Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
