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Hot Take on Fannie and Freddie Buying $200B of Mortgage Bonds 

Continue reading below or watch Stephen Rudner’s hot take on video.

Logistics questions are top of mind this morning for our customers who are Fannie, Freddie, and Ginnie MBS market participants because the logistics are the difference between a disrupted, confusing market and a more normal one.  

Here are some of the questions you probably asked yourselves last night: 

What coupons will be bought? Buying par coupons would likely have the most impact to lower primary rates, the mortgage rates to homeowners. But other coupons could be bought. 

Will Ginnie Mae issuances be emphasized, given their direct impact on first time homebuyers. Will purchase money loan collateral be favored over refinances? 

Over what period of time will the purchases take place? If purchased all at once, it would have the most material impact on mortgage rates, perhaps lowering them 20-30 bps, by some estimates, but wow that’s a lot of market disruption. And given how things go, after such action, mortgage rates could whipsaw back up. If MBS purchases are spread out over some time, are the mid-term elections a factor for timeline? In a measured gradual purchase timeline, wouldn’t the amount of mortgage rate lowering be much smaller, like in single digits of basis points? 

Will there be a $ cap perceived in the marketplace for expiration of this buying program? Are the agencies’ ability to issue debt to be considered? How do the capitalization requirements for the GSE’s dovetail into this, those imposed by the FHFA? 

Interesting the tweet from the President said Mortgage Bonds. Wouldn’t whole loans be more economic? But maybe the choice to buy and hold MBS, and not whole loans, is because of the capital regs. 

That’s just scratching the surface. 

But I’m interested in the policy aspect: using the GSEs portfolios to lower mortgage rates. Commentators have called it an end-around of the Fed to effectuate an aspect of Quantitative Easing. Sure, it’s unconventional. It is unnerving to veteran monetary policy wonks and requires some thought about unintended consequences and precedents. The Executive Branch of government, alone, has few options for acting to address home affordability. But this White House might have found one. 

High home prices, homeowners who can’t economically move because they don’t want to give up their epically low COVID stimulus mortgage rates, etc. are all part of the COVID stimulus hangover. It has lingered on for years. Our MBS trading customers may not like yesterday’s tweet from the President, and they have a lot of unanswered questions. But part of me celebrates the bold and the new. Having a politically controlled lever for moving mortgage rates is a scary concept. It’s why the Fed independence is so cherished. But maybe, if we are fortunate, this will work and will not become custom or practice. 

That’s my hot take for the morning of January 9th 2026. I think Larry David said you can’t really wish someone a Happy New Year after January 4th, but I hope this finds all of you well.  

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. 

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.

RiskSpan Launches Agentic AI for MBS Data — Instant, Transparent Insights for Agency MBS Professionals 

Arlington, VA – September 2, 2025 – RiskSpan, a leading provider of data analytics solutions for the structured finance industry, today announced the release of Agentic AI for MBS Data.

Watch a one-minute demo video or read on to learn about our new, AI-powered MBS analyst that delivers instant, sourced, and context-rich analysis in plain English.

Solving the MBS Data Bottleneck

While Agency MBS loan data is readily available, meaningful insights for many users often remain locked behind complex SQL or Python queries. This creates a barrier for traders, portfolio managers, and risk teams who need answers quickly to avoid missing opportunities. 

“Our clients told us they needed faster, clearer access to this data,” said Suhrud Dagli, RiskSpan’s Co-Founder and Chief Innovation Officer. “Agentic AI removes the technical barrier and delivers the kind of immediate, transparent insight that our clients have come to demand.” 

A 24/7 AI-Powered MBS Analyst 

With Agentic AI for MBS Data, users simply ask a question in plain English — such as “Which Ginnie Mae 2020 production pools had the fastest speeds in Q2?” — and get back clear, visualized, and fully-cited answers in seconds. Ask Complex Questions, Get Instant Answers – No SQL or Python required. 

