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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

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

Suggested Citation

  • Gordon W. Crawford & Michael C. Fratantoni, 2003. "Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(2), pages 223-243, June.
  • Handle: RePEc:bla:reesec:v:31:y:2003:i:2:p:223-243
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    1. Francis X. Diebold & Glenn D. Rudebusch, 1999. "Business Cycles: Durations, Dynamics, and Forecasting," Economics Books, Princeton University Press, edition 1, number 6636.
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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. Changha Jin & Terry V. Grissom, 2008. "Forecasting Dynamic Investment Timing under the Cyclic Behavior in Real Estate," International Real Estate Review, Asian Real Estate Society, vol. 11(2), pages 105-125.
    4. 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.
    5. Chyi Lin Lee, 2009. "Housing price volatility and its determinants," International Journal of Housing Markets and Analysis, Emerald Group Publishing, vol. 2(3), pages 293-308, August.
    6. repec:spr:empeco:v:52:y:2017:i:4:d:10.1007_s00181-016-1101-9 is not listed on IDEAS
    7. Geoffrey M. Ngene & Daniel P. Sohn & M. Kabir Hassan, 2017. "Time-Varying and Spatial Herding Behavior in the US Housing Market: Evidence from Direct Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 54(4), pages 482-514, May.
    8. Benoit Faye & Éric Le Fur, 2010. "L'étude du lien entre cycle et saisonnalité sur un marché immobilier résidentiel. Le cas de l'habitat ancien à Bordeaux," Revue d'économie régionale et urbaine, Armand Colin, vol. 0(5), pages 937-965.
    9. Levy, Moshe & Kaplanski, Guy, 2015. "Portfolio selection in a two-regime world," European Journal of Operational Research, Elsevier, vol. 242(2), pages 514-524.
    10. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    11. Chang, Chia-Chien, 2014. "Valuation of Mortgage Insurance Contracts with Counterparty Default Risk: Reduced-Form Approach," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 44(02), pages 303-334, May.
    12. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
    13. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, Elsevier.
    14. Adam Misiorek & Rafal Weron, 2006. "Interval forecasting of spot electricity prices," HSC Research Reports HSC/06/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    15. Canepa, Alessandra & Chini, Emilio Zanetti, 2016. "Dynamic asymmetries in house price cycles: A generalized smooth transition model," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 91-103.
    16. 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.
    17. Huang, MeiChi & Chiang, Hsiu-Hsuan, 2017. "An early alarm system for housing bubbles," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 34-49.
    18. Frontczak, Robert & Rostek, Stefan, 2015. "Modeling loss given default with stochastic collateral," Economic Modelling, Elsevier, vol. 44(C), pages 162-170.
    19. Tom Boot & Andreas Pick, 2014. "Optimal forecasts from Markov switching models," DNB Working Papers 452, Netherlands Central Bank, Research Department.
    20. Chang, Kuang-Liang, 2010. "House price dynamics, conditional higher-order moments, and density forecasts," Economic Modelling, Elsevier, vol. 27(5), pages 1029-1039, September.
    21. Robert I. Webb & Jian Yang & Jin Zhang, 2016. "Price Jump Risk in the US Housing Market," The Journal of Real Estate Finance and Economics, Springer, vol. 53(1), pages 29-49, July.
    22. 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.

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