Real estate ranking via mixed land-use latent models

Yanjie Fu, Guannan Liu, Spiros Papadimitriou, Hui Xiong, Yong Ge, Hengshu Zhuu, Chen Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages299-308
Number of pages10
ISBN (Electronic)9781450336642
DOIs
StatePublished - Aug 10 2015
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2015-August

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period8/10/158/13/15

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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