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Models & Markets Update: June 2026

Register here for next month’s call: Thursday, July 16th 2026, 1 p.m. ET. 

Key Takeaways 

  • Prepayment model continues to perform well; discount coupon speeds (WAC 5.5 and below) remain stable across Fannie/Freddie and GNMA, driven by housing turnover 
  • Premium coupon speeds (WAC 6.0+) declined as elevated rates reduced refi incentive; UPB rising steeply on new gross issuance of low-seasoning loans 
  • FN/FH S-curve has flattened materially from April to June; GNMA saw similar compression but at tighter intervals and with less magnitude 
  • Non-QM prepay speeds declined across all doc types in May remits; Non-QM DQ60+ rates ticked down marginally 
  • Non-QM Credit Model CM 7.1 in beta; available July 15; dedicated webinar coming before release 
  • Fed held rates at June 17–18 meeting; year-end expectation shifted to 375–400 bps; meaningful probability of a hike as early as September; dot plot revised to 3.8% for 2026 
  • Mortgage rates near 6.5%, back to summer 2025 levels; 6% viewed as 2026 floor; 10-yr TSY expected above 4% for years 
  • CPI at 3.8% YoY, real wage growth -0.4% (first negative since 2022); U-6 underemployment at 8.1% 
  • Home prices near-flat nationally (+0.67% YoY); inventory-constrained, not demand-driven; significant geographic divergence 

You can read the recap below or click here for the entire recording. 

Prepayment Model Back-Testing: June Factor Data Update 

The prepayment model continues to track realized speeds closely across Agency collateral (results available in Edge under Vertex). The June factor data captures May remittances. 

Fannie/Freddie — Discount Coupons (WAC 5.5 and Below) 

Model CPR closely tracks observed CPR, confirming the model is well-calibrated in the turnover regime. With minimal refi incentive at current rate levels, prepayment activity is driven almost entirely by housing turnover, which has held steady. 

Figure 1: FN/FH Discount Coupon Back-Testing — Model CPR vs. Observed CPR 

Fannie/Freddie — Premium Coupons (WAC 6.0 and Higher) and S-Curve 

Premium speeds declined due to elevated mortgage rates and fewer day counts in May vs. April. UPB is also rising steeply, reflecting gross issuance of newly originated loans with limited seasoning that further dampens prepay. The S-curve flattening tells the fuller story: comparing April, May, and June factor months, the curve has collapsed from the April peak (green), with borrowers responding progressively less to the same level of refi incentive. 

Figure 2: FN/FH Premium Coupon Back-Testing — Model CPR vs. Observed CPR 

Figure 3: FN/FH S-Curve Flattening — April, May, and June Factor Months 

GNMA — Discount and Premium Coupons 

GNMA discount speeds remain supported by turnover. Premium speeds are declining but proved more resilient than conventional — approximately 14% reduction vs. 24% for Fannie/Freddie. The GNMA S-curve is compressing, but at tighter intervals than the more pronounced slope collapse seen in conventional. 

Figure 4: GN/G2 Discount and Premium Coupon Back-Testing — Model CPR vs. Observed CPR 

Non-QM Historical Performance 

Based on Cotality data through the Edge platform’s historical performance module, capturing the June factor date (May remittances). 

Prepayment Speeds 

Speeds declined across all three major doc types reflecting the rate environment: bank statement 29→22 CPR, DSCR 21→16 CPR, full doc 17→16 CPR. Full doc loans carry a lower WAC (~5.1% vs. 6.9–7.1% for bank statement and DSCR) and are more locked in, though they show a steeper S-curve response when refi incentive is held constant. Many DSCR loans remain within prepayment penalty terms, though 2023 originations with three-year terms are now beginning to exit that window, which could lift speeds going forward. 

Figure 5: Non-QM CPR by Documentation Type (July 2023–June 2026) 

Delinquencies 

DQ60+ rates ticked down marginally in May remits: bank statement ~4%, DSCR ~3%, full doc ~0.72%. A wide gap persists across doc types even after controlling for FICO and LTV. The 2023 vintage remains the highest-delinquency cohort, driven by somewhat looser underwriting and a credit burnout effect — stronger borrowers in that high-WAC vintage have already paid down or refinanced, leaving a residual pool more likely to be credit-impaired. 

Figure 6: Non-QM DQ60+ by Documentation Type (July 2023–June 2026) 

Figure 7: Non-QM DQ60+ by Vintage and Loan Age 

Non-QM Credit Model: CM 7.1 Update 

CM 7.1 is in beta testing and will be generally available on July 15, 2026. A dedicated webinar is planned before the production release. The model uses a three-stage architecture with four independently estimated transition models (Bank Statement, DSCR, Full Doc, Other) feeding a unified liquidation timeline and severity model — capturing the meaningfully different performance characteristics across Non-QM documentation types seen in the historical data above. 

Figure 8: CM 7.1 Model Structure 

Macroeconomic Update: June 2026 

Federal Reserve — On Hold, With a Hike Now on the Table 

The Fed held rates at its June 17–18 meeting (350–375 bps). The big shift is in forward expectations: comparing CME FedWatch probabilities from May 20 to June 17, the likelihood of a rate hike has risen materially, with a meaningful probability of an increase as early as September. Year-end 2026 market expectation has shifted from 350–375 to 375–400 bps. The Fed’s own June dot plot revised the 2026 median funds rate to 3.8%, up from 3.4% in March, as inflation surprised to the upside and strong payrolls data reduce pressure to ease. 

Figure 9: CME FedWatch Conditional Probabilities (May 20 vs. June 17) and Fed Dot Plot 

Rates, Inflation, and Home Prices 

Treasury and mortgage rates: The 10-year TSY consensus forecast peaks near 4.6% by year-end 2026 and stays above 4% for the next several years. Mortgage rates have been notably volatile since late February, climbing from ~6% back to ~6.5% as of mid-June. The team views 6% as the effective 2026 floor. 

Inflation and labor: CPI at 3.8% YoY vs. wage growth of 3.4% produces real wage growth of -0.4% — the first negative year since 2022. U-3 unemployment is 4.3%; U-6 underemployment is 8.1%. Labor force participation is at its lowest since 2021. May payrolls came in at 172K vs. 80K expected but gains were concentrated in leisure/hospitality and local government. The Fed’s June projections raised 2026 PCE inflation to 3.6% (from 2.7% in March). 

Home prices: Case-Shiller National at +0.67% YoY (March 2026); 20-City Composite at +0.83%. Gains appear to be inventory-constrained rather than demand-driven. Geographic divergence is significant — some MSAs are in negative territory, which is a relevant risk factor for Non-QM collateral concentration. 

Figure 10: 10-Year Treasury Consensus Forecast and Mortgage Rate Trend 

Figure 11: Inflation, Wage Growth, and Labor Market Dashboard 

Figure 12: Case-Shiller National and 20-City Composite Home Price Indices 

Summary 

Topic Key Takeaway 
Prepayment Model Performing well; discount speeds stable (turnover-driven); premium speeds declined on elevated rates and fewer May day counts; UPB rising steeply on new gross issuance 
FN/FH S-Curve Materially flatter from April to June; borrowers responding less to refi incentive; newer vintages pulling down aggregate 
GNMA Performance Discount speeds stable; premium speeds down but more resilient than conventional (14% reduction vs. 24% for FN/FH); S-curve compression at tighter intervals 
Non-QM Prepay Bank statement 29→22 CPR; DSCR 21→16 CPR; Full doc 17→16 CPR; rate-driven; DSCR partly shielded by prepay penalty terms 
Non-QM DQ DQ60+ ticked down marginally: bank statement ~4%, DSCR ~3%, full doc ~0.72%; 2023 vintage remains highest-DQ cohort (loose underwriting + credit burnout) 
CM 7.1 Credit Model Beta testing now; available July 15; dedicated webinar before release; four doc-type transition models feeding unified liquidation and severity model 
Fed Policy Held at 350–375 bps (June 17–18 meeting); year-end expectation shifted to 375–400; hike possible as early as September; dot plot revised to 3.8% median for 2026 vs. 3.4% in March 
Rates 10-yr TSY consensus peaks ~4.6% year-end, stays above 4% for years; mortgage rates ~6.5%, back to summer 2025 levels; 6% viewed as 2026 floor 
Inflation & Labor CPI 3.8% YoY; wage growth 3.4%; real wages -0.4% (first negative since 2022); U-3 4.3%, U-6 8.1%; May payrolls 172K vs. 80K expected 
Home Prices Case-Shiller National +0.67% YoY (March 2026); gains inventory-driven, not demand-driven; significant geographic variation with some MSAs negative 


