Valuation and comps MCPs
A real estate valuation MCP lets an AI estimate a property's value, pull comparable sales and estimate rent. You size up a deal in a conversation instead of a spreadsheet.
[Top button: Run comps on the deal you're already working → demo]
What a valuation MCP returns
"Valuation" can mean three things:
- AVM (automated valuation model): one estimated value, usually with a range and a confidence score.
- Comps: recent nearby sales of similar homes.
- Rent estimate: expected monthly rent, often with rental comps.
Ask things like:
- "What's 4410 Birch Lane worth, and how confident is that number?"
- "Pull 5 sold comps within half a mile from the last 6 months."
- "Estimate rent for a 3/2 at this address and show rental comps."
- "If I buy at $140,000 and rent at the estimate, what's the cap rate?"
HouseCanary
HouseCanary's official MCP server has the most tools: 149, covering values, rents, market and risk data. Install it with pip install housecanary-mcp.
More on our HouseCanary review.
Homesage.ai
Homesage's official server covers values, value history, comps, rents and a seller-motivation score for about 160 million US properties.
Cost: from about $75 a month, using credits (1 to 5 per call).
More on our Homesage review.
RentCast
RentCast's official AI tools give live rent and value estimates through your API key. A community server also exists (robcerda/rentcast-mcp-server).
More on our RentCast review.
Open-source analysis servers
agentic-ops/real-estate-mcp packs 50+ tools for property search, valuation, mortgage math and investment analysis. The code is free, and optional Census, FEMA, FRED and RentCast data is partly paid.
Same 5 addresses, every tool
We valued five properties with each tool we had: a starter home, rental, home needing work, rural property and condo.
| Address type | HouseCanary | Homesage | RentCast | Goliath comps | Spread |
|---|---|---|---|---|---|
| Starter home | [fill] | [fill] | [fill] | [fill] | [fill] |
| Rental | [fill] | [fill] | [fill] | [fill] | [fill] |
| Needs work | [fill] | [fill] | [fill] | [fill] | [fill] |
| Rural | [fill] | [fill] | [fill] | [fill] | [fill] |
| Condo | [fill] | [fill] | [fill] | [fill] | [fill] |
Comps vs. AVM vs. rent estimate: which do you need?
| You're doing | Use |
|---|---|
| Quick screen of 200 leads | AVM: cheap and fast, good enough to sort |
| Making an offer | Comps you can see and adjust, not a single number |
| Buy-and-hold | Rent estimate plus rental comps |
| Wholesaling | Comps, plus a repair estimate, to set your maximum offer |
Pricing per call
| Server | Model | Rough cost per property looked up |
|---|---|---|
| HouseCanary | Plan + per call | $0.30–$6 per call, several calls per property |
| Homesage | Credits | 1–5 credits per call |
| RentCast | Plan with included requests | $0.015–$0.20 per request after the included amount |
| agentic-ops | Free code | Free, plus whatever data you plug in |
| Goliath | Included in plan | Comps included in the CRM plan |
The AI makes more calls than you'd expect. "Value these 20 properties" can mean 60 or more.
Our cost page puts those numbers next to a full stack.
Running comps inside your deal pipeline
A comp is only useful attached to a deal. A standalone valuation server gives you a number in chat to copy into your CRM by hand.
Questions
Which valuation MCP is most accurate? It depends on the market and property, which is why we test the same addresses across tools.
Is there a free comps MCP? RentCast's free tier gives 50 requests a month, and open-source servers are free but need a data source.
Can ChatGPT do comps on its own? Not reliably, because without a data connection it's guessing from training data.
Run comps on the deal you're already working. See Goliath pull comps, log the ARV and move the deal from one AI conversation, in a 20-minute demo. Book a demo