Forecasting House Prices in the United States with Multiple Structural Breaks

Year 2014, Volume: 6 Issue: 1, 1 - 23, 01.04.2014

Mahua Barari Nityananda Sarkar Srikanta Kundu Kushal Banik Chowdhury

https://doi.org/10.33818/ier.278028

Cited By: 6

Abstract

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), Self- Exciting 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.

Keywords

Structural Break, House Prices, Forecasting, Non-linear Models, Nonstationarity.

References

• Andreou, E. and E. Ghysels (2002). Detecting Multiple Breaks in Financial Market Volatility Dynamics. Journal of Applied Econometrics, 17 (5), 579–600.
• Andrews, D. W. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica, 61 (4), 821–856.
• Andrews, D.W. (2003). Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum. Econometrica, 71 (1), 395–397.
• Andrews, D. W. and W. Ploberger (1994). Optimal Tests when A Nuisance Parameter is Present Only Under the Alternative. Econometrica, 62 (6), 1383–1414.
• Bai, J. (1994). Least Squares Estimation of A Shift in Linear Processes. Journal of Time Series Analysis, 15 (5), 453-472.
• Bai, J. (1997a). Estimation of A Change Point in Multiple Regression Models. Review of Economics and Statistics, 79 (4), 551–563.
• Bai, J. (1997b). Estimating Multiple Breaks One at A Time. Econometric Theory, 13 (3), 315–352.
• Bai, J. and P. Perron (1998). Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica, 66 (1), 47–78.
• Bai, J. and P. Perron (2003). Computation and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics, 18 (1), 1–22.
• Bera, A. K. and M. L. Higgins (1993). A Survey of ARCH Models: Properties, Estimation and Testing. Journal of Economic Surveys, 7 (4), 305–366.
• Bernanke, B. S. (2011). The Near- and Longer-Term Prospects for the U.S. Economy. In Federal Reserve Bank of Kansas City Economic Symposium. Wyoming: Jackson Hole. http://www.federalreserve.gov/newsevents/speech/bernanke20110826a.htm October 28, 2011). (accessed
• Bernanke, B. S. (2010). Monetary Policy and the Housing Bubble. In Annual Meeting of the American Economic Association Meeting. Atlanta, Georgia, http://www.federalreserve. gov/newsevents/speech/bernanke20100103a.htm (accessed September 20, 2011).
• Berndt, E. R., B. H. Hall, R. E. Hall and J. A. Hausman (1974). Estimation and Inference in Nonlinear Statistical Models. Annals of Economic and Social Measurement, 3 (4), 653– 665.
• Bessec, M and O. Bouabdallah (2005). What Causes the Forecasting Failure of Markov- Switching Models? A Monte Carlo Study. Studies in Nonlinear Dynamics and Econometrics, 9 (2), DOI: 10.2202/1558-3708.1171.
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31 (3), 307–327.
• Case, K. and R. Shiller (1989). The Efficiency of the Market for Single-Family Homes. American Economic Review, 79 (1), 125–137.
• Chan, K. S and H. Tong (1990). On Likelihood Ratio Tests for Threshold Autoregression. Journal of the Royal Statistical Society B, 52 (3), 469–476.
• Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28 (3), 591–605.
• Crawford, G. and M. Fratantoni (2003). Assessing the Forecasting Performance of Regime- Switching, ARIMA and GARCH Models of House Prices. Real Estate Economics, 31 (2), 223–243.
• Dacco, R. and S. Satchell (1999). Why Do Regime–Switching Forecast So Badly? Journal of Forecasting, 18 (1), 1–16.
• Diebold, F. X. and R. S. Mariano (1995). Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13 (3), 253–263.
• Dokko, J., B. Doyle, M. T. Kiley, J. Kim, S. Sherlund, J. Sim and S. Van Den Heuvel (2009). Monetary Policy and the Housing Bubble. In Finance and Economics Discussion Series. Washington DC: Division of Research & Statistics and Monetary Affairs, Fed. Reserve Board. http://www.federalreserve.gov/pubs/feds/2009/200949/200949pap.pdf (accessed March 15, 2011).
• Ducca, J. V., D. Luttrell, and A. Murphy (2011). When Will the U.S. Housing Market Stabilize? Economic Letter, 6 (8), 1–4.
• Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50 (4), 987–1007.
• Gerlach, R., P. Wilson and R. Zurbruegg (2006). Structural Breaks and Diversification: The Impact of the 1997 Asian Financial Crisis on the Integration of Asia-Pacific Real Estate Markets. Journal of International Money and Finance, 25 (6), 974-91.