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

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


The Insurance ABF Stack: Panel Takeaways

We opened the insurance panel at the RiskSpan Summit earlier this month with an interesting statistic: according to Moody’s, almost a third of the $6 trillion in cash and invested assets held by US life insurers is now allocated across private credit sub-asset classes. Nancy Mueller Handal of Bayview confirmed it tracks with consideration to private placements, commercial mortgages, and infrastructure debt alongside direct lending. Bill Moretti of Equitable added a useful caveat: the industry still doesn’t have a settled definition of “private credit,” and ABF has only recently been pulled under that umbrella. However you count it, the scale is real and it’s happened fast.

I was joined by Nancy, Bill, and Larry Yang of Global Atlantic / KKR — three practitioners operating through fundamentally different models. What made the conversation worth having was exactly that diversity of structure. Here’s what stuck with me.

You may not know you’re in a bad vintage while you’re living through it.

This was the sharpest exchange of the panel. The group flagged the current non-QM environment specifically: massive appetite, compressed spreads, and layered risk that has never been tested in a declining home price environment. Non-QM has existed entirely within a rising housing market. There was even speculation that we may be experiencing a vintage risk event in data centers right now – and the industry won’t know for years. The most uncomfortable version of vintage risk isn’t the one you can see. It’s the one you’re inside.

The data problem is harder than the analytics problem.

One panelist observed that in structured finance, “you could get to a point where you don’t know what you own.” Larry described building proprietary end-to-end infrastructure — including custom waterfall models — because off-the-shelf systems aren’t granular enough. Nancy’s observation was the most memorable: her research team has grown as capabilities have improved, not shrunk. More tools enable more questions. The data was always there. The limiting factor has always been the ability to extract, manage, and act on it in real time.

AI is a co-pilot, not a substitute.

The panel’s most grounded take: “Seasoned people know what questions to ask — and know when the answer is wrong.” Junior analysts prompting a model without that judgment aren’t getting the same output. The firms making real progress are the ones embedding AI into durable processes, not just re-prompting the same task each month.

And on why they’re still leaning in: Nancy called it “the most exciting market out there.” Larry cited the breadth: in ABF, you’re always encountering asset classes you’ve never seen before. The undisputed line of the day: “Insurance companies are now sexy.”

Hard to argue with that.

The RiskSpan Summit 2026 brought together practitioners across insurance, asset management, and structured finance.


How Mortgage Teams Are Using RiskSpan’s AI Agent to Answer MBS Data Questions in Seconds 

Agency MBS data has never been more available and acting on it has never been harder. 

Mortgage servicers, secondary marketing desks, and risk teams are sitting on a mountain of GSE data that could be driving sharper decisions every day. The challenge isn’t access. It’s time. The typical path from question to answer — submit a data request, wait for an analyst, review a report — can take days. By then, the market has moved. 

RiskSpan built our AI MBS Data Agent to close that gap. Below are four ways mortgage teams are using it today. 

1. Secondary Marketing: Walking Into Negotiations With Data 

Spec pool pay-ups are negotiated daily, but the benchmarks that justify them — CPR performance by coupon, LTV band, servicer cohort — are rarely at your fingertips. 

Secondary marketing teams using the AI MBS Data Agent can ask questions like: 

“Show me our CPR performance for 3.5% coupons from high-FICO, low-LTV collateral over the past 24 months — compared to the market average.” 

The agent returns the answer in seconds, with auditable SQL and export-ready output. That’s the difference between entering a negotiation informed and entering it exposed. 

2. Risk & Underwriting: Surfacing Concentrations Before They Become Problems 

Portfolio risk doesn’t announce itself. It builds quietly in LTV bands, geographic clusters, and servicer cohorts — until a delinquency spike makes it visible. By then, options are limited. 

Risk and underwriting teams use the agent to run surveillance queries that would have previously required analyst hours: 

“Flag servicers in my cohort where 90+ day delinquency rates have increased more than 15% year-over-year — broken out by LTV band and geography.” 

The agent handles the data work. The team focuses on the decision. High-LTV surveillance, adverse selection monitoring, Ginnie vs. GSE comparisons — all available on demand, in plain English. 

3. Default Servicing: Early Warning Signals, Not Lagging Reports 

Default servicing teams need to see trouble coming — not confirm it after it arrives. But most teams are working from monthly data pulls that are already weeks old by the time they reach a desk. 

With the AI MBS Data Agent, monitoring becomes continuous and conversational. Teams can query delinquency trends across servicer cohorts, compare their performance against market benchmarks, and catch emerging signals before they escalate — all without submitting a ticket. 

This is especially valuable for servicers managing Ginnie portfolios, where early delinquency detection can directly affect buyout timing and loss severity. 

4. Executive Reporting: Competitive Benchmarking on Demand 

For mortgage executives, competitive intelligence is often a quarterly exercise — because assembling it takes weeks of analyst time. Market share by investor channel. CPR performance vs. peers. Spec pool trends. These are questions that should have instant answers. 

The AI MBS Data Agent makes competitive benchmarking a conversation, not a project. Executives can ask: 

“What is our market share by investor channel versus the top 10 servicers — and how has it trended over the past 12 months?” 

Fannie, Freddie, Ginnie. Any vintage. Any cohort. The answer comes back in seconds, ready for a board deck or an internal review. 

How It Works 

The AI MBS Data Agent connects to agency MBS data through a natural language interface. Users ask questions in plain English. The agent translates them into SQL, retrieves the data, and returns structured answers with the underlying query visible for audit. 

There’s no data team required to get started, no onboarding overhead for individual queries, and no waiting. The output is export-ready. 

Try It Free for 30 Days 

RiskSpan is offering a 30-day free trial of the AI MBS Data Agent — full access, no credit card required. 

🔗  https://riskspan.com/ai-mbs-agent-offering/ 


Models & Markets Update: May 2026 

Register here for next month’s call: Thursday, June 18th 2026, 1 p.m. ET. 

Key Takeaways 

  • Prepayment models continue to perform well; April discount coupon speeds remain stable, driven primarily by housing turnover 
  • Premium coupon speeds fell sharply in May factor data as the March mortgage rate sell-off reduced refinancing incentive and flattened the S-curve 
  • GNMA premium coupons showed a moderate speed decline but proved more resilient than conventional counterparts 
  • Non-QM Credit Model CM 7.1 enters beta testing in June and is targeted for production release around July 10th; a dedicated webinar is planned for the second half of June 
  • No Federal Reserve rate cuts are expected in 2026; CME FedWatch now shows a meaningful probability of a rate hike later this year or in early 2027 
  • Mortgage rates have climbed back to levels last seen in summer 2025, with the Freddie Mac survey rate at 6.51% and Mortgage News Daily at 6.65% 
  • The 10-year Treasury rose approximately 50 basis points since February; market consensus expects it to reach ~4.8% by year-end and remain above 4% for the next 2–3 years 
  • CPI inflation rose to 3.8% year-over-year in April; core CPI at 2.8% — both well above the Fed’s 2% target 
  • Home prices stagnating nationally (~0.67% YoY per Case-Shiller); San Francisco has turned negative while New York continues to grow at ~4.8% 
  • Consumer stress deepens: low- and middle-income households carry credit card debt roughly 3x their monthly spending at ~23% APR; buy now, pay later obligations add further hidden risk not captured in credit bureau data 

You can read the recap below or click here for the entire recording

Prepayment Model Back-Testing: May Factor Data Update 

The prepayment model continues to track realized speeds closely across Agency collateral. Results are available on the Edge platform under the Vertex module. 

