Adaptive Weighted Finite Mixture Model: Identifying the Feature-Influence of Real Estate

It is significant for real estate investors to understand how the construction environments and building characteristics impact the housing unit price. However, it is challenging for identifying the complex feature-influence from construction environments and building characteristics. It is also hard to alleviate the heterogeneity of real estate. In this article, we propose a framework named Adaptive Weighted Finite Mixture Model to identify the feature-influence and simultaneously alleviate the ill effect of heterogeneity. Applying this framework, we can predict the housing unit price based on the corresponding features. Besides, we discover that the feature-influence exists in the dissimilarity among similar cities. Specifically, we adaptively learn the weights of features to identify the feature-influence, and we model the estimation of the housing unit price with the feature-influence into a finite mixture model. We utilize the Principle Component Analysis algorithm to obtain a low-dimensional representation of housing features. The low-dimensional representation reduces the computational cost of model learning, and it avoids a potential catastrophe of the singular matrix inversion during the process of learning model parameters, which are estimated by the Expectation Maximization algorithm. To avoid the blind search for the latent group number used in the proposed framework, we employ the pre-clustering result as a guide of the searching range of the latent group numbers. We conduct numerous experiments on three real datasets from Shenyang, Changchun, and Harbin, which are the three provincial capital cities that have similar geography, economics, and cultures. The experimental results demonstrate the effectiveness of the proposed framework.