House Price Cycles and the Real Economy

1. House Price Cycles and the Real Economy Deniz Igan IMF – Research LIME Workshop Brussels- December 8, 2012 Disclaimer: Views expressed in the presentation and during the talk are those of the presenter and should not be ascribed to the IMF.

2. Background Work • The Changing Housing Cycle and the Implications for Monetary Policy – Chapter 3 in WEO April 2008 • Housing, Credit, and Real Activity Cycles: Characteristics and Comovement – JHE 2011 (revision of Three Cycles: Housing, Credit, and Real Activity, IMF WP 09/231) • Global Housing Cycles – IMF WP forthcoming • Early Warning Exercise – conducted twice a year

3. Outline • Research questions • Relevant literature (briefly) • Preview of results • Methodology and data (briefly) • Results • Conclusions and policy implications

4. Research Questions • How similar are the cycles: in duration, amplitude, etc.? • How do the cycles relate to the interest rate changes? • Are the cycles synchronized, or does one lead the others? • How important are global versus local factors? • What are the policy implications?

5. Literature—Domestic Context • Bank credit, house prices and aggregate demand tend to move in tandem • Financial accelerator effects through external finance premium and collateral prices result in procyclicality of bank credit • Bernanke and Gertler, 1989; Kiyotaki and Moore, 1997 • Liquidity and leverage of banks also contribute • Adrian and Shin (2008), Berger and Bouwman (2008) • Liquidity of housing wealth and availability of housing financing can also alter the relationship through consumption and investment

6. Literature—International Context • The degree of synchronization in real and financial cycles increased • Kose, Prasad and Terrones, 2003; Imbs, 2004; Otrok and Terrones, 2005 • International comovement in house prices may reflect comovement of interest rates • Especially with debt-financed ownership and adjustable-rate mortgages • Global liquidity may have also contributed • Belke and Orth, 2008

7. Literature—Monetary Policy • Transmission through the credit channel and the housing market • Monetary policy, credit and asset prices • Benign neglect: Monetary policy is not an effective tool for targeting asset prices, so better not to prick the bubble but mop up afterwards (Bernanke, Gertler, and Gilchrist, 1999) • Leaning against the wind: Monetary policy needs to play a more proactive role to prevent financial bubbles (Borio and Lowe, 2002, and Borio, 2006)

8. Contribution of This Paper • Multidirectional links • Goodhart and Hofman (2008) • Focus on output, bank credit, house prices (as well as residential investment) and interest rates • Descriptive approach • GDFM as opposed to VAR • Data properties • Unit root tests • Filtering as opposed to differencing • Different horizons • Short to medium term (6 to 16 quarters) • Long term (16 to 32 quarters)

9. Preview of Results • House price cycles lead credit and business cycles over the long run • Interest rates tend to lag other cycles at all horizons • Country cycles are largely driven by global factors • the role of which has increased over time, especially for credit and business cycles • U.S. cycles tend to lead other countries’ respective cycles

10. Generalized Dynamic Factor Model • Identifies a common component using a large number of series • Builds on the traditional factor models • Sargent and Sims (1977) and Geweke (1977) • By allowing for serial correlation and weakly cross-sectional correlation of idiosyncratic components • Chamberlain (1983) and Chamberlain and Rothschild (1983) • Recent examples • Giannone, Reichlin, and Sala (2002) • Forni and others (2005) • Eickmeier (2007)

11. (Approximate) GDFM where Yt is a (N x T) vector stochastic stationary process with zero mean and unit variance and Xt and Ξt are (N x T) vectors of common and idiosyncratic components, respectively. Xt can be written as: where Ft is a (r x T) vector of common factors and C is a (N x r) matrix of factor loadings. The model has N>>T and r<<N. The common factors assumed to follow an AR(1) process: with B and (r x r) matrix and ut a (r x T) vector of residuals.

12. Measures of Comovement The dynamic correlation varies between -1 and 1. Formally, where the numerator is the cospectrum between y1 and y2 processes at frequency λ and S (y1) and S (y2) are the spectral density functions of the processes at frequency λ defined over –π and π. Coherence is intrinsically related to dynamic correlation given by: Coherenceissymmetric and a real numberbetween 0 and 1.

