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Real estate appraisal is a complex and important task, that can be made moreprecise and faster with the help of automated valuation tools. Usually thevalue of some property is determined by taking into account both structural andgeographical characteristics. However, while geographical information is easilyfound, obtaining significant structural information requires the interventionof a real estate expert, a professional appraiser. In this paper we propose aWeb data acquisition methodology, and a Machine Learning model, that can beused to automatically evaluate real estate properties. This method uses datafrom previous appraisal documents, from the advertised prices of similarproperties found via Web crawling, and from open data describing thecharacteristics of a corresponding geographical area. We describe a case study,applicable to the whole Italian territory, and initially trained on a data setof individual homes located in the city of Turin, and analyze prediction andpractical applicability.

Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources