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Journal Article•DOI•

The Price Responsiveness of Housing Supply in OECD Countries

Aida Caldera1, Åsa Johansson1•
Organisation for Economic Co-operation and Development1
01 Sep 2013-Journal of Housing Economics (OECD Publishing)-Vol. 22, Iss: 3, pp 231-249
TL;DR: In this article, the authors estimate the long-run price elasticity of new housing supply in 21 OECD countries based on a stock-flow model of the housing market estimated within an error correction framework.
About: This article is published in Journal of Housing Economics.The article was published on 2013-09-01 and is currently open access. It has received 231 citations till now. The article focuses on the topics: Price elasticity of demand.

Summary (5 min read)

Jump to: [1. Introduction and main findings] – [2. A model of the housing sector] – [3.1 Econometric model] – [3.2 Data] – [4. Estimation results] – [4.1 Long-run estimates] – [4.2 Short-run estimates] – [4.3 Comparison of the findings relative to other studies] – [5. Factors influencing the responsiveness of housing supply to prices] – [6. Economic and policy implications of the price responsiveness of housing supply] – [6.1 Housing price developments] – [6.2 Labour market] – [6.3 Policy reforms to enhance supply responsiveness] and [WORKING PAPERS]

1. Introduction and main findings

  • Differences in supply responsiveness to prices are important since they determine the extent to which the housing market responds to demand side shocks with more construction or higher prices, with potential implications for the evolution of housing prices and housing affordability.
  • The error correction framework employed in the present paper takes into account this slow adjustment of housing markets.
  • In other words, house prices tend to rise faster in environments with less responsive housing supply, and the variability of house prices is also likely to be higher if the supply of housing is price-inelastic and if the demand for housing is subject to large shocks.
  • Country level estimates give a sense of differences in the overall responsiveness of housing supply across countries.

2. A model of the housing sector

  • This section presents the conceptual framework underlying the estimation of the long-run price elasticity of new housing supply.
  • One important feature of the housing market is that the housing stock adjusts slowly to changes in demand: housing investment is lumpy as building takes time and depreciation of the housing stock is slow.
  • The heterogeneity of housing generates search and transaction costs which make it difficult for households to react swiftly to price signals (DiPasquale and Wheaton, 1994).
  • Hence, stock equilibrium is achieved only in the long-run.

3.1 Econometric model

  • Building on the theoretical framework outlined above and on earlier OECD work (Hüfner and Lundsgaard, 2007; Rae and van den Noord, 2006), the following long-run price and investment equations are estimated in an error correction framework employing the Engle-Granger two-step estimation procedure (1987).
  • The resulting elasticity estimates and the country ranking in terms of responsiveness are in line with system results.
  • The explanatory variables in the investment equation include real residential construction costs (cct-1), real house prices ( ) and the same demographic variables included in the first equation ( td ), as the size and structure of the population is expected to influence the incentives to build.
  • In their seminal paper on co-integration, Engle and Granger (1987) suggest a two-step analysis to test for co-integration: estimate the co-integrating regression by ordinary least squares (OLS) in the first step and then test for a unit root in the residuals from the co-integrating regression in the second step.
  • The estimated residuals tpECT and tiECT are then included as error correction terms, in the following equations explaining the short-term evolution of prices and investment: ttt p ttttt ECTdsRyp.

3.2 Data

  • The estimation period differs across countries depending on data availability; however, the typical timeframe is from the 1980s to the mid-/late-2000s.
  • The measure of housing supply is real residential investment, more precisely defined as gross fixed capital formation in the housing sector expressed in volume indices, sourced from the OECD.
  • The other variables involved in the estimation include, real income, interest rates, population and construction costs and are sourced from different OECD databases.
  • In several countries, prices have increased by more than 90% since the early 1980s (e.g. Ireland, Spain, the United Kingdom, the Netherlands, Belgium etc.).
  • On the other hand, the increase in real house prices was accompanied by increased housing investment in several countries.

