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Are You Overpaying for VA Prepay Risk in Ginnie II Pools?

Recent history is showing a persistent (and widening) gap between VA and FHA loan prepayment speeds in Ginnie Mae securities.  

Over the past 33 months, VA 30-year loans are prepaying 40 percent faster than FHA 30-year loans (9.4% CPR for VA vs. Just 6.6% for FHA. VA speeds over this period are ranging from 1.15x to 1.77x FHA speeds. 

This divergence is not incidental. With a median spread between the two of around 230 bps, the difference compounds significantly in modeling cash flow expectations and MBS pricing. 

Why this divergence? 

At least three structural factors contribute to faster VA speeds relative to FHA: 

  1. Borrower Profiles: VA borrowers tend to have higher credit scores (727 average FICO as of Sept 2025) than FHA borrowers (678 average FICO). This makes VA borrowers more likely to refinance quickly when market conditions shift. 
  1. Program Rules: VA’s streamlined refinancing programs are generally more accessible, lowering the cost of refinancing compared to FHA. 
  1. Servicing Practices: The VA loan servicing ecosystem has historically been more efficient, which can accelerate churn relative to FHA pools. 
  1. Larger Loan Size: The average VA loan size is typically larger than the average FHA loan size, making refinancing more impactful for VA borrowers. 

What does this mean for Ginnie II TBA & Custom pools? 

Ginnie II TBAs typically combine both VA and FHA collateral. Most of the loans are FHA, but VA loans still account for a significant share. Because VA loans prepay substantially faster, TBA investors are effectively buying into faster prepayment risk than they would see in a purely FHA pool.  

This risk manifests itself both in the form of shorter duration and more negative convexity exposure. Investors in Ginnie II TBAs may see faster principal return than modeled if VA share is high, especially if the model fails to differentiate between VA and FHA loans, and the additional negative convexity in VA loans will adversely impact OASes, ceteris paribus. 

For investors seeking more tailored exposure, custom Ginnie pools provide a way to isolate or avoid VA prepayment risk. For instance: 

  • FHA-only pools offer slower, more stable prepayment behavior, attractive for investors prioritizing duration stability. 
  • VA-dominant pools may appeal to investors willing to take on higher turnover in exchange for price discounts or optionality in certain market environments. 

Given current market spreads, the differential between VA and FHA speeds is unlikely to narrow materially in the near term. As refinancing incentives fluctuate, VA borrowers will continue to exhibit faster churn than FHA counterparts. 

For Agency portfolio managers and traders, this reality underscores the importance of collateral composition within Ginnie II TBAs. It also highlights the importance of prepayment models capable of recognizing the differences between FHA and VA loans and taking those differences into account when making forecasts. 

Book a demo for RiskSpan’s Edge platform for Agency MBS Traders and Analysts. 

Consumers Under Pressure as Markets Seek Stability: October Models & Markets Recap 

Register here for next month’s call: Thursday, November 20th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call focused on the impact of the Fed rate cut, key macro indicators and a spotlight on the surging second-lien market. 

Here’s a quick recap in case you missed it. 

(Click here for the entire 23-minute recording or continue reading for a summary.)  

Rates Ease, but Headwinds Persist 

October has brought a modest reprieve in mortgage rates, with the 30-year fixed rate having fallen to approximately 6.2%, the lowest level in nearly a year. 

Affordability remains constrained, however, and long-term headwinds appear far from resolved. Specifically: 

Unemployment remains near 4.2%, and core PCE inflation continues to hover around 2.8%. While steady, this remains above the Fed’s comfort zone. 

Home price growth is slowing nationally, with several major metros posting month-over-month declines. 

Fed Funds futures suggest rates will stay elevated into 2026, with year-end 2025 expectations still in the 3.5–3.75% range. 

Together, these indicators suggest a “higher for longer” policy regime even as the market eyes rate cuts later this year. 

HELOC and Second-Lien Insights: Delinquencies on the Rise 

Leveraging the Equifax Analytic Dataset, a 10% sample of active U.S. credit borrowers with anonymized tradeline-level detail, enables us to dive deep into Home Equity Loans (HELs) and Home Equity Lines of Credit (HELOCs). These asset classes are gaining renewed investor attention as homeowners tap existing equity rather than selling into a high-rate market.

Delinquency rates are trending upward for both HELs and HELOCs, particularly among lower-credit-score borrowers. Aggregated five-year views on page 11 highlight the steady climb, with 600-score cohorts showing the sharpest deterioration. 

These findings echo broader signals of consumer strain visible across other loan products. 

Consumer Balance Sheets Under Pressure

The New York Fed’s Q2 2025 Household Debt and Credit Report underscored the strain many consumers face. Total household debt continues to climb, driven by non-housing credit categories—auto loans, student debt, and revolving balances in particular. 

Credit card and auto loan delinquencies have risen sharply, while mortgage and HELOC performance, though still comparatively solid, are trending downward. Even with stable macro indicators, consumers remain financially stretched. This dynamic is likely to influence credit performance and securitization trends into 2026.

Prepayment Model Updates 

Our prepayment models continue to align well with observed speeds across both Conventional and Ginnie collateral. Lower-coupon collateral (WAC ≤ 5.5%) experienced some deceleration versus forecasts—a function of seasonality and slower housing turnover.  

Higher-coupon cohorts (WAC ≥ 6.0%) reflected more volatility, consistent with recent refinance activity at the margins. 

We continue to add additional analytics reports on the platform. Please visit www.riskspan.com/request-access to request free access. 

Also, please feel free to contact us to discuss or learn more.

