House Price Prediction: A Multi-Source Data Fusion Perspective
House price prediction is of utmost importance in forecasting residential property prices, particularly as the demand for high-quality housing continues to rise. Accurate predictions have implications for real estate investors, financial institutions, urban planners, and policymakers. However, accur...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020019 |
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| author | Yaping Zhao Jichang Zhao Edmund Y. Lam |
| author_facet | Yaping Zhao Jichang Zhao Edmund Y. Lam |
| author_sort | Yaping Zhao |
| collection | DOAJ |
| description | House price prediction is of utmost importance in forecasting residential property prices, particularly as the demand for high-quality housing continues to rise. Accurate predictions have implications for real estate investors, financial institutions, urban planners, and policymakers. However, accurately predicting house prices is challenging due to the complex interplay of various influencing factors. Previous studies have primarily focused on basic property information, leaving room for further exploration of more intricate features, such as amenities, traffic, and social sentiments in the surrounding environment. In this paper, we propose a novel approach to house price prediction from a multi-source data fusion perspective. Our methodology involves analyzing house characteristics and incorporating factors from diverse aspects, including amenities, traffic, and emotions. We validate our approach using a dataset of 28550 real-world transactions in Beijing, China, providing a comprehensive analysis of the drivers influencing house prices. By adopting a multi-source data fusion perspective and considering a wide range of influential factors, our approach offers valuable insights into house price prediction. The findings from this study possess the capability to improve the accuracy and effectiveness of house price prediction models, benefiting stakeholders in the real estate market. |
| format | Article |
| id | doaj-art-225b2bf3635d49bf91fcff920e7428f0 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-225b2bf3635d49bf91fcff920e7428f02025-02-02T06:29:07ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-09-017360362010.26599/BDMA.2024.9020019House Price Prediction: A Multi-Source Data Fusion PerspectiveYaping Zhao0Jichang Zhao1Edmund Y. Lam2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077, ChinaSchool of Economics and Management, Beihang University, Beijing 100191, ChinaDepartment of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077, ChinaHouse price prediction is of utmost importance in forecasting residential property prices, particularly as the demand for high-quality housing continues to rise. Accurate predictions have implications for real estate investors, financial institutions, urban planners, and policymakers. However, accurately predicting house prices is challenging due to the complex interplay of various influencing factors. Previous studies have primarily focused on basic property information, leaving room for further exploration of more intricate features, such as amenities, traffic, and social sentiments in the surrounding environment. In this paper, we propose a novel approach to house price prediction from a multi-source data fusion perspective. Our methodology involves analyzing house characteristics and incorporating factors from diverse aspects, including amenities, traffic, and emotions. We validate our approach using a dataset of 28550 real-world transactions in Beijing, China, providing a comprehensive analysis of the drivers influencing house prices. By adopting a multi-source data fusion perspective and considering a wide range of influential factors, our approach offers valuable insights into house price prediction. The findings from this study possess the capability to improve the accuracy and effectiveness of house price prediction models, benefiting stakeholders in the real estate market.https://www.sciopen.com/article/10.26599/BDMA.2024.9020019price predictionreal estatedata miningdata fusionmachine learning |
| spellingShingle | Yaping Zhao Jichang Zhao Edmund Y. Lam House Price Prediction: A Multi-Source Data Fusion Perspective Big Data Mining and Analytics price prediction real estate data mining data fusion machine learning |
| title | House Price Prediction: A Multi-Source Data Fusion Perspective |
| title_full | House Price Prediction: A Multi-Source Data Fusion Perspective |
| title_fullStr | House Price Prediction: A Multi-Source Data Fusion Perspective |
| title_full_unstemmed | House Price Prediction: A Multi-Source Data Fusion Perspective |
| title_short | House Price Prediction: A Multi-Source Data Fusion Perspective |
| title_sort | house price prediction a multi source data fusion perspective |
| topic | price prediction real estate data mining data fusion machine learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020019 |
| work_keys_str_mv | AT yapingzhao housepricepredictionamultisourcedatafusionperspective AT jichangzhao housepricepredictionamultisourcedatafusionperspective AT edmundylam housepricepredictionamultisourcedatafusionperspective |