Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina.

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The real estate market plays an important role in the economy and society, therefore, the downgrading of cadastral valuations, particularly urban land, has harmful effects on tax, territorial and housing public policies, property market, as in the stability of the finance system. For this reason, th...

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Autores principales: Carranza, Juan Pablo, Piumetto, Mario Andrés, Salomón, Micael Jeremías, Monzani, Federico, Montenegro, Marcos Gaspar, Córdoba, Mariano Augusto
Formato: Artículo revista
Lenguaje:Español
Publicado: Instituto de Investigación de Vivienda y Hábitat 2019
Materias:
Land value
Mass appraisal
Machine Learning
Random Forest
Ordinary Kriging
Valor del Suelo
Valuación masiva
Kriging Ordinario
Acceso en línea:https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/27090
Aporte de:
Vivienda y Ciudad de Universidad Nacional de Córdoba
id I10-R355-article-27090
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spelling I10-R355-article-270902020-01-02T16:45:14Z Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina. Valuación masiva de la tierra urbana mediante inteligencia artificial. El caso de la ciudad de San Francisco, Córdoba, Argentina. Carranza, Juan Pablo Piumetto, Mario Andrés Salomón, Micael Jeremías Monzani, Federico Montenegro, Marcos Gaspar Córdoba, Mariano Augusto Land value Mass appraisal Machine Learning Random Forest Ordinary Kriging Valor del Suelo Valuación masiva Machine Learning Random Forest Kriging Ordinario The real estate market plays an important role in the economy and society, therefore, the downgrading of cadastral valuations, particularly urban land, has harmful effects on tax, territorial and housing public policies, property market, as in the stability of the finance system. For this reason, the cadastres face the challenge of developing massive valuations of a jurisdiction in order to provide updated and quality data, quickly and efficiently. Given the technological advance, the generation of large volumes of information and the progress associated with computer science, the ideas of massive appraisal of real estate by the catastres is increasingly taking hold. Under these needs and new situation, the results reflects the advantage of the predictive capacity in estimating the value of urban land by applying an algorithmic technique of machine learning, known as Random Forest, in combination with a geo-statistical technique called Ordinary Kriging for the treatment of error. El mercado inmobiliario desempeña un papel importante en la economía y la sociedad, por lo tanto, la desactualización de las valuaciones catastrales, en particular del suelo urbano, tiene efectos nocivos sobre las políticas públicas impositivas, territoriales y de vivienda, como en la estabilidad del sistema financiero. Por tal motivo, los catastros afrontan el desafío de desarrollar valuaciones masivas de una jurisdicción con el fin de proveer datos actualizados y de calidad, de manera rápida y eficiente. Dado el avance tecnológico, la generación de grandes volúmenes de información y los progresos asociados a las ciencias de la computación. Los resultados obtenidos permiten resaltar la ventaja de la capacidad predictiva en la estimación del valor del suelo urbano mediante la aplicación de una técnica algorítmica de aprendizaje automático, conocida como Random Forest, en combinación con una técnica geo-estadística llamada Kriging Ordinario para el tratamiento de los residuos frente a un método econométrico clásico, regresión lineal. Instituto de Investigación de Vivienda y Hábitat 2019-12-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/27090 Vivienda y Ciudad; Núm. 6 (2019); 90-112 2422-670X spa https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/27090/28749
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-355
container_title_str Vivienda y Ciudad
language Español
format Artículo revista
topic Land value
Mass appraisal
Machine Learning
Random Forest
Ordinary Kriging
Valor del Suelo
Valuación masiva
Machine Learning
Random Forest
Kriging Ordinario
spellingShingle Land value
Mass appraisal
Machine Learning
Random Forest
Ordinary Kriging
Valor del Suelo
Valuación masiva
Machine Learning
Random Forest
Kriging Ordinario
Carranza, Juan Pablo
Piumetto, Mario Andrés
Salomón, Micael Jeremías
Monzani, Federico
Montenegro, Marcos Gaspar
Córdoba, Mariano Augusto
Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina.
topic_facet Land value
Mass appraisal
Machine Learning
Random Forest
Ordinary Kriging
Valor del Suelo
Valuación masiva
Machine Learning
Random Forest
Kriging Ordinario
author Carranza, Juan Pablo
Piumetto, Mario Andrés
Salomón, Micael Jeremías
Monzani, Federico
Montenegro, Marcos Gaspar
Córdoba, Mariano Augusto
author_facet Carranza, Juan Pablo
Piumetto, Mario Andrés
Salomón, Micael Jeremías
Monzani, Federico
Montenegro, Marcos Gaspar
Córdoba, Mariano Augusto
author_sort Carranza, Juan Pablo
title Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina.
title_short Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina.
title_full Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina.
title_fullStr Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina.
title_full_unstemmed Mass appraisal of urban land value using artificial intelligence. The case of San Francisco city, Córdoba, Argentina.
title_sort mass appraisal of urban land value using artificial intelligence. the case of san francisco city, córdoba, argentina.
description The real estate market plays an important role in the economy and society, therefore, the downgrading of cadastral valuations, particularly urban land, has harmful effects on tax, territorial and housing public policies, property market, as in the stability of the finance system. For this reason, the cadastres face the challenge of developing massive valuations of a jurisdiction in order to provide updated and quality data, quickly and efficiently. Given the technological advance, the generation of large volumes of information and the progress associated with computer science, the ideas of massive appraisal of real estate by the catastres is increasingly taking hold. Under these needs and new situation, the results reflects the advantage of the predictive capacity in estimating the value of urban land by applying an algorithmic technique of machine learning, known as Random Forest, in combination with a geo-statistical technique called Ordinary Kriging for the treatment of error.
publisher Instituto de Investigación de Vivienda y Hábitat
publishDate 2019
url https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/27090
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first_indexed 2024-09-03T22:20:23Z
last_indexed 2024-09-03T22:20:23Z
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