Abstract
The latest applications of hedonic dwelling price models have included recent advances in spatial analysis that control for spatial dependence and heterogeneity. The study of spatial aspects of hedonic modelling pertains to spatial econometrics, which is relevant to this study because it clearly accounts for the influence and peculiarities related by space in real estate price modeling analysis.
The research reported herein introduces regression-kriging as a geostatistical method for obtaining econometric models in the analysis of real estate. The aim of this study is to compare the efficacy of regression-kriging (RK) with common regression and geographically weighted regression (GWR) methods of econometric modelling.
The spatial predictors, given as raster maps, were used as auxiliary inputs necessary for regression modeling. In addition to standard environmental predictors, some socio-economic data such as distribution, ages and income of inhabitants, were prepared in the same manner enabling their use in a GIS supported environment. Based upon global and local spatial analysis (Moran’s indices), we inspected spatial pattern and heterogeneity in model residuals for all considered methods. The obtained results indicate a similar spatial pattern of model residuals for RK and GWR methods. A spatial-econometric hedonic dwelling price model was developed and estimated for the Belgrade metropolitan area based on cross-sectional and georeferenced transaction data.
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Acknowledgments
This work was supported by the Ministry of Science of the Republic of Serbia (Contracts No. III 47014, TR 36035 and TR 36009).
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Bajat, B., Kilibarda, M., Pejović, M., Petrović, M.S. (2018). Spatial Hedonic Modeling of Housing Prices Using Auxiliary Maps. In: Thill, JC. (eds) Spatial Analysis and Location Modeling in Urban and Regional Systems. Advances in Geographic Information Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37896-6_5
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DOI: https://doi.org/10.1007/978-3-642-37896-6_5
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