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Will a Rising VQI Materially Impact Servicing Costs and MSR Valuations?

RiskSpan’s Vintage Quality Index computes and aggregates the percentage of Agency originations each month with one or more “risk factors” (low-FICO, high DTI, high LTV, cash-out refi, investment properties, etc.). Months with relatively few originations characterized by these risk factors are associated with lower VQI ratings. As the historical chart above shows, the index maxed out (i.e., had an unusually high number of loans with risk factors) leading up to the 2008 crisis.

RiskSpan uses the index principally to fine-tune its in-house credit and prepayment models by accounting for shifts in loan composition by monthly cohort.

Will a rising VQI translate into higher servicing costs?

The Vintage Quality Index continued to climb during the third quarter of 2021, reaching a value of 85.10, compared to 83.40 in the second quarter. The higher index value means that a higher percentage of loans were originated with one or more defined risk factors.

The rise in the index during Q3 was less dramatic than Q2’s increase but nevertheless continues a trend going back to the start of the pandemic. The increase continues to be driven by a subset of risk factors, notably the share of cash-out refinances and investor properties (both up significantly) and high-DTI loans (up modestly). On balance, fewer loans were characterized by the remaining risk metrics.

What might this mean for servicing costs?

Servicing costs are highly sensitive to loan performance. Performing Agency loans are comparatively inexpensive to service, while non-performing loans can cost thousands of dollars per year more — usually several times the amount a servicer can expect to earn in servicing fees and other ancillary servicing revenue.

For this reason, understanding the “vintage quality” of newly originated mortgage pools is an element to consider when forecasting servicing cash flows (and, by extension, MSR pricing).

Each of the risk layers that compose the VQI contributes to marginally higher default risk (and, therefore, a theoretically lower servicing valuation). But not all risk layers affect expected cash flows equally. It is also important to consider the VQI in relationship to its history. While the index has been rising since the pandemic, it remains relatively low by historical standards — still below a local high in early 2018 and certainly nowhere near the heights reached leading up to the 2008 financial crisis.

A look at the individual risk metrics driving the increase would also seem to reduce any cause for alarm. While the ever-increasing number of loans with high debt-to-income ratios could be a matter of some concern, the other two principal contributors to the overall VQI rise — loans on investment properties and cash-out refinances — do not appear to jeopardize servicing cash flows to the same degree as low credit scores and high DTI ratios do.

Consequently, while the gradual increase in loans with one or more risk factors bears watching, it likely should not have a significant bearing (for now) on how investors price Agency MSR assets.

Population assumptions:

  • Monthly data for Fannie Mae and Freddie Mac.
  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market conditions.
  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose, are also excluded. These loans do not represent credit availability in the market as they likely would not have been originated today but for the existence of HARP.

Data assumptions:

  • Freddie Mac data goes back to 12/2005. Fannie Mae only back to 12/2014.
  • Certain fields for Freddie Mac data were missing prior to 6/2008.

GSE historical loan performance data release in support of GSE Risk Transfer activities was used to help back-fill data where it was missing.

An outline of our approach to data imputation can be found in our VQI Blog Post from October 28, 2015.


Senior Housing Wealth Exceeds Record $9.57 Trillion

Homeowners 62 and older saw their housing wealth grow by 3.7 percent in the second quarter to a record $9.57 trillion, according to the latest quarterly release of the NRMLA/RiskSpan Reverse Mortgage Market Index.

For a comprehensive commentary, please see NRMLA’s press release.

How RiskSpan Computes the RMMI

To calculate the RMMI, RiskSpan developed an econometric tool to estimate senior housing value, mortgage balances, and equity using data gathered from various public resources. These resources include the American Community Survey (ACS), Federal Reserve Flow of Funds (Z.1), and FHFA housing price indexes (HPI). The RMMI represents the senior equity level at time of measure relative to that of the base quarter in 2000.[1] 

A limitation of the RMMI relates to Non-consecutive data, such as census population. We use a smoothing approach to estimate data in between the observable periods and continue to look for ways to improve our methodology and find more robust data to improve the precision of the results. Until then, the RMMI and its relative metrics (values, mortgages, home equities) are best analyzed at a trending macro level, rather than at more granular levels, such as MSA.


[1] There was a change in RMMI methodology in Q3 2015 mainly to calibrate senior homeowner population and senior housing values observed in 2013 American Community Survey (ACS).


How Are Ginnie’s New RG Pools Performing?

In February of this year, the Ginnie Mae II program began guaranteeing securities backed by pools of mortgages previously bought out of Ginnie Mae securities because of delinquency. In order to qualify for these new re-performing pools (known as “RG pools”) a loan must meet two (related) conditions: 

  • Borrower has made at least six months of timely payments prior to pool issuance. 
  • Pool issue date is at least 210 days from when the mortgage was last delinquent. 

The novelty of RG pools raises questions about their composition and performance relative to other Ginnie Mae pools. While it remains too early to make many conclusive statements, a preliminary look at the prepayment data indicates speeds somewhere between those of similar vintage Ginnie Mae multi and custom pools, with typical variability from servicer to servicer.  

In this post, we discuss the prepayment behaviors we have observed over the first seven months of RG pool securitization, issuance patterns, and collateral characteristics. 

