Planning, Public Policy and Property Markets (Real Estate Issues)

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Planning, Public Policy & Property Markets

Planning, Public Policy & Property Markets

Edited by

David Adams Ian Mactaggart Chair of Property and Urban Studies University of Glasgow

Craig Watkins Reader, Department of Town and Regional Planning University of Sheffield and

Michael White Senior Lecturer, Department of Property University of Aberdeen Business School

# 2005 by Blackwell Publishing Ltd Editorial offices: Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK Tel: þ44 (0)1865 776868 Blackwell Publishing Inc., 350 Main Street, Malden, MA 02148-5020, USA Tel: þ1 781 388 8250 Blackwell Publishing Asia Pty Ltd, 550 Swanston Street, Carlton, Victoria 3053, Australia Tel: þ61 (0)3 8359 1011 The rights of the Authors to be identified as the Authors of this Work have been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. First published 2005 by Blackwell Publishing Ltd Library of Congress Cataloging-in-Publication Data Planning, public policy & property markets / edited by David Adams, Craig Watkins, Michael White.—1st ed. p. cm. Includes bibliographical references and index. ISBN 1-4051-2430-X (pbk. : alk. paper) 1. Land use—Government policy—Great Britain. 2. Real estate development—Government policy—Great Britain. 3. Housing policy—Great Britain. 4. Commercial real estate—Great Britain. I. Title: Planning, public policy and property markets. II. Adams, David, 1954- III. Watkins, Craig. IV. White, Michael, 1965HD596.P56 2005 333.3’0941—dc22

2004022195

ISBN-10: 1-4051-2430-X ISBN-13: 978-14051-2430-0 A catalogue record for this title is available from the British Library Set in 10/13pt Trump Mediaeval by Kolam Information Services Pvt. Ltd, Pondicherry, India Printed and bound in India by Replika Press Pvt Ltd The publisher’s policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp processed using acid-free and elementary chlorine-free practices. Furthermore, the publisher ensures that the text paper and cover board used have met acceptable environmental accreditation standards. For further information on Blackwell Publishing, visit our website: www.thatconstructionsite.com

The Royal Institution of Chartered Surveyors is the mark of property professionalism worldwide, promoting best practice, regulation and consumer protection for business and the community. It is the home of property related knowledge and is an impartial advisor to governments and global organisations. It is committed to the promotion of research in support of the efficient and effective operation of land and property markets worldwide.

Real Estate Issues Series Managing Editors Stephen Brown RICS John Henneberry Department of Town & Regional Planning, University of Sheffield David Ho School of Design & Environment, National University of Singapore Elaine Worzala Real Estate Institute, School of Business Administration, University of San Diego Real Estate Issues is an international book series presenting the latest thinking into how real estate markets operate. The books have a strong theoretical basis – providing the underpinning for the development of new ideas. The books are inclusive in nature, drawing both upon established techniques for real estate market analysis and on those from other academic disciplines as appropriate. The series embraces a comparative approach, allowing theory and practice to be put forward and tested for their applicability and relevance to the understanding of new situations. It does not seek to impose solutions, but rather provides a more effective means by which solutions can be found. It will not make any presumptions as to the importance of real estate markets but will uncover and present, through the clarity of the thinking, the real significance of the operation of real estate markets.

Books in the series Adams & Watkins Greenfields, Brownfields & Housing Development Adams, Watkins & White Planning, Public Policy & Property Markets Allen, Barlow, Le´al, Maloutas & Padovani Housing & Welfare in Southern Europe Ball Markets and Institutions in Real Estate & Construction Ben-Shahar, Leung & Ong Mortgage Markets Worldwide Barras Building Cycles & Urban Development Beider Urban Regeneration & Neighbourhood Renewal Couch, Fraser & Percy Urban Regeneration in Europe Dixon, McAllister, Marston & Snow Real Estate & the New Economy Evans Economics & Land Use Planning Evans Economics, Real Estate & the Supply of Land Guy & Henneberry Development & Developers Jones & Murie The Right to Buy Leece Economics of the Mortgage Market McGough & Tsolacos Real Estate Market Analysis & Forecasting O’Sullivan & Gibb Housing Economics & Public Policy Seabrooke, Kent & How International Real Estate

Contents Preface Acknowledgements Contributors Abbreviations

Part 1 1

Examining Public Policy and Property Markets David Adams, Craig Watkins and Michael White Introduction The scope of the book Property research and public policy Structure of the book

Part 2 2

3

Introduction

Conceptualising Relationships

Conceptualising State–Market Relations in Land and Property: The Mainstream Contribution of Neo-Classical and Welfare Economics David Adams, Neil Dunse and Michael White Introduction Insights on supply, demand and public policy from neo-classical economics Insights on market failure and public policy from welfare economics Conclusions Notes Conceptualising State–Market Relations in Land and Property: The Growth of Institutionalism – Extension or Challenge to Mainstream Economics? David Adams, Neil Dunse and Michael White Introduction The institutional framework for land and property market operations New institutional economics The political economy of institutionalism

xi xiii xiv xx

1 3 3 5 6 9

15

17 17 19 31 35 35

37 37 39 40 44

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Contents

Uncertainty, risk containment and confidence building Conclusions Notes 4

Planning Tools and Markets: Towards an Extended Conceptualisation Steve Tiesdell and Philip Allmendinger Introduction Understanding ‘planning’ Mainstream economics The political economy of institutionalism A typology of planning tools Market characteristics Conclusions Notes

Part 3

Unravelling the Relationships

Section 3.1 5

6

50 52 55

56 56 57 58 61 63 71 73 76

77

Modelling Relationships

Modelling Local Housing Market Adjustment in England Glen Bramley and Chris Leishman Introduction Background Datasets Modelling framework and techniques Estimation of key relationships Policy simulations Conclusions Appendix 5.1 Variable definitions and sources Estimating the Impact of Planning on Commercial Property Markets John Henneberry, Tony McGough and Fotis Mouzakis Introduction Model development and specification Empirical study and results Conclusions Notes Appendix 6.1 Results

79 79 80 83 85 89 97 101 103

105 105 107 113 121 122 124

Contents

Section 3.2 7

8

9

10

Measurement Issues

UK Roads Policy, Accessibility and Industrial Property Rents Neil Dunse and Colin Jones Introduction Roads policy and the UK motorway network Transport infrastructure and the spatial economy Measuring the effect of transport investment Hedonic pricing analysis and the impact of the UK motorway network Discussion and policy implications Notes Urban Regeneration, Property Indices and Market Performance Alastair Adair, Jim Berry, Ken Gibb, Norman Hutchison, Stanley McGreal and Craig Watkins Introduction The urban regeneration policy context Data and research methods Comparing urban regeneration and prime property market performance The total returns index Conclusions

Section 3.3

ix

128 128 129 131 137 139 146 147

148

148 149 152 155 158 163

Surveys and Case Studies

Planning for Consumers’ New-Build Housing Choices Chris Leishman and Fran Warren Introduction Planning and new-build housing The new-build housing choice process The housing preference study Overview of the findings Preferences and satisfaction with room layouts and features Density and variety on new-build housing estates Conclusions Planning Obligations and Affordable Housing Sarah Monk, Christina Short and Christine Whitehead Introduction The research context

167 167 168 171 172 173 177 181 183 185 185 186

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Contents

Research methods How much affordable housing is being provided? Government evidence How is Section 106 operating? Evidence from local authorities What is happening on the ground? Evidence from the local authority case studies Conclusions Notes 11

Reinforcing Commercial Competitiveness through City Centre Renewal Gwyndaf Williams and Stuart Batho Introduction Urban governance and the entrepreneurial city centre The local governance and development context Mobilising regeneration capacity: the study approach Reinforcing commercial competitiveness Commercial impact of the renewal programme Conclusions

Part 4 12

Conclusions

Planning, Public Policy and Property Markets: Current Relations and Future Challenges David Adams, Craig Watkins and Michael White Introduction What state actors could learn about the market What market actors could learn about the state The evolving research agenda

References Index

187 188 189 193 206 207

209 209 209 211 216 225 231 235

237 239 239 241 245 249 253 273

Preface In the mass media, the ‘property market’ is where houses are bought and sold and ‘planning’ is all about the decisions of local authorities to approve or refuse permission for their construction or subsequent modification. The concept that some form of public policy may exist to link these aspects of the economic and political life, directly or indirectly, often escapes the notice of even experienced commentators. Academics, while well aware that the commercial property market is as important as the residential one and that planning is a much broader activity than that of simply reacting to applications submitted from the private sector, have tended to concentrate on either state or market activity and pay scant attention to the richness of state–market interconnections. This book reflects an increased realisation among academics and practitioners that, in an era where development is intended to be sustainable and where environmental protection needs to be matched by urban, rural and regional regeneration, effective state–market relations in land and property are critical to a prosperous economy and a robust democracy. This emphasis on theoretical integration and ‘joined-up’ practical application was central to the mission of the Department of Land Economy at the University of Aberdeen, in which we all worked during the period when this book was in preparation. Among many other projects, it encouraged a successful collaborative submission from Aberdeen, Cardiff, Sheffield and Ulster Universities for a new ESRC Seminar Series on Planning and Development. The first seminar of that new series was held at the Department of Land Economy in September 2003 and attracted some thirty prominent academics and practitioners to debate some of the latest research under the theme of planning, public policy and property markets. Along with some later invited contributions, this book has emerged from the papers originally presented at that seminar. The book is intended to spark the growing interest in the nature of state– market relations in land and property both by filling an obvious gap in the academic literature and by providing a launchpad for broader debate and a more pluralist property research agenda. Although the book focuses on the impact of planning, housing, land, regeneration and transport policies on land and property markets, the applied studies included are intended to

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Preface

exemplify current methodological debates and research techniques rather than to provide comprehensive coverage of the many ways in which policies and markets interact. Crucially, the various contributions are concerned not only with the direct impact of policy on market supply, demand and pricing but also with the role of institutions, information and actors in shaping policy and market outcomes. As editors, we hope that this book will encourage all those in property research who strive for theoretical and practical connectivity and whose work seeks to demonstrate that just as property market operations cannot be analysed without a sophisticated understanding of state processes, policy decisions impacting the market cannot be taken without an appreciation of how the market operates. David Adams, University of Glasgow Craig Watkins, University of Sheffield Michael White, University of Aberdeen December 2004

Acknowledgements The authors of Chapters 2, 3 and 4 would like to thank the Office of the Deputy Prime Minister for funding research under its New Horizons research programme on assessing the impact of planning, housing, transport and regeneration policies on land pricing, on which these chapters are based. They also wish to acknowledge the helpful comments received from members of the Research Advisory Group for the project. The contents of Chapter 5 are based on a contribution to the project on ‘Secondary Analysis of Housing Market Data Sets’ supported by the Joseph Rowntree Foundation in 2002–3. The authors are indebted to the foundation (and Theresa McDonagh, in particular) for supporting this exploratory work and for organising a series of workshops with other participants in the programme. Findings from this project were presented to members of the Foundation’s Housing & Neighbourhoods Committee and Board in February 2003, and have influenced the subsequent evolution of the foundation’s research programmes in this area. More specifically, Chapter 5 draws on an analysis of data derived essentially from administrative data systems and official statistics. The main dataset was originally constructed by Glen Bramley along with colleagues from the Universities of Newcastle and Leeds, as part of a DTLR/ODPMfunded project on ‘The Development of a Migration Model’ (University of Newcastle-upon-Tyne et al. 2002). This dataset was then substantially enhanced by the addition of further data, mainly relating to planning and land and also obtained from the ODPM. The authors gratefully acknowledge the assistance of colleagues at the Universities of Newcastle and Leeds and at the ODPM in accessing these data. The authors of Chapter 8 wish to acknowledge research funding from the Economic and Social Research Council (UK) for a project entitled Benchmarking of Urban Regeneration Performance (Grant Ref. R000239291), and from the Office of the Deputy Prime Minister (London) and the RICS Foundation (London). The authors of Chapter 9 would like to acknowledge the support of the Joseph Rowntree Foundation and, in particular, the valuable contribution made by the Project Advisory Group during the study. However, responsibility for any errors or omissions lies with the authors.

Contributors Alastair Adair holds a Chair in Property Investment at the University of Ulster School of the Built Environment. He has researched extensively into the dynamics of urban development and property market performance; financial aspects of development and investment appraisal; financing of urban regeneration and the comparative study of the international valuation research agenda. Currently he is the Deputy Editor of the Journal of Property Research and is a member of the editorial board of three other international journals. He holds a PhD in Urban Development from the University of Reading. He is a Fellow of the Royal Institution of Chartered Surveyors. David Adams is Ian Mactaggart Professor of Property and Urban Studies at the University of Glasgow. He was previously Professor of Land Economy at the University of Aberdeen. He has undertaken extensive research on the relationship between market processes and planning systems and published widely, most notably as author of Urban Planning and the Development Process (UCL Press 1994) and as co-author of Land for Industrial Development (E & FN Spon 1994, with Lynne Russell and Clare Taylor-Russell ) and Greenfields, Brownfields and Housing Development (with Craig Watkins, Blackwell 2002). He is both a Fellow of the Royal Town Planning Institute and a Member of the Royal Institution of Chartered Surveyors. Philip Allmendinger is Professor of Town Planning and Director of the Centre of Planning Studies in the Department of Real Estate and Planning at the University of Reading. He was previously Head of Department and Reader in Land Economy at the University of Aberdeen. He has undertaken a wide range of research into planning, planning theory, politics and governance and published a number of books including Planning in Postmodern Times (Routledge 2001) and New Directions in Planning Theory (with Mark Tewdwr-Jones, Routledge 2002). Stuart Batho works with the Urbanism Group within Building Design Partnership’s multi-disciplinary practice in Manchester, following a period of practice with David Lock Associates at Milton Keynes. With degrees in geography (Leeds) and in land economy (Aberdeen), he has recently completed a PhD (Manchester) on ‘The New Urbanist Vision for the Public Realm: Realising the Concept’. Prior to enrolling on his PhD

Contributors

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studies he was employed at University of Manchester as a Research Associate working on a Leverhulme Trust funded project on ‘Managing Urban Development Partnerships’. Jim Berry is a Reader in the School of the Built Environment, University of Ulster. He is also a member of the Centre for Research on Property and Planning, and Course Director of the Postgraduate Programme in Real Estate and Facilities Management at the University. As a Chartered Town Planner and Chartered Surveyor, Dr Berry’s research interests include Spatial/Urban Planning, Urban Regeneration, Property Investment and Development. He has published extensively in international, national and local journals. He is a member of a number of professional organisations including the RTPI, RICS, IPI, IAVI, British Urban Regeneration Association (BURA), the Society of Property Researchers (SPR), the Investment Property Forum (IPF), the European Network for Housing Research (ENHR), the European Real Estate Society (ERES ) and the American Real Estate Society (ARES ) and is chair of the Property Economics and Finance Research Network (PEFRN). Glen Bramley is Professor of Urban Studies at Heriot-Watt University in Edinburgh, where he leads a substantial research programme in housing and urban studies and directs the Centre for Research into Socially Inclusive Services (CRSIS ). Recent work is focused particularly on housing need and areas of low demand for housing, low-cost home ownership, flows of funds to local areas, planning for new housing and the impact of planning and infrastructure on city competitiveness. His publications include Key Issues in Housing (Palgrave forthcoming), and Planning, the Market and Private Housebuilding (UCL Press 1995). Neil Dunse is a Senior Lecturer in the Department of Property, University of Aberdeen Business School. His research interests include property market analysis, real estate valuation, and the impact of public policy on land values. He has published articles in a number of journals including Journal of Property Research and Journal of Property Investment, Finance and Urban Studies. He has undertaken research projects for the ODPM, Scottish Parliament, and the RICS. Kenneth Gibb is a Reader in the Department of Urban Studies at the University of Glasgow. Kenneth’s primary research interests lie in housing economics and housing finance but he has increasingly worked in wider real estate areas of research in recent years. Kenneth is his Department’s Director of Teaching. He is the co-editor of Housing Economics and

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Contributors

Public Policy (with Tony O’Sullivan) in this series. He is the 2004–2005 President of the European Real Estate Society. John Henneberry is Professor of Property in the University of Sheffield. His research interests are in the structure and behaviour of the property market and its relationship with the wider economy and the state regulatory system. His research has been funded by the EPSRC, ESRC, EC, ODPM, RICS Education Trust, and central and local government agencies. He is on the editorial board of the Journal of Property Research and is coeditor of the Blackwell/RICS book series Real Estate Issues. Norman Hutchison is a Professor in the Department of Property, University of Aberdeen Business School where he is also Director of the Centre for Property Research. His main research interests are in commercial property valuation and property development. Recent research projects have focused on the valuation of urban regeneration land, land ownership constraints to urban redevelopment, the valuation of goodwill and risk measurement. Colin Jones is Professor of Estate Management at Heriot-Watt University. He is an economist with research interests in the housing market, housing policy, planning and the urban land market, the commercial/industrial property markets and urban regeneration. Recent publications include journal articles on enterprise zones, local industrial property market areas, housing submarkets and forecasting urban office rents. Chris Leishman is a Senior Lecturer in Property Market Economics and Valuation in the School of the Built Environment at Heriot-Watt University. His research interests include the economics of the house building industry; housing market modelling, migration analysis, analysis of price dynamics; analysis of house buyers’ behaviour and preferences; commercial property market econometric modelling and forecasting; and analysis of spatial and structural submarkets. Tony McGough is Senior Lecturer in Real Estate Investment and Finance at the CASS Business School at City University and Director of their MSc Real Estate Courses. He is on the editorial board of the Journal of Property Research. He was formerly with the Prudential’s property research team. His work concentrates on econometric analysis and its implications in the property market. He has published and lectured widely in this field. He regularly presents papers at the American Real Estate Society International Conference, the European Real Estate Society Conference, and the American Real Estate and Urban Economics Association.

Contributors

xvii

Stanley McGreal is Director of the Centre for Research on Property and Planning at the University of Ulster. He has researched widely into issues relating to urban development and regeneration, planning, globalisation, property market performance, investment and housing. Professor McGreal has been involved in major research contracts funded by government departments and agencies, research councils and the private sector. Analysis of urban renewal strategies and regeneration outputs in the property sector and the investment market has been a central theme of this research with particular implications for policy. He has been an invited speaker at several conferences, holds membership of editorial boards, serves on various international committees and held the post of President of the International Real Estate Society in 2004–2005. Sarah Monk is a Senior Research Associate at the Department of Land Economy, University of Cambridge and Deputy Director of the Cambridge Centre for Housing and Planning Research. She is currently involved in a study of the relationship between planning gain and Social Housing Grant in the provision of affordable housing funded by the Joseph Rowntree Foundation and the Housing Corporation (jointly undertaken with the University of Sheffield). This is a follow-on to an earlier project on planning gain and the provision of affordable housing, which was published in 2003 as Planning Gain and Affordable Housing: Making it Count. Fotis Mouzakis is a Lecturer in Real Estate Investment and Finance at the CASS Business School, City University and was formerly with the Prudential’s property research team, DTZ Research and the Department of Economics of Surrey University. His research interests cover theoretical and empirical aspects in various areas of economic foundations, with a current focus on the integration of quantitative analysis in real estate markets and recent developments in urban economics. He has published in several books and refereed journals, and received awards for research contributions. Christina Short joined the Cambridge Centre for Housing and Planning Research after completing a MPhil in Land Economy followed by a position at the Cambridge International Land Institute where she developed a web-based library. Since joining the Centre she has worked on the complementary roles of Social Housing Grant and Section 106 in the provision of additional affordable housing and developing guidance on housing needs assessments and housing market assessments. She has also presented papers on the provision of affordable housing through the planning system. Steve Tiesdell is a Senior Lecturer, Department of Geography and Environment, University of Aberdeen. He is an architect-planner with teaching

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Contributors

and research interests in urban design, regeneration and the development process. He has published widely in these fields, most recently a book entitled Public Places – Urban Spaces: The Dimensions of Urban Design (with Carmona, Heath and Oc). He is currently working on a book project on the theme of safety and civility in urban public space and on a reader in urban design (with Matthew Carmona). Fran Warren has a background in politics and social research. She has been working as a Research Associate in the School of the Built Environment, Heriot-Watt University since 2001, where she works on a range of social science research projects involving primarily qualitative research methods. Recent projects include: housebuyers’ housing preferences; developing client participation in the private housebuilding design process using flexible house types; and a practical guide for local authorities to help address diversity issues in planning policies and procedures. Craig Watkins is a Reader in the Department of Town and Regional Planning at the University of Sheffield. Until 2004, he was Senior Lecturer in the Department of Land Economy at the University of Aberdeen. His research interests include housing economics and policy, urban regeneration and commercial property market analysis. He is co-author (with David Adams) of a book entitled Greenfields, Brownfields and Housing Development published recently in the RICS/Blackwells Real Estate Issues series. Michael White is a Senior Lecturer in the Department of Property, University of Aberdeen Business School. His research interests are in the field of economic analysis of commercial and residential property markets, the interaction between policy and property markets, and property and the macroeconomy. He has published academic articles in leading journals including the Journal of Real Estate Finance and Economics, the Journal of Housing Research, and Urban Studies. Some of his recent research work has been funded by the Office of the Deputy Prime Minister, the RICS and the Housing Research Foundation. Christine Whitehead is Professor of Housing in the Department of Economics, London School of Economics and Director of the Cambridge Centre for Housing and Planning Research, University of Cambridge. She has been working in the fields of urban and housing economics, finance and policy for many years and was awarded an OBE in 1991 for services to housing. She is author of a large number of academic and policy articles and reports on housing finance and related subjects. She is an honorary member of RICS and was elected fellow of the Society of Property

Contributors

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Researchers in 2001. She has been adviser to House of Commons Select Committees on many occasions, latterly with respect to empty homes and affordable housing. Gwyndaf Williams is Professor of Urban Planning and Development at the School of Environment and Development, University of Manchester. He has degrees in geography (Aberystwyth) and local government management (Birmingham) and a PhD in housing (Manchester). His main teaching and research interests are in urban regeneration, strategic planning, and on the interface between spatial planning and housing policy. Recent publications include Metropolitan Planning in Britain: A Comparative Review (with P. Roberts and K. Thomas (1999) ) and The Enterprising City Centre: Manchester’s Development Challenge (2003). He is currently undertaking research on the housing market renewal initiative, and on the development impacts of university expansion.

Abbreviations 2SLS 3SLS CBD CMDC DETR

Two stage least squares Three stage least squares Central business district Central Manchester Development Corporation Department of the Environment, Transport and the Regions DINKYs Younger single households and couples DTLR Department of Transport, Local Government and the Regions ESRC Economic and Social Research Council Higher SEG As middle SEG but more single person households and larger proportion of professional occupations HIP Housing Investment Programme HMA Housing market area IPD Investment Property Databank JLL Jones Lang LaSalle LPA Local planning authority MCC Manchester City Council Middle SEG Slightly older buyers over half of whom have children MIGMOD Migration model for England and Wales MML Manchester Millennium Task Force MNL Multinomial Logit MoD Ministry of Defence Neo-DINKYs As DINKYs but more couples and non-professional occupations NIE New institutional economics ODPM Office of the Deputy Prime Minister OLS Ordinary least squares PEI Political economy of institutionalism R&D Research and development RCEP Royal Commission on Environmental Pollution REIT Real Estate Investment Trust RICS Royal Institution of Chartered Surveyors RSL Registered social landlord SACTRA Standing Advisory Committee on Trunk Road Assessment SHG Social Housing Grant SME Small and medium-sized enterprises SPN Scottish Property Network

Planning, Public Policy & Property Markets Edited by David Adams, Craig Watkins & Michael White © 2005 by Blackwell Publishing Ltd

Part 1 INTRODUCTION


1 Examining Public Policy and Property Markets David Adams, Craig Watkins and Michael White

Introduction It is widely accepted that the performance of housing and commercial property markets has a profound impact on the social and economic well-being of our towns and cities. Property markets perform an important role as mediators of urban economic change (Gibb et al. 2002). The location of property development can enhance or inhibit the competitiveness of cities and regions (Begg 2002a). In addition, through its influence on the locational pattern of homes, employment opportunities, factors of production and the urban asset base, property plays an important role in the spatial distribution of social justice (Gibb & Hoesli 2003). It is thus no surprise that policy makers place particular importance on seeking to influence the performance of markets. Examples of governance strategies include the regulation of markets through the planning system, direct development channelled through land policy and regeneration initiatives, and indirect influences on the behaviour of property institutions through taxation and fiscal mechanisms. Although more difficult to detect, public sector decisions on transport infrastructure, public service provision, education and crime also influence the structure of the property markets. Household and business location decisions, and property development and investment strategies will all be shaped directly or indirectly by a myriad of policy initiatives.

4

Introduction

There are currently numerous highly topical policy issues occupying the minds of the general public and the property community. The recent Barker review, for instance, has focused on the role of government policy, especially planning polices, in tackling rapid price appreciation and volatility in the housing market (Barker 2003, 2004). Interestingly, the review was the responsibility of both the Treasury and the Office of the Deputy Prime Minister (which has general responsibility for housing and planning). This reflected the fact that it was a fundamental premise of the review that the instability of property markets in the UK has important macroeconomic consequences. As expected, Barker’s policy prescriptions highlight the need to reform the planning system. In addition, however, it is proposed that policy makers might consider alterations to the role of taxation mechanisms, the provision of infrastructure incentives, and reform of the construction industry. While these policy recommendations have received mixed reviews, the report ably demonstrates the difficulty associated with analysing the impact of public policies on the performance of property markets. These are not the only property-related policy areas subject to debate at present. Elsewhere, the property market impacts of congestion charging have been contested. Others have debated the likely property market ramifications of investment in the public transport system, especially the £10 billion east–west London Crossrail initiative (RICS & ODPM 2002). There has also been speculation about the likely impact of proposals to provide tax incentives for the formation of Property Investment Funds (HM Treasury 2004). The simple message from this brief review is that, for a variety of economic and social reasons, the relationship between public policy and the structure and operation of property markets is important. Unfortunately, however, these relationships are not well understood. While some policy issues have been subject to rigorous analysis, the extent to which initiatives and interventions in property markets have been informed by evidence is fairly limited. As we argue later in the book, this is somewhat disappointing given the considerable developments, both in terms of quality and quantity, in the property research agenda. Nevertheless, there are grounds for optimism. In recent years, it has become evident that policy makers see a much closer relationship emerging between public policy, including the planning system, and property development, in which they are regarded less as working in opposite directions but more in synergy with each other. In this context, this book is intended to provide an introduction to ‘state of the art’ research on the

Examining Public Policy and Property Markets

5

relationship between planning, public policy and property markets. Importantly, the contributors to the volume recognise that policy makers cannot rely on either the state or market alone to deliver policy objectives. Nor are they faced with straightforward choices between resolving government failure or market failure. Rather, successful public policy requires an appreciation of the role of the state as a participant in the market (Oxley 2004). Thus, although the book focuses on the impact of planning, housing, land, regeneration and transport polices on land and property markets, it begins to explore not just the direct impacts of policy on supply, demand and market pricing but also examines the role of market information, institutions and actors in meeting policy objectives.

The scope of the book This book is concerned with the relationship between planning, public policy and structure and operation of property markets and is relatively broad in scope. Our interest in property markets covers all sectors (residential, office, retail and industrial ) as well as the structures, institutions and processes that characterise the market and shape market outcomes. The analysis contained in the book explores the role of policy in the determination of property values, investment returns and levels of development activity. It also considers the behaviour of and interaction between a variety of actors including public agencies, planners, developers, investors and property users. Our use of the term planning in the title is reflective of the relatively high profile of current controversies associated with the relationship between planning and property markets. In the context of this text, ‘planning’ is thought of in much broader terms that its statutory functions. Indeed, following Adams et al. (2003), the conception of planning relates to the intentional interventions in market processes, usually (but not exclusively) by local government, in the pursuit of societal objectives. In this sense, the scope of the book is broader than that of many economic analyses where the focus has tended to be on land use regulation (see Evans 2003). This broad definition of ‘planning’ is indicative of an interest in public policy more generally and thus includes all land, land use planning, housing, transport and regeneration policies that are likely to have clear impacts on the use, investment and development of land and property. It is also consistent with the government view of the function of planning which highlights the role of the planning system in maintaining economic growth and in providing investment and jobs as well as homes and buildings.

6

Introduction

Clearly, given the breadth of the subject matter, it is difficult to cover all relevant current and recent research on the topic comprehensively. Instead, the treatment of policy debates and market sectors is necessarily selective. We do not attempt to tackle all policy issues, nor do we seek to explore all sectors of the property market in equal depth. There are thus some obvious gaps in coverage. For instance, none of our chapters is concerned with the private rented housing market, even though buy-to-let policies and proposals for Real Estate Investment Trust (REIT)-style vehicles are having important impacts on market performance. Nor do we focus explicitly on prominent housing policy issues such as ‘Right to Buy’. Instead our intention is to provide a flavour of contemporary issues in property research rather than an exhaustive treatment of all important topics. This is reflected in a much more pronounced concern with methodological debates and research methods that are relevant to the ways in which policy impacts on property. Analyses of particular policy initiatives and programmes are thus intended to be illustrative and are used to exemplify particular methodological and analytical approaches. Nevertheless, this approach enables the book to provide an overview of the available research evidence on a number of important policy questions.

Property research and public policy One of the main aims of the book is to demonstrate that property research can make important contributions to understanding public policy and its influence on urban dynamics. We argue that the maturing property research community now produces high quality research based on sound theoretical foundations, useable models and diverse research methods. This reflects the evolution of an increasingly coherent research agenda that has been enhanced by improved communication across disciplines and specialisms. Although the pioneers of classical and neo-classical economics often investigated land and housing market issues, it is only since the 1970s that a distinct property research community has emerged in the UK (O’Sullivan & Gibb 2003). This research drew initially on the traditional propertyrelated disciplines of economics, law, human geography, urban planning, construction and valuation. More recently, research output has begun to mirror trends in the US by encompassing advancements in business studies and finance. The diverse backgrounds of property researchers and practitioners have inevitably led to a fragmented research agenda characterised by a range of


7

quasi-independent specialisms in areas such as urban economics, housing economics, property investment and finance, property valuation, property and the macroeconomy, property and business economics, and planning and property development. Indeed, until relatively recently, there has been only limited cross-fertilisation between housing economics and commercial property market analysis and between economic analysis and planning research. Within each of the relatively narrow specialisms that emerged, the focus of research has varied from individual actors, through groups of actors to the market in aggregate; from the local to the national to the global dimension; from the demand side to the supply side; from the public sector to the private sector; and across the residential, retail, office and industrial sectors. Furthermore, theoretical and empirical studies of property market phenomena have also varied in terms of the methodological and epistemological approaches adopted (Adair et al. 2003). Encouragingly, however, there have been several initiatives, which have helped facilitate greater communication between property sub-specialisms. For instance, between 1998 and 2000, the ESRC funded a Property Economics and Finance Seminar Group. This group held four meetings on diverse themes including housing economics, urban regeneration, property investment and corporate real estate. Although the group helped establish links between housing and commercial property researchers, much of its activity focused on the role of property as a financial and corporate asset rather than as a mediator of urban economic and social change. More recently, an ESRC funded Planning and Development seminar group has been established. This group has a more explicit interest in public policy issues and has sought to foster interaction between property, housing and planning researchers. The first meeting of the group, hosted in September 2003 by the Department of Land Economy at the University of Aberdeen, focused on the relationship between planning, public policy and property markets. The meeting built on growing interest in this topic and provided the impetus for this book. In an interesting precursor to the establishment of the ESRC Planning and Development seminar series, Guy and Henneberry (2002a) edited a collection of papers on property development. The collection had been inspired by multi-disciplinary discussions of the development process at the annual Royal Institution of Chartered Surveyors (RICS ) conferences in 1994 and 1998 and sought to demonstrate the heterogeneous nature of research on property development. The resulting book included contributions from a

8

Introduction

variety of disciplines and the editors explicitly considered the ways in which different methodological perspectives on property have framed analyses of the development process. They suggest that the ‘interpretative flexibility’ relating to the built environment gives rise to the use of diverse theories (and associated research methods) when formal, analytical attempts are made to explain development. This diversity has generated considerable debate about the competing merits of alternative methodological approaches. It has also encouraged ongoing debate about the value of particular research methods. Superficially, it may appear that this debate has been won by the mainstream. Recent analyses of leading property economics journals show that journal publications have been dominated by the quantitative model building (Newell et al. 2001; Ong et al. 2001). This is particularly evident in the major US academic journals, where housing and commercial property are primarily analysed within an asset market framework (Levy & Henry 2003). However, this masks some underlying concerns with mainstream economics. Schiller (2001, p. 4), for instance, notes that ‘economic man may still exist, minimising cost and maximising utility, but he gives only part of the picture’. In the UK, the broader social science perspective adopted by several leading journals has allowed academics to continue to debate the merits of alternative approaches (see Guy & Henneberry 2000; Ball 2002, for instance). Furthermore, the case for considering alternative perspectives less as competitors and more as complements has received considerable support (Adams et al. 2003; Monk & Whitehead 1999; Lizieri 1995). As Guy and Henneberry (2002b, p. 299) state ‘instead of trying to shoehorn property research into competing disciplinary models or prioritising methodological approaches, we might begin to take a more heterogeneous route (or routes) in the future. While this would not mean abandoning critical debate between analytical approaches . . . , it would mean adopting a more catholic community of property researchers with some parity of esteem. The effect of this would be to open property studies to a much wider world of analytical innovation.’ It is hoped that this book will make a further contribution to this progressive view of property research. Although the book is dominated by the work of property economists it is no less supportive of the need for pluralist approaches to property research. Indeed, methodological diversity is seen as an essential ingredient in the improved analysis of the influence of public policy on commercial and residential property markets.


9

Structure of the book As we note above, the book focuses on theory and methods rather than policy themes or disciplines. This is reflected in the structure of the book. There are a further three parts to the text. Part 2 introduces some of the contemporary debates in conceptualising the relationship between the state and the market. In three chapters, this section explores the range of theoretical approaches that inform property and planning researchers’ studies of the relationship between public policies and property markets. In Chapter 2, David Adams, Neil Dunse and Michael White examine the methodological scope of mainstream economic analyses, which encompasses neo-classical and welfare economics. Although the authors highlight the limitations of the approach, especially in terms of its restrictive and unrealistic behavioural assumptions, they balance this by pointing to important developments in modelling approaches, especially with respect to modelling adjustment processes, choice and spatial phenomena, as well as advancements in information and search theory. In Chapter 3, the same authors explore the role for various forms of institutional economic analysis, including new institutional economics and more radical ‘political economy of institutionalism’ perspectives. Although it is established that, in overcoming the restrictions of mainstream approaches, institutional analyses has the potential to provide a fruitful basis for land and property market studies, the authors also acknowledge that empirical studies can tend towards description or ‘story telling’. Ultimately, Adams et al. conclude that what is required is greater methodological pluralism. This is a sentiment that is given strong support by the quality of insights derived from the diverse approaches employed in the empirical studies contained within this text. The fourth chapter seeks to link the conceptual and empirical issues. Steven Tiesdell and Philip Allmendinger develop a typology of ‘planning’ tools based on their relationship to the operation of land and property markets. The taxonomy proposes four types of planning tool – those intended to shape markets, those intended to regulate markets, those intended to stimulate markets and those directed at developing the capacity of market participants. The typology draws out the main relationships between public policy and property markets identified in mainstream and institutional economic analyses. Part 3 is the largest part of the book. It provides examples of empirical work consistent with the main theoretical traditions. Much of the work included

10

Introduction

in this part of the book has been undertaken with the support of research councils, government departments or charitable trusts and has been produced with the specific aim of contributing evidence to contemporary policy debates. In line with current policy concerns, the contributions provide further evidence of the level of interest in themes such as the effect of policies on property market outcomes and, in particular, property values; and the extent to which polices can alter market processes and help correct market failure through, for instance, assisting in the delivery of nonmarket homes, in facilitating the redevelopment of inner city areas and in shaping the nature and form of development in suburban growth areas. Although these studies do not reflect the totality of work on public policy and property markets, they demonstrate the diversity of research methods used in applied research. As such Part 3 is further sub-divided into three sections to highlight the approaches to developing economic models of the relationships between policy change and markets, to measuring and monitoring market performance and to using surveys, qualitative methods and detailed case studies to analyse market behaviour. In Section 3.1 Chapters 5 and 6 provide examples of attempts to model the relationship between land use planning and property values. Both studies exemplify current advancements in the development of quantitative economic models in the mainstream tradition. Glen Bramley and Chris Leishman focus on modelling outcomes in the residential property market. Although housing economists have long been concerned with the theoretical impacts of planning and other public interventions on the market, it is only in the last fifteen years that these have impacts have been empirically estimated. This chapter builds on a substantial body of previous work on the topic (including Bramley 1993a; Bramley 1999). It uses a panel data set to develop a system of equations, within a long-run equilibrium framework, to estimate the impact of different packages of public policies on a range of outcomes including the price and quantity of housing in ‘high’ and ‘low’ demand housing markets. The model also allows the authors to simulate the effects of policy changes, such as an increase in the level of building, over a seven-year period from the end of 1997. The results suggest that a combination of supply-side and demand-side (through the local economy and labour market) policies could achieve substantial impacts on the price of housing and quantity of new development. In Chapter 6 John Henneberry, Tony McGough and Fotis Mouzakis model the commercial and industrial property markets. Although a significant literature on the effects of land use regulation on housing markets has emerged in recent years, there has been very little attention paid to the


11

regulatory impacts on commercial markets. This chapter reports on what is to date the most systematic attempt to address this issue. The authors develop models of rental values and development output based on a system of equations covering the industrial, retail and office sectors of the property market over the 1998–2000 period. In the model, a proxy variable is constructed to represent the planning regime. The variable measures the proportion of planning decisions that are approvals. The behaviour of the variable implies that as planning regimes become tighter the percentage of approvals decreases and so does the local property supply. These lower levels of supply are in turn associated with higher rents and lower levels of local economic activity. The next two chapters (Section 3.2) move from model building to the indirect measurement of the impact of polices on property values. Neil Dunse and Colin Jones illustrate the use of hedonic methods in computing the ‘shadow’ or implicit price of public sector infrastructural improvements. The chapter has its intellectual roots in a voluminous mainstream urban economics literature that has long been concerned with the way that transport systems, land use, and residential and business location are inter-twined. The empirical work in the chapter derives from the approach used by researchers seeking to unpick the effects of public policy decisions, including the location of new airports and light rail systems, on property values. The study’s novelty is associated with the focus on the industrial property market rather than the housing market. The application of this mode of analysis to commercial and industrial property markets has largely been precluded by the paucity of good quality local market data (Dunse et al. 1998). The chapter shows that, in the contemporary industrial property market context, access to transport links is likely to have a more pronounced effect on rental structures than access to the marketplace. Thus, importantly for economic development policies, access to motorway networks dominates the locational choices of manufacturers. In Chapter 8, Alastair Adair, Jim Berry, Kenneth Gibb, Norman Hutchison, Stanley McGreal and Craig Watkins seek to measure the impact of urban regeneration policies on the performance of commercial and industrial property markets. The authors construct indices that measure property rents, returns and yield movements in areas of the property market that have been subject to urban regeneration interventions. The indices are constructed using a ‘bottom up’ method based on detailed field visits during which regeneration areas were delineated and individual property offerings identified. These properties acted as a sampling frame for the collation of market data from a variety of sources including a survey of

12

Introduction

valuers and property industry datasets. Ultimately, the performance of the regeneration indices is compared with that of the industry standard benchmarks typically consulted by the property investors. The research found that, contrary to the common perception that regeneration areas are characterised by high risk and low returns (Adair et al. 1999), regeneration areas have outperformed prime markets since the early 1990s. The next three chapters (which make up Section 3.3) are less reliant on quantitative methods. In Chapter 9, Chris Leishman and Fran Warren undertake a detailed exploration of the influences on demand for new housing. It is clear that, if the levels of new housing required are to be met, then significant numbers of new homes will need to be built. The development of these homes will need to be consistent with sustainable urban forms. However, little is understood about the levels of consumer satisfaction with new housing development. The chapter introduces research methods that are borrowed, in part, from psychology in that it experiments with use of visual images as part of a survey of consumers. It combines the survey results with the use of more conventional statistical techniques and concludes that housebuilders need to address numerous sources of dissatisfaction in order to establish strategies for the delivery of sustainable housing developments. Unless this happens, there will be an increasingly difficult mismatch between the requirements of house buyers and the dwellings offered – a challenge that may require the intervention of policy makers. Chapter 10, by Sarah Monk, Christina Short and Christine Whitehead, explores the role of the planning system in the delivery of affordable housing. They note that, in a marked departure from past approaches, the planning system locates social rented housing on the same development sites as new homes built for owner occupation. Thus the policy makes an explicit link between land use planning and both the financing of affordable homes and the desire to establish mixed tenure developments. The chapter considers the effectiveness of the system on the basis of evidence collated from a survey of local authority planning departments, face-to-face interviews with planning and housing officers and detailed examination of housing development sites. The research shows that the volume of housing provided is low relative to levels of need and that the majority of housing provision is not adding to the level of supply. Although there is a suggestion that the policy is now becoming widely accepted, the research also stresses difficulties associated with the negotiation process, and inconsistencies in the behaviour of public and private sector actors. The authors conclude by highlighting the extent to which the efficacy of the policy is linked to the level of market provision. They


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note that the system will not provide affordable homes in the numbers required if the current low levels of development activity prevail. In Chapter 11, Gwyndaf Williams and Stuart Batho examine the capacity of a city to respond to urban property market challenges using a range of urban redevelopment delivery mechanisms. The analysis is based on a detailed case study of the renewal of Manchester city centre in the wake of the IRA bombing in June 1996. The study explores local capacities for enhancing city competitiveness in a five-year period of intense renewal and development activity. In the context of this volume, the chapter provides a uniquely detailed examination of the processes through which governance and urban economic change shape the built environment, influencing the direction and flow of investment and nature and rate of development activity. The authors highlight the opportunistic, pragmatic and entrepreneurial behaviour of key actors, notably those in the public sector. The analysis invites us to consider the extent to which the lessons from Manchester’s renewal are transferable to other urban policy challenges. In the fourth and final part of the book, the editors provide some comments on contemporary challenges faced by planning and property researchers and on the future direction of research into the relations between the state and the (land and property) market. There are several messages. First, it is clear that we do not face simple choices between state and market solutions – or even between state failure and market failure. Rather the studies reported in this book provide clear evidence that the state must participate in the market. Second, the book highlights the progress being made in property research. We call for increased methodological pluralism and point to the diverse insights that can be derived and drawn together from the vast array of social science research methods and improved data that the property research community now has at its disposal. Finally, we would hope that the book demonstrates that the theories, models and empirical evidence collated by property researchers have an important role to play in improving the quality of public policy. There is clearly considerable attention being paid to developing useable models and to locating the analysis of policy impacts within its institutional context.


