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The Anisotropic Spatiotemporal Estimation of Housing Prices

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  • Jin Zhao

Abstract

This paper develops a method to identify three-dimensional anisotropic spatiotemporal autocorrelation with an application to real estate markets. A large literature modeling spatiotemporal autocorrelation in house prices assumes that the spatiotemporal dependence structure is isotropic: a function of only distances between observations, and therefore the direction effect is dismissed. If the importance of direction is dismissed or understated, an estimation result would be biased and therefore less precise unless the distribution of observations is in rare case of being directional homogeneous. This paper thus proposes a local anisotropic spatiotemporal approach to improve estimation performance. The methodology is illustrated by using data on single-family house transactions in the San Francisco Bay Area. The empirical results suggest that the proposed three-dimensional anisotropic modeling technique can reduce both in-sample estimation and out-of-sample forecast errors. Copyright Springer Science+Business Media New York 2015

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  • Jin Zhao, 2015. "The Anisotropic Spatiotemporal Estimation of Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 50(4), pages 484-516, May.
    • Handle: RePEc:kap:jrefec:v:50:y:2015:i:4:p:484-516
    DOI: 10.1007/s11146-014-9478-8
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    References listed on IDEAS

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    1. Peter Colwell & Henry Munneke, 2009. "Directional Land Value Gradients," The Journal of Real Estate Finance and Economics, Springer, vol. 39(1), pages 1-23, July.
    2. Jean Dubé & Diègo Legros, 2013. "A spatio-temporal measure of spatial dependence: An example using real estate data," Papers in Regional Science, Wiley Blackwell, vol. 92(1), pages 19-30, March.
    3. 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.
    4. Bing Zhu & Roland Füss & Nico Rottke, 2011. "The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 42(4), pages 542-565, May.
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    Cited by:

    1. Jin Zhao, 2019. "Information Entropy-Based Housing Spatiotemporal Dependence," The Journal of Real Estate Finance and Economics, Springer, vol. 58(1), pages 21-50, January.

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