Scalable Real Estate Data Extractor

I need a robust Python-based tool that accepts a street address or parcel number, crawls the web for matching property records, and pushes the results straight into a Google Sheet. Core data sources must include county appraisal sites, local tax offices, and FEMA flood maps; Zillow look-ups should be wired in as well so I can toggle them on or off.

Key expectations
• Clean, modular architecture—each data source in its own well-tested module so new states or counties can be added without touching existing code.
• Smart scraping logic that survives CAPTCHAs, inconsistent HTML, and slow servers; headless Selenium or similar only when truly necessary.
• Reliance on official or public APIs whenever available (e.g., FEMA, Google Maps/Geocode) to minimise scraping overhead.
• Automatic normalisation of results into a single schema, then export through the Google Sheets API with zero manual steps.
• Config file or simple UI for setting API keys, rate limits, and output sheet IDs.

Deliverables
1. Full source code with README covering setup, env variables, and deployment on a small VPS or serverless platform.
2. Google Sheet template and working demo sheet showing sample pulls from at least three counties.
3. Brief technical doc explaining how to plug in additional data providers, plus unit tests for the core extractor classes.

Acceptance criteria
• For any valid address or parcel number, the script returns assessor data, tax info, FEMA flood-zone status, and (when enabled) Zillow property details within one run.
• Results post to the specified Google Sheet in under 60 seconds for typical queries.
• New county modules can be registered by adding a single Python file and updating a config list.

A background in Python scraping, API integration, and basic GIS concepts will make the job smoother, and I value readable, well-documented code over quick hacks.