Par t 2 CONCEPTUALISING RELATIONSHIPS


2 Conceptualising State–Market Relations in Land and Property: The Mainstream Contribution of Neo-Classical and Welfare Economics David Adams, Neil Dunse and Michael White

Introduction Any analysis of state–market relations in land and property is highly dependent on the particular theoretical view taken of market operations. In this chapter, we concentrate on two well-established approaches within the mainstream of economic theory, namely, neo-classical and welfare economics. We review how each approach has been used both to specify ways in which the state should and should not intervene in land and property markets and to evaluate the impact of public policy on such markets. In neo-classical economics, which we examine in the first part of the chapter, prices are considered to be determined by the interaction of supply and demand in the market. In policy terms, the most important question then becomes how far policy directly affects the overall quantity of supply and demand. Yet, despite the importance of supply and demand relations in land and property markets, we argue that the distinctive nature of land and property as commodities ensures that contextual influences, such as the strength of property rights and the availability of information, can be just as significant in affecting supply and demand, even though their impact may be harder to discern. Considerable scope may

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thus exist to influence land and property markets by seeking to alter the context within which transactions take place. We therefore turn to welfare economics in the latter part of the chapter, where we investigate how market failure distorts market operations and produce differences between private and social calculations of costs and benefits. In policy terms, the most important question then becomes how far policy is able to overcome market failure. As the chapter demonstrates, mainstream economics can be conceived as a broad theoretical tradition that finds expression in a range of complementary approaches, each of which makes use of different assumptions in seeking to understand particular issues. Besides neo-classical and welfare economics, new institutional economics, which we discuss in the next chapter, now forms an important added component of the mainstream. For, as Samuels (1995, p. 578) explains, new institutional economics ‘works largely within neoclassicism, and shares its rationality, maximisation, and market or market-like orientation and likewise tends to seek, though with less formalisation, the conventional determinate, optimal, equilibrium solutions to problems.’ Over the past two decades, ‘institutionalism’ has grown to such an extent that its influence now infuses much of mainstream thought. Apart from new institutional economics, Ball (1998) identifies game theory, information theory and technical production characteristics as examples of institutionally related theories in mainstream economics. It is therefore mistaken to counterpoise mainstream economic approaches in land and property markets with ‘institutionalism’ in its broadest sense since as Ball (1998, p. 1502) emphasises: ‘the perceived dichotomy between the economics of property markets and institutions is a false one. It will be argued here that there is a continuum of issues rather than an opposition.’ While we leave substantive discussion of institutionalism until the next chapter, we highlight in this chapter how neo-classical and welfare economics have come to acknowledge the mediating role of institutions within state– market relations in land and property. According to Ball (1998), the widespread recognition across theoretical traditions that institutions matter means that debate between mainstream economics and other perspectives now centres on the nature and significance of institutions and specifically on their treatment in theory and method, rather than on their importance per se. In the next chapter, we contrast new institutional economics with more radical institutional perspectives on state–market relations, which we discuss using the term ‘the

Neo-Classical and Welfare Economics

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political economy of institutionalism’. We suggest that, whereas mainstream economics primarily seeks to explain market outcomes, the political economy of institutionalism is as much concerned with market processes as outcomes and so encourages more thorough exploration of what mainstream economics often unsatisfactorily leaves within a ‘black box’. Nevertheless, those who might expect us to use these chapters to propose the adoption of one of these theoretical traditions to the wholesale exclusion of the other will be disappointed. Instead, by acknowledging the value of theoretical pluralism (Hodgson 1993), we contend that mainstream economics and the political economy of institutionalism should be seen as supplementary to each other (Samuels 1995). The chapters thus echo the recent call of Guy and Henneberry (2002b: 299, 301) that ‘Instead of trying to shoe-horn property research into competing disciplinary models or prioritising methodological approaches, we might begin to take a more heterogeneous route (or routes) . . . If we approach developers and development from many perspectives, equipped with the theories and methodologies of many disciplines, we will take a challenging analytical path.’ It should thus be emphasised that each of the theoretical approaches reviewed in these two chapters takes us only part way along a conceptual journey that is necessarily heterogeneous. We contend that only by completing that journey in its entirety will a more rounded understanding of state–market relations in land and property be achieved.

Insights on supply, demand and public policy from neo-classical economics According to neo-classical economics, land and property prices are determined by the interaction of supply and demand in the market. If supply is constrained or demand stimulated by public policy, then, other things being equal, prices will rise. The price mechanism thus operates to return supply and demand to a state of equilibrium. According to Ball et al. (1998, p. 63) for equilibrium to be feasible ‘buyers and sellers must be able to use the full available information when making their decisions and operate according to the arguments of their demand and supply schedules. This may not occur, for example, if planning regulation freezes land supply or restrictive long leases severely distort demand.’ Whether and how fast equilibrium is achieved will therefore depend on the institutional characteristics of the market.

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Neo-classical assumptions In a perfectly competitive market, rapid changes in price balance the quantity demanded with the quantity supplied and ensure equilibrium. Perfectly competitive markets that are not distorted by external influences will therefore produce resource-efficient allocations. Perfect competition requires many buyers and sellers who each have freedom of entry and exit, perfect information and a homogeneous product. However, land and property markets are far removed from meeting the conditions of perfect competition. This affects their performance and suggests a role for public policy. Imperfections arise in the form of heterogeneity, infrequent trading, significant transactions costs, relative illiquidity and product differentiation. These in turn affect the quantity and quality of information. According to Balchin et al. (1988), the widespread nature of such imperfections makes property markets among the least efficient of all. Neo-classical land economics has often been criticised by institutional writers on account of its perceived unrealistic assumptions. Van der Krabben and Lambooy (1993, p. 1384), for example, suggest that: ‘Neoclassical economists assume that only rationally acting individual actors operate on the market. Price adjustments will automatically lead to an equilibrium.’ However, such comments ignore recent developments in neo-classical theory and have generated the response from Ball (1998) that neoclassical economics is often treated as a straw man, fashioned by its critics in a form that can be most easily knocked down. As Maclennan and Whitehead (1996) make clear, neo-classical theory is no longer dependent on such assumptions as perfect competition, full information and instant equilibrium. According to Ball (2002), even rationality should be regarded as merely a working hypothesis or methodological standard. Rational market outcomes do not necessarily require rational behaviour by all actors. Ball et al. (1998) suggest that, although neoclassical economics shares many of the assumptions of equilibrium models, it seeks to explain actual behaviour in terms of rational expectations, transaction costs and asymmetric information. This is a much broader view of neo-classical economics than taken by critics such as Hodgson (1999, p. 102) who defines it as ‘an approach which assumes rational, maximising behaviour by agents with given and stable preference functions, focuses on attained, or movements towards, equilib-


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rium states, and excludes chronic information problems.’ Critically, whereas Hodgson (1999) considers modern developments in mainstream economics such as game theory to be at the edge or beyond neo-classicism, much of the rapidly expanding literature on game theory is comfortably positioned within a neo-classical analytical framework (see, for example, Hargreaves-Heap & Varoufakis 1995; Montet & Serra 2003). Although neo-classical economic models are abstractions that ignore many aspects of reality, Ball (2002) argues that the real test of their value lies in whether or not they explain market outcomes. According to Needham (1994), the quantitative advantages of neo-classical economic theory make it better able to explain some property market outcomes than such non-quantitative approaches as Marxist economics.

Recent developments in neo-classical economic analysis of property markets The last decade has seen a substantial development in the contribution of neo-classical economics to the analysis of property markets. It has travelled far from the point where, for example, neo-classical models of the development process could be criticised for their failure to distinguish between user and investor demand (Healey 1991). Since then, Keogh (1994), Di Pasquale and Wheaton (1996), and Colwell (2002) have each examined how the functional divisions of the property market relate to each other and have identified how market signals link the use, investment and development sectors. Keogh (1994) provides a conceptualisation of the links between the functional divisions of property markets. He describes both short- and long-run responses to changes in user demand. Di Pasquale and Wheaton (1996) present a long-run equilibrium model connecting the use, investment and development sectors. While these conceptualisations do not, for example, fully integrate with capital markets, they can be applied to explain the current two-speed market in commercial (especially office) property where user demand remains weak while investor demand is strong. Differentiating between functions and identifying appropriate signals (e.g. yield gaps in the investment market, rental growth and security of income stream in the user market) has helped explain why investment demand, particularly from overseas, has remained buoyant in a weak user market. Colwell has recently extended what is often now called the Di Pasquale and Wheaton (DW) model by exploring protracted adjustment, vacancies and expectations. Tsolacos et al. (1998) apply the DW model to the UK

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office market and estimate, econometrically, the quantitative relationship between function-specific demand and supply variables and the dependent variables of rent, capital value and new office construction. These dependent variables reflect the respective characteristics of the user, investor and development markets. To generate this model, Tsolacos et al. adopt a reduced form approach where, for example, rent is written as a function of demand and supply variables. As this indicates, data availability often acts as a constraint in neo-classical model building, particularly in UK and European studies. In the US, many authors adopt a structural modelling approach, having separate equations for demand and supply. Most US models incorporate a rental adjustment equation where rental change is seen as a function of the difference between actual and natural (or long-run) vacancy rates. Hendershott (1995, 1996) also relates rental change to the difference between actual and natural rent levels as well as vacancy rates. While authors vary the exact specification of rental adjustment equations, the modelling of this process reflects a theoretical position that markets achieve equilibrium, even though such equilibrium may not be immediate or perpetual. In their study of the London office market, Wheaton et al. (1997) estimate equations for construction, net absorption and rental adjustment where the latter is written as a function of lagged vacancy rates, net absorption and lagged rents. Their results indicate that adjustment to equilibrium in the office market can take a number of years. However, modelling such adjustment processes in the rest of the UK, for example, cannot be undertaken using a structural model at the present time, due to lack of data, particularly on vacancy rates. Hendershott et al. (2002) seek to resolve this problem in their study of UK regional retail and office markets. They consider how best to handle shortrun disequilibrium in property markets in a neo-classical economic model. Using panel data, they construct a parsimonious econometric long-run model and use an error correction model to account for short-run dynamics. The model remains elegant and yet sophisticated since it permits modelling of disequilibrium within mainstream economics. It incorporates recent econometric advances and pays particular attention to the statistical properties of the time series examined. The authors’ estimated models are tested for cointegration1 to establish the validity of the longrun relationships under question. By using an error correction coefficient, they measure how long it takes property markets to overcome the significant imbalances that can occur between demand and supply. This coefficient parallels that in rental adjustment equations in structural models.


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Like the structural models, the error correction model embodies the possibility of slow adjustment to economic shocks and market imbalances. The extent to which market participants acquire new information and learn from it is thus crucial to the notion of equilibrium (Maclennan 1982). Property markets where information is scarce can be regarded as ‘weak form efficient’ (Ball et al. 1998). Econometric models have been criticised for using aggregate data and thus missing or underestimating the importance of distinct local factors affecting property markets. Hendershott et al. (2002) disaggregate to a regional level and empirically test for regional differences, finding a notable London effect. Jackson (2001, 2002) disaggregates to the level of local markets in her study of the retail sector. She finds that the size of the retail core and the demographic profile of the local population provide significant explanation for differential rental levels across towns and cities in Great Britain. Mainstream housing economic analysis has long acknowledged spatial differentiation. For example, Meen (1996a, p. 370) suggests that, ‘a [housing] market is defined by appropriately aggregating over sub-markets where coefficient equality exists.’ He argues that many studies at a national level of aggregation may be biased since they impose or assume parameter homogeneity where it does not exist. Meen proceeds to examine the degree of independence across regional housing markets. Consideration is given to the nature of spatial dependence, and the related concept of spatial convergence. This can also be associated with cointegration of regional house price time series. Meen finds evidence supporting a ripple effect and convergence within regions of the south, the midlands and the north of England. Maclennan and Tu (1996) and Tu (2003), disaggregating to a local level of analysis, discuss the structure of housing submarkets. In relating their analysis to housing search, various outcomes to this process are suggested. Often a housing mismatch can occur if the potential purchaser does not like the housing units on offer. Search is then prolonged, raising transactions costs. If multiple buyers exist, properties are sold to the highest bidder. Tu (2003, p. 40) states ‘Differences across housing product groups in the price of a particular housing attribute . . . give rise to topographically based housing submarkets.’ Furthermore, the existence of housing submarkets may imply that disequilibrium could be pervasive. This may be reflected in mismatch between demand and supply and continuous house search processes. This implies that consideration should be given to disequilibrium in modelling, for example, the response of housing market areas to planning policies that impact upon those markets.

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The recent literature, outlined above, shows how neo-classical analysis can be adapted to take into account information signalling, slow adjustment, spatial specificity and disequilibrium in property markets. It also reflects the transfer of developments in mainstream economics and econometrics to the analysis of property markets. The literature explicitly recognises the imperfections of property markets but seeks to analyse and accommodate them within a neo-classical framework. This has made the framework increasingly useful for policy analysis, especially since it can provide quantifiable results of policy impacts.

Direct policy impacts on supply and demand Research investigating the impact of public policies on land and property markets undertaken in the neo-classical tradition is strongly focused on how policy directly affects supply and demand outcomes. Although research is now emerging which investigates how business rents are influenced by different planning regimes (Henneberry et al., Chapter 6 in this volume), most work in this direction concentrates on the impact of planning constraints on land and housing markets. White and Allmendinger (2003), who review the relationship between planning policy and its impact on the housing market, find that most authors adopt a neo-classical approach. Indeed, there is a notable degree of sophistication in the econometric models developed in Bramley (1993a, 1993b), Bramley and Watkins (1996a) and Pryce (1999). Bramley (1993a, 1993b) constructs a multi equation (structural ) model covering demand, supply and planning permissions. Since the number of new planning permissions is responsive in part to the degree of pressure in the housing market, the behaviour of the planning system in this model is in part endogenous, rather than wholly exogenous. Pryce (1999) explores which econometric approach is most suited to modelling housing supply. He adopts a two stage least squares method, also called an instrumental variables approach. His study permits the price elasticity of housing supply to vary over time and raises a possibility that supply might fall at higher prices. This could occur if developers expect further price rises in the future and withhold new supply from the market by way of increased land banking. Work by these authors shows how econometric theory can construct models that provide unbiased and consistent parameter estimates. Even so, this does not result in agreement on the impact of planning policy on property markets. The earlier work by Bramley sparked a debate with Evans (Bramley 1996; Evans 1996) partly focusing on how best to measure the impact of planning in the housing market. Pryce (1999) also disputes


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the results suggested by Bramley, arguing that the estimation method employed can lead to inaccurate results. Despite these criticisms, Bramley clearly attempts to combine appropriate econometric techniques with a consideration of how to measure the impact of planning. He demonstrates how a neo-classical economic approach can capture the importance of planning impacts while maintaining intellectual and econometric modelling integrity. In their review of the extensive debate on the impact of planning policies on demand and supply, Adams and Watkins (2002) explore the work of such economists as Cheshire and Sheppard (1989 and 1996), Evans (1988 and 1991) and Meen and Andrew (1998) as well as that of Bramley (1993a, b, 1998, 1999). They conclude (op. cit., pp. 255–6) that: Overall, [such] studies seek to provide a partial [equilibrium] analysis of the distributional effects of planning intervention in the housing market. Although estimates of the magnitude and distribution of the effects differ, it is clear that planning constraints lead to higher prices, and densities, restrictions in the quantity of homes supplied and convergence in the type and design of new homes. Although these results are perceived in generally negative terms, there are winners and losers. Higher purchase prices force new buyers to pay more, but existing landowners gain from higher returns through the inflated selling prices in land and housing markets. Developers’ profits are dented by higher land prices and lower levels of development but are also inflated by higher selling prices. Residents derive unmeasured utility from the better urban environment associated with protected green belts but lose out through higher densities and smaller lot sizes within urban areas and at the urban fringe. Crucially, however, none of these studies is able to measure the less tangible social costs and benefits.

According to Barker (2003), supply constraints on new housebuilding also have important macroeconomic consequences. Using a general equilibrium model, the Barker report suggests that if housing supply had been more responsive to demand during the period since 1994, then between 82 000 and 380 000 additional homes would have been constructed in 2002, increasing GDP by between £3 billion and £16 billion and creating between 150 000 and 650 000 extra jobs. These significant macroeconomic benefits, it is argued, would have arisen both directly from higher employment and output in construction and indirectly from giving labour and industry greater scope to locate to where they are most productive. Of course, what some portray as the negative consequences of planning constraints may be regarded by others as evidence of the successful

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implementation of more sustainable forms of development. Take density, for example. To promote sustainability, recent policy advice in England discourages developments at less than 30 dwellings per hectare and encourages those of between 30 and 50 dwellings per hectare (Department of the Environment, Transport and the Regions 2000a). The indirect implementation of this policy through market processes that encourage high density development in areas where planning constraints have ensured high land values may well turn out to be far more important than its direct implementation through either the refusal of planning permission for low density development or the introduction of development plan policies against such development. Yet, as DTZ Pieda Consulting (2002) point out, where substantial amounts of urban windfall development result from high land values, redevelopment may take place in a piecemeal and haphazard fashion instead of being shaped by planning briefs or masterplans.2 Although neo-classical analysis of state–market relations in land and property has focused almost exclusively on how the planning system constrains the supply of land for new housing development, other direct impacts of public policy on supply or demand outcomes might also be worthy of further research. For example, in many locations that have suffered deindustrialisation, regeneration policies have tackled contamination and other physical constraints, so increasing the effective land supply in the locality. Alternatively, on the demand side, it has long been claimed that the completion of new transport infrastructure can have a positive impact on localised demand (Linneker & Spence 1996). Conversely, planning blight may have a negative impact across a much wider area while alternative routes for a new highway, for example, are being resolved. In this context, however, empirical evidence reviewed by Henneberry (1998) indicates that the impact of new transport infrastructure on house prices may be small (with a range from 0 to 6%) and limited to locations within ten minutes walking time of the improved system. Henneberry’s own analysis of the impact of the Sheffield supertram on local house prices found a modest inverse relationship during the construction period. He suggests that to find the full effect on house prices would require examining a longer time period. Related work by Dabinett (1998) found that the actual impact of the supertram on house prices was so small that it could not be separately distinguished. Dabinett concludes that road corridor improvements in Sheffield had more impact in stimulating development than investment in the supertram.


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A more recent literature review on the relationship between public transport improvements and land values found that research had been concentrated in North America, with most studies concerned with impacts on the commercial property market (RICS & ODPM 2002). Almost invariably, such research identified a positive influence of transport investment on values, but this varied widely from a marginal impact in some studies to an increase of over 100% in some North American markets. Most studies related to rail-based systems with relatively few concerned with investment in other forms of public transport (Weinberger 2000). Although the RICS and ODPM (2002) study attributes the variety of research conclusions partly to methodological differences and partly to the particular circumstances of each case, the report also highlights the importance of the economic and institutional context within which any investment in public transport takes place. The report suggests, for example, that financial benefits from transport investment are likely to be maximised where they occur in the context of a comprehensive plan for the development of associated sites (Walmsley & Perrett 1992), positive development incentives and coordinated land use planning (Dabinett 1998), and supporting public policies more generally (Banister & Berechman 2000). Moreover, according to Walmsley and Perrett (1992), public transit tends to accentuate existing economic trends since such investment may serve to boost expansion areas or boom periods but, at best, is likely only to stabilise not reverse processes of decline.

Absolute and relative policy impacts on land and property markets A prime concern of neo-classical economists with the efficient allocation of resources ensures that the potential costs of government intervention in the land market are often stressed by this tradition, which regularly warns about the dangers of government failure. In this context, it is useful to consider whether public policies merely move value3 around the market, thus having only a relative impact on land and property prices or whether they have the potential to create or destroy value as well, thus also having an absolute impact on land and property prices. Again much of the debate on this, which now goes back over half a century, revolves around the planning system and not public policy in a broader sense, although many of the insights gained do have wider applicability. In theoretical terms, the concepts of floating and shifting values were first brought to public attention by the Uthwatt Report (1942) to explain the relative impact of planning intervention on land and property markets. To

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simplify matters, the theory assumes that a steadily expanding city is surrounded on all sides by agricultural land. This encourages all landowners within the surrounding area to believe that one day they will be able to sell their land for development at a higher price than obtainable as agricultural land. In practice, even without a planning system, only a relatively small proportion of owners would experience actual demand for the conversion of their land to urban development. In this sense, development value can be said to ‘float’ over a much wider area than it eventually settles on. The introduction of planning intervention ensures that development is permitted at some locations but refused at others. Consequently, any potential development value on land refused planning permission is considered to ‘shift’ to land granted planning permission. However, the values gained on sites granted planning permission are felt to be outweighed by the values perceived to be lost by owners refused planning permission, since across the area as a whole, landowners’ past expectations had been unduly raised by floating value. Theoretically, the concept of floating and shifting values was employed to argue that planning intervention had a neutral impact on land and property markets since the actual rather than the perceived gains and losses cancelled each other out. Although the theory was at the heart of successive statutory attempts to appropriate development value for the community in 1947, 1965 and 1974, its static view of land price determination, reinforced by its unrealistic assumptions, make it of limited assistance in explaining the real impact of policy intervention in what is now acknowledged to be a highly differentiated and dynamic land market. Although research on property market change within and outside Enterprise Zones (Erickson & Syms 1986), for example, has shown how policy intervention can still have significant relative impacts across time and space, it is no longer believed that positive and negative effects cancel each other and result in a neutral outcome. The most interesting research questions in this direction thus concern the extent to which policy intervention is able to advantage certain locations or types of development relative to others. Nevertheless, in absolute terms, it should be recalled that since the demand for land and property is derived from the demand for the use to which it is to be put, bidding prices for land and property as a whole should increase if policy enhances economic efficiency by, for example, tackling congestion and improving overall accessibility. From a neo-classical perspective, value will thus be lost and not merely shifted if ‘bad’ planning produces a less efficient allocation of uses, causing users to locate less


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optimally, reducing their utility and profitability and causing them to bid less for land. Conversely, ‘good’ planning must have the potential to add value, by improving accessibility and complementarity within a city, so enhancing utility and profitability and enabling users to make higher bids. For ‘as the value of privately owned land may be increased by changes in the public land use infrastructure, town planning can be seen as a means of increasing the values of private and profitable uses of land’ (Balchin et al. 1995, pp. 106–7).

Public policy and the institutional construction of demand It could be argued that discussions of the relative and absolute impacts of public policy on land and property markets illustrate the concentration of neo-classical theory on explaining and predicting market outcomes through analysis of supply and demand. However, in unpacking supply and demand as concepts and in seeking to unravel the processes by which they operate, neo-classical theory can be argued to be less helpful. Guy and Henneberry (2002c) criticise Ball’s (2002) treatment of demand, which, they suggest, apparently ebbs and flows with no relation to wider society. They argue that: ‘The fact that demand is routinely used in property debates as a standard catch-all for an incredibly complex and deeply social process is no excuse for reifying it and leaving it unexplained’ (Guy & Henneberry 2002c, p. 1473). In this context, public policy in its widest sense may have a critical role in setting the context for individual demand decisions that ultimately work their way through to price outcomes. Take, for example, the much-trumpeted 60% brownfield housing land target. This clearly implies a broadening of demand for new urban housing from specific social groups to more general sections of the population who have been at the forefront of past residential decentralisation. Recognising this, the Urban Task Force (1999, pp. 35–6) commented that: ‘For many people, the crunch comes with having children. An urban environment previously perceived as diverse and stimulating starts to appear unsafe. Schools and health services become more important. While it is therefore accepted that, at this stage in their life cycle, many people will continue to move to more suburban or small town environments, we must look to persuade more families to stay. This means looking beyond the design, planning and building of the urban environment at the role played by health, education, security and social services.’ As this demonstrates, public policy in its widest sense is clearly important in setting the context for demand.

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Public policy and institutional constraints upon supply Although institutional analysis can certainly offer neo-classical theory some assistance in unpacking and inter-relating the social, economic and political components of demand, much of its previous research efforts have concentrated on demonstrating that the supply-side, especially in relation to the development process, ‘should not be regarded as a smoothly operating ‘‘service-hatch’’ ’ (Van der Krabben & Lambooy 1993, p. 1382). Monk and Whitehead (1999), for example, examined the behaviour of different planning authorities whose approach to structure plan policies differed. They found that the impact on new supply was substantially greater where authorities permitted land with outstanding planning permission to exceed that indicated by structure plans. Healey (1991) acknowledged that neo-classical models of the development process had earlier identified the significance of planning restrictions, monopoly public land ownership and the cost and availability of credit as supply-side constraints. However, she suggested that such a limited view of supply blockages meant that the neo-classical tradition still regarded the development process as relatively unproblematic and, as a result, over-concentrated on the demand side. Since then, considerable work has been undertaken on defining and identifying a much fuller range of supply-side blockages, especially in relation to physical and infrastructural difficulties (see, for example, Parliamentary Office of Science and Technology 1998; Syms and Knight 2000; Syms 2001) and ownership constraints (Adams et al. 2001). Such research suggests that processes can be as important as outcomes and that analysis of state– market relations in land and property markets cannot be restricted merely to matters of aggregate supply and demand. In the end, however, neo-classical economists might retort that process matters little if long-run outcomes are unaffected. Take, for example, recent debate on whether land ownership constraints to development attributable to varied owner behaviour undermine the neo-classical view. On the one hand, Ball et al. (1998) contend that the simple land supply model, which assumes that landowners behave rationally and have similar preference functions, is not necessarily invalidated by varied owner behaviour. Although variations in owner preferences may slow down market responsiveness or alter the spatial pattern of development, their impact on supply elasticities is more likely to occur in the short rather than the long run. They argue that: ‘the long-run land supply function should be expected to be more elastic, because, over the long run, more landowners are tempted to sell for development’ (Ball et al. 1998, p. 68).


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On the other hand, Adams et al. (2001) point to detailed studies that have shown that unresponsive ownership strategies often take many years to change. Llewelyn-Davies (1996), for example, found that 10 of the 28 potential housing sites they examined in Strathclyde that remained vacant at least between 1985 and 1995 were affected by ownership constraints and/or reluctance to sell. In a later nationwide survey, the Civic Trust (1999) revisited 54 potential housing sites in 1998 that had originally been identified by the House Builders Federation in 1986. The investigation identified landowner retention as a dominant reason why 11 sites either remained undeveloped at the end of the period or were developed for uses other than housing. Such evidence makes it important to consider whether demand changes are ever fully reflected in long-run landowner behaviour to the extent theorised in mainstream supply models.

Insights on market failure and public policy from welfare economics According to neo-classical theory, a perfectly competitive market that experiences no external distortions will achieve a resource-efficient allocation without any need for state intervention. In this context, efficiency in resource allocation can be defined by reference to the concept of Pareto optimality, in which it is impossible to make one person better off without making someone else worse off.4 This concept provides an important theoretical benchmark within welfare economics since it suggests that, where markets depart from the conditions of perfect competition or face external distortions, state intervention may be able to help improve the welfare of both consumers and producers. Essentially, welfare economics emerged from within neo-classical economics, since individuals were seen as the best judge of their own welfare, with social welfare then calculated as the mere summation of such individual judgements (Rutherford 1994). From a welfare economics perspective, it can be argued that land has public good characteristics. There are thus imperfections and failure in land and property markets that thwart the achievement of Pareto optimality and establish the case for state intervention to improve market efficiency and enhance economic welfare. For example, the physical and legal characteristics of land make it distinctive as a commodity. Real property is locationally specific and generally immovable (D’Arcy & Keogh 1999). Land ownership in strict legal

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terms refers not to land but rather to property rights in land, known as estates and interests. These rights exist as ‘bundles’ (Denman & Prodano 1972), the ownership of which may be divided, even for a single parcel, between different individuals or organisations. Once land is developed, it creates a built environment that becomes extremely durable. Yet, the optimal redevelopment rule in neo-classical theory, which envisages the smooth transfer of land to its ‘highest and best use’, assumes that once the price of land in a new use exceeds the price of land in its current use by the cost of demolition, rational owner behaviour readily accepts sale for redevelopment (Rosenthal & Helsley 1993). However, as Bourne (1967) suggests, despite such theoretical imperative, some owners may be hesitant or financially unable to forsake their existing property. Furthermore, if redevelopment requires substantial owner investment or carries significant risk, owners may prefer to accept the existing level of returns, even if they expect redevelopment to generate higher returns in the long term. Such market imperfections arise when the theoretical conditions of perfect competition are violated within the market. Where market operations are distorted by external influences, market failure can also occur. Externalities, public goods and lost opportunities all provide examples of such distortions within land and property markets (Adams 1994). An externality arises when the production or consumption of a commodity creates social costs or benefits that market mechanisms are unable to transmit into private costs or benefits. Externalities therefore create beneficial or harmful effects, for which no payment is either made or received by producers and consumers. For example, if the owner of a derelict site undertakes reclamation and landscaping, it may enhance the value of the neighbouring properties. However, if the site is subsequently brought back into use for car dismantling, property values in the neighbourhood may decrease. In this context, sustainability policies that seek to promote high-density housing on brownfield sites may be frustrated if dereliction in the immediate or wider neighbourhood is not also tackled. However, new urban housing, if successfully developed on one brownfield site, can help raise the value of adjacent such sites. Conversely, if development is considered to be over-intensive, it may threaten the value of adjoining land. Since individual owners usually take little or no account of such social costs or benefits, land and property markets tend to produce too few beneficial externalities and over provide harmful ones. Although externalities


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are often deployed as the classic justification for town planning and associated public policies, existing measurement methods such as cost–benefit analysis and community impact evaluation (Lichfield 1996) remain open to much dispute, on account of the subjectivity of many of their assumptions. This may help explain why applied welfare economics, with all its propensity to calculate distributive costs and benefits, has had less impact on public policy in recent decades than such broad faiths as ‘competition’, ‘deregulation’, ‘privatisation’ and ‘citizenship’ (Maclennan & Gibb 1993). Much research, for example, still needs to be done to pinpoint how externalities affect relative prices between different parcels of land and how this is influenced by policy interventions. A public good, once produced, can be enjoyed by more than one consumer at the same time, without diminishing the utility derived by any other consumer. Indeed, no consumer who wishes to benefit from a public good can usually be excluded. Since it is impossible to prevent free enjoyment of public goods, no charges can be enforced at the point of consumption. This makes it very hard to organise the supply of public goods through normal market mechanisms. A lighthouse is the classic example of a public good.5 Although public goods are normally supplied by the public sector, the definition of a public good is dependent on its distinctive qualities rather than on the means of supply. Even when supplied by the public sector, public goods tend to be heavily used but underproduced. Many urban goods, such as roads, parks and the external environment of the city, display elements of publicness, although they are not public goods in the strict economic sense. Much past debate has concerned the extent to which such infrastructure could be financed by collecting the betterment it produces in the surrounding areas. New motorway construction, for example, has a direct and often considerable impact on the value of land that it opens up for development. It can be argued that for any betterment to be collected in full, it would be necessary for the state to acquire not simply the route of the motorway itself but a much wider area, the value of which benefits from increased accessibility. Since in practical terms, this might be very wide, common sense might suggest limiting additional acquisitions to areas with the greatest and most immediate development potential. In this context, it is interesting to note that much of the finance for the development of the Mass Transit Rail (MTR) system in Hong Kong came from the sale of airspace above new stations for intensive office development. In the UK, the concept of Transport Development Zones (TDZs) was proposed by the RICS in 1997 as a means to help fund improvements

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in public transport by capturing consequent demonstrable increases in property values. It was suggested they would operate by allowing substantial increases in development density above normal planning polices within declared TDZs. These would typically extend about 200 m around a major local metro/bus interchange and 400 m around a mainline railway terminus. Contributions to funding transport infrastructure would be negotiated on a case-by-case basis through planning obligations. As a more radical proposal, Brittan (2001) supported the case for a 25% tax on the estimated annual returns to landowners from the publicly financed Jubilee Line extension in London. This, he suggested, would have more than paid for the capital cost of this new transport infrastructure over a 20-year period. In welfare economics, the operation of the Prisoners’ Dilemma (Pindyck & Rubinfeld 1995) can lead to a loss of those opportunities where social benefits would exceed social costs, or at least a significant delay in their realisation. A good example of such a lost opportunity in land and property markets occurs where development is frustrated by multiple or fragmented ownership. This happens where a site has no single owner, but is divided between two or more freehold owners. In a private market, the last owner to settle is in the strongest position to drive a hard bargain with any developer who has already bought out all other owners. Without compulsory purchase by the state, development cannot proceed unless agreement is reached with each owner. Developers who seek to assemble urban redevelopment sites in multiple ownership through the private market may thus have to proceed slowly and through third parties (Marriot 1967). Alternatively, on greenfield land, housebuilders making speculative planning applications for new settlements have been known to offer owner-occupying farmers ‘one-in all-in’ option arrangements, whereby no single option is taken up by the housebuilder until all the farmers have agreed to standard purchase terms. Apart from such techniques, the realisation of potential land value in cases of multiple ownership may be wholly dependent on policy intervention. Market imperfections and failure point to the importance of risk and uncertainty in land and property markets. They imply that public policy may have a potentially significant role in determining the context for market operations by helping to reduce the extent of market imperfections and failure. In this sense, while neo-classical economics highlights direct policy impacts on supply and demand, welfare economics reveals ways in which public policy might influence market decisions indirectly but no less significantly.


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Conclusions The first part of the chapter indicated how neo-classical economics has broadened its base in both theory and method, so that it is no longer dependent on restrictive behavioural assumptions. It explored the potential of public policy directly to alter the supply and demand for land and property and to have both absolute and relative impacts on the pattern of land values. The advances made by neo-classical economics in analysing property markets have been significant. Policy relevant issues such as the price elasticity of new housing supply, adjustment to market disequilibrium, the development of submarket level analysis and the impact of planning on commercial property markets have been analysed using sophisticated econometric techniques developed in other areas of mainstream economics. Indeed, the emergence of a submarket’s level of analysis recognises that the concept of a single market is not applicable to land and property markets. Hence, an important message of this chapter for policy makers is the need to consider the impact of changes at submarket level. Turning to welfare economics, we examined the extent to which public policy might seek more explicitly to resolve market failure, while recognising that the nature and scale of intervention may have to vary from one submarket to another to reflect the differential distribution of imperfections and failure between submarkets. The chapter has highlighted the need to investigate both process and outcome and, in so doing, to ensure that perspectives derived from mainstream economics are augmented by a strong emphasis on the institutional context as for market operations. Even within neo-classical economics, we stressed the need not to view demand for land and property as some conceptual abstraction but rather as shaped by public investment in schools, hospitals and the public realm, to name but three examples. The distinction between social and private costs and benefits made in welfare economics further emphasised the broader context and implications of property markets decisions. Such themes become more prominent in the next chapter, where we review the growth of institutionalism and assess its relevance in understanding state–market relations in land and property.

Notes 1 Cointegration effectively tests whether a statistically valid relationship exists between variables. It thus attempts to avoid spurious correlations to identify true causal relationships.

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2 The term ‘windfall development’ is used here to refer to sites that unexpectedly become available for development in the sense defined by Bibby and Shepherd (1993: 49) as ‘site(s) for housebuilding the availability of which was not anticipated in a local plan or a housing land availability study’. The term does not necessarily imply windfall gains for developers. 3 We use the term ‘price’ to refer to exact sum recorded for particular transactions. ‘Value’ can be considered a more general estimate of likely price, usually based on evidence from previous transactions. 4 In practice, since Pareto optimality may be impossible to achieve, the Kaldor– Hicks criterion may provide a more accessible definition of efficient resource allocation. This states that an efficient resource allocation occurs when those who have been made better off can potentially compensate those who have become worse off and still be better off themselves. 5 In this context, it is interesting to note that in the nineteenth century lighthouses in England were built privately rather than by the government (Coase 1974). This demonstrates how distinctive qualities, rather means of supply, determine whether or not a good is public.


3 Conceptualising State–Market Relations in Land and Property: The Growth of Institutionalism – Extension or Challenge to Mainstream Economics? David Adams, Neil Dunse and Michael White

Introduction During the past 20 years, there has been a remarkable growth in institutional approaches across the social sciences. Similar principles underpin the institutional analysis of state–market relations in economics, politics, sociology and related disciplines, despite the confusion often caused by the different terminology used in each (Adams & Watkins 2002). Nevertheless, while land and property markets have been extensively discussed from an institutional paradigm, such analysis remains controversial and open to misinterpretation. In this chapter, we therefore consider whether the advance of institutionalism should be regarded as an extension of, or challenge to, mainstream economics. According to Michael Ball, institutional analysis is far more prevalent in the UK than in the USA since: ‘At the heart of the British literature is a firm belief in the efficacy of public policy. It can either override markets or steer market forces to achieve desired political ends. . . . Although the form of intervention may have changed, the emphasis is still on putting policy rather than markets in the driving force of urban development’ (Ball 1998, p. 1502). Our concern, however, is not to prioritise either policy or markets but rather to explore how the relationship between

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them can be better understood by adopting an appropriate institutional framework. Although we construct this framework initially from a base within economics (since we consider this to be perhaps the best starting point from which to analyse how markets are influenced by public policy), we subsequently draw on insights from other disciplines within the social sciences. We begin the chapter by identifying three main institutional features of the land and property markets that deserve particular examination. The first is the formal rules within which transactions occur, which may be directly or indirectly determined by the processes of governance. The second is informal conventions or the unwritten ‘rules of the game’ that may also be affected by policy decisions. The third is the network of relationships between market operators or agents and the extent to which policy induces the development of trust and the creation of other forms of social capital within the marketplace. In the middle part of the chapter, we investigate new institutional economics, concentrating on the capacity of policy processes to alter transaction costs in land and property markets. We highlight how this approach differs from welfare economics in both its explanation of market failure and its recommended remedies. We then move on to explore more radical institutional perspectives on state–market relations, which we discuss under the term ‘the political economy of institutionalism’. Since these perspectives perceive markets as a social construct, we explore how they consider ‘context’, ‘process’ and ‘social relations’ essential in understanding market operations and price determination. In the later part of the chapter, we specifically examine the inter-relationship between policy, risk and uncertainty. The fragile nature of many urban land and property markets beyond the favoured cores of certain central areas suggests that a prime role for public policy is to reduce or contain risk and uncertainty in order to enhance user, developer and investor confidence in new forms of development. We thus highlight the need to bring risk and uncertainty to the fore in analysing the impact of public policy on land and property markets and to develop new ways of thinking that handle risk and uncertainty more explicitly in relation to policy intervention. We conclude the chapter by reinforcing our earlier plea for theoretical pluralism, and specifically for permeating mainstream economics with greater institutional input and linking insights so gained with those that can be derived from the political economy of institutionalism.

