Abstract
The price of a house depends on many factors, such as its size, location, amenities, surrounding establishments, and the season in which the house is being sold, just to name a few of them. As a seller, it is absolutely essential to price the property competitively else it will not attract any buyers. This problem has given rise to multiple companies as well as past research works that try to enhance the predictability of property prices using relevant mathematical models and machine learning techniques. In this research, we investigate the usage of machine learning in predicting the house price based on related estate attributes and visual images. To this end, we collect a dataset of 2,000 houses across different cities in the United States. For each house, we annotate 14 estate attributes and five visual images for exterior, interior-living room, kitchen, bedroom, and bathroom. Following the dataset collection, different features are extracted from the input data. Furthermore, a multi-kernel regression approach is used to predict the house price from both visual cues and estate attributes. The extensive experiments demonstrate the superiority of the proposed method over the baselines.
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This research was supported by the National Science Foundation (NSF) under Grant 2025234.
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Vaddi, S.S., Yousif, A., Baraheem, S., Shen, J., Nguyen, T.V. (2022). House Price Prediction via Visual Cues and Estate Attributes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13599. Springer, Cham. https://doi.org/10.1007/978-3-031-20716-7_8
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DOI: https://doi.org/10.1007/978-3-031-20716-7_8
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