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. ·
Done withClaude 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.
Source: Reddit · Jul 7, 2026
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