Time-aware latent hierarchical model for predicting house prices

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 introduced 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 complies with the real-world scenario. The empirical evaluation results demonstrate the outperformance of our approach over alternative competitive methods.