Fannie/Freddie — Discount Coupons (WAC 5.5 and Below) 

April speeds for Fannie/Freddie discount coupons remained relatively stable. Because these lower-coupon loans carry little to no refinancing incentive, prepayment activity is driven almost entirely by housing turnover, which has held steady. 

Figure 1: FN/FH Discount Coupon Back-Testing — Model CPR vs. Observed CPR 

Fannie/Freddie — Premium Coupons (WAC 6.0 and Higher) 

Premium coupon speeds fell sharply in the May factor data, reflecting the March mortgage rate sell-off. Rising rates reduced refinancing incentive and caused a notable flattening of the S-curve. The May S-curve sits meaningfully below both the April curve and the long-run historical average (January 2014–May 2026), with the gap widening at higher incentive levels. A diminishing media effect in the May cohort contributed to the flatter shape. 

Figure 2: FN/FH Premium Coupon Back-Testing — Model CPR vs. Observed CPR 

Figure 3: EDGE Historical Performance — FN/FH S-Curve (Refi Incentive vs. CPR) 

GNMA — Discount and Premium Coupons 

GNMA collateral showed a similar pattern. Discount coupon speeds remained supported by turnover activity, while premium coupon speeds saw a moderate decline consistent with the higher-rate environment. Notably, GNMA premiums proved more resilient than their conventional counterparts when compared against the conventional S-curve, reflecting structural differences in the GNMA borrower population. 

Figure 4: GN/G2 Discount and Premium Coupon Back-Testing — Model CPR vs. Observed CPR 

Non-QM Credit Model: CM 7.1 Update 

CM 7.1, RiskSpan’s new Non-QM Credit Model, is on track to enter beta testing in June with a targeted production release around July 10, 2026. A detailed webinar covering the model will be held in the second half of June. 

Model Structure 

CM 7.1 uses the same three-stage architecture as RiskSpan’s agency credit model: 

  • Transition Models (four, one per documentation type) — each independently estimated 
  • Bank Statement 
  • DSCR 
  • Full Doc 
  • Other 
  • Liquidation Timeline Model — applied once a loan enters default 
  • Severity Model — estimates final losses on the defaulted balance 

Each documentation type is modeled independently at the transition stage, then fed into a unified liquidation timeline and severity model. This segmentation reflects meaningfully different performance characteristics across Non-QM documentation types. 

Figure 5: CM 7.1 Model Structure — Four Transition Models Feed into Unified Liquidation and Severity Models 

Macroeconomic Update: May 2026 

Federal Reserve — No Rate Cuts Expected; Hike Risk Emerging 

The Fed funds rate remains at 350–375 bps. CME FedWatch futures indicate it is highly unlikely to be cut in 2026. More notably, the conditional probabilities have shifted over the past 4–6 weeks to reflect a meaningful likelihood of a rate hike in the latter part of 2026 or early 2027. With persistent inflation and a new Fed chair, the market sees little room for easing. 

Figure 6: Federal Funds Target Range — Upper Limit (Source: FRED) and CME FedWatch Conditional Probabilities 

Treasury Yield Curve — Significantly Higher Than February 

The Treasury yield curve has shifted materially upward since the February 2026 trough, when the 10-year yield was at its lowest recent level and mortgage rates briefly approached 6%. Since then: 

  • The 10-year and 30-year Treasuries rose approximately 50 basis points 
  • The 2-year Treasury rose approximately 75–80 basis points 
  • As of May 21, the 10-year Treasury stood at approximately 4.60% 

This move is attributed to geopolitical dynamics (including the situation around Iran) and a declining global appetite for U.S. Treasuries, with recent auctions clearing at progressively higher yields. Market consensus projects the 10-year to reach approximately 4.8% by December 2026 and to remain above 4% for the next 2–3 years. 

Figure 7: Treasury Yield Curves — January through May 2026 

Mortgage Rates — Back to Summer 2025 Levels 

Mortgage rates have given back much of the progress made earlier in the year. As of the call date, the Freddie Mac primary survey rate was 6.51% and the Mortgage News Daily rate was 6.65% — levels last seen in August 2025. The expectation is that mortgage rates will remain at or above 6.25% for the foreseeable future, with a sub-6% rate considered unlikely in the near term. 

Figure 8: 10-Year Treasury Yield Forecast and Primary Mortgage Rate Trend (Mortgage News Daily, MBA, Freddie Mac) 

Inflation — Staying Elevated 

The April 2026 CPI print came in at 3.8% year-over-year; core CPI (excluding food and energy) ran at 2.8%, well above the Fed’s 2% target. The PCE index was not yet published at call time but was expected to confirm continued inflationary pressure. Combined with stable unemployment, this leaves the Fed with limited flexibility to ease. 

Figure 9: PCE Inflation (ex. Food & Energy) and CPI — Year-over-Year % Change 

Home Prices — Stagnating, with Pronounced Regional Variation 

National home price growth has slowed to near-zero. The Case-Shiller National Index showed approximately +0.67% year-over-year as of February 2026, while the 10-City Composite came in at +1.74%, suggesting urban markets are modestly outperforming. Regional divergence is pronounced: San Francisco has recorded negative price growth for approximately the past six months (-0.34% YoY), while New York remains solidly positive at +4.78%. 

Figure 10: Case-Shiller National and 10-City Composite Home Price Indices — Year-over-Year % Change 

Figure 11: Case-Shiller San Francisco and New York Home Price Indices — Year-over-Year % Change 

Consumer Stress: Evidence from Credit Card Spending 

This month’s call featured a deep dive into consumer financial stress, drawing on research from the Federal Reserve Bank of Boston. The analysis is particularly relevant to mortgage credit risk given evidence of rising delinquencies in FHA and Non-QM collateral. 

Credit Landscape 

Agency loans (excluding FHA) continue to show low delinquency rates with no significant deterioration. FHA, however, is exhibiting elevated delinquencies that remain high even after accounting for the trial modification policy introduced in October 2025, which holds more loans in delinquent states during the modification process. The Non-QM universe has also begun to show a rising delinquency trend over the past 12–18 months. 

Boston Fed Analysis: Spending and Debt by Income Group 

A study from the Federal Reserve Bank of Boston segments credit card behavior across three income cohorts and reveals a striking disparity between spending levels and outstanding balances: 

  • Low-income ($0–$39K): monthly spending ~$25B vs. revolving debt ~$80B — a 3x ratio; this group is primarily revolving rather than paying off balances 
  • Middle-income ($59K–$83K): spending ~$37B vs. debt ~$105B — also approximately a 3x ratio 
  • High-income ($121K+): spending ~$170B vs. debt ~$185B — roughly 1:1, consistent with transactor behavior (spend and pay off monthly) 

Low- and middle-income households are therefore carrying roughly three months’ worth of spending as permanent revolving debt, at credit card APRs recently running around 23% on new issuances. Total credit card outstanding nationally has reached approximately $1.25 trillion. 

Figure 12: Aggregate Credit Card Spending by Income Group, January 2015–May 2025 (Source: Boston Fed / Federal Reserve Y-14M) 

Figure 13: Aggregate Credit Card Debt by Income Group, January 2015–April 2025 (Source: Boston Fed / Federal Reserve Y-14M) 

Buy Now, Pay Later: An Untracked Risk 

Buy now, pay later (BNPL) services have grown rapidly and appear to be used disproportionately by lower-income households. Because BNPL obligations are not reported to credit bureaus, they represent an invisible liability not reflected in standard debt figures. The team flagged this as a developing risk to monitor, particularly for its potential impact on borrower liquidity and mortgage performance in the FHA universe. 

