Housing Market Challenges in Europe and the United States: Any Solutions Available?

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Housing Market Challenges in Europe and the United States

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Housing Market Challenges in Europe and the United States Any Solutions Available? Edited by

Philip Arestis Peter Mooslechner and Karin Wagner

Editorial and selection matter © Philip Arestis, Peter Mooslechner and Karin Wagner 2009 Individual chapters © Contributors 2009 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No portion of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6–10 Kirby Street, London EC1N 8TS. Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The authors have asserted their rights to be identiﬁed as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2009 by PALGRAVE MACMILLAN Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS. Palgrave Macmillan in the US is a division of St Martin’s Press LLC, 175 Fifth Avenue, New York, NY 10010. Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world. Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries. ISBN 978–0–230–22903–7 hardback This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. Logging, pulping and manufacturing processes are expected to conform to the environmental regulations of the country of origin. A catalogue record for this book is available from the British Library. A catalog record for this book is available from the Library of Congress. 10 9 8 7 6 5 4 3 2 1 18 17 16 15 14 13 12 11 10 09 Printed and bound in Great Britain by CPI Antony Rowe, Chippenham and Eastbourne

Contents List of Figures

vii

List of Tables

ix

Notes on the Contributors

xi

1 Introduction: Housing Market Challenges in Europe and the United States Philip Arestis, Peter Mooslechner and Karin Wagner 2 Housing Markets in Europe and in the USA: What Are the Relevant Issues Today? Peter Mooslechner and Karin Wagner 3 Subprime Mortgage Market and Current Financial Crisis Philip Arestis and Elias Karakitsos 4 Determinants of Homeownership Rates: Housing Finance and the Role of the State Elisabeth Springler and Karin Wagner 5 The Rental Housing Market Dieter Gstach Housing Markets, Business Cycles and Economic Policies Christophe André and Nathalie Girouard

7

European Rental Markets: Regulation or Liberalization? The Spanish Case Montserrat Pareja-Eastaway and María Teresa Sánchez-Martínez Fiscal Aspects of Housing in Europe Guido Wolswijk

9 Towards a New Housing System in Transitional Countries: The Case of Hungary József Hegedüs 10

15 40

60 85

6

8

1

House Price and Other Housing Market Data: A User’s Perspective Anthony Murphy

v

109

131 158

178

203

vi

Contents

11 Residential Property Price Statistics for the Euro Area and the European Union Martin Eiglsperger

221

12 Housing and Financial Wealth in Austria: What Can Survey Data Tell Us for the Analysis of Financial Stability Issues? Pirmin Fessler, Peter Mooslechner, Martin Schürz and Karin Wagner

239

Index

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List of Figures 2.1 2.2 2.3 4.1 4.2 4.3 5.1a 5.1b 5.2a 5.2b 5.3a 5.3b 5.4 5.5 5.6a 5.6b 6.1 6.2 7.1 7.2 7.3 7.4 8.1 9.1 9.2 9.3 9.4 9.5 9.6

Real housing investment Mortgage debt outstanding and disposable income Share of variable rate lending in new loans for house purchase and mortgage debt to GDP in 2007 Annual changes in homeownership rates in percentage points, 1997–2006 Selected economies: Supply side allowances to GDP ratio in per cent in 2001 Development of subsidized housing supply and housing permissions and completions in Austria, 1996–2003 Yearly growth rates of real rents in per cent Yearly growth rates of real rents in per cent Yearly growth rates of real rents in per cent Yearly growth rates of real rents in per cent Distribution of disposable household income Distribution of disposable household income Rented market shares by household size Rented market shares by age group Distribution of rent-to-income ratio in per cent Distribution of rent-to-income ratio in per cent OECD real house prices and the business cycle Marginal propensities to consume out of housing wealth and mortgage market indicators Rented housing in several European countries Contracts and rents in Barcelona, 1984–2007 Regulated and unregulated housing, 1962–2007 ‘Disqualified’ regulated housing Housing taxes, 2006 Macroeconomic trends: GDP, CPI, and interest on housing (%) New construction and building permits between 1989 and 2008 House price changes Housing loans, 1989–2008 Housing subsidies, 1998–2007, as per cent of the GDP Household borrowing, 1996–2008 vii

21 27 29 63 69 70 93 93 94 94 99 99 101 102 103 103 111 116 134 144 146 147 164 181 183 183 187 188 190

viii

List of Figures

10.1 Channels of transmission of the mortgage and housing crisis 10.2 Various measures of UK house prices 10.3 Various measures of Greater London house prices 10.4 Estimated long-run effects of changes in the credit conditions index 10.5 Simulated real US house prices 11.1 Residential property prices for euro area countries, annual percentage changes 11.2 Residential property prices for non-euro area EU countries, annual percentage changes 11.3 Residential property prices for the euro area at annual and semi-annual frequency, annual percentage changes 12.1 Stock and/or mutual fund share holdings by gross financial wealth decile 12.2 Which sources do you rely on when you seek information on financial issues?

203 209 209 215 216 224 225 234 252 257

List of tables 2.1 Housing market indicators 2.2 Timing of maximum correlation 4.1 Introduction and usage of securitization for housing finance in European economies and the USA 4.2 Fixed effects model for ownership rate 4.3 Fixed effects model for ownership rate (incl. interest rates) A4.1 Cost of financing – time series A4.2 Fixed effects model with instrumental variables 6.1 House prices in real terms and relative to rents and income 6.2 Short-term and long-term impact of financial and housing wealth on consumption 6.3 Long-term impact of housing equity withdrawal on consumption 7.1 Housing tenure 7.2 Tenure in Spain 7.3 Cost of rented dwellings according to the year of signature 8.1 Main tax categories affecting housing/mortgages in the euro area, 2007 8.2 Overall housing subsidy measures, 1999 A8.1 Tax on imputed rents A8.2 Mortgage interest payments tax deductibility A8.3 Tax on housing capital gains A8.4 Indirect taxes 9.1 Basic indicators of housing conditions in Hungary, 1970–2000 9.2 Housing allowance, 2000–2007 10.1 Simple mix adjusted house price examples 10.2 Start dates of some European house price data 10.3 Various measures of quarterly ln house prices for the UK

ix

17 22

67 74 76 79 80 112 115 116 135 144 150 161 165 171 172 173 174 182 196 205 206 210

x

List of Tables

10.4 Data for modeling aggregate house prices A11.1 Overview table of residential property prices in EU countries, annual percentage changes 12.1 Ownership rates by socio-economic characteristics 12.2 Logit-regression: determinants of homeownership

217 236 243 248

Notes on the Contributors Christophe André is an economist in the Economics Department of the Organisation for Economic Cooperation and Development (OECD). He has contributed to several editions of the OECD Economic Outlook and has been involved in macroeconomic modelling and forecasting, as well as in research in areas ranging from housing to monetary and fiscal policy. Philip Arestis is University Director of Research, Cambridge Centre for Economics and Public Policy, Department of Land Economy, University of Cambridge, UK; Distinguished Adjunct Professor of Economics, Department of Economics, University of Utah, USA; Senior Scholar, Levy Economics Institute, New York, USA; Visiting Professor, Leeds Business School, University of Leeds; Department of Finance and Management Studies, School of Oriental and African Studies (SOAS), University of London, UK. Martin Eiglsperger is Senior Economist Statistician in the Euro Area Accounts and Economic Data Division of the ECB’s Directorate General Statistics. He held previous positions at the Deutsche Bundesbank, the German Federal Statistical Office and the Department of Business Administration and Economics of the University of Bamberg. He received a Ph.D. degree in economics from the University of Bamberg. Pirmin Fessler is an economist at the Austrian central bank. He studied economics at the University of Vienna. His current research focuses on wealth inequality, intergenerational transfers and private households’ portfolio choice. He is member of the Household Finance and Consumption Network of the European Central Bank. Nathalie Girouard is adviser to the OECD Secretary-General. Before assuming her responsibilities in the cabinet, Nathalie was part of the team editing the OECD Economic Outlook. Her fields of research include consumption behaviour, housing and mortgage markets and their effects on the wider economy. She has published several working papers and OECD documents.

xi

xii

Notes on the Contributors

Dieter Gstach is associate professor of economics at Vienna University of Economics and Business. He is also a member of the Research Institute for Spatial and Real Estate Economics. His current research focuses on the macroeconomic role of the housing market. Jószef Hegedüs is a founding member of the Metropolitan Research Institute, which was established in 1989 in Budapest, Hungary. He has been co-organizer of the East European Working Group of the European Network for Housing Research since 1989, and a member of the Housing Policy Council, a high-level advisory group in housing policy matters in Hungary, since 1996. He has been Affiliated Professor at Corvinus University since 2007. Elias Karakitsos is Director of Guildhall Asset Management; chairman of Global Economic Research; and Associate Member of the Centre for Economic and Public Policy, University of Cambridge. He was Professor at Imperial College, Head of Economics for ten years and has acted as an adviser to governments and financial institutions, including Citibank, Oppenheimer, Allianz, Credit Agricole and Standard Chartered. He is the author of five books/monographs, 80 papers in learned journals and more than 300 reports on financial markets. Peter Mooslechner is Director of the Economic Analysis and Research Department of the Oesterreichische Nationalbank, Vienna. He teaches economics and economic policy at the Vienna University of Economics and Business Administration and is a member of the Monetary Policy Committee of the ECB. His publications cover macroeconomics, monetary and fiscal policy, financial markets and Eastern European issues. Anthony Murphy is Economics Fellow at Hertford College, University of Oxford. He is an applied econometrician and works on housing, savings and labour markets, as well as empirical finance. Montserrat Pareja-Eastaway is Associate Professor of Economics at the University of Barcelona, Spain. Housing affordability, tenure and housing policy measures are, among others, key aspects of her research. She has been the Spanish partner responsible for several EU-funded projects and is a member of the ENHR Co-ordination Committee.

Notes on the Contributors xiii

María Teresa Sánchez-Martínez is Associate Professor of Economics at the University of Granada, Spain. Her research interests are housing finance, distributive aspects of public expenditure on housing and housing policies from a comparative perspective. She has published numerous articles in specialized journals in the field of housing economy. Martin Schürz is Head of the Monetary Unit of the Austrian central bank’s Economic Analysis Division. He teaches at the Vienna University of Economics and Business Administration and at the University of Applied Sciences (bfi), Vienna. He has published several books and his research interests are in the field of wealth inequality. Elisabeth Springler is Assistant Professor at the Vienna University of Economics and Business Administration, Austria. She has a strong interest in Post-Keynesian Economics. Her current research interests are monetary economics, financial structures, regulation of financial systems and institutions, as well as housing economics. Karin Wagner works as an economist at the Oesterreichische Nationalbank. Since 1997 she has worked in the Economic Analysis Division of the Economic Analysis and Research Department. Before this, she did various research projects. Her research interests include various aspects of the Austrian economy from a macroeconomic perspective, especially housing market developments and, from a microeconomic perspective, wealth survey topics. Guido Wolswijk is Principal Economist at the European Central Bank, focusing on fiscal policies in the euro area. Before that, he worked for the Dutch central bank and at ING Bank. His research interests include fiscal policy, monetary policy and housing market developments, including the interaction between these topics.

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1 Introduction: Housing Market Challenges in Europe and the United States Philip Arestis, Peter Mooslechner and Karin Wagner

The Oesterreichische Nationalbank (Central Bank of Austria) in September 2008 organized a conference entitled ‘Housing Market Challenges in Europe and the United States – Any Solutions Available?’ All the papers presented at the conference have been substantially revised since September 2008 to account for more recent developments in the housing markets in both Europe and the United States (USA). The result is the current book with a slightly different title, comprising 12 chapters. In what follows in this Introduction we attempt to put the contents of the book in context and at the same time summarize the contributions. Peter Mooslechner and Karin Wagner, in Chapter 2, open the discussion with their contribution entitled, ‘Housing Markets in Europe and in the USA: What are the Relevant Issues Today?’. In a significant number of recessions, the housing sector has preceded if not caused the economic downturn – a stylized fact also underlined by the current financial turmoil and economic crisis. The chapter analyzes some of the key aspects of the economic relevance of housing markets in the present context. First, it considers the effects that the housing sector and housing finance have on the macro economy. Since the housing sector accounts for a considerable part of a country’s welfare, wealth and GDP, it significantly shapes a country’s long-term economic development. In this respect it is important to pin down the underlying drivers of house prices, and to assess the economic implications of house price volatility. The differences in house price developments across countries may reflect country-specific factors like demographic differences, institutional regulations or cyclical positions; but changes in the structure of housing finance driven by globalization and liberalization are likely to play an important role as well. Above all, current macroeconomic analysis highlights the role of housing wealth as a household’s principal asset 1