Prepayments 

Latest September prepayment prints show that RG pools’ speeds generally fell in between those of similar coupon/vintage multi and custom pools.  Below charts shows that 2015/2016 3.5% RG pools prepaid at around 37-38 CPR in September, a couple of CPR slower than similarly aged multi pools and almost 10 CPR faster than custom pools.  


Prepayments for G2 3.5% RG, Custom and Multi Pools by Vintages, September Factor Month Note: Loan level data


Below, we plot S-curves for 49 to 72 wala RG loans against S-curves for similarly aged multi and other custom loans from April to September factor months Speeds for RG loans with 25 to 100 bp of rate incentives have prepaid in mid-30s CPRs (Green line in below figure).  During the same period, similar multi pools have prepaid 5 to 8 CPR faster (blue line) than RG pools while similar custom pools have prepaid around 5 CPR slower (black line) We also overlaid a s-curve for 7 to 18 wala G2 multi pools as a comparison (orange line).


S-curves for RG, Custom and Multi Pools (49 to 72 WALA) April to September Factor Months 
Note: Loan level data, orange line is the s-curve for 7-18 wala G2 multi pools with one-year lookback period 


Not surprisingly, prepayment behavior differs by servicer. Wells-serviced RG pools that are seasoned 49 to 72 months with 25 to 100 bp of rate incentives appear to be prepaying in low 30s CPRs (black line in below figure).  Similar loans from Penny Mac are prepaying 5 to 10 CPR faster, which tends to be the case for non-RG loans as well. 


S-curves for RG loans by servicers, 49 to 72 WALA, April to September Factor MonthsNote: Loan level data 


While the re-performing loans that are being securitized into RG pools are already seasoned loans, prepayments have been increasing as pool seasons.  For example, one-month old RG 3.5% pools have prepaid at 27 CPR while 6- and 7-month 3.5% pools prepaid at 45-50 CPR (black line below). In addition, overall prepayment speeds for same-pool-age 3.0%, 3.5%, and 4.0% have been on top of each other. 


 Prepayments for RG 3.0%, 3.5% and 4.0% Pools by Pool Age, March to September 2021 Note: only showing data points for cohorts with more than 50 loans


Issuance Volume 

Following a brief ramp-up period in February and March, issuance of RG pools has averaged around $2 billion (and roughly 300 pools) per month for the past five months (see Issuance chart below). The outstanding UPB of these pools stands at nearly $11 billion as of the September factor month. 


Note: RiskSpan uses reporting month as a factor month. For this chart, we adjust our factor date by one month to match the collection period.


RG pools already account for a sizable share of Ginnie II custom issuance, as illustrated in the following chart, making up 18% of G2 custom issuance and 3% of all G2 issuance since April.

Note: RiskSpan uses reporting month as a factor month. For this chart, we adjust our factor date by one month to match the collection period. 


RG Pool Characteristics 

Nearly all of RG pool issuance has been in 3.0% to 4.5% coupons, with a plurality at 3.5%. As of the September factor month, almost $4 billion (37%) of the outstanding RG pools are in 3.5% coupons. The 4% coupon accounted for the next-largest share–$2.5 billion (23%)—followed by $2.3 billion in 3.0% (20.9%) and $1.3 billion in 4.5% (11.8%). 


RG Pool Outstanding Amount by Coupon — September Factor Month 


 The following table compares the characteristics of RG pools issued since February with those of G2 single-family custom and multi pools issued during the same period.  The table highlights some interesting differences: 

  • Issuance of RG pools seems to be concentrated in higher coupons (3% to 4%) compared to issuances for G2 custom pools (concentrated on 2.5% and 3.0%) and G2 multi-lender pools (concentrated on 2.0% and 2.5%). 
  • Loan sizes in RG pools tend to fall between those of G2 customs and smaller than G2 multis.  For example, WAOLS for 3.5% RG pools is around 245k and is around 50k smaller than multi pools and 30k larger than other custom pools. 
  • RG pools consist almost exclusively of FHA loans while G2 multis have a much higher share of VA loans.  Almost 98% of 3.5% RG loans are FHA loans. 


 G2 RG vs. G2 Custom and G2 Multi (pools issued since February), Stat as of September Factor Month 

Wells Fargo and Penny Mac are far and away the leaders in RG issuance, accounting collectively for 62% of outstanding RG pools.  


RG Pools by Servicer, September Factor Month 


 How to Run RG Pools in Edge Perspective 

Subscribers to Edge Perspective can run these comparisons (and countless others) themselves using the “GN RG” pool type filter. The “Custom/Multi-lender” filter can likewise be applied to separate those pools in G2SF. 


Contact Us

Contact us if you are interested in seeing variations on this theme. Using Edge, we can examine any loan characteristic and generate an S-curve, aging curve, or time series.


Value Opportunities in Private-Label Investor Loan Deals

The supply of investor loan collateral in private securitizations has surged in 2021 and projects to remain high (more on this below). To gain an informational edge while selecting bonds among this new issuance, traders and investors have asked RiskSpan for data and tools to dissect the performance of investor loans. Below, we first show the performance of investor loans compared to owner-occupied loans, and then offer a glimpse into a few relative value opportunities using our data and analytics platform, Edge.

