Abstract

The affordable housing crisis has become a critical socio-economic challenge, disproportionately affecting underserved communities and exacerbating inequalities. Outdated data, reactive strategies, and inefficiencies in resource allocation have often hindered traditional policy interventions. This paper explores the role of smart data-driven analysis in addressing housing affordability challenges through predictive analytics, big data integration, and artificial intelligence. By leveraging large-scale datasets, including census data, rental market trends, and socioeconomic indicators, data-driven methodologies can provide real-time insights into housing affordability, enabling policymakers to design targeted and proactive interventions. The study reviews existing research on housing affordability, highlighting the limitations of conventional approaches and the emerging role of machine learning, geospatial analysis, and blockchain in improving transparency and efficiency in housing markets. A methodological framework is proposed that integrates predictive modeling, geographic information systems, and real-time data processing to assess housing crises at both macro and micro levels. Furthermore, the paper discusses policy implications and technology-enabled solutions such as AI-driven rent control models, smart subsidies, and blockchain-based housing registries. Key findings emphasize the necessity of public-private collaboration, ethical AI implementation, and the development of dynamic affordability tracking systems. Future research directions include refining predictive modeling techniques, enhancing real-time monitoring capabilities, and exploring AI-driven decision-support systems for urban planning. This paper provides a foundation for more effective, equitable, and sustainable housing interventions by integrating data science and housing policy.