4. Estimation results

  • Tables 1 and 2 below report the estimation results for the 21 OECD countries, for which the data are available, showing the results for the long-run and short-run price and investment relationships respectively.
  • As a first step in the estimation, the order of integration of the series involved in the estimation of the long-run relationship is verified using the standard Augmented Dickey Fuller (ADF) test for the presence of unit roots.
  • Alternative Dickey-Fuller GLS tests, with higher power and better overall performance in terms of sample size than the standard ADF, yielded similar results.
  • Then, the existence of a long-run relationship between real house prices or investment and the explanatory variables in equations (7) and (8) is verified including the error correction terms derived from the long-run relationship into the dynamic regressions (9) and (10).
  • The error correction terms all have a negative sign and are statistically significant, suggesting that equations (7) and (8) can indeed be interpreted as long-run relationships.

4.1 Long-run estimates

  • Panels A of Tables 1 and 2 give the estimates for the long-run price and investment equation.
  • The alternative test based on the significance of the error correction term in the short-run equation is, therefore, used (see Banarjee et al. 1993).
  • The stock of housing is, however, positive and significant in 2/20 specifications (i.e. Spain and Denmark).
  • The housing stock tends to have a positive and significant effect on prices, in line with the theoretical prediction that increases in the stock of housing, and thus larger supply, lead to lower prices in the long-run.
  • Housing supply is particularly unresponsive to prices in Switzerland relative to other countries, about three times less responsive than in countries like France or Germany, and more than ten times less responsive than in the United States and Sweden where supply is the most responsive among the countries in the sample.

4.2 Short-run estimates

  • Turning to the short-run adjustment equations, in the price equation the growth rates of income and population have a positive effect on price inflation, while increases in the housing stock lead to house price deflation.
  • The coefficients on the error correction term in the price equation are significant and range between -0.027 and -0.776, suggesting that there are wide differences across countries in the implied speed of price adjustment.
  • The other explanatory variables are only significant for few countries.
  • These estimates imply that between 20% and over 100% of the differences between actual and equilibrium investment are closed within a year depending on the country.
  • There appears to be a positive relationship between the price-responsiveness of housing supply and the speed of adjustment of investment for countries with a very responsive housing supply (e.g. the United States and Sweden) and for countries with a low responsiveness of housing supply (e.g. Switzerland and Belgium).

4.3 Comparison of the findings relative to other studies

  • Various studies have estimated the price elasticity of housing supply for a reduced number of countries (See e.g. Vermeulen and Rouwendal, 2007 for a review), mostly focusing on the United States and the United Kingdom.
  • Even so, evidence from other studies can serve as a rough benchmark for the estimates of the long-term elasticity of supply reported here.
  • Coefficient on the error correction term in the short-run investment equation.
  • It suggests that housing supply is particularly inelastic in these countries, translating into poor supply responses to demand shocks and strong rises in house prices (e.g. André, 2010; Barker, 2004; Vermeulen and Rouwendal, 2008).
  • Evidence for this group of countries is only available for New Zealand and Spain.

5. Factors influencing the responsiveness of housing supply to prices

  • Cross-country differences in the long-run price responsiveness of housing supply can be related to both policy and non-policy factors.
  • Figure 5 shows that housing supply responsiveness tends to be lower in countries where it takes longer to acquire a building permit.10 10 This correlation is robust to controlling for scarcity of land in a simple regression explaining the elasticity of supply with land-use regulation and scarcity of land.
  • Population density measured as population per square mile.
  • Housing supply responsiveness is also potentially affected by the degree of competition in the residential construction industry (Barker, 2004).

6. Economic and policy implications of the price responsiveness of housing supply

  • The responsiveness of housing supply could affect housing market developments and impact the wider economy through its effects on house prices in various ways.
  • In the face of demand shocks, an unresponsive housing supply can lead to higher prices compared to a more responsive housing supply environment, constraining households’ private consumption and portfolio decisions in the short to medium term.
  • In addition, constraints in the supply of housing may alter local employment and wage dynamics across residential areas with repercussions for labour mobility.
  • The flip side is that in flexiblesupply countries, housing investment adjusts more rapidly to large changes in demand and contributes to more cyclical swings in economic growth, as witnessed by recent developments in OECD housing markets.
  • The last part of this section outlines some policy options to increase the responsiveness of housing supply.