As background, the increase of investor loan collateral in PLS was spurred by a new FHFA policy, recently suspended, that capped GSE acquisitions of investor and second home loans at 7% of seller volume. This cap forced originators to explore private-label securitization which, while operationally more burdensome than GSE execution, has been more profitable because it bypasses the GSEs’ high loan-level pricing adjustments. Now that this difficult but rewarding PLS path has been more widely traveled, we expect it to become more efficient and to remain popular, even with the GSE channel reopening.

Subsector Performance Comparison: Investor Vs. Owner-Occupied Loans

Investor Loans Promise Longer Collection of Above-Market Rates

Compared to owner-occupants, investors have historically paid above-market mortgage rates for longer periods before refinancing. Figure 1 shows the prepayment rates of investors vs. owner-occupants as a function of refinance incentive (the borrower’s note rate minus the prevailing mortgage rate). As their flatter “s-curve” shows, the rise in investor prepayments as refinance incentive increases is much more subdued than for owner-occupants.

Crucially, this relationship is not fully explained by higher risk-based pricing premiums on investor loans. Figure 2 shows the same comparison as Figure 1 but only for loans with spreads at origination (SATO) between 50 and 75 bps. The categorical difference between owner-occupied and investor prepay speeds is partially reduced but clearly remains. We also tried controlling for property type, but the difference persists. The relative slowness of investors may result from investors spreading their attention across many elements of their P&L besides interest expense, from higher underwriting obstacles for a rental income-driven loan, and/or from lenders limiting allocation of credit to the investor type.

While we plot these graphs over a five-year lookback period to balance desires for recency and sample size, this relationship holds over shorter and longer performance periods as well.


Figure 1: The Investor Loans S-Curve is Significantly Flatter Than the Owner-Occupied Curve
Investor s-curve vs. owner-occupied s-curve. Includes prime credit, no prepayment penalty, original loan size $200K-$400K, ages 6-48 months for the past 5yr period performance.

Source: CoreLogic’s Private-Label RMBS Collateral Dataset, RiskSpan. Note: because the increase in private-label investor loan volume is coming from Agency cutbacks, the historical performance of investor loans within both Agency and private-label datasets are relevant to private-label investor loan future performance. In this analysis we show private-label data because it straightforwardly parses voluntary prepays vs. defaults, which of course is a critical distinction for PL RMBS investors. Nonetheless, where applicable, we have run the analyses in both datasets, each of which corroborates the performance patterns we show.


Figure 2: Even Controlling for SATO, The Investor vs. Owner-Occupied S-Curve Difference Persists

Same as Figure 1 but includes only loans with SATO between 50-75 bps Source: CoreLogic, RiskSpan


Investor Loans Pose Comparable Baseline Risk, Greater Downside Risk to Credit Investors

Credit performance of investor loans has been worse than owner-occupied loans during crises, which justifies a pricing premium. During benign periods, investor loans have defaulted at similar or lower rates than owner-occupied loans – presumably due to more conservative LTVs, FICOs and DTIs among the investor loan type – and have therefore been profitable for credit investors during these periods. See Figure 3.


Figure 3: Investor Loans Have Defaulted at Greater Rates During Crises and Similar Rates in Other Periods vs. Owner-Occupied Loans
Default rates over time, investor loans vs. owner-occupied. Includes prime credit, ages 12-360 months.

Source: CoreLogic, RiskSpan

Relative Value Opportunities Within Investor Loans

California Quicker to Refinance

California has the largest share of U.S. investor mortgages, as it does with all residential mortgages. California borrowers, both investors and owner-occupieds, have exhibited a steeper response to refinance incentives than have borrowers in other states. Figure 4 shows the comparison focusing on investors. While historical home price appreciation has enabled refinances in California, it has done the same in many states. Therefore, the speed differences point to a more active refinance market in California. All else equal, then, RMBS investors will prefer less California collateral.


Figure 4: California Prepays Significantly Faster In the Money
Investor s-curves bucketed by geography (California vs. Other). Includes prime credit, no prepayment penalty, original loan size $200k-$400k, ages 6-48 months for the past 3yr performance period.

Source: CoreLogic, RiskSpan


For AAA Investors, Limited-Doc Investor Loans May Offer a Two-Sided Benefit: They Buoy Premium Bonds, and a Small Sample Suggests They Lift Discount Bonds, Too

Limited-doc investor loans offer senior tranche holders the chance to earn above-market rates for longer than full-doc investor loans, a relative edge for premium bonds (Figure 5). This is intuitive; we would expect limited-doc borrowers to face greater obstacles to refinancing. This difference holds even controlling for spread at origination. Based on a smaller sample, limited-doc investor loans have also turned over more (see greater prepay rates in the negative refinance incentive bucket). This may result from a correlation between limited documentation and more rapid flipping into the rising HPI environment we have had nationally throughout the past seven years. If so, this would mean that limited-doc investor loans also help discount bonds, relative to full-doc investor loans, accelerate repayments at par.

Because limited-doc investor loans are rare in the RMBS 2.0 era, we widened the performance period to the past seven years to get some sample in each of the refinance incentive buckets. Nonetheless, with all the filters we have put on to isolate the effect of documentation type, there are only a few hundred limited-doc investor loans in the negative refinance incentive buckets.


