Screen incoming real estate deals and research tenants
Investors use AI to read incoming deals and filter weak ones, and one used ChatGPT to research deals, tenant information, and local rental rates.
- What they did
- Commenters described using Claude or ChatGPT as a first-pass analyst rather than a decision maker. They fed in deal details, comps, rent rolls or other gathered data, had the model pull comparable properties, organize them in spreadsheets, run basic metrics, summarize terms, and flag weak deals; one investor said they used ChatGPT to research deals, tenant information, and local rental rates, then manually verified numbers and assumptions.
- How it went
- The reported upside was time savings: several said it saved hours, reduced underwriting time, or got them most of the way to a rough screen. The consistent drawback was hallucination and overconfidence, so nobody in the thread recommended relying on it for final underwriting or purchase decisions.
- Worth knowing
- The main catch was that it only helped when paired with tested spreadsheets, real market data, and manual double-checking; without that, users said it confidently guessed.
Try it yourself with Claude
I'm a real estate investor and I'll paste in incoming deal packages one at a time. For each one, pull out the asking price, location, unit count, rents, and expenses, then flag anything that fails my criteria: [your criteria, e.g. cap rate above 7%, under $2M, in specific markets]. Finish with a pass, maybe, or reject call for each deal and a one-line reason, plus a short list of tenant and local rental rate questions to research for the ones that pass.
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1 more account of this
Source: Reddit · Undated
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