ai-property-analysis

Natural language property queries are translated into structured filters like min_price, max_price, min_beds, and locations using Claude-based NLP. This allows real estate teams to input plain text requests and receive structured property data validated by Zod schemas.

Property solar potential is analyzed using NREL PVWatts integration, while flood risk and storm events are assessed via FEMA NFHL flood zones and NOAA Storm Events data. These cross-source insights provide comprehensive property intelligence for real estate analysis.

Cross-source property analysis across multiple regions is orchestrated by applying natural language inputs to integrated data pipelines. The system processes demographics, risk assessments, and solar potential to return validated structured data outputs for downstream real estate systems.

Structured outputs are validated using Zod schemas to ensure compatibility with downstream systems. This validation guarantees that property search results and intelligence data conform to expected formats for seamless integration into real estate workflows.

Searching for properties with specific features under price limits is handled by translating natural language requests into structured filters. A broker can query for properties with strong solar potential under $400k and receive a ready dataset with a narrative summary.

Limitations depend on the availability of integrated data sources like NREL PVWatts, NOAA Storm Events, and FEMA NFHL flood zones. Property analysis accuracy is constrained by the coverage and update frequency of these external data providers.