Summary 

Topic Key Takeaway 
Prepayment Model Performing well overall; April discount speeds stable (turnover-driven); May premium speeds fell sharply on March rate sell-off and S-curve flattening 
GNMA Performance Discount speeds supported by turnover; premium speeds declined moderately but more resilient than conventional counterparts 
Non-QM Credit Model CM 7.1 beta in June; production release ~July 10; dedicated webinar in second half of June 
Mortgage Rates Freddie Mac at 6.51%; Mortgage News Daily at 6.65%; back to summer 2025 levels; sub-6.25% rate unlikely near-term 
Fed Policy No cuts expected in 2026; CME FedWatch shows meaningful probability of hike later in 2026 or early 2027 
Treasury Yields 10-year up ~50 bps since February; 2-year up ~75–80 bps; consensus at ~4.82% by year-end 
Inflation CPI 3.8% YoY (April); core CPI 2.8%; PCE similarly elevated; well above 2% target 
Home Prices National ~+0.67% YoY; San Francisco negative; New York +4.78%; highly geography-dependent 
Consumer Stress Low/mid-income households revolving 3x monthly spending at ~23% APR; BNPL obligations add hidden risk; FHA and Non-QM delinquencies trending higher 


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

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


AI’s Uneven Impact on Labor Demands a Local Housing View

By: Scott Anderson and Bernadette Kogler

AI is already disrupting parts of the U.S. labor market. The more contested question for residential mortgage investors is how and where. The dooms-day camp sees broad structural displacement where white-collar knowledge workers are replaced by AI agents. The optimists counter that AI makes workers more productive, that falling labor costs will expand demand leading to net employment gains. Both camps agree on one thing: the impact will not be uniform. Disruption will concentrate in pockets — specific occupations, metro centers, and income bands.

That specificity is precisely what makes this a mortgage credit problem. Local labor shocks have always transmitted to local housing markets through a familiar mechanism: income impairment reduces demand which pressures home prices, and price declines erode the collateral cushion that protects investors. What varies is the speed, the depth, and whether prices recover once the shock stabilizes.

Early indicators are consistent with the thesis. A Wall Street Journal analysis found the unemployment rate for recent college graduates ages 20–29 at 7.1% as of October 2024 — nearly double the 4.1% rate for the general population — with computer science graduates at 6.1% unemployment and computer engineering majors at 7.5%. The Federal Reserve of NY also published data showing labor market conditions worsened for recent college graduates at the end of 2025, with the unemployment rate climbing to about 5.7% in Q4 2025 — and underemployment rising to 42.5%, its highest level since 2020.

The occupations most exposed to AI displacement cluster in precisely the metros where high-balance prime mortgages are most concentrated, changing the traditional metrics that investors rely on for early warning. AI-driven labor disruption is not a macro thesis to be managed with national averages. While mortgage borrower occupation data is not complete or granular enough to drive AI job loss assumptions, property location data is. The risk needs to be identified, stress-tested, and monitored at a granular level — before it shows up in delinquency data.

The good news is that the data, models and historical reference points exist. With the growth of Non-QM lending, RTL and loan products that carry credit risk without an insurance wrapper, treating AI-driven labor disruption as a present-tense portfolio risk is not optional. To move from thesis to numbers, we ran a portfolio-level stress test on the universe of securitized Non-QM mortgages – starting with how AI exposure is measured at the local level.

Key Findings for investor consideration:

  1. AI labor disruption is expected to have a higher disproportional impact on white-collar professional jobs that are concentrated in certain “knowledge-work” centers.
  2. The largest job markets and the deepest credit pools are systematically the most AI-exposed, suggesting AI risk and portfolio risk overlap rather than diversify.
  3. Stress-testing the $196B universe of securitized Non-QM mortgage loans shows the magnitude is meaningful: under an AI-driven stress scenario calibrated to GFC-level shocks but re-allocated to AI-exposed geographies, RiskSpan’s credit model projects universe-wide cumulative defaults to nearly double and losses more than quadruple.
  4. Securitized deals benefit from built-in geographic diversification, which dampens the absolute impact across deals. Whole-loan investors, who construct their own geographic mix, face concentration risk that accumulates silently if exposure isn’t an explicit input into selection and surveillance.
  5. Act now — put the infrastructure in place to use granular data to monitor unemployment, HPI and loan performance and apply scenarios where relevant.

Measuring AI Exposure at the Local Level

Leveraging research published by the Brookings Institution, RiskSpan used their geographic AI exposure scores as the foundation for a credit stress scenario. The resulting county- and metro-level scores follow a similarly clear geographic pattern to that published by Brookings. Highly educated, high-paying knowledge-work centers — Santa Clara (42.8% exposure), King County WA, New York County, DC, Boston — show the highest exposure. Smaller industrial and rural counties score considerably lower. As a sanity check on the methodology, we aggregated and plotted state-level AI exposure against the share of each state’s adult population with a bachelor’s degree or higher (a well-understood proxy for knowledge-work concentration). The two track closely, which is the result one would expect — it confirms the Brookings measure is capturing what it intends to and isn’t producing arbitrary geographic rankings.

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Figure 1

Unlike earlier automation, generative AI concentrates in exactly the metros that house the largest mortgage markets: as the chart below shows, the most AI-exposed MSAs are also among the deepest non-agency RMBS markets.

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Figure 2

One important caveat before moving on to how we used this data. As Brookings is careful to note, “‘exposure’ does not speak only to the displacement of workers; it also may involve their ‘augmentation’ through rapidly improving AI tools.” The same coder whose role is highly exposed could be made dramatically more productive rather than displaced; the same financial analyst could spend less time on rote modeling and more on judgment-intensive work. Whether exposure plays out as net displacement, net augmentation, or some mix will vary by occupation, employer, and how quickly the technology evolves. For purposes of this exercise, we focus on the displacement story: the scenario where AI exposure translates into labor market shocks that transmit to housing markets and mortgage credit. That’s not the only possible outcome, but it’s the one investors need to be prepared for.

Translating that exposure into a portfolio-level credit stress test on the Non-QM universe, the largest segment of the non-agency RMBS market, required two steps.

Step 1: Construct an AI-driven stress scenario calibrated to the GFC.

Rather than forecast AI displacement magnitude — a genuinely uncertain exercise — we sized the stress to a known reference point: the total HPI decline and unemployment increase observed during the GFC. The GFC offers a useful calibration episode because the empirical relationship between local unemployment shocks and HPI declines is strong and well-documented, regardless of which direction the causation primarily ran. The chart below shows that relationship across MSAs — a clear negative slope linking the spike in unemployment rates to the depth of the HPI decline.

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Figure 3

The critical observation for our purposes is what happened in 2008: the metros most exposed to AI today (DC, Boston, San Francisco, Seattle) cluster toward the middle of that scatter plot. They experienced more moderate impacts than the hardest-hit metros, because the speculative and underwriting excesses that drove the GFC were concentrated elsewhere — Las Vegas, Phoenix, Miami, the Inland Empire. An AI-driven shock would invert that geography.

We took the GFC’s national-level shock magnitudes and redistributed them, concentrating the stress in MSAs with high AI exposure. Starting from Brookings’ occupation-level AI exposure scores rolled up to MSA and state-level, we scaled both the unemployment and HPI shocks to each MSA in proportion to its AI exposure share. The result is a per-MSA stress profile that adds up to GFC-magnitude pain at the national level but lands very differently on the map.

