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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/11/11/2100 |
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| author | Raul-Tomas Mora-Garcia Maria-Francisca Cespedes-Lopez V. Raul Perez-Sanchez |
| author_facet | Raul-Tomas Mora-Garcia Maria-Francisca Cespedes-Lopez V. Raul Perez-Sanchez |
| author_sort | Raul-Tomas Mora-Garcia |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T18:14:02Z |
| format | Article |
| id | doaj.art-ab4470e8dfc64527a7438682b5bf26c6 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T18:14:02Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-ab4470e8dfc64527a7438682b5bf26c62023-11-24T08:56:47ZengMDPI AGLand2073-445X2022-11-011111210010.3390/land11112100Housing Price Prediction Using Machine Learning Algorithms in COVID-19 TimesRaul-Tomas Mora-Garcia0Maria-Francisca Cespedes-Lopez1V. Raul Perez-Sanchez2Building Sciences and Urbanism Department, University of Alicante, 03690 San Vicente del Raspeig, SpainBuilding Sciences and Urbanism Department, University of Alicante, 03690 San Vicente del Raspeig, SpainBuilding Sciences and Urbanism Department, University of Alicante, 03690 San Vicente del Raspeig, SpainMachine 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.https://www.mdpi.com/2073-445X/11/11/2100machine learningmass appraisalreal estate marketpartial dependence plotsCOVID-19 |
| spellingShingle | Raul-Tomas Mora-Garcia Maria-Francisca Cespedes-Lopez V. Raul Perez-Sanchez Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times Land machine learning mass appraisal real estate market partial dependence plots COVID-19 |
| title | 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_short | Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times |
| title_sort | housing price prediction using machine learning algorithms in covid 19 times |
| topic | machine learning mass appraisal real estate market partial dependence plots COVID-19 |
| url | https://www.mdpi.com/2073-445X/11/11/2100 |
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