Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values

Fil: Carranza, Juan Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales. Instituto de Investigación y Formación en Administración Pública; Argentina.

Detalles Bibliográficos
Autores principales: Carranza, Juan Pablo, Piumetto, Mario, Lucca, Carlos, Da Silva, Everton
Otros Autores: https://orcid.org/0000-0003-4793-1323
Formato: publishedVersion article
Lenguaje:Inglés
Publicado: 2024
Materias:
Machine learning
Open data
Mass appraisal
Urban land value
Acceso en línea:http://hdl.handle.net/11086/554563
https://www.sciencedirect.com/journal/land-use-policy/vol/119/suppl/C
https://doi.org/10.1016/j.landusepol.2022.106211
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-554563
record_format dspace
spelling I10-R141-11086-5545632024-12-13T06:24:59Z Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values Carranza, Juan Pablo Piumetto, Mario Lucca, Carlos Da Silva, Everton https://orcid.org/0000-0003-4793-1323 https://orcid.org/0000-0002-3679-9761 https://orcid.org/0000-0001-9724-8384 Machine learning Open data Mass appraisal Urban land value info:eu-repo/semantics/publishedVersion Fil: Carranza, Juan Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales. Instituto de Investigación y Formación en Administración Pública; Argentina. Fil: Piumetto, Mario. Universidad Nacional de Cordoba. Facultad de Ciencias Exactas, Físicas y Naturales. Centro de Estudios Territoriales; Argentina. Fil: Lucca, Carlos. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales. Instituto de Investigación y Formación en Administración Pública; Argentina. Fil: Da Silva, Everton. Universidade Federal de Santa Catarina. Departamento de Geociencias; Brazil. Updated cadastral land values are a matter of critical importance for local governments: higher revenue ofproperty taxes, more equitable treatment to taxpayers, a fundamental input in the design of public policiesrelated to access to land and housing for the most vulnerable and a key feature in land value capture strategies tofinance public infrastructure, to name just a few public policies that require correct valuations of land. However,in Latin America, outdated cadastral values are common to most cities. The reasons for this can be found in thecomplexity of the mass appraisal process, lack of institutional and fiscal capacity to undertake it and bureaucraticresistance to its implementation.The objective of this paper is to present a mass appraisal methodology that uses only free and open data toachieve robust urban land valuations. Information from the OpenStreetMap Project is used to generate severalland variables. In addition, the Global Human Settlement Layer of the European Commission is used to determinethe level of consolidation of urban sprawl. Land value data were obtained from the Mapa de Valores de Am´ericaLatina, a collaborative initiative that systemizes more than 68,000 data from more than 900 cities.This information is used to train three tree-based machine learning models: Random Forest, Quantile RandomForest and Gradient Boosting Model. The results support the viability of the proposed strategy, simplifying themass appraisal process in terms of costs, time and complexity of the information used. https://www.sciencedirect.com/journal/land-use-policy/vol/119/suppl/C info:eu-repo/semantics/publishedVersion Fil: Carranza, Juan Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales. Instituto de Investigación y Formación en Administración Pública; Argentina. Fil: Piumetto, Mario. Universidad Nacional de Cordoba. Facultad de Ciencias Exactas, Físicas y Naturales. Centro de Estudios Territoriales; Argentina. Fil: Lucca, Carlos. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales. Instituto de Investigación y Formación en Administración Pública; Argentina. Fil: Da Silva, Everton. Universidade Federal de Santa Catarina. Departamento de Geociencias; Brazil. Estudios Urbanos (Planeamiento y Desarrollo) 2024-12-12T19:21:27Z 2024-12-12T19:21:27Z 2022 article http://hdl.handle.net/11086/554563 1873-5754 https://www.sciencedirect.com/journal/land-use-policy/vol/119/suppl/C https://doi.org/10.1016/j.landusepol.2022.106211 eng Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ Electrónico y/o Digital
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Machine learning
Open data
Mass appraisal
Urban land value
spellingShingle Machine learning
Open data
Mass appraisal
Urban land value
Carranza, Juan Pablo
Piumetto, Mario
Lucca, Carlos
Da Silva, Everton
Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values
topic_facet Machine learning
Open data
Mass appraisal
Urban land value
description Fil: Carranza, Juan Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales. Instituto de Investigación y Formación en Administración Pública; Argentina.
author2 https://orcid.org/0000-0003-4793-1323
author_facet https://orcid.org/0000-0003-4793-1323
Carranza, Juan Pablo
Piumetto, Mario
Lucca, Carlos
Da Silva, Everton
format publishedVersion
article
author Carranza, Juan Pablo
Piumetto, Mario
Lucca, Carlos
Da Silva, Everton
author_sort Carranza, Juan Pablo
title Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values
title_short Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values
title_full Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values
title_fullStr Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values
title_full_unstemmed Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values
title_sort mass appraisal as affordable public policy: open data and machine learning for mapping urban land values
publishDate 2024
url http://hdl.handle.net/11086/554563
https://www.sciencedirect.com/journal/land-use-policy/vol/119/suppl/C
https://doi.org/10.1016/j.landusepol.2022.106211
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