The chart below translates that scaling into implied HPI shock magnitudes across the metros where non-agency RMBS exposure actually sits. The metros at the bottom end of both axes (high portfolio balance and severe implied HPI shock) are where the AI thesis would hit hardest if it materializes.

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Figure 4

Step 2: Run the loan universe through two scenarios.

We took the $196B universe of Non-QM mortgage loans (~500K loans across 810 active Non-QM deals as of April 2026) and ran these loans through RiskSpan’s proprietary credit model under two scenarios: a baseline and the combined AI-driven Unemployment + HPI stress scenario described above. Every loan received a projected cumulative default rate and cumulative loss rate under each scenario, isolating the AI-driven impact at the loan level.

Findings

Under the AI-driven combined stress scenario, universe-wide cumulative defaults nearly double (3.6% → 7.4%) and cumulative losses more than quadruple (0.5% → 2.2%). The steeper loss response reflects the familiar non-linearity between defaults and severities under HPI stress. The mechanism behind those aggregate numbers becomes clearer when you look at it geographically.

Finding 1 — AI exposure drives MSA-level credit performance.

This finding is by construction — we built the stress scenario by re-allocating shocks to AI-exposed MSAs, so the model output should track AI exposure. The MSA-level chart confirms it does. MSAs at the high end of AI exposure (DC, San Francisco, Boston, Seattle) see over an 8% percentage point (pp) increase in cumulative defaults compared to MSAs at the low end. Riverside, near the lower end of the AI exposure range at just under 30%, sees only a 2.6% pp increase in cumulative defaults. There’s MSA-specific noise from collateral mix, but the geographic risk concentration the AI thesis predicts shows up cleanly in the model output. This is the core mechanism: re-allocate the stress to AI-exposed geographies, and the credit impact tracks the re-allocation.

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Figure 5

Finding 2 — The aggregate impact is shaped primarily by leverage.

Decomposing the universe results by current LTV bucket explains where the headline numbers come from. The first thing worth noting is what the LTV distribution looks like: less than 5% of total non-QM balance sits above 80% LTV, and roughly half is below 60%. That’s a meaningfully larger equity cushion than the borrower base of the mid-2000s, when the proliferation of low- and no-down-payment products left investors with thin protection against any HPI decline. Today’s Non-QM universe starts the AI stress scenario with a lot more skin in the game.

Within that distribution, stress impact scales monotonically with leverage, exactly as one would expect: low-LTV loans (≤40%) see a 1.3pp increase in defaults (from 0.96% to 2.24%), while high-LTV loans (>80%) see a 6.7pp increase (from 5.97% to 12.62%), though the relative impact on cumulative defaults is broadly stable across LTV buckets, with defaults about doubling in each bucket. The same pattern holds for losses, where the highest LTV buckets see losses rise by over 3pp under stress versus 0.16pp for the lowest LTV bucket. FICO declines slightly as LTV increases (from 756 for the lowest LTV bucket to 745 for the highest), so the projected cumulative default differences across LTV buckets are driven, at least partly, by underlying borrower credit as well.

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Finding 3 — Deal-level concentration matters, but only in relative terms.

When we rolled up by deal rather than by MSA, the picture got more interesting. The absolute (pp) impact across deals shows essentially no relationship with the deal’s AI exposure share — high-AI-exposure deals don’t show systematically larger pp increases. But the relative impact (stress / base − 1) is clearly correlated with AI exposure.

One plausible explanation is that deal-level base default rates vary widely based on traditional credit characteristics — LTV mix, FICO mix, doc type (Full Doc vs. DSCR vs. Bank Statement), occupancy. That variation in base rates may be dominating the AI signal in absolute terms while leaving it visible on a relative basis. Either way, the relative measure is the cleaner lens for isolating AI exposure’s marginal contribution to deal-level risk.

There’s also a more reassuring story embedded in these charts, at least for securitized mortgage assets. Compare the deal-level scatter to the MSA-level scatter and you’ll notice the deal-level points cluster in a meaningfully narrower range — both on the AI exposure axis (X) and on the impact axis (Y). That tighter clustering is geographic diversification at work: even the most AI-concentrated deal in the universe pulls in loans from enough different MSAs that its blended AI exposure sits closer to the universe average than the most exposed individual MSA does. Diversification within deals is doing what it’s supposed to do — dampening the variance of outcomes — even though the unit of analysis is smaller than an MSA. The magnitude of the differential across deals is bounded by how concentrated any individual issuer is willing to get.

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The implication for whole-loan portfolios is sharper. Whole-loan investors don’t get the benefit of pre-built deal diversification — they’re constructing their own geographic mix one loan or pool at a time, and concentration risk accumulates silently if it isn’t an explicit input into selection and surveillance. AI exposure is one such risk, and a relatively new one, but it sits alongside more familiar concentrations — natural disaster exposure, regional employment dependencies, single-industry metros — that warrant the same lens. The toolkit is the same in each case: granular geographic data, stress scenarios calibrated to defensible reference points, and loan-level credit modeling. Whether the goal is initial portfolio construction or ongoing surveillance of a seasoned book, the ability to identify these concentrations early — before they show up in delinquency data — is what separates active risk management from passive observation.

What This Means for Investors

History offers instructive analogies including the energy-patch metros of the mid-2010s when oil prices collapsed — Houston, Oklahoma City, and Calgary experienced home price corrections in the range of 5 to 15 percent. Regional manufacturing declines showed a similar story over a longer time period — the long contraction of auto-sector employment in Detroit, for example, played out in local housing markets over years while national HPA remained positive. In neither case did the national trend warn you that specific MSAs were in distress.

Acting now means two things in practice. The first is tracking — building the early-warning infrastructure before you need it. That means monitoring MSA-level unemployment, not just the national rate; watching labor force participation shifts; and tracking occupational mix changes in the markets where your collateral is heaviest. These indicators move before delinquency does. The second is scenario preparation — running stylized AI displacement shocks against the portfolio today using MSA-level or county-level HPA overrides and occupational exposure overlays. The goal is not a forecast but rather a map of your exposure — which corners of the portfolio are most sensitive to particular labor market stress, and what the loss distribution looks like if the more adverse scenarios begin to materialize. That map is worth building now, while the cost of being wrong about the timing is low.

RiskSpan is releasing a new Non-QM-specific credit model in the coming weeks, estimated directly on Non-QM performance data and designed around the underwriting features (DSCR, bank statement, expanded LTV) that distinguish these segments. Re-running this scenario and other stress scenarios on the new model is on the near-term roadmap and may sharpen some of the findings reported here. For investors who want to evaluate their own exposure, the analytical infrastructure described here can be applied to specific portfolios on request.

Note: The Brookings Institution has been tracking AI’s labor market impact since 2019. A 2024 Brookings piece analyzed data from OpenAI to measure occupational AI exposure as the share of tasks where AI could reduce human completion time by at least 50%. AS one would expect, the cognitive/manual divide is evident in the rankings, with computer and mathematical work atop the list at 75% while construction sits at the bottom at 5.6%.

Their 2025 piece extends that work to geography by multiplying each occupation’s exposure rating by its share of local employment in each county or metro, then aggregating. Counties with large workforces in highly exposed occupations (software developers, financial analysts, lawyers, marketing professionals) score higher than those whose employment skews toward construction, agriculture, or in-person services.

Key References:

Generative AI, the American worker, and the future of work | Brookings

The geography of generative AI’s workforce impacts will likely differ from those of previous technologies | Brookings

There Is Now Clearer Evidence AI Is Wrecking Young Americans’ Job Prospects – WSJ

www.newyorkfed.org/research/college-labor-market


Models & Markets Update: April 2026 

Register here for next month’s call: Thursday, May 21st, 2026, 1 p.m. ET. 