Figure 5: Limited-Doc Investor Loans Have Prepaid Slower In-The-Money and Faster Out-of-the-Money
Investor s-curves bucketed by doc type. Includes prime credit, no prepayment penalty, original loan size $400K-$800K, ages 6-48 months, SATO 25-125bps for the past 7yr performance period.

Source: CoreLogic, RiskSpan


Size Affects Refi Behavior – But Not How You Think

An assumption carried over from Agency performance is that rate-driven prepays get likelier as loan size increases. This pattern holds across conforming loan sizes, but then reverses and refinance response gets flatter again as balances cross $800K. This is true for investor and owner-occupied loans in both Agency and private-label loan data, though of course the number of loans above $800K in the Agency data is small. Figure 6 shows this pattern for private-label investor loans. As shown, in-the-money prepayments are slowest among loans below $200K, as we would expect. But despite their much higher motivation to refinance, loans above $800K have similar S-curves to loans of just $200K-$400K.

The SATO is generally a few basis points higher for these largest loans, but this does not explain away the speed differences. Figure 7 shows the same comparison as Figure 6 except only for loans with SATO between 50-75 bps. Except for a slightly choppier graph because of the reduced sample size, the same rank-ordering is evident. Nor does controlling for property type or geography remove the speed differences. The largest loans, we conclude, have fewer credit alternatives and/or face more stringent underwriting hurdles than smaller loans, hampering their refi rates.

Rate refinances are fastest among the mid-sized loans between $400K-$600K and $600K-$800K. That these last two groups have similar S-curves – despite the greater dollar motivation to refinance for the $600K-$800Kgroup – suggests that the countervailing effect of lower ability to find refinancing outlets is already kicking in for the $600K-$800K size range.

All of this means that high-balance collateral should be more attractive to investors than some traditional prepayment models will appreciate.


Figure 6: The Largest Investor Loans Refinance Slower Than Medium-Sized
Investor s-curves bucketed by loan size. Includes prime credit, no prepayment penalty, ages 6-48 months for the past 5yr performance period.

Source: CoreLogic, RiskSpan


Figure 7: Controlling For SATO, Largest Investor Loans Still Refinance Slower Than Medium-Sized
Same as Figure 4 but includes only loans with SATO between 50-75 bps

Source: CoreLogic, RiskSpan


Preliminarily, Chimera Has Lowest Stressed Delinquencies of Top Investor Shelves

For junior-tranche, credit-exposed investors in the COVID era, 60-day-plus delinquencies have been significantly rarer on Chimera’s shelf than on other top investor shelves. The observable credit mixes of the three shelves appear similar. We ran this analysis with only full-doc loans and from only one state (California), and the rank-ordering of delinquency rates by shelves remains the same. Further to this point, note that the spread at origination of Chimera’s shelf is nearly as high as Flagstar’s. All of this suggests there is something not directly observable about Chimera’s shelf that has generated better credit performance during this stressed period. We caution that differences in servicer reporting of COVID forbearances can distort delinquency data, so we will continue to monitor this performance as our data updates each month.


Figure 8: Chimera Posts Lowest COVID Delinquencies, with Nearly Highest SATO of Top Investor Shelves
Investor DQ60+ rates over time, bucketed by shelf. Includes prime credit, ages 12-60 months.


Source: CoreLogic, RiskSpan


The Greater Default Risk of Low-Doc Investor Loans Lasts About 10 Years

Low-doc investors default more frequently than full-doc investors, but only during the first roughly 120 months of loan age. Around this age, the default rates converge. For loans seasoned beyond this age, full-doc loans begin to default slightly more frequently than low-doc loans, likely due to a survivorship bias. This suggests that credit investors are wise to require a price discount for new issuance with low-doc collateral. For deals with heavily seasoned collateral, junior-tranche investors may counterintuitively prefer low-doc collateral — certainly if they can earn an extra risk premium for it, as it would seem they are not actually bearing any extra credit risk.


Figure 9: Low-Doc Investor Loans Default More Frequently Than Full-Doc Until Loan Age = 120
Investor default rates over time, bucketed by doc type. Includes prime credit, RMBS 2.0 era, for the past 7yr performance period.

Source: CoreLogic, RiskSpan


Summary

  • Investor loans face higher barriers to refinance than owner-occupied, offering RMBS investors the opportunity to earn higher coupons for longer periods.
  • For junior tranche investors, the credit performance of investor loans has been similar to owner-occupied loans during benign economic periods and worse during stressed times.
  • California borrowers respond more quickly to refinance incentives than borrowers from other states; investors will prefer less California collateral.
  • Limited-doc investor loans offer AAA investors a double benefit: slower refinances in the money, extending premium bonds; and faster turnover out of the money, limiting extension risk.
  • Low loan balances are attractive for their slow refinance response – as are non-conforming (high) loan balances above $800K. Traditional prepay models may miss this latter dynamic.
  • For credit investors, Chimera’s delinquency rates have been significantly better during the pandemic than other investor shelves. We will continue to monitor this as different ways of reporting COVID forbearances may confound such comparisons.
  • For credit investors, limited-doc investor loans default at higher rates than full-doc loans for about the first ten years of loan age; after this point the two perform very similarly, with limited-doc loans defaulting at slightly lower rates among these seasoned loans, likely due to survivor biases.


Contact Us

Contact us if you are interested in seeing variations on this theme. Using Edge, we can examine any loan characteristic and generate an S-curve, aging curve, or time series.