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and the role of mortgage debt as the largest liability in households’ balance sheets. Besides possible substantial wealth effects on consumption, changes in house prices can generate significant spillover effects for the macro economy as a whole, an obvious aspect of the current financial crisis. Second, fiscal and regulation aspects are important. Specific institutional settings together with the tax treatment of housing expenses have direct implications for a household’s income and wealth position. Again, there are fairly strong national differences in the tax deductibility of interest rates, the amount of allowances and subsidies given to households, and the instruments used for this purpose. Third, housing finance patterns have a direct effect on housing markets and on households’ wealth position. In this respect, lately there has been a decrease in the spread between interest rates on loans and the cost of funding, which may be due to increased competition. In parallel, a tendency to move away from traditional methods of financing that relied on specialized intermediaries or government programs towards more market-based systems of housing finance has become visible. At the same time, the funding of housing loans has changed significantly. In particular, there has been a boom in securitization, through which house financing has become international. As a result, fluctuations spill over much more easily from one ‘national’ market to others, indeed to international capital markets. This is a particular challenge for supervisors because of national differences in supervisory structures and practices. Finally, the chapter looks at the relationship between monetary policy and housing markets. Cross-country differences concerning the structure of mortgage loans provide for a heterogeneous transmission process. In the USA long-term fixed-rate mortgages prevail, while other countries mostly use variablerate loans, which lead to a faster transmission of monetary policy shocks. Despite marked changes in the overall transmission of monetary policy, the housing market is still one of the main channels of monetary policy. Philip Arestis and Elias Karakitsos proceed in Chapter 3 to the theme of ‘Subprime Mortgage Market and Current Financial Crisis’. The thesis of this contribution is that the current financial crisis is the result of three forces: Financial liberalization, financial innovation (what we now know as the subprime mortgage market) and easy monetary policy in the USA, the UK and other countries. The first feature is the financial liberalization policies initiated by governments both in the developed and developing world since the 1970s. The second feature is an important financial innovation that emerged following the financial liberalization experience. The financial innovation in question is based on the issue of financial structured products, such as Collateralized Debt Obligations

Philip Arestis, Peter Mooslechner, Karin Wagner 3

(CDOs) that played a key role in the swelling of the subprime market. Other forms of asset backed securities were also issued related to commercial real estate, auto loans and student loans, whereas Credit Default Swaps (CDSs) were issued to insure investors against the risk of default of the issuer. The third feature springs from the type of new economic policies pursued by a significant number of central banks around the world. This new policy is entirely focused on monetary policy, and the emphasis on frequent interest rate changes as a means of controlling inflation. In the USA, Alan Greenspan injected liquidity and cut interest rates following the Asian–Russian crisis of 1997–98, which was only partially drained later on. Afraid of deflation in the aftermath of the burst of the internet bubble, Greenspan cut interest rates from 6.5 per cent to 1 per cent and injected huge liquidity. More important, he was late and slow in draining that liquidity and reversing the rate cuts from the middle of 2004. Ben Bernanke imitated Greenspan and injected further liquidity following the ongoing credit crisis that erupted in the summer of 2007. This liquidity financed the last and most pronounced phase of the commodity bubble in the first half of 2008 which pushed the price of oil to US$ 147 per barrel. The commodity bubble was the last one in the current cycle, as it affected CPI-inflation. Whereas central banks are loath in hiking rates to curb asset price inflation, a surge in CPI-inflation falls squarely into their realm. The surge in commodity prices forced some central banks, like the ECB, to tighten monetary policy, whereas it delayed others, like the Fed and the Bank of England, from the urgently needed rate cuts, thus contributing to the downturn in the autumn of 2008. The acceleration of the economic downturn in the third quarter of 2008 burst the commodity bubble and demolished the myth of decoupling between developing and developed countries. The impact of these three types of development has been the creation of enormous liquidity and household debt in the major economies but which, in the USA and UK in particular, has reached unsustainable magnitudes and produced the current crisis. This contribution relies on these three features for an explanation of the origins of the current crisis. But ultimately the focus of this contribution is on the creation and subsequent developments in the subprime mortgage market. Elisabeth Springler and Karin Wagner turn their attention in Chapter 4 to homeownership in an attempt to examine the ‘Determinants of Homeownership Rates: Housing Finance and the Role of the State’. They argue that the current financial and real economic crises started off in the USA when the housing bubble burst and led to a global economic recession affecting both industrialized and emerging markets. Given similar

4


developments in the housing markets in numerous European economies compared to the pre-housing-crises experience observed in the USA, the fear has arisen recently that a similar experience is inevitable in Europe. A number of contributions have avoided analyzing the impact of institutions and the role of the state in implementing economic policies to promote the housing sector. Additionally, studies of housing bubbles, with only a few exceptions, neglect completely the impact of homeownership rates. Although – and similar to the development of house prices – homeownership rates also increased in most economies recently. Combining these two missing elements in current economic research, this chapter focuses on the determination of homeownership rates in the European economies and in the USA. Special attention is paid to the role of the state in providing tax exemptions and other indirect subsidies. These incentives of the state can additionally be promoted by measures in the financial sector. In this context, especially the liberalization of mortgage markets that led to a decrease in interest rates can be seen as a further incentive for the promotion of homeownership rates. The underlying hypotheses for the determination of homeownership rates in the light of the current crises are the following: First of all, tax incentives have a positive impact on homeownership rates. Second, interest rates have an inverse impact on homeownership rates, which lead to the conclusion that the current liberalization of European mortgage markets and the ongoing innovations in the field of housing finance have a positive impact on homeownership rates. If these interrelations can be detected, further light can be shed on the explanation of the housing crises, by connecting this evidence with the existing literature on house bubbles. As interest rates serve as an important explanatory variable of house price developments, they also turn out to be a crucial factor of housing bubbles; consequently, a clear causal interrelation between economic policies of the state and house prices can be detected. Economic policies become a direct factor for homeownership rates and gain indirectly increasing importance in the determination of housing bubbles. Taking the observed negative effects of the housing crises in the USA for individual households into account, the need to rethink economic policy emerges. Not only do housing crises not seem to be determined by market forces, they are directly fostered by the policies of the public sector owing to the increase in homeownership rates. To empirically test for the described interrelations between homeownership rates, the role of the state and housing finance products, a time series model of mortgage debt and cost of financing an owner-occupied home is applied to European economies and to the United States.


Dieter Gstach in Chapter 5, ‘The Rental Housing Market’, analyzes the current and potential role of rental housing in the macroeconomic housing literature. Recent data on rental housing are contrasted with the handling of rental housing in the literature. Particularly, results from 2006 micro data are used to provide relevant statistics regarding the role of rental housing. It is argued that the neglect of rental markets is a serious gap in macroeconomic analysis involving housing markets. In housing studies rental housing has always been an important topic. But, when it comes to the economic role of housing within more general setups, matters are very different. In this more scattered literature the rental housing sector is largely neglected. This observation even applies to special journal issues dedicated especially to the macroeconomics of housing, such as volume 13/4 of the Journal of Housing Economics or volume 24/1 of the Oxford Review of Economic Policy. This leads to the question of whether it is indeed justified to proceed with as-if-economics and simply assume the rental market away. Two variations of this question emerge: Can we really ignore the possibility that rising house prices via rising rents exert a negative long-run impact upon consumption? Is the widespread institution of rent indexing, which directly affects the consumer price index, really irrelevant for price stability? As, for example, the above-mentioned micro data suggest, renting households on average have significantly lower disposable incomes than owneroccupiers. The ratio is roughly 3:2 between owner incomes and incomes from renting with rising tendency. Data also show that households that rent typically spend 25 per cent of disposable income on rent with rising tendency again. These figures together with average shares from renting of 30 per cent in industrialized countries, amount to almost 6 per cent of aggregate consumption taking the form of rental payments (for equal average consumption propensities). Consequently, 6 per cent would also be the weight of rents in national price indexes (typically they are higher, for various reasons). So, the story about the housing market and the macro economy should be augmented by considerations of a rent channel. While it may take some time for this channel to become effective, it points in the opposite direction of the collateral effect. A major reason behind this effect could be different consumption propensities of tenants and landlords, which should come as no surprise given the significantly different incomes of homeowners and renters. Furthermore, as various policy measures influence rents directly, the latter may also play a macroeconomic role in the short run. Such measures include not only the most obvious case of direct rent and tenancy regulations, but many more, such as tax treatment of rental income, subsidization of ‘second

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homes’ and, more generally, the various measures affecting the relative price between rental and owner-occupied housing. Many deficiencies of housing related statistics are well documented in the literature and have been pointed out in various contributions to this book. This is not true for the notorious lack of reliable and internationally comparable rental rate data. But these are required to test whether indeed rental housing is irrelevant for macroeconomic analysis as the relevant strand of literature on housing implies. Christophe André and Nathalie Girouard in Chapter 6, entitled ‘Housing Markets, Business Cycles and Economic Policies’, argue that from the mid-1990s to 2006–07, the vast majority of OECD countries experienced an exceptional expansion of their housing markets, both in terms of magnitude and duration. Moreover, deviating from historical patterns, the latest housing upswing has been disconnected from the business cycle, as the economic downturn of the early years of the century was not accompanied by a slowdown of housing markets. Housing has contributed to the expansion of economic activity by enhancing the effect of interest rate cuts on economic growth: Residential investment has been strong and wealth effects have supported private consumption, especially in English-speaking countries. Estimates of long-run marginal propensities to consume out of housing wealth are in the range of 0.05 and 0.08 in Australia, Canada, the Netherlands, the United Kingdom and the United States, while they are much lower in other continental European countries and Japan. Econometric investigations point to a transmission of housing wealth to private consumption through the refinancing of mortgages and home equity loans (more generally housing equity withdrawal). This extraction of liquidity from the housing market has been strongest in countries with the most innovative mortgage markets, which offered a wide range of products and allowed broad access to financing; for example through high loan-to-value ratios, second mortgages, equity release products, alternative interest rate adjustment and repayment structures or subprime loans. Financial innovations have contributed to excessive lending and housing booms, which are at the root of the current financial turmoil. Tax systems favoring home ownership through tax deductibility have sometimes exacerbated the problem. These developments have revived the debate on the role of monetary policy in asset price cycles and on the optimal features of regulation and supervision of financial markets and institutions. For monetary policy to be efficient in containing asset price bubbles, it is generally considered that three conditions need to be met: First, monetary authorities need to detect the development of a bubble in a timely manner; second, a


modest tightening of monetary policy needs to be able to control speculation; and third, expected gains from avoiding a bubble in terms of medium-term macroeconomic performance must be substantial. Obviously, the severity of the current recession requires preventive action. However, the difficulty in meeting the first two conditions implies that monetary policy is probably too blunt a tool to deal with asset bubbles, even if ‘leaning against the wind’ strategies are sometimes applied. While loose monetary policy has fuelled the housing boom, many excesses in credit expansion would have been prevented by adequate regulation and supervision. In particular, a dramatic growth in the share of assets held outside the traditional banking system, especially in investment banks, structured investment vehicles, conduits and hedge funds, has weakened the control over the financial system. Restoring conditions for a sustainable development of housing markets will imply reinforcing the supervision and regulation of the financial system, in particular controlling the level of leverage, avoiding pro-cyclical provisioning and capital standards. Improving risk management and transparency and ensuring appropriate underwriting standards are further requirements. Montserrat Pareja-Eastaway and María Teresa Sánchez-Martínez focus on the rented markets in Chapter 7, ‘European Rental Markets: Regulation or Liberalization? The Spanish Case’. They argue that housing markets show nowadays a peculiar transformation in Europe and the United States. Not only do they reflect the current economic scenario but also they portray the results of recurrent housing policies over time. The European landscape provides quite a diverse picture in terms of the dominance of one form of tenure from another. Academic literature has described the evolution of European housing systems by means of comparative analysis and the selection of representative case studies. In the majority of European countries, a massive construction of social housing, mainly publicly rented, took place after the Second World War in order to provide shelter for low-income earners. However, the tenure pattern has been developed and transformed by each country according to its own socio-political system, especially so in terms of the interaction of the demand and supply forces and housing policy priorities. Nowadays, the change of the economic scenario directly affects the approach adopted towards the mechanisms used by housing policies in order to achieve their targets. Housing markets are currently unstable and uncertainty characterizes any expectation on the final market outcome. However, in terms of tenure, there are some general trends at the European scale, that is to say, a general increase in homeownership and a fall in size and quality of the rented dwellings. The characteristics of the rented markets

