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Population synthesis for urban resident modeling using deep generative models

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Abstract

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.

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Author notes

  1. Martin Johnsen and Oliver Brandt contributed equally to this work.

Authors and Affiliations

  1. Department of Technology, Management and Economics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark

    Martin Johnsen, Oliver Brandt, Sergio Garrido & Francisco Pereira

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  1. Martin Johnsen
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  2. Oliver Brandt
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  3. Sergio Garrido
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  4. Francisco Pereira
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Correspondence to Sergio Garrido.

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Appendix

Appendix

1.1 Partial joints in the extended application data set

See Fig. 8.

Fig. 8
figure 8

Performance of the partial joints in the Extended application data set. From left to right; (1) the bivariate distribution between age and nationality, (2) the trivariate distribution between age, nationality, and prior home district, and (3) the trivariate distribution between age, prior home district, and investor. The scatter plot represents the partial joint distribution between the sampled agents from the Extended application set against the real agents from the Extended application set. The axes are denoted in normalized bin frequencies on both the vertical and the horizontal axis

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Johnsen, M., Brandt, O., Garrido, S. et al. Population synthesis for urban resident modeling using deep generative models. Neural Comput & Applic 34, 4677–4692 (2022). https://doi.org/10.1007/s00521-021-06622-2

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