A note on real estate appraisal in Brazil

Brazilian banks commonly use linear regression to appraise real estate: they regress price on features like area, location, etc, and use the resulting model to estimate the market value of the target property. But Brazilian banks do not test the predictive performance of those models, which for all...

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Bibliographic Details
Published inRevista brasileira de economia Vol. 75; no. 1; p. 29
Main Authors Marzagão, Thiago, Ferreira, Rodrigo, Sales, Leonardo
Format Journal Article
LanguageEnglish
Published Rio de Janeiro Fundação Getulio Vargas 2021
Subjects
Algorithms
Appraisal
Appraisers
Econometrics
Machine learning
Market value
Neural networks
Python
Real estate
Real estate appraisal
Regression analysis
Support vector machines
Trees
Online AccessGet full text
ISSN0034-7140
1806-9134
DOI10.5935/0034-7140.20210003

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Summary:Brazilian banks commonly use linear regression to appraise real estate: they regress price on features like area, location, etc, and use the resulting model to estimate the market value of the target property. But Brazilian banks do not test the predictive performance of those models, which for all we know are no better than random guesses. That introduces huge inefficiencies in the real estate market. Here we propose a machine learning approach to the problem. We use real estate data scraped from 15 thousand online listings and use it to fit a boosted trees model. The resulting model has a median absolute error of 8,16%. We provide all data and source code.
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ISSN:0034-7140
1806-9134
DOI:10.5935/0034-7140.20210003