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and their regulations vary across Europe. In some cases, the percentage of dwellings oriented to this segment has been stable (e.g. Germany); in others, it has fallen dramatically (e.g. Spain). In many cases, rent controls or certain regulations are implemented. Different reasons lie behind the need to regulate or control the market; the question is the extent to which the evolution of the rented markets in terms of rent and size depends upon the attributes of their regulation. In other words, are there any other variables to take into account to analyze and forecast the relative importance of rented markets in Europe? In particular, how do the economic scenario and the specific measures of housing policy affect the rented sector development? The aim of Chapter 7 is two-fold: First, it provides a broad picture of the situation in Europe, covering not only structural facts of the market but also typologies of regulation; and, secondly, using this as a framework, it will go in depth on the peculiarities of the Spanish case. Regulations on the rented sector in Spain have proved to cover a wide spectrum of measures: From the freeze of rents and duration of contracts to a minimum agreement between landlords and tenants going through a period of complete liberalization. However, nowadays the size of the private rented sector scarcely reaches 10 per cent of the housing stock. Other measures may have certainly influenced this situation. Finally, the future challenges for policy measures oriented towards this sector regarding not only the current Spanish situation but also other European markets, are discussed. Guido Wolswijk in Chapter 8, ‘Fiscal Aspects of Housing in Europe’, discusses the role of housing and mortgage markets fiscal instruments. A large degree of volatility in housing markets and mortgage markets may have disruptive effects on the economy and also impair financial stability. National governments have various tax and subsidy measures at their disposal that affect housing decisions. The effectiveness of these instruments has not changed much since the start of EMU as the volume of cross-border activities in housing remains limited. The chapter focuses on the euro area countries given the lack of national monetary policy options for them. This aspect potentially increases the value-added of national fiscal instruments in affecting housing market developments. The first part of the chapter describes the set-up of fiscal housing instruments in euro area countries as in the year 2007. It focuses on the taxation of the imputed rental value of the house, income tax deductibility of mortgage interest payments, capital gains taxes on housing gains, and VAT rates applying to new houses. While revealing some degree of diversity, in most countries the fiscal system overall financially favors owner-occupied housing compared with investment in other assets. This


preferential treatment is usually motivated by positive external effects of owner-occupied housing; it is also possible, though, that incomedistribution considerations and effects may also play a role. The chapter also mentions some possible drawbacks of a non-neutral government approach to housing markets, including the need to raise taxes on other activities to finance the preferential treatment of housing. The potential of fiscal instruments for preventing or correcting housing market imbalances is discussed in the second part of the chapter. Given that housing markets in the euro area are still predominantly national, fiscal instruments, unlike the euro area monetary policy, can be geared towards country-specific developments in housing and housing finance. The potential roles of such micro policy measures, however, do not always receive sufficient attention. Structural fiscal measures may contribute to reducing volatility on housing markets and in this way contribute to limiting busts that can have disruptive effects on the economy and on financial stability. Options for governments, for instance, include increasing the sensitivity of taxes to house prices (for instance, by updating more regularly the market value of houses, which often acts as tax base of various taxes), and reducing mortgage interest relief which homeowners receive via the tax deductibility of interest payments. Although there appear to be more arguments in favoring downsizing, the implicit subsidy given via mortgage interest tax relief and the capitalization of these benefits in house prices, make introducing or extending limits on interest deductibility politically difficult. Caution is needed in the use of fiscal fine-tuning measures, aimed at ad hoc correction of a housing market imbalance, in normal circumstances. Doubts prevail about possibilities for identifying situations when fiscal authorities should react; also in terms of the ability to provide adequate time and calibrate such actions, which may cause a government measure to have pro-cyclical effects. Precise modalities of fiscal measures, however, need to take into account the exact situation and the specific national housing market characteristics. József Hegedüs, in Chapter 9, ‘Towards a New Housing System in Transitional Countries: The Case of Hungary’, suggests that governments in the region worked under constant fiscal pressure caused by the social and economic costs of the bankrupted socialist economy. As a consequence, the state had to ‘withdraw’ from the housing sector thereby privatizing the housing sector in its entirety. The chapter uses the case of Hungary to show the emergence of the new housing model in the region. After the transitional recession of the 1990s, Hungary launched a new housing policy in 2000 focusing on three areas of the sector: Development of the

10


mortgage market, renovation of the existing housing stock (especially urban, multi-unit housing estates) and the rebirth of the social housing sector. Housing programs went through different stages under the influence of different pressure groups (construction industry, banking sector, local governments) and political forces (party experts group). The mortgage market was developed by competitive private banks, which were ready to service emerging middle-class families. The process was supported by a substantial interest rate subsidy, and as a result the share of outstanding housing loan to GDP increased from 2 per cent to 10 per cent in four years. Because of the high fiscal cost, the Hungarian government cut the interest rate subsidies in 2004, which, however, did not stop the growth of the mortgage market. The typically foreign-owned banks with easy access to the capital market provided low interest rate foreign currency loans (with high exchange risk and interest rate risk). From 2004, the share of the foreign currency loans increased from 20 per cent to 60 per cent, which made the country highly vulnerable to the economic downturn. The social housing program (construction subsidy for local governments to build or buy social rental units) was halted in 2004 because of the huge budgetary burden, and it was replaced with a rent-subsidy program that did not prove to be operational. The rehabilitation of the multi-family, prefabricated housing estates has proved to be the most successful program since 2004, because it is relatively cheap and politically effective (reaching a large number of families). Despite results of the housing programs (increased housing output, growth of the mortgage market, and increasing renovation in the housing estates), signs of the housing market crisis emerged, such as a fiscal pressure on the programs, uneven house price trends, growing inequality in housing conditions, lack of adequate social housing and the regressive subsidy system. The chapter argues that countries in transition followed the same trends as they faced the same housing market challenges after the transition, although differences in institutional solutions in housing finance and housing welfare schemes have become more and more important. The economic crisis of 2008 has reached the region in a transitional stage of development of the housing market. The expected economic hardship caused by the recession will affect the housing market drastically, such as house price decrease, increasing mortgage arrears, and difficulty to have access to mortgage. The housing policy response to the crisis will determine the future housing model in the region. In Chapter 10, ‘House Price and Other Housing Market Data: A User’s Perspective’, Anthony Murphy argues that there are data needs for policy makers and others who want to analyze and model housing markets.


A range of mix-adjusted and other house price indices are available for some countries, notably the UK and the USA. However, in many other countries house price data are limited. Disaggregated house price data for first time buyers are useful since credit conditions matter a great deal. In both the UK and the USA, one may infer changes in credit conditions from changes in the distribution of loan to value (LTV) and loan to income (LTI) ratios, as well as from surveys of loan officers. Mix-adjusted house price data are clearly preferable to average or median house price data. The construction of these mix-adjusted house price indices involves many choices. For example, one choice is whether mix-adjusted house prices indices based on one type or another of regressions are more accurate than another. Another question is whether the mix-adjusted results be grossed up to the stock of owner-occupied housing or the flow of house purchases. There is no single ideal index and, apart from very short-run forecasting, the choice of mix-adjustment procedure is generally a second order issue. Various UK and USA house price indices display the same medium and long-run trends, since they are cointegrated with each other. For modeling purposes, it does not matter much which UK or USA house price series are used. When modeling European housing markets, the important issue is not the choice of house price index but the shortage of long-run, disaggregated average and/or mixadjusted house prices data, as well as other housing related data. What data are needed depends on the models used. The most basic theory of what determines house prices is just a story of supply and demand, where the supply, the stock of houses, is given in the short run. Then prices depend on the stock of housing and the factors driving demand. In the USA, where rental markets are well developed and with rents generally market determined in contrast to the heavily regulated rental markets of the UK, the most popular model of house prices is the house price to rent model. When applying the inverted housing demand and house price to rent models to European housing markets, one is immediately confronted by large gaps in the house price and rent time series. At the macro level, one would like consistent, medium-run time series data on the housing stock (including vacancy rates) and the main components of household wealth. In addition, the distribution of LTV and LTI data for first time and other borrowers would be very useful for tracking changes in credit conditions. At the micro level, one would like survey measures of house price expectations and lending, as well as more information on housing finance and wealth in household and panel surveys. The chapter deals with both the micro and macro issues that relate to housing data.

12


In Chapter 11, ‘Residential Property Price Statistics for the Euro Area and the European Union’, Martin Eiglsperger presents the residential property price indices for the euro area and EU Member States compiled by the ECB in co-operation with the EU national central banks. Data on dwelling prices used for calculating these indices have been collected from various national sources, mostly from private or government statistical institutes, ministries, registers, real estate agencies and associations and mortgage lenders. Since 2001, the ECB has calculated an aggregate for the euro area by weighting together changes in prices for houses and flats for the euro area countries. Since then, the statistical quality of both the indicators for the countries and the euro area aggregate has improved, but still remain below the standards of other economic statistics and price indicators for the euro area. Dwelling price statistics for non-euro area EU countries have been collected since early 2005. This chapter starts with briefly introducing the work of the ECB and the EU national central banks in this area. This is followed by an outline of the relevance of statistics on residential property prices for ECB analyses, in particular with respect to housing wealth and the channels through which changes in house prices may impact on overall inflation. For these purposes, the ECB requires good quality statistics on house prices both on national levels and for the euro area as a whole. Breakdowns into indicators for urban and non-urban areas and for new and existing houses and flats can help identify different price dynamics. The presentation of the national sources focuses on the statistical features of the dwelling price data currently collected for euro area and EU countries. While for each EU country at least one indicator is available, the collected statistics differ in various statistical dimensions. National residential property price indicators do not only vary in terms of reporting frequency and timeliness at which new data become available. They also vary in the type of price information used in the index and the way the indicators control for the differences in the composition of the sample. For the calculation of an aggregated euro area indicator for residential property prices, the ECB identifies those national indicators which best represent price developments in the respective country and which are most appropriate for identifying pure price changes over time. This is followed by an explanation of how the weights of the euro area residential property price indicator have been derived and which aggregation formula is used for its compilation. While annual data of the euro area dwelling price index are provided from 1981 on, the semi-annual index starts in 1996 when sufficient country data on infra-annual price changes are available, including an interpolation of the annual price index for


Germany. Finally, the chapter provides an outline of possible future developments in the area of house price statistics. Most promising is an EU-wide project of the European Statistical System from which a substantial contribution to the improvement and the harmonization of EU residential property prices can be expected. Finally, in Chapter 12, Pirmin Fessler, Peter Mooslechner, Martin Schürz and Karin Wagner turn their attention to microeconomic data in ‘Housing and Financial Wealth in Austria: What Can Survey Data Tell Us for the Analysis of Financial Stability Issues?’. Microeconomic data on private households provide increasingly important information for many economic policy issues. This chapter examines how micro data can be used to improve the analysis of financial stability. At the beginning of the chapter the authors provide a brief survey of the recent literature that employs survey data on household finance and consumption with a focus on financial stability. The availability of micro data for understanding the impact of shocks, policies and institutional changes is particularly important in view of the extremely large heterogeneity in economic behavior of households. The recent financial crisis has demonstrated that a relatively small fraction of households can have important effects on market outcomes. The authors use data of two surveys, the 2008 survey on Austrian households’ housing wealth and the 2004 survey on financial wealth, both conducted by Oesterreichische Nationalbank. Housing assets are the most important form of collateral and the value of housing property affects households’ expenditure by improving access to credit for liquidity-constrained households. High-income households tend to have more debt than low-income households; however, the latter are more burdened by their debt. By combining both financial and housing wealth data, the chapter draws a better picture of households’ vulnerability and checks whether households’ debt in Austria represents a risk to financial stability. The dynamics of economic aggregates are determined not only by macroeconomic variables, but also by household-specific factors. This is particularly true for household consumption, savings and balance sheets, which are to a large extent driven by expectations about future individual income and demographic and social characteristics. The Austrian household-level data allows quantifying the size and relevance of the impact of shocks, policies and institutional changes on various groups of individuals. For example, financial integration and financial innovation made it easier for households to borrow against their future income, smooth consumption and diversify their portfolios. The resulting changes in the composition of the assets and their potential implications for wealth distribution, the

14


relative impact of policies on different household groups and the ultimate effect on macroeconomic variables can be judged with the micro data. Micro data allows the authors to analyze this structure, assessing the mismatch between assets and liabilities of households and identifying how many individuals have accumulated too much debt and what risks such over-accumulation poses to their finances and ultimately to the economy. The evidence shows that the structure of portfolios depends on age, wealth and household characteristics. A major factor in increasing stock market participation has been a surge in indirect holdings through financial intermediaries such as mutual funds and retirement accounts. Risk-taking still remains strongly correlated with wealth. Monitoring further changes in portfolio behavior is particularly relevant for an assessment of the impact of financial innovation. The recent financial turmoil has shown that numerous households and lenders underestimated the risks associated with high indebtedness. We would like to thank the authors for their stimulating contributions to the conference, and also the research department of the central bank of Austria for their support and sponsoring of the conference. Taiba Batool and Gemma Papageorgiou at Palgrave Macmillan, their staff, and Rita Schwarz from Editorial Processing at the Oesterreichische Nationalbank have been extremely supportive throughout the life of this project.

2 Housing Markets in Europe and in the USA: What Are the Relevant Issues Today? Peter Mooslechner and Karin Wagner

2.1 Introduction While housing markets and housing finance have undergone remarkable changes over the past decades, both in the USA and in Europe, housing continues to play an important role in the economy. In a significant number of recessions, the housing sector has preceded if not caused the economic downturn – a stylized fact – also underlined by the current financial turmoil and economic crisis. At the same time, there has been a significant increase in indebtedness of private households. In this chapter we take a closer look at some of the fundamental factors behind this trend of increasing mortgage growth. In particular, the chapter analyzes some of the recent changes observed in housing finance and studies key aspects of the economic relevance of housing markets in the present context. The chapter is organized as follows. First, we take a look at the relationship of housing markets to their national but also international macroeconomic surroundings (Section 2.2) and analyze how, for instance, house prices are linked to other macroeconomic variables. Thereafter (Section 2.3), we deal with some fiscal and regulation issues and Section 2.4 focuses on the tremendous changes in housing finance we have faced since the beginning of the 1990s. Central banks’ opportunities to react proactively to asset prices movements are the topics discussed in Section 2.5. We end with some policy conclusions that can be drawn in the light of the current economic crisis which – once again – originates from the housing market. 15

16


2.2 The housing sector, housing finance and the macro economy As the housing sector accounts for a considerable part of a country’s welfare, wealth and GDP, it significantly shapes a country’s long-term development. In this respect, it is important to pin down the underlying drivers of house prices, and to assess their economic implications, as well as the fact that house price developments diverge strongly across countries. In the following section we want to show some issues concerning the interaction of housing markets and the business cycle, especially the impact of house price developments through wealth channels on consumption. The question of how monetary policy mechanism can by interest rates changes affect house prices and wealth will be discussed later (Section 2.5). 2.2.1 Variation in house price developments and ownership rates While house prices have risen to record levels in Spain, UK and in Ireland, Austria and Germany show, if any, just slight increases in house prices (Table 2.1). These differences may reflect country-specific factors such as demographic differences, institutional regulations or cyclical positions; but changes in the structure of housing finance driven by globalization and liberalization are likely to play an important role as well. Above all, current macroeconomic analysis underlines the role of housing wealth as a household’s principal asset and the role of mortgage debt as the largest liability in households’ balance sheets. Housing tenancy structures differ considerably across European and US countries. While it is 43 per cent in Germany, it is 86 per cent in Spain and 75 per cent in Ireland (see Table 2.1). This can be explained by different tax incentives (see Section 2.3) and by differences in the access to mortgage financing. Home ownership rates were rising in almost all OECD-countries from the Second World War until the early 1990s. Against the fact that it is still out of reach for some income groups, owing one’s home has become less exclusive over time. It seems as if ownership rates reached a level and remained there, no additional increase has been seen since the 1990s (Atterhög and Song, 2004). The reached level depends on social attitudes to home ownership, legal and tax systems, etc. (Scanlon and Whitehead, 2004). In general, the late 1980s and early 1990s were periods when many governments cut back on subsidies and liberalization in mortgage markets and new legal arrangements and financial instruments were created. Furthermore, increased uncertainty took place, as labor markets and

