Housing Deserves Better Tooling
Audio overview of an AI-powered property analysis pipeline for public housing — spatial intelligence, ML valuations, and Tasmania's LIST.
Generated for project: Strategic Acquisitions
Housing shortages are not information problems, but they have information problems inside them. When a public housing organisation needs to acquire properties, the analysis that should take hours takes weeks — listing discovery, valuation modelling, planning zone verification, hazard assessment, infrastructure proximity. Each step involves a different data source and a different manual process.
This episode walks through Strategic Acquisitions, an automated pipeline that connects all of those sources into a single analytical flow. GPT-4 generates insights. ML-based property valuations train on engineered features — land per bedroom, bed-bath ratios, latitude-longitude interactions — using cross-validated gradient boosting. The spatial intelligence layer integrates with Tasmania’s Land Information System to pull government-grade data on planning zones, bushfire risk, flood hazards, heritage constraints, and proximity to schools, transport, and healthcare.
The most interesting part is the scoring model: infrastructure access weighted at 30%, planning at 25%, hazards at 20%, cadastral at 15%, heritage at 10% — producing a BUY/CONSIDER/PASS recommendation with detailed reasoning. The data existed. It just was not connected.