An Integrated Model for Urban Subregion House Price Forecasting: A Multi-source Data Perspective

Urban housing price is widely accepted as an economic indicator of both business and research interest in urban computing. In this work, we propose an effective and fine-grained model for urban subregion housing price predictions. Compared to existing works, our proposal improves the forecasting granularity from city-level to mile-level in spite of data sparsity and complex factors. The fine-grained housing price forecasting has the potential to support a broad scope of applications, ranging from urban planning to housing market recommendations. To achieve that, in this paper, we propose a novel integrated framework, FTD_DenseNet, which incorporates more social and economic features and makes full use of all-level spatiotemporal features. Specifically, the Kalman Filter-based future expection is firstly involved as an influence factor in our model. Extensive empirical studies on real data show the effectiveness of our proposals.