Peter Mooslechner and Karin Wagner Table 2.1

17

Housing market indicators Real house prices year-on-year in %

Price-to- Price-torent income ratio ratio

2000–05 2006 2007 2

Level relative to long-term average1

United States 5.6 Germany –3.1 France 9.4 Italy 6.5 United Kingdom 9.8 Denmark 5.7 Finland 4.0 Ireland 7.9 The Netherlands 2.9 Norway 4.5 Spain 12.2 Sweden 6.0 Austria –0.8

4.5 –1.8 10.0 4.1 3.8 19.4 8.4 10.5 2.9 10.7 6.3 10.6 1.4

–0.3 –2.2 4.9 3.1 8.4 2.9 5.5 –1.8 2.6 11.5 2.6 8.6 1.5

123 71 159 127 151 162 146 167 156 158 187 160 –

102 64 138 114 141 143 105 133 158 121 147 120 –

Ownership Typical rates LTV in % in % 2007

68 43 80 70 70 54 58 75 54 77 86 50 57

2007

78 70 91 65 69 80 81 83 101 – 72.5 77 84

Note: House prices deflated by the Consumer Price Index. 1. Long-term average = 100, latest quarter available. 2. Average of available quarters where full year is not yet complete. Source: Girouard et al. (2006), OECD (2008), European Mortgage Federation, ECB (2009), Univerity of Technology Vienna.

governments were more exposed to competition through globalization and privatization. Tenure choice is determined by permanent income, the cost on owneroccupying versus renting (price–rent ratio) and by demographic variables. Households asking for a mortgage are often required equity contributions from borrowers/lending institutions. Thus, besides income, accumulated savings are important as a down-payment. They are an additional borrowing constraint and therefore also determine the timing of home purchase. In the 1990s down-payments were lowered in many countries (Duca and Rosenthal, 1994) – one of the changes in housing finance that increased competition between lenders (for further changes in the financing conditions for households see Section 2.4). Chiuri and Jappelli (2003) find that in countries with relatively high down-payment ratios ownership rates of the young are relatively low. Owning one’s home is typically considered as reaching the peak in the ‘housing career’. As mentioned earlier, differences in the access to mortgage markets and mortgage financing may be also one of the reasons

18


for the different ownership rates across countries. The share of homeowners of British and Dutch households aged 25–29 years is higher than in France, Italy, Germany or Spain (Catte et al., 2004, p. 26). However, countries showing high ownership rates such as Spain or Italy are not among those countries with the highest developed mortgage markets. This is to say, there seem to be other reasons responsible for that – besides tax regulations and mortgage markets. Bequests and other types of wealth transfers are considerably important in home purchases: e.g. more than a third of Italian households report that they got their home as a gift or bequest or that they got financial support when buying their home (Guiso and Jappelli, 1999). Scanlon and Whitehead (2004) conclude that younger households in most European countries tend to enter homeownership at a later period in their life than in former years because of higher house prices and greater access problems. Chiuri and Jappelli (2003), using micro data of 14 OECD countries, show that ownership rates vary significantly across countries. Whereas first homes are typically bought in the 20s in Australia, Finland, Sweden, UK, Canada and US, in other countries such as Austria, Spain, Italy people buy their first homes between 30 and 40 years old. Homeownership rates and, therefore, the size of the rental market, differ across Europe and the US (Table 2.1). The share of rented dwellings has decreased since the 1980s in most European countries (ECB, 2003). Nevertheless, in some countries the proportion of the private rented sector is still high. Additionally, not all homeowners live in their homes. Furthermore, the size of social rented homes and cooperatives play a crucial role and can be highly influenced by (national) policy-makers. According to a survey, German households own about 75 per cent of all residential property – but more than 30 per cent of all housing is rented out by private individuals to other households. 18 per cent of all housing is rented out by private enterprises, including cooperatives. Contrary, in the Netherlands private (and enterprise-owned) rental housing plays a minor role (just 10 per cent of dwellings to be rented), social housing is important (over 30 per cent). In Italy, about a quarter of the total rental housing stock is owned by the public. In France and Finland around 20 per cent is social renting by the government or public enterprises (ECB, 2009). 2.2.2 House price movements as a major cause of wealth effects Why are developments of house prices so important for the economy and how can house price movements influence other macroeconomic

Peter Mooslechner and Karin Wagner

19

variables and economic activity? House price increases or decreases have to be seen in the context of a country’s business cycle as housing markets tend to track the country’s business cycle. There are wealth effects on consumption from changes in house prices. The life cycle model (Ando and Modigliani, 1963) with consumption depending on a household’s lifetime income and wealth suggests that consumers will distribute increases in anticipated wealth over time and the marginal propensity to consume (MPC) out of all wealth – from any form of wealth – should be the same number, something just over the real interest rate. But one has to take into account that for those having a bequest motive the consumption effect derived from housing wealth may be smaller than that derived from other forms of assets. To assess the strength of the links between residential prices and consumption Catte et al. (2004) calculate the longrun marginal propensity to consume out of financial wealth to be in the range from 0.01 in Italy to 0.07 in Japan. They estimate the OECD average to be 0.035 and for the US their estimate is 0.03. In general, wealth effects from the real estate market are not of the same magnitude as wealth effects for the stock market. Consumption is affected differently by the form in which wealth is held for several reasons. An obvious point is that while households get information on changes in their stock market wealth easily by various media (internet, TV, newspapers, etc.), they may be less aware of changes in the value of their housing wealth. A number of empirical studies have analyzed the impact of housing wealth on consumption in the United States (for a survey, see Altissimo et al., 2005). The results point to a marginal propensity to consume between 2 and 9 cents out of each dollar of nominal housing wealth. Case et al. (2005) compare stock market wealth effects versus housing market wealth effects using data for a panel of 14 countries (Canada, the US and 12 European countries) over the period 1975 to 1996. One of the econometric models they apply shows that a 10 per cent increase in housing wealth leads to a growth of consumption by roughly 1.1 per cent. Contrary, a 10 per cent increase in stock market wealth has almost no effect on consumption. Results for a panel of US states tell that a 10 per cent increase in housing wealth and in stock market wealth has the same effect on consumption – both cause an increase of 0.4 per cent. However, all the econometric specification presented supports the conclusion that changes in house prices have a larger effect than changes in stock market prices to influence households’ consumption in Europe and in the US. Muellbauer (2007) suggests that the broader access to credit had important implications on consumer behavior in many countries. Credit

20


market liberalization raised consumption-to-income ratios and reduced consumer savings by increasing the collateral value of housing wealth. According to Muellbauer, this is the main reason for the increase in the size of housing wealth effects. The second reason is that house price changes now have larger effects on consumer consumption than changes in stock prices. Furthermore, house price movements may have led to income effects through the rental market (Giuliodori, 2004). With deregulated renting systems, higher house prices my cause higher rental prices. This income effect is positive for landlords or institutional investors while it is negative for tenants. As mentioned earlier, the size of the rental sector differs across countries. Therefore, the higher the share of owner-occupiers and the lower the rental market’s size in a country, the larger the house price effect will be on income and on consumption decisions of households. 2.2.3 Housing prices and the business cycle In many recessions the housing sector preceded overall economic downturn, even if the housing markets’ share in GDP is too small to cause a recession by itself. Changes in house prices generate not only substantial wealth effects on consumption, but also – as evidenced by the current financial crisis – significant spillover effects, which can cause severe problems for the macro economy as a whole. For example, booms in housing investment were responsible for increased employment, as the construction sector accounted for more than 20 per cent of all employment gains since 2000 in the US, France, Spain, Denmark, Norway, Sweden, Ireland and Greece (OECD, 2007). In the past the volume of residential investment tended to turn prior to house prices in a country’s business cycle. Over recent decades housing investment has shown high growth rates in many countries. Low interest rates have been one of the driving factors as they stimulate the demand for housing, which leads to higher house prices and, in turn, stimulate residential investment. If house prices rise faster than construction costs it makes sense for individuals or construction companies to invest in new dwellings. The extent of this supply effect of increased house prices by residential construction and housing investment can differ depending on the national construction regulations, availability of specialized workers, etc. (Giuliodori, 2004).1 Many countries which had reached a ten-year peak in housing investment growth (Figure 2.1, left panel) had experienced rapid population growth, especially from immigration. While housing investment was on the rise till 2007, in 2008 a downturn started in almost all countries (Figure 2.1, right panel).

Decline since end of 2007

Ten-year peak

year-on year in %

in % of GDP

10

12

5 10

0 5

8

10 15

6

20 4

25 30

2

Ø1998–2008

to 2007q4

Ireland

UK

Spain

Norway

France

Denmark

Italy

Finland

Sweden

Belgium

Austria

Germany

Ireland

UK

Spain

Norway

France

Denmark

Italy

Finland

Sweden

2008

to 2008q4

Real housing investment

Source: OeNB, OECD Economic Outlook 84 database.

10.1057/9780230246980 - Housing Market Challenges in Europe and the United States, Edited by Philip Arestis, Peter Mooslechner and Karin Wagner

21

Figure 2.1

Belgium

Austria

Germany

Netherlands

USA

2007

Netherlands

40

0

USA

35

22


Table 2.2

Timing of maximum correlation Output gap contemporaneous or lagged < 1 year

Output gap lagged 1–2 years

Output gap lagged 3–4 years

Strong

Denmark, Finland, Ireland, United Kingdom

Spain

Average

Japan

Canada, France, Sweden

Australia, Germany, Switzerland

Weak

New Zealand

Norway, United States

Belgium, Italy, Netherlands

Intensity of correlation

Note: Correlations are between de-trended real-house price levels and the output gap. They are calculated for the period 1970–2002, based on semi-annual data. Countries are ranked according to the value of the maximum correlations and of the lags at which these are found. The intensity of correlation is indicated as strong if the maximum correlation coefficient is >0.65, average if 0.50–0.65, weak if F

= =

29.32 0.0000

(Std. Err. adjusted for 16 clusters in varland) Owner

Coef.

Robust Std. Err.

t

P>|t|

[95% Conf. Interval]

.1001613 –.521102

.0521042 .0560829

1.92 –0.93

0.074 0.368

–.010896 –.171648

.2112187 .0674276

gdpcap –. L1. population∼h L1. permit L1. mortgdebtr∼o costfin _cons

.0103811

.0039163

2.65

0.018

.0020337

.0187285

–.0304139 .0279287 –.0245943 3.811632

.0121474 .0192121 .0074942 .2912128

–2.50 1.45 –3.28 13.09

0.024 0.167 0.005 0.000

–.0563055 –.0130209 –.0405678 3.190926

–.0045223 .0688783 –.0086209 4.432337

sigma_u sigma _e rho

.20886496 .0153427 .99469296

(fraction of variance due to u_i)

Source: Authors’ calculations.

population growth leads to a higher demand for housing. Over the last 10 years many countries which showed remarkable growth rates in housing investment had experienced a rapid population growth, especially from immigration. Simultaneously ownership rates rose in this period. The significant coefficient in the results underlines these developments. The variable in the center of our focus which we took as indicator for the role of the state – the cost of financing – is significantly negative. As expected, the higher the costs for financing a home, the lower the ownership rate is. This important result ‘proves’ the fundamental role that tax deductibility and fiscal measures can play for the level of a country’s

Elisabeth Springler and Karin Wagner 75

ownership rate. Additionally, the number of permissions (as indicator for the supply of housing) and mortgage debt per capita are crucial for the level of the ownership rate. As discussed in Section 4.4.1, the design of the housing finance system affects households’ ability to finance their home loans. Availability and variety of mortgage instruments are important determinants for the structure of a country’s mortgage market. So a higher ownership rate may expect a higher mortgage debt ratio of households. Both significant coefficients – for permissions and for the mortgage debt ratio – showed the expected sign. The negative coefficient of the variable permits which represented the number of permissions is not surprising, when taking the arguments of Figure 4.3 above into account.15 Even if we include the time series of the representative interest rates on new mortgage loans, the coefficients remain nearly unchanged (see Table 4.3). Additionally we checked for endogeneity of interest rates (see Table A4.2 in the Annex). A dynamic GMM model showed that the period of 10 years is quite short to show significant dynamics inside the time period.16

4.7 Conclusions and policy implications Ownership rates vary much across euro area countries and the US. The chapter searches for the factors behind this empirical observation. The results obtained from the empirical models above add some policy as well as theoretical contributions to the existing research literature on the development and determination of homeownership rates. Concerning the contributions of this chapter to housing policy recommendations the following can be stated: The model confirms the importance of housing costs for the development of homeownership rates for European economies and the US. Despite these similar results, compared to other research studies presented above, the results obtained in this model further strengthen the importance of the role of the state and highlight the immediate effects that housing policies have. Although the aim to increase homeownership rates is not manifested in many European economies as clearly as in the US, state policies show their indirect willingness to increase homeownership. Regardless of the clear impact that tax policies have on the development of homeownership rates, the empirical results do not enable us to derive a normative statement for European housing policies. This means that the results do not suggest the

76


Table 4.3

Fixed effects model for ownership rate (incl. interest rates)

. xtreg owner gdpcap 1.gdpcap 1. populationgrowth 1.permit mortgdebtratio costfin interestnew, fe vce(robust) Fixed-effects (within) regresssion Group variable: varland R-sq:

within between overall

= = =

0.4630 0.0120 0.0124

corr(u_i, xb)

=

–0.4356

Number of obs Number of groups

= =

96 15

Obs per group: min avg max

= = =

2 6.4 9

F(7,14) Prob > F

= =

86.38 0.0000

(Std. Err. adjusted for 15 clusters in varland) Owner

gdpcap –. L1. population∼h L1. permit L1. mortgdebtr∼o costfin interestnew _cons sigma_u sigma _e rho

Coef.

