A Multi-Modal Deep Learning Based Approach for House Price Prediction
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913493976875008 |
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| author | Hasan, Md Hasebul Jahan, Md Abid Ali, Mohammed Eunus Li, Yuan-Fang Sellis, Timos |
| author_facet | Hasan, Md Hasebul Jahan, Md Abid Ali, Mohammed Eunus Li, Yuan-Fang Sellis, Timos |
| contents | Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-Modal Deep Learning Based Approach for House Price Prediction Hasan, Md Hasebul Jahan, Md Abid Ali, Mohammed Eunus Li, Yuan-Fang Sellis, Timos Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2.7; I.2.10 Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp |
| title | A Multi-Modal Deep Learning Based Approach for House Price Prediction |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2.7; I.2.10 |
| url | https://arxiv.org/abs/2409.05335 |