RHPMF: A context-aware matrix factorization approach for understanding regional real estate market

The real estate market has a significant impact on people's daily life. Therefore, it is crucial to understand the real estate market from both spatial and temporal perspectives, while there is still a lack of research in real estate industries. In this paper, a regional house price mining and forecasting (RHPMF) framework is proposed to help people intuitively understand the spatial distribution and temporal evolution of the urban estate market based on real-world housing data and urban contexts such as demographics and criminal records. Specifically, the RHPMF framework introduces a context-aware matrix factorization to extract crucial spatial and temporal price factors for revealing the housing market. Meanwhile, the RHPMF can forecast future regional house prices by manipulating the two price factors. Consequently, this study presents extensive exploratory analysis and experiments in Virginia Beach, Philadelphia, and Los Angeles to verify the proposed RHPMF. These case studies indicate that the RHPMF framework can accurately capture the market's spatial distribution and temporal evolution and forecast future regional house prices compared with recent baselines. The experimental results suggest the great potential of the proposed RHPMF for applications in real estate industries.

Automated summary

This study proposes a regional house price mining and forecasting framework to help people understand the spatial distribution and temporal evolution of the urban real estate market. By extracting crucial spatial and temporal price factors, the framework accurately captures the market's spatial distribution and temporal evolution. Case studies in Virginia Beach, Philadelphia, and Los Angeles indicate the great potential of the proposed framework for applications in the real estate industry.