Robust Std. Err.

t

P>|t|

[95% Conf. Interal]

.1517806 –.0960605

.0643399 .0672633

2.36 –1.43

0.033 0.175

.0137852 –.240326

.2897759 .0482051

.0133821

.0039155

3.42

0.004

.0049842

.02178

–.0368985 .0357541 –.041917 .0273823 3.670055

.0117886 .0237002 .0132649 .0202113 .2766834

–3.13 1.51 –3.16 1.35 13.26

0.007 0.154 0.007 0.197 0.000

.21984747 .01538199 .99516626

(fraction of variance due to u_ i)

–.0621825 –.0116145 –.0150778 .0865861 –.07036748 –.0134667 –.0159667 .0707312 3.076628 4.263482


preference for the promotion of homeownership rates over other housing policy aims (such as providing an affordable rental sector, etc.) as the rise in homeownership rates is not discussed from a sociological or socioeconomic point of view in this study. Increasing homeownership rates should not be counted as ‘success of housing policies’. In this respect it would be necessary to analyze the cultural and sociological dimension of these developments. Although such an analysis is clearly beyond the scope of this study the empirical results presented stress their importance with the development of housing permissions in relation to homeownership rates. The negative coefficient in the regression model shows


that housing permissions decrease with an increase in homeownership rates. This suggests the potential of a form of crowding out on housing supply between the public and private sectors. Especially in economies with large rental sectors and a strong supply of housing from public or private limited-profit organizations, which fulfill in general similar social aims as public housing manufacturers, an increase in homeownership rates would imply a strong structural change in overall housing provision as well as a shift in the goal of social housing. Another aspect that can be pointed out in this empirical study is the effect of housing finance manifested in the development of interest rates. Other research studies suggested that an enlargement and deepening of the overall housing finance system would lead to a decrease in interest rates, which in turn would affect homeownership rates positively. The results obtained from our empirical models for European economies and the US suggest that a decrease in interest rates in combination with tax reliefs promotes homeownership further. The coefficients and results did not change significantly, when the representative interest rate on new mortgages – as an indicator for changes in the housing finance structure – was added. From a theoretical point of view the extension of already existing models of the literature allows for changes in tax structures during the period from 1997 to 2006 and leads to a gain in the explanatory value of federal housing policies. For the first time (to our knowledge) a time series of the user cost of financing is available and provides the possibility to test for its influence. Despite this contribution on macroeconomic level, weaknesses similar to other empirical studies can be found when it comes to the explanatory value for regional and national differences. The structure of the model does not allow for a distinctive national analysis. In combination with the policy conclusions that can be derived from a macroeconomic perspective, it opens the door and shows the necessity for further comparative socio-economic studies in this field.

Annex: Tax models used for the calculation of cost of financing • deduction with a ceiling but no time limit e.g. Austria 1999: Interest payments are deductible as special expenses up to ATS 20 000 (IBFD, 2000). ia = i − MTR ∗ min (20 000/P, i)

78


e.g. Ireland, 1997: deductibility of interest paid on a loan applied for the purchase, improvement or repair of only or main residence, up to . . . the lower of 80 per cent (100 per cent for a first-time buyer) of the interest paid or IEP 5 000 for a married couple, the first IEP 200 (married) is not deductible, except for first-time buyers (IBFD, 1998). ia = i − 0.8 ∗ MTR ∗ min (5000/P, i) + 0.8 ∗ MTR ∗ min (200/P, i) • deduction with or without a ceiling, taxation of imputed rent e.g. Belgium, till 2004: interest on mortgages may be deducted from taxable income up to the total amount of income from immovable property. Imputed rental income from the taxpayer’s main dwelling-house included in the taxable income. The basic rate of the levy is 1.25 per cent for the Brussels and the Walloon regions (IBFD, 2002). ia = i − MTR ∗ min (i − 0.012, 0) e.g. Belgium, since 2005: Additionally, interest on a mortgage contracted on or after 1 January 2005 may be deducted up to a2000 for the first 10 years and a1500 thereafter. ia =

i − MTR ∗ min (2000/P, i − 0.0125), t ≤ 10 i − MTR ∗ min (1500/P, i − 0.0125), t 10

• deduction of a fixed fraction of the acquisition value e.g. Germany, 1996: a taxpayer acquiring or constructing a new owneroccupied dwelling receives a cash grant up to 5 per cent of the construction or acquisition costs in the year of completion or acquisition and in the following seven years, with a ceiling of DEM 5000 per annum. Acquisition or construction cost is the cost of the dwelling and the cost of the land (IBFD, 1997). ia =

i − MTR ∗ min (0.05, 5000/P), t ≤ 8 i, t 8

Germany, since 2000 no tax relief: The tax relief for owner-occupied dwellings abolished. Instead of tax relief, the taxpayer may currently be entitled to a tax-free cash grant for acquiring or constructing a new dwelling to be used by himself (IBFD, 2001). ia = i


• tax credit with indefinite duration e.g. Italy, 2003: A tax credit equal to 19 per cent of certain personal expenses is granted. The expenses which qualify for credit are among others: interest paid on mortgage loans contracted to finance the purchase of an owner-occupied dwelling-house, up to a maximum credit of a686.89 (IBFD, 2004). ia = i − min (686.89/P, 0.19i)

Additional regression results Table A4.1

Cost of financing – time series in %

Austria Germany France Nether- Belgium Denmark Finland Sweden lands 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

5.195 5.153 4.191 4.184 5.071 n.a. 4.409 3.687 3.625 2.996 3.418

5.925 5.525 4.593 4.190 5.263 4.798 4.783 4.071 4.037 3.353 3.763

5.919 5.582 4.640 4.609 5.394 n.a. 4.860 4.130 4.098 3.410 3.797

4.686 3.686 3.265 3.250 3.825 n.a. 3.054 2.614 2.622 2.115 2.174

6.493 5.753 4.752 4.749 5.593 5.131 4.987 4.181 4.153 2.931 2.397

3.805 3.350 2.726 2.675 3.092 2.829 2.867 2.443 2.423 1.916 2.146

UK

Greece

Italy

Spain

Ireland

Luxembourg

6.748 6.069 4.742 4.513 4.794 n.a. 3.342 3.068 3.300 2.987 2.931

11.270 8.090 6.165 5.500 5.359 4.789 4.660 3.628 3.617 2.868 3.256

9.314 6.050 4.205 4.728 4.922 n.a. 4.427 3.645 3.664 2.972 3.473

7.426 5.441 4.108 3.771 4.144 n.a. 3.966 3.308 3.280 2.722 2.839

5.405 4.719 3.680 3.617 4.354 n.a. 4.008 3.306 3.261 2.663 2.996

5.137 4.821 3.714 3.158 3.908 n.a. 4.066 2.511 3.158 2.477 3.051


5.338 4.345 3.190 3.270 4.108 n.a. 4.233 2.957 2.990 2.294 2.766

3.306 4.258 3.213 3.157 3.478 3.361 3.622 2.992 2.978 1.624 1.793

Portugal United States 7.704 5.297 4.388 3.605 4.460 n.a. 3.907 3.106 3.093 2.681 3.164

3.150 3.108 2.576 2.765 2.981 2.483 2.282 2.192 2.337 2.358 2.634

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Table A4.2

Fixed effects model with instrumental variables

Fixed-effects (within) IV regresssion Group variable: varland R-sq:

within = 0.3749 between = 0.1765 overall = 0.1507

Coef.

gdpcap –. .0909136 L1. –.043141 interestnew .0180565 population∼h L1. .0085948 mortgdebtr∼o L1. .0262298 costfin –.0290365 _cons 3.434435

102 15

Obs per group: min = avg = max =

1 6.8 9

Wald chi2(6) Prob > chi2

corr(u_i, xb) = –0.5152 Owner

Number of obs = Number of groups =

Std. Err.

z

P>|z|

= 6.80e+06 = 0.0000

[95% Conf. Interval]

.0769655 .0724821 0.136501

1.18 0.238 –.059936 –0.60 0.552 –.1852032 1.32 0.186 –.0086972

.2417632 .0989213 .0448101

.0047537

1.81 0.071 –.0007223

.0179118

.0163827 .0137941 .2639153

1.60 0.109 –.0058797 –2.10 0.035 –.0560724 13.01 0.000 2.91717

–.0583392 –.0020006 3.951699

sigma_u .21459137 sigma _e .01597688 rho .99448737 (fraction of variance due to u_i) F

test that all u_i = 0:

F(14,81) = 481.92

Prob > F = 0.0000

Instrumented: gdpcap L.gdpcap interestnew Instruments: L. populationgrowth L.mortgdebtratio costfin interestnew gdpcap L.gdpcap Source: Authors’ calculations.

Notes 1. The authors wish to thank Dieter Gstach for his valuable comments. 2. Additionally, in a few cases the explanatory value of institutional factors is acknowledged, but not integrated into the econometric modeling. Additionally, innovations in the area of housing finance and institutional factors facilitating or hindering the access of households to housing finance are mostly neglected. 3. Apart from the fact that also most European economies showed a strong upward trend in house prices over recent years, which seems to follow the trend observed in the US in the past, European economies were also affected by the financial crisis of the US because of the strong interrelations of banks


4. 5.

6.

7. 8.

9. 10.

11.

12. 13.

14. 15.

16.

and financial institutions on international financial markets (see Shiller, 2008, p. 8). See the discussion in Czerny and Wagner, 2003. This field of research includes numerous studies, which try to connect household characteristics, such as income, with the impact of interest rates. This is done especially in the light of the subprime market in the US analysis. See, among others, Barth et al., 2008; Bicakova and Sierminska, 2008; Weicher, 2007. See, in this respect, the discussion of economic analysis on the impact of interest rates on homeownership in Painter and Redfearn (2002, pp. 245ff and pp. 259ff). Zhu (2006) focuses in his empirical analysis on the impact of housing finance for house prices. Homeownership rates are not investigated. Furthermore, Mishkin (2007, p. 381) claims that ‘the innovations we have been seeing in mortgage markets have the potential to weaken the response of overall aggregate demand to changes in income driven by monetary policy, thereby altering the overall transmission of monetary policy shocks to the economy.’ These causalities presented can be interpreted as evidence of the important role of consumer spending and household debt for the macroeconomic development of the US economy in recent years. Innovative mortgage products enabled a continuous increase in household debt and facilitated economic growth via the transmission channel. See among others Levine, 1997, 2002; Allen and Gale, 2000. The models of Poterba, 1984 and Van den Noord, 2005, analyzing the impacts of taxation on house prices, are used as a starting point for the modeling in this chapter and are described in more detail below. Esteban and Altuzarra (2007) point clearly in their study on the Spanish housing market at the specific cultural and sociological dimension of the sector, which affects housing demand. In the case of Spain, on the one hand the demand for secondary housing is comparatively high, while on the other the rental sector is not highly developed, so that households lack any real choice between homeownership and renting a home. Sometimes 15 countries are included, depending on the model used. We applied such a restricted time horizon as we wanted to use balanced panels within the calculations. Starting earlier and employing interpolation or other estimation techniques of unbalanced panels seemed not that favorable to us as we wanted to test for ‘real’ influencing factors to the size of ownership rates. Furthermore, within the last 10 years the harmonization of interest rates and debt statistics took place, data of earlier years are even less harmonized. For the equations used to calculate ia and then if , see the Annex. The negative coefficient with GDP per capita is also found by other papers. For example, also Fisher and Jaffe (2002) found in their cross country data that homeownership rates are negatively correlated with GDP per capita. Another possibility for dynamic panels are the so-called pooled mean group estimators (PMG) for dynamic panels (Pesaran et al., 1999) where intercepts, short-run coefficients and error variances are allowed to differ across groups but the long-run coefficient is fixed. Similar to the dynamic GMM these models also did not seem suitable for our situation as the time horizon is quite short.

