Population synthesis for urban resident modeling using deep generative models

Martin Johnsen, Oliver Brandt, Sergio Garrido*, Francisco Pereira

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
JournalNeural Computing and Applications
Pages (from-to)4677–4692
Number of pages16
Publication statusPublished - 2022


  • Deep generative models
  • Neural networks
  • Population synthesis
  • Real estate
  • Urban resident modeling


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