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The house price determination process: Rational expectations with a spatial context

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  • Kiefer, Hua

Abstract

Applying the rational expectations hypothesis, this essay models the current value of a house as the conditional expectation of the discounted stream of housing services accruing to the owner of the house. The value of housing services is determined by neighborhood effects as well as the physical attributes of the property itself. In the existing hedonic literature, future transactions have not been utilized to describe neighborhood effects. The rational expectations asset pricing model in this study accounts for expected future neighborhood effects as well as observed current neighborhood effects. The reduced form of the rational expectations model is a spatial autoregressive (SAR) model with two spatial lags. After employing the generalized method of moments (GMM) in estimating the spatial asset pricing model, I find that both expected future transactions and prior transactions in the neighborhood are significant. The inclusion of expected future transaction prices in the neighborhood takes into account the influence of expected changes in the community and factors these potential changes into the current house price. This is consistent with forward-looking households. The forward-looking model generates superior out-of-sample prediction performance relative to both the conventional hedonic model without considering neighborhood effects or the standard spatial hedonic model including only past transactions.

Suggested Citation

  • Kiefer, Hua, 2011. "The house price determination process: Rational expectations with a spatial context," Journal of Housing Economics, Elsevier, vol. 20(4), pages 249-266.
  • Handle: RePEc:eee:jhouse:v:20:y:2011:i:4:p:249-266
    DOI: 10.1016/j.jhe.2011.08.002
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    2. Yuan, Feng & Wu, Jiawei & Wei, Yehua Dennis & Wang, Lei, 2018. "Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China," Land Use Policy, Elsevier, vol. 75(C), pages 225-236.
    3. Xun Fan & Mengdi Liu & Bing Zhang & Weicheng Zhang, 2024. "The Green Premium: Environmental Regulation, Environmental Risk and Property Value," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(5), pages 1061-1096, May.
    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. Huang, MeiChi, 2018. "Time-varying diversification strategies: The roles of state-level housing assets in optimal portfolios," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 145-172.
    6. MeiChi Huang, 2019. "Risk diversification gains from metropolitan housing assets," Review of Financial Economics, John Wiley & Sons, vol. 37(4), pages 453-481, October.
    7. Chomsisengphet, Souphala & Kiefer, Hua & Liu, Xiaodong, 2018. "Spillover effects in home mortgage defaults: Identifying the power neighbor," Regional Science and Urban Economics, Elsevier, vol. 73(C), pages 68-82.
    8. 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.
    9. Doszyń Mariusz & Gnat Sebastian, 2017. "Econometric Identification of the Impact of Real Estate Characteristics Based on Predictive and Studentized Residuals," Real Estate Management and Valuation, Sciendo, vol. 25(1), pages 84-92, March.
    10. Aoife K. Hurley & James Sweeney, 2024. "Irish Property Price Estimation Using A Flexible Geo-spatial Smoothing Approach: What is the Impact of an Address?," The Journal of Real Estate Finance and Economics, Springer, vol. 68(3), pages 355-393, April.
    11. Xiaoqi Zhang & Yanqiao Zheng & Lei Sun & Qiwen Dai, 2019. "Urban Structure, Subway Systemand Housing Price: Evidence from Beijing and Hangzhou, China," Sustainability, MDPI, vol. 11(3), pages 1-23, January.

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