Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources

Francesco Bergadano, Roberto Bertilone, Daniela Paolotti, Giancarlo Francesco RUFFO

Risultato della ricerca: Contributo su rivistaArticolo in rivistapeer review

Abstract

In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM described here can help the stakeholders in this process (experts, appraisal company) to provide a reference price to be used by the expert, to allow the appraisal company to validate their evaluations in a faster and cheaper way, to help the expert in listing a set of comparable properties, that need to be included in the appraisal document.
Lingua originaleInglese
pagine (da-a)72-85
Numero di pagine14
RivistaINTERNATIONAL JOURNAL OF REAL ESTATE STUDIES
Volume15
Numero di pubblicazione1
Stato di pubblicazionePubblicato - 1 gen 2021

Keywords

  • Automated Valuation Models
  • real estate appraisal
  • open data
  • Machine Learning
  • Web Crawling

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