Key benefits include: 

  • Full Transparency – All sources cited for confidence and compliance. 
  • Deeper Insights – Surfaces patterns and drivers you might not think to look for. 
  • Narrative-Ready Reporting – Polished summaries and visuals for executives and clients. 
  • Efficiency – Eliminates dependence on technical teams for everyday queries. 

Designed for How MBS Professionals Think 

From spotting prepayment anomalies to explaining performance shifts, Agentic AI identifies not just the what but the why. It transforms hours of manual analysis into seconds of actionable insight, enabling faster, more informed decision-making. 

Agentic AI for MBS Data is available now. RiskSpan is offering live demos to showcase how the platform transforms the way MBS data is interrogated and reported. 

About RiskSpan 

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions. 

Learn more at www.riskspan.com.  

Is Your Prepay Analysis Ready for the Rate Cut?

The forthcoming Federal Reserve interest rate cuts loom large in minds of mortgage traders and originators. The only remaining question is by how much rates will be cut. As the economy cools and unemployment rises, recent remarks by the Fed Chair have made the expectation of rate cuts essentially universal, with the market quickly repricing to a 50bp ease in September. This anticipated move by the Fed is already influencing mortgage rates, which have already experienced a notable decline.

Understanding the Lock-in Effect

One of the key factors influencing prepayments in the current environment is the lock-in effect, where borrowers are deterred from selling their current home due to the large difference between their current mortgage rate and prevailing market rates (which they would incur when purchasing their next home). As rates decrease, the gap narrows, reducing the lock-in effect and freeing more borrowers to sell and move.

As Chart 1 illustrates, a significant share of borrowers continues to hold mortgages between 2 and 3 percent. These borrowers clearly still have no incentive to refinance. But historical data suggests that the sizeable lock-in effect, which is currently depressing turnover, diminishes as the magnitude of their out-of-the-moneyness comes down. In other words, even a 100-basis point reduction can significantly increase housing turnover, as borrowers who were previously 300 basis points out of the money move to 200 basis points, making selling their old home and buying a new one, despite the higher interest rate, more palatable.

CHART 1: Distribution of Note Rates for 30-Year Conventional Mortgages: July 2024

Current Market Dynamics

Recent data from Mortgage News Daily indicates that mortgage rates have dropped over the past four weeks from around 6.8% to nearly 6.4%. This decrease is expected to continue, potentially bringing rates below 6% by the end of the year. This will likely have a profound impact on mortgage prepayments, particularly in the Agency MBS market.

Most outstanding mortgages, particularly those in Fannie and Freddie securities, currently have low prepayment speeds, with many loans sitting at 2% to 3% coupons. While a drop in mortgage rates to 6% (or lower) will still leave most of these mortgages out of the money for traditional rate-and-term refinances, it may bring a growing number of them into play for cash-out refinances, given significant home price appreciation and equity buildup over last 4 years. It will also loosen the grip of the lock-in effect for a growing number of homeowners currently paying below-market interest rates.

Implications for Prepayment Speeds

Factoring in the potential increase in turnover and cash-out refis, the impact of rate cuts on prepayment speeds could be substantial. For instance, with a 100-bp drop in rates, loans that are deeply out of the money could see their prepayment speeds increase by 1 to 2 CPR based on the turnover effect alone. Loans that are just at the money or slightly out of the money will see a more pronounced effect, with prepayment speeds potentially doubling. Chart 2, below, illustrates both the huge volume of loans deep out of the money to refinance as well as the small (but significant) uptick in CPR that a 100-bp shift in interest rates can have on CPR even for loans as much as 300 bps out of the money.

CHART 2: CPR by Refinance Incentive (dotted line reflects UPB of each bucket)

Historical data suggests that if mortgage rates move to 6.4%, the volume of loans moving into the money to refinance could increase up to eightfold — from $39 billion to $247 billion (see chart 3, below.) This surge in refinance activity will significantly influence prepays — impacting both turnover and refi volumes.

