Assessing euro area residential property prices through the lens of key fundamentals* L. Gattini European Central Bank December 2011 * This presentation represents the views of the presenter which do not necessarily correspond with those of the ECB.

2 Outline 1.Background 2. Data and modelling approach 3. Results 5. Conclusions 4. Other approaches for the euro area & additional results for countries

3 Background Two approaches to the analysis of house price developments: Analysis of the boom and bust cycles – Kennedy and Andersen (1994), Alessi and Detken (2009), Borgy et al. (2009) – costly and not costly cycles Understanding the contribution of the fundamental components of housing market to price developments – Tsatsaronis et al. (2009), Goodhart and Hofmann (2008) Mixed set of potential explanatory variables Real economic variables “Credit view”: credit, leverage, overall bank balance sheet Interest rate and risk taking channel

4 Data and modelling approach “Forecasting and assessing euro area house prices through the lens of key fundamentals” – Gattini & Hiebert, ECB WP 1249 An empirical framework for the analysis of house prices in the euro area using a vector error-correction model (VECM) Real house prices related to selected housing demand and supply fundamentals We employ a structural decomposition – its is more suitable for policy analysis and interpretation of results in light of equilibrium/dis-equilibrium

5 Data and modelling approach Vector Error Correction model – parsimonious specification 4 variables: Real Disposable Income, Real Mixed Interest Rate, Real Private Residential Investment, Real House Prices Estimated on quarterly data – 1970/ Cointegrating Relation – Johansen Method – unrestricted cointegration rank tests (trace and maximum eigenvalue) Sources: ECB, OECD, Eurostat Data for some countries are interpolated (e.g. DE) 5 lags – Akaike Schawarz and FPE criteria

6 Data and modelling approach

7 Data and modelling approach – literature using ECM approach

8 Modelling approach – Structural decomposition - Common trend approach – King, Plosser, Stock and Watson (1991), Iacoviello (2002) - We distinguish between structural shocks with permanent and transitory effects Permanent shocks - baseline identification imposes zero restrictions on the first and second columns of the D(1) elements - (S)VECM system more suitable for policy analysis - structural decomposition is useful to analyse the responsiveness of the system – k*(k − 1)/2=6 restrictions imposed

9 Modelling approach – Structural decomposition Housing market technology shock - Technological shocks to the construction industry - rarely observed - Motivated by changes in the regulatory framework (e.g. building regulations and/or the modification of various zoning laws) - This could cause changes in housing production virtually indistinguishable from housing building technology - Matsuyama (1999)

10 Modelling approach – Structural decomposition The impact on real interest rates would be ambiguous The euro area - relatively closed economy - A substitution effect between categories of investment should nullify possible discrepancies in terms of returns between different categories of investment in the long-run - Sectoral specific technology shocks can have an impact in the short-run on interest rates – zero in the long-run given counterbalancing effects

11 Modelling approach – Structural decomposition Economy wide technological shock - Expected to exert some impact on all the variables in the system Financing cost shock The outcome of features that permanently alter interest rate risk premia, such as financial innovation or –specific to the case of euro area– convergence in the run up to European Monetary Union.

12 Modelling approach – Structural decomposition Transitory shocks - A two way short-run interaction between real interest rate and real income has been excluded via imposing two zero restrictions - standard lags in the monetary policy transmission mechanism Housing demand shock - The temporary shift in preferences toward housing assets - Rationalized in the context of literature on a time-varying housing risk premia (seeWeeken, 2004) - A temporary shift from non-residential demand to residential demand

13 Results – Forecast as a testing tool

14 Results – Impulse Response – housing demand shock

15 Results – Impulse Response – housing supply shock

16 Results – Historical decomposition

17 Results – Transitory-permanent component – B-N type

18 Results – Transitory-permanent component – B-N type

19 Other approaches for the euro area Crude affordability in the euro area – measured by the ratio of per capita GDP to the house price index – computed relative to long-term trends Residual of a simple error-correction framework with real house prices regressed on real GDP per capita, population and the real interest rate House price-to-rent ratio computed relative to its long-run average – a simplified static dividend discount model or asset pricing approach The evolution of the house price-to-rent ratio computed relative to the real long term interest rate - return on a housing investment should be equal to the returns on alternative investment opportunities

20 Other approaches - euro area - MB

21 Other approaches - euro area countries - FSR

22 Conclusions The structural model suggests that euro area housing has been overvalued in recent years – 2006/2007 by 10% -, implying a period of stagnation, which is already started in 2009, to bring housing valuation back in line with its modelled fundamentals During the last house price boom much of the increase appears to reflect a permanent component with an increasing importance of real disposable income per capita Proposed a structural methodology capturing fundamental demand and supply factors A transitory component has also contributed – particularly since In particular, housing preference and income shocks were a key driver in explaining house price dynamics over this period

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