Real estate ranking: from black magic to data science

Yanjie Fu
2016
With the advent of mobile, Internet, and sensing technologies, large-scale urban and mobile data are available and are linked with locations near real properties. These data can be a source of rich intelligence for classifying high-rated residential locations, developing livable communities, and enhancing urban planning in big cities. In this dissertation, we aim to address the unique challenges of real estate ranking, especially (i) how to build an effective ranking system by exploiting
more » ... y exploiting heterogeneous mobile data and modeling geographic dependencies; (ii) what are the underlying drivers for livable and sustainable communities. Along these lines, I first introduced a method for ranking residential complexes based on invest- ment ratings by mining users opinions about residential complexes from online user reviews and offline moving behaviors (e.g., taxi traces, smart card transactions, check-ins). While a variety of features could be extracted from these data, these features are intercorrelated and redundant. Thus, selecting good features and integrating the feature selection into the fitting of a ranking model are essential. To this end, I first strategically mined the fine-grained discriminative features from user reviews and moving behaviors. Then, I proposed a Sparse Pairwise Ranking method by combining a pairwise ranking objective and a sparsity regularization in a unified probabilistic framework. In addition, with the development of new ways to collect estate-related mobile data, there is a potential to leverage geographic dependencies of residential complexes for enhancing real estate evaluation. Indeed, the geographic dependencies of the value of a residential complex can be from the characteristics of its own neighborhood (individual), the values of its nearby residential complexes (peer), and the prosperity of the affiliated latent business area (zone). To this end, I proposed an enhanced method, named ClusRanking, for real estate evaluation by leveraging the mutual enforcement of ranking and clustering [...]
doi:10.7282/t3-6hyw-r428 fatcat:dqj2h2445vehzgnh42y7k6ollu