CHART 3: Volume and CPR by Coupon (dotted line reflects UPB of each bucket)

The Broader Housing Market

Beyond prepayments, the broader housing market may also feel the effects of rate cuts, but perhaps in a nuanced way. A reduction in rates generally improves affordability, potentially sustaining or even increasing home prices despite the increased supply from unlocked homes. However, this dynamic is complex. While lower rates make homes more affordable, the release of previously locked-in homes could counterintuitively depress home prices due to increased supply. With housing affordability at multi-decade lows, an uptick in housing supply could swamp any effect of somewhat lower rates.

While a modest rate cut may primarily boost turnover, a more significant cut could trigger a wave of refinancing. Additionally, cash-out refinances may become more attractive, offering a cheaper alternative to HELOCs and other more expensive options.

Conclusion

The forthcoming Fed interest rate cuts are poised to have a significant impact on mortgage prepayments. As rates decline, the lock-in effect will ease, encouraging more refinancing and increasing prepayment speeds. The broader housing market will also feel the effects, with potential implications for home prices and overall market dynamics. Monitoring these trends closely will be crucial for market participants, particularly those in the agency MBS market, as they navigate the changing landscape.

Contact us to staying informed and prepared and learn more about how RiskSpan can help you make strategic decisions that align with evolving market conditions.

RiskSpan Launches MBS Loan Level Historical Data on Snowflake Marketplace

ARLINGTON, Va., June 18, 2024 – RiskSpan, a leading provider of data analytics and risk management solutions for the mortgage industry, announced today that it has launched MBS Loan Level Historical Data on Snowflake Marketplace. RiskSpan’s MBS Loan Level Historical Data on Snowflake Marketplace enables joint customers to access RiskSpan’s normalized and enriched loan-level data for Fannie Mae, Freddie Mac, and Ginnie Mae mortgage-backed securities.

“We are thrilled to join the Snowflake Marketplace and offer our loan-level MBS data to a wider audience of Snowflake users,” said Janet Jozwik, Senior Managing Director at RiskSpan. “This is a first step in what we believe will ultimately become a cloud-based analytical hub for MBS investors everywhere.”

RiskSpan and Snowflake, the AI Data Cloud company, are working together to help joint customers inform business decisions and drive innovations by enabling them to query the data using SQL, join it with other data sources, and scale up or down as needed. RiskSpan also provides sample code and calculations to help users get started with common metrics such as CPR, aging curves, and S-curves.

“RiskSpan’s launch of a unique blend of enriched data onto Snowflake Marketplace represents a major opportunity for Snowflake customers to unlock new value through data on their business journey,” said Kieran Kennedy, Head of Marketplace at Snowflake. “We welcome RiskSpan to the ecosystem and look forward to exploring how we can support our customers as they look to leverage the breadth of the Snowflake platform more effectively.”

Joint customers can now leverage Loan-Level MBS Data on Snowflake Marketplace, allowing them to access RiskSpan data enhancements, including servicer normalization, refinements, mark-to-market LTV calculations, current coupon. These and other enhancements make it easier and faster for users to perform analysis and modeling.

Snowflake Marketplace is powered by Snowflake’s ground-breaking cross-cloud technology, Snowgrid, allowing companies direct access to raw data products and the ability to leverage data, data services, and applications quickly, securely, and cost-effectively. Snowflake Marketplace simplifies discovery, access, and the commercialization of data products, enabling companies to unlock entirely new revenue streams and extended insights across the AI Data Cloud. To learn more about Snowflake Marketplace and how to find, try and buy the data, data services, and applications needed for innovative business solutions, click here.

About RiskSpan, Inc. 

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions. Learn more at www.riskspan.com.

