Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm.
Knowledge and monitoring land values are necessary for the implementation of public policies and territorial management, as well as a genuine source of resources for local governments. However, the different territorial characteristics and dynamics demand adequate processes and techniques for each r...
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| Lenguaje: | Español |
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Instituto de Investigación de Vivienda y Hábitat
2021
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| Acceso en línea: | https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/35166 |
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I10-R355-article-351662021-12-21T23:37:09Z Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm. Propuesta metodológica para la valuación masiva del suelo urbano.: Aplicación espacial del algoritmo Quantile Regression Forest. Cerino, Rocío Mariel Carranza, Juan Pablo Piumetto, Mario Andrés Bullano, María Emilia Caffaratti Donalisio, Vania Monzani, Federico urban land value mass appraisal machine learning quantile regression forest valor de la tierra urbana valuación masiva aprendizaje automático árbol de regresión cuantílica Knowledge and monitoring land values are necessary for the implementation of public policies and territorial management, as well as a genuine source of resources for local governments. However, the different territorial characteristics and dynamics demand adequate processes and techniques for each reality. This paper describes the techniques and results obtained for the massive valuation of land in the province of Córdoba, particularly in mountain localities with a tourist profile. The performance of the Quantile Regression Forest machine learning technique is analyzed in terms of the level of accuracy in predicting land value and the resulting value structures are presented. In addition, the main innovation of the proposed technique consists in the possibility of generating a map of the prediction consistency, in terms of the dispersion coefficient at each point of the space. This last feature is considered a key input in the implementation of public policies for the periodic updating of urban land tax values, by informing about possible areas of the city where the results are of higher or lower quality in relation to the surroundings. El conocimiento y monitoreo de los precios del mercado inmobiliario se consideran necesarios para la implementación de políticas públicas y la gestión territorial, así como una fuente genuina de recursos para el Estado. Sin embargo, las diferentes características y dinámicas territoriales demandan procesos y técnicas que posibiliten la actualización adecuada y eficiente a cada realidad. El presente documento describe las técnicas y resultados obtenidos para la valuación masiva de la tierra de la provincia de Córdoba, particularmente en localidades serranas de perfil turístico. Se analiza el desempeño de la técnica de aprendizaje automático Quantile Regression Forest, en términos del nivel de precisión para predecir el valor de la tierra y se presentan las estructuras de valor resultantes. Además, la principal innovación de la técnica propuesta consiste en la posibilidad de generar un mapa de la consistencia de la predicción, en términos del coeficiente de dispersión en cada punto del espacio. Esta última característica se considera un insumo clave en la implementación de políticas públicas de actualización periódica de los valores fiscales de la tierra urbana, al informar sobre posibles áreas de la ciudad en donde los resultados son de mayor o menor calidad en relación al entorno. Instituto de Investigación de Vivienda y Hábitat 2021-12-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/html https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/35166 Vivienda y Ciudad; Núm. 8 (2021); 261-274 2422-670X spa https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/35166/36453 https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/35166/36454 Derechos de autor 2021 Rocío Mariel Cerino, Juan Pablo Carranza, Mario Andrés Piumetto, María Emilia Bullano, Vania Caffaratti Donalisio, Federico Monzani https://creativecommons.org/licenses/by-sa/4.0 |
| 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 |
urban land value mass appraisal machine learning quantile regression forest valor de la tierra urbana valuación masiva aprendizaje automático árbol de regresión cuantílica |
| spellingShingle |
urban land value mass appraisal machine learning quantile regression forest valor de la tierra urbana valuación masiva aprendizaje automático árbol de regresión cuantílica Cerino, Rocío Mariel Carranza, Juan Pablo Piumetto, Mario Andrés Bullano, María Emilia Caffaratti Donalisio, Vania Monzani, Federico Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm. |
| topic_facet |
urban land value mass appraisal machine learning quantile regression forest valor de la tierra urbana valuación masiva aprendizaje automático árbol de regresión cuantílica |
| author |
Cerino, Rocío Mariel Carranza, Juan Pablo Piumetto, Mario Andrés Bullano, María Emilia Caffaratti Donalisio, Vania Monzani, Federico |
| author_facet |
Cerino, Rocío Mariel Carranza, Juan Pablo Piumetto, Mario Andrés Bullano, María Emilia Caffaratti Donalisio, Vania Monzani, Federico |
| author_sort |
Cerino, Rocío Mariel |
| title |
Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm. |
| title_short |
Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm. |
| title_full |
Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm. |
| title_fullStr |
Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm. |
| title_full_unstemmed |
Proposal for the mass appraisal of urban land.: Spatial application of the Quantile Regression Forest algorithm. |
| title_sort |
proposal for the mass appraisal of urban land.: spatial application of the quantile regression forest algorithm. |
| description |
Knowledge and monitoring land values are necessary for the implementation of public policies and territorial management, as well as a genuine source of resources for local governments. However, the different territorial characteristics and dynamics demand adequate processes and techniques for each reality. This paper describes the techniques and results obtained for the massive valuation of land in the province of Córdoba, particularly in mountain localities with a tourist profile. The performance of the Quantile Regression Forest machine learning technique is analyzed in terms of the level of accuracy in predicting land value and the resulting value structures are presented. In addition, the main innovation of the proposed technique consists in the possibility of generating a map of the prediction consistency, in terms of the dispersion coefficient at each point of the space. This last feature is considered a key input in the implementation of public policies for the periodic updating of urban land tax values, by informing about possible areas of the city where the results are of higher or lower quality in relation to the surroundings. |
| publisher |
Instituto de Investigación de Vivienda y Hábitat |
| publishDate |
2021 |
| url |
https://revistas.unc.edu.ar/index.php/ReViyCi/article/view/35166 |
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