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Spatial Prediction Models for Real Estate Market Analysis

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  • Krzysztof Chrostek
  • Katarzyna Kopczewska

Abstract

The econometric modeling of real estate prices is an important step in their valuation. As shown in the theory and practice of valuation, the most important determinant of these prices is location. Therefore, models comprising the spatial components give better estimates than a-spatial models. The purpose of this paper is to compare the quality of prediction for several models: a classical linear model estimated with OLS, linear OLS model including geographical coordinates, Spatial Expansion model, spatial lag and spatial error models, and geographically weighted regression. The evaluation will be based on the calibrated models for the real estate market data in Wroclaw in 2011. The study confirms that the inclusion of the spatial aspect of the analysis may result in improvement in the quality of models. Best fit to the data among the presented methods has proved a geographically weighted regression.

Suggested Citation

  • Krzysztof Chrostek & Katarzyna Kopczewska, 2013. "Spatial Prediction Models for Real Estate Market Analysis," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 35.
    • Handle: RePEc:eko:ekoeko:35_25
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    File URL: http://ekonomia.wne.uw.edu.pl/ekonomia/getFile/376
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    References listed on IDEAS

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    1. Danlin Yu & Yehua Dennis Wei & Changshan Wu, 2007. "Modeling Spatial Dimensions of Housing Prices in Milwaukee, WI," Environment and Planning B, , vol. 34(6), pages 1085-1102, December.
    2. Shmuel S. Oren & Stephen A. Smith & Robert B. Wilson, 1982. "Nonlinear Pricing in Markets with Interdependent Demand," Marketing Science, INFORMS, vol. 1(3), pages 287-313.
    3. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
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    Cited by:

    1. Katarzyna Kopczewska & Mateusz Kopyt & Piotr Ćwiakowski, 2021. "Spatial Interactions in Business and Housing Location Models," Land, MDPI, vol. 10(12), pages 1-25, December.
    2. Marco Locurcio & Pierluigi Morano & Francesco Tajani & Felicia Di Liddo, 2020. "An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context," Sustainability, MDPI, vol. 12(14), pages 1-29, July.
    3. Felix Lorenz & Jonas Willwersch & Marcelo Cajias & Franz Fuerst, 2023. "Interpretable machine learning for real estate market analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(5), pages 1178-1208, September.
    4. Marcelo Cajias, 2017. "Is there room for another hedonic model? –The advantages of the GAMLSS approach in real estate research," ERES eres2017_226, European Real Estate Society (ERES).

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