The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw

Dawid Siwicki

No 2021-05, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: The principal aim of this paper is to investigate the potential of machine learning algorithms in context of predicting housing prices. The most important issue in modelling spatial data is to consider spatial heterogeneity that can bias obtained results when is not taken into consideration. The purpose of this research is to compare prediction power of such methods: linear regression, artificial neural network, random forest, extreme gradient boosting and spatial error model. The evaluation was conducted using train, validation, test and k-Fold Cross-Validation methods. We also examined the ability of the above models to identify spatial dependencies, by calculating Moran’s I for residuals obtained on in-sample and out-of-sample data.

Keywords: spatial analysis; machine learning; housing market; random forest; gradient boosting (search for similar items in EconPapers)
JEL-codes: C31 C45 C52 C53 C55 R31 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-geo, nep-ore and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://www.wne.uw.edu.pl/index.php/download_file/6326/ First version, 2021 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2021-05

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