NDLI: Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering

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Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering

Content Provider	ACM Digital Library
Author	Ge, Yong Yao, Zijun Fu, Yanjie Zhou, Zhi-Hua Xiong, Hui Zheng, Yu
Abstract	It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, 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 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 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 via ClusRanking. Also, we use a linear model to fuse these three influential factors and predict estate investment values. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Finally, we conduct a comprehensive evaluation with real-world estate related data, and the experimental results demonstrate the effectiveness of our method.
Starting Page	1047
Ending Page	1056
Page Count	10
File Format	PDF MP4
ISBN	9781450329569
DOI	10.1145/2623330.2623675
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Real estate appraisal Geographic dependencies Clusranking
Content Type	Audio Text
Resource Type	Article

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