Each referral partner's deals with you now against the year before, sorted into growing, steady and drifting, with a call list.
Your assistant runs these steps against Model Match through the MCP:
A sample Austin loan officer, agents anonymized. Run on Oct 18, 2026. Your numbers will differ with your market and the date you run it.
| Partner | Prior 12 months | Last 12 months | Read |
|---|---|---|---|
| Agent A | 12 loans | 10 loans | Steady, slightly down |
| Agent B | 0 loans | 3 loans | New and growing |
| Agent C | 11 loans | 0–1 loans | Drifting, call this week |
Loan Officers
Which real estate agents were on the other side of my loans most often in the last 12 months? My NMLS ID is your NMLS ID. Also show which cities most of my business came from.
The agents tied to your closed loans, ranked by volume, plus your top cities by share of your book.
Loan Officers
My NMLS ID is your NMLS ID. Find the top 25 buyer's agents with an office in Austin, TX by financed buyer deals over the last 12 months, and remove anyone who has already closed a loan with me. That's my warm prospect list.
A ranked list of productive local buyer's agents who have never closed a deal with you, your warmest cold-call list.
Loan Officers
Who are the top 20 buyer's agents with an office in Austin, TX by buyer-side transactions over the last 12 months, counting only agents with at least 5 financed buyer deals? Include each agent's brokerage and how many different loan officers they sent business to.
A ranked list of the most active buyer-side agents in your market, with brokerage, buyer units and volume, and how many loan officers they spread their deals across.
Connect the Model Match MCP to your AI assistant and ask about loan officers, agents, lenders and markets in plain English, answered from real records.
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