Enhancing real estate price prediction using optimized least squares moment balanced machine
Real estate price prediction is crucial for urban planning and economic forecasting, as property values are influenced by factors such as location and infrastructure. Traditional methods often struggle to capture the complex relationships between these variables, resulting in inefficient land use an...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/05/e3sconf_icenis2024_01007.pdf |
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| author | Radian Khasani Riqi |
| author_facet | Radian Khasani Riqi |
| author_sort | Radian Khasani Riqi |
| collection | DOAJ |
| description | Real estate price prediction is crucial for urban planning and economic forecasting, as property values are influenced by factors such as location and infrastructure. Traditional methods often struggle to capture the complex relationships between these variables, resulting in inefficient land use and suboptimal resource allocation. To address this challenge, this study introduces the Optimized Least Squares Moment Balanced Machine (OLSMBM), an advanced machine learning model designed to enhance the accuracy of real estate price predictions. The model incorporates key features such as transaction date, house age, proximity to MRT stations, number of convenience stores, and geographic location. The OLSMBM was benchmarked against five other machine learning models, including LSSVM, BPNN, ELSIM, Decision Tree, and Linear Regression. The results from a 10-fold cross-validation demonstrate that OLSMBM consistently outperforms other models across five evaluation metrics, including RMSE (6.977), MAE (4.752), MAPE (13.76%), R (0.846), and R² (0.728). The comprehensive evaluation, summarized by the Reference Index (RI), showed that OLSMBM achieved a perfect RI score of 1.000, highlighting its superior performance. These findings underscore the potential of the OLSMBM as a decision-support tool, enhancing the accuracy of real estate price predictions and reinforcing data-driven strategies in urban planning. |
| format | Article |
| id | doaj-art-274c70ea2a044467857a671f3741f5f4 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-274c70ea2a044467857a671f3741f5f42025-02-05T10:49:10ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016050100710.1051/e3sconf/202560501007e3sconf_icenis2024_01007Enhancing real estate price prediction using optimized least squares moment balanced machineRadian Khasani Riqi0Department of Civil Engineering Diponegoro University, Jalan Prof Sudharto SH SemarangReal estate price prediction is crucial for urban planning and economic forecasting, as property values are influenced by factors such as location and infrastructure. Traditional methods often struggle to capture the complex relationships between these variables, resulting in inefficient land use and suboptimal resource allocation. To address this challenge, this study introduces the Optimized Least Squares Moment Balanced Machine (OLSMBM), an advanced machine learning model designed to enhance the accuracy of real estate price predictions. The model incorporates key features such as transaction date, house age, proximity to MRT stations, number of convenience stores, and geographic location. The OLSMBM was benchmarked against five other machine learning models, including LSSVM, BPNN, ELSIM, Decision Tree, and Linear Regression. The results from a 10-fold cross-validation demonstrate that OLSMBM consistently outperforms other models across five evaluation metrics, including RMSE (6.977), MAE (4.752), MAPE (13.76%), R (0.846), and R² (0.728). The comprehensive evaluation, summarized by the Reference Index (RI), showed that OLSMBM achieved a perfect RI score of 1.000, highlighting its superior performance. These findings underscore the potential of the OLSMBM as a decision-support tool, enhancing the accuracy of real estate price predictions and reinforcing data-driven strategies in urban planning.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/05/e3sconf_icenis2024_01007.pdf |
| spellingShingle | Radian Khasani Riqi Enhancing real estate price prediction using optimized least squares moment balanced machine E3S Web of Conferences |
| title | Enhancing real estate price prediction using optimized least squares moment balanced machine |
| title_full | Enhancing real estate price prediction using optimized least squares moment balanced machine |
| title_fullStr | Enhancing real estate price prediction using optimized least squares moment balanced machine |
| title_full_unstemmed | Enhancing real estate price prediction using optimized least squares moment balanced machine |
| title_short | Enhancing real estate price prediction using optimized least squares moment balanced machine |
| title_sort | enhancing real estate price prediction using optimized least squares moment balanced machine |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/05/e3sconf_icenis2024_01007.pdf |
| work_keys_str_mv | AT radiankhasaniriqi enhancingrealestatepricepredictionusingoptimizedleastsquaresmomentbalancedmachine |