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Findings from a Cross-Sectional Housing Risk-Factor Model

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  • Eli Beracha
  • Hilla Skiba

Abstract

Housing data from the last 25 years show that returns to residential real estate in the U.S. can be volatile and vary significantly among locations. The variations in returns are driven by economically as well as geographically and psychologically motivated factors, but so far, no asset pricing model that adequately explains systematic risks in cross-sectional housing returns is widely accepted. This paper proposes an asset pricing model for housing returns that includes a market-wide return factor, an economically motivated factor derived from income growth, a geographically based factor derived from land supply elasticity and a momentum factor, which is psychological in nature. The model explains well the systematic risks in housing returns and is robust to different portfolio segmentations. Moreover, the model illustrates that local risk factors indirectly capture the risk previously attributed to market-wide price changes. While housing is not actively traded when compared to other financial assets, understanding the risk-factors that explain housing return in cross-section provides important insight for real estate investors, builders, real estate future traders, homeowners, banks and other mortgage lenders. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Eli Beracha & Hilla Skiba, 2013. "Findings from a Cross-Sectional Housing Risk-Factor Model," The Journal of Real Estate Finance and Economics, Springer, vol. 47(2), pages 289-309, August.
    • Handle: RePEc:kap:jrefec:v:47:y:2013:i:2:p:289-309
    DOI: 10.1007/s11146-011-9360-x
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    3. P. S. Morawakage & G. Earl & B. Liu & E. Roca & A. Omura, 2023. "Housing Risk and Returns in Submarkets with Spatial Dependence and Heterogeneity," The Journal of Real Estate Finance and Economics, Springer, vol. 67(4), pages 695-734, November.
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    6. Yuming Li & Jing Yang, 2020. "Momentum Strategies with Home Price Indices and Stocks," International Real Estate Review, Global Social Science Institute, vol. 23(2), pages 235-266.

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