House price prediction modeling using machine learning techniques: a comparative study
In the literature, there are two basic approaches regarding the determination of house prices. One of them is the prediction of house price using macroeconomic variables in the country where the house is produced, and another one is the price prediction models, which we can express as micro-variable...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Firenze University Press
2023-02-01
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| Series: | Aestimum |
| Subjects: | |
| Online Access: | https://oaj.fupress.net/index.php/ceset/article/view/13703 |
| _version_ | 1827993503057051648 |
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| author | Ayten Yağmur Mehmet Kayakuş Mustafa Terzioğlu |
| author_facet | Ayten Yağmur Mehmet Kayakuş Mustafa Terzioğlu |
| author_sort | Ayten Yağmur |
| collection | DOAJ |
| description | In the literature, there are two basic approaches regarding the determination of house prices. One of them is the prediction of house price using macroeconomic variables in the country where the house is produced, and another one is the price prediction models, which we can express as micro-variables, by considering the features of the house. In this study, the price of the house was attempted to be predicted using machine learning methods by establishing a model with micro variables that reveal the features of the house. The study was conducted in Turkey’ Antalya province, where household housing demand of foreigners is also high. The house advertisements in locations belonging to the lower, middle- and upper-income groups were selected as the sample. In the results, it was observed that the artificial neural network (ANN) method made predictions with more meaningful results compared to support vector regression (SVR) and multiple linear regression (MLR). These results appear to be a viable model for institutions that supply housing, mediate housing sales, and provide housing financing and valuation. It is considered that this model, which can be used to predict fluctuating house prices, especially in developing countries, will regulate the housing market.
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| first_indexed | 2024-04-10T04:26:33Z |
| format | Article |
| id | doaj.art-a44ee9a52fb64fc08e0434d70fd68c96 |
| institution | Directory Open Access Journal |
| issn | 1592-6117 1724-2118 |
| language | English |
| last_indexed | 2024-04-10T04:26:33Z |
| publishDate | 2023-02-01 |
| publisher | Firenze University Press |
| record_format | Article |
| series | Aestimum |
| spelling | doaj.art-a44ee9a52fb64fc08e0434d70fd68c962023-03-10T13:41:48ZengFirenze University PressAestimum1592-61171724-21182023-02-018110.36253/aestim-13703House price prediction modeling using machine learning techniques: a comparative studyAyten Yağmur0Mehmet Kayakuş1Mustafa Terzioğlu2Department of Labour Economics and Industrial Relations, Akdeniz UniversityDepartment of Management Information Systems, Akdeniz UniversityAccounting and Tax Department, Akdeniz UniversityIn the literature, there are two basic approaches regarding the determination of house prices. One of them is the prediction of house price using macroeconomic variables in the country where the house is produced, and another one is the price prediction models, which we can express as micro-variables, by considering the features of the house. In this study, the price of the house was attempted to be predicted using machine learning methods by establishing a model with micro variables that reveal the features of the house. The study was conducted in Turkey’ Antalya province, where household housing demand of foreigners is also high. The house advertisements in locations belonging to the lower, middle- and upper-income groups were selected as the sample. In the results, it was observed that the artificial neural network (ANN) method made predictions with more meaningful results compared to support vector regression (SVR) and multiple linear regression (MLR). These results appear to be a viable model for institutions that supply housing, mediate housing sales, and provide housing financing and valuation. It is considered that this model, which can be used to predict fluctuating house prices, especially in developing countries, will regulate the housing market. https://oaj.fupress.net/index.php/ceset/article/view/13703Home PricePredictionSupport Vector RegressionArtificial Neural NetworksMultiple Linear Regression |
| spellingShingle | Ayten Yağmur Mehmet Kayakuş Mustafa Terzioğlu House price prediction modeling using machine learning techniques: a comparative study Aestimum Home Price Prediction Support Vector Regression Artificial Neural Networks Multiple Linear Regression |
| title | House price prediction modeling using machine learning techniques: a comparative study |
| title_full | House price prediction modeling using machine learning techniques: a comparative study |
| title_fullStr | House price prediction modeling using machine learning techniques: a comparative study |
| title_full_unstemmed | House price prediction modeling using machine learning techniques: a comparative study |
| title_short | House price prediction modeling using machine learning techniques: a comparative study |
| title_sort | house price prediction modeling using machine learning techniques a comparative study |
| topic | Home Price Prediction Support Vector Regression Artificial Neural Networks Multiple Linear Regression |
| url | https://oaj.fupress.net/index.php/ceset/article/view/13703 |
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