An Emerging Climate Risk Consensus for Mortgages?

That climate change poses a growing—and largely unmeasured—risk to housing and mortgage investors is not news. As is often the case with looming threats whose timing and magnitude are only vaguely understood, increased natural hazard risks have most often been discussed anecdotally and in broad generalities. This, however, is beginning to change as the reality of these risks becomes increasingly clear to an increasing number of market participants and industry-sponsored research begins to emerge.

This past week’s special report by the Mortgage Bankers Association’s Research Institute for Housing America, The Impact of Climate Change on Housing and Housing Finance, raises a number of red flags about our industry’s general lack of preparedness and the need for the mortgage industry to take climate risk seriously as a part of a holistic risk management framework. Clearly this cannot happen until appropriate risk scenarios are generated and introduced into credit and prepayment models.

One of the puzzles we are focusing on here at RiskSpan is an approach to creating climate risk stress testing that can be easily incorporated into existing mortgage modeling frameworks—at the loan level—using home price projections and other stress model inputs already in use. We are also partnering with firms who have been developing climate stress scenarios for insurance companies and other related industries to help ensure that the climate risk scenarios we create are consistent with the best and most recently scientific research available.

Also on the short-term horizon is the implementation of FEMA’s new NFIP premiums for Risk Rating 2.0. Phase I of this new framework will begin applying to all new policies issued on or after October 1, 2021. (Phase II kicks in next April.) We wrote about this change back in February when these changes were slated to take effect back in the spring. Political pressure, which delayed the original implementation may also impact the October date, of course. We’ll be keeping a close eye on this and are preparing to help our clients estimate the likely impact of FEMA’s new framework on mortgages (and the properties securing them) in their portfolios.

Finally, this past week’s SEC statement detailing the commission’s expectations for climate-related 10-K disclosures is also garnering significant (and warranted) attention. By reiterating existing guidelines around disclosing material risks and applying them specifically to climate change, the SEC is issuing an unmistakable warning shot at filing companies who fail to take climate risk seriously in their disclosures.

Contact us (or just email me directly if you prefer) to talk about how we are incorporating climate risk scenarios into our in-house credit and prepayment models and how we can help incorporate this into your existing risk management framework.  



Prepayment Spikes in Ida’s Wake – What to Expect

It is, of course, impossible to view the human suffering wrought by Hurricane Ida without being reminded of Hurricane Katrina’s impact 16 years ago. Fortunately, the levees are holding and Ida’s toll appears likely to be less severe. It is nevertheless worth taking a look at what happened to mortgages in the wake of New Orleans’s last major catastrophic weather event as it is reasonable to assume that prepayments could follow a similar pattern (though likely in a more muted way).

Following Katrina, prepayment speeds for pools of mortgages located entirely in Louisiana spiked between November 2005 and June 2006. As the following graph shows, prepayment speeds on Louisiana properties (the black curve) remained elevated relative to properties nationally (the blue curve) until the end of 2006. 

Comparing S-curves of Louisiana loans (the black curve in the chart below) versus all loans (the green curve) during the spike period (Nov. 2005 to Jun. 2006) reveals speeds ranging from 10 to 20 CPR faster across all refinance incentives. The figure below depicts an S-curve for non-spec 100% Louisiana pools and all non-spec pools with a weighted average loan age of 7 to 60 months during the period indicated.

The impact of Katrina on Louisiana prepayments becomes even more apparent when we consider speeds prior to the storm. As the S-curves below show, non-specified 100% Louisiana pools (the black curve) actually paid slightly slower than all non-spec pools between November 2003 and October 2005.

As we pointed out in June, a significant majority of prepayments caused by natural disaster events are likely to be voluntary, as opposed to the result of default as one might expect. This is because mortgages on homes that are fully indemnified against these perils are likely to be prepaid using insurance proceeds. This dynamic is reflected in the charts below, which show elevated voluntary prepayment rates running considerably higher than the delinquency spike in the wake of Katrina. We are able to isolate voluntary prepayment activity by looking at the GSE Loan Level Historical Performance datasets that include detailed credit information. This enables us to confirm that the prepay spike is largely driven by voluntary prepayments. Consequently, recent covid-era policy changes that may reduce the incidence of delinquent loan buyouts from MBS are unlikely to affect the dynamics underlying the prepayment behavior described above.

RiskSpan’s Edge Platform enables users to identify Louisiana-based loans and pools by drilling down into cohort details. The example below returns over $1 billion in Louisiana-only pools and $70 billion in Louisiana loans as of the August 2021 factor month.


Edge also allows users to structure more specified queries to identify the exposure of any portfolio or portfolio subset. Edge, in fact, can be used to examine any loan characteristic to generate S-curves, aging curves, and time series.  Contact us to learn more.



EDGE: QM vs Non-QM Prepayments

Prepayment speeds for qualified mortgages (QM loans) have anecdotally been faster than non-QM loans. For various reasons, the data necessary to analyze interest rate incentive response has not been readily available for these categories of mortgages.

In order to facilitate the generation of traditional refinancing curves (S-curves) over the last year, we have normalized data to improve the differentiation of QM versus non-QM loans within non-agency securities.

Additionally, we isolated the population to remove prepay impact from loan balance and seasoning.

