Mass appraisal of apartments using Random Forest and Gradient Boosting algorithms: case study of Florianópolis, Brazil
DOI:
https://doi.org/10.11606/eISSN.2236-2878.rdg.2024.212297Keywords:
Mass appraisal of properties, Property tax, Machine learning, Web scrappingAbstract
Property tax is an important tool of urban policy, and its calculation is based on the assessed market value of the property, typically determined through mass appraisals. This study evaluates the predictive performance of the machine learning algorithms random forest and gradient boosting in the mass appraisal of urban properties, comparing them to classical linear regression. A total of 8,694 real estate market data points were collected using web scraping techniques, and after initial processing with inclusion criteria, 1,572 apartment data points from the central region of Florianópolis, Brazil, were selected for modeling. The results indicated that the gradient boosting model outperformed all others across metrics such as RMSE, MAE, MAPE, COD, PRD, and R², with predictions up to 30% more accurate, confirming its potential to estimate apartment market values in a robust and equitable manner. These findings reinforce gradient boosting as a viable alternative for generating the property tax base, enabling fairer and more equitable taxation, thereby achieving fiscal justice and tax transparency.
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