Disentangling Spatial and Structural Drivers of Housing Prices through Bayesian Networks: A Case Study of Madrid, Barcelona, and Valencia

TL;DR

This study uses Bayesian networks to analyze and interpret the key spatial and structural factors influencing housing prices in Madrid, Barcelona, and Valencia, providing city-specific insights and a transparent, actionable modeling framework.

Contribution

It introduces a Bayesian network approach for modeling housing prices that captures city-specific drivers and enhances interpretability and policy relevance.

Findings

Madrid's prices driven by amenities

Barcelona emphasizes typology and classification

Valencia shaped by spatial and structural fundamentals

Abstract

Understanding how housing prices respond to spatial accessibility, structural attributes, and typological distinctions is central to contemporary urban research and policy. In cities marked by affordability stress and market segmentation, models that offer both predictive capability and interpretive clarity are increasingly needed. This study applies discrete Bayesian networks to model residential price formation across Madrid, Barcelona, and Valencia using over 180,000 geo-referenced housing listings. The resulting probabilistic structures reveal distinct city-specific logics. Madrid exhibits amenity-driven stratification, Barcelona emphasizes typology and classification, while Valencia is shaped by spatial and structural fundamentals. By enabling joint inference, scenario simulation, and sensitivity analysis within a transparent framework, the approach advances housing analytics toward models that are not only accurate but actionable, interpretable, and aligned with the demands of equitable urban governance.