Conceptualising State–Market Relations: The Growth of Institutionalism

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The institutional framework for land and property market operations Within both new and old institutional economics, institutions are often regarded as the ‘rules of the game’ in contrast to the ‘players’ or organisations (North 1990). More specifically, Hamilton (1932, p. 84) describes an institution as: ‘a way of thought or action of some prevalence, which is embedded in the habits of a group or the customs of people’ suggesting that ‘institutions fix the confines of and impose structure upon the activities of human beings.’ Taking a similar approach, Lawson (1997) applies the term institution to those systems, or structured processes of interaction (collecting together rules, relations and positions as well as habits and other practices) that are relatively enduring and can be identified as such. In this sense, it is possible to conceive of land and property markets as networks of rules, conventions and relationships (Keogh & D’Arcy 1999). Institutional economics thus opposes the simple neo-classical notion that resources are allocated merely by market processes since it holds that markets both reflect and help to operationalise the institutional structure of society (Samuels 1995). A broad range of explanatory variables, including cultural influences and power distribution, are required to explain market outcomes since ‘the market economy per se is itself a system of social control’ (Samuels 1995, p. 573). As the rules, norms and regulations are created by society to enable the market to function properly, institutions reflect prevailing power and interests. Yet, to be successful, institutions must be effective in generating ‘workable mutuality’ out of the formal and informal processes of conflict resolution from which they develop (Rutherford 1994). In this context, what is legally or culturally feasible may deserve as much attention as what is technologically feasible (Keogh & D’Arcy 1999). Although regarded as a social institution, the market is not considered by institutional theory to be a single uniform entity. Indeed, a strongly disaggregated view is taken of market structures, with each particular market seen as having its own routines and procedures alongside its own distinctive relations with a particular social culture and other institutions. ‘Accordingly, there is not just one type or set of markets – perhaps differentiated merely by the type and degree of market structure and competition according to textbook typology – but many different markets, each depending on its cultural and institutional context’ (Hodgson 1999, p. 94).

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Reflecting perspectives across the social sciences, Jepperson (1991) suggests that the ‘rules of the game’ can be divided between ‘regimes’ and ‘cultures’, which each act as important carriers of institutionalisation. The former refers to explicitly codified rules and sanctions that are monitored by a central authority, while the latter are customary or conventional in character and are not monitored in the same way.1 This is an important distinction in the context of land and property markets. The planning system might be considered an important regime, as might the rules of conduct within property professions, including their mandatory requirements on valuation methods. However, embedded cultures can also have an important influence, for example, on what is considered adventurous or conservative behaviour by market operators. An illustration of this is the notion of what should be regarded as ‘prime property’, which, while never firmly defined, is transmitted and refined from one generation of surveyor to the next. A third carrier of institutionalisation alongside regimes and cultures is formal organisations (Jepperson 1991). Our interest here is not in the formal structure of organisations per se, but the way in which organisations operate and relate to each other, accepting and reinforcing or challenging and transforming prevalent regimes and cultures. This might be described as assessing the richness of ‘actor–network relationships’, which is often termed ‘institutional thickness’, with its important implications in institutional theory for examining, for example, the nature of relations between the public and private sectors in urban land development.

New institutional economics New institutional economics is conceptually grounded within mainstream economic theory and represents an extension of the neo-classical tradition discussed in the previous chapter. As Samuels (1995, p. 578) explains, new institutional economics ‘works largely within neoclassicism, and shares its rationality, maximisation, and market or market-like orientation and likewise tends to seek, though with less formalisation, the conventional determinate, optimal, equilibrium solutions to problems.’ This view is shared by Hodgson (1989) who considers the remarkable growth of new institutional economics since the 1970s to be at the heart of developments in modern orthodox theory in contrast to approaches in ‘old’ institutional economics that derive from such writers as Veblen (1899) and Commons (1934) and challenge such basic tenets of neo-classicism as profit-maximising behaviour.2 Rutherford (1994) also sees the vast majority of work in new institutional economics as an


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extension of neo-classical economics, while Ball (1998), in his review of institutions in British property research, firmly places new institutional economics as one of four specific institutionally related theories within mainstream economics. Four main strands can be identified in new institutional economics (Rutherford 1994), the first three of which are discussed in detail below: (1)

(2)

(3) (4)

Transaction cost theory deriving from the work of Coase (1937; 1960) and developed by Williamson (1975; 1985), with its associated organisational and agency theories. Property rights theory in which economic analysis is subjected to, and interpreted within, a framework of legal concepts (Demsetz 1967; Alchian & Demsetz 1973). Public choice theory employing analytical tools from economics within political science (Olson 1965; Mueller 1989). Game theory that seeks to predict action within given institutional circumstances (Shubik 1975) and explain the evolution of social institutions (Schotter 1981).

The principle that institutions evolve to minimise the transactions costs of commodity production and exchange is well rehearsed in new institutional economics (Coase 1937; Williamson 1985; North 1990). Although Coase (1937) originally developed transaction cost theory as a means to explore the relationship between individual firms and the market as a whole, he subsequently broadened discussion to examine the arguments for and against state intervention as a method of resolving the problem of social costs and benefits. In this context, the powerful connections made by Coase (1960) between transaction cost and property rights theories have important implications for exploring the nature of state–market relations in land and property. Specifically, Coase (1960) contends that the problem of social cost would not exist in a world of zero transaction costs since there would be no barriers to private individuals and companies resolving market failure through entering into voluntary arrangements for compensatory payments. As a result, market failure cannot be intrinsically ascribed to the presence of externalities per se but rather to the existence of high transaction costs that undermine attempts to allocate ownership over such externalities. Any case for state intervention must therefore derive from the absence of zero transaction costs rather than the mere existence of externalities. Governments may well be able to deal with market failure more effectively by creating a stronger system of private property rights that

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would reduce transaction costs by minimising risk and uncertainty (Jaffe 1996) and thus enable externalities to be internalised. According to public choice theory, the process of collective decision making through systems of governance does not necessarily take place in a logical and transparent fashion since the existence of the ‘political marketplace’ (Pennington 2000) ensures that well-organised pressure groups may become disproportionately successful in the messy business of vote trading, alliance building and political compromise. Bureaucracies too may promote strategies that primarily seek to perpetuate and expand their own activities rather than to maximise economic welfare. In the end, intervention that might originally have been intended to redress market failure may well break down in the embarrassment of government failure. New institutional economists therefore tend to portray governments as promoting the misallocation of resources, and creating laws, resources and regulations that are costly to society (Rutherford 1994). In land and property markets, for example, state intervention distorts prices and encourages rent-seeking behaviour (Keogh & Evans 1992). This may benefit those actors who have learnt how best to ‘play the system’ but does not necessarily contribute to overall production in the economy. To illustrate this, it is worth highlighting the difference in value between agricultural and residential development land (Adams & Watkins 2002). Agricultural land in the green belt is normally traded at agricultural land prices, with perhaps some additional hope value to represent the possibility of its eventual release for development in the distant future. In contrast, land allocated for residential development will be traded at residential land prices. The difference between these two sectors can be quite staggering. For instance, in the south-east of England, the average value of one hectare in spring 1999 was £1 370 000 if traded as bulk residential building land, but only at £8000 if traded as mixed agricultural land (Inland Revenue Valuation Office 1999). From an economist’s perspective, development planning thus creates rentals gaps between sectors that reinforce sectoral divisions in the land market (Keogh 1985). If it were possible to buy one hectare of land in the south-east at £8000 in the agricultural land market, have it reallocated for residential development and sell it for £1 370 000 in the residential land market, a gross return of 170 times the original sum invested would be achieved. In reality, however, it may take many years and great effort before reallocation happens or planning permission is granted. In the meantime, actors in


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the development process will engage in extensive rent-seeking behaviour to capture development value. Landowners, for example, may be prepared to devote substantial resources over a long period of time to instructing the best lawyers and consultants available eventually to secure the release of their land for development, while local planning authorities may be so motivated by the prospect of substantial planning gain that they begin to evaluate alternative potential land release strategies by the comparative financial contribution they might yield in the form of ‘community benefits’. New institutional economics therefore challenges the implicit assumption in welfare economics that market failure can necessarily be addressed by government intervention. Specifically, for example, it argues that Pareto optimality is a wholly artificial construct that ignores information costs, institutional systems, property rights and real scarcity (Rutherford 1994). Indeed, inefficiency rather than efficiency may well be embedded in the economic system as a result of uncertainty, individual risk aversion or moral hazard, all of which can be costly to reduce. New institutional economics thus calls on governments to consider instead how they might assign stronger rights of property to private decision makers and create or support institutional arrangements designed to promote greater certainty within markets. This approach primarily reflects the assumptions and methodology of the neo-classical paradigm, while opening up conventional neo-classicism to useful insights on corporate formation and organisation, market systems and institutions and the operation of transaction costs as a mediating device (Samuels 1995). As Van der Krabben and Lambooy (1993) contend, the collection of information may well be an important transaction cost. They emphasise the importance of uncertainty in explaining human behaviour and point out that, because the knowledge of decision makers is severely limited, people are boundedly rational and sometimes have to behave opportunistically. Although institutions are designed to reduce these uncertainties of human interaction, they also reflect prevalent power and influence. As a result, they may succeed in reducing transaction costs only for those groups who are most powerful in the market or most successful in lobbying policy makers in their favour. On this basis, it cannot therefore be assumed that land and property markets will always be moving towards greater efficiency and lower transaction costs (Keogh & D’Arcy 1999). At present, it is fair to say that policy processes impacting on land and property markets are not explicitly designed to reduce transaction costs and that such an approach would probably require both a philosophical and

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operational switch in the policy environment. Nevertheless, insights from new institutional economics highlight the possibility that policy actions might unconsciously and inadvertently serve to increase transaction costs in such markets, and thus damage the potential for effective market operations. Uncertainty, instability or delay in the planning system, for example, all potentially increase market transaction costs, while open and effective systems of land registration will reduce them.

The political economy of institutionalism The political economy of institutionalism can be said to derive from a broad range of enquiry across the social sciences that draws on such disciplines as economics, sociology, political science and economic geography. Although the most relevant ideas from economics can be traced back to such early writers as Veblen (1899), Commons (1934) and Mitchell (1937), who laid the foundations of old institutional economics, more recent contributions from Hodgson (1988, 1989, 1993, 1999) and other ‘evolutionary economists’ have refreshed this tradition. However, many commentators who approach political economy from other disciplines often fail to notice the distinction between old and new institutional economics or to acknowledge that habits, rules and conventions have long been seen as fundamental components within old institutional economics (Hodgson 1989). Amin and Thrift (1995), for example, who commend arenas such as new institutional sociology, suggest that: ‘the new institutional economics is radically undersocialised as an approach’ (p. 100) without mentioning how the social processes of institutionalisation are explicitly addressed in old institutional economics and its modern equivalent, evolutionary economics. For instance, in recognising that contracts cannot be designed to cover every eventuality in relations between people, evolutionary economics highlights the importance of such factors as trust, honesty and decency in making business deals (Hodgson 1999). Since the ‘economy’ is much broader than the ‘market’ (Samuels 1995), it is important not to restrict economics to the study of market systems but instead to investigate the whole range of institutions that determine the form and operation of markets. This approach regards the economy as fundamentally ‘processual’ with emphasis placed not on the achievement of some ultimate equilibrium but on the means by which it evolves from one state of existence to the next. In the operation of this process, institutional change may equally well result from changing ideas, norms


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and values as from changes in either technology, the ratio of factor prices or the costs of information (Van der Krabben 1995). In practice, therefore, there is much common ground between modern theories of economic evolution and the emphasis placed by Amin and Thrift (1995) on investigating the processes of institutionalism rather than the mere existence of networks of institutions. From such varied sources derives ‘the political economy of institutionalism’ which, we suggest, takes us much further along the journey of understanding state–market relations in land and property markets than has been achieved so far in mainstream economics. Significantly, the institutions of the market are considered by political economy both to be reflective of power relations in wider society and designed to provide certainty and stability in ‘an economy that is essentially non-equilibrating, imperfect and irrational’ (Amin 1999, p. 366). Nevertheless, these institutions should not be considered as static or immutable since they are shaped and fashioned by continuous interaction between the strategies, interests and actions of market actors and the inherited economic, social and institutional structure of the market. As Healey et al. (1995, p. 14) note: ‘This activity of structuration, the interrelation between structure and agency, is actively constructed both through the material flow of resources and through the construction of ideas, images, values and norms.’

Actor–network relationships Much has been written about the importance of actor–network relationships, especially in the context of building up local institutional capacity in economic development (see, for example, Amin 1999; Amin & Thrift 1995; Macleod 1997; Raco 1997). As local authorities have moved from their traditional role as providers to take on different responsibilities as enablers, so their connections with other public sector bodies and the private sector have become more important. A key issue here is whether stronger actor–network relationships, especially between the public and private sectors, merely add to bureaucratic complexity or whether they help make the urban development process smoother. In this context, the Civic Trust (1999) has called for a more subtle mix of knowledge and skills in local government, including project management, partnership development, grant brokering, market research and development economics. Other important actor–network relationships exist in which the public sector has but an indirect influence. Crucially, for example, the property market has been portrayed as a forum within which the interests of users,

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developers and investors are closely linked (Adams 1994; Keogh 1994), even if there is plenty of room for friction between these parties in interpreting market evidence and responding to market signals. Better communication between users, developers and investors may well have wider benefits. Examples would include the speed with which consumer preferences are understood and acted upon by the housebuilding industry and the extent to which the commercial development industry responds or fails to respond to demand from manufacturing companies for good quality but not over-elaborate workspace. A variety of initiatives could be taken to enhance the richness of market networks. For example, it may be worth building on the success of online Solicitors Property Centres in Scotland that ensure rapid access to information for private buyers and sellers alike. This could significantly improve the efficiency of the private housing market by removing the need to travel around five or six estate agents in any town to collect necessary information. Nevertheless, the creation of such voluntary arrangements to improve public access to market information critically depends on whether the professionals who service land and property transactions consider it will improve their efficiency without reducing their potential to earn fees from the professional negotiation of individual transactions. This may raise the question of whether the commercial surveying network, which appears to be a close-knit community in most cities and in which market information is shared extensively between those who happen to be inside the group, needs to be subject to greater external regulation in order to empower those outside the network. To further counter lack of information, for example, recent initiatives to create a National Land Information Service (NLIS ) in England provide a model of what could be achieved with public support and finance if all existing information systems on land and property are linked together and made available online. This might also reduce the costs of market entry and exit. However, as suggested in the paragraph above, the main barriers to better information systems in land and property markets are not necessarily technical but institutional. Since knowledge is power, certain interests may well be reluctant to allow their protected information to be placed in such a public domain, since immediate availability may reduce the demand for their services (Adair et al. 1998). These various examples suggest that public policy could play a more active role in ensuring the delivery of high quality reliable information to all market actors. At the local level, this might require local authorities or


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development agencies to bring together all relevant local information on market activities in much the same way as population or employment trends are already monitored. However, mindful of the potential for government failure mentioned earlier, any such initiative would need to involve a high degree of active cooperation between the public and private sectors. In sustainability terms, it may be that stronger institutional networks in urban regeneration areas would persuade developers and investors to apply lower property yields, thereby taking a longer-term view of the period over which development would need to show a return before it could be considered viable by the private sector. Recent research indicates that once a regeneration area becomes established and rental growth is apparent, competition between investors is likely to reduce property yields (Adair et al., Chapter 8 in this volume). Interestingly, regeneration property yields in the Adair et al. study decreased by 1.44% between 1995 and 2002 compared to a 0.4% increase in yields over the same period for investment property more generally. Since regeneration areas were found to offer significant investment returns over the long term, Adair et al. (see Chapter 8) were able to argue that high levels of public sector support enable such areas to mature and develop as sustainable urban environments capable of meeting private sector investment goals.

Formal rules and regulations Turning to the importance of a supportive regime of formal rules, D’Arcy and Keogh (1999) have demonstrated how the modernisation of property law in Spain during the 1980s helped create a system of commercial lease structures that was conducive to incoming property investment. Certainly, the legal framework for property market operations provides an essential element contributing to the extent to which investment can be considered secure, liquid and profitable. Other forms of state intervention that set important regime boundaries for the land and property markets include compulsory purchase law and compensation arrangements. The planning system can also play a critical role in creating a favourable regime for property market operations. The Department of Transport, Local Government and the Regions (2001a) itself considered planning reform in England essential to tackle such perceived problems as complexity, lack of predictability, delay and insufficient customer focus. Interestingly, these

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themes have appeared many times before, with substantial debate evident on whether the British planning system is too flexible or too uncertain. In this context, Syms (2001), in assessing how best to promote brownfield redevelopment, suggests that market confidence in such locations may well be best engendered by a more flexible approach to development regulation. Conceptually, it is important to regard the law not as objective, neutral and beyond reproach, but rather as shaped by the competing ideologies or philosophies that have dominated human debate at different periods in its construction (McAuslan 1980). There is an interconnection here between law, public policy and professional approaches to valuation, especially where land values may be reflective of statutory guidance or informal advice on how land compulsorily taken is to be valued. The substantial body of statute and case law that has been built up in this field now provides an essential institutional environment within which state–market relations in urban redevelopment may be framed. Although such a well-developed area of law and practice conveys clarity on what is and is not possible, it may also serve to stultify creativity and market innovation.

Informal customs and conventions In old institutional economics ‘the notion of the sovereign consumer with given tastes and preferences is replaced by a view of individuals as subject to the pressures of advertising and salesmanship and to the prescriptive power of existing social norms’ (Rutherford 1994, p. 130). The power of institutions to mould individual aims and ideals is therefore an important reason why economists in this tradition cannot accept the orthodox notion of individualism as the basis of economic decision making. Rutherford (1994, p. 6) suggests that while it is not necessarily irrational to follow accepted social norms and conventions ‘some types of norm guided behaviour do strongly resist explanation in rational terms’. Analysis of market customs and conventions demonstrates that the British property industry is highly fashion conscious, with much of its symbolism derived from advertising and the media. This is apparent not simply in the way in which properties are marketed (and sometimes designed primarily to catch market images rather than provide long-term substance) but also in the openness of the industry to new concepts and ideas, even if they could benefit from greater testing before market launch. In this context, Guy and Henneberry (2002c) draw attention to the connections between subjectivity, image and the creation of economic


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value, commenting that even a casual browse through architectural or property magazines or a cursory inspection of promotional literature for new development soon reveals the link between property, desire and identity. A very real issue in this respect is the way in which new products are devised and marketed and the extent to which this is open to policy influence. There is a sense in which innovative property concepts, such as business parks or regional shopping centres, emerged and were disseminated as new products, only for the planning system to take some time to catch up with the innovation and later to decide how best to react. Although the impact of this on land pricing is only indirect, it is important to question how new ideas arise in property, whether they have any long-term substance and how this process might best be influenced from a policy perspective. Moreover, as sustainability grows in importance across all business sectors, it becomes important to differentiate those in the property industry whose products have changed significantly as a result of the new agenda from those who pay merely lip service to the concept in order not to lose influence with policy makers. It is evident that the property industry is a past master in taking advantage of emerging customs and cultures in the public sector and turning them to its own advantage. Research on Stoke-on-Trent, for example, revealed how concerned the city council was about views and images of the city presented to travellers on the main west coast rail line. As a result, it was particularly keen to see the redevelopment of brownfield sites that were visually prominent from the railway. Similarly, in Dundee, it became apparent that the city council had promoted the development of a technology park on an inner urban site that was particularly prominent to passing motorists. The research suggested that this concept of visual prominence, for example, could be readily exploited, and indeed promoted, by welladvised owners seeking permission for that otherwise elusive retail development (Adams et al. 2002). Although the political economy of institutionalism thus begins to open up new territory well beyond mainstream economics and identifies numerous lines of interesting enquiry, much work still needs to be done to apply, test and develop its insights in both research and policy making. In research terms, Hodgson (1999) warns that institutional economics can degenerate into extensive data gathering and naı¨ve empiricism, without producing much of general value. He calls for an emphasis on developing meaningful and operational principles of categorisation as a basis for analysis. While the political economy of institutionalism has expanded

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rapidly in recent years and serves to counterbalance the strength of mainstream economics, much further research and thought will be required if it is ever to provide a wholly alternative and fully rounded body of knowledge.

Uncertainty, risk containment and confidence building A critical function of markets in a modern economy (including urban property markets) is to convert uncertainty into risk (Van der Krabben 1995). In a world where many future events are possible, uncertainty refers to a lack of knowledge of all possible outcomes and the impossibility of specifying their likelihood. Risk refers to specific calculations of the likelihood of each possible outcome taking place. Risk theory within mainstream economics provides the basis for risk pricing in the financial markets and for the development of modern portfolio theory, which has been extensively applied in property investment (Dubben & Sayce 1992; Ball et al. 1998). What is clear from such theory is that public policy can directly affect market pricing for good or ill through the extent to which it increases or diminishes the prevalence of risk. Yet, while mainstream economics can demonstrate the potential for this outcome with theoretical rigour supported by quantitative analysis, it leaves the processes by which it can be achieved and the comparative effectiveness of different policy mechanisms in doing so, under-explored. A more thorough investigation of the potential impact of public policy on risk pricing requires appreciation of the institutional context for land and property markets. For example, in relation to urban regeneration, Adair et al. (1998b, p. 16) comment that: ‘Reduction of risk is a key issue with the result that private sector investment depends on the facilitating role of the public sector’. They call for the public sector to take a lead in confidence building measures, including a guaranteed minimum standard of infrastructure, clarity in public policy and processes, targeting of initiatives, simplified planning processes and land assembly. Uncertainty is considered to have potentially significant transaction costs in new institutional economics. This tradition places emphasis on the capacity of strong property rights to reduce risk and uncertainty by minimising negative institutional interruptions to the expected future flow of returns from investment. However, as Hodgson (1988) argues, rules, norms and conventions can play an equally important role in making the world more certain, reflecting, according to Lawson (1997, p. 182), the Keynesian view that a certain amount of conventional investment practice in the


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midst of widespread speculative behaviour can be attributed to ‘the psychological need of individuals for continuity and sameness in all they are doing.’ Does such habit persistence provide a better indicator of developer response to price change than the neo-classical theory of current price taking as Antwi and Henneberry (1995) have argued? If so, the ironic impact of developer behaviour intended to gain shelter in an uncertain world may be to make that world more volatile by continuing to bring forward development supply when a closer reading of market conditions would suggest that it is time to stop building. If this is the case, acknowledging the detrimental impact of substantial volatility in property markets especially in secondary locations, should public policy (if it has the capacity so to do) take a more dynamic view of market operations and seek to respond to the changing circumstances of the property cycle especially during its recessionary phase? Neutze (1987) argues optimistically that by clearly specifying the level and form of development that will be permissible, land use plans can reduce uncertainty and hasten the level of development. He suggests that: ‘The result would be to increase the supply and reduce the price of land for development and redevelopment’ (Neutze 1987, p. 387). Unfortunately, this position requires almost heroic assumptions about the effectiveness of both the planning system in particular and government machinery in general. For although Neutze maintains that the overall impact of land use planning is to make the whole context within which development takes place less uncertain, he acknowledges that frequent changes in plans cause increased uncertainty about the future pattern of development. As Hodgson (1999) thus suggests, democratic institutions do not cope easily with uncertainty. It can be argued that the distinctive nature of land as a commodity and the significance of imperfections and failure within the market suggest the need for some central overview and management of supply and demand as a means of making market processes more certain. However, the whole bureaucratic machinery of government may instead deliver frequent policy switches as it struggles to keep up with market trends, so aggravating uncertainty and raising transaction costs. Moreover, if policy makers act as a constraint on innovative forms of development (such was argued in the case of business parks), risk loving actors prepared to challenge the system may, if successful, have the chance to make super profits from speculative activity unnecessarily created by state uncertainty.

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Some believe that the emergence of policy out of the broader processes of governance rather than the narrow machinery of a centralised state might itself help reduce uncertainty in the market (Healey et al. 1995). An explicit concern with uncertainty from within the political economy of institutionalism might also begin to highlight the need to ensure more transparent and accessible information systems across the property industry and to promote higher standards of customer service and ‘professionalism’ in those parts of the market not currently so served. It might focus on challenging rather than reinforcing the power of those whose control of information puts them at a privileged position to benefit from market uncertainty, while attempting to develop rules, norms, conventions and customs, along with institutional capacity that enable sophisticated and smoother forms of state intervention in market processes.

Conclusions As this chapter has indicated, the rich body of thought generated by the growth of institutionalism has manifest resonance in facilitating an improved understanding of state–market relations in land and property. Since it is apparent that particular institutional approaches can be used either to reinforce or to challenge mainstream thinking, it is important to appreciate the diversity and range of institutionalism and to avoid the danger of treating all institutionalists as belonging to a single and cohesive school of thought. While new institutional economics and the political economy of institutionalism both subscribe to the pervasive influence of institutions in market operations, they exhibit significant differences in the institutions they consider matter most, in their theoretical construction of the market and in the most appropriate role for the state to play within the market. Although new institutional economics shares much in common with neoclassical economics, its emphasis on transaction costs moves analysis of state–market relations in land and property beyond earlier consideration of the overall impact of policy on supply and demand. This helps to focus attention on how governments can devise and shape institutions with the capacity to eliminate or minimise those frictions and uncertainties that collectively constitute the essence of transaction costs. While new institutional economics can claim its place in mainstream thought as an extension of the neo-classical tradition, it nevertheless disputes many of the precepts of another branch of the mainstream, namely, welfare economics. As a result, it regards direct government intervention as only one of several strategies that might be adopted to enhance the institutional


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framework and usually considers such intervention less preferable to the creation of clear property rights, reinforced by robust systems of law and administration. The political economy of institutionalism represents a challenge to mainstream economics since it emphasises the social construction of economic life and takes a strongly disaggregated view of market structures, with distinctive routines, cultures, procedures and institutions evident in each submarket. This suggests that the notion of a single policy response to land and property market operations is inappropriate and that a more sophisticated and varied set of responses is needed that reflects the institutional context of each submarket. In this context, actor–network relationships, formal rules and regulations and informal customs and conventions serve as three important carriers of institutionalism, through which public policy impacts on land and property markets. However, institutional analysis faces a particular challenge in translating theoretical understanding into a consistent basis for empirical work. For example, in terms of measuring the impact of the planning system on new housing supply, many authors have adopted a neo-classical econometric approach. While estimates differ, this body of work highlights significant supply problems. Although institutional analysis might suggest or imply that these relate to housebuilders’ behaviour as much as the planning system, it needs to develop more rigorous research methods to substantiate such claims empirically. If this can be achieved, then the more qualitative nature of institutional analysis will be better placed to complement the emphasis on empirical hypothesis testing in mainstream economics. Across the two chapters, we have thus sought to demonstrate how analysis of state–market relations in land and property can benefit both from permeating mainstream economics with greater institutional input and from linking the insights so gained with those that can be derived from the political economy of institutionalism. Since both process and outcome are essential to understanding the complexity of state–market relations in land and property, we have argued the case for theoretical pluralism, and specifically, for the explanatory power of mainstream economics to be reinforced by an appropriate institutional framework. Yet, despite the importance of a conceptual continuum and the value of weaving institutional perspectives into mainstream approaches, it is apparent that particular conceptual frameworks may be better suited to particular submarkets. Much econometric modelling in the neo-classical

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tradition is centred on investment property markets in the City and West End of London where lengthy time series of appropriate data make it at least possible to attempt such an endeavour. Taking advantage of the richness of property data and the functional specialisation of the City of London office market, Blake et al. (2000), for example, have successfully developed an econometric model of market movements, based on interacting multiple equations. It is more difficult to apply such techniques to the brownfield land market, for instance, not merely because policy influences are that much greater but also because readily available and consistent data is so much harder to obtain. Yet, while the institutional emphasis within land and property market analysis has been pioneered primarily where public policy appears significant (in urban regeneration areas, for example), there remains a pressing need to address the under-development of this approach in well-developed commercial markets that have been less reliant on policy intervention. An essential challenge in market analysis is thus to investigate not only the direct impact of policy on property transactions but also its indirect impact on the context within which transactions occur. Although we recognise the inherent difficulties confronting democratic societies as they seek to cope with ever-greater uncertainty in an increasingly complex world, we believe that public policy might explicitly seek to reduce risk in land and property markets, rather than consider such an objective to be implicit in what policy already achieves. Indeed, through the way in which risk calculations are incorporated into discount rates and thus impact on project viability, a contextual strategy that seeks to confront risk directly, especially in regeneration areas, could have a direct impact on land pricing, development potential and long-term sustainability. In this respect, we have often returned to the importance of information to the effective operation of land and property markets and alluded to the potentially significant role of the state in reducing information shortages and asymmetry. Increasingly, it will have become apparent that information is power and that, taking a political economy perspective, there are those whose control of information enables them to obtain disproportionate bargaining strength in the marketplace(s). In the end, we would therefore argue that the main barriers to greater sharing and more effective use of the information that help determine land prices are not technical but institutional. This limited illustration from political economy of how land and property markets operate does not present policy makers with necessarily easy options but it does reinforce our view that to understand the full range of potential policy impacts on land and property markets, it is necessary to move well beyond supply and demand analysis and give


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considered attention to the actual and potential policy impacts on information, confidence, risk and uncertainty.

Notes 1 As an example, entrepreneurial behaviour may be constrained by both the formal rules of bankruptcy and by individual fear of the wider cultural connotations that bankruptcy might imply. 2 As Rutherford (1994: 182) explains: ‘The term ‘‘old’’ (in old institutional economics) does not imply that the tradition is dead, dying or old-fashioned. Its use here denotes only the longer tradition of continuous and central concern with institutional issues.’


4 Planning Tools and Markets: Towards an Extended Conceptualisation Steve Tiesdell and Philip Allmendinger

Introduction A more developed understanding of the relationship between planning tools and markets should enable policy makers both to better appreciate the likely impacts of planning tools and to tailor them to achieve desired outcomes (e.g. increased efficiency, equity and sustainability). While it is widely recognised that some planning tools are better suited to achieving certain policy objectives than others, this understanding is largely implicit. By exploring how planning tools interact with and affect land and property markets, this chapter seeks to encourage more market-aware forms of planning. Many writers have commented on the limited value of a planning–market dichotomy. Alexander (2001a, p. 1), for example, asserts that ‘Planning versus markets is a fallacy . . . comparing their merits is the wrong question: in theory and in reality it is impossible to separate them.’ Nonetheless, discussions of governance are frequently polarised. One view largely ignores – and sometimes demonises – markets, seeing policy implementation as primarily contained within state processes. A polar view demands the adoption of subservient ‘market-led’ planning for effective implementation (see, for example, Sorensen & Day 1981). By failing to engage with state–market relations, each provides little effective basis for transformative action. Thus, rather than a dichotomy of ‘market’ and ‘state’, marketaware planning requires a focus on interrelationships between these – that is, the state–market dialectic.

Planning Tools and Markets: Towards an Extended Conceptualisation

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Market-aware planning is informed by, and cognisant of, the dynamics and motivations of the various actors (e.g. individuals, firms and governmental bodies) in the land and property development process and of how the decisions that are made and actions taken, in aggregate form, become market outcomes. The key issue is how particular planning tools affect the ‘decision environment’ of land and property market actors.1 While this may suggest a separation of ‘structure’ (i.e. the decision environment or context) and ‘agency’ (i.e. the actor’s capability for decision making), it is both clear that structure influences agency and that agency is not hopelessly determined by structure. Moreover, over time, agents change structure. Hence, rather than a structure–agency dichotomy, it is better conceived as a more fluid and complex structure–agency dialectic. This can be taken further by recognising that not only is an actor’s opportunity space (i.e. the space for autonomous action) constrained, it is also uneven or in Jessop’s term ‘strategically selective’ (i.e. it favours some strategies over others) (Jessop 1996). Explaining Jessop’s concept, Hay (1995) suggests contexts should not be seen as level playing fields but, instead, may have sloping contours that advantage certain players and certain strategies. Continuing the analogy, planning tools act variously to shape the decision environment or context, to define its parameters, to restructure the contours within it and/or to develop an actor’s ability to identify and/or develop more effective strategies. To explore these issues, this chapter develops a typology of planning tools based on their relationship to particular market characteristics. The typology presents four broad types of planning tool – those intended to shape markets; those intended to regulate markets; those intended to stimulate markets; and those intended to develop the capacity of market actors. It then sets out how these interact with five salient characteristics of land and property markets – demand, supply, risk and confidence, information, and appraisal. By providing an extended conceptualisation of the impact of planning tools on land and property markets, it aids the search for a more market-aware form of planning. The chapter is in five main sections. The first briefly sets out a definition of planning, while the second and third review different perspectives on understanding land and property markets. The fourth presents a typology of planning tools. By relating the typology to salient market characteristics, the final section develops an extended analytical framework.

Understanding ‘planning’ For current purposes, (public) ‘planning’ refers to intentional public/governmental interventions in the land and property development process

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intended to achieve desirable societal objectives. By including all land, planning, housing, transport and regeneration policies intended to have a general and direct impact on the use and development of land, it is clearly much broader than simply land use regulation. By planning tool, we mean policy actions or initiatives intended to affect the decision environment (and, in turn, the behaviour) of market actors and to achieve desirable societal objectives. A number of analyses of the impact of planning tools on land and property markets have appeared over the years (e.g. Hall et al. 1973; Evans 1988; 1996; Cheshire & Sheppard 1996). Often reducing planning to land use regulation, such research has generally shown that it constrains supply, leading, inter alia, to an increase in land costs, development densities and a reduction in the amount of open space. More recent research has begun to recognise the broader scope of planning activity and has deconstructed the notion of it as a homogenous and regulatory activity, emphasising instead the heterogeneous and temporal nature of planning (showing, inter alia, that land use regulation is merely one part of planning activity), of planning regimes and of land and property markets (e.g. Monk & Whitehead 1996; Bramley & Watkins 1996a; Brindley et al. 1996; Bramley 1998, 1999; Allmendinger & Thomas 1998; Meen 1998). Meaningful studies of planning tools must be located within an understanding of market operations. Planning has a crucial role not only in affecting supply and demand but also in shaping the context for, social relations within, and process of land and property development through, for example, reducing risk, providing more authoritative information, affecting the weighing of cost, benefits and risks of a particular action (and especially the period over which reward or return is sought) and increasing or decreasing the number/range of participants in appraisal – impacts of planning activity that have often been overlooked. Furthermore, because much planning activity occurs in areas where development is desirable but does not occur, a much broader theoretical framework is required than is traditionally employed. Accordingly this chapter is framed by two theoretical traditions of thought – ‘mainstream’ economics and what Adams et al. (2003) have termed the ‘political economy of institutionalism’ (PEI). These are briefly discussed in the next two sections.

Mainstream economics Mainstream economics primarily seeks to explain market outcomes and, thereby, offers some insights into the eventual outcomes of policy deci-


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sions on markets. Under this general heading, we discuss neo-classical, welfare and new institutional economics.

(i) Neo-classical economics Neo-classical economics sees prices as being determined by the interaction of supply and demand. In neo-classical theory, entrepreneurs (i.e. property developers/investors) base their (development/investment) decisions on the market signals provided by prices. As a mode of analysis, such approaches generally provide good explanations where there are reasonably competitive markets with a high number and frequency of transactions. Nonetheless, the approach has various presumptions/limitations that other approaches have sought to address:

. That through the interaction of supply and demand, markets will (rapidly) arrive at a predictable, stable and desirable equilibrium (i.e. where prices stabilise). . That markets are (reasonably) competitive (i.e. that the necessary conditions of a ‘perfect’ market, including all actors having full information, are sufficiently realised to ensure that the market is competitive). . That decisions are reversible, can be made incrementally and are costless (i.e. minimal or zero transaction costs). . That market actors behave rationally2 and that, for example, developers and landowners readily respond to price signals. In practice, however, prices signals are necessary but not sufficient, because, despite price signals, for example, a series of constraints restrict the availability of land.

(ii) Welfare economics Welfare economics investigates how market imperfections and market failure distort market operations, by, for example, preventing or delaying equilibrium between supply and demand, and/or producing differences between private and social calculations of costs and benefits. Market imperfections relate to situations where real markets fall short of the conditions for perfect competition. One of these conditions is the need for homogeneous and readily transferable commodities, but the distinctiveness of land and property as a commodity almost inevitably means that land and, by extension, property markets are imperfect. Market failures typically include monopolies, externalities, public goods and common property resources. Justifications for public intervention in land and property markets traditionally stem from this perspective (e.g. Klosterman 1985).

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(iii) New institutional economics Institutions are variously ‘relationships’, ‘organisations’ and/or ‘rules’. Institutional economists argue that institutions emerge to reduce or minimise transaction costs associated with production and exchange and, more generally, the costs of voluntary cooperation (e.g. Williamson 1985; Martin 1993; Webster & Lai Wai Chung 2003). Under the general heading new institutional economics (NIE), a number of strands of thinking can be grouped, including transaction cost theory; property rights theory; public choice theory; and game theory (see Rutherford 1994; Ball 1998; Adams et al. 2003).

Transaction costs theory Explaining why firms (i.e. organised economic activity or institutions) existed, Ronald Coase (1937) attributed it to transaction costs (e.g. information costs and costs of performance monitoring and contract enforcement). Coase’s work was subsequently extended to explain not just the emergence of firms but also systems of governance. According to Alexander (2001b, pp. 46–47), Coase argued that contrary to the classic ‘perfect market’ where prices (allowing only for production costs and profit) find their optimal equilibrium between multiple buyers and sellers, transaction costs frequently obstruct, delay or prevent the achievement of equilibrium. To minimise transaction costs, markets are modified in predictable ways, through, for example, hierarchical organisation (i.e. the emergence of larger firms and corporations through horizontal or vertical integration) and/or institutional governance (i.e. self, bi-lateral or thirdparty regulation), which provides a framework for determining and enforcing market actors’ property rights.

Property rights theory Property rights are legally defined and enforceable rights relating to the ownership and use of resources and commodities. All commodities are viewed as having multiple attributes, the rights to which are, in principle, infinitely separable and assignable. Property rights advocates propose strengthening and more clearly defining property rights to enable negotiations and private contracts between property owners. Clarifying property rights reduces the transaction costs of creating legal institutions to create, monitor and police contracts and, inter alia, protect property rights. Fundamental problems of property rights approaches involve doubts regarding


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whether all property rights can be adequately or sufficiently assigned and the extent to which all or sufficient property rights are commodifiable.

Public choice economics Regarded as the ‘economics of politics’, public choice approaches explore the institutions of the state. Highlighting the prevalence of government as well as market failure, public choice theory focuses on planning as a political activity (e.g. Poulton 1997; Pennington 2000). By emphasising how state intervention distorts prices and encourages rent seeking, public choice theory expressly challenges welfare economics approaches. It also highlights how the costs of policy formulation, monitoring and enforcement are often neglected. Although, in principle, a mode of analysis (and effectively neutral between the costs of government and market failure), an overarching theme is that state intervention generally creates more problems than it solves and its most prominent analysts have tended to advocate a vision of the ‘minimal state’ (see Ward 2002).

