Population synthesis for urban resident modeling using deep generative models

The impact of new real estate developments is strongly associated with its target population distribution, that is, the characteristics that define a population such as composition of household, income, and socio-demographics, conditioned on characteristics of the development itself, such as dwelling typology, price, location, and floor level. This paper presents a machine learning-based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings. We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam and study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents: conditional variational auto-encoder (CVAE) and conditional generative adversarial networks (CGAN). A large experimental study was performed, showing that the CVAE outperforms both the empirical distribution, a non-trivial baseline model, and the CGAN in estimating the population distribution of new real estate development projects.