An Optimal House Price Prediction Algorithm: XGBoost
An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Me...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Analytics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2813-2203/3/1/3 |
| _version_ | 1797242377162719232 |
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| author | Hemlata Sharma Hitesh Harsora Bayode Ogunleye |
| author_facet | Hemlata Sharma Hitesh Harsora Bayode Ogunleye |
| author_sort | Hemlata Sharma |
| collection | DOAJ |
| description | An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints. |
| first_indexed | 2024-04-24T18:38:15Z |
| format | Article |
| id | doaj.art-e638a01854154eb78edd491de5815955 |
| institution | Directory Open Access Journal |
| issn | 2813-2203 |
| language | English |
| last_indexed | 2024-04-24T18:38:15Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Analytics |
| spelling | doaj.art-e638a01854154eb78edd491de58159552024-03-27T13:17:31ZengMDPI AGAnalytics2813-22032024-01-0131304510.3390/analytics3010003An Optimal House Price Prediction Algorithm: XGBoostHemlata Sharma0Hitesh Harsora1Bayode Ogunleye2Department of Computing, Sheffield Hallam University, Sheffield S1 2NU, UKDepartment of Computing, Sheffield Hallam University, Sheffield S1 2NU, UKDepartment of Computing & Mathematics, University of Brighton, Brighton BN2 4GJ, UKAn accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints.https://www.mdpi.com/2813-2203/3/1/3feature engineeringfeature importancehouse price predictionhyperparameter tuningmachine learningregression modeling |
| spellingShingle | Hemlata Sharma Hitesh Harsora Bayode Ogunleye An Optimal House Price Prediction Algorithm: XGBoost Analytics feature engineering feature importance house price prediction hyperparameter tuning machine learning regression modeling |
| title | An Optimal House Price Prediction Algorithm: XGBoost |
| title_full | An Optimal House Price Prediction Algorithm: XGBoost |
| title_fullStr | An Optimal House Price Prediction Algorithm: XGBoost |
| title_full_unstemmed | An Optimal House Price Prediction Algorithm: XGBoost |
| title_short | An Optimal House Price Prediction Algorithm: XGBoost |
| title_sort | optimal house price prediction algorithm xgboost |
| topic | feature engineering feature importance house price prediction hyperparameter tuning machine learning regression modeling |
| url | https://www.mdpi.com/2813-2203/3/1/3 |
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