Game theory Game theory seeks to model/predict action within given institutional circumstances and to explain the evolution of institutions. In essence, it deals with situations where an actor’s choice of strategy is affected by the choices of others and vice versa (Ball 1992). It is therefore concerned with exploring how different institutional structures (i.e. as systems of rules) affect actors’ strategic behaviour. Although underdeveloped in land and property research, various computer simulations/modelling suggest potential applications of this work.

The political economy of institutionalism While the above discussion of NIE has mainly been concerned with economic institutions, Hodgson (1997, p. 679) defines institutions more broadly as ‘individual habits’, which, when ‘shared and reinforced within a society or group’, ‘ . . . assume the form of socio-economic institutions . . . not in the narrow sense of formal organisations, but in the broad sense of socially habituated behaviour.’ Hodgson’s definition places institutional perspectives more readily within Adams et al.’s (2003) political economy of institutionalism. Going beyond narrower ‘economic’ interpretations, political economy includes consideration of political, legal,

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social and cultural factors. Moving beyond economic institutions to recognise legal, political, social and cultural institutions, PEI treats markets as an essentially social construct, in which ‘context’, ‘process’ and ‘social relations’ are regarded as essential to understanding market operations. Various (institutional ) models of the development process (Ball 1983; 1998; Healey 1992) demonstrate how actors (i.e. individuals, companies, governmental and other bodies) not only have their own motivations, resources and constraints and objectives but, as importantly, are connected in various ways. Drawing on (and extending) Giddens’ concept of structuration (the interrelation between structure and agency), Healey and Barrett (1990) consider that the structural framework for development is evident in the resources (i.e. knowledge, information, capital, land, labour, etc.) to which agents have access, the rules they consider govern their behaviour and the ideas that they draw upon in developing their strategies. Based on their awareness of the development context within which they operate and the motivations and objectives of other development actors, actors formulate strategies to master rules, capture resources and exploit ideas and achieve their objectives. As all actors have some power, autonomy and resources (e.g. expertise), plus value systems that frequently conflict with those of other development actors and a degree of self-interest, they variously interact, plot, scheme, form alliances, bargain, negotiate and cooperate with and against each other to achieve their objectives – a process that cannot credibly be regarded as unproblematic. Thus, rather than a relatively anonymous and asocial structural process dominated by economic forces, a PEI perspective suggests the development process is also a highly social process in which the character, personality, interpersonal skills and cultural perspectives of the various actors are highly significant (see Pryke & Lee 1995; Tiesdell & Adams 2004). As mainstream economics offers insights into the eventual outcomes of policy decisions on markets and PEI is concerned with market processes, context and social relations as outcomes, these traditions can be seen as supplementary (Samuels 1995; Adams et al. 2003). Such a multi-theoretic approach should also provide a more rounded appreciation of state–market relations in the context of land and property. The potential fallacy of relying on a single conceptual model or mode of analysis (i.e. situations where the availability of a particular tool determines how the problem is seen) is neatly encapsulated in Mark Twain’s epigram: ‘If your only tool is a hammer, all your problems are nails.’


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A typology of planning tools Constructing a typology involves organising all observations (i.e. all possible planning tools) into mutually exclusive and jointly exhaustive categories (i.e. types). A ‘good’ or ‘useful’ typology has discrete ‘ideal’ types with minimal ‘fuzziness’ or spillage between types (i.e. minimising ‘within type’ variability while maximising ‘between type’ variability). For the purpose of precision, each planning tool should only fit into one category, while, for the purpose of comprehensiveness, all possible tools should fit into at least one (and preferably only one) type category. Several studies have developed typologies to classify and organise the relationships between planning tools and land and property markets (see, for example, Lichfield & Darin-Drabkin 1980; Healey et al. 1988; Vigar et al. 2000). Typically derived from a welfare economics tradition, they have tended to concentrate on market failure and state interventions (i.e. the ‘state’ part of the state–market dialectic). Less attention has been given to the impact of planning tools on market actors’ decision environments. By highlighting the context, social relations and process dimensions, we hope to provide a more rounded study of state–market relations and interactions. The typology set out in Table 4.1 and explained below identifies four basic types of planning tool – ‘market shaping’; ‘market regulation’; ‘market stimulation’; and ‘capacity building’. Each type is characterised by how it is intended to affect the decision environment of market actors. Market regulation and market stimulation stem principally, but not solely, from a welfare economics perspective, while market shaping and capacity building derive principally from NIE and PEI perspectives. The main features of each type will now be outlined and discussed.

(i) Market-shaping tools The distinctive nature of land and property as commodities, together with the imperfect nature of their markets (Adams et al. 2001), ensures that, despite their impact often being harder to discern, contextual influences, such as the strength of property rights and the availability of information, can be significant. In principle, therefore, considerable scope exists to influence land and property markets by shaping the context within which transactions take place.

Capacity building (e.g. developing actor’s ability to identify and/or develop more effective/desirable strategies)

Stimulation (e.g. restructuring the contours of the decision environment)

Regulation (e.g. defining the parameters of the decision environment)

. Actor–network relationships . Social capital . Cultural perspectives

. Direct state action

. Indirect/fiscal measures

. State (or third party) regulation . Contractual (or bi-lateral) regulation

.

.

. Development plans (e.g. public infrastructure investment plans) Regulatory plans (e.g. statutory plans/policies/strategies) Indicative plans (e.g. non-statutory plans/policies/strategies and advice)

Typical sub-types

Shaping (e.g. shaping the decision environment or context)

Planning tool types.

Intended/characteristic market affect

Table 4.1

. Arenas for interaction/networking . Partnerships/partnering arrangements . Thinking ‘outside the box’

. .

desired activities (e.g. derelict land reclamation grants) and/or taxes to discourage certain activities (e.g. tax on greenfield development) Compulsory acquisition of land Joint ventures

. Subsidies (tax breaks) to encourage more of

. Planning/development controls . Restrictive covenants attached to land transfers

. Establishing spatial vision for area

. National planning policy and development plans

. Transport infrastructure investment plans

Typical example


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Providing the overarching context within which market actions and transactions occur, market shaping tools may be more (or less) directive (i.e. intended to achieve particular and identifiable purposes/intended outcomes). How the legal framework shapes, assigns and protects property rights is, in essence, a non-directive market shaping tool and has an important effect on how an economy works – property rights in a market economy, for example, differ significantly from those in a command economy. At the macro (or state) level, property rights advocates propose strengthening and more clearly defining property rights to enable negotiations and private contracts between property owners, enabling, for example, problems of externalities to be resolved through side payments. In the context of land and property markets, plans can be considered to be the principal directive market shaping tool. Hopkins (2001, pp. 5–6) contends that plans are useful where the value of the outcome of a decision now depends on other decisions (i.e. where decisions are interdependent); the decision cannot be made in infinitesimally small steps (i.e. where decisions are indivisible); the decision cannot be reversed later without cost (i.e. decisions are irreversible); and complete knowledge of the future is lacking (i.e. where decisions have to be made with imperfect information). By coordinating (and perhaps sequencing) otherwise independent actions and by seeking to shape subsequent market actions, plans increase the possibility of achieving a desired (future) state. The most important attribute of a plan is the provision of information. Plans have three primary information-giving/uncertainty-reducing functions. First, by providing general (coordinating) information. Such information may reduce uncertainty about, for example, any property or land parcel’s development value and/or the possibility of positive/negative neighbourhood effects. Second, by indicating government intentions regarding, for example, the location of public facilities and infrastructure. Third, by providing information about regulatory polices (e.g. permitted and prohibited land uses; development densities; development forms; etc.). The more authoritative a plan’s information content, then the more likely that the plan will be a significant consideration in market actors’ decision making. Authoritativeness is, however, socially constructed. Its value, as Alexander (2001b, p. 65) observes, depends on the development control system’s rigidity/flexibility and the government’s reputation for commitment to its stated intentions and its reliability in carrying them out. In more flexible and discretionary systems (such as the UK) the information content of statutory plans is less reliable than that in more rigid systems (e.g. the Netherlands).

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The implementation of plans may be through voluntary action (i.e. because it is beneficial to the particular market operator), through some form of compulsion (i.e. some form of state or contractual regulation) and/or through some form of incentive or encouragement (i.e. through financial inducements or other forms of market stimulation). Effective implementation may often depend on the market-shaping tool working in conjunction with other planning tool types. More generally, public choice analysts would also identify potential problems arising from, for example, special interest politics and poor coordination within the public sector (Alexander 2001b, p. 64). Three general types of market-shaping plan can be differentiated by their method/probability of effective implementation.

. ‘Development’ plans set out a set of intended public actions, such as the spatial pattern (and temporal schedule) of proposed investments in public infrastructure. Representing an authoritative commitment, the plan should be effective in shaping market-initiated development (i.e. because anticipation of public investment influences location decisions). . ‘Regulatory’ plans set out the basis for (land use) regulation, usually by state (i.e. third party) regulation. Involving an element of compulsion, regulation should ensure conforming development (but see discussion below). . ‘Indicative’ plans set out ‘guidance’, which is essentially advisory. As the plan can be ignored by market actors, compliance with the plan’s objectives is voluntary. Although Alexander (2001b, p. 64) accepts a plan’s ‘persuasive power’ may ensure conforming development, this may be the result of anticipating and incorporating development that would take place in any case or, alternatively, the plan’s database (i.e. as a common information source) being sufficient to ensure conforming development. If, however, the plan’s objectives propose development that, in some way, is contrary to market trends, then the indicative plan must be supported by local political, economic, and/or social coalitions of long-term stakeholder interests. As discussed below, the planmaking process may involve building this support coalition.

(ii) Market regulation tools Market regulation tools seek to regulate and control market actions and transactions. Whereas plans affect decisions by providing information (and, thereby, shaping the context for decision making), regulations affect decisions by restricting the set of choices available (i.e. by defining the boundaries of the actor’s opportunity space).


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In land and property markets, market regulation planning tools generally operate by the state taking certain rights in land and making subsequent exercise of those rights subject to express permission. An obvious example in the UK as the nationalisation of development rights in land through the Town and Country Planning Act 1947, which meant that all acts of ‘development’ required planning consent. The grant of planning consent, thereby, releases certain developments rights in the land. Under a zoning system, the detail of the zoning ordinance constrains the exercise of development rights within the zone (i.e. it restricts the range of uses, building height and land coverage that may be undertaken on a particular parcel ). Regulation tools may also be created voluntarily under force of contract, through, for example, restrictive covenants attached to land transfers. ‘State’ or ‘third party’ regulations are, in principle, universally applicable (i.e. except where expressly stated otherwise, they apply equally everywhere and to everyone). By contrast, ‘contractual’ or ‘bi-lateral’ regulations are only applicable to the parties to the contract. In each case, however, regulation is ultimately enforced through legal sanctions. Regulations enforced by law may be considered to be ‘regimes’ and are subject to legal sanctions (i.e. by loss of rights, by fine, by imprisonment and/or other sanctions specified in a contract). Regulations that are not (or cannot be) legally enforced can be termed ‘cultures’ and are usually subject to ‘social’ sanctions (i.e. loss of group privileges, group disapproval, conscience, harmed self-interest, etc.).3 Alexander (2001b, p. 68) argues that: ‘Strict regulation linked to relatively rigid statutory planning is the best assurance of knowledge about development potential based on predictable and authoritative information.’ However, he also notes that this has several shortcomings. Its success, for example, depends on the effectiveness of its implied and enacted sanctions and the efficiency of enforcement, which ‘ . . . often fall short of what is necessary, let alone ideal, especially when political will is needed to resist market development pressures’ (Alexander 2001a, p. 68). More generally, the third party responsible for regulation may act in its own, narrower interests rather than in any collective or societal interest or, alternatively, it may act in a partisan rather than in a neutral fashion.

(iii) Market stimulation tools By limiting choices, regulation often operates negatively – it can, for example, direct demand away from specified locations, but cannot generally attract demand (and development) to a location. In practice,

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therefore, regulation tools are often supplemented by market stimulation tools that seek to facilitate markets working better (i.e. they ‘lubricate’ the market by, for example, having a direct impact in financial appraisals). While regulations put parameters around (and, thereby, constrain) a market actor’s opportunity space, market stimulation tools do not limit choices but simply change the contours of that opportunity space making some strategies more (or less) advantageous to market actors. Such tools can be considered as having two main types – fiscal measures and direct (state) action.

. Fiscal measures – One way of encouraging market operators to produce more desirable outcomes (where ‘desirable’ is defined by the policy maker) is through subsidies and tax breaks that encourage more of a desired activity and/or taxes that discourage an undesired activity. The tax may be of a general nature (e.g. development land taxation) or a more specific nature (e.g. on a particular type of development). . Direct (state) action – Market stimulation tools may also work through direct state intervention into land and property markets. Examples of this include the provision of public infrastructure (e.g. roads, public spaces, etc.) and the state’s powers to acquire land ownership of private land through expropriation or (compulsory) purchase. Having acquired the land, the state may subsequently carry out development itself and/ or prepare the land for development (e.g. through land reclamation, provision of infrastructure or simply through site assembly) and release it to private developers.

(iv) Capacity building tools Capacity building tools build the abilities and capacity (e.g. skills, knowledge, networks, rules of operation, working practices, etc.) of market actors in various ways. While they could be regarded as simply further forms of market shaping or stimulation tools, they are better seen as means of facilitating the (better) operation of the other planning tools. An express focus on capacity building, in effect, creates space for a wider consideration of both NIE and PEI, whereby, it may constitute a means of institution design to support market shaping, market regulation and market stimulation tools. Appearing in many forms and difficult to define precisely, this planning tools includes the more diffuse – though nonetheless ‘real’ – phenomena of building relations, ‘trust’ and/or more commonly ‘social capital’ among the range of development actors. Although developing such relations and measuring their impact and significance is problematic, this does not


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detract from their value as a planning tool. In developing effective capacity building measures, public agencies often demonstrate both innovation and insight into market processes – the capacity built serves both to overcome obstacles to development and to release development potential. Three interrelated sub-types of capacity building tools are of particular interest here and are described under the next three subheadings. Actor-network relations Transaction cost theory suggests institutions will/should emerge spontaneously where market transaction costs are greater than the organisation and other costs of an appropriate institution. But, because appropriate institutions may frequently fail to emerge, action by a third party may be necessary to encourage them to emerge or to emerge sooner, thereby, encouraging market actors to produce the desired outcomes. Given that lower transaction costs increase the likelihood of the action occurring, capacity building might be directed at establishing stronger actor–network relationships that, inter alia, reduce transaction costs. In this regard, capacity building could involve establishing formal and informal arenas for the exchange of information and knowledge and for building or extending networks and relationship webs. The relationships – or institutions – so created may enlarge the pool of available resources and/or create synergies. It is, nonetheless, debatable whether stronger actor– network relationships on their own help make the development process smoother or whether they merely add to bureaucratic complexity. But, if and when stronger relationships result in greater trust and mutual respect among market actors, then the ‘social capital’ created can be advantageous (Adams et al. 2003). Social capital Defined as ‘ . . . the productive resources mobilised by interpersonal networks of cooperation and coordination for mutual benefit.’ (Amin 2003, p. 124), social capital has been recognised as an economic resource (see Coleman 1990; Putnam 2000). Hutton (1996), for example, highlights how economic agents need relationships of trust and mutual commitment as a basis for coping with problems of unequal power relationships, uncertainty and information asymmetry. He asserts that where market economies work well, there are ‘committed owners’, ‘long-term relationships’ (i.e. based on the expectation and need for ‘repeat custom’ rather than oneoff ‘transactions’) and ‘heavy penalties for those who cheat on bargains’ (Hutton 1996, p. 93). Capacity building in this regard may, therefore, involve building social institutions as means of reducing costs, risks and uncertainty in market-based transactions.

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Building social capital among actors may encourage or enable collective action, such as the effective implementation of plans. The process of plan making is, therefore, both a focus of deliberation and one of building social capital (Hopkins 2001, p. 42). Fostering a sense of ownership among market actors, it may be instrumental in convincing/persuading them to act in accordance with the resultant plan, vision or strategy. As Hopkins (2001, p. 38) argues, a ‘vision’ is a ‘ . . . desired future that can work if people can be persuaded that it can and will come true.’, which works by challenging and perhaps changing cultural perspectives and beliefs about how the world works (i.e. the relationship between actions and outcomes) and about the likelihood of success (i.e. raising aspirations or motivating effort). Cultural perspectives Taking many different forms, cultures may emerge, develop and be sustained in different ways (e.g. through informal agreements about conduct/ operation/practice; customs; internalised rule systems; and conventions). Cultural perspectives establish how ‘things’ are perceived, interpreted and appraised and may be an essential editing mechanism in situations of information overload. Nonetheless, cultural perspectives – and their concomitant operational rationality – can be constraining. Relevant cultures of interest here include those of the professions involved and the cultures (or ‘house views’) developed and sustained within particular firms and organisations. Products of education and expertise, professional cultures provide a predisposition to frame situations and problems in a particular way: ‘ . . . to analyse them according to specific categories, to synthesise them into specific structures, and to represent them in specific verbal, graphic, or numerical ways.’ (Fischler 1995, p. 21, from Guy et al. 2002, p. 1193). As Harvey (1989, p. 2) has observed, each profession has a cultural world view – viewing the same street scene, architects may appreciate the rules of architectural design, visual rhythms and historical references; traffic engineers see street design, traffic flow and ways of improving the phasing of lights; property developers see buildings in terms of rents per square foot, zoning regulations, set-backs and height limitations. Similarly, firms and other organisations develop ‘house views’ relating to how they see the world, how they make that world and, in essence, how they conduct business with the world. Again these may be particularly constraining – Pryke and Lee (1995, p. 338), for example, observe how in constructing a particular ‘house view’, investment advisors share in the development of a ‘limited universe of choice’ in respect of investment criteria. Whatever their other benefits, professional and house cultures are partial and may inhibit the seeing of the world in a more holistic – or even a


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simply different – fashion. Such cultural perspectives may also establish a relatively unquestioned ‘conventional wisdom’, which, inter alia, inhibits the development and exploitation of new ideas. While new ideas will ultimately be tested in the marketplace, because of entrenched culture perspectives and attitudes, they may never get to the market to be tested. Such cultural perspectives affect the reception given to proposals for new production and development types. The phenomenon of ‘loft living’ was an emergent market in the early 1990s, but the conventional wisdom of much of the development industry of the period was that people do not want to live in former industrial units with bare brickwork and exposed wooden floorboards and pipework. It has since become a well-established market sector. Challenging – and perhaps – altering established or conventional cultural perspectives involves creativity and encouraging actors to think ‘outside the box’ (i.e. outwith their normal cultural perspectives) – a process that may be alien to professions that have traditionally been highly risk averse. In The Creative City, for example, Charles Landry (2000, p. 12) describes how ‘linearity’ and ‘box-like thinking’ characterised his discussions with property developers, planners and accountants. By viewing the same ‘objective’ criteria differently, those outside the mainstream often bring different cultural perspectives to bear. Such perspectives challenge how the supposed underlying ‘reality’ is perceived, interpreted and appraised. This may be a crucial factor in stimulating development. Studies (Adair, et al. 1998, 1999, 2003; Guy et al. 2002) of institutional investment in regeneration areas, for example, have shown how attitudes to risk, to the period of time over which return is expected and to design (i.e. as means of reducing risk) affect decisions whether to invest or not. In the field of regeneration, for example, it is notable that some highly successful investment and development firms, such as Urban Splash in Manchester, have succeeded by bringing a different ‘house view’ to bear (see Guy et al. 2002).

Market characteristics By considering salient market characteristics, the typology presented in the previous section can be developed into an analytical framework or matrix for assessing the market impacts of planning tools. To do this, we need to consider whether, how and to what extent a particular policy tool affects a market actor’s decision environments. Thus, in addition to the classification of basic policy types, a set of five land and property market characteristics can be identified.

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Impact on demand for activity Key questions here are as follows – Does the tool change (i.e. increase or decrease) the magnitude (i.e. number/amount) of demand for the activity? Does the tool change the spatial incidence of demand for the activity (i.e. by re-direction, displacement or deflection)? Are there spatial effects on demand for the activity (i.e. increasing/reducing the magnitude or the spatial spread of positive/negative externalities)? Does the tool change the temporal incidence of demand for the activity (i.e. serving to bring forward or delaying demand for the activity)?

Impact on supply of activity Key questions here are as follows – Does the tool change (i.e. increase or decrease) the magnitude (i.e. number/amount) of the supply of the activity? Does the tool change the spatial incidence of the supply of the activity (i.e. by re-direction, displacement or deflection)? Are there other spatial effects on the supply of the activity (i.e. increasing/reducing the magnitude or spatial spread of positive/negative externalities)? Does the tool change the temporal incidence of supply of the activity (i.e. serving bring forward actions in time or delay the supply of the activity)?

Impact on risk and confidence Key questions here are as follows – What are the generic types of risk confronting the market actor? Financial risk? Development risk? ‘Political’ risk? Does the tool change (increase or decrease) the risks associated with the activity?

Impact on information/uncertainty Key questions here are as follows – Does the tool affect the quantity and quality of information available? Does the tool increase or reduce the information available for decision making about the activity? Does the planning tool increase the authoritativeness and reliability of the information available? Does the planning tool provide information that enables greater coordination of actions (i.e. situations where the coordination of otherwise independent actions results in a ‘whole’ that is greater than the sum of the parts)? Does the planning tool provide information that enables more strategic decision making by the market operator? Does the planning tool reduce information asymmetry (and associated moral hazards) between market operators? Does the planning tool enable


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information to be verified (i.e. by reference to an external or independent authority)?

Impact on (financial) appraisal In this instance ‘appraisal’ refers to the market operator’s decision making (i.e. whether and to what extent the proposed action is beneficial to the actor). Key questions here are as follows – Does the planning tool affect the weighing of costs, benefits and risk of a particular action? Does the planning tool reduce/extend the period over which reward or return is sought? Does the planning tool increase (or decrease) the number/range of appraisal participants, thereby enabling a more holistic or comprehensive appraisal (i.e. by including actors from the public, private and voluntary sectors, or, more generally, by including a wider range of stakeholders)? Combining these market characteristics with the typology of planning tools provides an extended conceptualisation of planning tools, market characteristics and market impacts (see Table 4.2). Note that the text in Table 4.2 is framed in terms of the generality of the planning tool type – particular planning tools may differ from this general picture.

Conclusions The framework presented in this chapter identifies four broad planning tools – market shaping, market regulation, market stimulation and capacity building – and relates these to a set of salient market characteristics. It serves to advance the study of state–market relations in several ways. By providing a comprehensive landscape of the intended impact of planning tools upon land and property markets, it provides a menu for future action, a way of assessing policy proposals and a framework against which to assess actual outcomes against the assumptions and theories underpinning the use of planning tools. It highlights, for example, how some tools may be better suited to affecting some forms of change than others. If regulatory tools have a major role in affecting an increase in the supply of land for development but only a minor impact on expanding demand, another planning tool (for example, a market stimulation tool such as financial inducements) may need to be used in combination with the regulatory tool to achieve the desired policy objectives. While providing a heuristic device to help develop a fuller conceptualisation of the impact of planning tools on land and property markets, there are inevitably circumstances and instances where planning tools have

Market regulation . State/third party regulation

plans

–

(Potentially) changes spatial incidence of demand for activity

. Indicative

plans



. Regulatory

Market shaping . Development plans

Demand for activity

Where regulation is disincentive to activity, relaxing or simplifying may encourage supply of activity. Changes spatial incidence of activity (typically by prohibiting it in certain locations)

(Potentially) changes spatial incidence of supply for activity



Supply of activity

Table 4.2 Planning tools and market characteristics.

By strengthening property rights, reduces risk and increases confidence – e.g. strict regulation reduces risk of adjacent development reducing site value (i.e. regulation protects site value)

Authoritative information reduces ‘political’ risk

Authoritative information and state commitment to (say) infrastructure provision reduces ‘political’ risk Authoritative information reduces ‘political’ risk

Confidence/risk associated with activity

Increases availability of information about activity (i.e. what is permissible and what is not)

Provides authoritative information about activity and enables market actor to act more strategically Provides authoritative information about activity and enables market actor to act more strategically Provides authoritative information about activity and enables market actor to act more strategically

Information about activity

–

Assists decision making by providing (authoritative) information about interdependent decisions/ developments Assists decision making by providing (authoritative) information about interdependent decisions/developments Assists decision making by providing (authoritative) information about interdependent decisions/developments

Appraisal/decision making

cultural perspectives

. Challenging

capital

. Building social

Capacity building . Fostering actor–network relations

. Direct state actions

Market stimulation . Fiscal measures

regulation

. Contractual/bi-lateral

–

–

–

(Potentially) changes spatial incidence of demand for activity. May increase (or reduce) effective demand for activity

–

–

–

–

–

Improves supply of desirable activity by lowering effective costs of supply (Potentially) improves supply of desirable activity

Reduces risk by providing opportunity to build working relationships and trust between market actors Reduces risk by building trust between market actors. Joint commitments increase confidence. Provokes reconsideration of risk

State commitment to activity reduces development risk

Reduces financial risk

Reduces risk

Provides new information about activity

Increase availability and verifiability of information

Increases availability of information about activity

–

–

Increases availability of information about activity (i.e. what is permissible and what is not)

Challenges conventional appraisal approaches

Potentially enlarges number of participants in appraisal

(Potentially) enlarges number of participants in appraisal

–

–

–

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impacts that are not captured by the text in Table 4.2. It is, therefore, important to stress the normative nature of this analysis. While we can determine what we might expect to happen in terms of likely outcomes, the framework requires further empirical work to determine what actually happens with regard to any particular planning tool or – since planning tools are rarely used in isolation – a package of planning tools. Such empirical work constitutes a research agenda for the further development of the ideas and concepts presented in this chapter. The framework is therefore a point of departure to be further informed by more fine-grained and empirical research.

Notes 1 This is not a one-way process, of course. Market actors will seek to change public policy in ways that are more beneficial to their particular interests. 2 The notion of rationality in economics is frequently misunderstood. As Ball (2002, p. 1456) argues, the belief that economics assumes real-world agents always behave rationally misunderstands the modelling approach of economic analysis. Rather than a model of reality (i.e. rationality should not be seen as a necessary axiom of all behaviour), rationality merely provides a methodological standard against which to measure departures from the ideal. 3 This distinction is adapted from Jepperson (1991). Regimes are imposed and explicitly codified public and private rules and sanctions that are monitored, and sometimes policed, by a central authority. Cultures, by contrast, are customary or conventional in character and are not monitored nor policed by a central authority in the same way.


Par t 3 UNRAVELLING THE RELATIONSHIPS

Section 3.1 Section 3.2 Section 3.3

Modelling Relationships 79 Measurement Issues 128 Surveys and Case Studies 167


5 Modelling Local Housing Market Adjustment in England Glen Bramley and Chris Leishman

Introduction This chapter presents the results of a robust and soundly-based economic model of the operation of local housing markets in England as part of a wider regional and national system. This model provides insights into the way these markets operate, and helps to understand the way market forces and responses combine to generate problems of great policy concern, including both the booms which affect housing affordability in some regions and the weaknesses which trigger low demand and failing markets in other areas. This model can demonstrate the quantitative impact of certain policy measures as well as a wide range of economic, demographic and environmental factors on market outcomes, and as such provide a guide to future policy. The level of analysis is relatively aggregated, focusing on the ‘housing market area’ of a city or group of towns. As such it emphasises the broader forces and constraints on supply and demand, which condition the behaviour of the overall market in each area over time. These provide a context for more micro-level choices and behaviour, which may be observed in studies of individuals or of small neighbourhoods. This work is timely in relation to key policy concerns in Britain at the present time. The housing market has experienced a major and prolonged boom, particularly in the south of England, where it has exacerbated problems of affordability of housing for key workers and many other households.

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There are some concerns that this boom may ‘overshoot’ unsustainably, leading to a subsequent correction with wider recessionary impacts. Underlying this development is a recognition that housing supply in Britain is particularly low and unresponsive, which has led to the Barker Inquiry into Housing Supply (Barker 2004) promoted by the Treasury as a result of its concerns about the wider macroeconomic impact of housing market imbalance in the context of attempts to secure convergence of the British economy with mainland Europe. At the same time, in other parts of the country the housing market has displayed serious symptoms of low demand and, in extreme cases, housing abandonment, with serious adverse impacts on local communities (Bramley et al. 2000). Our contribution draws on one major new and unique dataset, which was created as part of a major DTLR (now ODPM) project to establish a migration model for England. It provides a panel dataset describing variation over both space and time, so enabling simultaneous exploration of dynamic adjustment processes and local/spatial differences. Its scope in terms of types of variables included is wide-ranging, and it is particularly suitable for relating housing market performance to key demographic processes, especially migration, which link these local markets together in a wider system. Nevertheless, we have enhanced this basic dataset by obtaining and incorporating a number of additional measures which are particularly useful for the analysis of certain key policy questions. These measures relate to the land supply available through the planning system for new housing development, the share of recycled former urban land within this, particular spatial attributes, and the perceived status of certain areas as ‘low demand’. This chapter proceeds by firstly referring to the relevant research background. It then describes the core dataset and the additional variables included in more detail. The modelling framework is set out and the particular econometric approach adopted is described and justified. The relationships estimated are then used in simulations to examine the impact of certain policy and contextual variables on market performance in different types of area. The final section summarises the conclusions emerging from the work so far, both on policy issues and on the research approach exemplified.

Background Econometric models of housing have hitherto followed two traditional approaches: (a) macro and regional time series; (b) hedonic house price

Modelling Local Housing Market Adjustment in England

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models and related urban models. The former have become increasingly sophisticated in their treatment of dynamics and trends over time, including the application of cointegration techniques (Meen 1989, 1996b, 1998, 1999; Giussani & Hadjimatheou 1991; Muellbauer & Murphy 1997; Munro & Tu 1997). While the major emphasis has been on cycles in demand and house prices, there have been quite sophisticated models of new building supply and/or investment in this tradition (Tsoukis & Westaway 1994; Ball 1996b). However, up until now this type of research has lacked any data to get a handle on land supply and the influence of planning policy and practice. The latter type of research, the dominant tradition in US urban economics, typically involves cross-sectional analysis at either individual property level or at small area level. On the whole, most of this work has focused on demand, including measuring the demand for different attributes within the housing bundle, and measuring the demand/willingness to pay for environmental benefits (e.g. Michaels & Smith 1990). Cheshire and Sheppard (1989, 1997) explore this approach to measuring some of the benefits of planning regulation, for example urban open space protection. A good part of this kind of work has been concerned with the emergence or maintenance of ‘submarkets’ (Schnare & Struyk 1976; Goodman 1982; Adair et al. 1996; Maclennan & Tu 1996), and part of the reason for the existence of these may be supply constraints (Maclennan 1977, 1982; Ball & Kirwan 1977). Another tradition within urban economics has been the analysis of housing supply in terms of the intensity of land use (following Muth 1969; see reviews in Bartlett 1991 or Bramley et al. 1995, chapters 2 and 8). However, there seems to have been relatively little intra-urban and cross-sectional analysis of new build supply itself. US studies have mainly employed hedonic house price modelling to examine the effects of zoning and the recent proliferation of growth controls; useful reviews include Fischel (1990), Monk et al. (1991), Podogzinski and Sass (1991) and School of Planning and Housing (2001). There is a surprising lack of unanimity across different studies, with some appearing to show that land use controls are not an effective constraint while others argue that they are and that they do push up housing and developed land prices. Cheshire and Sheppard (1989, 1997) is the most directly comparable British work. Their general conclusion is that British planning control, by containing urban extension, tends to increase house prices moderately but has its main (adverse) welfare impact by increasing density. These conclusions are consistent with the qualitative observations of Evans

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(1991) concerning the type of housing produced under the relatively tight planning regime characteristic of southern England. Also consistent are the findings of Monk et al. (1996) showing much higher differentials between housing and agricultural land values in areas of greater planning constraint. One of the present authors has undertaken a series of studies employing a cross-sectional inter-urban scale of analysis with an explicit planning-land supply component and medium-term simulations of the impact of varying planning policies (Bramley 1993a, b, 1999; Bramley & Watkins 1996a). This work confirms the low supply elasticities found under British conditions, and estimates that system-wide changes in land release would impact on house prices with elasticity in the range 0.15 to 0.30. This model draws explicit attention to the transmission mechanism between planning policies and outcomes, part of the basis for subsequent debate with Evans (1995). As a flow-of-units model it also addresses the possibility that planning may be in part responsive to the market as well as the significance of landowner/developer behaviour in influencing the flow of development. Pryce (1999) reworked the same dataset suggesting among other things that supply may tend to ‘bend backwards’ at higher price levels. One of the central problems in empirical work in this area is that of how one actually measures planning restraint. Bramley (1998) reviews a range of measures and considers their logical interrelationships and some pitfalls of interpretation. In the US, Malpezzi (1996) reviews and tests measures of restrictiveness. Many of these measures are only effectively available on a cross-sectional basis, limiting the scope for time series or panel models. However, relying on cross-sections means that results are vulnerable to problems including the ecological fallacy, inconsistently defined (heterogeneous) areas, and an inability to model dynamic effects (lags and expectations) adequately. Some US work may be better than that in Britain, by utilising more consistently-defined metropolitan housing market areas, and/or by recognising the phenomenon of varying degrees of competing jurisdictions. The problem that demand-side adjustments to constraints in one area may be substantially displaced to other surrounding areas, but to differing degrees, means that econometric models applied to sets of such areas may not adequately capture price impacts. Some of the more obvious directions for development, within the econometric tradition, can be identified from the above. One is to incorporate systematic data, as it becomes available, on land supply and construction costs/conditions, into models within both the time series and the cross-


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sectional modes. Second, one may try to analyse supply at the micro-level, looking, for example, at supply at the level of sites rather than local authorities or zones. This in turn may entail a mixture of discrete and continuous variable modelling. Third, one may try to link the analysis with more explicitly spatial approaches, for example, spatial interaction models (common in related fields like migration) or methods which take more explicit account of spatial autocorrelation and/or spatially-varying response parameters. Fourth, one may seek to marry the time and crosssectional dimensions of variation in a panel data framework (Mayer and Somerville (2000) provide a recent US example). This chapter exemplifies the last two of these lines of development. We describe an inter-urban panel model of the supply and demand for new private housebuilding in England, providing a link between time series and cross-sectional research on housing markets. The model builds on an earlier, simpler version reported in Bramley (2002).

Datasets Central to this exercise is a unique new dataset created within a recent government-funded project to develop a policy model to predict internal migration flows within England and Wales. This model, known as MIGMOD, has been developed up to an operational level and is described in detail in a report recently published by ODPM (2002). The research team was led by Tony Champion, Stewart Fotheringham and Phil Rees and included Glen Bramley. The migration model comprises two main submodels: (1)

(2)

A panel model to predict gross out-migration flows by age and sex for 98 zones in England and Wales, calibrated on 15 years of data for 1983–97. A spatial interaction model to distribute flows across the 98 zones, calibrated cross-sectionally but separately for each origin zone and each age/sex group.

These are combined to predict flows conditional on assumed values of determinant variables. We do not use this full migration model directly, but take account of its findings on the determinants of migration in designing our own somewhat simplified submodels for migration. In order to calibrate MIGMOD, a uniquely large dataset was constructed, including a significant number of time-varying factors. This dataset is valuable for our purposes because it includes a wide range of economic,

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demographic, housing, social and environmental factors which potentially can influence housing market outcomes. It has also been subject to substantial scrutiny and systematic testing. For the purposes of this exercise data from additional sources have been incorporated. One is the annual ‘PS3’ return made by local planning authorities to central government (between 1987 and 1997) recording the stock of land with outstanding planning permission for new private housing development. The main measure used in the panel dataset (plsh) is the number of housing units capacity, divided by the population in thousands and by a notional average household size of 2.5 to yield a stock measure per 1000 households (or occupied dwellings). Unfortunately, this dataset has been temporarily discontinued, but it may be possible (as a refinement) to make use of data from another return, the ‘PS2’ on planning applications/ permissions, to extend the coverage and improve the imputation for missing values. We also use the PS2 data to provide an additional measure of the flow of new housing planning permissions (plfh), in the same units. The share of new housing built on recycled urban land (pnbu) has been incorporated in the dataset as an annual time series. This is a key policy variable and we are interested in estimating its impact on housing supply and market performance. A moving average is used to deal with some imprecision in the timing of returns. Low demand indicators have been taken from the DETR study of Low Demand Housing and Unpopular Neighbourhoods (Bramley et al. 2000). These measure the percentage of private and public sector stock defined by local authority officials in 1998/9 as being ‘low demand or unpopular’ based on a set of criteria supplied in that research. We mainly use these to categorise areas. House prices are measured in real terms and standardised using a typology of four ‘optimised’ housing types developed by the University of Newcastle. The data source is the Nationwide Building Society, one of the larger national mortgage lenders which has maintained data over a 20-year period. New housing output is measured by private completions as per local authority returns based on building control records. These data are not wholly reliable but they are the best available. This measure (pqpr) is expressed in annual units per 100 existing dwellings (all tenures). Other variables are listed and defined in Appendix 5.1 (see page 103). The geographical framework for this exercise has been essentially determined by the migration data underlying MIGMOD. We use a set of


85

90 zones in England, based on the former Health Authority Areas. These comprise: (a) each metropolitan district as a single zone; (b) London boroughs combined into small contiguous groups; (c) non-metropolitan counties treated each as a single zone. It is not pretended that these zones constitute an optimal set of housing market areas (HMAs). They are a convenient approximation, some being smaller than ideal HMA units and some rather larger. However, the variables and procedures used to reflect spatial characteristics, as described below, make adequate allowance for this variation.

Modelling framework and techniques One important way of trying to improve models of the relationships between planning, land supply, housebuilding and house prices would be to use panel datasets with a time dimension as well as a cross-section of local housing market areas. Following Hsaio (1986), it can be argued that this should improve the robustness of the models in the following ways:

. Greatly increasing the number of observations and degrees of freedom; . Discriminating between competing hypotheses which might be consistent with the same ‘facts’; . Enabling some account to be taken of lags and dynamic effects, subject to the limitations of length and detail of time series; . Enabling the stripping-out of the effect of certain omitted variables (e.g. fixed area effects). In addition, other potential benefits include lessening the danger of results being distorted by ecological correlations and spatial autocorrelation, and bringing important macroeconomic variables like interest rates into the models. The basic structure of the model developed in this chapter focuses on five key variables which are central to the operation of local housing markets:

. Real house prices (adjusted for mix, hprice); . New private construction output (completions, pqpr); . Out-migration (persons, expressed as a rate based on resident population, outm_r);

. In-migration (persons, also as a rate on same resident population, inm_r);

. Vacancy rate in housing stock (private tenures, pvac).

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These variables are endogenous, determined simultaneously as the market adjusts in each period and area to changing external factors affecting supply and demand. These external factors are a mixture of national time-varying ones (e.g. mortgage interest rates), locally-variable but relatively fixed attributes of localities (e.g. urban settlement character, class structure, climate), and factors which vary both locally and over time (e.g. unemployment, income, land availability and type). We use a basic model where the above key variables are expressed in levels. There is an implicit assumption underlying this approach, that local markets achieve equilibrium each year. This first model does not study the dynamics of adjustment in much detail, although some use is made of lagged and log-difference terms. A possible alternative model might postulate an adjustment process involving changes in vacancies (a measure of excess supply) related to changes in prices. We deal with the simultaneous relationships involved in this model structure through using the standard technique of two-stage least squares (2SLS, also known as the instrumental variables method). In the first stage, the endogenous variables are predicted using all exogenous variables. In the second stage, these variables are predicted using subsets of relevant variables, including where appropriate predicted values of endogenous variables from the first stage. This should yield unbiased estimates of the structural relationships involved. So, for example, the equation for price level includes the endogenous variable private completions (pqpr) as one of its explanatory factors. This enables the effects of changes/differences in supply to feed back into prices. Migration and vacancies variables are also included in this way, where appropriate. A dataset of this kind, consisting of geographical zones which sit alongside or near each other, creates potential econometric problems associated with spatial autocorrelation. Values of many variables, and residuals of regression equations fitted to these variables, may display patterns of association between values in spatially adjacent areas. This violates the standard assumptions of the regression model, that observations are independent of each other. We tackle this problem by creating special terms based on the values of the endogenous variables in adjacent zones. Adjacent may be defined in two ways: by spatial contiguity, or by a weighted function based on the inverse of distance from the zone in question to all other zones. The results reported here utilise the first of these options, contiguity. These spatial terms (e.g. hprice_astk) are included in the structural equations, where their function is to strip out this effect and leave the parameters on all the other variables unbiased.


