Modeling and elucidation of housing price

Abstract

It is widely acknowledged that the value of a house is the mixture of a large number of characteristics. House price prediction thus presents a unique set of challenges in practice. While a large body of works are dedicated to this task, their performance and applications have been limited by the shortage of long time span of transaction data, the absence of real-world settings and the insufficiency of housing features. To this end, a time-aware latent hierarchical model is developed to capture underlying spatiotemporal interactions behind the evolution of house prices. The hierarchical perspective obviates the need for historical transaction data of exactly same houses when temporal effects are considered. The proposed framework is examined on a large-scale dataset of the property transaction in Beijing. The whole experimental procedure strictly conforms to the real-world scenario. The empirical evaluation results demonstrate the outperformance of our approach over alternative competitive methods. We also group housing features into both external and internal clusters. The further experiment unveils that external component shapes house prices much more heavily than the internal one does. More interestingly, the inference of latent neighborhood value in our model is empirically shown to be able to lessen the dependence on the critical external cluster of features in house price prediction.