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Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices

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  • Gordon W. Crawford
  • Michael C. Fratantoni
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    Abstract

    While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime-switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime-switching models can perform better in-sample, simple ARIMA models generally perform better in out-of-sample forecasting. Copyright 2003 by the American Real Estate and Urban Economics Association

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    Bibliographic Info

    Article provided by American Real Estate and Urban Economics Association in its journal Real Estate Economics.

    Volume (Year): 31 (2003)
    Issue (Month): 2 (06)
    Pages: 223-243

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    Handle: RePEc:bla:reesec:v:31:y:2003:i:2:p:223-243

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    Cited by:
    1. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.
    2. Kuang-Liang Chang & Ming-Hui Yen, 2014. "The magnitude and significance of macroeconomic variables in explaining regional housing fluctuations," Economics Bulletin, AccessEcon, vol. 34(2), pages 828-841.
    3. Coulson, N. Edward & Liu, Crocker H. & Villupuram, Sriram V., 2013. "Urban economic base as a catalyst for movements in real estate prices," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 1023-1040.
    4. Chang, Kuang-Liang, 2010. "House price dynamics, conditional higher-order moments, and density forecasts," Economic Modelling, Elsevier, vol. 27(5), pages 1029-1039, September.
    5. Huang, MeiChi, 2014. "Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 2-16.
    6. Daniele Bianchi & Massimo Guidolin, 2014. "Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios," The Journal of Real Estate Finance and Economics, Springer, vol. 49(1), pages 116-164, July.

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