87

There may also be an element of serial autocorrelation present, and nonstationarity. The shortness of our time series limits our ability to address these problems in sophisticated ways. In certain equations we also make use of the values of particular endogenous variables in adjacent zones as additional predictors. For example, we hypothesise that new housing supply in adjacent zones may affect house prices or out-migration. Decisions about which variables to include/omit in each equation involve judgements based on both prior theoretical expectations and the results of other work. In the case of the migration equations, we were strongly influenced by the results of the exhaustive modelling efforts undertaken by the MIGMOD team. In the case of house prices and new house building, we have been mainly influenced by our own earlier work. It should also be noted that the prior efforts of the MIGMOD team largely eliminated serious problems of multi-colinearity between most of the variables in the dataset. We have largely avoided using dummy variables. While they give an apparently good fit, they do not contribute to an explanation of what is driving the model. However, we do include a dummy for London (londondu) in some equations, recognising that London has unique and extreme characteristics. In this initiative we are very conscious of the idea that market behaviour and responses may vary between different kinds of area, or between different phases or states of the market. There has been much discussion of ‘non-linearity’ as an expression of this in modelling jargon. We have approached this mainly by the tactic of splitting sample into three broad ‘types of area’. Given the central focus in this study on areas at opposite ends of the housing market spectrum, those characterised by low demand and those under high pressure, we have adopted this dimension for this exercise. Our basis for splitting is an independent set of data, that provided by local authorities to the DETR study of low demand in 1999. Category 1 areas are those where no low demand was reported in the private sector and had house prices above the national average. Category 3 reported non-trivial amounts of low demand areas in the private sector (more than 1.25% of private stock, half the national average figure), and typically these areas would also have significant low demand in the public sector as well.

88


Category 2 are the remaining, intermediate areas, with limited private sector low demand and/or lower than average house prices. Figure 5.1 shows the geographical location of these three categories. Category 3 (the darkest shaded areas) are as expected mainly in the northern conurbations, but also include some less urban areas in the midlands and remoter rural/coastal areas. Category 1 (the lightest shading) are mainly in London and the south-east, stretching into the south-west along the M4 corridor, with a few isolated hot spots elsewhere. There are other ways of examining non-linearity. One approach is to include non-linear terms in key variables, for example quadratic terms. Another possibility is to have interaction terms combining two or more variables. It is interesting to observe differences in the parameters estimated for certain variables between the different categories of area. This should help to suggest ideas about the differential responses, and provide some initial testing of hypotheses in this regard. However, in the final

Figure 5.1

Categorisation of levels housing demand in England.


89

model used for simulations we use a single set of parameters estimated for the whole dataset, including some lagged and squared terms. This is partly because, when conditions change, particular areas may shift from one category to another.

Estimation of key relationships Tables 5.1 to 5.5 show the results of our final model. For each of the five key endogenous variables (price, private completions, in-migration and out-migration, private vacancies) we show four equations, the first estimated on the whole sample and the remaining three estimated for each of the three categories separately. It should be noted that the three category sub-samples are of roughly similar sizes. Table 5.1 shows the house price equations which achieve a reasonable fit, explaining up to 89% of the variance. The level of explanation and the number of significant predictors fall off a bit as you move from the whole sample to the sub-categories, particularly Category 3. Most variables act as expected, although there are some apparently perverse effects: the mortgage interest rate (mgintr_z) is positive. Income (hhinc) is not consistently significant, but it may be affected by some colinearity with class structure (occmig), which is strongly positive. Particularly interesting are variables which appear to have a stronger or different effect between high and low demand areas. Urban settlement pattern (urbsing) is more strongly negative for prices in low demand areas. Vacancies depress prices overall, but this effect is reversed in high demand areas. New private build (pqpr) in the locality reduces prices, as expected, but the effect is greatest in low demand areas. However, new build in adjacent areas has a bigger negative effect in high demand areas. Migration is most significant in intermediate areas, where in-migration seems to have a negative effect and out-migration a positive one, and overall the direction of these effects seems counter-intuitive. The private completions equations in Table 5.2 involve fewer variables and achieve a poorer fit. However, the fit in high demand areas is good by past standards. Previous work (Bramley & Watkins 1996a) found crosssectional supply models like this worked less well in recession than in boom. The supply model is driven mainly by planning-determined land supply (stock and flow), and by house price growth (and hence expected profitability). Supply is modified (negatively) by urban conditions, picked up here mainly by the urban variable rather than the proportion of urban land variable (pnbu_ts), dropped from the final model because it was not

* ** ** *** ***

1.9 2.5 2.24 5.7 5.8

4511 5237 86 879 52625

810 481.0 0.894 0.893

N F statistic Rsquared R bar sq ***

*** *** *** *** *** ** *** *** *** ***

23.7 4.8 7.6 3.3 3.03 2.12 9.9 5.7 4.7 3.1

t stat.

0.499 882 3635 82 0.785 2042 248 161 120 108

Coeff.

Full sample

228 114.3 0.892 0.884

0.488 2065 55793 58.5 0.447 6281 425 253 133 199 18725 4418 1476 67 1568 90257

Coeff.

***

11.5 4.5 5.9 1.2 0.9 2.79 6.44 3.4 2.03 1.1 3.4 0.8 0.3 0.9 4.3 4.8

t stat.

Category 1 (high demand)

*** ***

***

*** *** *** **

*** *** ***

294 125.5 0.871 0.865

0.559 28 32721 130 1.193 2858 298 134 111 102 3209 7274 9194 22 881 72115

Coeff.

***

16.2 0.1 4.4 1.53 2.86 2.0 6.6 3.6 3.4 1.78 1.1 2.1 2.74 0.4 4.3 4.9

t stat.

Category 2

Five equation system segmented by low demand category: real house price levels equation.

hprice_astk asunem occmig urbsing hhinc Pvac (end) hpriceld hpriceld( 1) hpriceld( 2) Pqprsc (end) Pqpr_astk inm_r (end) outm_r (end) ter mgintr_z constant

Variable

Table 5.1

*** ***

** ***

*** ** *** *** *** *

***

***

288 64.1 0.790 0.778

0.513 92.9 36528 494 0.777 88 264 201 125 204 1000 198 4880 129 464 71928

Coeff.

***

12.5 0.3 4.0 5.4 1.38 0.0 7.5 4.9 3.4 3.0 0.3 0.1 1.44 2.32 1.94 4.4

t stat.

Category 3 (low demand)

** * ***

*** *** *** ***

*** ***

***

***

5.00 7.3 5.2 7.7 1.62 3.7 2.24

t stat.

*** ** **

*** *** *** ***

228 47.8 0.666 0.652 ***

3.7 5.9 0.1 5.7 1.38 1.68 2.18

t stat.

Category 1 (high demand) 0.217 0.0101 .00031 0.152 0.089 0.00251 0.0268

Coeff.

Note: these regression models are weighted by population.

810 106.5 0.545 0.540

0.245 0.004 0.0098 0.149 0.066 .00240 0.0136

pqpr_astk Plsh( 1) Plfh( 1) Urban londondu Hpriceld( 1) vacdrl constant

N F statistic Rsquared R bar sq

Coeff.

Variable

Full sample

* ** **

***

*** ***

294 28.2 0.473 0.456

0.283 0.00379 0.0066 0.230 0.385 0.00391 0.00847

Coeff.

***

3.7 5.1 2.22 3.2 1.91 3.7 0.9

t stat.

Category 2 *** *** ** *** * ***

288 31.7 0.507 0.491

0.144 0.00239 0.0178 0.222 0.068 0.00137 0.00958

Coeff.

***

1.66 2.37 5.6 3.8 0.8 1.39 1.0

t stat.


Table 5.2 Five equation system segmented by low demand category: new private housebuilding completions (% of stock).

* ** *** ***

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significant. The price effect is strongest in intermediate areas; the lower coefficient and significance in higher demand areas may hint at the possible ‘backward bending supply’ phenomenon. In-migration is significantly positive in this equation, particularly in high demand areas. The simulation model allows for feedback from new build to affect the other key endogenous variables. Broadly, more new build reduces house prices, increases in-migration and increases vacancies. The two migration equations in Tables 5.3 and 5.4 are similar-looking and achieve a reasonably high fit. We explain 66–70% of in-migration variation and 69–93% of out-migration variation. The fit is poorest in intermediate areas, and the fit in high and low demand areas is not markedly inferior to that for the whole sample. This suggests that segmenting the areas may improve the model in some cases. There are a number of variables where the effects seem to be perverse, relative to prior expectations, particularly in the in-migration equation. This applies to unemployment (overall and in high demand areas), for example. Turnover is the key factor in explaining some of the results, for example, urban areas have high turnover reflected in both the in- and outmigration equations. The coefficients in the two models can be compared for each explanatory variable, as they are in the same units, and by subtracting the out-migration coefficient from the in-migration coefficient one can see the implied effect on net migration. On this basis one can say, for example, that more urban areas tend to have net more out-migration in high demand and intermediate areas, and less in low demand areas. These comparisons confirm the following impacts on net migration: unemployment positive in high demand areas, negative elsewhere; job growth positive in high demand areas, negative elsewhere; income positive. Social rented completions are positive, as are private completions except in low demand areas. For house prices, the findings are interesting. High house price levels are associated with markedly higher rates of in-migration. This suggests that people are positively attracted to areas of high prices more than they are repelled by considerations of affordability. This is consistent with the view that house purchase is influenced as much or more by investment motives as it is by considerations of cost/value for money/affordability. High house prices may be interpreted as a signal that future price prospects are better,

810 132.0 0.699 0.694

0.371 0.0458 0.0481 .0000649 0.0909 0.0116 0.182 0.397 0.0045 0.0114 5.54E-7 .0000104 .00010 .00020 0.105


Coeff.

Variable

inm_r_astk Asunem Empgro Hhinc Londondu Nonwh Pqsr pqpr (end) Ter Urbsing Pop hprice (end) Hpriceldq Hpriceldq( 1) Constant ***

2.12 4.11 4.1 5.1 0.8 2.72 3.5 3.0 1.67 11.5 6.8 5.8 2.02 3.5 0.4

t stat.

Full sample

*** *** *** * *** *** *** ** ***

*** *** *** ***

228 34.9 0.697 0.677

0.0768 0.0108 0.0693 .0000241 0.0176 0.0106 0.0627 0.369 0.00864 0.0075 6.38E-7 .0000177 .000089 .00040 0.744

Coeff.

***

0.8 3.7 3.3 5.0 0.52 1.3 0.7 1.1 1.4 4.8 2.07 3.8 0.4 2.12 1.0

t stat.


**

*** ** ***

*** *** ***

294 46.7 0.701 0.686

***

8.8 6.9 1.85 4.8 6.3 2.80 1.0 2.70 0.4 11.91 9.0 3.6 1.42 0.1 5.9

t stat.

Category 2 0.458 0.105 0.0274 .0000826 5.18 0.0205 0.0758 0.388 0.00098 0.0950 8.45E-7 .0000081 .000069 .0000055 2.376

Coeff.

Table 5.3 Five equation system segmented by low demand category: in-migration.

***

*** *** ***

***

*** *** * *** *** ***

288 42.3 0.685 0.668

0.308 0.0077 0.0354 .0000740 1.832 0.0248 0.141 0.141 0.00155 0.0598 6.48E-7 .0000125 .000174 .000305 1.031

Coeff.

***

11.4 0.5 1.84 2.5 8.1 4.2 1.73 0.7 0.4 12.5 5.8 3.9 1.83 3.4 1.93

t stat.


*** *** *** * *** *

* ** *** *** *

***


outm_r_astk asunem climate empgro londondu mgintr_z nonwh occmig rgdpch_z urbsing pop hprice (end) Hprice_astk hpriceldq constant

Variable

Table 5.4

810 359.0 0.872 0.869

0.325 0.108 0.0778 0.0309 0.0828 0.0161 0.0389 1.95 0.0529 0.0051 4.25E-7 .000016 .000011 .00010

Coeff.

***

9.1 12.3 2.59 3.5 0.8 1.86 10.8 10.8 4.5 6.7 7.0 5.4 4.6 2.68

t stat.

Full sample

* *** *** *** *** *** *** *** *** ***

*** *** ** ***

228 194.7 0.932 0.928

0.345 0.091 0.256 0.062 0.781 0.00378 0.0381 2.22 0.0069 0.00432 1.63E-7 .000015 .0000096 .000058

Coeff.

***

16.5 5.1 3.8 5.1 4.5 0.2 7.8 6.4 0.3 4.5 1.1 2.33 2.13 0.5

t stat.


***

** **

***

*** ***

*** *** *** *** ***

294 45.4 0.710 0.694

***

10.3 2.15 3.7 1.59 2.7 2.45 0.8 4.2 2.4 7.7 5.6 0.2 1.1 0.9

t stat.

Category 2 0.484 0.031 0.155 0.0195 1.77 0.0249 0.0061 0.989 0.0338 0.0553 4.32E-7 7.3E-7 .0000032 .000033

Coeff.

Five equation system segmented by low demand category: out-migration.

*** ** *** ***

*** **

*** ** ***

288 107.6 0.856 0.848

0.176 0.079 0.045 0.0065 1.02 0.0371 0.0207 1.98 0.0734 0.0373 7.34E-7 .000019 .000015 .000168

Coeff.

***

2.54 5.7 0.9 0.4 5.0 2.75 3.21 6.1 4.0 9.0 7.4 3.9 3.8 2.21

t stat.


*** *** *** *** *** *** *** *** *** ** ***

** *** **


95

or correspondingly (in lower demand areas) that the risk of price collapse or negative equity is less. Interestingly, the effect is stronger in high demand areas (the speculative factor). High house prices are also a signal of areas of a higher social character/reputation, better environment, etc. On the outmigration side, there is also a positive relationship with prices, at least in some areas. This is consistent with evidence of higher transactions turnover being associated with higher prices. This would create a secondary reinforcing influence on in-migration (more houses coming on the market for in-migrants to buy). The reported version of the model, designed particularly for simulation purposes, makes use of lagged price changes (log differences and square of log differences). This formulation makes it the pattern that is more complex and difficult to interpret. The positive effects on migration seem to be more clearly associated with high demand areas. These findings on house price effects echo those of the MIGMOD team. The implications of this finding are not necessarily very helpful in terms of smooth market adjustment. They may inject a disequilibrating tendency into the market. Higher prices increase net in-migration and (ceteris paribus) this will tend to increase the excess demand that caused the high prices to start with. This process may be seen as one of the reasons the British housing market is so unstable and so regionally variable (other factors like inelastic supply in high demand areas also play a part, of course). The overall model allows for feedback from migration to affect the other key variables. The effects on price are counter-intuitive (in-migration negative, out-migration positive), but these effects are not significant in all areas. In-migration has a positive effect on new building. It also has this effect on vacancies, which are reduced by out-migration (again, counterintuitively). The model for vacancy rates in the private sector seems to be relatively successful (see Table 5.5). The proportion of variance explained is high, 89% overall rising to 97% in high demand areas. Vacancy rates are strongly positively related to urban conditions, although this effect is slightly lessened in London. The labour market effects are as expected: higher with high unemployment, lower where job growth is positive. Environment plays a part, with favourable climate reducing vacancies, and so does type of housing, with more terraced housing increasing vacancies. New housing supply, in both private and social sectors, increases vacancies. This could be either or both of new houses standing vacant


pvac_astk asunem climate empgro londondu Ter urban Pqsr Pqpr (end) Inm_r (end) Out_r (end) hprice (end) Hpriceld hpriceldq constant

Dependent outm_r

810 446.7 0.894 0.892

0.024 0.0432 0.141 0.0227 0.481 0.0038 1.18 0.180 0.341 0.0572 0.161 .0000125 0.0105 .0003 4.247

Coeff.

***

1.86 8.1 9.0 4.2 9.9 3.1 51.9 7.4 9.7 2.2 5.3 14.1 6.8 5.5 34.6

t stat.

Full sample * *** *** *** *** *** *** *** *** ** *** *** *** *** *** 228 521.7 0.972 0.970

0.0040 0.0404 0.00116 0.0381 0.584 0.00116 1.09 0.0743 0.286 0.0125 0.0675 .000012 0.0285 .000957 4.366

Coeff.

***

0.1 4.3 0.3 5.3 8.5 0.6 42.4 2.59 5.1 0.4 1.6 7.6 7.6 5.7 19.5

t stat.

Category 1

*** *** *** ***

*** ** ***

*** ***

***

294 33.9 0.630 0.611

0.0476 0.00898 0.176 0.0169 0.610 0.00382 1.14 0.369 0.311 0.135 .309 .0000144 0.00717 .000169 4.611

Coeff.

***

2.25 0.8 7.1 1.6 1.9 1.95 10.3 6.2 5.3 2.36 4.7 8.6 2.92 2.83 16.9

t stat.

Category 2

Table 5.5 Five equation system segmented by low demand category: private sector vacancy rate.

* * *** *** *** ** *** *** *** *** ***

***

**

288 173.1 0.899 0.894

0.0165 0.0427 0.193 0.0244 0.469 0.0014 1.58 0.229 0.372 0.161 .327 .0000132 .00558 .000098 4.622

Coeff.

***

1.1 5.7 8.2 2.8 4.8 0.9 24.2 5.8 7.5 3.2 6.6 9.6 2.17 1.1 23.6

t stat.

Category 3


97

awaiting sale or let, or displacement effects elsewhere in the market. Higher house price levels reduce vacancies (a basic demand effect), but house price growth may raise vacancies albeit at a diminishing rate (a possible speculative effect). The effects of migration are counterintuitive, being positive for in-migration and negative for out-migration, although these effects are less apparent in high demand areas. The overall simulation model allows for feedback effects from vacancies. In practice, these are only significant in the price equation.

Policy simulations The discussion of individual relationships is informative and provides significant insights into market processes. However, we are most interested in simulating the impact of possible policy measures or packages which might be tried, in different types of area, and what their effect would be on market outcomes, taking account of the complex interactions which our multi-equation model seeks to capture. The policy outcomes we are interested in are principally those identified by our key endogenous variables: house prices, output, net migration and vacancies. In high demand areas, for example, we would be interested in policies which might have the effect of reducing house prices (to promote affordability), increasing output, and possibly reducing net in-migration (although this is debatable, depending on one’s attitude to the balance between supporting economic growth versus protecting the countryside). In low demand areas, by contrast, we would be interested in increasing house prices (refloating collapsed markets), reducing net out-migration, reducing vacancies and (possibly) reducing new housing supply (to help correct excess supply). Within the model there are certain variables which are definitely subject to policy influence: planning can change the amount of land available, and the mix of urban land; new social housing provision can be changed. Policy may, indirectly, be able to change the value of a number of variables measuring local and regional economic performance: employment growth, unemployment, incomes. It may be able to improve local environments (reduce vacant/derelict land and air pollution). It may be able to change the local social environment (less crime) and improve the housing stock. We have developed a simulation model based on the final set of equations. The simulation is set up to run over a seven-year period from the end of the

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calibration period (1997). Results for a particular scenario (set of input assumptions) are compared with a baseline simulation where all input values are held constant in real terms. In the simulation model, in-migration must be subject to some wider system-wide constraint. The migration model refers to internal migration within the UK. This means that net migration for the system as a whole is fixed. In-migration in total cannot exceed the total number of outmigrants predicted in the simulation (plus or minus a constant amount of net migration from the rest of the UK to England). We have imposed a control factor into the in-migration function to give effect to this constraint. We have also found it necessary to impose some constraint on house price changes. Sometimes, the effect of this constraint is to force the migration constraint to be eased slightly. The combination of these constraints helps to prevent the system spiralling out of control. Another feature is the treatment of (sub-)regional effects. The approach adopted to spatial autocorrelation gives us terms in our model which are a function of the values of key endogenous variables in adjacent areas. These terms have a strong impact in all of the models. In some instances, values in adjacent zones of other endogenous variables are also included in the equations. These values are now recalculated in each cycle of the simulation. These variables, together with the migration effects mentioned above, help to transmit impacts from one part of the system to other parts. The model is capable of testing the impact of changes in many determinant variables on housing market outcomes. Here we test the impact of changes in combinations of eight variables. Four of these refer to the demand side: unemployment, job growth, income and mortgage interest rates. Four refer to the supply side: land stock with planning permission, flow of new planning permissions, social rented new supply, and vacant and derelict land. These are the main variables retained in the current model which appear to be amenable to policy influence in the short to medium term. As already explained, the model looks at impact in terms of five housing market outcomes: house prices, private new build, vacancies, in-and out-migration. These changes are measured as percentage differences from the baseline at year 5 in the simulation. We concentrate mainly on changes applied to two of the three categories of area, high and low demand areas. The scale of changes applied are towards the upper end of what might be reasonably feasible over a period of four to five years. The changes applied to high and low demand areas are broadly


99

symmetrical. We look separately at packages of policies on the supply side and on the demand side, then at packages which combine both types of measure.

Simulation results: high demand areas Table 5.6 summarises the results of this exercise. Looking first at the high demand areas, the supply side policy packages here consider increasing land with planning permission, with a doubling of the flow of permissions and a progressive increase in stock of land available by 20% per year (so doubling in four years). We also double social rented new supply. This leads to quite a large (though not proportionate) increase in private output of 38%. However, the impact on price appears modest (a reduction of 4% in year 5), with a moderate increase in vacancies (7%) and an increase in net in-migration. The relatively low impact on prices may be partly a product of this particular version of the model, which does not include the regional effect of increased supply in adjacent areas. A demand-side scenario of progressively increasing unemployment (by 10% per year), reducing job growth by 20%, and reducing income progressively by 1% per year, would have generally smaller effects on the housing market in high demand areas. Prices would fall by just under 3%, output

Table 5.6 Results of simulations using pooled five equation system and constrained in-migration after five years. Supply side scenarios (% difference from baseline) Increase in HD Reduce in LD – Liverpool

hprice

pqpr

pvac

inm_r

4.0 4.3 4.5

38.1 22.6 28.1

6.9 3.6 2.1

4.5 3.5 2.3

outm_r 0.1 0.2 0.2

Demand side scenarios (% difference from baseline) Reduce in HD Increase in LD – Liverpool

hprice

pqpr

pvac

inm_r

outm_r

2.7 4.4 7.4

1.2 1.7 4.3

3.5 3.2 4.0

1.5 3.9 10.2

8.2 11.8 17.8

Combined scenarios (% difference from baseline) HDA’s only HDA’s with LD LDA’s only LDA’s with HD – Liverpool

hprice

pqpr

pvac

inm_r

outm_r

8.0 6.6 10.0 8.7 11.9

43.0 39.4 26.0 24.3 32.3

11.0 10.4 7.2 6.8 6.0

7.8 6.0 8.6 7.3 12.9

9.4 8.3 12.7 11.6 17.6

100


would hardly change, and vacancies would rise slightly (3.5%). Outmigration would rise by a rather larger amount (8%), reducing net inmigration into this area. A rather larger effect in dampening house prices, which fall by 8% on average. Combining the two scenarios produces an impact which is broadly the sum of the two components. House prices would fall by 8%, output would increase by a massive 43%, vacancies would increase by 11% and outmigration would increase slightly more than in-migration. So this combined scenario could make a substantial impact in correcting the current imbalances in the high demand areas (essentially the south). These impacts would be slightly attenuated if symmetrical strategies were followed in low demand areas (see below), but the overall impact would still be of similar magnitude (e.g. 7% lower prices, 39% more output). The overall direction of this policy package on the supply side is similar to the ‘Sustainable communities’ policy package (ODPM 2003a) involving new growth areas in the south-east, although it should be noted that this announcement places emphasis on supporting rather than suppressing economic growth. This may mean that its impact would be less than our model suggests.

Simulation results: low demand areas Broadly equivalent but opposite packages have been tested for the low demand areas, mainly in the north and midlands. On the supply side we halve social housing output and the supply of new planning permissions, and progressively reduce the stock of outstanding permissions by 20% pa to a level of 60% below the initial figure. We also progressively reduce vacant and derelict land by 15% pa to a level of half that pertaining at the start. Again, the impact of supply-side measures like these is modest on prices, which only rise by 4.3%, and on vacancies which only fall by 3.6%. New build output falls by 23%, and in-migration falls slightly. The demand-side package this time involves progressively reducing unemployment (by 10% pa), raising job growth (by 20%) and raising income (1% pa). This has a slightly larger impact on prices in low demand than in high demand areas, with rises of 4.4%. There is also a bigger fall in out-migration. This is clearly a favourable set of outcomes for low demand areas. The impacts could be greater in the more extreme cases, for example, Liverpool where prices might rise by 7.4% and out-migration fall by 18%.


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The supply- and demand-side scenarios have very similar impacts on prices in low demand areas. Again, the combination of supply- and demand-side packages would have a larger effect. Prices would rise by 10% (12% in Liverpool), output would fall by 26% (32% in Liverpool), and outmigration would fall by 12% (4% points more than the fall in inmigration, but more in terms of absolute differences). Vacancy rates would still only fall by 7%. Combining this scenario with the mirror image package in the high demand areas, as described above, would only slightly attenuate these effects. House prices would rise by 8.7% in this case. Overall, this simulation gives grounds for cautious optimism about the possibility of using a combination of measures on the demand and supply sides to help to ‘refloat’ these currently stricken markets. The direction of the effects is generally as one would expect. Also the effects are proportionately greater in some more extreme cases such as Liverpool. However, the magnitude of these impacts is not very dramatic, relative to the quite large changes instituted in the policy variables, particularly on the supply side. It may not be practically or politically feasible, for example, to reduce new planning permissions for housing or the new build of social housing by as much as a half. Notwithstanding Regional Development Agency targets, it is not easy to raise economic growth rates by 1% pa or to reduce unemployment by 40% in economies with continuing structural weaknesses.

Conclusions The model described in this chapter represents a step forward in economic analysis of local housing markets in the UK. The panel dataset permits more robust econometric estimation and the model incorporates a wider, more balanced range of determinant factors than previous studies. We believe that a coherent set of relationships governing the key market outcomes – house prices, new building and migration – can be established. There are good grounds for expecting some differences in market responses between different types of areas, particularly between ‘low demand’ and ‘high demand’ areas. Price and output models are less well determined in the low demand areas. Urban conditions have more negative impacts on price in low demand areas. New private building in the locality depresses prices more in low demand areas, but this effect may work more at a sub-regional level in high

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demand areas. New building attracts more in-migration in intermediate areas but not in low demand areas. Output in adjoining areas generates more out-migration in low demand areas, while high house prices deter out-migration more in these areas. A number of influences on vacancies are stronger in low demand areas: climate, urban conditions, new build output and migration effects. In general, variables relating to local economic well-being (unemployment, jobs, income) have mixed effects. Higher house prices are associated with greater positive net in-migration, in high demand areas. This effect can be explained, in terms of investment/precautionary motives and turnover effects, but it is one factor which may impart disequilibrium/instability tendencies to local housing markets. New private housebuilding tends to draw in more net in-migrants. Therefore releasing more land in one area will have an effect which leaks out into a wider market area. Overall, the feedback effect from land supply to house prices appears weak on current simulations, compared with earlier studies such as Bramley (1999), but further work is needed on regional adjustment. New social housing has effects which appear to be positive in higher demand areas but negative in lower demand areas. Improving environmental conditions may be one way of achieving improved outcomes in low demand markets (see also MIGMOD study, University of Newcastle 2002). Improving housing conditions does not appear to have consistent or significant effects. Simulations take account of all the interactions within the model, including some element of dynamic price adjustment and sub-regional interaction. They demonstrate that packages of policies can be expected to have significant impacts on key housing market outcomes. Both demand-side policies, via the economy and the labour market, and supply-side policies through planning, land and social housing, appear to have significant impacts, which are of similar magnitude in terms of price. Plausible if quite strong combinations of policies of both kinds could, if applied in a concerted way, achieve substantial impacts. For example, policies could reduce house prices by 8% after five years in high demand areas, while increasing ouput by 40%. Corresponding policies of reduced supply and improved economic/employment conditions in low demand areas could raise house prices by


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10% or more (in extreme cases) and improve the migration balance, while reducing vacancies and out-migration. There are clearly considerable opportunities to build further on this work in future research. The model could address the dynamics of change and regional interactions in a more sophisticated way, and could more explicitly build in regional variations in response behaviour. Better data could improve the model, particularly data on factors affecting development costs in the supply model. Appendix 5.1 Variable definitions and sources. Variable

Definition

Source

Average real mix-adjusted house price New private completions per 100 households Gross in-migration rate (within-UK) persons % of resident persons (aged 30–44) Gross out-migration rate (within-UK) persons % of resident persons (aged 30–44) Private sector housing vacancy rate %

Nationwide Building Society ODPM Hsg Stats NHS Central Register

Endogenous Hprice Pqpr Inm_r Outm_r Pvac

NHS Central Register LA HIP returns

Adjacent Hprice_astk Pqpr_astk Inm_r_astk Outm_r_astk Pvac_astk

Mean value of hprice in contiguous zones Mean value of pqpr in contiguous zones Mean value of inm_r in contiguous zones Mean value of outm_r in contiguous zones Mean value of inm_r in contiguous zones

Lagged Hpriceld Hpriceldq

Log difference of house price year 0 minus yr 1 etc. (with differing lags) Squared Log diff house price yr 0 minus yr

Nationwide Building Society 1

Nationwide Building Society

National TS Mgintr_z Rgdpch_z

Mortgage interest rate % Real GDP change over previous year %

Economic Trends Economic Trends

Time and Space Varying Asunem Hhinc Empgro Pop Pqsr Plsh Plfh

Claimant unemployment rate % population (aged 30–44) Estimated real net household income £pa Annual growth rate in FTE jobs, 3-yr moving ave, % Total resident population New social housing completions % households Stock of outstanding planning permissions for private housing, per 1000 households Flow of new planning permissions, per 1000 households

NOMIS; ONS Mid-year pop. ests ONS regional accounts and Bramley affordability model Annual Census/ Survey of Employment ONS mid-year est ODPM Hsg Stats ODPM PS3 returns ODPM PS2 return

104


Appendix 5.1 (continued ) Variable

Definition

Source

Urban

Composite index of settlement pattern indicators

Urbsing Nonwh Occmig

Single person households Non-white ethnic population % Predicted gross migration rate based on occupational composition % of population Terraced houses, % of dwellings Vacant and derelict land Composite index of temperature and sunshine Dummy variable for Greater London

MIGMOD index, based on 1991 Census 1991 Census 1991 Census 1991 Census

Cross-sectional

Ter Vacdrl Climate Londondu

1991 Census ODPM MIGMOD index


6 Estimating the Impact of Planning on Commercial Property Markets John Henneberry, Tony McGough and Fotis Mouzakis

Introduction The economic effects of the land use planning system have been relatively little researched, despite the pervasiveness of this form of regulatory intervention in the market. It is argued that the greatest impact of planning on the economics of property markets is through the restriction of land supply, the consequent constraint on building development and the resulting effects on property rents/prices and on welfare (Evans 1985; Harvey 1987; Ball et al. 1998). The literature relating to this impact is uneven in its coverage. The housing literature includes macroeconomic analysis at national and regional level (see, for example, Meen 1990, 1996; Holly & Jones 1996) and microeconomic analysis at the local level. A subset of the latter focuses on the effects of planning (for reviews, see Monk et al. 1991; Henneberry et al. 2003). In contrast to the housing sector, little attention has been paid to planning in the modelling of the behaviour of the business property market. In the commercial property literature, macroeconomic models used to analyse national or regional data do not include a planning variable (see, for example, Silver & Goode 1990; McGough & Tsolacos 1994; RICS 1994). Some analyses, such as that by Tsolacos et al. (1998), use models based on simultaneous equations: but planning is still absent. All the analyses are of time series, and thus relate either to annual levels and flows or to changes in rents and the independent variables affecting them. They

106


emphasise the influence of demand variables because they focus on shortrun dynamic behaviour. Analyses of rent determination at local level can be divided into two broad types. The first is the hedonic pricing model. This assigns prices to an array of ‘characteristics’ of property when modelling rents. The approach has most commonly been applied to retail property (for example, by Ownbey et al. 1994; Hardin & Wolverton 2000). It has also been used in the industrial sector (see Buttimer et al. 1997). However, none of this work uses planning constraint variables in the analysis. The second approach involves the use of time series relating to locations or in panel data analysis. Generally, such models have looked at the interplay of standard economic variables1 (for example, Pollakowski et al. 1992; D’Arcy et al. 1997; Wheaton et al. 1997) – thus omitting consideration of planning. One exception is Blake et al. 2000. This study relates rents in the City of London office market to office employment density. The variable ‘Planning Permission (not currently being used)’ is taken as a measure of planning constraint. This feeds into the key equation on the supply side of the model, which is for ‘Starts’. The model does not estimate the impact of planning on past/current office rents. Instead medium-term (12-year) simulations are undertaken to indicate the possible implications of planning constraint. This rather indirect approach to the treatment of planning constraints is the only attempt to incorporate this issue into a rental model for commercial property. It highlights an obvious omission from the commercial real estate research literature. Consequently, discussion of this subject has not been particularly wellinformed. Claims that a range of productivity deficiencies in the UK could be attributed to over-restrictive land use regulation (McKinsey Global Institute 1998) were rebutted by the House of Commons, ODPM: Housing, Planning, Local Government and the Regions Committee (2003). The Committee concluded (page 13) that such claims seem to have been made without evidence (para 32). The ‘cost / benefit’ approach to planning has . . . tended to focus on the easier to measure costs but has . . . failed to produce definitive answers . . . (para 33)

This chapter describes an attempt to address this lacuna by developing a micro-economic, location model of business rents that explicitly examines planning issues.

Estimating the Impact of Planning on Commercial Property Markets

107

Model development and specification The functional form of the model The starting point for model development is the basic premise that rent is a function of the local demand for space and the local supply of land and buildings (see Figure 6.1). Taking the demand side first. Local demand for space ¼ f( þ Local economic activity space utilisation) (6:1) Demand increases with economic activity. But, for any given level of activity, space-efficient firms or industries will use less space than other firms or industries. Local economic activity is, in turn, determined by a complex set of factors. Local economic activity ¼f( þ National economic activity þ Local economic activity, Industrial structure þAgglomeration benefits þLocal supply of space Rent)

(6:2)

The national economy sets the general trajectory of economic growth for all local economies. A favourable local industrial structure – for example, a relatively large local representation of growth industries – will enhance local economic activity (Richardson 1968; Armstrong & Taylor 1993). Concentrations of declining industries will have the opposite effect. Supply of space allows for more growth. However, the more expensive that space is to rent, the less capital is available for investment in labour or equipment. Agglomeration benefits boost activity over and above that expected from the previous variables. Agglomeration benefits ¼ f( þ Urbanisation and localisation economiesjst Local supply of space)

(6:3)

They are determined by the development of urbanisation (scale) and localisation (scope) economies (Healey & Ilbery 1990; Armstrong & Taylor 1993; Ball et al. 1998). Agglomeration is based on a conditional constraint in terms of supply. That is, the present benefits of agglomeration are based, inter alia, on past rounds of supply of space that are already incorporated in the urbanisation and localisation variables. Consequently, local supply of space is not treated here as an agglomeration benefit. This is in contrast to

108


the supply variable in equation (6.2) which is the direct supply of local space. Space utilisation concerns the intensity with which space is used. Space utilisation ¼f( þ Rent þ Local economic activity Local supply of space)

(6:4)

If space is expensive, then firms will use it efficiently and intensively. A growing economy will put pressure on the use of space. Conversely, a generous supply of local space reduces the incentive to use space well. Turning next to the supply side. Local supply of space is dependent on the actual land available and the amount of that land which is granted planning permission (Bramley 1993a, b). The supply of land in any individual sector will also be affected by the relative rents prevailing in other sectors. For example, if higher rents can be achieved for land in another use, less will be supplied for the subject use (Ball et al. 1998; Di Pasquale & Wheaton 1996). Thus Local supply of space ¼f( þ Physical supply of land þPlanning Permissions

(6:5)

Relative rents of other sectors) Planning permissions are a function of planning applications and the planning regime (Bramley 1998). For any given planning regime, more planning applications will result in more planning permissions. For locations receiving a similar number of planning applications, where the planning regime is positive/relaxed more permissions will be granted than where the planning regime is negative/restrictive. Thus Planning permissions ¼ f( þ Planning regime þ Planning applications) (6:6) Furthermore, the number of planning applications will rise or fall in line with developer demand and the planning regime. In the latter case, the lower is the proportion of planning applications approved (the tighter the regime), the more likely it is for speculative planning applications to be submitted (Bramley 1993a, b; 1999). Planning applications ¼ f( þ Developer demand Planning regime) (6:7)


109

Developer demand is, in turn, determined by the profitability of development: that is, the balance between development costs and values (RICS 1994; Antwi & Henneberry 1995). Developer demand ¼ f( þ Rents Costs)

(6:8)

Finally, equation (6.9) equates rents to local demand and supply and also allows for the effect of rents in other sectors. Rent ¼f( þ Local demand for space Local supply of space Relative rents of other sectors)

(6:9)

The above portrayal greatly simplifies the development process. It assumes that the process is driven by two main actors: the developer and the planner. However, and for example, land owners may apply for planning permission and then sell on the land to developers. In addition, there are lags between each stage of the process. If economic circumstances change, these lags may be considerable. For example, the growth in rents that prompted the seeking of permission for development may have ceased during the time it takes to obtain planning permission. As a consequence, there may be a long lag between the grant of planning permission and the erection of a building on a site, because the developer will have to wait for another market up-turn before the scheme is viable. However, because our focus is on longer-term trends in the property market, it is assumed that over the full market cycle it is possible for the development process to be completed. The final issue on the supply side relates to the interaction of land and property markets and, in particular, the relation between land costs and development values and profitability. Here we take the view of Cheshire and Shepherd (1989). They argue that if planning restricts the supply of new development, it will raise development prices. Because, as a result, builders can get higher prices for property, they will pay more for land. High land prices do not cause high property prices, both are caused by the restriction of development. In addition, while there might be short-term perturbations in the land market which have a reverse effect, we incline to the view that the direction of causality flows from property prices to land prices, rather than the other way round (after Ball 1996b). Consequently we portray planning permissions resulting in the supply of both land and buildings. This is viewed, in the longer term, as roughly contemporaneous. The dominant effect is that of the supply of buildings on rents which, in turn (because land values are a residual after other development costs are subtracted from development values) have a major influence on land prices.

110


Model specification The theoretical model defined in Figure 6.1 and equations 6.1–6.9 now needs to be translated into a practical model for estimating. The initial changes incorporated reductions for data non-availability. The affected equations are as follows. Equation 1: Local demand for space This endogenous variable has to be dropped from the system of equations as it is not observable. If occupier demand is not observable using available data, it may be indirectly represented by its observable determinants. Consequently, while equation (6.1) cannot be utilised directly, local economic activity and space utilisation may take the place of local demand for space in the rent equation (6.9). This alteration brings the equation of rent determination (the one most commonly used in the applied study of

Building costs

Development costs

Finance costs

Land costs very long term National economy Development profitability very long term

Local industrial structure

specialised space

Agglomeration

Developer demand

general space

Planning applications (policy compliant and opportunistic)

Local economic activity

Local planning regime Planning permissions Local space utilisation Local supply of land Local development of buildings Local demand for space Local supply of buildings

RENT Relative rents in other sectors

Figure 6.1

Sector specific model of the local property market.


