Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices
Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/17/5832 |
| _version_ | 1797556158778572800 |
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| author | Ping-Feng Pai Wen-Chang Wang |
| author_facet | Ping-Feng Pai Wen-Chang Wang |
| author_sort | Ping-Feng Pai |
| collection | DOAJ |
| description | Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results. |
| first_indexed | 2024-03-10T16:58:00Z |
| format | Article |
| id | doaj.art-5f78badef8d04b15bfa245c8c6bf0043 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:58:00Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5f78badef8d04b15bfa245c8c6bf00432023-11-20T11:04:19ZengMDPI AGApplied Sciences2076-34172020-08-011017583210.3390/app10175832Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate PricesPing-Feng Pai0Wen-Chang Wang1Department of Information Management, National Chi Nan University, 1 University Rd., Puli, Nantou 54561, TaiwanDepartment of Information Management, National Chi Nan University, 1 University Rd., Puli, Nantou 54561, TaiwanReal estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.https://www.mdpi.com/2076-3417/10/17/5832real estate pricesmachine learningpredict |
| spellingShingle | Ping-Feng Pai Wen-Chang Wang Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices Applied Sciences real estate prices machine learning predict |
| title | Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices |
| title_full | Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices |
| title_fullStr | Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices |
| title_full_unstemmed | Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices |
| title_short | Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices |
| title_sort | using machine learning models and actual transaction data for predicting real estate prices |
| topic | real estate prices machine learning predict |
| url | https://www.mdpi.com/2076-3417/10/17/5832 |
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