| dc.creator | Marzagão, Thiago | |
| dc.creator | Ferreira, Rodrigo | |
| dc.creator | Sales, Leonardo | |
| dc.date | 2021-07-07 | |
| dc.date.accessioned | 2022-11-03T20:57:57Z | |
| dc.date.available | 2022-11-03T20:57:57Z | |
| dc.identifier | https://bibliotecadigital.fgv.br/ojs/index.php/rbe/article/view/79823 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5044286 | |
| dc.description | 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. | en-US |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | EGV EPGE | pt-BR |
| dc.relation | https://bibliotecadigital.fgv.br/ojs/index.php/rbe/article/view/79823/79909 | |
| dc.rights | Copyright (c) 2021 Revista Brasileira de Economia | pt-BR |
| dc.source | Revista Brasileira de Economia; Vol. 75 No. 1 (2021): JAN-MAR; 29-36 | en-US |
| dc.source | Revista Brasileira de Economia; v. 75 n. 1 (2021): JAN-MAR; 29-36 | pt-BR |
| dc.source | 1806-9134 | |
| dc.source | 0034-7140 | |
| dc.subject | real estate | en-US |
| dc.subject | hedonic pricing | en-US |
| dc.subject | market behavior | en-US |
| dc.title | A note on real estate appraisal in Brazil | en-US |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | Articles | en-US |
| dc.type | Artigos | pt-BR |