The analysis below was performed on securitized loans with 9 to 36 months of seasoning and an original balance between 200k and 500k. S-curves were generated for observation periods from January 2016 through July 2021.

Results are shown in the table and chart below.

 


 

For this analysis, refinance incentive was calculated as the difference between mortgage note rate and the 6-week lagged Freddie Mac primary mortgage market survey (PMMS) rate. Non-QM borrowers would not be able to easily refi into a conventional mortgage. We further analyzed the data by examining prepayments speeds for QM and non-QM loans at different level of SATO. SATO, the spread at origination, is calculated as the difference between mortgage note rate and the prevailing PMMS rate at time of loan’s origination.

 

Using empirical data maintained by RiskSpan, it can be seen the refinance response for QM loans remains significantly faster than Non-QM loans.

Using Edge, RiskSpan’s data analytics platform, we can examine any loan characteristic and generate S-curves, aging curves, and time series. If you are interested in performing historical analysis on securitized loan data, please contact us for a free demonstration.


RiskSpan Named to Inaugural STORM50 Ranking by Chartis Research – Winner of “A.I. Innovation in Capital Markets”

Chartis Research has named RiskSpan to its Inaugural “STORM50” Ranking of leading risk and analytics providers. The STORM report “focuses on the computational infrastructure and algorithmic efficiency of the vast array of technology tools used across the financial services industry” and identifies industry-leading vendors that excel in the delivery of Statistical Techniques, Optimization frameworks, and Risk Models of all types. 

RiskSpan’s flagship Edge Platform was a natural fit for the designation because of its positioning squarely at the nexus of statistical behavioral modeling (specifically around mortgage credit and prepayment risk) and functionality enabling users to optimize trading and asset management strategies.  Being named the winner of the “A.I. Innovation in Capital Markets” solutions category reflects the work of RiskSpan’s vibrant innovation lab, which includes researching and developing machine learning solutions to structured finance challenges. These solutions include mining a growing trove of alternative/unstructured data sources, anomaly detection in loan-level and other datasets, and natural language processing for constructing deal cash flow models from legal documents.

Learn more about the Edge Platform or contact us to discuss ways we might help you modernize and improve your mortgage and structured finance data and analytics challenges. 


Is the housing market overheated? It depends where you are.

Mortgage credit risk modeling has evolved slowly in the last few decades. While enhancements leveraging conventional and alternative data have improved underwriter insights into borrower income and assets, advances in data supporting underlying property valuations have been slow. With loan-to-value ratios being such a key driver of loan performance, the stability of a subject property’s value is arguably as important as the stability of a borrower’s income.

Most investors rely on current transaction prices to value comparable properties, largely ignoring the risks to the sustainability of those prices. Lacking the data necessary to identify crucial factors related to a property value’s long-term sustainability, investors generally have little choice but to rely on current snapshots. To address this problem, credit modelers at RiskSpan are embarking on an analytics journey to evaluate the long-term sustainability of a property’s value.

To this end, we are working to pull together a deep dataset of factors related to long-term home price resiliency. We plan to distill these factors into a framework that will enable homebuyers, underwriters, and investors to quickly assess the risk inherent to the property’s physical location. The data we are collecting falls into three broad categories:

  • Regional Economic Trends
  • Climate and Natural Hazard Risk
  • Community Factors

Although regional home price outlook sometimes factors into mortgage underwriting, the long-term sustainability of an individual home price is seldom, if ever, taken into account. The future value of a secured property is arguably of greater importance to mortgage investors than its value at origination. Shouldn’t they be taking an interest in regional economic condition, exposure to climate risk, and other contributors to a property valuation’s stability?

We plan to introduce analytics across all three of these dimensions in the coming months. We are particularly excited about the approach we’re developing to analyze climate and natural hazard risk. We will kick things off, however, with basic economic factors. We are tracking the long-term sustainability of house prices through time by tracking economic fundamentals at the regional level, starting with the ratio of home prices to median household income.

Economic Factors

Housing is hot. Home prices jumped 12.7% nationally in 2020, according to FHFA’s house price index[1]. Few economists are worried about a new housing bubble, and most attribute this rise to supply and demand dynamics. Housing supply is low and rising housing demand is a function of demography –millennials are hitting 40 and want a home of their own.

But even if the current dynamic is largely driven by low supply, there comes a certain point at which house prices deviate too much from area median household income to be sustainable. Those who bear the most significant exposure to mortgage credit risk, such as GSEs and mortgage insurers, track regional house price dynamics to monitor regions that might be pulling away from fundamentals.

Regional home-price-to-income ratio is a tried-and-true metric for judging whether a regional market is overheating or under-valued. We have scored each MSA by comparing its current home-price-to-income ratio to its long-term average. As the chart below illustrating this ratio’s trend shows, certain MSAs, such as New York, consistently have higher ratios than other, more affordable MSAs, such as Chicago.

Because comparing one MSA to another in this context is not particularly revealing, we instead compare each MSA’s current ratio to the long-term ratio for itself. MSAs where that ratio exceeds its long-term average are potentially over-heated, while MSAs under that ratio potentially have more room to grow. In the table below highlighting the top 25 MSAs based on population, we look at how the home-price-to-household-income ratio deviates from its MSA long-term average. The metric currently suggests that Dallas, Denver, Phoenix, and Portland are experiencing potential market dislocation.

