Investments in housing influence migration and landscape construction, making them a key component of human-environment interactions. However, the strategic decision-making that builds residential landscapes is an underdeveloped area of research in evolutionary approaches to human behavior. We develop a model of strategic settlement and fit it to data from Ulaanbaatar, Mongolia. We construct a stochastic dynamic programming model to explore the trade-offs between building, moving, and saving over time, finding different trade-offs depending on optimisation scenarios. Household strategies are estimated using data on 825 households that settled in the Ulaanbaatar Ger districts between 1942 and 2020. The Ger districts are areas of self-built housing featuring both mobile dwellings (gers) and immobile houses (bashins). Using Approximate Bayesian Computation, we find the parameters of our dynamic programming model that best fit the empirical data. The model is able to capture the time horizon of housing changes and their bi-directionality, showing that moving from a bashin to ger can also be an optimal strategy. However, the model under-predicts some types of dwelling change; we discuss deviations from model predictions in relation to housing changes. We identify a more detailed exploration of risk and population mixes of strategies as key steps for future research.

The dataset is a subset of a household survey that was collected in Ulaanbaatar between 2019-2020. Data from the household survey have been anonymised and represent only the set of variables needed for the analysis detailed in the manuscript. For more information on methods and data management and cleaning procedures, see the methods section of the manuscript.