TY - GEN
T1 - Real estate ranking via mixed land-use latent models
AU - Fu, Yanjie
AU - Liu, Guannan
AU - Papadimitriou, Spiros
AU - Xiong, Hui
AU - Ge, Yong
AU - Zhuu, Hengshu
AU - Zhu, Chen
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/10
Y1 - 2015/8/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84954175990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954175990&partnerID=8YFLogxK
U2 - 10.1145/2783258.2783383
DO - 10.1145/2783258.2783383
M3 - Conference contribution
AN - SCOPUS:84954175990
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 299
EP - 308
BT - KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
Y2 - 10 August 2015 through 13 August 2015
ER -