Urban housing price is widely accepted as an economic indicator which is of both business and research interest in urban computing. However, due to the complex nature of influencing factors and the sparse property of transaction records, to implement such a model is still challenging. To address these challenges, in this work, we study 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, with only publicly released transaction data. We employ a feature selection mechanism to select more relevant features. Then, we propose an integrated model, JGC MMN (Joint Gated Co-attention Based Multi-modal Network), to learn all-level features and capture spatiotemporal correlations in all-time stages with a modified densely connected convolutional network as well as current ingredients and future expectations. Next, we devise a novel JGC based fusion method to better fuse the heterogeneous data of multi-stage models by considering their interactions in temporal dimension. Finally, extensive empirical studies on real datasets demonstrate the effectiveness of our proposal, and this fine-grained housing price forecasting has the potential to support a broad scope of applications, ranging from urban planning to housing market recommendations.

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.

Community house price forecasting has been a livelihood issue for the governments and the residents, and accurate forecast of real estimate price is so important to urban planning as well as house-purchase suggestions. However, the price of residential communities involving many aspects including economic factors, community attributes and time series trend. What's more, in this paper, we take spatial dependence among communities into account, which is hard to capture in city-level. Finally, we propose a novel deep network framework to integrate all the aspects and model the spatial-temporal patterns for community house price forecasting.