2025 Purchase LLPA Heatmap

Loan Pricing

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Data Points

CLTV Range 0% to 30 30.01% to 60 60.01% to 70 70.01% to 75 75.01% to 80 80.01% to 85 85.01% to 90 90.01% to 95 95.01% to 97 97.01% to 100 100.01% and up Credit Score Range 0% to 30 30.01% to 60 60.01% to 70 70.01% to 75 75.01% to 80 80.01% to 85 85.01% to 90 90.01% to 95 95.01% to 97 97.01% to 100 100.01% and up Total 639 and lower 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 640 to < 660 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 660 to < 680 0% 0% 0% 0% 1% 0% 0% 1% 0% 0% 0% 3% 680 to < 700 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 4% 700 to < 720 0% 1% 0% 0% 1% 0% 1% 2% 1% 0% 0% 7% 720 to < 740 0% 1% 1% 1% 2% 0% 1% 3% 1% 0% 0% 10% 740 to < 760 0% 1% 1% 1% 3% 1% 2% 3% 1% 0% 0% 14% 760 to < 780 0% 2% 2% 2% 5% 1% 2% 4% 1% 0% 0% 19% 780 and greater 1% 7% 4% 4% 11% 2% 4% 6% 1% 0% 0% 41% Total 2% 13% 9% 9% 25% 4% 12% 20% 5% 0% 0% 100.0%

Date Published:

October 29, 2025

Date Updated:

October 29, 2025

Chart type:

Table

Suggested Citation:

Polygon Research. "Fixed Rate Conventional Purchase Originations Concentration Heatmap 2025 YTD." Polygon Pulse – MBS Pivot. Accessed October 2025. Filters: 2025 YTD through September; Purchase; Fixed Rate; Conventional Loans (excluding FHA, HomeReady/HomePossible, Rural, HUD Section 184); Sold to Fannie Mae and Freddie Mac.

Key Insight and Commentary

This heatmap illustrates the concentration of fixed-rate conventional purchase loan originations in 2025 YTD, segmented by borrower credit scores (rows) and combined loan-to-value (CLTV) ratios (columns) - an LLPA-like matrix. Data excludes FHA, HomeReady/HomePossible, rural, and HUD Section 184 loans, focusing on those sold to Fannie Mae and Freddie Mac. Colors intensify with higher percentages, revealing market patterns.

Key observations

Originations heavily skew toward prime borrowers.
Those with 780+ credit scores account for 41% of total volume. Mid-tier scores (740-780) contribute 19%, with concentrations in 75-90% CLTV.
Lower scores (<700) are minimal (under 10% combined), clustered in lower CLTVs, reflecting tighter underwriting standards.

Strategically, this data highlights credit quality as a core driver in a high-rate environment, where affordability pressures push buyers toward higher LTVs without triggering PMI thresholds. Stakeholders like investors or policymakers might use it to assess market resilience; for instance, if rates drop, expect shifts to higher CLTVs among mid-credit tiers. In real-world application, originators could refine credit overlays or marketing to underserved lower-CLTV high-score niches, enhancing profitability and compliance with fair lending.

From Analysis to Action

This analysis provides a clear blueprint for how to uncover meaningful market dynamics. Its true power is unleashed when you apply this same methodology to your own local markets. Because all real estate is local, this granular approach is essential for crafting precise strategies that effectively address the unique conditions of each community.

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