[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119377-en":3,"doc-seo-119377-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119377,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil - Article summary","Mass appraisal supports urban planning, real estate valuation, and property taxation by estimating property values across large datasets. Because analyzing massive models is challenging, semi-automatic assessment and machine learning are widely adopted. This study evaluates traditional statistical and machine learning appraisal models and tests whether adding spatial information improves the capture of typical spatial dependence in the real estate market, reducing spatial autocorrelation in residuals. Using data for 43,000+ apartments in Fortaleza, Brazil, nine algorithms produce comparable results, with XGBoost minimizing spatial autocorrelation and MRA/M5P/MARS offering the most interpretable outputs.","AES TIM UM  \nCENTRO STUDI DI ESTIMO E DI ECONOMIA TERRITORIALE-Ce.S. E.T.  \nFirenze University Press [www.fupress.com/ceset](www.fupress.com/ceset)  \nCitation: Oliveira, A. A. F. , ReyesBueno, F. , González, M. A . S. & Silva, E. (2024) . Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil. Aestimum 85: 21-38. doi: 10.36253/aestim-15344  \nReceived: November 15, 2023  \nAccepted: July 2, 2024  \nPublished: February 14, 2025  \n© 2024 Author(s) . This is an open access, peer-reviewed article published by Firenze University Press ([https://www.fupress.com](https://www.fupress.com)) and distributed, except where otherwise noted, under the terms of the CC BY 4.0 License for content and CC0 1.0 Universal for metadata.  \nData Availability Statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  \nConflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  \nORCID:  \nAAFO: 0000-0003-3890-4219  \nFRB: 0000-0002-5646-0263  \nMASG: 0000-0002-1975-0026  \nEdS: 0000-0001-9724-8384  \nComparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil  \nAntônio Augusto Ferreira de Oliveira1, Fabián Reyes-Bueno2, Marco Aurelio Stumpf González3,*, Everton da Silva4  \n1 Municipal treasury auditor of Fortaleza, Brazil  \n2 Facultad de Ciencias Exactas y Naturales, Universidad Técnica Particular de Loja, Loja, Ecuador  \n3 Polytechnic School, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil  \n4 Geosciences Department, Universidade Federal de Santa Catarina, Florianópolis, Brazil E-mail: [augusto.oliveira@sefin.fortaleza.ce.gov.br](augusto.oliveira@sefin.fortaleza.ce.gov.br) ; [frreyes@utpl.edu.ec](frreyes@utpl.edu.ec); mgonzalez@ [unisinos.br](unisinos.br); [everton.silva@ufsc.br](everton.silva@ufsc.br)  \n*Corresponding author  \nAbstract. 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.  \nKeywords: semi-automatic assessment methods, mass appraisal techniques, machine learning.  \nJEL codes: O18, R33 .  \n1. INTRODUCTION  \nThe real estate market is a segment of the economy, and as such, the importance of traded goods is measured through the sales prices reached because of buyer and seller agreements. This market presents macro and microeconomic aspects. Macroeconomic aspects are related to government decisions, the conduct of the economy, international influences, interest rates,  \nAestimum 85: 21-38, 2024  \nISSN 1592-6117 (print) | ISSN 1724-2118 (online) | DOI: 10.36253/aestim-15344  \n22 Antônio Augusto Ferreira de Oliveira et al.  \nand national and regional economic growth, among others. Microeconomic asp","cbCaipKuV3habAD9","https://ap.wps.com/l/cbCaipKuV3habAD9","pdf",1212794,1,18,"English","en",105,"# Introduction\n## Real estate market and pricing drivers\n## Mass appraisal and automated valuation models\n# Methodological approach\n## Spatial data analysis and representativeness\n## Traditional statistical vs machine learning models\n## Model comparison and evaluation targets\n# Results and discussion\n## Spatial autocorrelation in residuals\n## Accuracy–interpretability trade-off","[{\"question\":\"What problem does mass appraisal address in property taxation and planning?\",\"answer\":\"It provides a systematic way to value many properties simultaneously using standardized methods, enabling consistent valuation for taxation and urban planning.\"},{\"question\":\"How does the study use spatial information in appraisal models?\",\"answer\":\"It incorporates spatial information to better capture spatial dependence in the real estate market and to reduce spatial autocorrelation observed in model residuals.\"},{\"question\":\"Which machine learning approach performed best for spatial autocorrelation and which methods were easiest to interpret?\",\"answer\":\"XGBoost minimized spatial autocorrelation, while MRA, M5P, and MARS were the easiest to interpret, despite showing the greatest residual spatial autocorrelations.\"}]","Comparing traditional and machine learning techniques in apartments mass appraisal in Fortaleza, Brazil - 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