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| Housing price prediction has caught much attention and has been researched for a long time, and it is known to all that the value of a house is influenced by a wealth of determinants, some of which are irregular or even cannot be quantified. Moreover, housing prices fluctuate tremendously in reality. In this scenario, it remains a challenging task: To design an accurate, multi-dimensional predictive method of estimating housing prices. Previous work on this problem focuses on the value of housing independently and makes use of the structured features (such as floors, the number of rooms, etc.) to offer a valuation of the house. However, this assumption does not hold in reality since housing prices are strongly related to time characteristics, and there exist some ignored unstructured features (visual information) that will ultimately affect prices in housing transactions. Therefore, to address these limitations, we rethink the housing price prediction problem and leverage a multimodal fusion framework with the other two important factors taken into consideration: the time series of house prices and the unstructured information part of the house. We design an efficient differential housing price prediction model based on Multimodal Deep Learning. In this framework, we first propose an advanced time series correlation techniques to improve the predictive performance of average house price across a certain time scale. Next, we design an efficient image algorithm to mine more favorable features from the unstructured information. Then our prediction result is the sum of the mean and difference in house prices. We refine more about the deep features of house prices and the features of the house itself in order to provide better, richer housing price prediction results. Through extensive experiments on real-world datasets, we demonstrate that our algorithm performs better than the
baseline and state-of-art approaches. |
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| Keywords:Housing price prediction; Multimodal deep learning; Images-based; Time series |
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