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References Allen, F. and Gale, D. (2000) Comparing Financial Systems, Cambridge, MA: MIT Press. Amann, W. (2000) Schwerpunkt Subjektförderung. Auswirkungen und Optionen einer substantiellen Mittelverlagerung, Endbericht, FGW Publishing. Amann, W. (2005) Die Zukunft der Wohnbauförderung Chancen und Perspektiven in den Bundesländern, FGW Publishing. Anas, A. and R. Arnott (1997) Taxes and Allowances in a Dynamic Equilibrium Model of Urban Housing with a Size-quality Hierarchy, Regional Science and Urban Economics, Vol. 27, 547–80. Barth, J., T. Li, T. Phumiwasana, and G. Yago (2008) Perspectives on the Subprime Market, Miljen Institute, www.srrn.com Bicakova, A. and E. Sierminska (2008) Mortgage Market Maturity and Homeownership Inequality among Young Households: A Five Country Perspective, DIW Discussion Papers 778. Bourassa, S. and M. Yin (2006) Housing Tenure Choice in Australia and the United States: Impacts of Alternative Subsidy Policies, Real Estate Economics, Vol. 34, 303–28. Catte, P. et al. (2004) Housing Markets, Wealth and the Business Cycle, OECD Economics Department Working Papers, No. 394, OECD Publishing. Colton, K.W. (2002) Housing Finance in the United States: The Transformation of the US Housing Finance System, Joint Centre for Housing Studies Harvard University W02-5, July 2002. Commission of European Communities (2007) White Paper on the Integration of EU Mortgage Credit Markets, COM (2007) 807 final, http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2007:0807:FIN:EN:PDF Committee on the Global Financial System (2006) Housing Finance in the Global Financial Market, CGFS Working Group Report, No. 26, BIS Publishing. Czerny, M. (2001) Wohnungswirtschaft vor neuen Herausforderungen, Vienna: WIFO Publishing. Czerny, M. and K. Wagner (2003) Structural Factors in the Austrian Housing and Real Estate Market, Focus on Austria, 3/2003, OENB, http://www.oenb. at/en/img/foa_20033_tcm16-8301.pdf ECB (2003) Structural Factors in the EU Housing Markets, ECB Publishing. ECB (2009) Housing Finance in the Euro Area, ECB Occasional Paper, No. 101, March. Egert, B. and D. Mihaljek (2007) Determinants of House Prices in Central and Eastern Europe, BIS Working Paper Series, Working Paper No. 236, September 2007, BIS Publishing. Esteban, M. and A. Altuzarra (2007) A Model of the Spanish Housing Market, Paper presented at the Macro Workshop of the Boeckler Stiftung, Berling, October 2007. European Mortgage Federation (2007a) Hypostat 2006, Annual Report. European Mortgage Federation (2007b) Factsheet Germany 2007. European Mortgage Federation (2008) Hypostat 2007, Annual Report. European Securitisation Forum (2006) ESF Securitisation Data Report, Spring 2006. Fisher, L.M. and A.J. Jaffe (2002) Determinants of International Home Ownership Rates, paper proposed to be presented at the 7th Annual Conference of the

Elisabeth Springler and Karin Wagner 83 Asian Real Estate Society jointly held with the American Real Estate and Urban Economics Association at Seoul, Korea, 4–6 July, 2002. Florida, R. (1986) Overview, in: Florida, R. (ed.), Housing and the New Financial Markets, State University of New Jersey, Centre for Urban Policy. Frankel, A. (2006) Erstklassig oder auch nicht: Finanzierung von Wohneigentum in den USA im neuen Jahrhundert, BIS Quarterly Review, March 2006, BIS Publishing. Fukao M. and M. Hanazaki (1986) Internationalisation of Financial Markets: Some Implications for Macroeconomic policy and for the Allocation of Capital, Economics Department Working Paper, No. 37, OECD, Paris. Girouard, N. et al. (2006) Recent House Price Developments: The Role of Fundamentals, OECD Economics Department Working Papers, No. 475, OECD Publishing. Goodhart, C. and B. Hofmann (2008) House Prices, Money, Credit and the Macroeconomy, ECB Working Paper Series, No. 888, April 2008, ECB Publishing. Goulder, L. (1989) Tax Policy, Housing Prices and Housing Investment, Regional Science and Urban Economics, Vol. 19, 281–304. Green, R.K. and K. Vandell (1999) Giving Households Credit: How Changes in the US Tax Code Could Promote Homeownership, Regional Science and Urban Economics, Vol. 29, 419–44. Green, R.K. and S.M. Wachter (2007) The Housing Finance Revolution, paper presented at the Symposium of Housing, Housing Finance and Monetary Policy at the Federal Reserve Bank of Kansas City, 30 August–1 September, 2007. Hoeller, P. and D. Rae (2007) Housing Markets and Adjustment in Monetary Union, OECD Economics Department Working Papers, No. 550, OECD Publishing. International Bureau of Fiscal Documentation (IBFD): European Tax Handbook, years 1996 to 2000 and 2002 to 2006. International Union for Housing Finance (n.d.) Fact Sheet USA, www.housingfinance.org. IMF (2008) World Economic Outlook, Housing & Business Cycle, April 2008. Lea, M. (2001) Overview of Housing Finance Systems, International Housing Finance Sourcebook 2000, International Union of Housing Finance Publishing. Levine, R. (1997) Financial Development and Economic Growth: Views and Agenda, Journal of Economic Literature, 35 (2), 688–726. Levine, R. (2002) Bank-Based or Market-Based Financial Systems: Which Is Better?, Journal of Financial Intermediation, 11(4), 398–428. Miles, D. (1994) Housing, Financial Markets and the Wider Economy, Chichester, New York, Brisbane, Toronto, Singapore: John Wiley & Sons. Mishkin, F. (2007) Housing and the Monetary Transmission Mechanism, Working Paper Finance and Economics Discussion Series, Division of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Mooslechner, P. (1994) Das System der Wohnbaufinanzierung in makroökonomischer Perspektive, in: Korinek, K. and E. Nowotny (eds), Handbuch der gemeinnützigen Wohnungswirtschaft, Wien: Orac, pp. 185–204. OECD (a) The tax-benefit Position of Production Workers, OECD Publishing, editions 1996 and 1997.

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OECD (b) The tax-benefit Position of Employees, OECD Publishing, editions 1998 and 1999. OECD (c) Taxing Wages, OECD Publishing, editions 2000 to 2006. OECD (2008) Economic Outlook, Vol. 2008/1 No. 83, June, OECD Publishing. Office of Federal Housing Enterprise Oversight (2008) House Price Index, Report 2nd Quarter 2008, http://www.ofheo.gov/media/pdf/2q08hpi.pdf Painter, G. and Redfearn, C. (2002) The Role of Interest Rates in Influencing LongRun Homeownership Rates, Journal of Real Estate Finance and Economics, 25(2/3) 243–65. Pesaran, M.H., Y. Shin and R.P. Smith (1999) Pooled Mean Group Estimation of Dynamic Heterogeneous Panels, Journal of the American Statistical Association, 94, 621–34. Poterba, J.M. (1984) Tax Subsidies to Owner Occupied Housing: An Asset Market Approach, Quarterly Journal of Economics, 99 (4), 729–52. Poterba, J.M. (1991) House Price Dynamics: The Role of Tax Policy and Demography, Brookings Papers on Economic Activity, Vol. 2, 143–203 (including discussion). Pozdena, R.J. (1988) The Modern Economics of Housing, Westport, Connecticut: Quorum Books. RealtyTrac (2009) U.S. Foreclosure Market Report, February 2009, www.realtytrac.com Shiller, R. (2008) Understanding Recent Trends in House Prices and Homeownership, Paper presented at the Symposium of Housing, Housing Finance and Monetary Policy, at the Federal Reserve Bank of Kansas City, 30 August–1 September, 2007. Springler, E. (2008) Wohnbaufinanzierung aus volkswirtschaftlicher Sicht, in: Lugger, K. and M. Holoubek (eds), Die österreichische Wohnungsgemeinnützigkeit ein europäisches Erfolgsmodell, Wien: Manz, pp. 281–91. Stagel, W. (2004) Wohnbauförderung und Wohnversorgung im internationalen Vergleich, ISW Endbericht, im Auftrag der oö. Landesregierung, Ressort Wohnbau. Tsatsaronis, K. and H. Zhu (2004) What Drives Housing Price Dynamics: Cross Country Evidence, BIS Quarterly Review, March 2004, BIS Publishing. Van den Noord, P. (2005) Tax Incentives and House Price Volatility in the Euro Area: Theory and Evidence, Économie international, Vol. 101, 29–45. Volk, B. (2008) Rmbs vs. Covered Bonds, in: European Covered Bond Council (ed.) European Covered Bonds Factbook. Warnock, V.C. and F.E. Warnock (2007) Markets and Housing Finance, www.srrn.com Weicher, C. (2007) The Long and Short of Housing: The Home Ownership Boom and the Subprime Foreclosure Bust, Networks Financial Institute Policy Brief, 2007PB-09, www.srrn.com Weller, C. and K. Sabatini (2007) On Shaky Ground: the US Mortgage Boom and its Economic Consequences, unpublished working paper, presented at the 11thconference of the research network Macroeconomic Policies of the Boeckler Stiftung, October. Wolswijk, G. (2005) On Some Fiscal Effects on Mortgage Debt Growth in the EU, European Central Bank Working Paper, No. 526, September. Zhu, H. (2006) The Structure of Housing Finance Markets and House Prices in Asia, BIS Quarterly Review, December, BIS Publishing.

5 The Rental Housing Market Dieter Gstach1

5.1 Introduction This chapter surveys some recent scholarly literature about the macroeconomic role of housing with regard to its contribution to an understanding of rental housing. It will also present major stylized facts about rental housing in the form of simple statistics, partly collected from other sources, partly compiled from the most recent wave of EU-SILC microdata collection for 2006. Together this material constitutes an informed starting point for a discussion about the potential role of rental housing in macroeconomic analysis. The focal point of the following account is the question of whether or not it is justified to proceed by assuming, like in Ortalo-Magné and Rady (2004), that ‘(t)here is no rental market.’ To increase contrast, the material to be presented will be organized according to two working hypothesis: 1) Renters and homeowners differ significantly in economic terms. 2) Rising house prices exert a negative long-run impact upon aggregate consumption via rising rents. The reputation of rental housing is not very good. TV gossip shortly before Michael Jackson’s death revealed that he even had to move into a rental unit.2 The stress was on rental, implying that the tenure type as such constituted the disaster, irrespective of the value of housing services associated with this rental. This reputation contrasts with the typical asset market approach to housing where it is assumed that individuals in equilibrium would be indifferent between renting or owning. In industrialized countries housing markets typically consist of four thirds:3 Two-thirds of owner-occupiers, one-third of landlords renting out and, as ‘inevitable byproduct,’ one-third of renters. This classification is related to but not identical to the fact that housing is, simultaneously, 85

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an investment and a consumption good. However, in the ongoing discussion about the build-up and subsequent burst of the housing bubble the fourth third is rarely mentioned as if it were not directly affected and, at any rate, played no role in transmitting house price shocks to the wider economy. This is surprising for two reasons: One is that in the consumption demand perspective one may suspect changing house prices to translate into reverse changes in non-housing consumption because of lack of substitutability and strong income effects of housing consumption; second, because the burst of the housing bubble particularly affects renter households. More precisely: Those who were at the verge of ownership before mortgage markets deepened and lending standards were relaxed and who turned into owners afterwards. Clearly, these are exactly the households who are now, after the subprime crisis started to unfold, in greatest danger of arrears and foreclosures. The latter point is stated very clearly in Leamer (2007). It should be added, that among renter households, those at the verge of ownership are the ones relatively well off. Arguably, also most of the other renters would choose ownership, if only they were found creditworthy.4 This preference in practice arises, apart from idiosyncratic motives, simply because tenants through their term of lease must pay also for the landlords’ profit margin upon the house price itself, while this margin is based on a desired amortization period much shorter than a renter’s lifespan. This means, as often stated, that it is not the lack of income that prevents these renters from owning but credit constraints. The remainder of this chapter is organized as follows: In the next section some recent contributions to the literature about the macroeconomics of housing are briefly surveyed with respect to their rental market content. Section 3 discusses relevant data to describe rental markets and associated conceptual and availability problems. Section 4 presents some stylized facts based on EU-SILC data about differences between tenants and owners and the role of the rental in tenant household expenditures and Section 5 summarizes.

5.2 Selected macroeconomic literature A recent literature review on ‘Macroeconomics and housing’ by Leung (2004) suggests that the rental market is of no macroeconomic relevance: No hint regarding its mere existence can be found in the text. But also the other articles of volume 13/4 of the Journal of Housing Economics dedicated exclusively to the macroeconomics of housing, feature such an implicit

Dieter Gstach 87

irrelevance theorem. The situation improves only marginally in the special issue 24/1 of the Oxford Review of Economic Policy with the same focus. From the key article ‘Housing Markets and the Economy: The Assessment’ by Muellbauer and Murphy (2008), a distinct macroeconomic role of the rental sector cannot be inferred. The majority of the relevant literature is of an econometric nature, and studies particularly potential real estate wealth or collateral effects and, more generally, the monetary transmission mechanism in the face of changing housing market conditions. See e.g. Case (1992), Girouard and Blöndal (2001), Boone and Girouard (2002), Catte et al. (2004), Bernanke (2007), Mishkin (2007), André and Girouard (2008), Karakitsos (2008) and IMF (2008). The dominant impression arising from this rather scattered literature is that increasing house prices have positive effects on aggregate consumption and that the interest sensitivity of output has increased substantially along with mortgage market liberalizations. Only exceptionally, like in Kiss and Vadas (2005), is the possibility of a distinct macroeconomic role of the rental sector mentioned. Theoretically, as has been repeatedly noted, the impact of increasing house prices upon consumption is unclear and could as well be negative. See e.g. Catte et al. (2004), Al-Eyd et al. (2005) or Attanasio et al. (2009). The last point out that the observed co-movements of house prices and consumption might be due to common causality rather than anything else. This at least leaves the theoretical possibility of a relevant rent channel, through which rising house prices could ultimately unfold a dampening effect upon aggregate consumption. In Sheiner (1995) the working of such a channel has been studied by estimating the savings response of renters to rising house prices. As Sheiner argues, theoretically this response could be positive or negative: Positive, because due to rising house prices renters would be forced to increase savings for a larger future down-payment. Negative, because renters could also react with delaying the purchase of a house or skipping this idea completely. Her estimation results, based on micro-data from the 1984 US panel study of income dynamics, provide strong evidence for significantly increased savings of renters as a result of house price appreciation. As no evidence of a significant real estate wealth effect was available at that time, Sheiner conjectured that the net impact of rising house prices upon savings would be positive as well. In other words, the macroeconomic effects resulting from renters’ response dominates those from owner-occupiers. Sheiner might correct her assessment in the light of today’s evidence about positive collateral effects of rising house prices, but this would leave the relevance of her primary results untouched.

