An early warning system to predict house price bubbles

Transcription

1 An early warning system to predict house price bubbles Christian Dreger, Konstantin Kholodilin DIW Berlin Why is the topic relevant? Bubbles in housing prices can impede the real economic evolution Prices do not indicate scarcity, distorts allocation of resources House price bubbles can inflate over long periods, high risk of recession after their burst Observed prices include both fundamental and speculative components Separation depends on underlying model Early detection can improve policy reactions 1

2 Early warning system Based on two-stage procedure Previous bubbles in housing markets need to be identified (bubble chronology) Statistical model fitted to explain the chronology Results might vary with empirical models Robustness tests to identify critical price drivers Early warning system for panel of countries or regions, as bubbles are rare events Monetary policy identical for regions within a country Chronology of price bubbles Identification using structural econometric models and filter methods Country-by-country since bubbles are distributed heterogeneously across panel members Dominance of structural models Prices can be linked to macroeconomic conditions (fundmentals) Filter methods to validate structural bubbles Structural bubbles need to be confirmed, periods should at least partially overlap 2

3 Filter and structural models Filter: cycle = rp trend > θσ ( cycle ) it it it i i Regression: p = α X + ε, ˆ ε > ρσ ( ε ) it i it it it i it Deviations from HP trend and (smoothed) residuals from structural regression should exceed threshold Threshold expressed as a multiple of standard deviations, might differ across panel members Thresholds by iteration to obtain maximum concordance Set of fundamentals Potential drivers of excessive house price excluded in model fit If not, bubbles are wiped out, as OLS provides optimal fit Fundamental variables bear correct sign Higher income triggers rising house prices Increasing real interest rates will worse financing conditions and dampen house prices High urbanization implies lower migration pressure, negative impact on house prices 3

4 Chronology house price bubbles Australien Kanada Frankreich Deutschland q3 1989q3 1992q3 1995q3 1998q3 2001q3 2004q3 2007q3 1970q2 1975q2 1980q2 1985q2 1990q2 1995q2 2000q2 2005q2 1970q2 1975q2 1980q2 1985q2 1990q2 1995q2 2000q2 2005q2 1991q2 1994q2 1997q2 2000q2 2003q2 2006q2 2009q2 Italien Japan Niederlande Portugal q2 1985q2 1989q2 1993q2 1997q2 2001q2 2005q2 2009q2 1969q4 1974q4 1979q4 1984q4 1989q4 1994q4 1999q4 2004q4 2009q4 1977q2 1982q2 1987q2 1992q2 1997q2 2002q2 2007q2 1995q2 1997q2 1999q2 2001q2 2003q2 2005q2 2007q2 2009q Spanien Schweden Schweiz UK 1971q2 1976q2 1981q2 1986q2 1991q2 1996q2 2001q2 2006q2 1993q2 1996q2 1999q2 2002q2 2005q2 2008q2 1970q2 1975q2 1980q2 1985q2 1990q2 1995q2 2000q2 1971q1 1976q1 1981q1 1986q1 1991q1 1996q1 2001q1 2006q1 USA q2 1980q2 1985q2 1990q2 1995q2 2000q2 2005q2 Distribution of bubbles Number of bubbles Duration (Quarter) Australia Canada France Germany Italy Japan Netherlands Portugal Spain Sweden Switzerland UK USA

5 Prediction of bubbles Identification of potential variables driving the emergence and inflation of bubbles Calibrated to explain the bubble chronology Signal approach Signal for bubble extracted, if particular variable exceeds critical value Probability for presence of bubbles captured by logit and probit models Panel with country fixed effects, institutional conditions Signal approach Evolution of variables in periods of previous bubbles Standardization of variables to eliminate country specific impact on overall volatility Thresholds ensure optimal bubble forecast for individual variable Bubbles should be detected, false alarms avoided Signal extracted, if variable above threshold Overall indicator results from the aggregation of individual signals 5

6 Thresholds for signal extraction Event Bubble No bubble Signal a b No signal c d z i a d = + a + c b + d max Ratio of correctly predicted bubbles non bubbles) to all bubbles (non bubbles) Variables with more accurate forecasts receive higher weight in overall indicator Logit and probit models Binary response model (bubble, no bubble) Probability for the presence of bubble traced to set of variables Logistic or standard normal distribution Instead of thresholds, regressors are evaluated by statistical significance Ordering of contribution of regressors by their log-odds ratio Goodness-of fit measures to rank different approaches 6

7 Empirical results Signal-, logit- and probit model based on the same set of variables Excessive liquidity and credit conditions are highly relevant to predict speculative housing bubbles Confirms previous findings But other variables are also important Investment ratios, public finances, interest rates Logit and probit models outperform the signal approach Variables to predict bubbles Weights Signal Logit Short term interest rates 15.0 Real exchange rate Rent 5.0 House prices / income House prices / rent Investment ratio Credit growth GDP growth Liquidity (based on M3) Public finances Financial regulation 7.3 7

8 Ranking of models In sample Out of sample Signal approach Logit model Probit model QPS = y p T j 2 ( it it ) min T t = 1 Quadratic probability score: y is (0, 1) variable p predicted probability for a bubble One-step recursive forecasts, perfect foresight for exogeneous variables Logit detection of bubbles Australien Kanada Frankreich Deutschland 1986Q3 1993Q3 2000Q3 2007Q3 1970Q4 1981Q4 1992Q4 2003Q4 1980Q3 1988Q3 1996Q3 2004Q3 1991Q2 1996Q2 2001Q2 2006Q2 Italien Japan Niederlande Spanien 1997Q2 2001Q2 2005Q2 2009Q2 1991Q4 1996Q4 2001Q4 2006Q4 1991Q1 1996Q1 2001Q1 2006Q1 1998Q2 2002Q2 2006Q2 Schweden Schweiz UK USA 1996Q2 2000Q2 2004Q2 2008Q2 1985Q4 1990Q4 1995Q4 2000Q4 1983Q1 1991Q1 1999Q1 2007Q1 1975Q2 1985Q2 1995Q2 2005Q2 8

9 Conclusions Development of a tool for early detection of speculative bubbles in housing markets Robustness analysis of results Logit and probit outperform signal approach Probably not in general, since different weights for threshold components can affect results Liquidity and credit variables not sufficient to predict bubbles Represent 35 (logit) and 55 (signal) percent of forecast of overall indicator Further information cdreger@diw.de Early warning systems to detect price bubbles in the stock and housing market Sponsored by the German Ministry of Finance An early warning system to predict speculative house price bubbles, Economics: Open Access, Open Assessment E-Journal 7, (Dreger and Kholodilin). 9