Forecasting House Prices in the United States with Multiple Structural Breaks
The boom-bust cycle in U.S. house prices has been a fundamental determinant of the recent financial crisis leading up to the Great Recession. The risky financial innovations in the housing market prior to the recent crisis fueled the speculative housing boom. In this backdrop, the main objectives...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Econometric Research Association
2014-04-01
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| Series: | International Econometric Review |
| Subjects: | |
| Online Access: | http://www.era.org.tr/makaleler/15040090.pdf |
| Summary: | The boom-bust cycle in U.S. house prices has been a fundamental determinant of the
recent financial crisis leading up to the Great Recession. The risky financial innovations
in the housing market prior to the recent crisis fueled the speculative housing boom. In
this backdrop, the main objectives of this empirical study are to i) detect the possibility of
multiple structural breaks in the US house price data during 1995-2010, exhibiting very
sharp upturns and downturns; ii) endogenously determine the break points and iii)
conduct house price forecasting exercises to see how models with structural breaks fare
with competing time series models – linear and nonlinear. Using a very general
methodology (Bai-Perron, 1998, 2003), we found four break points in the trend in the
S&P/Case-Shiller 10 city aggregate house-price index series. Next, we compared the
forecasting performance of the model with structural breaks to four competing models –
namely, Random Acceleration (RA), Autoregressive Moving Average (ARMA), SelfExciting Threshold Autoregressive (SETAR), and Smooth Transition Autoregressive
(STAR). Our findings suggest that house price series not only has undergone structural
changes but also regime shifts. Hence, forecasting models that assume constant
coefficients such as ARMA may not accurately capture house price dynamics. |
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| ISSN: | 1308-8793 1308-8815 |