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Spatial and Temporal Dependence in House Price Prediction

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  • Xiaolong Liu

Abstract

This paper incorporates spatial and temporal dependence among housing transactions in predicting future house prices. We employ the spatiotemporal autoregressive model and structure the spatial and temporal weighting matrices as in Pace et al. (1998) . We control for the time variation of both the attribute prices and the spatial and temporal dependence parameters through performing the analysis on an annual basis. Spatial heterogeneity is accounted for using experience-based definition of submarkets by real estate professionals. Using a comprehensive housing transaction data set from the Dutch Randstad region, we show that integrating the spatial and temporal dependence within the hedonic modeling improves the prediction power as compared to traditional hedonic model that neglects these effects. Copyright Springer Science+Business Media, LLC 2013

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  • Xiaolong Liu, 2013. "Spatial and Temporal Dependence in House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 47(2), pages 341-369, August.
    • Handle: RePEc:kap:jrefec:v:47:y:2013:i:2:p:341-369
    DOI: 10.1007/s11146-011-9359-3
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    1. Pace, R. Kelley & Barry, Ronald & Gilley, Otis W. & Sirmans, C. F., 2000. "A method for spatial-temporal forecasting with an application to real estate prices," International Journal of Forecasting, Elsevier, vol. 16(2), pages 229-246.
    2. Pace, R Kelley & Barry, Ronald & Clapp, John M. & Rodriquez, Mauricio, 1998. "Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 15-33, July.
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