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The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients


  • Hany Guirguis


  • Christos Giannikos
  • Randy Anderson


The US housing market has experienced significant cyclical volatility over the last twenty-five years due to major structural changes and economic fluctuations. In addition, the housing market is generally considered to be weak form inefficient. Houses are relatively illiquid, exceptionally heterogeneous, and are associated with large transactions costs. As such, past research has shown that it is possible to predict, at least partially, the time path of housing prices. The ability to predict housing prices is important such that investors can make better asset allocation decisions, including the pricing and underwriting of mortgages. Most of the prior studies examining the US housing market have employed constant coefficient approaches to forecast house price movements. However, this approach is not optimal as an examination of data reveals substantial sub-sample parameter instability. To account for the parameter instability, we employ alternative estimation methodologies where the estimated parameters are allowed to vary over time. The results provide strong empirical evidence in favor of utilizing the rolling Generalized Autoregressive Conditional Heteroskedastic (GARCH) Model and the Kalman Filter with an Autoregressive Presentation (KAR) for the parameters’ time variation. Lastly, we provide out-of-sample forecasts and demonstrate the precision of our approach. Copyright Springer Science + Business Media, Inc. 2004

Suggested Citation

  • Hany Guirguis & Christos Giannikos & Randy Anderson, 2004. "The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients," The Journal of Real Estate Finance and Economics, Springer, vol. 30(1), pages 33-53, October.
  • Handle: RePEc:kap:jrefec:v:30:y:2004:i:1:p:33-53 DOI: 10.1007/s11146-004-4830-z

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    References listed on IDEAS

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    Cited by:

    1. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
    2. Park, Donghyun & Xiao, Qin, 2009. "Housing Prices and the Role of Speculation: The Case of Seoul," ADB Economics Working Paper Series 146, Asian Development Bank.
    3. Michail Karoglou & Bruce Morley & Dennis Thomas, 2013. "Risk and Structural Instability in US House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(3), pages 424-436, April.
    4. Mahua Barari & Nityananda Sarkar & Srikanta Kundu & Kushal Banik Chowdhury, 2014. "Forecasting House Prices in the United States with Multiple Structural Breaks," International Econometric Review (IER), Econometric Research Association, vol. 6(1), pages 1-23, April.
    5. 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.
    6. Qin Xiao & Donghyun Park, 2010. "Seoul housing prices and the role of speculation," Empirical Economics, Springer, vol. 38(3), pages 619-644, June.
    7. Chang, Kuang-Liang, 2010. "House price dynamics, conditional higher-order moments, and density forecasts," Economic Modelling, Elsevier, vol. 27(5), pages 1029-1039, September.
    8. 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.


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