Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources

TL;DR

This paper presents a novel machine learning approach that combines heterogeneous web-sourced data to automate real estate valuation, reducing reliance on expert structural data and enabling scalable, accurate property assessments across Italy.

Contribution

It introduces a new web data acquisition methodology and a machine learning model that integrates diverse data sources for automated real estate valuation.

Findings

Effective valuation accuracy demonstrated in the Turin case study

Method scalable to the entire Italian territory

Potential to reduce reliance on expert structural data

Abstract

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