Autonomous AI Agents for Real-Time Affordable Housing Site Selection: Multi-Objective Reinforcement Learning Under Regulatory Constraints

TL;DR

This paper introduces AURA, a hierarchical multi-agent reinforcement learning system that efficiently selects affordable housing sites in real-time while adhering to complex regulatory constraints and optimizing multiple social and environmental objectives.

Contribution

It develops a novel regulatory-aware multi-objective reinforcement learning framework with feasibility guarantees for real-time site selection under regulatory constraints.

Findings

Achieves 94.3% regulatory compliance on US datasets.

Reduces site selection time from 18 months to 72 hours in NYC case study.

Identifies 23% more viable sites with better transit access and lower environmental impact.

Abstract

Affordable housing shortages affect billions, while land scarcity and regulations make site selection slow. We present AURA (Autonomous Urban Resource Allocator), a hierarchical multi-agent reinforcement learning system for real-time affordable housing site selection under hard regulatory constraints (QCT, DDA, LIHTC). We model the task as a constrained multi-objective Markov decision process optimizing accessibility, environmental impact, construction cost, and social equity while enforcing feasibility. AURA uses a regulatory-aware state encoding 127 federal and local constraints, Pareto-constrained policy gradients with feasibility guarantees, and reward decomposition separating immediate costs from long-term social outcomes. On datasets from 8 U.S. metros (47,392 candidate parcels), AURA attains 94.3% regulatory compliance and improves Pareto hypervolume by 37.2% over strong baselines. In a New York City 2026 case study, it reduces selection time from 18 months to 72 hours and identifies 23% more viable sites; chosen sites have 31% better transit access and 19% lower environmental impact than expert picks.