Key Takeaways 

  • Prepayment models continue to perform well, with March speeds driven by a February rate rally and day count effects 
  • A new Non-QM Credit Model (CM 7.1) is on track for release near end of Q2 2026, with a dedicated webinar planned for end of May or early June 
  • Housing turnover analysis reveals rate sensitivity at positive refinancing incentive levels — a finding that will inform the next prepayment model 
  • Mortgage rates hit a six-month high in March before pulling back; rates are expected to remain above 6% through 2026 and 2027 
  • No Federal Reserve rate cuts are expected in 2026; the consumer remains under pressure from elevated rates and rising credit card balances 

You can read the recap below or click here for the entire recording. 

Prepayment Model Back-Testing: April Factor Data Update 

The prepayment model continues to track realized speeds closely across Agency collateral. Results are available on the Edge platform under the Vertex module. 

Fannie/Freddie — Discount Coupons (WAC 5.5 and Below) 

Discount coupons showed a modest uptick in March speeds, driven primarily by two factors: 

  • A day count effect: March had three more collection days than February 
  • Seasonal turnover patterns typical of the spring housing market 

Figure 1: FN/FH Discount Coupon Back-Testing — Model CPR vs. Observed CPR 

Fannie/Freddie — Premium Coupons (WAC 6.0 and Higher) 

Premium coupons saw a sharper increase in prepayment speeds in March, driven primarily by the rates rally in February. With rates subsequently moving higher in March, May factor data is expected to show a decline in speeds — a clear convex response consistent with model expectations. 

Figure 2: FN/FH Premium Coupon Back-Testing — Model CPR vs. Observed CPR 

GNMA — FHA and VA Segmentation 

GNMA performance showed a similar pattern to Fannie/Freddie across discount and premium coupons. A notable enhancement this month: the team has introduced the ability to split GNMA back-testing results by FHA vs. VA segments on the Vertex report, providing additional analytical granularity. 

Two segment-level observations: 

  • FHA: FHA: The model has shown a slight drift in prepayment speeds over the past year. This is primarily attributed to the FHA trial modification policy change in 2025, under which servicers are no longer required to buy out delinquent loans — a policy shift that has meaningfully reduced prepayment speeds relative to historical levels. 
  • VA: VA: A discrepancy between modeled and actual speeds reflects the default VA-to-PMMS spread assumption being too tight. Users can adjust the VA spread to current market levels within the platform to bring results into closer alignment with observed speeds. 

Figure 3: FHA Segment Back-Testing 

Figure 4: VA Segment Back-Testing 

New Non-QM Credit Model: CM 7.1 

Guanlin Chen from the Quantitative Modeling Group presented an overview of the upcoming Non-QM Credit Model, version 7.1, expected to be available to all users near end of Q2 2026. 

Model Structure 

CM 7.1 follows the same three-component framework as RiskSpan’s agency credit model: 

  • 1. Transition Model — generates a time-varying transition matrix estimating transition probabilities across delinquency states and time periods 
  • 2. Liquidation Timeline Model — applied once a loan enters default 
  • 3. Severity Model — estimates final losses on the defaulted balance 

A key design decision: the model is built with separate sub-models for each documentation type, consistent with the segmentation used in the Non-QM prepayment model: Bank Statement, DSCR, Full Doc, and Other. This segmentation reflects the meaningfully different performance characteristics across these loan types and allows for more accurate, documentation-specific projections. 

Transition Matrix Design 

The transition matrix tracks loans across delinquency states — current (0), one-month delinquent (1), two-month delinquent (2), foreclosure (F), and REO (R) — with additional granularity for delinquency history.

Back-Testing Results 

Initial back-testing of the 30-day delinquency transition demonstrates that the model captures the overall trend in Non-QM credit performance well across all four documentation types. The COVID period was intentionally excluded from model training — including it would have caused extreme unemployment levels to dominate the model and distort sensitivity to other risk factors. 

Figure 5: NonQM Credit Model — Current to 30DPD Transition: Actual vs. Projection by Documentation Type 

A dedicated webinar covering CM 7.1 in detail is planned for end of May or early June. Please stay tuned. 

Housing Turnover Analysis: Rate Sensitivity at Positive Refinancing Incentive 

Shane Lee from the Quantitative Modeling team presented new research on housing turnover behavior in a positive refinancing incentive environment. 

Background 

Total prepayment has two components: housing turnover (prepayment driven by home sales) and refinancing. In current model design, housing turnover is assumed to be weakly rate sensitive — and in the positive refinancing incentive regime, sensitivity is held at zero. The question the team set out to answer: is that assumption correct? 

Data Sources 

  • NAR: National Association of Realtors (NAR) Existing Home Sales — measures the number of homes sold including single-family, condo, and co-op properties (including sales without mortgages) 
  • Equifax ADS: Equifax ADS Data — tracks trade lines per consumer, allowing the team to identify housing turnover by flagging cases where an existing mortgage closes and a new mortgage originates at a different ZIP code for the same borrower 

Figure 6: ADS vs. NAR Data Comparison — Prepaid Mortgages vs. Home Sale Units 

Key Finding 

During the post-COVID refinancing boom, housing turnover activity increased significantly — by almost 50% above the baseline level. This elevated turnover coincided with the period of low rates and high refinancing activity, driven in part by the work-from-home migration wave. 

Figure 7: Housing Turnover CPR — ADS Data (nearly 50% above baseline during COVID refi boom) 

This finding suggests that housing turnover is more rate-sensitive in a positive refinancing incentive environment than current models assume — a potential source of underestimation when projecting prepayment speeds in a low-rate environment. Research is underway to incorporate this into Prepayment 4.0. 

Macroeconomic Update: April 2026 

Federal Reserve — No Rate Cuts Expected in 2026 

CME FedWatch futures currently indicate no Federal Reserve rate cuts this year. The overall expectation is that the Fed funds rate (currently 350–375 bps) will remain unchanged through year-end 2026. 

Figure 8: Federal Funds Target Range — Upper Limit (Source: FRED) 

Mortgage Rates — Elevated and Volatile 

Mortgage rates hit a six-month high in March, with Freddie Mac’s primary rate reaching 6.45% and Mortgage News Daily data showing rates approaching 6.64%. Rates have since pulled back modestly as some geopolitical uncertainty subsided. The 10-year Treasury rate is expected by market consensus (econforecasting.com) to remain above 4% for the next three to five years — implying mortgage rates are unlikely to fall significantly below 6%. 

Figure 9: 10-Year Treasury Yield — Historical and Market Consensus Forecast 

Figure 10: Primary Mortgage Rate Trend 

Unemployment and Inflation 

  • March unemployment rate: 4.3% — trending upward 
  • PCE (excluding food and energy): approximately 3% — still above the Fed’s 2% target 

Figure 11: Unemployment Rate 

Figure 12: PCE Inflation (ex. Food & Energy) 

Consumers continue to face pressure from elevated gasoline and oil prices. Credit card balances have risen significantly over the past two years, adding to the financial strain on households. 

Home Prices — Stabilizing but Elevated 

Home price growth remains positive but has decelerated substantially from the 20%+ year-over-year peaks observed in mid-2022: 

  • Case-Shiller National Index: approximately 1% year-over-year growth 
  • 10-City Composite: slightly above the national index 
  • FHFA All-Transaction Index: somewhat stronger, indicating variation across market segments 

Figure 13: Case-Shiller U.S. National Home Price Index — Year-over-Year % Change 

Two regional case studies highlight the range of outcomes: Austin, TX and Boise, ID both experienced peak growth of 30–35% in 2021–2022, followed by sharp declines through 2023, and are now returning to modest positive territory. Housing supply remains severely constrained. 

