Vol. 85 (2024)
Original Articles - Urban, Land, Environmental Appraisal and Economics

Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil

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  • Antônio Augusto Ferreira de Oliveira,
  • Fabián Reyes-Bueno,
  • Marco Aurelio Stumpf Gonzalez,
  • Éverton da Silva
Antônio Augusto Ferreira de Oliveira
Municipal treasury auditor of Fortaleza, Brazil
Fabián Reyes-Bueno
Facultad de Ciencias Exactas y Naturales, Universidad Técnica Particular de Loja, Loja, Ecuador
Marco Aurelio Stumpf Gonzalez
Polytechnic School, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil
Éverton da Silva
Geosciences Department, Universidade Federal de Santa Catarina, Florianópolis, Brazil
DOI: https://doi.org/10.36253/aestim-15344

Published 2025-02-14

Keywords

  • semi-automatic assessment methods,
  • mass appraisal techniques,
  • machine learning

Copyright (c) 2024 Augusto, Fabián, MARCO AURELIO STUMPF GONZALEZ, Éverton

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

Mass appraisal has significant applications, such as urban planning, real estate appraisal, and property tax. Due to the challenges of analyzing massive models, they are often developed using semi-automatic assessment methods and machine learning techniques. This article explores different appraisal model methods that utilize statistics and machine learning. It also looks at incorporating spatial information to see if the chosen method can effectively capture the typical spatial dependency of the real estate market. This can help reduce the spatial autocorrelation observed in the residuals. The study compared nine machine learning methods with traditional statistical approaches using a dataset of over 43,000 apartments in Fortaleza, Brazil. The results of the machine learning algorithms were similar. The XGBoost minimized spatial autocorrelation. The easiest interpretations were with MRA, M5P, and MARS techniques. Although, these techniques had the greatest residual spatial autocorrelations. There is a trade-off between the methods, depending on whether the aim is to improve accuracy or provide a clear explanation for property taxation.

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