PropHunt
3,000 listings a day. The 4 that matter.
PropHunt pulls listings from Zillow, LoopNet, and Realtor and ranks the rare properties — the ones on the fewest sites, freshest to market — so good deals don't slip past while you're still scrolling. Pick 3–5 favorites and have 10 AI agents deep-analyze each: crime, income, comps, news, and open-city data. It compresses a 3,000-listings-a-day analyst job down to the 4 that matter — same hit rate, a fraction of the time. The whole wedge is speed: you stop losing fast-moving deals.
Features
AI agents screen 3,000+ listings a day and surface only the rare ones
Ranks by scarcity: fewest sites listed + freshest to market
Pick 3–5 favorites, then 4/7/10 agents deep-analyze each
Per-property analysis: crime, income, comps, news, open-city data
40+ cities of open public data
Web app — nothing to install
Use Cases
Investors who keep losing fast-moving deals to speed
Flippers and landlords screening new markets daily
Small funds and deal-hunters who want analyst-grade screening without hiring an analyst
Comments
I built PropHunt because the best deals don't wait. Investors refresh Zillow, LoopNet, and Realtor all day and still lose the rare ones to whoever moved faster. So I built a team of AI agents that screen ~3,000 listings a day, rank the rare ones (fewest sites, freshest to market), then deep-analyze the few you pick — crime, income, comps, news, open-city data — into one honest memo that also flags what it couldn't verify. The whole bet is speed: 3,000 down to the 4 that matter. Would love feedback from the builders and investors here, especially on what would make you actually trust an AI deal score.
The "flags what it couldn't verify" line is the part that earns trust here, most AI scorers hide their gaps instead of showing them. If I'm acting on a deal memo, I want every number in the score traceable to a source and a timestamp, plus a confidence level per factor (comps from three sales last month shouldn't weigh the same as one stale listing). And the 3,000-to-4 funnel is only as convincing as the rejection reasons: surfacing why the other 2,996 got dropped would make me trust the 4 a lot more. Curious how you're handling that explainability.
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I built PropHunt because the best deals don't wait. Investors refresh Zillow, LoopNet, and Realtor all day and still lose the rare ones to whoever moved faster. So I built a team of AI agents that screen ~3,000 listings a day, rank the rare ones (fewest sites, freshest to market), then deep-analyze the few you pick — crime, income, comps, news, open-city data — into one honest memo that also flags what it couldn't verify. The whole bet is speed: 3,000 down to the 4 that matter. Would love feedback from the builders and investors here, especially on what would make you actually trust an AI deal score.
The "flags what it couldn't verify" line is the part that earns trust here, most AI scorers hide their gaps instead of showing them. If I'm acting on a deal memo, I want every number in the score traceable to a source and a timestamp, plus a confidence level per factor (comps from three sales last month shouldn't weigh the same as one stale listing). And the 3,000-to-4 funnel is only as convincing as the rejection reasons: surfacing why the other 2,996 got dropped would make me trust the 4 a lot more. Curious how you're handling that explainability.
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