• Granger, C. J. and T. Terasvirta (1993). Modeling Nonlinear Economic Relationships. Oxford University Press.
• Guirguis, H. S., C. Giannikos and R. I. Anderson (2005). The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients. The Journal of Real Estate Finance and Economics, 30 (1), 33–53.
• Guirguis, H. S. and R. Vogel (2006). Asymmetry in Regional Real House Prices. Journal of Real Estate Portfolio Management, 12 (3), 293–298.
• Hansen, B. E. (2001). The New Econometrics of Structural Change: Dating Breaks in U.S. Labor Productivity. Journal of Economic Perspectives, 15 (4), 117–128.
• Harvey, D., S. Leybourne and P. Newbold (1997). Testing the Equality of Prediction Mean Squared Errors. International Journal of Forecasting, 13 (2), 281-291.
• Ho, A. K. F. and A. T. K. Wan (2002). Testing for Covariance Stationarity of Stock Returns In the Presence of Structural Breaks: An Intervention Analysis. Applied Economics Letters, 9 (7), 441–47.
• Kohn, D. L. (2007). Economic Outlook. http://www.federalreserve.gov/newsevents/speech/ kohn20071004a.htm (accessed March 15, 2011).
• Leamer, E. (2007). Housing is the Business Cycle. NBER Working Paper No.13428. http://www.nber.org/papers/w13428 (accessed March 20, 2011).
• Lucey, B. M. and S. Voronkova (2008). Russian Equity Market Linkages Before and After the 1998 Crisis: Evidence from Stochastic and Regime-Switching Cointegration Tests. Journal of International Money and Finance, 27 (8), 1303–24.
• Luukkonen, R., P. Saikkonen and T. Terasvirta (1988). Testing Linearity Against Smooth Transition Autoregressive Models. Biometrika, 75 (3), 491–499.
• Maasoumi, E., A. Zaman, and M. Ahmed (2010). Tests for Structural Change, Homogeneity, and Aggregation. Economic Modeling, 27 (6), 1382–1391.
• Maddala, G. S. and In-Moo. Kim (1998). Unit Roots, Cointegration, and Structural Change. Cambridge University Press.
• Marquardt, D. (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM Journal on Applied Mathematics, 11 (2), 431–441.
• Miles, W. (2008). Boom-Bust Cycles and Forecasting Performance of Linear and Nonlinear Models of House Prices. Journal of Real Estate Finance and Economics, 36 (3), 249– 264.
• Mills, T. C. (1999). The Econometric Modeling of Financial Time Series. Cambridge University Press.
• Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59 (2), 347–370.
• Nelson D. B. and C. Q. Cao (1992). Inequality Constraints in the Univariate GARCH Model. Journal of Business & Economic Statistics, 10 (2), 229–235.
• Obtsfeld, M. and K. Rogoff (2009). Global Imbalances and the Financial Crisis: Products of Common Causes. In Asia Economic Policy Conference. San Francisco: Federal Reserve Bank. http://elsa.berkeley.edu/~obstfeld/santabarbara.pdf (accessed March 25, 2011).
• Quandt, R. E. (1960). Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes. Journal of the American Statistical Association, 55, 324–330.
• Shiller, R. J. (2007). Understanding Recent Trends in House Prices and Home Ownership. http://www.kc.frb.org/publicat/sympos/2007/pdf/2007.09.27.shiller.pdf (accessed March 2, 2011).
• Terasvirta, T. (1998). Modeling Economic Relationships with Smooth Transition Regressions. In Handbook of Applied Economic Statistics, New York: Marcel Dekker Publisher, 507- 552, ED. A. Ullah and D. E. Giles.
• Terasvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89 (425), 208– 218.
• Terasvirta, T. and H. M. Anderson (1992). Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models. Journal of Applied Econometrics, 7 (S), 119–136.
• Tong, H. and K. S. Lim (1980). Threshold Autoregressions, Limit Cycles, and Data . Journal of the Royal Statistical Society, B 42 (3), 245–292.
• Tsouma, E. (2007). Stock Return Dynamics and Stock Market Interdependencies. Applied Financial Economics, 17 (10), 805–25.
• White, H. (1980). A Heteroskedasticity- Consistent Covariance Matrix Estimator and A Direct Test for Heteroskedasticity. Econometrica, 48 (4), 817–838.
• Zhou, Z. (1997). Forecasting Sales and Price for Existing Single-Family Homes: a VAR Model with Error Correction. Journal of Real Estate Research, 14 (2), 155–167.