Population migration and housing market dynamics in California agglomerations

Purpose This paper investigates how residential sorting shapes local housing costs in California. It aims to understand the impact of internal population movements – particularly the inflow of high-income, educated and younger residents – on housing affordability and to explore the underlying household decision-making processes that drive migration patterns. Design/methodology/approach Using 2005–2020 US census data, the study applies a spatial Durbin model to quantify regional housing cost impacts and spillovers. It also employs a random utility maximization framework to model joint household decisions involving residence, workplace, housing and commuting mode, allowing for preference heterogeneity across demographic groups. Findings The study finds that the influx of positively selected populations significantly increases housing costs and produces strong spatial spillovers. Household migration decisions are driven by systematically heterogeneous preferences. Simulation results suggest that improved transport accessibility near employment hubs can reduce residential sorting intensity and help ease housing pressure in urban cores. Originality/value The paper uniquely links spatial econometrics with behavioral modeling to analyze residential sorting and housing costs. By combining regional-level impacts with household-level decision mechanisms, it offers a comprehensive view of urban migration dynamics and provides actionable insights for housing and transportation policy aimed at mitigating affordability issues.