Inferring Housing Demand Based On Express Delivery Data

Estimation of housing requirement is beneficial for many applications such as guidance of house trading and real estate market regulation. Although there have been studies focusing on the demand analysis of urban resources, estimation of housing requirement is still under explored. To this end, in this paper we propose a systematic housing demand inference method, named Housing Demand Inference Model (HDIM), to estimate housing demand by exploiting the residential mobility of communities based on express delivery data. In this work, we first aggregate the express delivery records at community scale with clustering methods. Then, we propose a useful method to infer residential mobility by extracting express delivery related features and community related features. Since the features extracted are sparse for some residents, we utilize Regularized Singular Value Decomposition Model (RSVD) to construct missing values of features. After that, we infer residential mobility probability of each community by taking advantage of the less sparse features. We also consider community attractiveness as one of the factors influencing housing demand with the help of community profiles and geographical data. With the residential mobility probability and community attractiveness being obtained, we estimate housing demand with a regression model. Finally, experimental results on real-world data show that our model is effective to infer housing demand for communities in urban areas.