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.