Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times

Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to...

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Autores: Mora García, Raúl Tomás, Céspedes López, María Francisca, Pérez Sánchez, Vicente Raúl
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Consejo General de la Arquitectura Técnica de España (CGATE)
Repositorio:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/2861
Acceso en línea:http://hdl.handle.net/20.500.12251/2861
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147935396&doi=10.3390%2fland11112100&partnerID=40&md5=bef3e1a86b9f4b0d6ab29f4962c8ea65
Access Level:acceso abierto
Palabra clave:Algoritmos
Precio de venta
Edificación residencial
Covid-19
Tasaciones
Modelo de precios
Mercado Inmobiliario
5302.02 Modelos Econométricos
3305.14 Viviendas
5311.06 Estudio de Mercado
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spelling Housing Price Prediction Using Machine Learning Algorithms in COVID-19 TimesMora García, Raúl TomásCéspedes López, María FranciscaPérez Sánchez, Vicente RaúlAlgoritmosPrecio de ventaEdificación residencialCovid-19TasacionesModelo de preciosMercado Inmobiliario5302.02 Modelos Econométricos3305.14 Viviendas5311.06 Estudio de MercadoMachine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to make predictions that help users in decision making. This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city. The methodology addresses the phases of data preparation, feature engineering, hyperparameter training and optimization, model evaluation and selection, and finally model interpretation. Ensemble learning algorithms based on boosting (Gradient Boosting Regressor, Extreme Gradient Boosting, and Light Gradient Boosting Machine) and bagging (random forest and extra-trees regressor) are used and compared with a linear regression model. A case study is developed with georeferenced microdata of the real estate market in Alicante (Spain), before and after the pandemic declaration derived from COVID-19, together with information from other complementary sources such as the cadastre, socio-demographic and economic indicators, and satellite images. The results show that machine learning algorithms perform better than traditional linear models because they are better adapted to the nonlinearities of complex data such as real estate market data. Algorithms based on bagging show overfitting problems (random forest and extra-trees regressor) and those based on boosting have better performance and lower overfitting. This research contributes to the literature on the Spanish real estate market by being one of the first studies to use machine learning and microdata to explore the incidence of the COVID-19 pandemic on house prices. © 2022 by the authors.MDPI2022info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12251/2861https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147935396&doi=10.3390%2fland11112100&partnerID=40&md5=bef3e1a86b9f4b0d6ab29f4962c8ea65reponame:RIARTEinstname:Consejo General de la Arquitectura Técnica de España (CGATE)Ingléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:www.riarte.es:20.500.12251/28612026-06-02T12:44:41Z
dc.title.none.fl_str_mv Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
title Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
spellingShingle Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
Mora García, Raúl Tomás
Algoritmos
Precio de venta
Edificación residencial
Covid-19
Tasaciones
Modelo de precios
Mercado Inmobiliario
5302.02 Modelos Econométricos
3305.14 Viviendas
5311.06 Estudio de Mercado
title_short Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
title_full Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
title_fullStr Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
title_full_unstemmed Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
title_sort Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times
dc.creator.none.fl_str_mv Mora García, Raúl Tomás
Céspedes López, María Francisca
Pérez Sánchez, Vicente Raúl
author Mora García, Raúl Tomás
author_facet Mora García, Raúl Tomás
Céspedes López, María Francisca
Pérez Sánchez, Vicente Raúl
author_role author
author2 Céspedes López, María Francisca
Pérez Sánchez, Vicente Raúl
author2_role author
author
dc.subject.none.fl_str_mv Algoritmos
Precio de venta
Edificación residencial
Covid-19
Tasaciones
Modelo de precios
Mercado Inmobiliario
5302.02 Modelos Econométricos
3305.14 Viviendas
5311.06 Estudio de Mercado
topic Algoritmos
Precio de venta
Edificación residencial
Covid-19
Tasaciones
Modelo de precios
Mercado Inmobiliario
5302.02 Modelos Econométricos
3305.14 Viviendas
5311.06 Estudio de Mercado
description Machine learning algorithms are being used for multiple real-life applications and in research. As a consequence of digital technology, large structured and georeferenced datasets are now more widely available, facilitating the use of these algorithms to analyze and identify patterns, as well as to make predictions that help users in decision making. This research aims to identify the best machine learning algorithms to predict house prices, and to quantify the impact of the COVID-19 pandemic on house prices in a Spanish city. The methodology addresses the phases of data preparation, feature engineering, hyperparameter training and optimization, model evaluation and selection, and finally model interpretation. Ensemble learning algorithms based on boosting (Gradient Boosting Regressor, Extreme Gradient Boosting, and Light Gradient Boosting Machine) and bagging (random forest and extra-trees regressor) are used and compared with a linear regression model. A case study is developed with georeferenced microdata of the real estate market in Alicante (Spain), before and after the pandemic declaration derived from COVID-19, together with information from other complementary sources such as the cadastre, socio-demographic and economic indicators, and satellite images. The results show that machine learning algorithms perform better than traditional linear models because they are better adapted to the nonlinearities of complex data such as real estate market data. Algorithms based on bagging show overfitting problems (random forest and extra-trees regressor) and those based on boosting have better performance and lower overfitting. This research contributes to the literature on the Spanish real estate market by being one of the first studies to use machine learning and microdata to explore the incidence of the COVID-19 pandemic on house prices. © 2022 by the authors.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12251/2861
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147935396&doi=10.3390%2fland11112100&partnerID=40&md5=bef3e1a86b9f4b0d6ab29f4962c8ea65
url http://hdl.handle.net/20.500.12251/2861
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147935396&doi=10.3390%2fland11112100&partnerID=40&md5=bef3e1a86b9f4b0d6ab29f4962c8ea65
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RIARTE
instname:Consejo General de la Arquitectura Técnica de España (CGATE)
instname_str Consejo General de la Arquitectura Técnica de España (CGATE)
reponame_str RIARTE
collection RIARTE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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