Uneven Flows: Bayesian Best–Worst Method and GIS Analysis of Spatial Barriers to Housing Finance in an Iranian Mid-Sized City

چکیده مقاله

Abstract Housing financialization has transformed residential property from a basic social necessity into a speculative asset, intensifying socio-spatial inequalities in the Global South. In Iran, a bank-dominated financial regime and high spatial inequality severely restrict access to housing finance, particularly for low-income households and climate-induced migrants in mid-sized cities. While the spatial consequences of financialization have been extensively studied in major metropolises, neighborhood-scale exclusion mechanisms in secondary cities remain significantly understudied. This study addresses this gap by employing a hybrid methodology that integrates the Bayesian Best–Worst Method (BWM) with Geographic Information Systems (GISs) and Fuzzy Overlay analysis to quantify and spatially visualize barriers to housing finance in Birjand, Iran. Eleven indicators were weighted through probabilistic expert elicitation (n = 10) using Markov Chain Monte Carlo (MCMC) sampling, with excellent convergence confirmed by Gelman–Rubin diagnostics (R-hat ≈ 1.00). Results identify land use as the dominant barrier (posterior mean = 0.256; 95% CrI [0.244, 0.270]), followed by homeownership rate and education level (≈0.132 each). Spatial modeling reveals a pronounced north–south monetary–spatial divide: southern districts concentrate financial services and formal tenure, while northern peripheries, predominantly inhabited by low-income and climate-migrant populations, constitute financial deserts. These findings demonstrate that spatial barriers to housing finance are structurally embedded in land-use classification and asset-based gatekeeping. The study offers a replicable probabilistic–spatial framework for diagnosing financial exclusion in Global South mid-sized cities and provides evidence-based guidance for spatially targeted policy interventions aligned with SDGs 1, 10, and 11.