6.1 Housing price developments

  • Evidence suggests that regions with high supply responsiveness have relatively small price rises following demand shocks (e.g. Grimes and Aitken, 2006).
  • Recent cross-country estimates (Andrews et al., 2011, Box 4) indeed suggest that positive housing demand shocks caused by either financial, labour market or demographic shocks translate into larger increases in real house prices in countries with more rigid housing supply.
  • The magnitude of these effects is reasonably large.
  • If the supply elasticity is 11 However, there is no apparent cross-country correlation between available measures of mark-ups in the construction industry and the estimated supply responsiveness.
  • These effects of supply restrictions on house price volatility may have implications for aggregate demand and economic activity because such volatility may affect private demand and residential investment.

6.2 Labour market

  • Housing supply responsiveness to prices may also influence the functioning of labour markets and how they adjust to changing economic conditions.
  • Constraints on the supply of housing can alter local employment and wage dynamics in those areas in which restrictions are more severe (Saks, 2008).
  • In the face of a positive demand shock, an increase in labour demand may translate into less employment growth and higher wages in areas where it is relatively more difficult to build new housing in order to accommodate an inflow of workers.
  • Housing supply conditions can also influence the reallocation of workers over the national territory as relative house prices influence migration decisions across labour market areas.
  • Large price differentials between areas caused, for instance, by region-specific shocks in combination with rigid housing supply, can lead to lower geographical mobility since households in lower-priced areas have a larger credit hurdle to clear if they wish to move to the higher priced region (Saks, 2008; Barker, 2004; Cameron and Muellbauer, 1998).

6.3 Policy reforms to enhance supply responsiveness

  • A number of structural policy reforms, notably in the areas of housing regulations and taxation, offer potential means to increase the responsiveness of housing supply and to avoid excessive increases in house prices and negative impacts on the economy (for a detailed discussion see Andrews et al., 2011).
  • Land-use policies and regulations could ensure a more efficient use of land in countries where scarcity of land restricts supply and pushes up price.
  • Linking the assessment of property value for tax purposes to the market value may increase incentives for developing vacant land (Johansson et al., 2008).
  • In countries where the construction industry is characterised by a few large constructors, competition policy hindering collusive behaviour in the construction sector is also important for a flexible supply.
  • The design of such policies should, however, balance the benefit of additional supply against the potential cost of new developments in terms of congestion and environmental amenity losses.

WORKING PAPERS

  • The full series of Economics Department Working Papers can be consulted at www.oecd.org/eco/workingpapers/.
  • Improving fiscal performance through fiscal councils (December 2010) by Robert Hagemann 828.
  • Current account imbalances in the euro area: a comparative perspective (December 2010) by Sebastian Barnes, Jeremy Lawson and Artur Radziwill 825.
  • Mitigating risks of macroeconomic instability in Turkey (December 2010) by Łukasz Rawdanowicz 819, also known as After the crisis.

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Figures (10)
Citations
More filters
Report Series•DOI•
Housing Markets and Structural Policies in OECD Countries

[...]

Dan Andrews1, Åsa Johansson1, A Caldera Sanchez1•
Organisation for Economic Co-operation and Development1
25 Jan 2011-Research Papers in Economics
TL;DR: In this article, the authors compared a number of housing policies such as housing taxation, land use and rental regulations and social housing policies for OECD countries relying on new data, and investigated whether these housing-related policies achieve their objectives in an efficient and equitable way and whether there are any side effects on other aspects of housing markets or on the wider economy.
Abstract: This paper compares a number of housing policies such as housing taxation, land use and rental regulations and social housing policies for OECD countries relying on new data. Based on a range of econometric analyses, it also investigates whether these housing-related policies achieve their objectives in an efficient and equitable way and whether there are any side effects on other aspects of housing markets or on the wider economy. One main finding is that badly-designed policies can have substantial negative effects on the economy, for instance by increasing the level and volatility of real house prices and preventing people from moving easily to follow employment opportunities. The paper makes some recommendations for the design of efficient and equitable housing policies that can improve the functioning of housing markets and contribute to macroeconomic stability and growth.

