Multimodal Information Fusion Based Housing Prices Prediction
首发时间:2019-06-03
Abstract: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.
keywords: Housing price prediction Multimodal deep learning Images-based Time series
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基于多模态信息融合的房价预测
摘要:房价预测作为一项研究项目已有较多相关工作,多数研究均指出房屋价值受多方面因素影响,且其中一些决定性因素存在不规则波动情况,甚至难以量化。在此背景下,设计一种准确、多维度估算房屋价格方法仍然具有较大挑战性。此前大多数方法主要关注于房屋结构化特征(如楼层,房间数等),利用结构化特征预测房屋价值。然而,房屋成交价格也受一些非结构化特征影响(如视觉信息),使用非结构化特征有利于补充结构化特征无法覆盖的信息。因此,我们重新考虑房屋价格预测问题并利用基于多模态信息的深度融合模型来囊括额外的非结构化特征:房屋的时间序列信息和房屋视觉信息。我们设计了一种基于多模态深度学习的房价预测模型。在此框架中,我们首先提出了一种时间序列模型挖掘房价成交时间对价格的影响,进行房屋均价预测;接下来,我们提出一种有效的图像算法,从非结构化视觉信息中挖掘有利特征,进行房屋差价预测;最后我们设计融合模型整合多模态信息,以便提供更好的、更具有泛化能力的房价预测结果。通过真实数据集进行对比实验,我们证明了方法的有效性和先进性。
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