The newest, fastest and easiest way to access and analyze Agency MBS data

TL;DR Summary of Benefits

  • Data normalization and enhancement: RiskSpan’s MBS data on Snowflake normalizes Fannie, Freddie, and Ginnie loan-level data, consolidating everything into one set of field names. It also offers enhanced loan level-data fields, including current coupon, spec pool category, and mark-to-market LTV, which are not available in the raw data from the agencies. The data also includes pool-level factors like pool prefix and pool age, as well as full loan histories not available from the GSEs directly.
  • Data access and querying: Users access the data in Snowflake using SQL or Python connectors. Snowflake functions essentially as a cloud SQL server that allows for instantaneous data sharing across entities. In just a few clicks, users can start analyzing MBS data using their preferred coding language—no data, ETL, or IT Teams required.
  • Data merging and analytics: Users can merge the data in Snowflake with other available loan level or macroeconomic data, including interest rates, home prices, and unemployment, for advanced analytics. Users can also project performance, monitor portfolios, and create spec pools, among other features.
CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

The Problem

Even though Fannie, Freddie and Ginnie have been making MBS performance data publicly available for years, working with the raw data can be challenging for traders and back-office analysts.

Traders and analysts already have many of the tools they need to write powerful queries that can reveal hidden patterns and insights across different markets – patterns that can reveal lucrative trading opportunities based on prepayment analysis. But one big obstacle often stands in the way of getting the most out of these tools: the data from the agencies is large and unwieldy and is not formatted in a consistent way, making it hard to compare and combine.

What’s more, the Agencies do not maintain full history of published data on the websites for download. Only recent history is available.

The Solution: RiskSpan’s new MBS loan-level historical offering on Snowflake Marketplace

Using RiskSpan’s new MBS Loan-Level Historical Data Offering, MBS traders and analysts can now leverage the power of Snowflake, the leading cloud data platform, to perform complex queries and merge data from multiple sources like never before.

This comprehensive data offering provides a fully normalized view of the entire history of loan-level performance data across Agencies – allowing users to interact with the full $9T Agency MBS market in unprecedented ways.

A list of normalized Fannie and Freddie fields can be found at the end of this post.

In addition to being able to easily compare different segments of the market using a single set of standardized data fields, MBS traders and analysts also benefit from derived and enhanced data, such as current coupon, refinance incentive, current loan-to-value ratio, original specified pool designation, and normalized seller and servicer names.

The use cases are practically limitless.

MBS traders and analystscan track historical prepayment speeds, find trading opportunities that offer relative value, and build, improve, or calibrate prepayment models. They can see how prepayment rates vary by loan size, credit score, geographic location, or other factors. They can also identify pools that have faster or slower prepayments than expected and exploit the differences in price.

Loan originators can see how their loans perform compared to similar loans issued by other originators, servicers, or agencies, allowing them to showcase their ability to originate high-quality loans that command premium pricing.

Enhanced fields provide users with more comprehensive insights and analysis capabilities. They include a range of derived and enhanced data attributes beyond the standard dataset: derived fields useful for calculations, additional macroeconomic data, and normalized field names and enumerations. These fields give users the flexibility to customize their analyses by incorporating additional data elements tailored to their specific needs or research objectives.

Enhanced loan-level fields include:

  • Refi Incentive: The extent to which a borrower’s interest rate exceeds current prevailing market rates
  • Spread at Origination (SATO): a representation of the total opportunities for refinancing within a mortgage servicing portfolio. SATO encompasses all potential refinance candidates based on prevailing market conditions, borrower eligibility, and loan characteristics
  • Servicer Normalization: A standardization of servicer names to ensure consistency and accuracy in reporting and analysis
  • Scheduled Balance: A helper field necessary to easily calculate CPR and other performance metrics
  • Spec Pool Type: A designation of the type of spec story on the loan’s pool at origination
  • Current LTV: a walked forward LTV based on FHFA’s HPI and the current balance of the loan

Not available in the raw data from the agencies, these fields allow MBS traders and analysts to seamlessly project loan and pool performance, monitor portfolios, create and evaluate spec pools, and more.

