The global affordable housing crisis affects 2.8 billion people living in inadequate conditions, with urban areas facing acute land scarcity and complex regulatory frameworks.
This paper presents AURA (Autonomous Urban Resource Al locator), a novel multi-agent reinforcement learning system for real-time affordable housing site selection under hard regulatory constraints. AURA employs a hierarchical architecture with specialized autonomous agents for geospatial analysis, regulatory compliance verification, and multi-objective optimization.
Our framework introduces three key innovations: (1) a regulatory-aware state representation encoding 127 federal and local constraints, (2) a Pareto-constrained policy gradient algorithm with feasibility guarantees, and (3) a multi-fidelity reward decomposition separating immediate costs from long-term social impact. Evaluated on real metropolitan datasets from 8 U.S. cities comprising 47,392 candidate parcels, AURA achieves 94.3% regulatory compliance while improving Pareto hypervolume by 37.2% over baseline methods. For New York City’s 2026 affordable housing initiative, AURA reduced site selection time from 18 months to 72 hours while identifying 23% more viable locations meeting all regulatory requirements.
Deployment in partnership with housing authorities demonstrates practical viability, with selected sites showing 31% better transit
accessibility and 19% lower environmental impact compared to human expert selections. These results establish autonomous AI agents as transformative tools for addressing the urban housing crisis highlighted at WUF13, combining computational efficiency with regulatory rigor and social equity considerations.