350 citations

Cites background or methods from "The Price Responsiveness of Housing..."

  • ...Full description on data and estimation are provided in Caldera Sánchez and Johansson 2011....

    [...]

  • ...See Box 3 and Caldera Sánchez and Johansson (2011) for details....

    [...]

  • ...OECD estimates of the long-run price responsiveness of new housing supply for some 20 countries show that housing responsiveness varies substantially across countries (Box 3 and Caldera Sánchez and Johansson 2011)....

    [...]

Journal Article•DOI•
The Evolution of Homeownership Rates in Selected OECD Countries: Demographic and Public Policy Influences

[...]

Dan Andrews1, Aida Caldera Sánchez1•
Organisation for Economic Co-operation and Development1
09 Dec 2011-Oecd Journal: Economic Studies
TL;DR: In this paper, the authors used micro-econometric decomposition techniques to investigate the role of public policy in explaining the increase in homeownership rates in many OECD countries over recent decades.
Abstract: Homeownership rates have increased significantly in many OECD countries over recent decades. Using micro-econometric decomposition techniques, this paper shows that part of this increase can be explained by changes in the characteristics of households, including age, household structure, income and education. Nevertheless, a significant portion of the change in homeownership rates remains unexplained by shifts in household characteristics, leaving a potential role for public policy in explaining developments in homeownership rates. Panel estimates suggest that the relaxation of down-payment constraints on mortgage loans has increased homeownership rates among credit-constrained households over recent decades, resulting in a rise in the aggregate homeownership rate that is comparable with the impact of population ageing. In countries where tax relief on mortgage debt financing is generous, however, the expansionary impact of mortgage market innovations on homeownership is smaller. This is consistent with the tendency for such housing tax relief to be capitalised into real house prices, which may crowd-out some financially constrained households from homeownership at the margin. The impact of housing policies regulating the functioning of the rental market, such as rent regulation and provisions for tenure security, on tenure choice is also explored. JEL classification: R21, R31, G21, H24. Keywords: Housing markets, homeownership, mortgage markets, financial regulation, taxation.

196 citations

Cites background or methods from "The Price Responsiveness of Housing..."

  • ...For a discussion on housing taxation, see Andrews, Caldera Sánchez and Johansson (2011)....

    [...]

  • ...LTVs are plotted in Figure 14 of Andrews, Caldera Sánchez and Johansson (2011) and are sourced from Chiuri and Jappelli (2003), Catte et al. (2004) and ECB (2009)....

    [...]

  • ...See Andrews, Caldera Sánchez and Johansson (2011) for a discussion of the tax treatment of housing investment and mortgage market developments....

    [...]

  • ...Moreover, while alleviating credit constraints is generally desirable, it is important to acknowledge that the relaxation in lending standards can go too far, especially if this is associated with insufficient regulatory supervision as illustrated by recent developments in the United States (see Andrews, Caldera Sánchez and Johansson, 2011)....

    [...]

  • ...…is generally desirable, it is important to acknowledge that the relaxation in lending standards can go too far, especially if this is associated with insufficient regulatory supervision as illustrated by recent developments in the United States (see Andrews, Caldera Sánchez and Johansson, 2011)....

    [...]

Book Chapter•DOI•
Regulation and Housing Supply

[...]

Joseph Gyourko1, Raven Molloy2•
University of Pennsylvania1, Federal Reserve System2
02 Oct 2014-Handbook of Regional and Urban Economics
TL;DR: A wide array of local government regulations influences the amount, location, and shape of residential development as discussed by the authors, and many theories have been developed to explain why regulation arises, including the role of homeowners in the local political process, the influence of historical density, and the fiscal and exclusionary motives for zoning.
Abstract: A wide array of local government regulations influences the amount, location, and shape of residential development. In this chapter, we review the literature on the causes and effects of this type of regulation. We begin with a discussion of how researchers measure regulation empirically, which highlights the variety of methods that are used to constrain development. Many theories have been developed to explain why regulation arises, including the role of homeowners in the local political process, the influence of historical density, and the fiscal and exclusionary motives for zoning. As for the effects of regulation, most studies have found substantial effects on the housing market. In particular, regulation appears to raise house prices, reduce construction, reduce the elasticity of housing supply, and alter urban form. Other research has found that regulation influences local labor markets and household sorting across communities. Finally, we discuss the welfare implications of regulation. Although some specific rules clearly mitigate negative externalities, the benefits of more general forms of regulation are very difficult to quantify. On balance, a few recent studies suggest that the overall efficiency losses from binding constraints on residential development could be quite large.