Access the Data on Your Terms

Traders and analysts can access the data in Snowflake using SQL or Python connectors. Alternatively, they can also access the data through the Edge UI, our well-established product for ad hoc querying and visualization. RiskSpan’s Snowflake listing provides sample queries and a data dictionary for reference. Data can be merged with macroeconomic data from other sources – rates, HPI data, unemployment – for deeper insights and analytics.

The listing is available for a 15-day free trial and can be purchased on a monthly or annual basis. Users don’t need to have a Snowflake account to try it out. Learn more and get started at the Snowflake Marketplace or contact us to schedule a demo or discussion.

CHECK OUT RISKSPAN’S OFFERING ON SNOWFLAKE MARKETPLACE

Fannie/Freddie Normalized Fields

NAMETYPEDESCRIPTION
AGENumberLoan Age in Months
AGENCYVarcharFN [Fannie Mae], FH [Freddie Mac]
ALTDQRESOLUTIONVarcharPayment deferral type: CovidPaymentDeferral,DisasterPaymentDeferral,PaymentDeferral,Other/NA
BORROWERASSISTPLANVarcharType of Assistance: Forbearance, Repayment, TrialPeriod, OtherWorkOut, NoWorkOut, NotApplicable, NotAvailable
BUSINESSDAYSNumberBusiness Day in Factor Period
COMBINEDLTVFloatOriginal Combined LTV
CONTRIBUTIONFloatContribution of Loan to the Pool, to be used to correctly attribution Freddie Mirror Pools
COUPONFloatNet Coupon or NWAC in %
CURRBALANCEFloatCurrent Balance Amount
CURRENTCOUPONFloatPrimary rate in the market (PMMS)
CURRENTLTVFloatCurrent Loan to Value Ratio based on rolled-forward home value calculated by RiskSpan based on FHFA All-Transaction data
CURTAILAMOUNTFloatDollar amount curtailed in the period
DEFERRALAMOUNTFloatDollar amount deferred
DQSTRINGVarcharDelinquency History String, left most field in the current period
DTIFloatDebt to Income Ratio %
FACTORDATEDatePerformance Period
FICONumberBorrower FICO Score [300,850]
FIRSTTIMEBUYERVarcharFirst time home buyer flag Y,N,NA
ISSUEDATEDateLoan Origination Date
LOANPURPOSEVarcharLoan Purpose: REFI,PURCHASE,NA
LTVFloatOriginal Loan to Value Ratio in %
MATURITYDATEDateLoan Maturity Date
MICOVERAGEFloatMortgage Insurance Coverage %
MOSDELINQVarcharDelinquency Status: Current, DQ_30_Day, DQ_60_Day, DQ_90_Day, DQ_120_Day, DQ_150_Day, DQ_180_Day, DQ_210_Day, DQ_240_Day, DQ_270_Day, DQ_300_Day, DQ_330_Day, DQ_360_Day, DQ_390_Day, DQ_420_Day, DQ_450_Day, DQ_480_Day, DQ_510_Day, DQ_540_Day, DQ_570_Day, DQ_600_Day, DQ_630_Day, DQ_660_Day, DQ_690_Day, DQ_720pls_Day
MSAVarcharMetropolitian Statistical Area
NUMBEROFBORROWERSNumberNumber of Borrowers
NUMBEROFUNITSVarcharNumber of Units
OCCUPANCYTYPEVarcharOccupancy Type: NA,INVESTOR,OWNER,SECOND
ORIGBALANCEFloatOriginal Loan Balance
ORIGSPECPOOLTYPEVarcharSpec Story of the pool that the loan is a part of. Please see Spec Pool Logic in our linked documentation
PERCENTDEFERRALFloatPercentage of the loan balance that is deferred
PIWVarcharProperty Inspection Waiver Type: Appraisal,Waiver,OnsiteDataCollection, GSETargetedRefi, Other,NotAvailable
POOLAGENumberAge of the Pool
POOLIDVarcharPool ID

What Do 2024 Origination Trends Mean for MSRs?

