Abstract
Abstract The spatial distribution of real estate in specific geographic locations, real estate transactions, and the prices and values of properties are a highly complex spatial phenomena that should be analyzed with the use of multidimensional methods. Spatial factors are taken into account in the modeling process to increase the reliability of real estate market analyses, and spatial autoregressive models are applied to determine the effect of spatial factors on real estate prices and values. The present study relies on a review of the literature and the results of an experiment. The concept and principles of market analysis were designed with the use of spatial autoregressive models, and the influence of selected spatial factors on real estate prices was presented on maps. Analyses involving autoregressive models enable reliable modeling and support correct interpretation of the observed processes.
Highlights
Spatial factors play a very important role in the analyses of the real estate market, but they are not easy to incorporate in models of the evaluated space
The significance of parameters in classical regression models is not influenced by the spatial structure of the investigated phenomenon, which can lead to an incorrect interpretation of the results (Charlton & Fotheringham, 2009), in particular when real estate markets are assumed to be spatially heterogeneous
The results of the analysis indicate that transaction prices are spatially autocorrelated; spatial regression models can be applied
Summary
Spatial factors play a very important role in the analyses of the real estate market, but they are not easy to incorporate in models of the evaluated space. If spatial correlations exist between real estate prices in market datasets, classical regression models produce similar results because they do not meet the assumption of independent observations; the sample carries less information than an independent sample of the same size. These autocorrelations are taken into account in spatial autoregressive models (SAR), where the prices of neighboring real estate constitute additional explanatory variables (Ligas, 2006)
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