Loans originated during periods of over-heating have a higher probability of default, as illustrated in the scatterplot below. This plot shows the correlation between the extent of the house-price-to-income ratio’s deviation from its long-term average and mortgage default rates. Each dot represents all loan originations in a given MSA for a given year[1]. Only regions with large deviations in house price to income ratio saw explosive default rates during the housing crisis. This metric can be a valuable tool for loan and SFR investors to flag metros to be wary of (or conversely, which metros might be a good buy).

Although admittedly a simple view of regional economic dynamics driving house prices (fundamentals such as employment, housing starts per capita, and population trends also play important roles) median income is an appropriate place to start. Median income has historically proven itself a valuable tool for spotting regional price dislocations and we expect it will continue to be. Watch this space as we continue to add these and other elements to further refine how we measure property value stability and its likely impact on mortgage credit.


[1] FHFA Purchase Only USA NSA % Change over last 4 quarters

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Is Your Enterprise Risk Management Keeping Up with Recent Regulatory Changes?​

For enterprise risk managers, ensuring that all the firm’s various risk management structures and frameworks are keeping up with ever-changing regulatory guidance can be a daunting task. Regulatory updates take on particular importance for model risk managers. MRM is required not only to understand and comply with the regulatory guidance specific to model risk management itself, but also to understand the regulatory ramifications of the risk models they validate.

This post focuses on recent updates to eight ERM areas that can sometimes seem like a moving target when it comes to risk compliance.

The timeline below illustrates the extensive variability that can exist from regulator to regulator when it comes to which ERM components are of most concern and the nature and speed of adoption. To take one example, model risk management guidance was issued in 2011 and all Fed- and OCC-regulated institutions were in general compliance with it by 2014. The FDIC, however, did not issue the same guidance until 2017 and enforcement varies considerably. Although every FDIC-regulated institution is technically required to be in compliance with the MRM guidance, several have yet to undergo even their first MRM exam. Things get even cloudier for credit unions as the NCUA has not issued any guidance or regulation pertaining to MRM. The NCUA requires MRM practices to be observed during Capital Planning and Stress Testing (per its 2019 capital planning guide). But this narrow definition allows most credit unions to skirt regulator-required MRM entirely.

Because it can be difficult to stay on top of which regulator is requiring what and when, here is a quick summary of recent updates, organized by risk area.

Bank Secrecy Act (BSA/ Anti Money Laundering (AML) 

The past year has seen five guidance updates pertaining to BSA/AML. Most of these seek to increase the effectiveness, predictability, and transparency of BSA/AML regulatory exams. Other updates clarify specific aspects of BSA/AML risk.

  1. Updated Sections of the FFIEC BSA/AML Examination Manual (OCC 2021-10/SR 21-9 & OCC 2021-28). The updated sections:
    • Reinforce the risk-focused approach to BSA/AML examinations, and
    • Clarify regulatory requirements and include updated information for examiners regarding transaction testing, including examples.
  1. Interagency Statement on Model Risk Management for Bank Systems Supporting BSA/AML Compliance and Request for Information (OCC 2021-19/SR 21-8) as of April 12, 2021. This guidance:
    • Outlines the importance of MRM governance to AML exams,
    • Is designed to be flexible when applying MRM principals to BSA/AML models,
    • Updates MRM principles and validation to be more responsive,
    • Seeks not to apply a single industry-wide approach, and
    • Directs validators to consider third-party documentation when reviewing AML models.
  1. Answers to Frequently Asked Questions Regarding Suspicious Activity Reporting and Other AML Considerations (OCC 2021-4) as of January 21, 2021. These include instructions around:
    • Requests by law enforcement for financial institutions to maintain accounts,
    • Receipt of grand jury subpoenas/law enforcement inquiries and suspicious activity report (SAR) filing,
    • Maintaining a customer relationship following the filing of a SAR or multiple SARs,
    • SAR filing on negative news identified in media searches,
    • SAR monitoring on multiple negative media alerts,
    • Information in data fields and narrative, and
    • SAR character limits.
  1. Joint Statement on Bank Secrecy Act Due Diligence Requirements for Customers Who May Be Considered Politically Exposed Persons (OCC 2020-77/SR 20-19) as of August 21, 2020. This statement:
    • Explains that the BSA/AML regulations do not define what constitutes a politically exposed person (PEP),
    • Clarifies that the customer due diligence rule does not create a regulatory requirement and that there is no supervisory expectation for banks to have unique, additional due diligence steps for PEPs,
    • Clarifies how banks can apply a risk-based approach to customer due diligence in developing risk profiles for their customers, and
    • Discusses potential risk factors, levels and types of due diligence.
  1. OCC-Proposed Rule Regarding Exemptions to Suspicious Activity Report Requirements as of December 17, 2020:
    • The proposed rule would amend the agency’s SAR regulations to allow the OCC to issue exemptions from the requirements of those regulations on when and how to file suspicious activity reports (SARs).

Allowance for Loan and Lease Losses (ALLL)/ Current Expected Credit Losses (CECL) 

Current Expected Credit Losses: Final Rule (OCC 2020-85/SR 19-8/FIL-7-2021) as of October 1, 2020. The rule:

    • Applies to all community banks that adopted CECL in 2020 per GAAP requirements,
    • Exempts all other institutions until 2023,
    • Adopts all of the 2020 CECL IFR, and
    • Clarifies that a banking organization is not required to use the transition during fiscal quarters in which it would not generate a regulatory capital benefit.