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Another form of such a rental channel is studied empirically in Gstach (2007) based on panel data for several OECD countries over the 1970– 2004 period. Starting point of this study is a typical consumption function specification found in the above-cited literature on real estate wealth effects. This is augmented by real rental rates, rented market shares and an interaction term of these two variables (to allow for country specific results) to test for the significance of rental rates in explaining consumption. The results provide robust evidence for a negative contribution of rising rents upon consumption, coexisting with a significantly positive wealth effect quantitatively in line with the above-cited literature. Furthermore, assuming long-run proportionality between changes of rents and house prices (for given interest rates and other user cost relevant variables), his results indicate negative net effects from rising house prices upon aggregate (!) consumption, albeit insignificant ones for typical rented market shares. Similarly, Muellbauer and Murata (2008) find a negative long-run impact of land prices upon consumption for Italy and Japan. But they view the illiquid mortgage markets in these countries as ultimately responsible for this negative net effect, rather than a rental channel. A serious challenge for the analysis of a potential macroeconomic role of rental housing is the slow response of rental rates to changing house prices (see e.g. Muellbauer and Murphy, 2008). This implies for house prices as triggering variable, that the role of rental housing in the transmission of such changes to the wider economy could be revealed only in a long-run perspective. On the other hand, various policy measures influence rents rather more directly. For example, rent regulations, as described for major European countries in Haffner et al. (2007), which affect also sitting tenants, exert their influence comparably fast. This would make estimation of macroeconomic effects associated with changing rental market conditions possible without the need to assume questionable long-run equilibrium relationships. But such attempts would be subject to another major challenge: The notoriously poor quality of available rental data. This is stated for example in Muellbauer and Murphy (2008), who question the strategy of testing the existence of a house price bubble by analyzing the priceto-rent ratio. But while the deficiencies of housing related statistics for the owner-occupied segment are well documented (see e.g. the contributions of Murphy, 2008 and Eiglsperger, 2008 in this volume), the lack of reliable and internationally comparable data on rental housing markets is rarely discussed. So I conjecture that the statement by Leamer (2007) about the macroeconomics of housing, namely that there is ‘. . . too much theory and not enough data’, applies in particular to rental housing.

Dieter Gstach 89

However, such data are necessary to test whether indeed rental housing is irrelevant for macroeconomic analysis as this survey of some recent relevant literature suggests. Given the general neglect of rental rates in actual consumption function specifications in the applied macroeconomic housing literature, the latter displays a surprisingly widespread awareness of the negative impacts of rising house prices upon renters. Muellbauer and Murphy (2008), for example state the redistributing effects of rising house prices ‘towards the haves from the have-nots’, where the latter are meant to include renters. The same point is also made in Leamer (2007), who explicitly talks about the undesirable redistribution from renters to current homeowners in the course of rising house prices. Edelstein and Lum (2004) also discuss the ambiguity of wealth effects. As they note ‘. . . house price inflation widens the real income gap between the “haves” and the “have-nots” but the distributional effects depend on relative numbers of private and public housing owners and renters.’ Similarly in Goodhart and Hofmann (2008) who claim that a permanent increase in house prices ‘works in favor of a positive wealth or collateral effect (. . .) on consumption’, because of the ‘asymmetry between gainers and losers’, the latter being smaller and consisting of tenants and first-time buyers. However, this awareness only goes half-way towards a macroeconomic conception of rental housing, because it does not include the possibility of a significant feedback from renters to aggregate consumption. A potential feedback channel of rental housing upon the wider economy is related to the size of this market: When home ownership is associated with less mobility, as found for example in Barcélo (2006), higher rented market shares might contribute to lower overall unemployment. Consequently, as indicated e.g. in Henley (1998), ECB (2003) or OECD (2005), one must ask whether the actual size of rental markets is too small compared with what should be regarded as healthy for labor mobility. As Oswald (1997) formulates it, ‘the large rise in European home-ownership may be the missing piece of the unemployment puzzle.’ Unfortunately a serious quantification of this potential feedback is a demanding econometric exercise, which may explain why it does not exist hitherto. The relationship between house prices and rents is a regular topic in housing research and is usually tackled from the perspective of capital asset pricing. In this perspective house prices equal the discounted present value of market rents under arbitrage in perfect capital markets. This suggests a rather simple relationship between the two variables and calls for a consideration of rents as additional explanatory variable in house price determination. This was stated earlier for example by

90


Muellbauer and Murphy (1997). As they note, ‘(t)his is an issue future modellers will need to address’ (p. 1722). But unanimous findings about this relationship exist only for the short run and these clearly reject the idea of proportionality between house prices and rents. It remains dubious, however, whether the often found stickiness of rents is the reason for, or merely a reflection of, this lacking proportionality. According to Meen (2006), it may be due to the fact that discount rates in the user cost formula are likely to be time-varying and that nominal interest rates affect house prices too. But also for the long run corresponding problems exist. To remedy the latter Murphy (2008) suggests that a ‘measure of credit constraints should also appear in equilibrium housing rent to house price equations.’ Looking at the relationship between house prices and rents from reverse angle it is tempting to speculate that rental prices will simply follow house prices, at least in the long run. But the corresponding cointegration analysis of Gallin (2004) does not yield clear-cut results in this respect. This calls for the introduction of a separate rental rate variable in econometric models along with house prices as in Gstach (2007), where both variables turned out to be significant in the long run also. A recent study on the economically relevant differences between owners and renters and wealth and income distributions within these two groups is Bicáková and Sierminska (2007). They investigate microdata from the Luxembourg Wealth Study and confirm earlier findings about the relative economic disadvantage of renters compared to owner-occupiers. Regarding the economic behavior of these two groups, Mishkin (2007) notes that they would change their spending in similar ways in response to movements in house prices. This clearly contradicts the findings by Sheiner (1995), cited above. The increasing importance of the land component in house prices is discussed explicitly in Davis and Heathcote (2007) for the US and, more cursorily, also e.g. in Mishkin (2007). Of course, this issue also applies to rentals. But here this change appears in the form of rising economic rents, usually the main reason for rent controls as stated e.g. ECB (2003). For homeowners, instead, this land component in house prices is a different matter owing to their simultaneous ownership of the rent-creating factor and consumption of its services. So, particularly in the rented market segment, this issue calls for a distinct treatment of land and structure in the analysis of supply side effects associated with house price/rental rate changes. Rent indexation, i.e. the automatic increase of rentals in line with the CPI, is analyzed in Gstach (2006). It is shown, unsurprisingly, that

Dieter Gstach 91

such indexation has an inflation accelerating consequence, which is reinforced by the use of imputed rents in the CPI. Rent controls in many countries were changed in the past to allow for more widespread use of this instrument. At the same time the comparable instrument of wage indexation has been largely abolished, also because of ample evidence about the undesirable consequences of such indexation. While the attention of economists at the moment may be rather more attracted by deflationary developments, the issue of rent indexation certainly deserves further analysis. Unlike in the above-cited macroeconomically oriented literature, rental markets are covered well in the microeconomic literature, populated by rational, optimizing agents. One strand of this literature has analyzed the (in)efficiency of rent control, see for example the overviews of Arnott (1998) or Klappholz (2004). Another currently more active branch of this literature investigates the role of credit constraints and the question of tenure choice. See e.g. the recent related contributions of Davis and Heathcote (2005), Jeske (2005), Ortalo-Magné and Rady (2005), Finocchiaro and von Heideken (2007), Silos (2007) or Calza et al. (2007). But the key question of the feedback of rental rates and rented market shares upon the wider economy is not tackled. It should also be noted, that in these models after removal of credit constraints a true preference for owner-occupation rather than rented accommodation requires the introduction of some market distortion. Of course this can be found easily in reality in form of the widespread subsidization of home ownership, for example, through deductibility of mortgage interest payments and/or renunciation of taxing imputed rents. Recent accounts of this are found in Wolswijk (2008) and Springler and Wagner (2008). Also this issue has been analyzed in a long series of papers initiated by Poterba (1984). But this does not capture the essential argument from the introductory section about the higher relative price of rental housing.

5.3 Data issues The main variables to characterize rental housing markets are rents, rented market shares, tenant turnover and vacancy rates of rental units. The relevance of each of these and some related variables is discussed below. Recent figures for these variables will be presented along the way to put things into perspective. The treatment of turnover rates for the two market segments is postponed to the next section, while rent-to-income ratios will be discussed here and in the following section.

92


5.3.1 Rental rates Despite their obvious significance, rental rate data comparable across time and countries are in fact hard to find. Country-specific housing market regulations gave rise to rather different national practices of defining the relevant cost items to be included in the rental measure. Furthermore, in many countries these regulations have changed considerably in recent decades. Whether it is energy, property management, parking space or other accommodation related items, these have been treated differently across time, as many breaks in national series tell, and across countries, as the ILO tables about national definitions of rental costs highlight. The problem of controlling adequately for the quality of housing, at any rate, cannot be used as explanation for this poor state of affairs, because various proposals for dealing with this issue can be found in the extensive literature covering the same problem for owner-occupied housing (see e.g. the discussion in Murphy, 2008). But there are also reasons to expect data availability on rental rates to be actually better than for house prices: The frequency with which the rental for an individual unit changes is much higher than for owneroccupied units. This has two reasons: First, the typical tenure duration of renters is considerably shorter than that of owner-occupiers. According to the EU-SILC data, for example, the median is seven years for renters compared with 18 years for owner-occupiers. This implies a turnover rate in rental units of almost three times that of owner-occupied units. Secondly, typical tenancy durations exceed the duration of the underlying rental agreements, which may be renegotiated after extension of each single contract. So, present rental housing market conditions are priced into monthly rates even faster than the typical tenure duration suggests. If at all, rental rate data are usually available only in index form containing no level information. This lack of level information, a typical feature also of house price statistics, can either be ignored by talking about growth rates only, or, with panel data covering several countries, by assuming some sort of structural homogeneity across national economies and addressing cross-country differences in price levels via unobserved components techniques. The latter approach is particularly useful because it stands to reason that cross-country differences in rental levels may affect non-housing consumption. Alternatively, rent-to-income ratios could provide relevant real level information for comparative international studies. For some countries this information is provided below. The OECD, the ECB and the BIS compile series for house prices that are regularly used in relevant studies, despite some lack of international comparability. But, to my knowledge, the only source for a listing of

Dieter Gstach 93

rental price indices for many countries over more than just a few years is the ILO. The ILO compiles nominal rental rate series as table 7F in the consumer prices category with yearly coverage typically starting 1970. The sources of these data are the national statistical offices, which publish domestic rental rates in one form or another. Although the ILO tries to harmonize the national series by occasionally choosing indices of more appropriate subcategories of ‘rent’, the extensive documentation reveals remaining shortcomings of the so constructed indices. Figures 5.1 and 5.2 show these ILO data as yearly growth rates of the deflated series (by country-specific harmonized index of consumer prices – HICPs), i.e. real rental growth: 12 EU countries are displayed for

US UK JP

4

2

0 ⫺2 1990 Figure 5.1a

1995

2000

2005

Yearly growth rates of real rents in per cent (US, UK and Japan)

Source: ILO data.

DE IT ES

4

2

0 ⫺2 1990

1995

2000

2005

Figure 5.1b Yearly growth rates of real rents in per cent (Germany, Italy and Spain) Source: ILO data.

94


the 1987–2007 period. The period 1970–1987 (not shown) was characterized by much higher volatility of real rental rates, but this is about the only commonality across countries to be found for this period. As the figures show, volatility of real rental rates has generally decreased further over the 1980s and the 1990s. The Figure 5.1a shows data for the US, the UK and Japan. What distinguishes them from the other countries is the consistent rise of real rental rates in the years around 2000 and an almost uninterrupted rise in real rentals for the whole period considered. The data for Germany, Italy and Spain are displayed in Figure 5.1b. In these countries there has been a

FR NL AT

4

2

0 ⫺2 1990

1995

2000

2005

Figure 5.2a Yearly growth rates of real rents in per cent (France, Netherlands and Austria) Source: ILO data.

CA DK CH

4

2

0 ⫺2 1990

1995

2000

2005

Figure 5.2b Yearly growth rates of real rents in per cent (Canada, Denmark and Switzerland) Source: ILO data.