111

market equilibria), nearer to the forms most widely discussed and used in the academic literature and commercial analytical practice. Equation 3: Agglomeration benefits The agglomeration equation (6.3) is substituted into equation (6.2). The identification of the agglomeration variables was carried out using calibration techniques, and the resultant significant variables indicating agglomeration issues were incorporated into the simplified model via substitution in equation (6.2). Equation 5: Local supply of space There are no data for the physical supply of land. However, given the absence of spatial integration within this model, the physical supply of land can be considered fixed. Equation 6: Planning permissions The planning regime is not observable. A measure of planning restrictiveness may be used as a proxy for the planning regime (as discussed previously) and substituted into the equation (but see below for further complications). Equation 7: Planning applications These data are needed but are not available. The solution adopted is to assume that applications are a fixed proportion of planning decisions. This is not perfect. Problems may arise in locations where the relationship between applications and decisions differs significantly from the fixed proportion. Consequently, the use of planning decisions data will need to be monitored in the system via, for example, examination of residuals in the relevant equations. Equation 8: Developer demand Developer demand is not observable. As with occupier demand, it may be indirectly represented by its observable determinants. Thus, the righthand side of equation (6.8) is substituted for developer demand in the equation of planning applications (6.7).

Structural characteristics of the system Planning data The proxy measure of the planning regime is based on the observed relationship between planning decisions and planning permissions. Consequently, one of the three variables involved must be excluded from the

112


model, as a linear combination of the other two. As a solution, planning permissions are not directly included in the system but only used in the definition of planning restrictiveness, the planning regime proxy. The latter is therefore exogenous, participating as an instrument in the system. This change results in the planning regime being treated as a policy instrument, rather than as a product of market forces that follows stable behavioural rules. The definition of the planning regime The proportion of planning decisions that are approvals provides a successful measure of the planning regime. This judgement is based on empirical tests of the performance of alternative measures of planning restrictiveness (for example, the relative area of green belt). For the purpose of econometric analysis an equally weighted index of the percentages of the decisions which are approvals for major and minor development is used. The calibration of the weights was determined by empirical testing. Rent differential between sectors By incorporating the differential in actual observed rents, we maintain some simplicity to the model and can treat these differentials as exogenous to the model. Individual competing sector rents were analysed one at a time within the model. This may well be a simplified, if rational approach to how the sectors interact. It could be argued that over the long term this interaction has already taken place. Only where planning restraints are stopping this process has the flow of land from one sector to another not taken place. This would result in insignificance of the variables, and/or very low coefficients. If the interaction is more complicated, we will observe indications of missing information within the relevant equation. Examining the effect of local regimes on the national economy The model is cross-sectional. In this pilot study, empirically, the model does not cover the full economy. Consequently, it is not possible to analyse whether the local effects of planning regime changes are transferred to other locations, or the extent of the aggregate effects on the economy as a whole. These issues are addressed by developing a separate time series model of the UK as a whole, based on the relevant equations, and then comparing the coefficients in the local and national models (see below). The changes discussed in this sub-section are summarised in Box 6.1.


Box 6.1

113

Simplified functional form of the model

Demand Local economic activity ¼ f( þ Industrial structure þ Local supply of space þ urbanisation economies þLocalisation economies Rent) Space utilisation ¼ f( þ Rent þ Local economic activity Local supply of space)

(i)

(ii)

Supply Local supply of space ¼ f( þ Planning applications þ Planning regime Relative rents of other sectors) Planning applications ¼ f( þ Rents Costs Planning regime þ Local economic activity)

(iii) (iv)

Rent Rent ¼ f( þ Local economic activity Space utilisation Local supply of space Relative rents of other sectors)

(v)

Empirical study and results Model specification Within the constraints of data availability and the theoretical framework, a cross-sectional econometric study is feasible. It covers each of the three business property sectors and is based on a medium-sized sample of the main UK urban property markets. The final sample sizes for the three sectors are: 49 for office and retail property; and 40 for industrial property. Since measures for some of the variables included in the theoretical model are not available, the model has been restructured in order to remove latent variables from the estimated forms, substituting out the unobserved variables. The form derived from this restructuring, shown in Box 6.1, does not impose any undesirable alterations in the implicit model structure and is, therefore, directly related to the original model (described in Figure 6.1 and equations 6.1–6.9). The estimated system of equations in Box 6.1 can be formalised by using the following standard notational conventions:

114

. . . .


Scalar variable symbols are written in italic font. Matrix and vector symbols are in bold font. Variable symbols are in upper case using an index i for the observation. Coefficient symbols are in lower case.

The proposed system of equations can be written as Yc þ Xb ¼ u where Y and X are endogenous and exogenous variable matrices respectively. Each matrix has as many rows as observations and columns equal to the number of endogenous and exogenous variables: that is, 5 and 8 (including the intercept term), respectively. Coefficient matrices c and b have columns equal to the number of equations and endogenous variables in the system (to achieve completeness) and as many rows as the variables in each group: that is, 5 5 and 8 5 respectively. Every row of Y and X consists of the vectors of the two groups of variables yi ¼ (Yi , Ti , Si , Pi , Ri ) and xi ¼ (1, Ii , Ui , Li , Ci , Gi , X1i , X2i ). Where Y ¼ local economic activity, T ¼ space utilisation, S ¼ local supply of space, P ¼ planning applications, R ¼ rent; and where I ¼ industrial structure, U ¼ urbanisation economies, L ¼ localisation economies, C ¼ developers’ costs, G ¼ planning regime, X 1 , X 2 ¼ relative rents in other sectors. The matrix u contains the disturbance terms for the five equations. If cAB and bAB are the elements of c and b respectively, with A indicating the dependent variable and B the associated right-hand side variable and aA is the intercept term in the first row of b (i.e. ai ¼ bi1 ), then the system of equations can be written without the use of matrix expressions as: (i) (ii) (iii) (iv) (v)

Yi ¼ aY þ cYS Si þ cYR Ri þ bYI Ii þ bYU Ui þ bYL Li þ u1i Ti ¼ aT þ cTY Yi þ cTS Si þ cTR Ri þ u2i Si ¼ aS þ cSP Pi þ bSC Ci þ bSG Gi þ bSX1 Xi1 þ bSX2 Xi2 þ u3i Pi ¼ aP þ cPY Yi þ cPR Ri þ bPC Ci þ bPG Gi þ u4i Ri ¼ aR þ cRY Yi þ cRT Ti þ cRS Si þ bRX1 Xi1 þ bRX2 Xi2 þ u5i

It is a complete system with all equations satisfying rank and order conditions for identification. Table 6.1 presents the identification characteristics of this system. A calculation of the reduced form of the system shows the cumulative effects of exogenous to endogenous variables, taking into account the multiplicity of simultaneous impacts throughout the system. The 1 8 5 matrix p ¼ bc contains the reduced form elasticities eAB between endogenous variable A and exogenous variable B, in a column of coefficients for each equation (see Appendix 6.1, Table 4, page 127).


115

Table 6.1 Identification of the system.

Equation

Var.

Excluded exogenous Var. (Kj )

i ii iii iv v

Yi Ti Si Pi Ri

5 7 4 6 6

Local economic activity Space utilisation Local supply of space Planning applications Rents

Included endogenous Var. (Mj 1)

Rank (4)

Identification

2 3 1 2 3

full full full full full

over over over over over

Econometric issues Very little formal modelling of the impacts of planning on property markets and the local economy has been undertaken. This research is an initial attempt to extend previous work and to produce new empirical evidence on planning’s market effects for evaluation. Overwhelming proofs of such effects are not likely to be identified. Market equilibrium is a common assumption in static cross-sectional studies. This allows deviations of local markets from equilibrium but requires them to be independent from the exogenous variables and randomly distributed. Regarding the adequacy of the sample size, the local markets used in the study do not cover the entire population. However, they constitute the bulk of the three use sectors. According to IPD figures, those Jones Lang LaSalle’s (JLLs) ‘50 Centres’ that are used account for 91% of the capital value of the national offices stock and approximately 75% of the retail and industrial stocks. This provides reasonable coverage. Increasing the sample could be useful, but may raise heterogeneity issues when very small centres are compared with very large ones. The use of general to specific model specification techniques was considered. It did not appear appropriate for the needs of this study. The presence of measurement errors and the imposition of structural simplifications because of data limitations suggest that theorising on the basis of unguided empirical analysis is unlikely to be effective. Furthermore, observation of the rank condition in the current form of the model demonstrates that ex-post identification is very unlikely in this case. The specification technique followed here started from a form the theory indicated, with a confirmed full rank. Then, a limited number of alternative forms of the model that appeared consistent with the broader theory were empirically tested; but only if ex-ante identification was successful. Judgement was used in the application of these tests. A compromise was

116


sought between the degree to which the estimates comply with theory and the overall statistical goodness of the model. Following wide-ranging tests of limited and full information methods, the latter was chosen as the more appropriate method. This decision was based on evidence of interdependence in the stochastic structure of the model, the significant correlation of the error structure and the theoretical acceptability of the outcome. Single iteration 3SLS estimates for all three sectors were compared using a range of different approximations and lag structures, employing calibration techniques mentioned above. These enable the selection of representative proxies from alternatives in the light of the relative theoretical and statistical quality of the results. In practice, this requires the production of a separate set of full results for each part of the range of alternatives examined. The data used for the variables in the system of equations are summarised in Table 6.2. Subsequent discussion focuses on key attributes of the data and variables. There were three available approximations of a proxy for localisation economies, local employment in R&D, in government and in SMEs. The use of the relative concentration of R&D employment dominated any mix of the three measures in the retail and industrial sectors. In the office sector an average of R&D and SME employment slightly improved the outcome. To maintain the symmetry in the model, the simplest measure (relative Table 6.2 Variables and data. Variable

Proxy

Data period

Local economic activity Space utilisation Local supply of space Planning applications

Sector employment Floorspace stock per employee Local floorspace stock Average major and minor planning applications Achievable prime rent Share of sector’s employment to local total employment Total employment share to sample total R&D employment share of total local employment Regional price index Average proportion of major and minor permissions granted Ratio to other sector rent

Avg. 1998–2000 Avg. 1994, 2000 2000 Avg. 1991–1996

Rent Industrial structure Urbanisation economies Localisation economies Developers’ costs Planning regime Relative rents in other sectors

Avg. 1998–2000 Avg. 1996–1998 2000 2000 Avg. 1995–1998 Avg. 1991–1996 Avg. 1995–1997


117

employment in R&D) was used for all three sectors. Rents and local economic activity (employment by sector) are averages of 1998–2000 data. The JLLs rental series are end year and some missing fourth quarter observations had to be interpolated. Building stock figures for 2000 were used as the measure of local supply of space. The average of the 1994 and 2000 stock figures were used in combination with total employment data for the respective years as an indicator of space utilisation. The best performing lag between subject sector rents and relative rents in other sectors was found to be three years, so the average of 1995–1997 observations was used for the latter variable. The best performing lag between local economic activity and industrial structure was found to be two years, so 1996–1998 data for the latter was used. The best performing lag between planning applications and developers’ costs was found to be three years, so 1995–1997 data for the latter was used. From a detailed examination of a large number of alternatives for each variable, the best performing lag for both planning applications and planning regime was five years, so the average of 1991–1996 was used for all three sectors. This coincidence in timing may be an indication of the robustness of this timing in the representation of the markets’ information structure.

Interpretation and evaluation of the results Results from the estimation of the structural equations are reported in Appendix 6.1, Tables 1–3. A summary analysis of the performance of the models is presented in Table 6.3. A 10% one-tail test is used as the benchmark of the statistical significance of relationships. Signs of coefficients are assessed for correctness only when theory provides a prediction for the direction of the relationship: positive or negative. Table 6.3 shows the numbers of coefficients that are theoretically correct or incorrect and that are statistically significant or insignificant, and combinations of these properties. The counts are out of 21 coefficients in

Table 6.3 Coefficient properties (out of 21 slope coefficients in total).

Sector

Correct (%)

Significant (%)

Correct and significant (%)

Incorrect and insignificant (%)

Incorrect and significant (%)

Offices (21) Retail (21) Industrial (21) All (63)

17 (81%) 17 (81%) 17 (81%) 51 (81%)

13 (62%) 10 (43%) 12 (57%) 35 (56%)

13 (62%) 9 (43%) 11 (52%) 33 (52%)

4 (19%) 3 (14%) 3 (14%) 10 (16%)

0 (0%) 1 (5%) 1 (5%) 2 (3%)

118


each model, excluding intercept terms. The cross-sector elasticities are not restricted by the theory, so they are always counted as theoretically correct. The table shows clearly that the success ratio is high in all sectors. This is reinforced by the observation that the large majority of theoretically incorrect signs relate to coefficients that are very close to zero. There are only two cases out of 63 where a theoretically incorrect, statistically significant coefficient occurs. Due to the large amount of output, the results for the three sectors need careful examination in the context of the underlying theory. The results of the modelling appear generally supportive of the theory. They suggest that the planning regime has a positive effect on the supply of space, which is elastic and significant in the case of offices. That is, as planning regimes become more lax and the percentage of planning decisions that are approvals increases, so local supply of space increases. Supply also has a near proportional relationship with planning applications, the proxy for developers’ demand. That is, for a given change in the number of applications submitted there is a near 1 to 1 change in the overall supply of space. The largest departure from this unitary link is in the retail sector, which has a coefficient of 0.77. This may be the result of more speculative applications in the retail sector. Supply of space has a positive effect on local economic activity – the greater the supply of space the greater the economic activity – with a clear and significant effect in the case of the industrial sector. Supply has a negative effect on rents, with elastic and significant impacts in the office and industrial sectors. Local economic activity also has near unitary positive relationships with agglomeration, as expressed jointly by the urbanisation and localisation variable. However, the localisation coefficients are very small. That is, the more important a sector’s concentration in an area the greater its output, and also, though again not at unity, the more employment in a sector (the urbanisation variable) the greater the output. The relationship between employment and output was, as expected, strongest in the retail sector (0.9) and weakest in the industrial sector (0.7). Localisation coefficients had small values in all the sectors and were not significant. Additionally, the signs of the localisation coefficients differed between sectors, indicating that the relationship between this variable and others was inconsistent. Space utilisation was found to increase with demand and local economic activity and to decline with the supply of new space in all sectors. However, the expected negative effect of rents on space utilisation is only statistically significant in the retail sector. Planning applications were found to increase


119

in proportion with local economic activity but a positive effect of rents and a negative effect of developer cost on planning applications were not established in all cases. Planning applications were found to increase with the tightness of planning regime in all three sectors. This indicates that speculative applications increase when the rate of planning approvals reduces. Developers’ behaviour in this regard in the business sector appears to mirror their behaviour in the housing market (Bramley 1993a; 1993b; 1999). The structure of the rental equations appears to be in line with the outcome of other empirical studies, with balanced and elastic responses by rents to variations in demand and supply. In the office sector the elasticities are above unity. In the industrial sector elasticities are higher, at about þ and 2.5. These results had strong statistical support. The response of rent to variation of space utilisation is also positive and elastic in both office and industrial sectors. The structure of the retail rental equation has smaller elasticities and for the supply side is near zero. This may indicate the distinctive nature of the retail sector. It appears to be less pricesensitive than the office and industrial sectors. This may be because area representation is sought regardless of cost and availability, in pursuit of market share and penetration. The sector also displays an inverse relationship between space utilisation and rent. The equation of rental determination is intended to capture the forces involved in the equilibrium of each tenant market, involving price, demand and supply in each sector. According to the standard model the parameterisation of such an equation is affected by dependencies between the good in question and other goods with which substitution or complementation might take place. In the case of tenant markets, imperfect substitution or complementation would be expected. In terms of such possible impacts, it is important to remember that the aggregate figures of this study capture the central tendency of both spatially and qualitatively differentiated data. The rental series used are based on the average rent achievable for prime quality space. This means that an increase in prime office rents might cause a tendency for decentralisation and an increase in next to prime locations. The difference between this market equilibrium and the previous one is not reflected in the measured prime rental values but in the distribution of rents over the qualitative spectrum of the city’s stock. All cross-price effects estimated so far are negative with the exception of positive impacts to supply of industrial space, with a significant impact from retail relative rents. This may indicate a link in the supply side of the two sectors, possibly due to the opportunities of switching the use of

120


the developed industrial space to retail warehouses, or to complementarity of the two types of space in their industrial use (warehouses in support of retail operations). Substitution/complementation forces can also involve the demand side of the market. For instance, increased retail rents might reduce the demand for prime offices, as demand tends to relocate in less congested and lower price areas. The clear alignment of both negative cross-price elasticities in the rental equations of all three sectors points to a degree of substitution in the space market. From an analytical point of view, the alignment of these results (all negative) and the significance of some of them points to their theoretical importance. From the econometric point of view there are two important reasons for their inclusion. One is that they significantly improved the theoretical properties and goodness of fit of both the supply of space and rental determination equations. The second, mentioned earlier, is that their inclusion (double counting) is not, a priori, problematic. On the contrary, their omission would be expected to cause difficulties. Reduced form estimates are selectively summarised in Table 6.4 and presented in detail in Appendix 6.1, Table 4. These estimates provide an additional source of information on the behaviour of the market and the performance of the model. The majority of the cumulative effects of the exogenous on the endogenous variables appear in line with theoretical expectations. Most interesting are the results for the office and industrial sectors. The planning regime has a clear effect on the supply side of the market, with elasticities between the regime and the supply of space substantially greater than 1. The effect of the planning regime on local economic activity is at the moderate levels of 16% and 38%. The effect of the planning regime on rents varies from proportional in the office sector (near unitary elasticity) to a less elastic response of about 50% in industrials. The estimated structural coefficients for retail have reasonable sizes and signs. However, some of the coefficients in the reduced form have high values and two of their signs are incorrect. This may be due to the small determinant value in the inversion of b. The reason for the strong impacts of exogenous coefficients in this sector is the high correlation in the

Table 6.4 Selected reduced form coefficients for effect of planning regime. Office sector

Retail sector

Industrial sector

Variable

Yi

Si

Ri

Yi

Si

Ri

Yi

Si

Ri

Coefficient

16%

160%

82%

31%

673%

939%

38%

121%

50%


121

structure of endogenous variables. This is indicated by the low value of the gamma determinant in Table 4 of Appendix 6.1. A likely source of this outcome is high correlation of the proxies used for the variables and possible correlations in measurement errors contained in them. This study has made the maximum use of available information, so the problem of limited orthogonality was anticipated. Conversely, while this is not a major problem overall, it is welcome that it only occurs in one sector (retail ). It should be clear that this form of structural multi-colinearity is not likely to affect the statistical confidence of the estimated structural coefficients, which is similar to those of the other sectors. An important consideration when estimating with the use of full information techniques is that any misrepresentations or measurement quality shortages in one equation affect the results of all the other equations. It is therefore essential that when testing alternative structural hypotheses of measurements, the decision is based not merely on the outcome of the individual coefficient but on the overall appearance of the system. This should include the goodness of fit, the theoretical acceptability and the statistical significance of a hierarchical rank of coefficients from all equations. Note that when using 3SLS the R2 statistic has different properties from the one commonly used in OLS. It might take negative values while the statistics of the coefficients remain strong. It needs to be treated as a relative indicator of performance, with more weight being given to the confidence intervals of the coefficients. An overall measurement of the performance of the system is the log likelihood of the estimation. A large majority of the coefficients was found to be in line with theoretical expectations and most were statistically significant (cf. Table 6.3). Given the cross-sectional nature of the data, the goodness of fit ranges from intermediate to high levels. The exception is the rental equation for the industrial sector. However, it displayed strong results from the coefficient tests.2

Conclusions Despite the pervasiveness of planning intervention in the land and property market, the economic effects of the planning system have been little researched. While there is a growing body of work addressing this issue in the housing sector, similar work considering the business sector is marked only by its absence. The chapter describes an initial attempt to explore this subject area by considering the effect of planning on the supply and price of business space at a particular time. The cross-sectional analysis identified a local effect of planning that was consistent with theory. As planning regimes become tighter and the percentage of planning decisions that are

122


approvals decreases, so the local supply of space decreases. Lower levels of supply of space are associated with less local economic activity and higher rents. The study identified a proportionate relationship (elasticity) between the local planning regime and the level of local economic activity of 15.5% in the office sector and 38% in the industrial sector.3 So, taking the office sector as an example, this would imply that a 10% decrease in the proportion of office planning decisions that are approvals would result in a decrease in local economic (business service) activity of 1.55%. However, in its current form the model does not incorporate spatial adjustment. Consequently, the net effect of aggregate local planning impacts cannot be estimated. The impact of the planning system on the national economy remains moot. This is an important avenue for future research. Our findings need much qualification. This was an initial analysis constrained by significant data limitations. The cross-sectional approach, while using averaged data for appropriate lagged time periods, could not effectively capture market dynamics. This is particularly so when the data were insufficient to support even a second cross-sectional analysis. In addition, the study did not cover the whole market or economy and was based on local authority districts: under-bounded spatial units that were not contiguous. Finally, there was significant unevenness in the performance of the model, both between and within market sectors. The model was more successful in dealing with the behaviour of the office and industrial sectors than with the retail sector. For each sector, the variables of most interest to this study, particularly the planning regime, performed less well than others, such as local economic activity. Despite these various constraints and limitations, the results of the study, taken in the round, suggest that we have developed a theoretically robust, cross-sectional model for analysing and estimating the effect of planning on the property sector of the local economy. The techniques have identified an effect of planning on the local economy that is significant.

Notes 1 Rent is determined by previous rent, demand and supply; where demand is a function of (sector) output or consumer expenditure and supply is a function of development profitability (development values minus costs).


123

2 All the individual equations of the system have been analysed using limited information OLS technique. All equations were found to produce normal error terms and to be structurally robust. Detailed results of these tests are available from the authors on request. 3 The result for the industrial sector was less clear-cut than that for offices. The result for the retail sector (30.8%) runs counter to theory.

1

55.79%

0.2844

0.1667

Average R&D emp. shares and SMEs 2000

Weigh. av 75% major 25% minor permis. granted av av 1995–1998 1991–1996

Regional price index

0.9546 87.74%

1.6% 0.4% 2.3% 1.1% 0.2% Yi 1.4% 1.3% 0.5% 2.0% Ti 26.6% 24.5% 0.1% Si 26.8% 1.4% Pi 6.5% Ri

12.5% 25.7% 36.3% 17.6% 7.3% Yi

12.0% 20.5% 7.5% 65.1% Ti

51.5% 91.8% 0.4% Si

51.8% 10.7% Pi

Yi Ti Si Pi Ri

2000 av 1996–1998

1.2954 98.90%

Yi Ti Si Pi Ri

0.52%

9.0558

29.62%

0.1473

0.0179

0.7776

1.4613

Gi

6.4102

Total emp. share to sample total

17.53%

0.0282

0.0382

Ci

6.02%

Share of sector’s employment to total

0.00%

0.1839

0.7863

Li

0.00%

3.1726

0.00%

4.7829

0.00% 0.00%

0.1154

Ui

Urbanisation Localisation Developer Planning variable variable costs regime

Standard deviation / correlation matrix

av av 1991–1996 1998–2000

Ii

10.1605 0.8172

1

Intercept Industrial term structure

Variance / covariance matrix

2000 av av 1998–2000 1994, 2000

1

7.85%

0.0620

0.1092

67.13%

0.0860

0.0365

Ri

Local Average Achievable floorspace major/minor rent stock planning applications

0.04%

0.3699

Lag

0.24%

0.6266

Florspace Sector employment stock per employee

0.01%

p-value

1.3159

Proxy

0.3665

determination st. err.

1.3955

0.00%

Ri

0.1303

st. err.

p-value

applications

Rent

0.9692

Pi

0.00%

Planning

0.1170

st. err.

p-value

0.9912

supply

1

0.01%

0.1138

Pi

Planning applications Rent

of space

Si

0.01%

st. err.

p-value

utilisation

Local

0.1279

Ti

1.9011

p-value

activity

Space

0.4556

48.39%

st. err.

0.4865

0.1653

Yi

0.1157

Si

economic 1

Ti

Local supply of space

Local

1

Yi

Space utilisation

Note: All equations are identified. P-values from coefficient t-tests are two-tail. One-tail 5% significant estimates are set in boldface.

v

iv

iii

ii

i

No. Equation

Local economic Variable activity

Appendix 6.1 Results Table 1 Offices – single iteration 3SLS estimates; final stage log-likelihood: 2.5346

Ratio to other sectors rent

0.00%

0.0870

0.3748

95.19%

0.1276

0.0077

XiR

25.4% Ri

av av 1995–1997 1995–1997


0.05%

0.1973

0.6861

19.39%

0.2663

0.3459

XiI

Relative Relative rent – rent – industrials retail

0.00%

55.2%

0.00%

58.0%

0.00%

50.3%

0.00%

60.1%

0.00%

96.4%

R squared/ F-test p-value

Yi

p-value

0.2125

Ii

R&D emp. shares to total 2000

Weigh. av 75% major 25% minor permis. granted av av 1995–1997 1991–1996

Regional price index

2.0% 1.2% 1.4% 2.7% Ti 15.3% 18.2% 0.5% Si 28.4% 4.7% Pi

6.4% Ri

6.9% 10.4% 12.6% 12.1% 24.8% Yi

14.1% 21.3% 18.9% 75.9% Ti

0.7725 92.16%

39.2% 87.5% 4.5% Si

53.3% 34.8% Pi

Yi Ti Si Pi Ri

2000 av 1996–1998

1.3146 50.76%

Yi Ti Si Pi Ri

17.87%

5.3778

76.37%


0.5% 0.1% 0.3% 0.4% 0.4% Yi

0.5794

0.1395

Gi

0.0760 0.8711

Ci

2.0229

Total emp. share to sample total

75.74%

0.0241

0.0074

Li

80.97%


0.25%

0.3010

0.9095

Ui

Urbanisation Localisation Developer Planning variable variable regime costs

0.03%

1.9704

0.00%

5.3061

0.00% 0.00%

0.1903

10.7795 0.7964

1

Intercept Industrial structure term


av av 1991–1996 1998–2000

av av 2000 1998–2000 1994, 2000

Lag

1

0.18%

0.2862

0.8955

Floorspace Local Sector Average Achievable employment stock per floorspace major/minor rent employee stock planning applications

0.6743 99.20%

0.0068

1

0.03%

0.0884

0.3178

93.31%

0.1100

0.0092

Ri

Proxy

Ri 0.5985 1.57%

Rent

determination st. err. 0.6365 p-value 58.59%

0.00%

p-value

1.4456

0.1783

st. err.

applications

0.3467

1.4911

Pi

Planning

0.0868 0.00%

st. err.

p-value

0.7683

supply

1

0.91%

Pi

Planning applications Rent

of space

Si

7.28%

st. err.

utilisation

Local

0.2091

Ti

0.5543

86.00%

1

p-value

activity

Space

0.3752

0.3321

st. err.

economic

0.0586

Si

Ti

Yi

Local

1

Local supply of space

Space utilisation


v

iv

iii

ii

i

No. Equation

Local economic Variable activity

Table 2 Retail – single iteration 3SLS estimates; final stage log-likelihood: 2.75164


0.2002 34.92%

0.1874

0.49%

0.1008

0.2839

XiO

25.3% Ri

av av 1995–1997 1995–1997


0.1658 11.97%

0.2580

12.85%

0.1471

0.2236

XiI

Relative Relative rent – rent – industrials offices

0.70%

30.7%

0.00%

53.0%

0.11%

34.1%

0.79%

23.4%

0.00%

97.9%


0.2981 0.3662 Total emp. share to sample total

2000 av 1996–1998

3.3% 6.1% 3.3% 5.3% Ti 35.4% 29.7% 9.6% Si 8.7% Ri

7.0% 76.0% 34.4% 16.6% 76.7% Yi

18.2% 56.6% 34.4% 99.3% Ti

59.5% 93.8% 54.8% Si

Yi Ti Si Pi Ri

0.5% 1.0% 1.4% 0.6% 1.6% Yi

Yi Ti Si Pi Ri

2000

1.5340 64.97%

53.2% 34.2% Pi

Weigh. av 75% major 25% minor permis. granted av av 1995–1997 1991–1996 Regional price index


28.3% 5.4% Pi

R&D emp. shares to total

1.9414 29.75%


av av 1991–1996 1998–2000

av av 2000 1998–2000 1994, 2000

0.00%

11.1044

9.56%

0.6967

2.0224

15.3573

1.5239

1.5452 31.06%

Lag

0.00%

85.04%

0.0060

0.0011

0.00%


0.00%

1

75.80%

0.3048

0.0939

0.00% 3.3386

0.00%

0.1457

0.6911

Urbanisation Localisation Developer Planning variable variable costs regime Ui Li Ci Gi

Achievable Average Sector Floorspace Local employment stock per floorspace major/minor rent planning employee stock applications

p-value

2.6505

1

0.1009 0.00%

7.1196

0.00% 0.00%

0.1028

7.8843 0.7990

Intercept Industrial term structure Ii 1

Proxy

0.3716

0.00%

determination st. err.

2.5796

p-value

Ri

0.1267

0.00%

st. err.

applications

Rent

0.8328

Pi

0.00%

Planning

0.1422

st. err.

1.0136

p-value

1

supply

1.6799

0.1530 0.00%

0.4996

25.46%

0.0825

0.0940

Planning applications Rent Pi Ri

of space

Si

st. err.

p-value

utilisation

Local

0.1447

0.00%

Ti

Space

1.6339

4.45%

1

p-value

activity

1.5884

0.1375

Yi

st. err.

Local

0.2762

Local supply of space Si

economic

1

Space utilisation Ti


v

iv

iii

ii

i

Local economic Variable activity Yi

Industrial – single iteration 3SLS estimates; final stage log-likelihood: 14.1601

No. Equation

Table 3

Ratio to other sectors rent Ratio to other sectors rent

29.5% Ri

av av 1995–1997 1995–1997

70.71%

0.1240

0.0466

44.37%

0.2705

0.2072

Relative rent – offices XiO

10.66%

0.0813

0.1311

4.26%

0.1602

0.3247

Relative rent – retail XiR

11.0%

0.00%

58.1%

0.11%

41.6%

0.00%

60.0%

0.00%

99.1%


Intercept Ii U L Ci Gi Xi1 Xi2

Table 4

Ti Si Ri

3.88 2.64 88.4% 4.9% 85.0% 4.7% 4.1% 0.2% 2.2% 1.1% 13.9% 81.5% 6.6% 74.2% 7.5% 51.8%

Pi

Gamma determinant: 0.6376

11.07 2.88 7.02 92.0% 4.3% 87.6% 88.6% 4.2% 84.3% 4.1% 4.3% 0.2% 0.2% 0.8% 2.2% 15.5% 56.4% 159.9% 6.0% 18.0% 28.1% 1.1% 2.1% 6.7%

Yi

Offices

Reduced form coefficients


379.20 417.33 122.3% 264.1% 139.7% 301.6% 2.5% 1.1% 7229.0% 7747.1% 894.1% 938.8% 1102.3% 1164.0% 527.1% 560.5%

2.32 76.6% 87.5% 0.7% 253.8% 30.8% 40.2% 16.9%

Ri

Pi

Si 289.38 94.0% 107.3% 0.9% 5554.2% 673.0% 869.3% 376.6%

Ti 286.87 164.7% 188.2% 1.5% 5445.9% 659.9% 836.8% 380.5%

Yi

Retail 15.51 25.7% 22.2% 0.0% 395.1% 161.7% 9.4% 21.0%

Ti

Pi

Ri 7.79 10.98 2.29 86.5% 85.4% 6.2% 74.8% 73.8% 5.4% 0.1% 0.1% 0.0% 295.0% 291.0% 121.6% 120.8% 33.3% 49.8% 27.3% 5.1% 66.3% 22.9% 2.1% 18.1%

Si


5.52 103.2% 89.3% 0.1% 92.9% 38.0% 1.3% 4.6%

Yi

Industrial


7 UK Roads Policy, Accessibility and Industrial Property Rents Neil Dunse and Colin Jones

Introduction The relationship between investment in transport infrastructure, the spatial economy and hence to the property market is subject of some debate. The kernel of the problem is set out by the Standing Advisory Committee on Trunk Road Assessment (SACTRA) (1999) who note that roads operate in two directions and that there is no guarantee that transport improvements will benefit a particular end. The essential issue is the causal linkage between changing transport infrastructure and regional/ sub-regional/urban patterns of spatial economic activity. The particular focus of this chapter is on the role of inter-urban transport freight costs as reflected in the motorway/road network. Undoubtedly the development of this network has brought about a major change in road transport and the objective of this chapter is to consider what impact these changes have had on the commercial property market. This chapter begins with an overview of post-war road policy. It then proceeds to review the evidence on the how the motorway system has impacted on accessibility, the potential implications and examines what location theory has to offer on the effects of changing transport costs and the pattern of economic development. The following section discusses the methods by which the impacts of transport investment have been assessed. The penultimate section develops a hedonic price model, using Glasgow and the surrounding hinterland as a case study, to measure the impact of the motorway investment as revealed through commercial

UK Roads Policy, Accessibility and Industrial Property Rents

129

property values. The final section concludes with a discussion of the policy implications of the analysis.

Roads policy and the UK motorway network The basic tenets of UK road policy have remained relatively unchanged since 1945. Successive governments on both sides of the political spectrum have primarily focused their policies on regenerating the post-war economy and promoting the use of the motor vehicle. This was facilitated through ambitious programmes of constructing an extensive interurban motorway network and trunk road system enabling the efficient movement of large volumes of traffic throughout the UK. Policy was implemented through predicting traffic levels several years ahead and identifying congestion points on the road network and providing the necessary road infrastructure. This straightforward implementation of ‘predict and provide’ remained the core concern of roads policy of successive administrations until the early 1990s and is arguably still a dominant influence on current policy (Walton 2003). Many commentators argue that the end of the 1980s saw the pinnacle of predict and provide with the publication of the White Paper, Roads for Prosperity in 1989 by the Conservative government (Docherty 2003). This document set out ambitious plans for major road building, improvement and maintenance. However, the policy was almost immediately thwarted by a succession of economic and political events, for example the UN Earth Summit in Rio de Janeiro in 1992, the recession of the 1990s and subsequent concerns about funding, and an increasing public awareness of environmental issues and hostility towards road building programmes. In particular, two independent reports produced in 1994 by SACTRA and Royal Commission on Environmental Pollution (RCEP) provided strong evidence that the predict and provide policies do not work and lead to an increase, not a reduction, in traffic congestion. The government transformed its official position, making roads policy more about managing the motor vehicle and its impacts than accommodating them. The predict and provide policy was abandoned and, from 1994 to their departure from office in 1997, the Conservative government considerably rationalised the road building programme as set out in their 1989 White Paper. On assuming office in 1997 the Labour government immediately cancelled or shelved the majority of schemes in the motorway and trunk roads construction programme established by the outgoing administration with a view to shifting investment to public transport, and actually

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reducing the amount of road capacity available. In 1998 they published the White Paper, A New Deal for Transport which set out a strategy for tackling the many transport related problems that exist within the UK. Although enthusiastically received it was not as radical as they had indicated while in opposition. In 2000 Labour formalised its vision of what could be achieved in Transport 2010: The Ten Year Plan for Transport.1 Elements of the predict and provide policies still dominate since this plan primarily targets congestion as the primary problem affecting the transport system, and as a consequence one-third of the total planned expenditure in England is targeted for road construction and improvements. A significant element of past road policy, particularly during the 1960s and 1970s, has been the construction of an inter-urban UK motorway network. Inspired by the North American freeways, motorways differ from trunk roads in their design and use regulations. They are high speed, multi-lane roads with an adjoining hard shoulder to prevent congestion by vehicles that have broken down. Slow moving vehicles and unqualified drivers are disallowed and access and entry is limited to designated junctions along the route. They are designed for fast, safe and efficient movement of motor vehicles although, with continually increasing traffic growth throughout the post-war period, many of these roads are now reaching capacity and suffering congestion during peak traffic flows. There now exists in the UK an extensive motorway network totalling approximately 3360 kilometres in 2003 (see Figure 7.1). This network has been incrementally constructed over a period of 45 years, beginning with the opening of the by-pass of Preston, Lancashire in 1959. As indicated in Table 7.1 and Figure 7.2, the 1960s and 1970s were the principal decades of motorway growth with only relatively minor additions to the network over the last ten years. Current government policy focuses on improvement and upgrading of the current system. For example, Transport 2010: The Ten Year Plan for Transport has targeted approximately 650 kilometres of the English network for widening to reduce congestion at certain stress points. A few additions are either planned or under construction but they are not on the scale of previous development, an example is the 15.2 kilometre M77 extension due for completion in 2005. Undoubtedly, the development of and continued investment in this network has brought (and will continue to bring) about a major change in road transport in Great Britain. We will now proceed to consider what the impacts of those changes are.


131

M90 M9

M8

A1

M8

M77 M77

M74

N

A74(M)

A1(M) M6

A1 A1(M)

M55

M53 A55

M65 M62

58 M

6

M5

M1 80

M18

0 M6 M1

M6

A1

M

69

M54

A1(M)

M42 M45

A14 M1

M50

M4

M11

M4

0

M4

M25

M2

M

5

M3 M2

M23 7

M20

A38

0

Figure 7.1

50

100 km

The 2003 UK motorway network.

Transport infrastructure and the spatial economy Changes to transport systems affect absolute and relative accessibility. For example, Dodgson (1974) examines the route of the trans-Pennine M62 motorway built in the 1970s and shows that there were differential impacts on accessibility along its route. These effects were greatest in west Yorkshire followed by areas close to the M62 but located at either end, e.g. Liverpool and Leeds (prior to its extension further east). Logically as a consequence Gwilliam and Judge (1978) estimate that the building of this motorway increased traffic along its route and it is now accepted, as noted above, that new road development generates further traffic. However, the precise impact of building individual new roads or expanding the existing transport networks is clouded by the counterfactual, namely what would have happened if the new roads had not been built, as highlighted by Botham (1983).

132


Table 7.1 The UK motorway network in chronological order. Motorway M6 M1 M4 M20 M50 M63 A1(M) M5 M2 M90 M8 M32 M48 M74 A57(M) M18 M40 M9 A102(M) M61 A194(M) A40(M)/M41 M3 M56 M602 M62 A38(M) A627(M) M53 M57 M606 M621

Length (km) Opening dates Motorway 365 327 318 82 34 23 159 262 40 52 81 6 9 61 2 42 141 65 6 35 6 4 95 53 6 170 4 6 33 15 4 12

1958–1972 1959–1999 1959–1996 1960–1991 1960–1962 1960–1982 1961–1998 1962–1977 1963–1965 1964–1980 1965–1995 1966–1975 1966 1966–1994 1967 1967–1979 1967–1991 1968–1980 1969–1973 1969–1970 1970 1970 1971–1995 1971–1981 1971–1982 1971–1976 1972 1972 1972–1982 1972–1974 1972–1973 1972–1975

M73 A329(M) M23 M58 M80 A167(M) A58(M)/A64(M) M11 M25 M27 M271 M54 M55 M66 M275 M42 M69 M180 M77 M67 A3(M) M26 M876 M65 A601(M) A6144(M) A74(M) M49 A823(M) M60 M6 Toll Total

Length (km) Opening dates 9 7 27 18 15 2 3 80 184 39 4 35 19 20 3 66 26 40 12 8 9 14 12 41 1 2 74 9 2 15 43 3359

1972 1973 1974–1975 1974–1977 1974–1992 1975 1975 1975–1980 1975–1986 1975–1983 1975 1975–1983 1975 1975–1989 1976 1976–1989 1976–1977 1977–1978 1977–1996 1978–1981 1979 1980 1980 1981–1997 1987 1987 1992–1999 1996 1998 2000 2003

Source: Compiled from Motorway Archive website (http://www.ukmotorwayarchive.org/ accessed on 9 February 2004.)

