Modeling the Dynamics of Growth in Master-Planned Communities

TL;DR

This paper introduces a data-driven, time-varying Markov model to forecast housing development in master-planned communities, effectively capturing nonlinear growth changes and external shocks for better planning during various economic conditions.

Contribution

The paper presents a novel application of a time-varying Markov model that accurately models growth dynamics and external shocks in master-planned communities, improving forecasting accuracy over traditional methods.

Findings

Successfully captures buildout onset and economic shocks

Produces unbiased, data-driven forecasts

Applicable for urban planning and stakeholder decision-making

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.