Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil
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...
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| Format: | Article |
| Language: | English |
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Firenze University Press
2025-02-01
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| Series: | Aestimum |
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| Online Access: | https://oaj.fupress.net/index.php/ceset/article/view/15344 |
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| author | Antônio Augusto Ferreira de Oliveira Fabián Reyes-Bueno Marco Aurelio Stumpf Gonzalez Éverton da Silva |
| author_facet | Antônio Augusto Ferreira de Oliveira Fabián Reyes-Bueno Marco Aurelio Stumpf Gonzalez Éverton da Silva |
| author_sort | Antônio Augusto Ferreira de Oliveira |
| collection | DOAJ |
| description | 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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| format | Article |
| id | doaj-art-14ac92bb3d1a4078b4dab6d7a3fc5211 |
| institution | DOAJ |
| issn | 1592-6117 1724-2118 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Firenze University Press |
| record_format | Article |
| series | Aestimum |
| spelling | doaj-art-14ac92bb3d1a4078b4dab6d7a3fc52112025-08-20T03:11:39ZengFirenze University PressAestimum1592-61171724-21182025-02-018510.36253/aestim-15344Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, BrazilAntônio Augusto Ferreira de Oliveira0Fabián Reyes-Bueno1Marco Aurelio Stumpf Gonzalez2Éverton da Silva3Municipal treasury auditor of Fortaleza, BrazilFacultad de Ciencias Exactas y Naturales, Universidad Técnica Particular de Loja, Loja, EcuadorPolytechnic School, Universidade do Vale do Rio dos Sinos, São Leopoldo, BrazilGeosciences Department, Universidade Federal de Santa Catarina, Florianópolis, BrazilMass 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. https://oaj.fupress.net/index.php/ceset/article/view/15344 semi-automatic assessment methodsmass appraisal techniquesmachine learning |
| spellingShingle | Antônio Augusto Ferreira de Oliveira Fabián Reyes-Bueno Marco Aurelio Stumpf Gonzalez Éverton da Silva Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil Aestimum semi-automatic assessment methods mass appraisal techniques machine learning |
| title | Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil |
| title_full | Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil |
| title_fullStr | Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil |
| title_full_unstemmed | Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil |
| title_short | Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil |
| title_sort | comparing traditional and machine learning techniques in apartments mass appraisal in fortaleza brazil |
| topic | semi-automatic assessment methods mass appraisal techniques machine learning |
| url | https://oaj.fupress.net/index.php/ceset/article/view/15344 |
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