Use Case: RiskSpan’s Agentic AI for MBS Data Tool 

Breaking Down VA vs FHA Prepayment Speeds 

This use case demonstrates how agentic interaction with the MBS Data Tool helps investors move from a general observation (“VA speeds are fast”) to a data-backed, multidimensional explanation tied directly to credit, LTV, and program structure. 

Business Question 

Why are VA loans — particularly those with low FICO and high LTV profiles — prepaying so much faster than historically, and faster than FHA loans with comparable characteristics? 

For the full output of our AI MBS Agent, read on or watch our two-minute demo video:

Workflow with Agentic AI 

1. Baseline Comparison 

Based on the data, I can now provide a comprehensive analysis of VA vs FHA loan prepayment trends since 2020:

Key Findings:

1. Current Prepayment Speeds (as of September 2025):

– VA loans: 11.74% CPR

– FHA loans: 8.68% CPR

– VA loans are currently prepaying about 3 percentage points faster than FHA loans

2. Historical Trends:

2020-2021 (Refinance Wave):

– Both loan types experienced very high prepayment speeds

– Peak CPRs:

* FHA: 38.48% (April 2021)

* VA: ~30-31% (Late 2021)

– VA loans consistently showed higher prepayment sensitivity during this period

2022-2023 (Rising Rate Environment):

– Both loan types saw significant slowdown in prepayment speeds

– Lowest CPRs:

* FHA: 4.42% (February 2023)

* VA: 5.07% (February 2023)

– The spread between VA and FHA prepayment speeds narrowed but remained positive

2024-2025 (Current Environment):

– Moderate prepayment speeds for both loan types

– VA loans maintain faster prepayment speeds

– Recent range:

* FHA: 5-10% CPR

* VA: 7-14% CPR

3. Key Observations:

– VA loans consistently prepay faster than FHA loans across different rate environments

– The spread between VA and FHA prepayment speeds tends to widen in more favorable refinancing environments

– Both loan types show similar seasonal patterns but with different magnitudes

– VA loans show greater prepayment sensitivity to market conditions

4. Portfolio Sizes:

– FHA: Currently ~7.45 million loans with $1.47 trillion in balance

– VA: Currently ~3.77 million loans with $1.03 trillion in balance

This analysis suggests that investors should expect consistently faster prepayment speeds from VA loans compared to FHA loans, particularly during periods of favorable refinancing opportunities. The higher prepayment sensitivity of VA loans likely reflects the generally better credit quality of VA borrowers and their greater ability to refinance when rates are favorable.

2. High-Credit Borrowers 

Based on the analysis of high-FICO (740+) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-FICO VA: 9.37% CPR

– High-FICO FHA: 5.96% CPR

– Spread: 3.41% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-FICO VA Loans:

– Average FICO: 779

– Average WAC: 3.85%

– Average CLTV: 58.1%

– Balance: $443.4B (43% of VA portfolio)

– Loan Count: 1.47M loans

High-FICO FHA Loans:

– Average FICO: 769

– Average WAC: 4.55%

– Average CLTV: 58.4%

– Balance: $188.0B (13.8% of FHA portfolio)

– Loan Count: 879K loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-FICO VA: 4.26% to 14.76% CPR

– High-FICO FHA: 3.56% to 8.68% CPR

Seasonal Patterns:

– Both show seasonal increases in spring/summer months

– VA loans show more pronounced seasonal effects

– Peak prepayment typically occurs in October/November

4. Notable Observations:

– The prepayment speed differential between VA and FHA remains significant even at high FICO levels

– High-FICO VA loans have consistently lower WACs (about 70bps lower)

– Similar CLTV profiles between high-FICO VA and FHA

– VA has a much larger proportion of high-FICO borrowers (43% vs 13.8% for FHA)

– High-FICO VA loans show greater prepayment sensitivity to market conditions

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– The spread between VA and FHA prepayment speeds has remained relatively stable

– High-FICO VA loans maintain faster prepayment speeds even in rising rate environments

This analysis suggests that even when controlling for credit quality (FICO ≥ 740), VA loans consistently prepay faster than FHA loans. This is likely due to:

1. Lower average note rates (WAC)

2. VA’s no-down-payment benefit making it easier to refinance

3. Generally better borrower demographics and income profiles

4. More streamlined refinancing options for VA loans

Investors should expect continued faster prepayment speeds from high-FICO VA loans compared to high-FICO FHA loans, particularly during favorable rate environments or seasonal peaks.

3. Low-Credit Borrowers 

Based on the analysis of low-FICO (<640) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– Low-FICO VA: 21.04% CPR

– Low-FICO FHA: 12.07% CPR

– Spread: 8.97% faster for VA

2. Portfolio Characteristics (as of September 2025):

Low-FICO VA Loans:

– Average FICO: 611 – Average WAC: 4.77%

– Average CLTV: 66.2%

– Average Age: 52.3 months

– Balance: $77.5B (8.3% of VA portfolio)

– Loan Count: 316K loans

Low-FICO FHA Loans:

– Average FICO: 609

– Average WAC: 4.83%

– Average CLTV: 60.4%

– Average Age: 62.8 months

– Balance: $290.7B (21.3% of FHA portfolio)

– Loan Count: 1.49M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– Low-FICO VA: 9.08% to 27.54% CPR

– Low-FICO FHA: 6.31% to 12.73% CPR

Notable Patterns:

– Much higher volatility in VA prepayment speeds

– Larger seasonal effects in VA loans

– Consistently wider spread between VA and FHA vs high-FICO segment

4. Key Differences:

Portfolio Composition:

– FHA has a much larger concentration in low-FICO loans (21.3% vs 8.3% for VA)

– FHA’s low-FICO portfolio is nearly 4x larger than VA’s by balance

Performance Characteristics:

– VA loans show significantly higher prepayment speeds despite similar credit profiles