More generally Spence and Linneker (1994) examine the relative changes in the British regional accessibility surface brought about by the extension of the motorway network during two stages of development 1971–76 and 1976–89, and the overall period 1971–89. Accessibility is defined using a gravity type model in respect of accessibility to employment and so relates to accessibility to the location of economic activity. All regions experienced motorway-induced increased accessibility. The largest gainers were the north-west, Wales, and the south-west regions; there were also subregional beneficiaries and losers that are not recorded in the paper. However, this research is complicated by the changing composition of the spatial pattern of employment over time and the degree to which this is


133

1200

Kilometres opened

1000

800

600

400

200

0

58 − 62 63 − 67 68 − 72 73 − 77 78 − 82 83 − 87 88 − 92 93 − 97 98 − 02 03 − 07

Five year period

Figure 7.2 Length of motorway opened in each five-year period since 1958. Source: www.ukmotorwayarchive.org/.

induced by the development of the motorway network (although it is not a simple relationship). This issue is considered later. Transport costs are arguably a small percentage of industrial costs with one study finding that average transport costs account for 5–10% of the value of net output (Parkinson 1981). Other studies have pointed to variations between industries and suggested higher figures for some industries, for example Diamond and Spence (1989) found that while transport costs represented less than 3% of operating costs for some industries they accounted for 12% for wholesale distribution. Whatever the precise figures it is generally accepted that transport costs have an influence on the location of industry (Botham 1980). Neo-classical or Weberian theories have seen transport costs as a key determinant of location. These models evolved around cost minimisation triangles (Moses 1958). In these models optimum location revolves around the cost of materials and transportation costs: exogenous changes in costs can lead to alterations in the input mix of raw materials or to a new location. These simple models do not derive practical hypotheses to test. McCann (1995) also more generally criticises these models for having an incomplete production function which renders them devoid of any realworld economic meaning despite their mathematical consistency. As a result they are ‘meaningless as a basis for discussing why we observe that

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particular types of firm producing particular products exhibit particular types of locational behaviour’ (McCann 1995, p. 568). An alternative neo-classical view of the determination of industrial location is given by the role of externalities. Within this model firms are attracted to locations by the externalities available (Parr 2002). These include the geographical concentration of similar industries enabling specialist subcontractors, or complementary firms, for example in business services, and a pool of skilled specialist labour (and associated education facilities) to develop. Gordon and McCann (2000) review other models of industrial clustering, distinguishing between the classic agglomeration model, the industrial complex model where firms group as part of the production process and a network of firms which is derived from a sociological perspective. None of these models are linked to (variations in) inter-urban transport costs. There have been two models specifically aimed at examining the impact of changes in inter-regional transport costs. Sharp (1980) finds that when two regions are brought together by a road then the region with lower production costs benefits, but this conclusion states little more than the obvious. Venables and Gasiorek (1998) quoted in SACTRA (1999) develop a theoretical model that examines the impact of improving inter-regional transport costs in three regional development scenarios – a central and a peripheral region, three identical regions, and three regions along a centre–periphery corridor. Their conclusions, despite being based on models with very simplistic assumptions, are that there are no simple rules about the impact of changing transport costs. Overall the use of industrial location models does not seem to progress our understanding. There are, however, a wide range of studies that have suggested generalised influences of transport costs on spatial economic development. From these studies there has been a whole gamut of potential conflicting outcomes postulated from road improvements, including:

. Increased transport network facilitates long distance office and manufacturing plant dispersal (Pye 1977; Keeble 1976).

. Improvements reduce the previous disadvantages of indigenous industry in peripheral regions by opening up markets (Manners 1962).

. Lower costs of distribution encourage the concentration of production into fewer plants (plant size previously constrained by transport costs) which tend to locate with reference to the major markets. The need to disperse to peripheral locations is reduced, while by opening them up to increased competition the well-being of indigenous industry in


135

peripheral regions is undermined as reported by Scherer et al. (1975) and Transport 2000 (1980). . Encouraging long-distance dispersal of branch plants while the competitiveness of indigenous industry is undermined and administrative functions are centralised (Botham 1983). . Enabling a retailer to serve the same number of outlets from a smaller number of distribution points (Quarmby 1989; Mackie & Tweddle 1993). . Increasing the absolute level of accessibility by reducing significance attached to transport costs and enabling other factors to determine location patterns. These may include residential preferences, behavioural factors or new sources of unexploited labour and environmental factors. These views are expressed primarily at the regional level. Yet it is clear that the development of the motorway system will have implications for both inter-regional and sub-regional/inter-urban spatial development. Local empirical studies of the economic impact of particular new interurban roads are difficult because of data limitations. They have therefore tended to be qualitative and have found it impossible to isolate local additionality and identify spatial displacement effects (Halden & Sharman, 1994; WERU, 1997). Threshold effects on market size and distribution costs muddy the picture also. An illustration of the processes of adaptation is given by examining the underlying influences on how distribution is changing. McKinnon and Woodburn (1996) note that ICT developments combined with motorways and the increasing size of lorries has led to changes in the supply chain. The motivation is primarily the desire to cut inventory levels and the consequences are centralisation of production and changes in market area. Today, movement of most commodities from raw material source to final point of sale comprises several separate journeys. There has been an increase in the number of separate links in the supply chain, increased weight loss in the production process and changes in the amount of packaging. In the food and drinks sector, for example, large supermarket chains facilitate this by channelling the products of smaller regional producers through their distribution systems. As a result there is a sharp increase in volumes of food and drink being funnelled through the distribution centres where they are consolidated into bulk loads for direct delivery to shops. The role of ICT in the above example is only one of a number of other factors that have influenced spatial economic development besides inter-urban

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transport improvements. In the twentieth century there has been a considerable process of urban industrial change. Three different processes have contributed to this – de-industrialisation, decentralisation and decongestion. De-industrialisation is a consequence of traditional industries declining in the face of international competition and the globalisation of markets. Cities that had a high concentration of this type of industry have suffered severe economic decline. However, this structural explanation for decline will vary widely between cities, depending on the nature of their traditional industries (Robson et al. 2000). Decentralisation involves the movement of manufacturing industries to better locations outside city core areas. The reasons for this include lower inter-urban transport costs already discussed and the increasing requirement for land due to changing production and storage requirements. This has encouraged industry to move to decentralised locations where there is plentiful supply of land and easy access to the national motorway networks. Other factors have also contributed to a decline in the requirement for industries to be close to core city areas. Technological change has weakened the agglomeration economies of many cities. Transport costs and the need for large pools of labour have also declined as a result of: (1) (2) (3) (4)

Miniaturisation of products; The introduction of new lightweight materials; A reduced number of moveable parts in machinery; The increased use of electronics, as opposed to mechanical, parts and processes.

The globalisation of industry and markets has reinforced these trends further. The creation of large multi-national companies has helped to increase the average size of an industrial plant and therefore increase industrial land requirements. Decongestion is the decentralisation of manufacturing industries to suburban locations or locations at the periphery of the city. Decongestion is distinct from decentralisation because it occurs strictly within the urban setting. It is a form of extended suburbanisation that involves an intraurban move as opposed to an inter-urban move. It is, however, a consequence of the same forces that cause decentralisation. Headicar (1996) chronicles this phenomenon along the M40 corridor where the location of new development is determined in large part by opportunities to interact with the transport system. These opportunities increase over time with improvements in transport technology and investment in the transport network. Shifts in demand for development in these areas are supported by


137

lower initial land prices, easier land assembly, better physical and social environment than many alternative inner urban sites. Parr and Jones (1983) identify a wider interaction between intra-urban and inter-urban transport costs in urban development. They synthesise many of these ideas discussed above into a (five) stages model of urban change beginning with the pre-urban stage where both weak intra- and inter-urban transport links stymie development. In the final urban dispersal stage inter-urban transport improvements are the dominant influence supported by ICT, while intra-urban transport links are suffering from congestion. These improvements permit hitherto latent locational advantages to be exploited so that export bases are expanded and new ones developed. This is reinforced by markets outside the national core areas being sufficiently large that more local or regional patterns of production are feasible. Thus, ‘ . . . the advantages of concentrated patterns of production (based on scale and agglomeration economies) in the older, larger metropolitan areas become subordinate to the even greater advantages of access to labour, power or regional markets, with the medium sized cities becoming the principal beneficiaries of these locational shifts.’ (Parr & Jones 1983, p. 302). In this new dispersed sub-regional urban system the key accessibility relationships have been transformed from those centring on the urban core to regional centrality nodes. With the locational demand for property a derived demand from the spatial distribution of economic activity, these relationships are reflected in the pattern of industrial rents.

Measuring the effect of transport investment The direct impact of the motorway network is a changed accessibility landscape defined in relative and absolute terms. There have, as noted above, been various inconclusive attempts to measure the benefits to local economies of the changes to the transport network. These studies have focused directly on economic activity. In this chapter the impact of motorway links are examined via industrial rental values. The logic of this approach is that the financial benefits of accessible locations in terms of costs lead to (potential ) occupiers bidding up rents to reflect these advantages. In a perfect property market all the financial benefit would be bid away and subsumed into rental values, but even recognising that the market is imperfect there should be a positive relationship between rents and advantaged locations. To fully understand this relationship between rent and location it is useful to review the underpinnings of intra-urban

138


location theory. Unlike regional location theory the property market is a central element of intra-urban location theory. Intra-urban location theory originates with Alonso’s seminal land use model (Alonso 1964). This model implicitly assumes agglomeration economies in the city centre. It is based on a city located on a featureless plain where land use is allocated to the highest bidder in a competitive land market. In this uni-nodal city the central business district (CBD) is the point of maximum accessibility where business revenue is at a maximum and costs (other than land costs) are minimised. As the point of maximum accessibility rental values are highest. Differences in the optimum locations of industrial and commercial land uses relate directly to the responsiveness of revenue and costs to distance from the centre. This in turn determines the rent bid ability of a particular land use. In the original Alonso model it is presumed that revenues fall and costs rise with distance from the CBD. Di Pasquale and Wheaton (1996) develop a more sophisticated approach. In their stylised nineteenth century city the CBD is replaced with a central shipment/transportation terminal. All firms are initially assumed to produce identical products, use the same production process and have equal outputs. There is no factor substitution so that the plot size and buildings are fixed for each firm, and output per acre is fixed. In this model, the rent for buildings is fixed but the land rent per acre varies with location. Revenue and production costs are spatially invariant and given a competitive land market land rents in equilibrium will exactly compensate for transport costs which rise with distance from the transportation terminals. In this way a rent gradient is formed. When the assumption of identical production processes is relaxed, the model can be extended to groups of identical firms or land uses. This leads to a range of potential different rent gradients. The land market is presumed to be competitive, allocating land use on the basis of the highest rent. Both the Alonso (1964) and Di Pasquale and Wheaton (1996) models above lead to similar outputs, notably a declining rent gradient from the CBD. A major distinction in their assumptions is that while Di Pasquale and Wheaton presume revenue is spatially constant, Alonso has revenue decreasing with distance from the CBD. Logically the Alonso model would lead to a steeper distance decay gradient. These models provide valuable insights to the underlying mechanisms of spatial property markets that can now be applied to the sub-regional


139

inter-urban context that also incorporates intra-urban differences. It has been seen that much of industry now serves regional or national markets and hence revenue will not vary according to spatial location within a city or region. Industrial change means that the spatial structure of costs has changed over time. Distribution costs were originally minimised at core urban locations near rail and sea terminals. With the advent of the national road network and the associated greater use of containers and large trucks at the intra-urban level costs are now minimised at peripheral locations accessible to the national road network. Di Pasquale and Wheaton (1996) argue that this has led to an almost flat industrial rent gradient. But with revenue constant throughout the city, and costs falling with distance from a central point, the rent gradient is logically upward sloping. However, this is intuitively an incomplete argument, for example, it ignores the benefits of access to business services in the city centre. From a sub-regional perspective rental structures reflect and extend these forces. With the presumption of national or regional markets the industrial revenue is independent of location. Similarly labour costs and other nonland costs are taken to be broadly constant within a sub-region, closely linked to a local labour market area. With these assumptions the spatial pattern of rents should reflect the regional accessibility landscape which encompasses say major motorway intercepts and motorway junctions. Within the scenario set out by Parr and Jones (1983) we hypothesise that key crossover points on the motorway network will be associated with rent maxima with a negative rental gradient emanating from such locations. Nearness to motorway junctions will also be locations of secondary rent premia.

Hedonic pricing analysis and the impact of the UK motorway network To determine the impact of transport investment on property values, hedonic theory is adopted in this study. Hedonic pricing models have been applied to housing markets for numerous purposes including the impact of policy decisions, such as environmental impact assessment (Garrod & Willis 1992); to examine the effect of the planning system on property prices (Cheshire & Sheppard 1989); to assess the impact of transport infrastructure on property values (Damm et al. 1980; Forrest et al. 1992; Henneberry 1998). The application to industrial markets is limited by comparison and has been principally confined to a few studies undertaken in the United States (Ambrose 1990; Fehribach et al. 1993; Lockwood & Rutterford 1996; Buttimer et al. 1997; Jackson 2002). Although

140


limited in their policy application these studies provide useful insights as to the significant determinants of industrial rents and provide guidance as to the selection of explanatory variables. The hedonic approach to property market analysis utilises the heterogeneous nature of property and adopts the view that a unit of property is best viewed as a bundle of attributes which contribute to the provision of a flow of one or more property services. When considering industrial property each occupier is assumed to derive profit directly from the property characteristics and to maximise it by choosing the industrial unit (bundle of characteristics) which provides the greatest efficiency. Within this framework the rent of the industrial unit will depend upon the quantities of the various attributes of which the unit is composed. More formally the rent can be expressed as some function, R(Z) ¼ f(z1 , z2 . . . zn )

(7:1)

where R(Z) is the rent of the industrial unit and the zi ’s are the individual characteristics. Empirical testing of this involves obtaining a price measure, R(zk ) and the corresponding zi ’s for the kth property, and estimating the hedonic equation using multiple regression analysis. R(zk ) ¼ b0 þ

n X

bi zik

(7:2)

i¼1

An important step in producing a hedonic model of a property market is the selection of explanatory variables. Different variables are likely to be important in different markets and at different times. Rather than experimenting with an unlimited number of attributes, the best option open to the researcher is to review some of the existing hedonic industrial studies. The studies, noted above, typically include characteristics relating to the physical characteristics (age, size, access), use (manufacturing, warehousing), economic and financial indicators (year of transaction), location (distance to points of accessibility, submarket) and tenure (lease terms). For the purposes of this study, the key characteristic for inclusion in the model is clearly whether proximity to transport interchanges and motorway junctions is a statistically significant determinant of the rent on industrial property and if so the direction and magnitude of the effect. The chosen study area is Glasgow and its surrounding hinterland accounting for much of the mainland area covered by the former Strathclyde


141

Regional Council. This region of Scotland has experienced considerable de-industrialisation and decentralisation of industry over the last three decades. It has suffered from a severe decline in shipbuilding, coal mining and heavy industries and consequently industry is now predominantly light engineering, electronics, distribution and other service industries. The region has an extensive motorway network as illustrated in Figure 7.3 that has been developed in incremental stages since 1965. The network consists of a system of five motorways: the M8 running east–west connecting Glasgow and Edinburgh, the M74(A74(M)) running south to the Scotland/England border, the M73 and M80 to the north and the M77 connecting the south-west coast of Scotland. The system was planned in the mid-1960s and was a major part of post-war development aimed at improving the accessibility of the area with the rest of Scotland and the UK. The data pertain to industrial property transactions during the calendar years 1994 to 2000. The source is the Scottish Property Network (SPN)

CLYDEBANK M8

M898

M80

GLASGOW M8

M73

PAISLEY

M74 HAMILTON

M77 EAST KILBRIDE

Figure 7.3

Case study area and motorway network.

M74 & A74(M) to M6

142


which collects availability and transaction level information on commercial transactions from real estate agents. The data set, comprising 3180 observations within recognised industrial estates, includes asking rents2 and the location and physical characteristics of the units. To this data accessibility to transportation nodes – major transport interchanges and motorway junctions – have been added. Table 7.2 explains the dependent and independent variables, the majority of which require no further explanation. However, the specification of the accessibility to transport nodes does. To quantify the impact of transport investment we develop two measures. First, distance to the major transport interchange located to the east of the region which joins the M8 running east–west, the M73 to the north and the M74 to the south (see Figure 7.3). Second, distance to the nearest major trunk road or motorway junction. These are constructed using a piecewise linear distance formulation similar to that used by Colwell et al. (1999). This specification measures distance within a series of incremental boundaries. The distance within each boundary is defined as the boundary distance (DBoundary ) less the straight-line distance (SLD) (DBoundary SLD). If the industrial unit is located within the boundary it is given a dummy variable (d) of 1, if the unit is beyond the boundary it is designated a dummy variable of 0. Hence, each distance variable is defined as (DBoundary SLD) d. We define four boundaries. The first is defined by reference to the observation within the sample that has the maximum distance from the measured point, i.e. the observation furthest away from the motorway junction or transport interchange. The remaining boundaries are set at 10, 5 and 2 kilometres respectively. This specification is used for both the nearest motorway junction and transport interchange. It enables an examination of the rental gradient as we approach the measured point. Based upon our theoretical discussion we hypothesise that the gradients will get steeper as distance gets closer to both the nearest motorway junction and central access point. Based upon the information provided in Table 7.2, we apply a logarithmic transformation3 to the regression equation, the results of which are presented in Table 7.3. Before proceeding with a discussion of the results it is important to interpret the constant correctly. The constant represents the starting point of any examination of results produced by hedonic modelling. This term includes the influence of all attributes not included in the regression equation and is the base from which other variables are added. The

143

UK Roads Policy, Accessibility and Industrial Property Rents Table 7.2

Variable descriptions.

Variable

Description

LnRent

Natural logarithm of asking rent per square metre Natural logarithm of gross internal area measured in square metres 1 if unit constructed between 1960–1969, otherwise 0 1 if unit constructed between 1970–1979, otherwise 0 1 if unit constructed between 1980–1989, otherwise 0 1 if unit constructed from 1990 onwards, otherwise 0 1 if principal use is warehousing, otherwise 0 (Manufacturing ¼ 0) 1 if unit is divisible, otherwise 0 1 if unit is in good state of repair, otherwise 0 (poor state of repair ¼ 0) 1 if unit is ‘fitted-out’, otherwise 0 1 if unit is located within East Ayrshire Unitary Local Authority (ULA), otherwise 0 1 if unit is located within East Dunbartonshire ULA, otherwise 0 1 if unit is located within East Renfrewshire ULA, otherwise 0 1 if unit is located within Inverclyde ULA, otherwise 0 1 if unit is located within North Ayrshire ULA, otherwise 0 1 if unit is located within North Lanarkshire ULA, otherwise 0 1 if unit is located within Renfrewshire ULA, otherwise 0 1 if unit is located within South Ayrshire ULA, otherwise 0 1 if unit is located within South Lanarkshire ULA, otherwise 0 1 if unit is located within West Dunbartonshire ULA, otherwise 0 1 if unit let in 1995, otherwise 0 1 if unit let in 1996, otherwise 0 1 if unit let in 1997, otherwise 0 1 if unit let in 1998, otherwise 0 1 if unit let in 1999, otherwise 0 1 if unit let in 2000, otherwise 0 (Dmax SLD) where Dmax equals sample boundary defined by the observation furthest away from a motorway junction (D10km SLD) if distance to nearest motorway junction is less than 10 km, otherwise 0 (D5km SLD) if distance to nearest motorway junction is less than 5 km, otherwise 0 (D2km SLD) if distance to nearest motorway junction is less than 2 km, otherwise 0 Straight-line distance in metres to nearest principal town centre (population > 20 000) (Dmax SLD) where Dmax equals the distance to the sample boundary defined by the observation furthest away from the CAP (D10km SLD) if distance to high concentration of roads (CAP) is less than 10 km, otherwise 0 (D5km SLD) if distance to CAP is less than 5 km, otherwise 0 D2km SLD) if distance to CAP is less than 2 km, otherwise 0

LnSize AGE_1 AGE_2 AGE_3 AGE_4 Warehouse Flexible G_Cond Fitted Ulaea Ulaed Ulaer Ulainv Ulana Ulanl Ularen Ulasa Ulasl Ulawd Y95 Y96 Y97 Y98 Y99 Y00 D nmjmax

nmj10km nmj5km nmj2km Nrsttown D capmax

cap10km cap5km cap2km

Predicted sign N/A þ þ þ þ þ/ þ þ þ þ/ þ/ þ/ þ/ þ/ þ/ þ/ þ/ þ/ þ/ þ þ þ þ þ þ þ

þ þ þ þ

þ þ þ

144


Table 7.3 Regression results.

(Constant) LnSize AGE_1 AGE_2 AGE_3 AGE_4 Warehouse Flexible G_Cond Fitted Ulaea Ulaed Ulaer Ulainv Ulana Ulanl Ularen Ulasa Ulasl Ulawd Y95 Y96 Y97 Y98 Y99 Y00 D nmjmax nmj10km nmj5km nmj2km Nrsttown Dcapmax cap10km cap5km cap2km

Beta

t-stat

3.4800 0.1490 0.0730 0.0901 0.1010 0.2260 0.0014 0.0157 0.0637 0.0301 0.0238 0.0566 0.0211 0.2890 0.2250 0.0020 0.0504 0.4600 0.1040 0.2200 0.0316 0.0085 0.0177 0.0097 0.0700 0.0666 0.0037 0.0154 0.0378 0.1130 0.0123 0.0172 0.0475 0.1090 3.4400

23.00 31.92 3.67 5.06 6.04 12.16 0.13 0.85 5.63 2.79 0.36 1.39 0.33 3.85 3.49 0.12 2.23 4.93 4.73 5.91 1.53 0.49 1.02 0.64 4.80 2.16 1.24 1.57 2.79 6.63 4.01 8.76 8.50 4.52 0.94

*** *** *** *** *** ***

*** ***

*** *** ** *** *** ***

*** **

*** *** *** *** *** ***

*** significant at 99% ** significant at 95%

coefficients generated in the hedonic model represent an implicit value of buying that attribute which is not included in the constant term. In this study the constant term represents a pre-1960s indivisible manufacturing unit, let in 1994, in poor condition, and located within Glasgow City ULA. An examination of the regression coefficients in the hedonic model accords with the results of the previous North American studies and emphasises the importance of location (general and specific), size, physical condition, economic conditions and age in explaining the variation in rent across industrial units.


145

Location appears to have a major influence upon the rent supporting arguments for a negative rent gradient from the city centre. As the distance increases from the nearest major town rent reduces by 1.2% per kilometre. However, the effect while significant suggests a shallow negative rent gradient and is significantly overshadowed by an industrial unit’s proximity and access to the motorway network. The analysis suggests that proximity to motorway junctions is only of significance to occupiers who are located within 5 km of the junction. Beyond 5 km the coefficients are statistically insignificant. The coefficient indicates that as the junction is approached from the boundary of 5 km the rent increase is 3.78% per kilometre. From a boundary of 2 km the rent increases by 15.08%. This is an entirely logical result since a location close to the junction will not only be more accessible but also more prestigious in terms of visibility from the road. Access to the central access point is also statistically significant. However, in comparison to proximity to motorway junctions, the significance covers a broader geographic area. D_capmax is statistically significant, suggesting that proximity to a major network of roads is of relevance to all occupiers, including those located on the outer boundary of the sample area. As we approach this point the gradient increases; however, in contrast to motorway junctions, being in close proximity to this network of roads (i.e. within two kilometres) does not add value. As illustrated in Figure 7.4, we uphold the hypothesis set out by Parr and Jones (1983) where key crossover points on the motorway network will be 160

Estimated rent index

140 120 100 80 60 40 20

Distance (km) to transport interchange

Figure 7.4

Simulated industrial rent gradient.

9 7 5 3 1

67 65 63 61 59 57 55 53 51 49 47 45 43 41 39 37 35 33 31 29 27 25 23 21 19 17 15 13 11

0

146


associated with rent maxima with a negative rental gradient emanating from such locations and nearness to motorway junctions are also locations of secondary rent premia.

Discussion and policy implications The establishment of the road/motorway network has transformed the accessibility landscape of the UK both between and within regions. At the sub-regional level the extension and improvement of the road network has had fundamental influences on industrial location decisions. Together with parallel changes, including developments in ICT and production technologies, the road system has contributed to a new dispersed urban system with the substantive accessibility relationships transformed from those centring on the urban core to regional centrality nodes. These nodes encompass both the access to motorway junctions and major crossover points on the motorway network. To date, empirical studies of the impact have focused on examining spatial economic activity and its divisions. This approach suffers from data limitations and therefore tends to be qualitative and not entirely conclusive. Also, there has been less interest in the commercial property market effects. The approach here has been to measure the benefits by examining the impact on industrial property values using hedonic analysis. This is the first major study in the UK known to the authors and provides a useful comparator with American studies and provides an insight into the commercial property markets impacts of road policy and investment, albeit in a narrow way. Hedonic theory is not without its critics. There are some caveats to conclusions drawn from this research method. The essential premise of the paper is that the property market is competitive so that rents reflect accessibility benefits. There are arguments that the property market is imperfect and this could deflate/inflate rent premia, and bias the results. In particular imperfections caused by weak knowledge could occur in small industrial centres where trading is thin. There is some evidence to support this may have occurred with some local authority areas with a small industrial stock having insignificant coefficients. However, the results of the analysis are consistent with a priori theoretical expectations. The treatment of time is important in such studies and further insight into the impact of the road network could be achieved with a time series analysis of property rents and the supply of industrial stock. Changes


147

will take place in land and property values in advance of the completion of the transport investment since developers will invest in the expectation of improvements in the transport infrastructure. Effects might also be expected immediately after the transport investment is opened, and further in the future as the full benefits are recognised. Ideally, data should be available from before the decision to build was taken and immediately after opening, as well as sometime after. Despite these criticisms, the evidence from the study suggests that the motorway network does bring economic benefits to locations along its path at least in the immediate areas of its junctions. These benefits are not even and they are magnified close to motorway crossover points. These findings give support to the view that extensions to the motorway network have positive effects for peripheral sub-regional locations close to junctions but also that the greatest benefits occur at central regional locations.

Notes 1 Applies to England only. 2 The use of asking rents is often open to criticism but we would argue that they represent open market valuations undertaken by professionals operating within their local market. The asking rent is set at a level which will attract tenants and it should fairly reflect the quality and location of the property. Naturally a margin is included to allow for negotiation but provided the time span of a study is short, this should not be a problem. Glascock et al. (1990) examine the stability of asking rents against the actual transacted rent and find an extremely close relationship between the two variables. 3 For a discussion on the choice of a suitable functional form, see Halvorsen and Pollakowski (1981) and Thibodeau (1992), p. 14–18. In our analysis we choose between alternative models through a series of Box–Cox transformation and likelihood ratio tests (Box & Cox 1964). Following Brennan et al. (1984) five functional forms are considered: log-linear, logarithmic, reciprocal, semi-log, and linear. It should be noted that log transformations could not be used for dummy variables and other independent variables that have possible magnitudes of zero. We conclude that the logarithmic model is the most appropriate of the five models initially considered, on the basis of empirical evidence.


8 Urban Regeneration, Property Indices and Market Performance Alastair Adair, Jim Berry, Ken Gibb, Norman Hutchison, Stanley McGreal and Craig Watkins

Introduction As discussed in the previous chapter, there is a long history of researchers using property indices to help unravel the impact of public policies on market performance. As Dunse and Jones (in Chapter 7) demonstrate, typically indices have been constructed using regression techniques and the impact of particular policy initiatives has been revealed through the interpretation of the parameter estimates. This approach has allowed researchers to identify the impact of a wide range of public policy interventions including transport infrastructure changes, environmental schemes and land use planning measures. In this chapter we also set out to use indices as a means of exploring the impact of public policies. In particular the focus is on the long-term relationship between urban regeneration policies and commercial property market performance. The research, however, uses property index methodologies in a very different way to the studies mentioned in the previous chapter. Specifically, using two different approaches, we construct indicators that measure the performance of a particular segment of the property market. The market segment of interest comprises those properties that have directly benefited from, or are located within areas that have benefited

Urban Regeneration, Property Indices and Market Performance

149

from, funding under any of the raft of urban regeneration initiatives introduced by British governments since the late 1970s. Data on returns, rental values and yields produced by a sub-set of the regeneration properties were collected and combined to produce performance indices that are directly comparable with the major property industry indicators produced by the Investment Property Databank (IPD) and CBRE. It is the analysis of the changes in our indices relative to those developed for traditional markets that helps illuminate the long-term performance of the parts of the property market in which urban regeneration policies have been implemented. This analysis is important in two ways. First, it provides information on the performance of property markets influenced by regeneration policies. This information has not been captured by existing evaluative studies. Second, the provision of measures of regeneration market performance will help improve the information set upon which investment decisions are based and may, in turn, help remove some of the barriers to private sector investment in declining areas in British cities. In addition, given the lack of published information on the index construction approaches employed by the UK property industry, the chapter provides unique methodological insights. The chapter is organised in four main sections. The first explores the context of the construction of the urban regeneration property performance index. It examines the role of property markets in urban regeneration policy and sets out the rationale for measuring the performance of regeneration property markets. The second section provides details of the data sources and research methods used in constructing the indices. A summary of the empirical results is outlined in the third section. The results section examines indices for retail, office and industrial sectors as well as all property. The performance of the regeneration indices is compared to the industry benchmarks. The conclusions section considers the implications for policy makers and property investors.

The urban regeneration policy context The emergence of a comprehensive urban policy in the UK can be traced to a number of urban experiments undertaken in the 1960s and the introduction of the Urban Programme in 1968 (Atkinson & Moon 1994). By the early 1980s, regeneration policy mainly took the form of private sector led property development (Healey et al. 1992). This emphasis on physical property-led projects coincided with the property market boom and a general increase in the level of finance supporting speculative

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development. A range of policy initiatives and instruments were developed to support this activity including Enterprise Zones and Urban Development Corporations as well as a range of grants and subsidies. However, the approach became discredited by the early 1990s when the property slump and withdrawal of private finance undermined many projects (Turok 1992; Jones 1996). Despite this, the rationale for urban regeneration policy is still provided by the need to reverse the economic, social and physical decay in towns and cities, especially where it has reached that stage when market forces alone will not suffice (Adair 1998). While policy content has switched towards a more holistic, people-oriented focus, one element of continuity that remains is the fact that the natural mechanism for intervention is no longer the public sector. As such, a key part of the role of policy is to restore private sector confidence in investing in urban areas and, despite the downgrading of physical property projects as policy outcomes, investment in property remains an important vehicle for securing private sector involvement. This is clearly apparent in the current work of Urban Regeneration Companies and Business Improvement Districts and in contemporary debates about the use of tax-based instruments in the promotion of regeneration schemes (McGreal et al. 2002). In this context and the current public policy environment the efficacy of regeneration schemes is often measured in terms of value for money, including the extent to which public sector funds achieve a high leverage ratio. However, despite the emergence of voluminous regeneration evaluation literature, there are two important issues that are rarely addressed. First, there have been few substantive studies of the property market outcomes of the vast array of urban regeneration initiatives undertaken. In a recent review, Tyler et al. (2001) summarises evaluation studies of the principal area-based urban regeneration initiatives. Tyler et al.’s work includes those initiatives that have generated outputs entering the property investment market. However, while this, and other, value for money (VFM) studies indicate the importance of the property dimension within area-based initiatives, these major studies tell policy makers very little about market performance (see Robson et al. 1994; PA Cambridge Economic Consultants 1995; Hall et al. 1998; Imrie & Thomas 1999; DETR 2000b). In fact, the Tyler et al. study merely tells us about the number of new housing units constructed. Elsewhere case study-based literature has also evolved, providing in-depth coverage of urban regeneration including the role of fiscal incentives, the


151

nature of partnership arrangements, and the evaluation of local impacts of particular policy initiatives. These studies tend to have a spatial focus and have given rise to clearly identifiable strands of the literature based on, for example, the regeneration of waterfront areas, inner city locations and peripheral estates (Berry et al. 1993). However, again the analysis of property market outcomes is partial. Where property issues have been explored, most market analyses have tended to focus on the residential market and, in particular, on relatively short-term house price trends (see Imrie & Thomas 1993; Cameron 1994; Rosenburg & Watkins 1999; Couch 1999; Leishman & Watkins 2004). Of the few commercial property market studies that exist the research has been narrowly targeted at specific initiatives or locations (see Erickson & Syms 1986; Greenhalgh et al. 2000; Jones et al. 2003). Typically the analysis focuses on the relative success of initiatives in terms of generating new supply and establishing new market activity. Overall, despite this research, it is fair to conclude that measurement of (commercial ) property returns remains an under-developed part of the urban regeneration literature. Second, the failure to measure returns means that there is little information available to private sector investors about the performance of regeneration property markets. If we think about the context in which private sector property investment decisions are made, this information gap is of considerable policy significance. For instance, it is worth noting that property as an investment provides institutions with diversification benefits within a mixed asset portfolio in which real estate has to compete with other asset classes primarily equities and bonds. Key criteria in the decision making process and in determining allocations across the respective asset classes are performance-based measures of expected return and risk (Hoesli et al. 2002). Furthermore, property investment in the UK and in particular institutional funds are heavily focused upon the prime commercial markets (retail, office and industrial ) for which transaction evidence, although partial, is best developed. In contrast evidence for secondary, tertiary and regeneration markets is more fragmented. Thus, in practice, investment in regeneration property suffers from competing with alternative property and non-property investment vehicles. While investment in property has increased through the 1980s, this has tended to be focused in prime markets. This trend is in part a reflection of investors’ perceptions of regeneration projects and a general lack of information about investment opportunities and performance. Indeed, institutional investors are reluctant to invest in regeneration areas in spite of

152


the desire by government and regeneration agencies to increase institutional involvement and attract private finance to regeneration areas. Instead, private sector involvement frequently has been driven by property development and investment companies (often of local origin), which seem to adopt less risk-averse strategies than institutional investors (Adair et al. 2002). Evidence from the literature suggests that the lack of rigorous and consistent measures/signals of market performance in regeneration locations has acted as a major deterrent to the redevelopment of brownfield sites and has had a detrimental impact on the wider regeneration agenda. Authors such as Syms (1997) emphasise the confidential nature of most property transactions in the UK with limited access to such information. This scenario is exacerbated in regeneration areas due to fewer transactions and therefore less market evidence. Such conditions of uncertainty are not conducive to property investment decisions. The problem of information transparency and resulting uncertainty in urban regeneration investment is amply demonstrated by the perception gap between those investors who have achieved anticipated returns and those who perceive that this is not possible. However, analysis by Adair et al. (1999) indicates that for both investors and non-investors in regeneration there is broad consensus concerning the range of factors that would facilitate the mitigation of risk and the enhancement of return. The challenge for regeneration policy makers and those agencies seeking to stimulate greater private sector investment is how to bridge the gap in perception between investors and non-investors. One way in which this may be resolved is through the provision of enhanced information on regeneration investment thereby illuminating return and risk and facilitating a more accurate and comprehensive understanding for decision making. Against this backdrop, the remainder of this chapter seeks to address these two issues: the absence of rigorous property market analysis and the associated lack of transparency. The focus now turns to the construction of indices designed to measure the comparative performance of urban regeneration property markets and the results produced over the last twenty years.

Data and research methods The empirical part of this study involved the construction of property indicators comparable to those conventionally used to monitor prime


153

market performance, namely the IPD Total Returns Index and the CBRE Rent Index and Yield Monitor (or Beacon methodology). This part of the paper focuses on the methods used and data collected. It describes a fourstage research design. The first two stages of the methodology were common to both the TRI and Beacon approaches. Stage 1 required an operational definition of the urban regeneration property market. Unlike many other property market studies where market areas are generally assumed to be coterminous with the city, local authority or regional boundaries, this research requires more precision in order to be able to demarcate between regeneration areas and prime markets. Although area-based regeneration programmes provide a useful starting point, this misses some of the important strands to policy and, in particular, ignores many of the local project-specific property-led flagships schemes of the 1980s. Consequently, in this study, the regeneration property market is taken to comprise all properties located within identifiable area-based locations plus those properties that have been the subject of some form of intervention, including grants and subsidies. Stage 2 of the methodology required the selection of case studies from which data on property offerings would be collected. The cities were chosen to capture a range of social, economic and property market characteristics and the varying nature of regeneration intervention in urban Britain in the last two decades. A number of indicators including GDP, employment change, population change, Index of Multiple Deprivation and prime property values were consulted. Once the case study areas had been selected, the research team undertook a series of field visits involving detailed consultation with local experts on the spatial extent of regeneration markets and the usage and incidence of subsidies and grant expenditure. These visits provided the framework for the data collection and were conducted in Birmingham, Bristol, Cardiff, Glasgow, Greater Manchester, Nottingham, Sheffield, Tyne and Wear. Stage 3 focused in on the data required for the TRI index, while Stage 4 sought to capture the Beacon data. The TRI approach requires the construction of an index that measures changes in capital value from one yearend valuation to the next, net of any capital expenditure and net income receivable through the year. Given the paucity of data available for local commercial property markets (Dunse et al. 1998), the research team sought to identify all potential investment quality properties within the eight chosen study areas. This exercise yielded information on 1208 properties. Initially the team sought to capture data on these properties from

154


agents, owners and developers. However, this produced few returns. Time and cost factors as well as what appeared to be poor database management in real estate firms inhibited the process. The only significant source of good quality data was IPD. In the end, this dataset effectively acted as a sampling frame utilised by IPD. Indeed, collaborative work with IPD who undertook the process of matching the regeneration properties to their data on investment holdings was vital to the generation of the raw data. This matching process captured data over a twenty-year time period, with a peak of 223 properties in 1998 (see Adair et al. 2003a and 2003b for further details). Stage 4 focused on collating the data required for the Beacon Index. This part of the research relied heavily on the cooperation of the valuation community. The Beacon index requires local market experts to estimate rents and yields for hypothetical, standardised offerings for a range of regeneration locations within each of the eleven study cities (the eight used for the TRI plus Edinburgh, Belfast and London Docklands). The standard offerings were based on those used by CBRE, although there were some modifications to reflect the distinctiveness of the regeneration market (see CB Hillier Parker 2000; Adair et al. 2004 for details). The experts’ estimates were collected using a standard proforma on which were entered the open market rental value and initial yield of a rack-rented property at 31st December in each year from 1995 to 2002. The regeneration locations were pre-determined and included the main regeneration locations within each market. The estimates were combined in three steps. First, the individual estimates for each rent point were averaged to provide an average rental change. Second, the average annual rental change across all rental points was then computed on a sector by sector basis. Third, using 1995 as a base year, the rent points were combined to compute an all property and sectorspecific indices. In combining the data from different cities, the differences in size and importance of the commercial property markets were recognised. The rental index and yield monitor are based on weighted results. After experimentation with a range of weighting schema, the level of employment was found to be the most reliable and consistent proxy for market size. This weighting proved to be superior to the IPD universe, used by CBRE, and sector-specific floor space which was undermined by missing data for some areas outside England and Wales as well as the divergence between official definitions of use classes and the definitions of sectors employed by the research team. The average yield monitor was calculated following similar steps. In total, data were collected on 89


155

valuation points across four property types in 11 study areas (44 office, 12 retail, 8 retail warehouse and 25 industrial ).

Comparing urban regeneration and prime property market performance This section of the paper summarises the indices constructed. It also compares the urban regeneration indices with those produced by CBRE and IPD.

Rent index and average yield monitor We start with the analysis of the Beacon findings. These are benchmarked to the CBRE index. Results are presented based upon two parameters, namely the rent index and average yield monitor. It should be appreciated that while the two rent indices have been compiled using very similar approaches, based on new lettings of hypothetical, standardised offerings, the CBRE index identifies headline rents while the Beacon index records effective rents (see Adair et al. 2003b for more details). Analysis of the Beacon rental index demonstrates an initial divergence from the CBRE benchmark but convergence between the two indices is apparent over the last two years (Figure 8.1). On a sector basis retail warehousing emerges as the strongest performer with the rental index increasing to 167.05 by 2002. This is significantly higher than any of the other sectors within the Beacon analysis and outperforms the CBRE all property benchmark. The performance of the office and retail sectors (excluding

160 Beacon CBHP

150 140 130 120 110 100 1996

Figure 8.1

1997

1998

Rent index: Beacon vs CBRE.