13. The phase angle between processes helps identify the lead-lag relationship where q (y1 y2) is the quadrature spectrum. Only when K (y1y2) ≠0, the phase angle converges in distribution to a normal random variable. Lead-Lag Relationship

14. Panel Data • 18 industrial countries • 1981 Q1 to 2006 Q4 • 1,283 series • Real activity indicators • Consumption • Investment, including residential • International trade • Confidence indicators • Portfolio and FDI flows • Financial variables • Credit to the private sector and other monetary aggregates • Short-term and long-term interest rates • House prices and stock prices • Balance sheet data • Household savings and assets (in particular, housing stock) • Capital stock of business sector

15. Unit Root Tests • ERS (Elliott, Rothenberg and Stock, 1996) • Generalized least squares • More powerful than standard Dickey-Fuller test • KPSS (Kwiatowski, Phillips, Schmidt and Shin, 1992) • Cross check • Stationarity as H0 instead of unit root • Constant and deterministic trend • Lags chosen based on the Schwarz information criterion

16. Results of Unit Root Tests • House prices are sometimes I(2) • France, Ireland, the Netherlands, New Zealand, Sweden and the United States • Credit series are also I(2) in some cases • Japan and Spain • Over-differencing versus under-differencing

17. Unit Root Tests (Details)

18. Band-Pass Filter • Corbae and Ouliaris (2006) ideal band-pass filter • Passes through the components of time series with periodic fluctuations between 6 and 32 quarters (in line with widely used minor-major cycle lengths) • Consistent • No finite sampling error • Not subject to end-point problems

19. Advantages of Filtering over Differencing

20. Wrong data transformation may introduce a downward bias in the degree of economic integration and an upward bias in the efficiency of uncoordinated macroeconomic policies.

21. Characteristics of Cycles • Credit and house price cycles tend to be slightly more protracted on average than business cycles... • but peaks and troughsgenerally are not too far. • Credit and house price cycles have larger swings than real activity cycles). • In some countries, there are significantdifferencesacrossthesecharacteristics… • Countries thatallow MEW and refinancing tend to have larger amplitude and longer duration in house price cycles relative to the business cycle if the share of variable rate mortgagesis large (e.g., New Zealand versus Germany).

22. Characteristics of Cycles

23. Procyclicality • Financial accelerator model is not empirically supported in all countries—correlations vary in significance and sign. • Collateral is a driver of procyclicality more than lending is—stronger correlation between output and house prices than between output and bank credit.

24. Procyclicality

25. Common Components • A large share of common components drive the three cycles. • The share of the common component in GDP and in credit has increased over time. • The importance of common shocks and/or the speed of transmission of shocks has increased over time.

26. Common Components GDP

27. Common Components Credit

28. Common Components • House prices

29. Evolution of Common Components

30. Leads and Lags • During the minor cycle, house priceslead output and creditonly in a few cases. • In the long run, thereissome support for financialacceleratormechanism, but whichchannel? • balance sheetimprovements -> credit -> house prices or • house prices -> creditworthiness -> credit • Short-terminterest rates neverlead house prices. • House priceslead output, which in turnleadscredit. • U.S. cycles lead the corresponding cycles in the long run in most cases, and U.S. credit cycles leadonly in the short run.

31. Leads and Lags (Details - 1)

32. Leads and Lags (Details – 2)

33. Conclusions • House price cycles lead other cycles in the long run • Interest rates tend to lag other cycles at all horizons • Global factors are important for all cycles, especially for credit and real activity in the latter part of the sample period • U.S. leads other countries in all cycles

34. Similar conclusions from other research • A six-variable VAR: real GDP, private consumption, residential investment, CPI inflation, the nominal (short term) interest rate and real house prices. 20 countries, sample from 1986-2009. • House price shocks are identified with a Cholesky decomposition. In practice they look like “housing demand shocks” because they lead to a strong comovement between real house prices and residential investment. Alternative identification through sign restrictions. • VAR delivers average responses, but there could be asymmetries between house price booms and busts.

35. Impact on Real GDP of a 10% decline in real house prices

36. Mortgage market characteristics matter

37. Policy Implications • Can a uniform policy prescription of taking into account asset prices in monetary policy making be made? It seems not, statistical properties of house prices vary across countries. In addition, some shocks to house price inflation are persistent. • The U.S. business, house price, and interest rate cycles tend to lead the respective cycles in other countries over all horizons and more so recently. Questions domestically-focused economic policies.