– VA loans exhibit greater prepayment volatility

– VA loans have slightly better average FICO scores within the low-FICO segment

– VA loans have higher CLTVs but lower WACs

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds are consistently higher and more volatile

– Seasonal patterns are more pronounced in VA loans

– The spread between VA and FHA prepayment speeds has remained wide

6. Notable Observations:

– The prepayment speed differential between VA and FHA is actually larger in the low-FICO segment than in the high-FICO segment

– Low-FICO VA loans show surprisingly high prepayment speeds despite lower credit scores

– FHA loans show more consistent, less volatile prepayment patterns

– VA loans maintain faster speeds despite higher CLTVs

This analysis reveals that the prepayment behavior difference between VA and FHA loans is even more pronounced in the low-FICO segment than in the high-FICO segment. This could be due to:

1. VA’s stronger refinancing program benefits for lower-credit borrowers

2. More flexible VA streamline refinance requirements

3. Better servicing support for VA borrowers

4. Lower average note rates in the VA program

5. Potentially different borrower demographics within the same FICO band

Investors should expect:

– Higher and more volatile prepayment speeds from low-FICO VA loans

– More stable, lower prepayment speeds from low-FICO FHA loans

– Larger seasonal effects in VA loans

– Greater market sensitivity in VA loans

4. High-LTV Loans 

Based on the analysis of high-LTV (≥95%) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-LTV VA: 12.37% CPR

– High-LTV FHA: 7.77% CPR

– Spread: 4.60% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-LTV VA Loans:

– Average FICO: 714

– Average WAC: 4.33%

– Average CLTV: 70.9%

– Average Original LTV: 100.3%

– Average Age: 52.9 months

– Balance: $607.2B (61.1% of VA portfolio)

– Loan Count: 2.11M loans

High-LTV FHA Loans:

– Average FICO: 679

– Average WAC: 4.61%

– Average CLTV: 62.9%

– Average Original LTV: 97.6%

– Average Age: 71.7 months

– Balance: $1.04T (72.0% of FHA portfolio)

– Loan Count: 5.08M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-LTV VA: 5.60% to 19.73% CPR

– High-LTV FHA: 4.50% to 8.89% CPR

Notable Patterns:

– Higher volatility in VA prepayment speeds

– Strong seasonal patterns in VA prepayments

– More stable FHA prepayment patterns

– Consistent spread between VA and FHA

4. Key Differences:

Portfolio Composition:

– Both programs have significant high-LTV concentration

– FHA has larger absolute exposure but similar relative exposure

– VA allows higher original LTV (100%+) compared to FHA

Performance Characteristics:

– VA loans consistently prepay faster despite higher LTVs

– VA borrowers have notably higher FICO scores (+35 points)

– VA loans have lower WACs (-28bps)

– VA loans are significantly younger (-19 months)

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds remain more volatile and responsive to seasonality

– Peak prepayment periods occur in spring/summer months

– The spread between VA and FHA has remained relatively stable

6. Notable Observations:

– High-LTV concentration is a defining characteristic of both programs

– VA’s higher prepayment speeds persist despite higher LTVs

– Better credit quality of VA borrowers likely contributes to faster speeds

– Younger average loan age in VA portfolio may influence prepayment behavior

– Lower WACs in VA portfolio suggest more efficient refinancing history

Implications for Investors:

1. Portfolio Management:

– Expect faster and more volatile prepayments from high-LTV VA loans

– Consider seasonal patterns in VA prepayments for timing decisions

– Factor in the significant WAC differential when evaluating prepayment risk

2. Risk Considerations:

– Higher prepayment risk in VA despite similar LTV profiles

– Greater market sensitivity in VA portfolio

– More stable, predictable prepayment patterns in FHA

3. Valuation Impact:

– Need to apply larger prepayment speed differentials for VA vs FHA

– Consider stronger seasonal effects in VA valuations

– Account for credit quality differences within high-LTV segment

The analysis suggests that even in the high-LTV segment, VA loans maintain significantly faster prepayment speeds than FHA loans, driven by:

1. Better borrower credit quality

2. Lower note rates

3. More flexible refinancing options

4. Younger loan age

5. More efficient VA refinancing program

These factors appear to overcome the theoretical constraints of high LTV ratios on refinancing ability.

5. Program Features as Drivers

The data strongly supports that program features drive faster VA prepayment speeds. Here’s the detailed analysis:

1. Early Prepayment Behavior (0-24 months):

– VA loans age 13-24 months: 25.29% CPR

– FHA loans age 13-24 months: 17.04% CPR

– The 8.25% difference reflects VA’s more flexible refinancing rules

– No seasoning requirement for VA vs 210 days for FHA

2. Refinance Incentive Response:

– High incentive (>100bp) VA: 50.43% CPR

– High incentive (>100bp) FHA: 26.55% CPR

– The dramatic difference (23.88%) shows VA borrowers can more easily act on rate incentives

– Even medium incentive (0-100bp) shows large gap:

* VA: 29.59% CPR

* FHA: 18.37% CPR

3. Credit Quality Impact:

– VA average FICO: 715-724 across age buckets

– FHA average FICO: 672-681 across age buckets

– Better VA credit quality + easier program rules = faster speeds

4. Cost Structure Effects:

VA Advantages:

– Lower funding fee (0.5%)

– No ongoing MIP

– Can include closing costs

– Results in lower all-in refinancing costs FHA Constraints:

– Higher upfront MIP (1.75%)

– Ongoing annual MIP

– More restricted closing cost inclusion

– Higher total refinancing costs

5. LTV Considerations:

– VA has no maximum LTV for IRRRL

– FHA generally limited to 97.75% LTV

– Current data shows:

* VA high-LTV loans (>95%): 12.37% CPR

* FHA high-LTV loans (>95%): 7.77% CPR

6. Program Flexibility Evidence:

Response to Rate Environment:

– VA loans show stronger reaction to refi incentives

– Higher CPRs across all incentive buckets

– Faster response to market opportunities

Seasoning Effects:

– VA shows earlier prepayment spikes

– More pronounced early-age prepayment behavior

– Less constrained by seasoning rules

7. Servicer Impact:

– VA servicers may be more proactive due to program ease

– Lower documentation requirements reduce operational friction

– Simpler approval process enables faster execution

The data demonstrates that VA’s program features create a more efficient refinancing mechanism:

1. Lower Costs:

– Reduced fees

– No ongoing MIP

– More included costs

2. Fewer Restrictions:

– No LTV limits

– No seasoning requirement

– Limited credit checks

3. Simpler Process:

– Less documentation

– Faster approval

– Lower operational burden

These advantages manifest in:

– Consistently faster prepayment speeds

– Stronger response to rate incentives

– Earlier refinancing activity

– Higher refinancing success rates

For investors, this means:

1. Expect faster VA prepayments across market cycles

2. Anticipate stronger response to refinancing opportunities

3. Factor in program differences when valuing VA vs FHA MBS

4. Consider seasoning rules when evaluating young loans

The combination of program features and data evidence clearly shows that VA’s more flexible refinancing rules are a primary driver of faster prepayment speeds compared to FHA.

6. Scenario Stress Test 

Based on the current portfolio characteristics and historical refinancing behavior, here’s how a 100bps rate drop would likely impact VA vs FHA prepayment speeds:

1. Current Portfolio Rate Distribution:

VA Portfolio:

– 7.5%+ WAC: $4.6B (0.4%)

– 7.0-7.49% WAC: $18.5B (1.8%)

– 6.5-6.99% WAC: $71.1B (6.9%)

– 6.0-6.49% WAC: $105.3B (10.2%)

– 5.5-5.99% WAC: $118.7B (11.5%)

– <5.5% WAC: $713.0B (69.2%)

FHA Portfolio:

– 7.5%+ WAC: $23.6B (1.6%)

– 7.0-7.49% WAC: $68.5B (4.7%)

– 6.5-6.99% WAC: $163.8B (11.2%)

– 6.0-6.49% WAC: $158.4B (10.8%)

– 5.5-5.99% WAC: $149.0B (10.2%)

– <5.5% WAC: $903.5B (61.5%)

2. Projected Prepayment Speeds by Refi Incentive:

Based on current behavior with similar incentives:

High Incentive (>100bps):

– VA: 50-55% CPR

– FHA: 25-27% CPR

– Differential: ~25-28% faster for VA

Medium Incentive (50-100bps):

– VA: 35-40% CPR

– FHA: 20-25% CPR

– Differential: ~15% faster for VA

Low Incentive (0-50bps):

– VA: 25-30% CPR – FHA: 15-18% CPR

– Differential: ~10-12% faster for VA

3. Portfolio Impact of 100bps Rate Drop:

VA Portfolio:

– ~$200B would move to >100bps incentive

– ~$119B would move to 50-100bps incentive

– Weighted average CPR projection: 32-37% CPR

FHA Portfolio:

– ~$256B would move to >100bps incentive

– ~$149B would move to 50-100bps incentive

– Weighted average CPR projection: 18-22% CPR

4. Key Factors Driving Divergence:

Program Features:

– VA IRRRL has no LTV restrictions

– VA has lower funding fees

– VA has simpler documentation requirements

– VA has no seasoning requirement

Borrower Characteristics:

– VA has higher average FICOs (714 vs 675)

– VA has lower average WACs

– VA has younger average loan age

5. Projected Timeline of Response:

Months 1-3:

– VA speeds increase rapidly to 30-35% CPR

– FHA speeds lag, reaching 15-18% CPR

– Differential: 15-17% CPR

Months 4-6:

– VA peaks at 35-40% CPR – FHA reaches 20-22% CPR

– Differential: 15-18% CPR

Months 7-12:

– VA moderates to 30-35% CPR

– FHA stabilizes at 18-20% CPR

– Differential: 12-15% CPR

6. Risk Factors to Consider:

Upside Risks (faster speeds):

– Pent-up refinance demand

– Improved technology/processing

– Competitive lender environment

Downside Risks (slower speeds):

– Housing market uncertainty

– Credit tightening

– Operational capacity constraints

7. Investment Implications:

1. Premium Pools:

– Expect significantly faster VA prepayments

– Greater price impact on VA premium pools

– Wider VA-FHA OAS spreads

2. Current Coupon Pools:

– Moderate prepayment differential

– Less price impact

– Smaller OAS spread widening

3. Discount Pools:

– Minimal impact on speeds

– Limited price effect

– Stable OAS spreads

Recommendations:

1. Portfolio Positioning:

– Reduce exposure to premium VA pools

– Consider FHA alternatives in premium segment

– Maintain VA exposure in current coupon/discount segments

2. Risk Management:

– Increase prepayment protection in VA holdings

– Consider specified pool stories

– Monitor servicer behavior closely

3. Relative Value:

– Look for opportunities in FHA premium pools

– Consider VA-FHA switches in premium segment

– Evaluate specified pool pay-ups

The analysis suggests a significant divergence in VA vs FHA prepayment speeds following a 100bps rate drop, with VA speeds likely to be 12-25% CPR faster depending on the segment and timing. This differential is driven by both program features and borrower characteristics, with the impact most pronounced in premium pools.

Questions or Need Support? 

Our team is standing by to help you make the most of these new features. Contact us at techsupport@riskspan.com or reach out to your dedicated account manager. 

Interested in a Complimentary Trial or Demo?