Dieter Gstach 95

massive increase in real rents in the 1990s until about 2000 with yearly growth rates exceeding 4 per cent followed by little change thereafter. The striking commonality of the group of countries including France, the Netherlands, Austria, shown in Figure 5.2a, and (not shown) the Nordic countries is a more or less consistent and significant increase of real rents up to 2000 followed by marked slump, contrary to developments in the first group described above. This common feature is surprising because these countries are rather heterogeneous. The housing market characteristics of the Netherlands, for example, are usually described as more similar to those of the UK than to those of France. The French housing market in turn is usually considered incomparable to that of the Nordic countries. Figure 5.2b comprises Canada, Denmark and Switzerland, all of which exhibit comparably low variability and only a modest overall increase or even decrease in real rental rates between the mid-1990s and 2007. The case of Canada is particularly striking as it not only displays by far the lowest variability of real rentals, but also the only instance of continuous reduction of real rentals since the mid-1990s. 5.3.1.1 Price-to-rental ratio An important variable constructed via rental rates is the price-to-rental ratio. In the textbook housing market equilibrium, to be found for example in Miles (1994) or DiPasquale and Wheaton (1996), this ratio equals the reciprocal of the user cost of housing. So, deviations from this equality might be used to check for the existence of a house price bubble as suggested e.g. in Case and Shiller (2003). Even simpler instead, one might consider deviations of this variable from its own long-run trend as potential indicator for a real estate bubble, as in Ayuso and Restoy (2006). An overview of corresponding figures in this deviation form is found in André and Girouard (2008) for OECD countries. As a criterion in tenure decisions or to assess the rentability of real estate investment (for which it has to be compared with user costs) the original form is required. Girouard et al. (2006) reports corresponding figures for various countries. 5.3.1.2 Rent-to-income ratio A variable often used to address the question of affordability of housing is the rent-to-income ratio. Typical values of this ratio are around 25 per cent when disposable household income is used for comparison. More precise figures for this ratio for major EU countries derived from the EUSILC data-set will be given below. Based on evidence for stationarity of user costs (including house prices but not rents!) in the UK, Meen (1996) argues why the house-price-to-income ratio is not necessarily constant in

96


the long run. Given co-integration of rent and income, this would imply the non-existence of a stable long-run relationship between rent and income too. This is potentially relevant information for mortgage lenders as it renders simple extrapolative procedures for risk assessment flawed. 5.3.2 Rented market shares The macroeconomic literature on housing typically does not distinguish between renters, landlords and owners, although a few empirical studies cited above indeed have used rented market shares as explanatory variable. This neglect may lead to questionable results, if, for example, the consumption behavior of these groups differs significantly. This in turn could be due to various reasons such as different incomes, different composition of renter households compared with owner-households, different exposure to unemployment risk or different mortality rates, all of which have been investigated and found significant indeed. Therefore, the potential feedback of rental markets to the wider economy hinges crucially on the size of the rental market. Accounting for this in empirical analysis involving several countries, consequently requires the usage of an indicator for rental market size in the regression specification. The size of this rented market is usually expressed via the share of rented accommodations and varies widely between countries in a range of roughly 10 to 50 per cent. As documented below, rented market shares around the globe followed a secular decline in the past decades. One reason behind this development, apart from rising per capita incomes, is that economic policy in most countries fosters home ownership in various ways. This decline in rented market shares was rather steep for some countries (Italy, Spain,5 UK and Belgium) where corresponding figures dropped by more than 30 per cent between 1970 and 2007, with a more modest drop (less than 15 per cent) for many others (including the US, Canada and France). In Japan, Germany and Australia rented market shares did not change significantly during this period, while it increased considerably only in New Zealand and Finland. But, as the example of Finland nicely illustrates, figures about rented market shares should be interpreted cautiously in each case: While Catte et al. (2004) (citing the Royal Institute of Chartered Surveyors as source) report a rented market share for Finland in 2002 of 42 per cent, the Danish National agency for enterprise and housing (NAG) gives a figure of only 36 per cent for the pooled group of regular renters and all other non-owners. Furthermore, the latter source sees rented market shares in Finland in 2000 at 42 per cent, while ECB (2003) for the same year report 32. Likewise, figures reported for Germany in 2000 range from 53 per cent (Statistisches Bundesamt) to 60 per cent (ECB, 2003), while

Dieter Gstach 97

those for Sweden cover an even wider range from 39 (Catte et al., 2004) to 54 per cent (NAG). This gives an impression of the ambiguity of available data. It is not clear to what extent this problem could be remedied simply by stating proper definitions of reported data. It would help avoid some misunderstandings, for example, if it were mentioned whether rented market shares apply to households or individuals. For a typical rented market ratio of 30 per cent on a household basis and owner-occupier households’ sizes 15 per cent larger than for renters (see below), the corresponding rented market ratio would only be 27 per cent on an individual basis. Likewise, it would also be highly desirable if authors made clear whether their reported rented market shares apply to all contracts or just contracts with market rents. That this distinction is quantitatively important is illustrated by the rented market shares reported in O’Sullivan and de Decker (2007) for the competitive segment only, which, for some countries, are only a fraction of the corresponding figures in the literature cited above. In ECB (2003) it was noted that the shrinking of the rental sector may damage the functioning of the whole housing market. So it could be seen as good news, that the long downward trend in rented market shares in quite a few countries has come to a halt or even showed signs of reversal. Clearly, the evidence in this regard must be cautiously interpreted in the light of the above-mentioned data problems. Nevertheless, taken at face value, the figures for the recent 10 years suggest that this trend break in rented market shares applies for example to Denmark, Belgium (Ball, 2005), Spain (European Commission, 2005, p. 50), Japan, Norway, Australia (Catte et al., 2004) and the US (JCHS, 2007). According to Census Bureau data, for example, the US rented market share reached an all time low of 30.8 per cent in the fourth quarter of 2004 and since then increased again (32.2 per cent in first quarter 2008). 5.3.3 Vacancy rates The indicator most directly reflecting relative strength of demand and supply in housing markets are vacancy rates, which is true for owneroccupied housing and rental housing alike. Meen (2006), citing a study by Evans and Hartwich (2005) and the US Census Bureau as source, provides some vacancy rate estimates for the rental market: UK 3.4 per cent, Germany 8.2 per cent, France 6.8 per cent, Italy 20 per cent, US 10 per cent. It should be added that these vacancy rates often display significant volatility, as a look at the Census Bureau figures shows. This high volatility in the case of the rental market also reflects the sluggish price response (particularly downward) upon changing demand

98


conditions in this market. As Leamer (2007) points out, a strong rental market does not suffice to prevent a slowdown in new construction, when house prices tumble. Rather, changes of the vacancy rate seem to absorb much of the short-run fluctuations in demand. So, using the rate of new construction of rental units instead of vacancy rates may give a highly misleading impression of current market conditions. Unfortunately, internationally comparable vacancy rates for extended periods of time are compiled nowhere, leaving yet another important statistical job for the future.

5.4 Stylized facts from EU-SILC data This section presents some stylized facts about renters and rental housing based on the most recent EU-SILC micro-data for 2006. This data set about social indicators and living conditions (SILC) also contains a useful set of tenure related information. While EU-SILC data are collected in all EU member states, the former eastern European countries as well as some special cases like Luxembourg were skipped to improve data comparability. This yielded data covering 19 countries with a total sample of roughly 80 000 households consisting of 230 000 individuals. The average rented market share on a household basis in this sample is around 25 per cent, significantly lower than the average figure of around 30 per cent, which can be calculated from the figures reported by national statistical offices. This indicates a slight bias of the sampling design towards owner-occupiers. 5.4.1 Renters vs. owners Income differences between renters and owners are the key distinguishing feature of these two groups from an economic point of view. As EU-SILC data confirm, renter households on average have significantly lower disposable incomes than owner-occupiers. As Figure 5.3 shows the ratio based on the medians is roughly 3:2 between owner incomes (wide, light-shaded bars) and renter incomes (narrow, dark-shaded bars) for most countries.6 Particularly high differences are observed for the Nordic block (Denmark, the Netherlands, Sweden) where the ratio is more around 5:3 and comparably low differences for Italy and Spain. In any case, being renter rather than owner goes along with significantly lower incomes, although the causality is not clear and in fact may run in both directions. Note that a comparison of income levels for the UK and Denmark with those of the other countries is not meaningful because they are based on exchange rates from 2004 rather than on purchasing power parities for 2006.

99

Income (1000 Euro)

60 50 40 30 20 10 0 #(Owners): #(Renters):

DE 5095 4171

ES 7054 793

FR 5161 2681

IT 11459 2255

UK 4844 1457

Figure 5.3a Distribution of disposable household income (Germany, Spain, France, Italy and UK) Legend: Boxes represent the two interquartile ranges of incomes. Whiskers extend to the most extreme data points which are no more than 50 per cent of the interquartile range from the box. This typically covers around 90 per cent of the sample observations. Source: EU-SILC data.

Income (1000 Euro)

60 50 40 30 20 10 0 #(Owners): #(Renters):

AT 2238 1364

DK 2432 741

IE 2645 464

NL 4657 1924

SE 3021 1381

Figure 5.3b Distribution of disposable household income (Austria, Denmark, Ireland, Netherlands, Sweden) Legend: Boxes represent the two interquartile ranges of incomes. Whiskers extend to the most extreme data points which are no more than 50 per cent of the interquartile range from the box. This typically covers around 90 per cent of the sample observations. Source: EU-SILC data.

100 Housing Market Challenges in Europe and the United States

It is worthwhile noting that this distinction is not only a European phenomenon. Statistics New Zealand, for example, also reports incomes by tenure type based on ‘residual income’. This is defined as disposable equivalized income after deduction of mandatory housing related costs, in particular rents and mortgage repayments. Using the corresponding values of this variable to construct the ratio between renter and owner incomes leads to very similar results compared to the above for Europe (US$31 400 for owner households versus US$22 600 for renter households in 2004). Since renters, according to EU-SILC definitions, include all kinds of non-owners, one may suspect that the subgroup of renters actually paying market rents would be much better off. However, the exclusion of subsidized renters would not change the median income of renters significantly. The exceptions to this indifference claim are the UK and Ireland, where median incomes would increase to roughly a26 000 when excluding subsidized renters compared to the a20 000 reported in Figure 5.3. This indicates an extraordinarily high level of segregation in the UK rental market, which has no parallel in mainland Europe.7 Next to income the exposure to unemployment risk is another important distinguishing criterion between renters and owners. The reference group in constructing unemployment ratios consists of the selfemployed, employed and unemployed persons (all together referred to as ‘active’ below) and excludes, according to EU-SILC definitions, retired and other inactive persons. It turns out that the unemployment ratio among active renters over all countries is around 12.9 per cent as opposed to 6 per cent for active owners. So, not only is the average income of renters far lower than that of owner-occupiers, they are also subject to an a priori risk of becoming unemployed that is more than twice as high. This finding, of course, must not be interpreted causally, as if the ownership status as such would lower unemployment. The above-cited studies on labor mobility should have made this clear. Changing perspective one finds, furthermore, that the renter ratio among the unemployed is 36.7 per cent, significantly above the average of roughly 25 per cent of renters in the overall sample. These findings strengthen the impression of economic disadvantage of renters. One might suspect that these disadvantages are in fact much smaller when taking household size into account. But as EU-SILC data tell, this leads to only minor corrections of the above picture. Owner households have average equivalized size of 1.75, slightly above the size of 1.52 of renter households, but the median size of 1.5 is the same for both tenure types.

Dieter Gstach 101

The rented market shares in the different household size classes (measured in heads) are displayed in Figure 5.4. The share of renters for the class ‘single parent households’ (SP) is displayed separately.8 The situation in southern European countries (Italy, Spain, Portugal, Greece), represented via narrow, dark-shaded bars is depicted separately from that in northern EU countries represented via the wide, light-shaded bars. The repeatedly reported different housing habits for these two groups of countries suggest this distinction. As can be seen, the idea that rented market shares would decrease monotonically with household size is wrong, irrespective of North or South. In northern EU countries about 23 per cent of households with six persons or more live in rented accommodation compared with only 17 per cent for households with only five members. In southern Europe the rented market share already starts increasing again with households of four members. EU-SILC data about rented market shares by age are shown in Figure 5.5, again distinguishing northern from southern EU countries. EU-SILC reports (at most) two responsible persons per household. If two are reported, the older person is used for the age classification of the household. As can be seen, the rented market share for the northern European countries falls with the age cohort and reaches a minimum for the group of households with head aged 50–60 years. But for the groups with older heads the rented market share starts increasing again reaching 32 per cent for those aged 80 years and above.9 It can also be seen, that 70 Percentage of renters

EU North 60 EU South 50 40 30 20 10 0 #(North, all ): #(South, all):

1 2 3 13820 18818 8200 5690 8035 6183

4 8842 5747

5 3306 1625

Household size Figure 5.4 Rented market shares by household size Source: EU-SILC data.

6⫹ 1065 598

SP 1510 276

102 Housing Market Challenges in Europe and the United States

70 Percentage of renters

EU North 60 EU South 50 40 30 20 10 0 20 #(EU–North): #(EU–South):

4274 1042

40 60 9360 12516 11531 9096 3933 5455 5584 5178

80 5556 4410

3028 2542

Age Figure 5.5

Rented market shares by age group

Source: EU-SILC data.

this U-shaped figure of rented market shares across age profiles does not apply to southern Europe. There, instead, it seems that apart from the highest age group (more than 80 years) rented market shares would be ever increasing. 5.4.2 Rent to income ratio The rent concept of EU-SILC refers to the total monthly amount paid for the use of an unfurnished dwelling used as main residence of a household. Rentals may include payments for the use of a garage in connection with the dwelling but should exclude payments for electricity, heating, repairs and maintenance. As Figure 5.6 shows, renter households (wide, light-shaded bars) typically spend around 22 per cent of disposable income on rent. Restricting attention to tenancies of less than five years duration (narrow, dark-shaded bars) reveals, that for more recent tenancies this ratio is significantly higher than the average in almost all countries, implying rising rent-to-income ratios. This rise is in the 10 per cent range with Denmark being an outlier as more recently formed rental households pay almost 30 per cent more than the average of all rental households. The decrease of the rent-to-income ratio of more recent rentals in Ireland may be explained also with the extremely high starting level compared to all other European countries. The Netherlands and Sweden did not report tenancy durations.

103

Rent/income in %

50 40 30 20 10 0 #(All): #(Dur

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