Abstract Understanding house price appreciation benefits place-based decision makings and real estate market analyses. Although large amounts of interests have been paid in the house price modeling, limited work has focused on evaluating the price appreciation rate. In this study, we propose a data-fusion framework to examine how well house price appreciation potentials can be predicted by combining multiple data sources. We used data sets including house structural attributes, house photos, locational amenities, street view images, transportation accessibility, visitor patterns, and socioeconomic attributes of neighborhoods to enrich our understanding of the real estate appreciation and its predictive modeling. As a case study, we investigate more than 20,000 houses in the Greater Boston Area, and discuss the spatial dependency of house price appreciations, influential variables and their relationships. In detail, we extract deep features from street view images and house photos using a deep learning model, merging features from multi-source data and modeling house price appreciation using machine learning models and the geographically weighted regression at two spatial scales: fine-scale point level and aggregated neighborhood level. Results show that the house price appreciation rate can be modeled with high accuracy using the proposed framework ( R 2 = 0.74 for gradient boosting machine at neighborhood-scale). We discovered that houses with low house prices and small house areas may have a higher house appreciation potential. Our results provide insights into how multi-source big geo-data can be employed in machine learning frameworks to characterize real estate price trends and help understand human settlements for policy-making.

中文翻译：

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使用多源大地理数据和机器学习了解房价上涨

摘要 了解房价升值有利于地方决策和房地产市场分析。尽管在房价建模中已经支付了大量利息，但有限的工作集中在评估价格升值率上。在这项研究中，我们提出了一个数据融合框架，以检查通过结合多个数据源可以如何预测房价升值潜力。我们使用的数据集包括房屋结构属性、房屋照片、位置便利设施、街景图像、交通可达性、访客模式和社区的社会经济属性，以丰富我们对房地产升值及其预测模型的理解。作为案例研究，我们调查了大波士顿地区的 20,000 多栋房屋，并讨论房价升值的空间依赖性、影响变量及其关系。具体来说，我们使用深度学习模型从街景图像和房屋照片中提取深度特征，合并来自多源数据的特征，并使用机器学习模型和两个空间尺度的地理加权回归建模房价升值：细尺度点水平和聚合邻域水平。结果表明，使用所提出的框架（对于邻域尺度的梯度提升机，R 2 = 0.74）可以高精度地对房价升值率进行建模。我们发现，房价低、房子面积小的房子可能有更高的房子升值潜力。