While mortgage rates remain stubbornly high by recent historical standards, accurately forecasting MSR performance and valuations requires a thoughtful evaluation of loan characteristics that go beyond the standard “refi incentive” measure.

As we pointed out in 2023, these characteristics are particularly important when it comes to predicting involuntary prepayments.

This post updates our mortgage origination trends for the first quarter of 2024 and takes a look at what they could be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned and stayed near the averages observed during the latter half of the 2010s.

The most credible explanation for this most recent reversion to the mean is the fact that the Covid years were accompanied by an historically strong refinance market. Refis traditionally have higher FICO scores than purchase mortgages, and this is apparent in the recent trend.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period.

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Consequently, in 2023 and 2024, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

Between rates and home prices, ​​borrowers simply need to borrow more now than they would have just a few years ago to buy a comparable house. This is reflected not just in the average DTI and LTV, but also the average loan size (below) which, unsurprisingly, continues to trend higher as well.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward? 

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends. 

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level. 

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

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Impact of Mr. Cooper’s Cyber Security Incident on Agency Prepayment Reporting

Amid the fallout of the cyberattack against Mr. Cooper on October 31st was an inability on the large servicer’s part to report prepayment activity to investors.

According to Freddie Mac, the incident “resulted in [Mr. Cooper’s] shutting down certain systems as a precautionary measure. As a result, Freddie Mac did not receive loan activity reporting, which includes loan payoffs and payment corrections, from Mr. Cooper during the last few days of the reporting period related to October loan activity.”

Owing to Mr. Cooper’s size, were curious to measure what (if any) impact its missing days of reporting might have on overall agency speeds.

Not a whole lot, it turns out.

This came as little surprise given the very low prepayment environment in which we find ourselves, but we wanted to run the numbers to be sure. Here is what we found.

We do not know precisely how much reporting was missed and assumed “the last few days of the reporting period” to mean 3 days.

Assuming 3 days means that Mr. Cooper’s reported speeds of 4.5 CPR to Freddie and 4.6 CPR to Fannie likely should have been 5.2 CPR and 5.4 CPR, respectively. While these differences are relatively small for to Mr. Cooper’s portfolio (less than 1 CPR) the impact on overall Agency speeds is downright trivial — less than 0.05 CPR.

Fannie MBSFreddie MBS
Sch. Bal.195,221,550,383168,711,346,228
CPR (reported)4.64.5
CPR (estimated*)5.45.2
*assumes three days of unreported loan activity and constant daily prepayments for the month

Fannie Mae and Freddie Mac will distribute scheduled principal and interest when servicers do not report the loan activity. Prepayments that were not reported “will be distributed to MBS certificateholders on the first distribution date that follows our receipt and reconciliation of the required prepayment information from Mr. Cooper.”

What Do 2023 Origination Trends Mean for MSRs?

When it comes to forecasting MSR performance and valuations, much is made of the interest rate environment, and rightly so. But other loan characteristics also play a role, particularly when it comes to predicting involuntary prepayments.

So let’s take a look at what 2023 mortgage originations might be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned during the first part of 2023 to averages observed during the latter half of the 2010s.

FICO

The most credible explanation for this most recent reversion to the mean is the fact that the Covid years were accompanied by an historically strong refinance market. Refis traditionally have higher FICO scores than purchase mortgages, and this is apparent in the recent trend.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period

LTV

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Consequently, in 2023, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

DTI

Between rates and home prices,​​borrowers simply need to borrow more now than they would have just a few years ago to buy a comparable house. This is reflected not just in the average DTI and LTV, but also the average loan size (below) which, unsurprisingly, is trending higher as well.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

WOLS

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward? 

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends. 

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level. 

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

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