192 citations

Journal Article•DOI•
How long do housing cycles last? A duration analysis for 19 OECD countries

[...]

Philippe Bracke1•
London School of Economics and Political Science1
01 Sep 2013-Journal of Housing Economics
TL;DR: In this article, the authors analyzed the duration of house price upturns and downturns in the last 40 years for 19 OECD countries and found that both upturn and downturn displays duration dependence: they are more likely to end as their duration increases.

94 citations

Cites background from "The Price Responsiveness of Housing..."

  • ...22 Caldera and Johansson (2013) provide a time-invariant measure of housing supply elasticity for the OECD countries studied here....

    [...]

Report Series•DOI•
Skill Mismatch and Public Policy in OECD Countries

[...]

Müge Adalet McGowan1, Dan Andrews1•
Organisation for Economic Co-operation and Development1
28 Apr 2015-Research Papers in Economics
TL;DR: This paper explored the relationship between skill mismatch and public policies using micro data for 22 OECD countries from the recent OECD Survey of Adult Skills (PIAAC) and found that differences in skill mismatch across countries are related to differences in public policies.
Abstract: This paper explores the relationship between skill mismatch and public policies using micro data for 22 OECD countries from the recent OECD Survey of Adult Skills (PIAAC). Results suggest that differences in skill mismatch across countries are related to differences in public policies. After controlling for individual and job characteristics, well-designed product and labour markets and bankruptcy laws that do not overly penalise business failure are associated with lower skill mismatch. Given the negative relationship between skill mismatch and labour productivity, reducing skill mismatch emerges as a new channel through which well-designed framework policies can boost labour productivity. Skill mismatch is also lower in countries with housing policies that do not impede residential mobility (e.g. transaction costs on buying property and stringent planning regulations). Greater flexibility in wage negotiations and higher participation in lifelong learning as well higher managerial quality are also associated with a better matching of skills to jobs.

91 citations

Cites background from "The Price Responsiveness of Housing..."

  • ...Housing market interventions which limit the supply of housing, such as restrictive land use regulations and building costs, can also constrain residential mobility (Caldera Sánchez and Johansson, 2011)....

    [...]

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TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
Abstract: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a becomes a matrix. Interpreting a'x,= 0 as a long run equilibrium, co-integration implies that deviations from equilibrium are stationary, with finite variance, even though the series themselves are nonstationary and have infinite variance. The paper presents a representation theorem based on Granger (1983), which connects the moving average, autoregressive, and error correction representations for co-integrated systems. A vector autoregression in differenced variables is incompatible with these representations. Estimation of these models is discussed and a simple but asymptotically efficient two-step estimator is proposed. Testing for co-integration combines the problems of unit root tests and tests with parameters unidentified under the null. Seven statistics are formulated and analyzed. The critical values of these statistics are calculated based on a Monte Carlo simulation. Using these critical values, the power properties of the tests are examined and one test procedure is recommended for application. In a series of examples it is found that consumption and income are co-integrated, wages and prices are not, short and long interest rates are, and nominal GNP is co-integrated with M2, but not M1, M3, or aggregate liquid assets.