1999

2000

2001

2002

156


retail warehouse) reflects the all property analysis but performance of the industrial sector, on the basis of this analysis, is considerably weaker (see Figure 8.2). The Beacon all property rental index produced annualised nominal rental value growth of 5.45% over the seven-year period to 31 December 2002 (Table 8.1). The retail warehouse sector enjoyed the highest level of growth at 7.61%, followed by the office sector at 5.76%, the retail sector at 5.45%, with industrials returning a more modest 2.8%. In comparison, the CBRE all property rent index recorded annualised nominal rental growth of 6.56% over the same period with the retail warehouse, office and industrial sectors returning higher rental growth rates than the Beacon data. The reverse was true in the retail sector where the Beacon rental growth rate was marginally higher than the CBRE return (5.45% compared with 5.35%).

180 Office Retail Retail warehouse Industrial All property

170 160 150 140 130 120 110 100 1996

Figure 8.2

1997

1998

1999

2000

2001

2002

Beacon rent index 1996 to 2002 (31 December 1995¼100).

Table 8.1 Beacon rent index vs CBRE rent index 1996 to 2002. Annual rental value growth % (nominal) Sector Office Retail Retail warehouse Industrial All property

Beacon

CB Hillier Parker

5.76 5.45 7.61 2.80 5.45

7.88 5.35 10.49 4.05 6.56


157

Over the period 1995 to 2002, the Beacon all property average yield improved by 144 basis points from 8.49% to 7.05% (Figure 8.3). Average yields in all three sectors experienced downward pressure: office yields hardened from 9.27% to 7.71% (156 basis points), retail yields from 7.07% to 5.2% (205 basis points), retail warehouse yields from 8.01% to 7.14% (87 basis points) and industrial yields from 9.62% to 8.15% (147 basis points). In contrast, the CBRE all property average yield (Figure 8.4) rose

12 Office Retail Retail warehouse Industrial All property

10

Percentage

8

6

4

2

0 1995

Figure 8.3

1996

1997

1998

1999

2000

2001

2002

Beacon average yields by sector.

9 Beacon HP

8.5

Percentage

8 7.5 7 6.5 6 5.5 5 1995

Figure 8.4

1996

1997

1998

1999

All property average yields: Beacon vs CBRE.

2000

2001

2002

158


from 6.80% to 7.2% (40 basis points), with two of the sectors, offices and retail, recording upward movement in yields and the retail warehouse and industrial sectors experiencing downward movement. The effect of this convergence between yields is to remove and then reverse the yield gap between the Beacon yields and CBRE yields. In quantifying the yield shift, the Beacon analysis indicates a significant hardening of regeneration property yields by 1.44% whereas in contrast the CBRE national benchmark has moved out by 0.4%. Based on the all property returns, the gap was 169 basis points in 1995 but this turned into a ‘reverse yield gap’ of 15 basis points by 2002.

The total returns index In this section the total returns index is benchmarked against the IPD Total Returns Index. Approximately 20% of properties in the regeneration stock database generated by the field visits have been identified through the process of matching the properties against the IPD database. The sample size varies over time to reflect the churn in the IPD data. The number of properties within the index has declined from a peak of 223 in 1998 reflecting the behaviour of the overall IPD index. The decline is particularly apparent in the office sector, which has dropped in both capital value terms (£636 m) and number of properties (91) in the index since 1998. Indeed, the sector weighting for offices in the Eight City Regeneration Index has fallen from 33% in 1994 (which with 107 is the year with the greatest number of offices in the index), to 22.9% in 1998 (the year of maximum capital value) to the current weighting of 17.5%. The general reduction in sample size at the end of the period reflects the recent pattern of disposal of smaller properties by institutions. The statistics for property in the regeneration portfolio for 2001 provide an illustration of the robustness upon which the Eight City Regeneration Index is constructed. In 2001, 187 properties with a total capital value of £3135 m are included in the index: 73 are in the retail sector (capital value £2360 m), 64 offices (capital value £548 m) and 41 industrial (capital value £155 m). Hence, on a value basis the retail sector has a high weighting in the overall index (75.3%) whereas for the IPD universe for England the sector weighting of retail property is 43.6%. The analysis infers that regeneration areas are particularly attractive to retail property, often of high capital value. The significance of the retail sector is a growing characteristic of the regeneration areas, for example in 1995 retail property had a value weighting of 63.6% compared to 45.5% for 1985. The strength


159

of the retail sector in the regeneration index is in broad agreement with the recent IPD analysis for Morley Fund Management and English Partnerships (IPD/Morley/EP) that identified a 60.1% sector weight for retail property in the 10% most deprived wards in England (IPD 2002). The annualised returns for all property in the Eight City Regeneration Index (see Figure 8.5), over the long term, average 12.8% per annum in comparison to 10.3% for the IPD universe for all of England (IPD 2002). This difference is statistically significant (t ¼ 2.3, p ¼ 0.03) inferring that returns for regeneration locations do, in fact, in the long run exceed those across the market as a whole. Further analysis on a sector basis (see Figures 8.6, 8.7 and 8.8) highlights that retail property, with an annualised return of 15.5% over the period

40 UR figure 10% deprived UK Benchmark Eight City IPD Benchmark

35 30

Percentage

25 20 15 10 5 0

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

−5 −10 −15

Figure 8.5

Total returns: all property.

40

UR figure 10% deprived UK Benchmark Eight City IPD Benchmark

35 30

Percentage

25 20 15 10 5 0

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

−5 −10 −15

Figure 8.6

Total returns: retail.

160

Unravelling the Relationships 40 UR figure 10% deprived UK Benchmark Eight City IPD Benchmark

30

Percentage

20 10 0

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

-10 -20

Figure 8.7

Total returns: office.

50 UR figure 10% deprived UK Benchmark Eight City IPD Benchmark

Percentage

40 30 20 10 0

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

-10

Figure 8.8

Total returns: industrial.

1980–2001, outperforms both industrial (12.8%) and office (10.0%) property. The difference in mean return is statistically significant for the latter (t ¼ 3.32, p ¼ 0.003) but not for the former (t ¼ 1.16). On the basis of weighting and total return criteria, retail property is the best performing sector within urban regeneration locations. The high annualised figure reflects the occurrence of major retail development including shopping centres and retail parks within designated renewal areas. This finding is consistent with analysis by O’Roarty (1999) showing that retail warehouses and supermarkets have, on a returns basis, outperformed more traditional high street shops since 1991. The pattern of returns over time is highly variable reflecting the property market cycle (Figures 8.5, 8.6, 8.7 and 8.8) notably the period of the

161


property crash in the early 1990s. The significant issue is that for regeneration property, returns during this period did not become negative compared to the experience of the wider property market and national benchmarks for which negative returns were apparent over the period 1990–1992 inclusive.

Comparison with benchmarks The Eight City All Property Regeneration Index has significantly outperformed comparable benchmarks since 1990, notably the IPD UK Benchmark for all property and a benchmark drawn from the IPD universe for all property in these metropolitan areas. The latter is employed to provide a finer grain to the analysis and correct for any London/south-east factor and thereby place comparisons on a like-for-like basis. Nevertheless the key message from this research is that regeneration returns over the long term are better than national benchmarks. In terms of performance over time, contrasting patterns are apparent between the Eight City Regeneration Index and the IPD UK index. Throughout the 1980s the two index series paralleled each other but differences emerge at the start of the property market downturn that saw the UK Benchmark in particular decline in the early 1990s and show negative returns. A similar pattern is apparent for the Eight City Benchmark indicating that the negative returns are not entirely a London effect and confirming the better performance of regeneration property (see Figure 8.9).

1500 All Eight Cities UK Benchmark(IPD) Eight Cities Benchmark Index

1300 1100 900 700 500 300

01

00

20

99

20

98

19

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The early 1990s mark a step change in performance between the Eight City Regeneration Index and the benchmark indices. The former is characterised by a relatively flat effect/slow growth over the period 1990–92 in contrast to the negative figures for the benchmarks, followed by a rapid increase and growing divergence from both the IPD UK All Property Index and the Eight City Benchmark (Figure 8.9). The close performance and high correlation (r ¼ 0.905) between the two benchmark series indicates that the Eight City Regeneration Index is essentially capturing a regeneration effect rather than rather some characteristic peculiar of the eight cities. This adds to the argument that this period signified a clear step change in performance between regeneration markets and established commercial property market locations. The behaviour of the Regeneration Property Index over the cycle supports qualitative opinion that highlighted greater relative investment in urban regeneration property in the down-cycle due to cushioning effects provided by public sector mechanisms (Adair, Berry, Deddis, Hirst & McGreal 1998). In particular occupancy of such stock by public sector agencies and a possible capitalisation of subsidies into property values may explain the better relative performance of regeneration areas. This was followed by significantly greater returns in the mid- to late 1990s as regeneration locations matured. Respective values for the Eight City All Property Index was 1286 in 2001 compared to 778 for the UK Benchmark. The differential in these figures may reflect regeneration locations reaching near prime market rents and capital values starting from a lower base and thereby generating higher percentage return. Again, analysis on a sector basis further highlights the contrasting performances. In this context the Eight City Retail Index for regeneration property has continually out-performed the UK retail benchmark and the Eight City retail benchmark since 1987 to the extent that the index reached a value of 2074 in 2001 compared to a value of 773 for the IPD universe (see Figure 8.10). The same pattern prevails for office and industrial property. The Eight City Office (Figure 8.11) and Eight City Industrial (Figure 8.12) indices for regeneration property outperform the respective benchmarks but the differences are appreciably less than for retail property with trends closely paralleling each other over the time series. In both the office and industrial sectors the step change is again apparent during the property market slump, an effect particularly noticeable for offices, whereby performance returns from regeneration started to outperform the benchmarks and have continued to do so over the past ten years.


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Conclusions This chapter is based on research undertaken as part of a project entitled ‘Benchmarking of Urban Regeneration Performance’ funded by Economic and Social Research Council (ESRC), Office of the Deputy Prime Minister and RICS Foundation. The research exemplifies the use of index methodologies as a means of measuring property market performance. It also provides a useful basis for comparing the performance of segments of the market subject to public sector intervention with prime submarkets.

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The chapter has set out to address two specific gaps in the urban regeneration literature. First, it offers complementary evidence to the major evaluative studies of regenerations initiatives. These have rarely included substantive assessments of the property market impacts. Second, even when property market analyses have been undertaken, there has been little systematic assessment of market performance and the assessment of urban regeneration projects has tended to emphasise short-term rather than long-term effects (Jones & Watkins 1996). This research provides a rigorously constructed long-term analysis of the performance of property markets subject to urban regeneration interventions. Specifically, using indicators computed from two alternative index construction techniques, we summarise the trends revealed by two new urban regeneration property indices. These indices provide measures of the performance of urban regeneration property markets and facilitate comparison of trends in rental values, yields and total returns with those derived to measure the performance of prime markets. The results show that investment property in regeneration areas can outperform national and local benchmarks. The comparison of market performance on a sector-by-sector and varied geographic basis demonstrates the presence of a clear ‘regeneration effect’ that is independent of differences in the mix of properties included in the indices. Interestingly there is a suggestion that public sector intervention may have helped cushion regeneration areas against the general market downturn in the early 1990s. The analysis demonstrates that over the long-term perspective regeneration areas offer significant investment opportunities. These findings


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challenge perceptions regarding investment returns and suggest that judgements concerning low investment returns in regeneration areas are misplaced. Hence, the message to major institutional investors from this research is the need to reconsider strategies regarding the potential of property within regeneration areas. Complementary evidence is apparent from the two performance methodologies employed in this analysis, with total returns evidence as the lead indicator. Both demonstrate the superior performance of regeneration property in recent years relative to recognised benchmarks. The total returns approach highlights the property market downturn of the early 1990s as marking a step-change. The Beacon approach indicates a major yield shift in regeneration areas in the short to medium term. The study demonstrates that the systematic under-pricing of regeneration markets on the part of the property industry, a symptom of the information deficit, has been based on incomplete evidence. Potentially the absence of returns information has been detrimental to investment strategies in relation to regeneration areas and the pricing of investment opportunities. The research is of enormous relevance in unpicking the effects of state intervention in property markets. Importantly for policy makers, it confirms the maturing of locations that have received high levels of public sector support and indicates the effectiveness of regeneration policy mechanisms in creating sustainable urban environments capable of meeting private sector investment goals. As government agencies are increasingly looking for greater private sector participation in regeneration the success of previous and current policy mechanisms is fundamental. In addition, the finding that regeneration areas can offer vibrant property markets and new development/investment opportunities has wider relevance to the economic competitiveness of UK cities and investability objectives (Begg 2002b; Gibb et al. 2002). The ODPM work on core cities, several of which overlap with the urban areas included in this study, has raised concerns over UK urban competitiveness (Core Cities 2002). As regeneration areas frequently offer the most significant opportunities within these cities the potential clearly is there to attract investment, raise value and increase competitiveness. The policy agenda therefore needs to be consistent and focussed to facilitate delivery of these goals. There are also several useful methodological lessons to be learned from this research. For instance, there are challenges associated with the

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replicability of the two approaches. Although the IPD and CBRE indices are widely used by the property industry, there is little published information on the construction or technical aspects of the indices. This, of course, is quite different to residential markets where a plethora of indices are published, often constructed using well understood hedonic or repeatsales index techniques and accompanied by widely available technical reports on the specific of each index as well as a voluminous academic literature on the general subject area. In this context, repeatability over time is relatively straightforward as the approach is readily replicable. This is not such a simple process when dealing with our indices. The total returns index is based on a costly and time-consuming process. As new regeneration initiatives emerge the stock of properties subject to the matching index will need to be expanded. This requires further fieldwork and an extended matching process. There are also difficult issues relating to the period of time over which a property should be considered to be part of the regeneration market. The Beacon method, on the other hand, is easier to replicate and update. However, it is, of course, subject to the vagaries of valuer error. Finally, it is also worth noting that the methodological approach adopted in this study provides a fairly novel basis for policy evaluation. This approach provides a less direct analysis of the impact of policy and relies more on comparative analysis and interpretation. Conventionally, when hedonic methods are deployed in policy analysis, the model tells us about the marginal effect of state intervention on property values. In other words, it unravels the implicit value of the policy measure and allows us to determine what market values would otherwise have been. In this research, the focus on measuring total returns, rental values and yield levels in aggregate means that we learn very little about what would have happened to the market in the absence of the policy intervention. Rather we are reliant on our comparison with the prime benchmarks to tell us about general market trends. As such it is more difficult to control for other extraneous influences on market performance. Thus, in contrast to Chapter 7, we simply have to assume that exogenous shocks have an equal impact on both the regeneration market and the prime market.


9 Planning for Consumers’ New-Build Housing Choices Chris Leishman and Fran Warren

Introduction By design, the planning system exerts significant control over the private housing market and particularly the new-build sector of the market. Recently, policy interest has centred on the ability of the planning system to facilitate an adequate supply of new-build housing (see Barker 2004). Meanwhile, there is also an ongoing debate focused on the potential impact of the planning system at the urban level. In particular, there is growing awareness of the importance of urban or neighbourhood design issues and the concept of sustainable urban form has progressively moved up the policy agenda since the late 1990s. Recent developments in government policy suggest a renewed impetus for the use of the planning system to promote a range of policy goals including higher neighbourhood and urban design standards, encouragement for the re-use of urban brownfield land and the creation of sustainable communities. The planning system is an important interface between housing demand (households) and housebuilders. This chapter examines new-build house buyers’ preferences and choice processes and considers how the planning system can take account of these to promote the government’s evolving policy objectives. The empirical work examined in this chapter includes elements of a recently completed study funded by the Joseph Rowntree Foundation (see Leishman et al. 2004 for a full discussion of the study findings). The

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study is focused on the housing preferences of a sample of new-build housing purchasers. In particular, preferences for physical housing characteristics, location, price and design factors are considered and several distinct consumer groups are profiled. The chapter focuses on the importance of housing and neighbourhood design issues to the identified consumers groups and considers the implications of the findings on the planning policy debates.

Planning and new-build housing Planning may influence housing market outcomes on a number of different levels. During the 1980s and early 1990s, the academic literature focused on the issue of private housing development land supply (for a review, see Chapter 5). More recently, the planning policy debate has moved on from the question of land supply and associated economic effects. In particular, the questions of sustainable urban form and densification/intensification are high on the policy agenda. There is an ongoing debate concerning the definition of sustainable urban form and the potential environmental, social and economic benefits (see Jenks et al. 1996 and Williams et al. 2000 for a thorough review of these debates). Much is predicated on perceived failures of suburban living including lack of social interaction and high consumption of energy, greenfield land and time spent commuting (see, for example, Fulford 1996). However, a broad consensus is emerging that the promotion of sustainable urban form through planning policy will lead to environmental, social and economic benefits. As yet, there is no clear consensus on the magnitude of these potential benefits. Although there is an ongoing debate concerning exactly what constitutes a sustainable urban form, the concept of the ‘compact city’ is now centrestage on the planning agenda (see Williams 1999). The compact city is defined by Williams (1999) as an urban form characterised by high density mixed urban uses. Government policy has increasingly encouraged the reuse of previously urbanised, or brownfield, land since the mid-1990s (see DETR 1998; 1999). Together with this, urban design or neighbourhood design, considerations have recently moved up the planning agenda (see ODPM 2003a; Scottish Executive 2003). For example: People have moved out of our cities to seek a better life in suburbs, creating urban sprawl. There has been inadequate long-term planning of communities. Too much of what we have built has been poorly designed. ODPM 2003a: 5

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Too many recent developments have a rigid and standard layout. A development’s mix of housing types and uses and its three-dimensional physical form are important in determining what sort of place is to be created. Scottish Executive 2003: 20

The ODPM (2003a), citing survey evidence, argue that the public’s perception of new-build housing neighbourhoods is generally poor and note that only 36% of households would consider buying new-build housing. While it is not explicitly clear from these survey results that the public’s low perception of new-build housing relates to poor neighbourhood design, it seems clear that the house building industry, the planning system, or some combination of both is failing to deliver housing that is attractive to the majority of house buyers. ODPM (2003a) go on to point out that there is some disparity between the type of housing being built, and the type of housing for which there is greatest demand: . . . too many large homes are being built when the new demand is mainly for small households. In recent years more than one in three homes built in the South East have been larger, four-bedroom homes. ODPM 2003a: 9

Planning for consumers’ housing preferences is not straightforward. Relatively little is known about the role played by local amenities, proximity to employment and to family and neighbourhood considerations in buyers’ housing choices. Some studies report these as important, though not dominant, factors (Chapman Hendy Associates 1997; School of Planning and Housing 2001; Taylor Nelson Sofres 2001) while others pay little consideration to them. In a study focused on households’ propensity to walk rather than drive, Farthing et al. (1996) present evidence that the local provision of facilities does not alter the balance between trips made by car and on foot. They also find that the distance travelled by households is reduced when local facilities are provided in large-scale new-build housing developments. The clear implication is that households use local facilities when they are provided but that they continue to use a private car to access them. One of the inherent problems in the sustainable urban form debate is the fact that the inter-relationships between density, intensity, land use mix and sustainability and economic indicators are highly complex. Furthermore, there has been relatively little research focused on these relationships with the result that the impacts and importance of urban form are not yet well understood. Even at the level of individual site or estate design and individual housing unit design, there has been very little research focused on design and design process (see Hooper & Nicol 2000). However,

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there is evidence that the UK private housebuilding industry has become increasingly concentrated since the 1970s (Ball 1996a; Hooper & Nicol 2000). This move is associated with a relative move from non-standardisation towards greater standardisation of housing designs and ‘house types’ over a similar period (Nicol & Hooper 1999; Hooper & Nicol 2000) with the result that 90% of volume housebuilders use standardised house type designs while 75% use 20 or more different designs or house types. To a certain extent, the findings of some preference and choice studies may partly reflect survey design. Some studies are focused on dominant factors such as price and location while others are designed to yield information on the detailed factors behind housing choice. For example, the Popular Housing Forum (1998) and the School of Planning and Housing (2001) demonstrate that variety in housing designs, layouts and features is valued by new-build housing buyers. Similarly, estates with little variety in housing design or with insufficient open space and parking are disliked by house buyers. Meanwhile, Fulford (1996), reporting on a survey of 14 major UK housebuilders, notes a number of conflicts between the planning system and the planning policy agenda. This includes, for example, the idea that planning authorities often view high density planning proposals as a profit-maximising push by housebuilders despite the fact that high-density development is a requirement for the compact city. Fulford (1996) also points out the reticence of many housebuilders to the idea of mixed use development. The suggestion is that housebuilders are responding to the attitudes of consumers who have witnessed several decades of the promotion of single use residential neighbourhoods and are consequently resistant to change. Existing housing choice studies provide useful insights into the important factors in buyers’ housing choices. However, an obvious problem arises in attempting to generalise the findings. Almost all studies show that there are dominant factors in the housing choice process. Meanwhile, studies with a greater focus on more detailed housing and locational attributes tend to conclude that these are the factors that drive housing choice. The difficulty arises from a number of underlying problems in interpreting choice studies:

. The impact of post-purchase rationalisation is unknown (buyers may be biased in favour of the attributes present in the housing choice they have made). . Few housing choice studies examine interaction between housing and location attributes. . The importance of each attribute is examined in isolation or simply rank ordered.


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These difficulties, in turn, give rise to several planning-relevant questions. For example, how do consumers trade-off the dominant factors in housing choice? Dominant factors include price, location, property size and, possibly, property type as noted earlier. Second, how do the detailed aspects of housing and locational choice interact with the dominant factors?

The new-build housing choice process A supply of new-build housing is required for a number of reasons. For example, the existing housing stock has a finite life and periodic additions to it are therefore required to replace dwellings as they become obsolete. Furthermore, population and/or demographic changes may alter the requirements of the housing stock – a reduction in average household size may increase the need for smaller dwellings. Perhaps most significantly, individual housing attributes may move in and out of favour with consumers, reflecting changes in households’ tastes and preferences. While some alterations and refurbishment works are possible in the second-hand sector, changes in demand for some housing attributes can only be met through a supply of new-build housing. This process is best described as functional, as distinct from physical, obsolescence. Households’ housing choice processes are not generally well understood. However, what is known is that the new-build sector of the housing market has come under sustained criticism in recent years. Indeed, some commentators point to the fact that households in the UK have a tendency to extend the economic life of dwellings well beyond their intended life (see Ball 1996a). This suggests that many consumers have a preference for second-hand housing (see also ODPM 2003a). Despite the obvious importance of consumers’ tastes and preferences in the housing choice process, there are surprisingly few published empirical studies. Of those that do exist, many studies emphasise the importance of property size or price in driving households’ choices (see, for example, Chapman Hendy Associates 1997; Munro et al. 1995; School of Planning and Housing 2001). The importance of location and property size (or the number of rooms) are also well rehearsed as key determinants of choice (Chapman Hendy Associates 1997; Taylor Nelson Sofres 2001). These studies also suggest that property type is of secondary importance to price, location and size. The remainder of this chapter provides a more detailed examination of the preferences, choices and trade-offs made by new-build house buyers.

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The chapter draws from the findings of a recent research project funded by the Joseph Rowntree Foundation (see Leishman et al. 2004). The next section describes the objectives and hypotheses of the study and links these to the brief review of economic theory set out in this section. The chapter then goes on to describe the main findings of the study. The chapter concludes by linking the empirical findings to economic theory and considering the implications for the planning system.

The housing preference study Based on evidence from Glasgow and Edinburgh, the study encompasses a cross-section of private new-build housing design and buyers’ preferences in the UK in the late 1990s and early millennium. A number of data collection methods are used to investigate buyers’ preferences and tradeoffs including focus groups, interviews with recent and prospective buyers, information from planning applications and selling price data. The study adopts a ‘bottom up’ approach to profiling new-build house buyers and their preferences and trade-offs. In the first stage of the analysis, a series of focus group transcripts identifies key influences on housing choice. The focus groups include participants recruited evenly between Glasgow and Edinburgh and between inner urban and suburban locations in both cities. Land Register data on transaction prices is used to ensure representation across a broad spectrum of house prices. The findings of the focus groups sessions feed into the design of the survey instrument used to collect choice data from a sample of new-build housing purchasers. The data collected in the choice-based survey are analysed with multinomial logistic (MNL) regression – an approach also known as ‘conjoint analysis’, a form of stated-preference choice modelling. The choices described in the household survey instrument are derived from a list of attributes and their respective levels defined principally by the findings of the focus group sessions. The attributes and levels are summarised below:

. Price (six different price bands are used). . Location (city centre, near city centre, suburban and out of town). . Neighbourhood (five types which vary in terms of density, amenities and transportation links).

. Property type (six types ranging from detached to several flat types). . Public room layout (six different room options and configurations).


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. Bedroom layout (five different options in terms of number of rooms, size and layout).

. Front garden (none, small or large). . Back garden (none, small or large).

Overview of the findings A preliminary analysis of the choice-based survey results suggests that the 400 respondents can be classified into four identifiable groups, based on the similarity of their individual preferences. An analysis of the socioeconomic and demographic data collected as a precursor to the choice tasks reveals similarities within groups. A brief socio-economic profile as well as a working name for each of the four groups is given in Table 9.1. For a more detailed analysis of the composition of these consumer groups, see Leishman et al. (2004). In the second step of the analysis, utility estimates are obtained for each of the four groups using multinomial logistic (MNL) regression. The specification of the MNL model is such that all ‘products’ or housing choices are described by a bundle which includes exactly one level or category from each of the eight attributes. Where an attribute level has a negative utility estimate this implies that the presence of this feature in the housing bundle reduces the probability of it being purchased. Meanwhile, where the level of an attribute has a positive utility estimate, the inclusion of that level increases the probability of the product being purchased. For example, the ‘detached’ level of the property type attribute has a positive utility estimate for all consumer groups while the ‘terraced’ level has a negative estimate for three of the four consumer groups. Detached properties are therefore more likely to be purchased than terraced house types, all other factors held constant.

Table 9.1

Profile of the four consumer groups.

Group

Working name

Socio-economic/demographic profile

1 2

‘DINKYs’ ‘neo-DINKYs’

3 4

‘middle-SEG families’ ‘higher-SEG families’

Predominantly younger single households and couples As group 1 but a slightly higher prevalence of couples and non-professional occupations Slightly older buyers, over half of whom have children As group 3 but with a higher prevalence of single person households and a greater predominance of professional occupations

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Parameter estimates in a multinomial logistic regression model are more difficult to interpret than those of an ordinary linear regression model. The range between the least preferred and best preferred levels of an attribute determine the relative importance of that attribute. Attributes for which this range is large have a strong effect on probability of purchase and vice versa. Table 9.2 summarises the relative importance of the conjoint attributes. The figures are calculated as the range between the ‘worst’ and ‘best’ attribute levels as a proportion of the ‘worst’ attribute level. These proportions are recalculated to sum to 100. The most important, second most important and third most important attributes are also highlighted in Table 9.2. Fully interpreting the results requires the joint consideration of the summary shown in Table 9.1 and the detailed estimates shown in Tables 9.3 to 9.6. For example, Table 9.2 reveals that property type is the single most important factor to DINKYs while location is the least important factor. DINKYs are likely to be drawn to new-build housing sites that feature the property types and public room options that appeal to them. With reference to the detailed results shown in Table 9.3, the important property type revealed is flats with abundant external space and detached houses. The preferred public room layouts include large living rooms, small kitchens and utility rooms. The revealed preferences of the neo-DINKYs are a stark contrast. These buyers rate location as the most important attribute by a considerable margin and, consequently, will be drawn to particular locations rather than property types (out-of-town locations are preferred). The detailed results are shown in Table 9.4. These locational preferences are difficult to overcome through the provision of better public room and garden characteristics despite these factors being the next most important in the decision process of these buyers.

Table 9.2 Relative importance of the conjoint attributes. Attribute Price Neighbourhood Location Public room Bedroom Property Front garden Back garden

Group 1 11.62 11.19 9.45 12.55 11.48 20.43 12.50 10.78

Rank

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1 2

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15.46 11.93 10.31 14.59 11.12 13.49 12.27 10.83

1

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Table 9.3 Conjoint results for the DINKYs consumer group. Effect

Std err

t Ratio

£75 000 – £100 000 £100 000 – £125 000 £125 000 – £150 000 £150 000 – £200 000 £200 000 – £250 000 £250 000 þ

2.278 6.795 5.591 1.625 2.716 3.267

1.481 1.467 1.497 1.492 1.461 3.126

1.538 4.631 3.736 1.089 1.859 1.045

Walkable to local amenities, high density, transportation links Just walkable to local amenities, high density, transportation links Not walkable to local amenities, low density, no transportation links Walkable to local amenities, low density, transportation links Not walkable to local amenities, low density, transportation links

6.684 2.851

1.469 1.449

4.549 1.967

1.463

1.456

1.004

7.57 2.274

1.463 2.709

5.174 0.84

Located in the city centre Located close to the city centre In a suburban location In an out-of-town location

2.717 2.176 1.701 1.16

1.454 1.453 1.414 2.397

1.869 1.498 1.203 0.484

Small living room, small kitchen, utility room, dining room Small living room, large kitchen, utility room, no dining room Small living room, large kitchen, no utility room, dining room Large living room, small kitchen, utility room, no dining room Large living room, small kitchen, no utility room, dining room Large living room, large kitchen, no utility room, no dining room

2.519 2.155 6.062 8.439 3.346 0.605

1.468 1.489 1.487 1.471 1.465 3.089

1.716 1.447 4.077 5.735 2.283 0.196

Two large bedrooms, extra storage, extra bathrooms Three small bedrooms, extra storage, no extra bathrooms Three small bedrooms, no extra storage, extra bathrooms Three large bedrooms, no extra storage, no extra bathrooms Four small bedrooms, no extra storage, no extra bathrooms

3.672 2.161 0.415 2.44 4.365

1.476 1.479 1.492 1.454 2.882

2.487 1.461 0.278 1.678 1.515

Detached Semi-detached Terraced Flat – no external space Flat – some external space Flat – with external space

2.613 5.003 4.822 4.631 2.63 14.473

1.52 1.526 1.516 1.525 1.532 3.344

1.719 3.278 3.181 3.036 1.717 4.328

Small front garden Large front garden No front garden

0.264 0.191 0.073

1.461 1.468 2.152

0.181 0.13 0.034

Small back garden Large back garden No back garden

1.425 1.352 0.073

1.454 1.47 2.152

0.98 0.919 0.034

Price category 1

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Table 9.4 Conjoint results for the neo-DINKYs consumer group. Price category 2

Effect

Std err

t Ratio

£75 000 – £100 000 £100 000 – £125 000 £125 000 – £150 000 £150 000 – £200 000 £200 000 – £250 000 £250 000 þ

5.375 2.744 1.625 1.496 0.405 8.395

1.483 1.496 1.501 1.431 1.498 2.823

3.626 1.835 1.083 1.046 0.27 2.974

0.756

1.501

0.503

3.733

1.488

2.509

1.024

1.495

0.685

2.812 6.276

1.472 2.859

1.91 2.196


1.714 1.829 2.09 5.633

1.424 1.472 1.433 2.394

1.204 1.243 1.459 2.353


9.803 7.056 10.702 9.304 4.396 3.047

1.464 1.477 1.507 1.501 1.487 2.872

6.697 4.776 7.102 6.198 2.956 1.061


3.477 9.148 0.472 3.519 1.68

1.481 1.46 1.426 1.486 2.517

2.349 6.265 0.331 2.368 0.667

7.949 10.583 6.726 3.677 10.754 26.237

1.56 1.544 1.558 1.513 1.545 3.334

5.096 6.856 4.318 2.43 6.959 7.869


3.717 0.222 3.938

1.481 1.496 2.181

2.509 0.148 1.806


0.222 3.717 3.938

1.496 1.481 2.181

0.148 2.509 1.806

Walkable to local amenities, high density, transportation links Just walkable to local amenities, high density, transportation links Not walkable to local amenities, low density, no transportation links Walkable to local amenities, low density, transportation links Not walkable to local amenities, low density, transportation links



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Middle SEG families are attracted to housing options that are in their preferred price bracket. However, public room options and property characteristics are only marginally less important than price. These buyers prefer functional public rooms layouts (large living rooms, small kitchens and utility rooms) and semi-detached or detached houses (see Table 9.5). The analysis suggests that this group’s preference for suburban locations and low density neighbourhoods can be overcome through the provision of preferred public room options or property types in alternative locations provided properties are marketed at the right price. Buyers in this group have a strong aversion to flatted property types. In theory, it would be possible to compensate failure to achieve preferred location (suburbs) through the provision of similarly priced low density housing in centrally located low density neighbourhoods. However, there are obvious difficulties in actually providing such a combination. The results for higher SEG families are shown in Table 9.6. Respondents in this consumer group are attracted to location, garden characteristics and property type. Internal property characteristics and price are relatively unimportant factors to these buyers although detached housing is generally preferred to higher density property types. Buyers in this group have a strong preference for out-of-town locations. In summary, analysis of the choice-based survey dataset yields interesting findings with respect to the relative importance of price, location and property specific factors in consumers’ housing choice processes. It is generally accepted that price and location are dominant factors in these processes but the analysis suggests that this does not hold true for all groups of new-build buyers. The analysis is suggestive that propertyspecific factors such as the number of bedrooms, bedroom layout, public room layout, property type and neighbourhood design are potentially very significant in some buyers’ housing choice processes. In the next section we consider preferences for bedroom space/size, public room layouts and housing estate design in more detail.

Preferences and satisfaction with room layouts and features There is some consensus that unlike other major industries in contemporary society, the private house building industry is producer, rather than consumer, led and takes little account of consumers’ needs and preferences. House types are developed specifically for pre-defined target groups (first-time buyers, family homes and so on) rather than through detailed

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Table 9.5 Conjoint results for middle SEG families. Effect

Std err

t Ratio

13.696 8.423 12.105 25.577 0.34 8.307

1.55 1.77 1.626 1.74 1.721 2.914

8.836 4.76 7.446 14.699 0.198 2.851

Walkable to local amenities, high density, transportation 1.665 links Just walkable to local amenities, high density, transportation 2.473 links Not walkable to local amenities, low density, no transportation 10.921 links Walkable to local amenities, low density, transportation links 13.253 Not walkable to local amenities, low density, transportation 3.14 links

1.668

0.999

1.636

1.512

1.695

6.444

1.743 2.021

7.605 1.554

Price category 3 £75 000 – £100 000 £100 000 – £125 000 £125 000 – £150 000 £150 000 – £200 000 £200 000 – £250 000 £250 000 þ


21.762 0.416 19.838 2.34

1.533 1.52 1.634 1.819

14.191 0.273 12.143 1.286


17.816 6.48 3.902 33.141 19.42 1.517

1.574 1.679 1.673 1.672 1.625 2.545

11.319 3.858 2.333 19.821 11.949 0.596


24.106 25.617 5.479 6.474 2.507

1.574 1.748 1.603 1.524 1.745

15.313 14.652 3.418 4.248 1.436


58.323 109.129 36.416 72.609 59.303 71.957

1.948 2.084 1.918 1.756 1.85 3.207

29.947 52.375 18.991 41.352 32.047 22.44


45.92 12.648 58.568

1.655 1.699 2.623

27.747 7.443 22.328


0.181 20.481 20.663

1.666 1.672 2.608

0.109 12.252 7.923


Table 9.6

179

Conjoint results for higher SEG families. Effect

Std err

t Ratio

0.452 1.612 3.628 3.096 0.298 7.586

1.464 1.485 1.481 1.479 1.477 3.178

0.309 1.086 2.449 2.094 0.202 2.387

2.355 0.861

1.462 1.468

1.611 0.586

3.106

1.456

2.134

3.987 2.334

1.45 2.733

2.75 0.854


0.392 2.702 1.689 3.998

1.466 1.421 1.454 2.35

0.268 1.901 1.161 1.701


1.167 2.653 0.306 1.149 0.465 4.197

1.468 1.458 1.478 1.463 1.484 3.073

0.795 1.82 0.207 0.785 0.314 1.366


0.79 0.289 0.17 2.288 1.379

1.47 1.472 1.456 1.477 2.823

0.537 0.197 0.117 1.55 0.488


4.206 2.411 0.067 1.505 3.457 3.681

1.499 1.496 1.503 1.498 1.493 3.289

2.806 1.611 0.044 1.005 2.315 1.119


0.55 6.058 6.608

1.451 1.469 2.071

0.379 4.125 3.19


0.678 0.813 0.135

1.476 1.477 2.127

0.46 0.551 0.063

Price category 4 £75 000 – £100 000 £100 000 – £125 000 £125 000 – £150 000 £150 000 – £200 000 £200 000 – £250 000 £250 000 þ Walkable to local amenities, high density, transportation links Just walkable to local amenities, high density, transportation links Not walkable to local amenities, low density, no transportation links Walkable to local amenities, low density, transportation links Not walkable to local amenities, low density, transportation links

180


research (Barlow & Ozaki 2003). Similarly, housebuilders limit consumer involvement to choices regarding fixtures and fittings, rather than layout or design (Nicol & Hooper 1999). Other commentators warn that the growth in standardisation of house types, enhanced by concentration in the industry, will result in the development of monotonous designs and layouts (Leopold & Bishop 1983). In their study of change in the housebuilding industry, Nicol and Hooper (1999) challenge the latter argument, asserting that while the use of standardised designs is on the rise, most builders have increased their range of house types. Even within a constrained plot and building ‘footprint’ there is potential for some variation in the internal space: bedroom sizes can vary, as can the provision of bathroom(s), toilets and the configuration of downstairs space between living, cooking and eating areas. This provides at least some scope for buyers to choose a design that suits their needs and preferences. The remainder of this section examines housebuyers’ views of room options and design features and considers the importance of these factors in the overall housing choice process. The results of the choice-based survey show that public room configuration is the second most important factor for three of the four consumer groups identified in the choice-based survey. The ‘public room’ attribute consists of different configurations of kitchen, utility room, dining room and living room options as described earlier. The results show that DINKYs and middle SEG families have a preference for a large living room, small kitchen and a utility room with no dining room. Meanwhile, the preferred combination of neo-DINKYs was a small living room, large kitchen, no utility room and no dining room. Higher SEG families rate a small living room, large kitchen and a utility room with no dining room. Further insights to consumers’ internal layout and room configuration preferences are obtained from the focus group transcripts. These suggest, for example, that preferences for different types of public room and room layout are linked to life-cycle and life-style factors. Mixed messages emerge from the qualitative and conjoint analyses with respect to the provision of bathrooms. The focus group results indicate that en-suite bathrooms are popular with most buyers but also raised concerns about en-suite and bedroom sizes. This interaction between bathroom provision and room size is reinforced by the statistical analysis


181

which shows that consumers strongly prefer additional bedrooms or larger room sizes to additional bathrooms. In summary, the qualitative and conjoint analysis results do not show that one particular combination of non-bedroom layout features is always preferable to another. Some features are consistently valued by new house buyers, for example, additional bathrooms are considered by many new-build house buyers as a luxury feature but in the conjoint analysis results it seems clear that buyers ultimately prefer larger rooms and more bedrooms to such features. A significant finding of the study is that public room layouts and options are very important factors in many house buyers’ choice processes. Moreover, it is very difficult to predict or explain the internal design and layout preferences of different groups of buyers. To an extent, these findings are not surprising – most respondents felt that housebuilders uniformly provide standardised bedroom options, normally involving a relatively large number of bedrooms that are on the small side. In the context of this, it is perhaps unsurprising that buyers are responding more strongly to the nonbedroom layout and specification options.

Density and variety on new-build housing estates New-build housing estates are often criticised for their homogeneous design and layout. This aspect of new-build housing design was not considered explicitly in the choice-based survey although the wider study did consider external and estate design issues through focus group sessions and interviews (see Leishman et al. 2004). A simple descriptive analysis of new-build housing sites linked to sale price information yields some interesting trends. Figure 9.1 shows the frequency with which different house types were employed on the private housing sites examined during the study. The analysis suggests that homogeneity, or repeated use of the same house type, is more noticeable at the lower end of the market. Although house type variety increases progressively in the middle price bands, variety drops back again at the top end of the market. This is likely to be partly because the capacities of the more expensive sites tend to be lower than compared with the lower and middle priced sites. Figure 9.2 considers variety from the perspective of individual houses. It shows the average number of different house types immediately neighbouring or facing each house. Figure 9.2 suggests that lower priced houses are much more likely to be situated next to, or facing, identical house types than more expensive ones.

182


190k+ 170−190k

Price band

150−170k 130−150k 110−130k 90−110k 70−90k 50−70k

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