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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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    3. Monica Palma & Claudia Cappello & Sandra De Iaco & Daniela Pellegrino, 2019. "The residential real estate market in Italy: a spatio-temporal analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2451-2472, September.
    4. Thanos, Sotirios & Dubé, Jean & Legros, Diègo, 2016. "Putting time into space: the temporal coherence of spatial applications in the housing market," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 78-88.
    5. Daams, Michiel N. & Proietti, Paola & Veneri, Paolo, 2019. "The effect of asylum seeker reception centers on nearby house prices: Evidence from The Netherlands," Journal of Housing Economics, Elsevier, vol. 46(C).
    6. Brady, Ryan R., 2014. "The spatial diffusion of regional housing prices across U.S. states," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 150-166.
    7. Jorge Chica-Olmo & Rafael Cano-Guervos & Mario Chica-Rivas, 2019. "Estimation of Housing Price Variations Using Spatio-Temporal Data," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    8. Jin Hu & Xuelei Xiong & Yuanyuan Cai & Feng Yuan, 2020. "The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    9. R. Kelley Pace & Raffaella Calabrese, 2022. "Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey," The Journal of Real Estate Finance and Economics, Springer, vol. 65(1), pages 1-21, July.
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    11. Eddie Chi Man Hui & Cong Liang & Ziyou Wang & Yuan Wang, 2016. "The roles of developer’s status and competitive intensity in presale pricing in a residential market: A study of the spatio-temporal model in Hangzhou, China," Urban Studies, Urban Studies Journal Limited, vol. 53(6), pages 1203-1224, May.
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    13. Federico Dell’Anna & Marina Bravi & Carlos Marmolejo-Duarte & Marta Carla Bottero & Ai Chen, 2019. "EPC Green Premium in Two Different European Climate Zones: A Comparative Study between Barcelona and Turin," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
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