Contact us here.

Prepayments Hold Steady, Second Liens Surge: September Models & Markets Recap

Register here for next month’s call: Thursday, October 16th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call focused on the impact of the Fed rate cut, key macro indicators and a spotlight on the surging second-lien market. 

Here’s a quick recap in case you missed it. 

(Click here to listen to the entire 27-minute recording or continue reading for a summary.)  

Market Backdrop: September 2025

Mortgage rates have hit their lowest levels in nearly a year, averaging close to six percent. The Federal Reserve delivered its first rate cut of the current cycle in September 2025, reducing the target range from 400–425 basis points to a projected 350–375 basis points by year-end. Despite this easing, markets continue to anticipate relatively high rates into 2026. 

Inflation and unemployment are holding stable, but long-term headwinds persist, including sluggish real wage growth and affordability constraints in the housing market. Longer-dated Treasury yields are the key driver of mortgage rates, making them essential for investors to keep tabs on. As bond yields set the tone for borrowing costs across the economy, their movement will be critical in shaping both origination volumes and prepayment activity in the coming quarters. 

Spotlight on Second Liens 

The second lien mortgage market continues its emergence as one of the most active areas in structured finance. Issuance and securitization of second lien products have been increasing rapidly, with no signs of slowing. This expansion is driven in part by rising homeowner demand for tapping into the accumulated equity and lenders’ interest in capturing additional credit exposure in a higher-rate environment. 

Prepayment behavior in second lien mortgages, however, differs significantly from that of first liens. This divergence makes specialized model calibration critical. RiskSpan’s Prepayment Models, calibrated against actual second lien performance, indicate that the models are capturing observed dynamics effectively. With issuance expected to continue climbing, accurate modeling of second lien prepayment risk will remain an essential tool for market participants seeking to price and manage these assets. 

Prepayment Model Updates 

Back-testing continues to show that RiskSpan’s prepayment models are tracking well against observed performance across a variety of collateral types. Recent analysis of agency MBS vintages from 2021 and 2022 revealed that higher-coupon pools, particularly those in the 6.5% range, are slowing more than originally anticipated. 

FN/FH 2021-2022 6.5s

By contrast, lower- and mid-coupon pools—those ranging from 1.5% through 5.5%—have remained steady and closely aligned with model expectations. This outcome reinforces the robustness of the models across different coupon bands and provides confidence in their ability to capture nuanced prepayment behavior. 

FN/FH 2021-2022 1.5s – 3.5s

FN/FH 2021-2022 4s – 5.5s

As we introduced during our August call, consumer credit remains a major focus of RiskSpan’s modeling enhancements. Using the Equifax Analytic Dataset, the team has constructed prepayment aging curves for both auto loans and personal loans. These analyses confirm that borrower credit score bands, measured using VantageScore 4.0, influence prepayment behavior in a manner similar to mortgage loans. For auto loans, the score sensitivity is particularly evident across borrower segments. Personal loan data show similar trends, with one notable difference: the effect of loan term is more pronounced after the first year of loan seasoning. This suggests that term structure plays a more significant role in personal loan prepayment decisions compared to auto loans. 

Auto Loan Prepayment Aging Curves

Personal Loan Prepayment Aging Curves

We are in the process of finalizing these consumer loan prepayment models and will release them shortly on the RiskSpan Platform. This will give clients the ability to incorporate a new level of borrower insight into their own portfolio analytics. 

Looking Ahead 

The integration of Equifax ADS into the construction of prepayment aging curves is just the beginning. We continue to expand our modeling capabilities and data integration in order to provide clients with deeper and more actionable insights. Credit card and student loan models are already in the pipeline, and their release will extend RiskSpan’s modeling coverage across the full spectrum of consumer credit products. 

In addition, the team is adding new analytics reports to the Platform, giving clients free access to timely updates and market intelligence. These ongoing enhancements underscore our commitment to equipping the investment management community with the tools and data needed to navigate complex and evolving credit markets. 

Contact us to discuss or learn more.

Higher Rates, Smarter Models, and Fresher Credit Insights: August Models & Markets Recap

Register here for next month’s call: Thursday, September 18th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call was a wide-ranging update on new model developments, consumer credit insights, and macroeconomic trends shaping structured finance. 

Here’s a quick recap in case you missed it. 

(Click here to listen to the entire 30-minute recording or continue reading for a summary.)  

Market Outlook: August 2025

Stable employment and inflation notwithstanding, the macro backdrop remains dominated by persistent headwinds: 

  • Mortgage Rates: Still above 6.5% and expected to stay above 6% for the next several years. 
  • Home Prices: Case-Shiller data shows relative stability, with modest month-over-month declines and low year-over-year growth. 
  • Labor & Inflation: Both unemployment and PCE inflation are holding steady. 
  • Fed Policy: The Fed Funds Rate remains in the 4.25%–4.50% range, with the first cut expected in September 2025. Markets anticipate a year-end rate of 3.75%–4.00%, but long-term rates remain elevated. 
  • 10Yr rates unlikely to see a significant decline over next few years, leading to a high mortgage rate environment (>~ 6%) for next 3-5 years. 

New Equifax Data Integration 

We introduced our latest research leveraging the Equifax Analytic Dataset (ADS), a borrower-level anonymized sample representing 10% of the U.S. active credit population. Using tradeline-level detail (credit scores, balances, payments, etc.), we have constructed aging curves for auto loans and personal loans segmented by credit score bands. 

Some key takeaways: 

  • Auto Loan Defaults: Clear segmentation appears across credit score bands, with default curves validated against Federal Reserve data. 

  • Personal Loan Defaults: Similar segmentation trends, with early results indicating significant variation across risk tiers. 
  • Credit card and student loan performance curves: Coming soon. 

