Your volume, units and average loan size against the prior 12 months, with a quarter-by-quarter purchase and refinance split.
Your assistant runs these steps against Model Match through the MCP:
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.
Loan Officers
In ZIP 78704, find agents with at least 4 financed buyer deals in the last 12 months who worked with only 1 or 2 loan officers. Rank them by buyer volume so I know which relationships are worth trying to win.
A short list of productive agents who send nearly all their financed buyers to one or two loan officers.
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.
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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