TY - JOUR
T1 - Developing Real Estate Automated Valuation Models by Learning from Heterogeneous
Data Sources
AU - Bergadano, Francesco
AU - Bertilone, Roberto
AU - Paolotti, Daniela
AU - RUFFO, Giancarlo Francesco
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - Automated Valuation Models
KW - real estate appraisal
KW - open data
KW - Machine Learning
KW - Web Crawling
KW - Automated Valuation Models
KW - real estate appraisal
KW - open data
KW - Machine Learning
KW - Web Crawling
UR - https://iris.uniupo.it/handle/11579/144949
M3 - Article
SN - 2231-7643
VL - 15
SP - 72
EP - 85
JO - INTERNATIONAL JOURNAL OF REAL ESTATE STUDIES
JF - INTERNATIONAL JOURNAL OF REAL ESTATE STUDIES
IS - 1
ER -