Summary 

Topic Key Takeaway 
Prepayment Model Performing well overall; March speeds driven by February rate rally and day count effects 
GNMA Segmentation New FHA/VA split available in Vertex; FHA drift tied to 2025 trial mod policy change 
NonQM Credit Model CM 7.1 on track for end of Q2 2026; dedicated webinar coming end of May / early June 
Housing Turnover Rate sensitivity confirmed in positive refi regime; ~50% above baseline during low-rate period; research underway for Prepayment 4.0 
Mortgage Rates Hit six-month high of 6.64% in March; expected to remain above 6% through 2026–27 
Fed Policy No rate cuts expected in 2026; Fed funds rate at 350–375 bps 
Home Prices Growth slowing (~1% nationally); supply constraints persist 


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

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


Models & Markets Update: March 2026 

Register here for next month’s call: Thursday, April 16th, 2026, 1 p.m. ET.

Key takeaways from this month’s call: 

  • Non-mortgage credit is deteriorating more rapidly than mortgage credit 
  • BNPL usage may be masking underlying financial strain 
  • Macroeconomic conditions are likely to remain restrictive, reinforcing current trends 
  • Prepayment models remain well-calibrated, even as borrower behavior begins to shift 

You can read the recap below or click here for the entire 20-minute recording.  

Credit Performance by Asset Class 

The data shows a clear divergence between mortgage and non-mortgage credit: 

  • Mortgage delinquencies remain relatively low, supported by tighter underwriting standards 
  • Credit card delinquencies have increased meaningfully since 2022 
  • Auto loan delinquencies are approaching levels observed during the Global Financial Crisis, particularly among younger borrowers  

The following charts from NYFed illustrate how younger age cohorts are consistently exhibiting higher delinquency rates across credit types (mortgages, credit cards, and autos). 

BNPL Usage as a Potential Blind Spot 

Buy Now, Pay Later (BNPL) usage continues to expand. 

Adoption is highest among younger borrowers. 

A meaningful portion of usage is for essential expenses such as groceries  

Because BNPL obligations are not consistently captured in traditional credit metrics, they may obscure underlying levels of consumer leverage and stress. 

Macroeconomic Outlook: Rates Expected to Remain Elevated 

The macroeconomic environment continues to support a “higher-for-longer” rate outlook. 

  • Market expectations suggest no Federal Reserve rate cuts through 2026. 
  • The 10-year Treasury rate is expected to remain above 4% over the next several years. 
  • Mortgage rates, after declining earlier in 2026, have risen again and are expected to remain near or above 6%. 

At the same time: 

  • Inflation remains above target levels 
  • Unemployment is trending upward  

These conditions suggest a continued tightening backdrop for borrowers, with limited relief from monetary policy in the near term. 

Housing Market: Moderation Continues 

Home price growth remains positive but has slowed: 

  • Case-Shiller index shows modest annual growth (~1.3%) 
  • FHFA index indicates somewhat stronger growth (~3.3%)  

Differences between indices suggest variation across market segments, with relatively stronger performance in more affordable segments and geographic differences on home prices. 

Against this macro and consumer backdrop, prepayment behavior continues to evolve. 

  • Prepayment models remain closely aligned with realized speeds across FN/FH and GNMA collateral, as shown in the coupon-level comparisons.  
  • Refinance behavior is well captured, including sensitivity to changes in mortgage rates.  

There are, however, early indications of shifting borrower behavior: 

  • Prepayment speeds increased in February despite fewer collection days, suggesting a gradual weakening of the mortgage rate “lock-in” effect.  
  • Short-term rate increases may moderate this trend, but the directional change is notable. 

GNMA Segmentation Enhancements 

The introduction of FHA and VA segmentation in GNMA back-testing provides additional analytical detail. 

FHA performance shows some divergence, likely reflecting recent policy changes affecting delinquent loan buyouts. 

VA results are more sensitive to spread assumptions and can be adjusted to align with market conditions. 


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

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


From Household Debt to Non-QM Credit: February Models & Markets Recap 

Register here for next month’s call: Thursday, March 19th, 2026, 1 p.m. ET. 

In this month’s Models & Markets call, RiskSpan’s quantitative modeling team tackled: 

  • The record debt levels now carried by U.S. households (and the consumer stress that is building beneath the surface); 
  • The likely persistence of higher rates; 
  • RiskSpan’s forthcoming non-QM credit model, and; 
  • (as always) how RiskSpan’s prepayment model is performing 

You can read the recap below or click here for the entire 20-minute recording.  

$18 Trillion in Household Debt (and Growing) 

 U.S. household debt reached $18.8 trillion at the end of 2025 and continues to climb. Mortgages account for the largest share at roughly $13 trillion, with auto loans, student loans, credit cards, and HELOCs making up the balance. 

Using conservative assumptions for average interest rates for each category, we estimate that these balances equate to roughly $1.1 trillion in annual interest payments and $2.5 trillion in total annual debt service payments – the approximate cost required each year just to keep households current. 

A Distributional Problem

The aggregate debt figure masks meaningful stress at the lower end of the income spectrum: 

  • A median household (~$80K gross income) may devote roughly 37% of disposable income to debt service. 
  • Bottom-quartile households (~$32K gross income) may spend 40–55% of disposable income servicing debt. 

Lower-income households are disproportionately exposed to higher-rate revolving credit and subprime auto loans, as opposed to 3–4% fixed-rate mortgages. The averages therefore understate the severity of strain on the more vulnerable segments. 

Are Reported Delinquencies Understating Stress?

Delinquencies are rising across income levels, particularly in lower-income areas. Lenders, however, may be quietly modifying or re-aging loans, particularly in consumer credit categories (e.g., auto loans). Such modifications can: 

  • Push missed payments to the back of the loan 
  • Reset accounts to “current” status 
  • Avoid immediate charge-offs 

While this suppresses reported delinquency statistics, borrower balances may continue to grow. This implies that reported delinquency rates may really be more of a floor, as aggregate DSCR and household stress may be greatly understated. 

Consumer strain is real and potentially worse than what is suggested by the headline metrics. 

Coming in Q2: RiskSpan’s Non-QM Credit Model! 

RiskSpan’s forthcoming non-QM credit model will feature four distinct documentation categories: 

  1. Bank Statement 
  1. Full Documentation 
  1. DSCR/Investor 
  1. Other (e.g., VOE, asset depletion) 

Each segment is modeled independently through a transition-matrix framework covering: 

  • Current 
  • 30-day DQ 
  • 60-day DQ 
  • 90+ DQ 
  • Termination (voluntary and involuntary) 

Prior delinquency figures prominently in the model, with clean loans having relatively low base transition rates from current to delinquent, while loans with prior delinquency history can experience transition probabilities up to 10x higher. Capturing this conditional risk dynamic is central to the model’s design. 

Back-testing (shown below for the Full Doc segment) indicates the model is tracking historical delinquency transition rates reasonably well, though development remains ongoing.

Macro Considerations

Consistent with prior months, the macro backdrop continues to reinforce a “higher-for-longer” rate environment. 

Fed and Policy Outlook 

  • Fed Funds expectations imply limited cuts in 2026. 
  • No immediate expectation of a March rate cut. 

10-Year Treasury 

  • Consensus forecasts suggest the 10-year Treasury will remain above 4% for the next 2–3 years. 
  • Recently, it has hovered around ~4.1%, down slightly from prior highs. 

Mortgage Rates 

Primary mortgage rates are approaching 6%, but not sustainably breaking below it. In our view, mortgage rates are likely to remain around or above 6% through 2026, possibly into 2027. 

Labor, Inflation, and Home Prices 

  • Unemployment ticked down slightly. 
  • Job creation surprised to the upside. 
  • Inflation remains sticky in the 2.5–3% range  
  • National home prices showed modest year-over-year growth (~1.4%). 

Traditional seasonal adjustments may be less reliable in today’s inventory-constrained housing market. Turnover seasonality appears to be shifting earlier in the year, with implications for both pricing and prepayment dynamics. 

Prepayment Model Performance: Stable & Improving

Despite macro headwinds and rising consumer stress, RiskSpan’s prepayment models continue to perform well. 