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Serena Ng1, Pierre Perron2•
Johns Hopkins University1, Boston University2
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Anindya Banerjee1, Juan J. Dolado, John W. Galbraith, David F. Hendry•
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01 Jan 1993-Research Papers in Economics
TL;DR: This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling, and the asymptotic theory of integrated processes is described.
Abstract: This book provides a wide-ranging account of the literature on co-integration and the modelling of integrated processes (those which accumulate the effects of past shocks). Data series which display integrated behaviour are common in economics, although techniques appropriate to analysing such data are of recent origin and there are few existing expositions of the literature. This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling. The concepts of co-integration and error-correction models are fundamental components of the modelling strategy. This area of time-series econometrics has grown in importance over the past decade and is of interest to econometric theorists and applied econometricians alike. By explaining the important concepts informally, but also presenting them formally, the book bridges the gap between purely descriptive and purely theoretical accounts of the literature. The asymptotic theory of integrated processes is described and the tools provided by this theory are used to develop the distributions of estimators and test statistics. Practical modelling advice, and the use of techniques for systems estimation, are also emphasized. A knowledge of econometrics, statistics, and matrix algebra at the level of a final-year undergraduate or first-year undergraduate course in econometrics is sufficient for most of the book. Other mathematical tools are described as they occur.

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Co-integration, Error Correction and the Econometric Analysis of Non-stationary Data

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Anindya Banerjee1, Juan J. Dolado, John W. Galbraith, David F. Hendry•
North Carolina State University1
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TL;DR: In this article, a wide-ranging account of the literature on co-integration and the modelling of integrated processes is provided, with a focus on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling.
Abstract: This book provides a wide-ranging account of the literature on co-integration and the modelling of integrated processes (those which accumulate the effects of past shocks). Data series which display integrated behaviour are common in economics, although techniques appropriate to analysing such data are of recent origin and there are few existing expositions of the literature. This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling. The concepts of co-integration and error-correction models are fundamental components of the modelling strategy. This area of time-series econometrics has grown in importance over the past decade and is of interest to econometric theorists and applied econometricians alike. By explaining the important concepts informally, but also presenting them formally, the book bridges the gap between purely descriptive and purely theoretical accounts of the literature. The asymptotic theory of integrated processes is described and the tools provided by this theory are used to develop the distributions of estimators and test statistics. Practical modelling advice, and the use of techniques for systems estimation, are also emphasized. A knowledge of econometrics, statistics, and matrix algebra at the level of a final-year undergraduate or first-year undergraduate course in econometrics is sufficient for most of the book. Other mathematical tools are described as they occur.

1,726 citations

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Frequently Asked Questions (10)
Q1. What is the effect of a major demand shock on housing prices?

In the face of a major demand shock, an increase in labour demand may translate into less employment growth and higher wages in areas where it is relatively more difficult to build new housing in order to accommodate an inflow of workers. 

Housing supply responsiveness to prices may also influence the functioning of labour markets and how they adjust to changing economic conditions. 

Housing supply conditions can also influence the reallocation of workers over the national territory as relative house prices influence migration decisions across labour market areas. 

Apart from regulations on land-use, the provision of infrastructure and other public services complementary to housing, such as road junctionsor water drainage, is also likely to influence supply, though hard evidence of this link is not available (e.g. Barker, 2008, for a discussion). 

The elasticity of the stock of housing with respect to prices would then be (following equation (2)) β1 divided by the depreciation rate (∂ ) of the housing stock. 

These estimates imply that between 20% and over 100% of the differences between actual and equilibrium investment are closed within a year depending on the country. 

Evidence provided in Andrews (2010) shows that low responsiveness of housing supply has tended to exacerbate the price effect of changes in housing demand caused by financial, labour market or demographic shocks across OECD countries over the past decades. 

As a first step in the estimation, the order of integration of the series involved in the estimation of the long-run relationship is verified using the standard Augmented Dickey Fuller (ADF) test for the presence of unit roots. 

For the United Kingdom, Swank et al. (2002) report a low elasticity of supply of 0.45 over 1976 to 1999 using the number of permits as a measurement of new housing supply. 

In the case of Spain, restricting the sample to the period 1995- 2007, which would reflect recent developments in housing markets (such as the large stock of unsold houses resulting from thenegative and significant coefficient for Spain and non significant for Denmark, while the long-run price elasticities of new housing supply are close to the baseline resultsconstruction boom starting in 2000 and peaking in 2007-09), only slightly increases the estimate of the elasticity of housing supply from 0.45 to 0.58.