Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics framework oriented towards policy monitoring. Official data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM-inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an uneven supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts.

This work is supported by UIDB/04466/2023, UIDP/04466/2023, and UID/04516/2025 with the financial support of FCT—Fundação para a Ciência e Tecnologia.

Housing inequalities,Territorial disparities,Urban analytics,Business intelligence,Visual analytics,Official statistics

• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
• Other Social Sciences - Social Sciences