AI housing assistant reaches 95% accuracy on real queries — but only in a lab setting with curated data

Researchers built a multi-agent AI system that breaks housing consultation into four specialized tasks: memory management, retrieval, generation, and fact-checking. In tests on 100 real scenarios, it reached 95% accuracy compared to 75% for simpler approaches, suggesting AI can handle the multi-constraint complexity of house hunting better than existing platforms.

Most housing platforms and AI assistants today just rank listings or make recommendations without explaining reasoning or checking facts. This system attempts to change that by making the AI reasoning auditable and the conclusions verifiable. The question is whether this lab result translates to production: the test used 100 real scenarios, but deployed systems will encounter edge cases, bad data, and users with goals the training set never saw.