Modeling of Geographic Dependencies for Real Estate Ranking on Site Selection
Abstract
With the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed, the geographic dependencies of the investment value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this dissertation, we propose a geographic method, named ClusRanking, for estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas. Also, we fuse these three influential factors and predict real estate investment value. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Furthermore, we propose an improved method named CR-ClusRanking by incorporating checkin information as a regularization term which reduces the performance volatility of estate ranking system. Finally, we conduct a comprehensive evaluation with the real estate related data of Beijing, and the experimental results demonstrate the effectiveness of our proposed methods.
Recommended Citation
Y. Fu and H. Xiong, "Modeling of Geographic Dependencies for Real Estate Ranking on Site Selection," Proceedings of the IEEE 15th International Conference on Data Mining Workshop (2015, Atlantic City, NJ), pp. 1506 - 1513, Institute of Electrical and Electronics Engineers (IEEE), Nov 2015.
The definitive version is available at https://doi.org/10.1109/ICDMW.2015.83
Meeting Name
IEEE 15th International Conference on Data Mining Workshops, ICDMW 2015 (2015: Nov. 14-17, Atlantic City, NJ)
Department(s)
Computer Science
Keywords and Phrases
Investments; Site selection; Taxicabs; Comprehensive evaluation; Influential factors; Investment value; Objective functions; Probabilistic ranking; Real estate investment; Regularization terms; Trajectory data; Data mining
International Standard Book Number (ISBN)
978-1-4673-8493-3
International Standard Serial Number (ISSN)
2375-9259
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Nov 2015
