Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing

Jose Torres-Pruñonosa, Pablo García-Estévez and Camilo Prado-Roman ()
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Jose Torres-Pruñonosa: Facultad de Empresa y Comunicación, Universidad Internacional de la Rioja, 26006 Logroño, Spain
Pablo García-Estévez: Colegio Universitario de Estudios Financieros (CUNEF), 28040 Madrid, Spain

Mathematics, 2021, vol. 9, issue 7, 1-16

Abstract: We used a large sample of 188,652 properties, which represented 4.88% of the total housing stock in Catalonia from 1994 to 2013, to make a comparison between different real estate valuation methods based on artificial neural networks (ANNs), quantile regressions (QRs) and semi-log regressions (SLRs). A literature gap in regard to the comparison between ANN and QR modelling of hedonic prices in housing was identified, with this article being the first paper to include this comparison. Therefore, this study aimed to answer (1) whether QR valuation modelling of hedonic prices in the housing market is an alternative to ANNs, (2) whether it is confirmed that ANNs produce better results than SLRs when assessing housing in Catalonia, and (3) which of the three mass appraisal models should be used by Spanish banks to assess real estate. The results suggested that the ANNs and SLRs obtained similar and better performances than the QRs and that the SLRs performed better when the datasets were smaller. Therefore, (1) QRs were not found to be an alternative to ANNs, (2) it could not be confirmed whether ANNs performed better than SLRs when assessing properties in Catalonia and (3) whereas small and medium banks should use SLRs, large banks should use either SLRs or ANNs in real estate mass appraisal.

Keywords: artificial neural networks; banking; hedonic prices; housing; quantile regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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