GSE Discounts (WAC 5.5 and Below)

Prepayments declined slightly in the most recent month, primarily due to fewer collection days and normal January seasonality (lower turnover). Overall model fit remains strong. 

One identified refinement: the model’s seasonal peak appears slightly delayed (June/July shifting toward August). This will be addressed in the next version update 

GSE Premiums (WAC 6 and Above)

Refinance speeds have been largely unchanged over the past two months. S-curve comparisons between December and January show no material differences once recount adjustments are made. A modest ~1.5 CPR change in recent data appears driven by turnover rather than refi activity.

Ginnie Mae: FHA vs. VA Enhancement

Performance across Ginnie segments remains solid, with recent prepayment dips again attributable to fewer collection days. However, we have observed divergence between FHA and VA: Modeled FHA speeds tend to be overestimated, while modeled VA speeds tend to be underestimated compared to recent historicals.

To address this, RiskSpan is adding a loan guarantor filter to the back-testing report, enabling FHA and VA splits (expected early March). This enhancement will improve transparency and precision in Ginnie performance analysis.


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

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


AI Isn’t Coming to Structured Finance. It’s Already Here.

At SFVegas 2026, RiskSpan had a front-row seat to one of the most consequential conversations happening in our industry right now. I moderated a session on Agentic AI and the Securitization Lifecycle, while our CEO, Bernadette Kogler, participated in a panel on AI applications in structured finance. Across both rooms, the message was the same: we are done experimenting. We are redesigning workflows. 

The distinction that framed everything was simple but important. Copilots assist. Agents orchestrate. We are moving from AI that enhances what humans do to AI that executes complex, multi-step workflows — with humans supervising the process rather than driving every step. That shift changes everything about how we think about structured finance operations. 

And structured finance, it turns out, is almost perfectly suited for this moment. It is document-heavy, multi-party, process-driven, and data-intensive. From origination through surveillance, the lifecycle is fundamentally workflow-based. Panelists across both sessions shared real examples already in production: legal document review compressed from days to hours, AI-powered loan tape scrubbing before cash flow calculations, prompt-driven scenario generation replacing manual model configuration, and surveillance scaled across hundreds of deals per month. One striking observation: the future interface for structured finance may not be a UI at all — it may be entirely driven by prompts. 

But the panels were equally clear-eyed about what has to come first. Sixty-five percent of financial services firms are actively using AI, yet only 13% have deployed it across production processes. The gap between those two numbers is largely a data problem. The most sophisticated AI cannot overcome poor inputs, inconsistent loan tapes, or legacy system constraints. Firms that want to lead need to fix the foundation before layering on orchestration. 

Explainability matters just as much. With the EU AI Act and US fair lending enforcement raising the stakes, auditable, transparent models are not optional. And governance is shifting from “human in the loop” to “human over the loop” — a subtle but meaningful difference that requires defined accountability, model drift monitoring, and clear operational guardrails. 

The one-year outlook from both panels was notably concrete. Expect AI agents managing defined surveillance workflows, deeper cross-platform integration, and a sharper divide between early movers and everyone else. The competitive advantage will go to firms that clean their data, build explainability into their models from day one, and embed AI into operations — not just into pilot programs. 

Structured finance has always rewarded process discipline and deep domain expertise. That doesn’t change in an agentic world — if anything, it becomes more critical. The quality of an AI agent’s output is only as good as the prompts and parameters guiding it, and designing those well requires people who understand cash flow waterfalls, covenant structures, and credit risk at a fundamental level. Agentic AI doesn’t replace that expertise. It amplifies it. The firms that understand that will define the next chapter. 

At RiskSpan, we have spent years building at exactly this intersection — combining deep structured finance domain knowledge with purpose-built analytics infrastructure. That foundation is what makes it possible to deploy AI that actually works in production, not just in demos. The opportunity in front of our clients right now is significant, and we are focused on helping them capture it. 


Why AI Won’t Kill Asset-Backed Finance Software — and Why the Last Mile is the Moat

Every wave of financial technology innovation brings the same prediction: software will be commoditized. Today, that prediction is being applied to AI. If AI models can reason, summarize, and generate code, the thinking goes, B2B vertical SaaS becomes unnecessary. 

That conclusion is inherently wrong. ABF platforms are not feature layers, they are governed systems.  

The last mile of AI deployment isn’t friction—it’s the moat. 

ABF Is Not a “Promptable” Problem 

ABF platforms sit directly in the flow of capital allocation, risk management, and regulation. For asset managers deploying institutional capital, this creates a very high bar for reliable data, validated models and domain-specific workflow.  

The real question isn’t whether a system can produce answers. It’s whether it can produce results that are: 

  • Consistent over reporting periods and market cycles 
  • Explainable under stress and investor scrutiny 
  • Defensible and robust enough for LPs, investment committees, and regulators 

That high bar changes everything. It explains why technology adoption in financial markets moves cautiously and why legacy systems persist. These systems embed decision rights, controls, and institutional logic that can’t simply be recreated with better prompts. Any platform that ignores this reality will struggle to scale beyond pilots. 

Which leads to the obvious question: if AI is so powerful, where does it actually help? 

AI Accelerates Workflow — Not Accountability 

Applied correctly, AI can materially improve ABF workflows. It can ingest complex credit agreements faster, reconcile data across counterparties, flag covenant breaches, and reduce manual reporting work. In other words, AI increases operational leverage. 

But AI does not remove the need for explicit deployment configuration and governance. Institutions still must define who owns key assumptions, which decisions can be automated, and where accountability sits when outcomes affect capital. These embedded design choices (not prompts) ultimately determine whether a platform is trusted. 

AI compresses timelines, but responsibility remains fixed. Once this distinction is recognized, the broader implication becomes clear: AI does not eliminate the need for software. It raises the bar for it. 

Software Remains the System of Record 

The idea that AI replaces SaaS also misunderstands where SaaS enterprise value lives. Enterprise value in ABF doesn’t live in isolated insights. It lives in controlled systems of record and durable platforms that provide: 

  • Governed data and the system of record 
  • Embedded domain expertise 
  • Repeatable processes that survive personnel turnover 
  • A shared source of truth across counterparties, investment, risk, accounting, and investor relations 

AI without software discipline creates speed without stability. With it, AI becomes force-multiplying. The question, then, is what separates platforms that successfully integrate AI from those that don’t. 

The Real Differentiator: Deployment Intelligence at Scale 

What separates enduring platforms from feature-rich tools is not model sophistication—it’s deployment intelligence — the ability to integrate AI into live production environments without weakening controls. That requires: 

  • Controlled data pipelines designed for real-world imperfections 
  • Configuration layers that adapt to fund-specific structures without breaking controls 
  • AI outputs that are transparent, and auditable 
  • Implementation treated as a repeatable product, not bespoke services 

This is where defensibility emerges. Deployment intelligence compounds with each client rollout. Each successful implementation strengthens the next, deepening institutional trust and operational resilience. AI amplifies this flywheel but cannot replace it. 

The Mispriced Risk of “AI-Only” Narratives 

In private credit, trust is earned slowly and lost quickly. It is built through consistent valuations, defensible reporting, and reliability during market dislocations. 

A system that produces faster answers, but weaker confidence does not displace incumbents. It increases operational and reputational risk. Investors should be wary of platforms that promise instant replacement without acknowledging institutional reality of fiduciary-grade infrastructure. 

The Investment Takeaway 

AI is not commoditizing ABF software solutions. It is widening the moat for platforms that integrate AI responsibly into governed systems. 

The next phase of growth for category leaders such as RiskSpan will be driven by combining deep domain knowledge with AI-native architecture. Leaders will treat the last mile – data integration, workflow configuration, and control design — as a core product capability, not an implementation afterthought. 

In markets where trillions in capital allocation depend on data integrity and institutional trust, the last mile isn’t an implementation detail. 

It’s the moat. 


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