The final versions of these datasets will be accessible directly within the RiskSpan platform, allowing clients to benchmark their portfolios against robust national trends. 

Model Updates 

Prepayment Models (Versions 3.2 & 3.7) 

Our prepayment models continue to perform strongly against observed market behavior. The latest back-testing of agency cohorts (Fannie Mae and Freddie Mac 2021/2022 vintages across 1.5%–6.5% coupons) shows that speeds remain broadly consistent with expectations. However, higher coupon pools have recently exhibited slower-than-expected speeds, reflecting both tighter refinancing conditions and borrower credit constraints. 

1.5 to 3.5 Coupons 

6.5 Coupons 

Credit Model 7.0 

Our much-anticipated Credit Model v7 is now available in production on the RiskSpan Platform. Key features include: 

  • Delinquency Transition Matrix – A granular 3-D framework tracking monthly movement of loans through delinquency buckets (30D, 60D, 90D, 120D, 150D, 180D+, Foreclosure, REO). 
  • Severity & Liquidation Enhancements – Expanded severity vectors and a liquidation timeline module allow for more nuanced control of loss projections. 
  • Integration with MSR Engine – Provides detailed P&I and T&I cash flow accounting that captures probabilistic delinquency transitions. 

These enhancements equip investors and risk managers with deeper tools for analyzing loss dynamics across mortgage, GSE, FHA, and VA loan cohorts. 

Contact us to learn more.

Monitoring Non-QM Mortgage Delinquencies in a Shifting Market

This post provides an update on delinquency rate trends observed in the Non-Agency mortgage market with a deep dive on different segments of the fast growing Non-QM mortgage market. All of the figures in this post are based on queries of historical CoreLogic Non-Agency data via our proprietary RiskSpan Edge Historical module.

After reaching post-Covid highs in May 2025, delinquency rates have stabilized at slightly lower levels in August 2025, the most recent factor date available from CoreLogic: 

  • As shown in figures 1 and 2, the 60+ delinquency rate for Private Label Securities 2.0 (loans originated after 2010) is 2.21%, while the DQ rate for Legacy products (originated prior to 2010) continues to fall below the 10% threshold, hitting a post-COVID low of 9.61% 
  • Prime Jumbo mortgages continue to demonstrate the strongest performance from a credit perspective, with delinquency rates at 0.57%. 
  • 2nd Lien loans, comprising HELOCs and closed end mortgages, had a delinquency rate of 1.01%. 
  • Non-QM loans saw delinquency rates remain stable at 3.05%, slightly below the post-COVID peak of 3.17% in May.  

Figure 1. 

Figure 2.

Figure 3 shows the relative delinquency performance of mortgages across 4 segments of the Non-QM population, which represents the largest portion of the PLS 2.0 market. While loans with full documentation represent the largest segment of this market from a total outstanding balance perspective, originations have been shifting towards DSCR/Investor and Bank statement loans since 2022 (see Figure 4). In 2025, the combined volume of originations in the DSCR/Investor and Bank statement segments was about four times the volume of loans originated with full documentation. 

  • Fully documented loans have the lowest 60+ delinquency rate at 0.89%, though as this segment seasons, the DQ rate continues to creep up from the post-COVID lows of 0.39% seen in October 2022. 
  • Delinquency rates for DSCR/Investor and Bank Statement loans stabilized in August at 3.34% and 4.41% respectively, slightly lower than their post-COVID peaks seen in May 2025  

Figure 3. 

Figure 4. 

Figures 5 and 6 show the relative delinquency performance of Non-QM mortgages by year of origination. For these charts, we exclude vintages prior to 2021 to avoid the distorting impact of the COVID delinquency shock. 

Figure 5 shows the 60+ delinquency rate for each vintage by factor date. 

  • The delinquency rate for the 2023 vintage hit 5.25% in August, surpassing 2022 as the vintage with the highest delinquency rate. 
  • In spite of being the most seasoned, the 2021 vintage’s 2.04% DQ rate was significantly lower than the subsequent 2022 and 2023 vintage. This is largely due to the disproportionately high share of full documentation loans in this first post-COVID cohort of Non-QM rates, which can be seen in Figure 4. By contrast, the 2022 and 2023 vintages  are composed primarily of the higher risk DSCR and Bank Statement originations. 

Figure 6 shows the 60+ delinquency rate for each vintage by loan age.

  • Consistent with the trends observed in Figure 5, we see the 2023 vintage DQ rates ramp up faster than any of the other vintages. 
  • The 2024 vintage is tracking between the 2022 and 2023 vintages. 
  • While there are only a few months of observations available the 2025 vintage, its delinquency ramp-up is tracking with the other post-2021 vintages 

Figure 5. 

Figure 6.

Given the elevated delinquency rates of Non-QM mortgages relative to Agency and Prime Jumbo mortgages and the backdrop of housing and macroeconomic uncertainty, it is important for investors to monitor their portfolios that have Non-QM exposure. Our credit models at RiskSpan model these delinquency roll rates directly, and our modeling team calibrates our suite of models to capture both the overall trends and the differentiated performance across loan and product types. These models are just one component of our scaled analytics solutions to help our clients evaluate risk and make investment decisions.

Navigating Headwinds with Data and AI: July Models & Markets Recap

Register here for next month’s call: Thursday, August 21st, 2025, 1 p.m.

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month, as interest rates remain elevated and economic uncertainty persists, we addressed how both conventional and AI-based modeling techniques are shaping decision-making processes across agency, non-QM, and ARM products.

Here’s a quick recap in case you missed it.

(Click here to listen to the entire 30-minute recording, or continue reading for a summary.)

