Real Estate Ranking Via Mixed Land-Use Latent Models


Mixed land use refers to the effort of putting residential, commercial and recreational uses in close proximity to one another. This can contribute economic benefits, support viable public transit, and enhance the perceived security of an area. It is naturally promising to investigate how to rank real estate from the viewpoint of diverse mixed land use, which can be reflected by the portfolio of community functions in the observed area. To that end, in this paper, we develop a geographical function ranking method, named FuncDivRank, by incorporating the functional diversity of communities into real estate appraisal. Specifically, we first design a geographic function learning model to jointly capture the correlations among estate neighborhoods, urban functions, temporal effects, and user mobility patterns. In this way we can learn latent community functions and the corresponding portfolios of estates from human mobility data and Point of Interest (POI) data. Then, we learn the estate ranking indicator by simultaneously maximizing ranking consistency and functional diversity, in a unified probabilistic optimization framework. Finally, we conduct a comprehensive evaluation with real-world data. The experimental results demonstrate the enhanced performance of the proposed method for real estate appraisal.

Meeting Name

21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015 (2015: Aug. 10-13, Sydney, Australia)


Computer Science


This research was partially supported by National Science Foundation (NSF) via the grant number CCF-1018151. Also, it was supported in part by Natural Science Foundation of China (71028002). In addition, this research was supported in part by National Institutes of Health under Grant 1R21AA023975-01.

Keywords and Phrases

Data mining; Economics; Community function; Comprehensive evaluation; Economic benefits; Functional diversity; Perceived securities; Probabilistic optimization; Real estate appraisals; User mobility pattern; Land use

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2015 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Aug 2015