Zillow-Full: An OpenClaw Skill That Turned Manual Property Research Into an Automated Deal Pipeline
A part-time wholesaler spent 3 years scraping Zillow data manually — pulling zestimate, tax history, price history, schools, and comps for each candidate property took ~4 hours each. Existing tools like Apify (cost walls) and RentCast (data gaps) didn't give LLMs the structured data needed to reason about deals. So they built zillow-full, an OpenClaw skill now live.
Installation
openclaw skills install zillow-fullAvailable Tools
search_listings(filters)— search by bounding box, zip, or listing statuslookup_property_by_address(addr)— geocode + resolve ZPIDlookup_property_by_zpid(zpid)— core attributes, price history, tax historyget_zestimate(zpid)— zestimate + rent zestimate
Use Case: Automated Wholesale Deal Sourcing
A nightly cron runs to pull every new listing across 4 target ZIP codes. Claude scores each against the user's deal criteria, and results (80+ scored listings) are texted at 6am. Before this skill, they closed 2 wholesale deals per month; after, 11 per month.
Other Potential Use Cases
- Short-term rental analysis: compare cap rate vs rent zestimate
- Fix-and-flip lead scoring agents
- Buyer's agents auto-screening listings for clients
- Relocation househunting bots: 'find me a house under $X with...'
- Portfolio underwriting for small LPs
Planned Additions
- Permit history (renovation potential)
- Listing-description sentiment analysis (e.g., 'motivated seller', 'estate', 'as-is')
- Async optimization for batch lookups exceeding 500 ZPIDs
📖 Read the full source: r/openclaw
👀 See Also
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