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Scout and underwrite real estate deals nightly

A builder made a Claude Code agent that scans hundreds of MLS listings nightly, underwrites those passing filters, and emails qualifying deals to investors before 6am. · Jul 7, 2026

Done withCCClaude Code

What they did
The builder says the agent runs on a nightly schedule with Inngest, scans hundreds of MLS listings, filters by price and cap rate, then underwrites the survivors. It pulls listing data from a property-data API, backfills missing beds, baths, and square footage from a secondary endpoint, runs multiple AI models in parallel to synthesize a score, and uses user actions like saves, passes, and closes to tune fit scoring over time.
How it went
They say it became Dealsletter, with almost 200 users and over 2,400 newsletter subscribers. The main shortfalls were sparse listing data, cold-start learning, and duplicate properties across feeds, which required confidence scoring and reconciliation.
Worth knowing
The biggest catch was data quality rather than scheduling: missing multifamily fields forced a backfill layer, and duplicate listings across sources needed extra reconciliation logic.

Try it yourself with Claude Code

Build me an agent that each night pulls new listings from [MLS export, feed, or site] in [market], filters them by [price, location, property type], and underwrites those that pass using [my assumptions: rent, expenses, financing, target return]. It should produce a short summary for each deal that meets [return threshold] and email the list to [recipient] by [time]. Start with a dry run that shows me the output before it sends any email.

Want help getting this working? Join the Vibe Coding Academy ↗ Run by Alex Finn, who also runs this site.

Read the original ↗

Source: Reddit · Jul 7, 2026

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