Model Performance: Prepayment Dynamics in Focus

RiskSpan’s prepayment model continues to perform well based on benchmarking against actuals across coupon stacks. The team noted:

  • Speeds in higher coupons have slowed relative to expectations, in line with broader refinancing trends as mortgage rates remain high.
  • RiskSpan’s Non-QM Prepayment Model (v3.11) shows strong back-testing performance. While most vintages perform as expected, the 2022 vintage diverged, potentially due to ambiguous underwriting guidelines in QM loans that may have led to adverse selection in the Non-QM space. One possible reason is that this reflects borrower composition differences not captured by traditional metrics.

New ARM Model Launch

An enhanced ARM Prepayment Model (v3.8) is now live in production. It exhibits refined sensitivity to rate shocks and aims to provide improved accuracy for adjustable-rate portfolios in today’s volatile environment.

Claude the Research Assistant: AI in Action

One of the highlights of the call was a deep dive into how we are testing Claude (Anthropic’s well-known LLM) as a mortgage research assistant.

Using a dataset from RiskSpan’s Snowflake instance, Claude orchestrated an end-to-end analytical workflow, including:

  • Retrieving and aggregating partially pre-aggregated loan-level data
  • Generating Python code for analysis and visualization
  • Annotating charts and analyzing prepayment trends

Key Insights from Claude’s Analysis

Claude surfaced several noteworthy trends:

  • FICO Score Sensitivity: Higher credit score bands (>750) showed dramatically higher prepayment rates than lower bands (<650), highlighting the refinancing advantage for more creditworthy borrowers.
  • Loan Size Effect: A positive correlation (0.22) between loan size and prepayment rates suggests that larger loan holders are more motivated to refinance.
  • Mortgage Vintage: Newer vintages (especially 2015–2016) demonstrated greater prepayment sensitivity, likely due to looser underwriting and seasoning effects.
  • Interest Rate Sensitivity: Claude captured the sharp inverse relationship between rates and prepayment, particularly the COVID-era spike and the post-2022 slowdown.

Claude correctly reasoned with the provided data but could not identify some features (like “Spread at Origination”). This raises interesting questions about LLMs’ capacity to reason beyond their training corpus.

Market Outlook: Economic Signals Turning Cautionary

The macro backdrop continues to weigh on securitization and borrower behavior. Highlights from July’s indicators:

  • Mortgage Rates: Remain above 6.5%, with little sign of easing before the Fed’s expected first rate cut in September.
  • Fed Funds Rate: Currently 4.25–4.50%, with year-end projections settling around 3.75–4.00%.
  • Home Prices: Showing stability with little YoY movement in the Case-Shiller Index.
  • Labor and Inflation: Both unemployment and PCE inflation measures remain steady, but signs of economic headwinds are beginning to appear.

On the Horizon

  • RiskSpan’s new credit model (v7), which includes a new delinquency transition matrix, is on track for release by the end of the month.
  • Continued enhancements are being made to the Platform, including new prepayment and performance visualizations for private credit and agency MBS sectors.

Contact us to learn more.

Humans in the Loop: Ensuring Trustworthy AI in Private ABF Deal Modeling

As generative AI becomes a powerful tool in Private asset-backed finance (ABF), the need for precision and transparency is more critical than ever. At RiskSpan, we’re applying Large Language Models (LLMs) to automate and accelerate private ABF deal modeling and surveillance. But speed is only half the battle—accuracy is non-negotiable.

That’s where Human-in-the-Loop (HITL) validation plays a vital role. While the RiskSpan platform incorporates sophisticated AI guardrails, we believe the right blend of automation and expert oversight ensures results that are not just fast—but reliable, auditable, and production-ready.

The AI-Powered Workflow: What’s Automated

Our private ABF modeling and surveillance system uses LLMs to handle several critical tasks:

  • Data Extraction: Parsing offering memos, indentures, and loan tapes to extract structural and financial data.
  • Deal Code Generation: Producing executable waterfall models based on extracted rules.
  • Database Ingestion: Uploading validated deal terms and triggers into the RiskSpan system of record.
  • Surveillance Automation: Running periodic deal performance analyses and compliance checks.

But What About Hallucinations?

Generative models are powerful but imperfect. Without the right controls, they can fabricate securitization tranches or fees that are not present in the deal documents. They can also misinterpret waterfall rules or omit critical override conditions or generate semantically incorrect code for cashflow models. To address these challenges, RiskSpan employs a multi-layered safeguard framework, combining asset class based extraction; LLM-as-Judge; Rule-Based Guardrails and Inline Human Review

Humans in the Loop: Three Layers of Oversight

We’ve embedded human validation at three key points in the deal lifecycle:

  1. Pre-Modeling Validation: before LLM-generated outputs are finalized, RiskSpan analysts review extracted terms and model prompts—correcting anything misaligned with the source documents.
  2. Inline Oversight: during waterfall code generation, humans validate AI-generated logic in context, ensuring correct treatment of subordination, triggers, caps/floors and other.
  3. Post-Deployment Monitoring Surveillance: outputs are reviewed both by the RiskSpan team and client-side structuring or credit teams. Feedback is looped back into model tuning and prompt optimization.

Looking Ahead: RAG and Continuous Improvement

We’re actively exploring Retrieval-Augmented Generation (RAG) to reduce hallucinations even further. By grounding AI responses in deal-specific material such as offering documents, trustee reports, and internal risk memos—we aim to: 1) eliminate off-topic responses. 2) increase trust in model-derived outputs and 3) enable deeper customization per issuer or asset class.

The Takeaway

AI is transforming how private ABF deals are modeled and monitored—but it must be grounded and guided by human expertise and built for institutional rigor. At RiskSpan, we’re not just accelerating workflows—we’re raising the bar for accuracy and trust in AI-assisted private structures. Human-in-the-loop is not a fallback—it’s a strategic pillar. Want to see how our AI platform works in action? Reach out to schedule a demo or contact your RiskSpan representative to learn more.

