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Modeling the Dynamics of Growth in Master-Planned Communities

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  • Christopher K. Allsup
  • Irene S. Gabashvili

Abstract

This paper describes how a time-varying Markov model was used to forecast housing development at a master-planned community during a transition from high to low growth. Our approach draws on detailed historical data to model the dynamics of the market participants, producing results that are entirely data-driven and free of bias. While traditional time series forecasting methods often struggle to account for nonlinear regime changes in growth, our approach successfully captures the onset of buildout as well as external economic shocks, such as the 1990 and 2008-2011 recessions and the 2021 post-pandemic boom. This research serves as a valuable tool for urban planners, homeowner associations, and property stakeholders aiming to navigate the complexities of growth at master-planned communities during periods of both system stability and instability.

Suggested Citation

  • Christopher K. Allsup & Irene S. Gabashvili, 2024. "Modeling the Dynamics of Growth in Master-Planned Communities," Papers 2408.14214, arXiv.org, revised Aug 2024.
    • Handle: RePEc:arx:papers:2408.14214
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    References listed on IDEAS

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    1. Greg T. Smersh & Marc T. Smith & Arthur L. Schwartz, Jr., 2003. "Factors Affecting Residential Property Development Patterns," Journal of Real Estate Research, American Real Estate Society, vol. 25(1), pages 61-76.
    2. Václav Adamec, 2018. "Disparity in Performance of the Czech Construction Sector: Evidence from the Markov-Switching Model," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1383-1391.
    3. Melissa Shakro, 2013. "Tracking neighborhood development and behavioral trends with building permits in Austin, Texas," Journal of Maps, Taylor & Francis Journals, vol. 9(2), pages 189-197, June.
    4. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    5. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
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