Before-and-after production for low, mid and high producers who switched companies, a reality check for any recruiting pitch.
Your assistant runs these steps against Model Match through the MCP:
Texas, 3,815 movers analyzed. Run on Oct 18, 2026. Your numbers will differ with your market and the date you run it.
| Tier | Movers | Volume change after the move |
|---|---|---|
| Low (under 25 loans/yr) | 2,771 | +119% |
| Mid (25–99 loans/yr) | 806 | −26% |
| High (100+ loans/yr) | 238 | −53% |
Recruiters / Business Development
Which companies had the biggest net gain of loan officers in Texas over the last 6 months? Show gained, lost and net for the top 5.
Companies ranked by net loan officer gain, with gained and lost counts and the production of the loan officers they added.
Recruiters / Business Development
Show me the highest-producing loan officers in Florida who changed companies in the last 3 months, with where they came from, where they went and their trailing volume.
Recent movers with previous and new employer, move date, city and trailing volume and units.
Recruiters / Business Development
For competitor name, show the net change in LO headcount over the last 12 months, which companies its hires came from and where its departures went, and what share of new hires left within 90 days.
Headcount over time, top source and destination companies, and early attrition by hire cohort.
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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