June 2025 Models & Markets Update – Predictive Power Amid Economic Uncertainty

Register here for next month’s call: Thursday, July 17th, 2025, 1 p.m.

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month, we showcased our responsiveness to shifting macroeconomic dynamics and introduced new transparency elements (i.e., back-testing tools) to our prepayment and credit modeling.

Click here to listen to the entire 23-minute recording, or continue reading for a summary.

Agency Prepay Model: Back-testing and Enhanced Control

We are launching a new loan-level prepayment back-testing tool using nearly all agency loans (FN/FH/GN) aged 10 years or less. The tool runs every month through our models with historical home prices and interest rates. Based on this data, we have an interactive dashboard that will allow users to drill down into model performance with far more granularity than currently possible.

Key Enhancements to Prepay Model v. 3.8

A soon to be released version of the prepay model will include:

  • User-defined slope multipliers for both Out-of-the-Money (OTM) and In-the-Money (ITM) performance, offering finer control over refinance sensitivity and turnover behavior.
  • Independent knob control across CONV 30, CONV 15, FHA, and VA loan types.

A redesigned ARM prepayment framework, derived from the fixed-rate model. The new ARM component includes:

  • A realistic payment shock element that aligns prepayment spikes with rate reset events.
  • Improved seasonality and aging ramp that reflects empirical loan behavior

These updates give users the ability to more precisely tune model responses under a variety of macroeconomic and borrower scenarios.

Credit Model: V7 and Delinquency Transitions

The delinquency transition matrix incorporated into our new Credit Model V7 provides users a more nuanced credit risk assessment. This model works in conjunction with the enhanced prepayment model to better simulate the joint dynamics of default and prepay behavior across economic cycles.

Macroeconomic Context: Rates and Risk in a Holding Pattern

We remain cautious in our outlook for the remainder of 2025 and into 2026:

The Fed Funds Rate is expected to remain elevated—currently in the 4.25–4.50% range—with the first rate cut likely in September. By year-end 2025, the market expects it to settle around 3.75–4.00%.

Mortgage rates remain stubbornly high, hovering above 6.5%, putting pressure on origination volumes and reinforcing the value of accurate prepayment modeling.

Home prices and broader macro indicators like unemployment and PCE inflation remain stable, suggesting a “wait-and-see” mode for both consumers and investors.

What’s Next: More Models, More Tools, More Insights

We continue to expand our Platform with new analytics, model documentation, and client-facing tools. Users can soon access the new back-testing report directly within the Platform, alongside these updated prepayment and credit models. These developments reflect our commitment to model transparency, data-driven innovation, and practical tools for real-time market adaptation.

Contact us to learn more.

Private Credit Market Pulse: What LPs Want from Their Data and How to Deliver It

Limited Partners (LPs) continue to demand better data, faster, and with full transparency. At this week’s Private Credit Tech Summit in New York, I moderated a panel of industry leaders for a discussion on where LP expectations are heading and the challenges managers face trying to meet them.

My fellow panelists included:

·      Charlie Tafoya, Co-Founder and CEO of Chronograph

·      Marios Tsiptis, Senior Portfolio Manager

·      Arnab Mazumdar, Partner at Pantheon

We explored the evolving expectations of LPs, the operational hurdles General Partners (GPs) face, and the technology shaping the next frontier of data transparency in private credit.

What follows is where we landed.

LP Expectations Are Outpacing the Status Quo

Quarterly performance reports and aggregate numbers used to be enough. But Marios Tsiptis explained that today’s LPs (particularly insurance companies) want detailed, timely insight into exactly what they own and what risks they’re carrying.

From Pantheon’s fund-of-funds vantage point, Arnab Mazumdar laid out three foundational data pillars:

1.     Performance metrics

2.     Operating and credit-level data

3.     Consistency across managers

Lagging information and inconsistencies across investment structures, especially between SMAs and feeder funds, create significant friction. Structure matters, and data must be complete, accurate, and delivered on LP terms.

The Operational Reality: Bridging Ambition and Execution

Charlie Tafoya, who works closely with GPs via Chronograph, provided a sobering view of the day-to-day realities. Many managers are eager to modernize, but they’re grappling with:

·      Delays in data delivery

·      Data quality and validation issues

·      Fragmented internal processes

There’s just no getting around the fact that investment teams simply must be embedded in the data flow. Too often, front-office insights are siloed from the operations teams responsible for reporting. “The investment team is the source of truth,” Charlie noted, making their engagement essential to any successful data transformation.

Meanwhile, Arnab called for a convergence of internal monitoring and external reporting. Aligning what GPs see internally with what they share externally could yield benefits across the board—but cultural and technological hurdles remain.

Technology as Enabler, Not Panacea

Tools that support data interoperability, real-time reporting, and workflow automation are rapidly maturing. But challenges persist around integration with legacy systems and data standardization.

From the LP side, Marios painted a picture of an ideal future. Intuitive dashboards, seamless access across portfolios, and fully integrated delivery pipelines all featured prominently in this future. But the road leading there is still under construction.

According to Arnab, the next few years will hinge on industry standardization and early adopters gaining a competitive edge, while laggards risk being left behind.

Transparency builds trust—but it requires real operational change.

More than just a tech problem, this requires a cultural shift. GPs and LPs need to work as true partners in designing data ecosystems that are not only robust and scalable, but also reflect the growing sophistication of the private credit space.

As the private markets evolve, so too must the infrastructure that supports them. Those who succeed in this transition will not only meet the expectations of today’s LPs, but also shape tomorrow’s.

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