Leveraging AI-Driven Analytics and Marketing to Enhance Affordable Housing Access and Inform National Housing Policies.

  ABSTRACT: As affordable housing crises deepen globally, there is a growing need for data-driven solutions to address systemic inequities in housing access. This paper explores the transformative potential of artificial intelligence (AI)-driven analytics and marketing in enhancing access to affordable housing and shaping responsive national housing policies. AI tools such as predictive modeling, geospatial mapping, and behavioral segmentation are being leveraged to identify at-risk populations, optimize resource allocation, and increase engagement with underserved demographics. AI-powered marketing campaigns also enable housing organizations to reach marginalized groups with tailored, culturally aware outreach efforts that drive higher application and retention rates. Drawing insights from successful green energy equity frameworks (Ogbemudia et al., 2024), the article highlights how data-centric strategies can be adapted for equitable housing outcomes. It discusses the integration of AI into housing policy planning, illustrating how public agencies can harness machine learning to simulate policy scenarios, detect housing supply-demand mismatches, and proactively mitigate displacement risks. The analysis also explores key challenges, including algorithmic bias, data privacy, and ethical deployment, which must be addressed to ensure technology enhances — rather than exacerbates — housing inequality.