 Artificial Neural Networks vs Spatial Regression Approach in Property ValuationDamian Przekop ()
Central European Journal of Economic Modelling and Econometrics, 2022, vol. 14, issue 2, 199-223 Abstract: The purpose of this paper is to compare two approaches applied in property valuation: artificial neural networks and spatial regression. Despite the fact that artificial neural networks are often the first choice for modeling in the big data era, spatial econometrics methods offer incorporation of information on dependences between multiple objects in the studied space. Although this dependency structure can be incorporated into artificial neural network via feature engineering, this study is focused on abilities of reproducing it with machine learning method from crude coordinate data. The research is based on the database of 18,166 property sale transactions in Warsaw, Poland. According to this study, such volume of data does not allow artificial neural networks to compete in reflecting spatial dependence structure with spatial regression models. Keywords: artificial neural networks; spatial regression; SDEM; GNS; property valuation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:psc:journl:v:14:y:2022:i:2:p:199-223 Access Statistics for this article More articles in Central European Journal of Economic Modelling and Econometrics from Central European Journal of Economic Modelling and Econometrics |
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