Modeling of Geographic Dependencies for Real Estate Ranking


It is traditionally a challenge for home buyers to understand, compare, and contrast the investment value of real estate. Although a number of appraisal methods have been developed to value real properties, the performances of these methods have been limited by traditional data sources for real estate appraisal. With the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of real estate for enhancing real 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 paper, we propose a geographic method, named ClusRanking, for real 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 real 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.


Computer Science


Z.-H. Zhou was partially supported by the National Science Foundation of China (61333014) and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China. H. Xiong was partially supported by the 111 Project (B14020), NSFC (71329201) and the Rutgers 2015 Chancellors Seed Grant Program.

Keywords and Phrases

Taxicabs; Clustering; Comprehensive evaluation; Geographic dependencies; Probabilistic ranking; Ranking; Real estate; Real estate appraisals; Real estate investment; Investments

International Standard Serial Number (ISSN)

2157-6904; 2157-6912

Document Type

Article - Journal

Document Version


File Type





© 2016 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Aug 2016