Asset Liability Management (ALM) and Liquidity Risk Management 

Four important updates to ALM and liquidity risk guidance were issued in the past year.

  1. Net Stable Funding Ratio: Final Rule (OCC 2021-9) as of February 24, 2021. The rule:
    • Implements a minimum stable funding requirement designed to reduce the likelihood that disruptions to a covered company’s regular sources of funding will compromise its liquidity position,
    • Requires the maintenance a ratio of “available stable funding” to “required stable funding” of at least 1.0 on an ongoing basis,
    • Defines “available stable funding” as the stability of a banking organization’s funding sources,
    • Defines “required stable funding” as the liquidity characteristics of a banking organization’s assets, derivatives, and off-balance-sheet exposures,
    • Requires notification of a shortfall, realized or potential within 10 business days, and
    • Provides public disclosure rules for a consolidated NSFR.
  1. Volcker Rule Covered Funds: Final Rule (OCC 2020-71) as of July 41, 2020. The rule:
    • Permits the activities of qualifying foreign excluded funds,
    • Revises the exclusions from the definition of “covered fund,”
    • Creates new exclusions from the definition of covered fund for credit funds, qualifying venture capital funds, family wealth management vehicles, and customer facilitation vehicles, and
    • Modifies the definition of “ownership interest.”
  1. Interest Rate Risk: Revised Comptroller’s Handbook Booklet (OCC 2020-26) as of March 26, 2020. The updated Handbook:
    • Expands discussions on MRM expectations for reviewing and testing model assumptions,
    • Addresses funds transfer pricing (FTP), and
    • Adds guidelines for advanced approaches to interest rate risk management consistent with the Pillar 2 supervisory approach.
  1. Capital and Liquidity Treatment for Money Market Liquidity Facility and Paycheck Protection Program: Final Rule (OCC 2020-96) as of November 3, 2020. This rule:
    • Permits a zero-percent risk weight for PPP loans,
    • Eliminates the regulatory capital impact and liquidity rule provisions for participating in the PPP and Money Market Liquidity Facility.

Artificial Intelligence (AL)/ Machine Learning (ML) 

The only recent regulatory update pertaining to AI/Machine Learning has been a request for comment related to usage, controls, governance, and risk. At present, there is no formal guidance specifically related to AI or ML models. The OCC’s semi-annual risk perspectives includes just a couple of sentences stating that users of ML models should be able to defend and explain their risks. The Fed’s feedback has been similarly broad. Movement seems afoot to issue more detailed guidance on how ML models should be governed and monitored. But this is likely to be limited to specific applications and not to the ML models themselves.

The Request for Information and Comment on Financial Institutions’ Use of Artificial Intelligence, Including Machine Learning (OCC 2021-17) as of March 31, 2021, seeks respondents’ views on appropriate governance, risk management, and controls over artificial intelligence, and any challenges in developing, adopting, and managing artificial intelligence approaches.

Capital Risk 

We focus on the two items of capital risk guidance issued in the past year. The rule applies to community banks with total assets of less than $10 billion as of December 31, 2019.

  1. Temporary Asset Thresholds: Interim Final Rule (OCC 2020-107) as of December 2, 2020:
    • The rule allows these institutions to use asset data as of December 31, 2019, to determine the applicability of various regulatory asset thresholds during calendar years 2020 and 2021.
  1. Regulatory Capital Rule: Eligible Retained Income: Final Rule (OCC 2020-87) as of October 8, 2020:

The final rule revises the definition of eligible retained income to the greater of:

    • Net income for the four preceding calendar quarters, net of any distributions and associated tax effects not already reflected in net income, and
    • The average of a Bank’s net income over the preceding four quarters.

Fair Lending 
  1. Community Reinvestment Act: Key Provisions of the June 2020 CRA Rule and Frequently Asked Questions (OCC 2020-99) as of November 9, 2020:

The rule establishes new criteria for designating bank assessment areas, including:

    • Facility-based assessment areas based on the location of a bank’s main office and branches and, at a bank’s discretion, on the location of the bank’s deposit-taking automated teller machines, and
    • Deposit-based assessment areas, which apply to a bank with 50 percent or more of its retail domestic deposits outside its facility-based assessment areas.
  1. Community Reinvestment Act: Implementation of the June 2020 Final Rule (OCC 2021-24) as of May 18, 2021. The OCC has determined that it will reconsider its June 2020 rule. While this reconsideration is ongoing, the OCC will not implement or rely on the evaluation criteria in the June 2020 rule pertaining to:
    • Quantification of qualifying activities
    • Assessment areas
    • General performance standards
    • Data collection
    • Recordkeeping
    • Reporting

Market Risk 
  1. Libor Transition: Self-Assessment Tool for Banks (OCC 2021-7) as of February 10, 2021. The self-assessment tool can be used to assess the following:
    • Five primary topics: Assets and contracts; LIBOR risk exposure; Fallback language; Consumer impact; Third-party service provider
    • The appropriateness of a bank’s Libor transition plan
    • Bank management’s execution of the bank’s transition plan
    • Related oversight and reporting
  1. Standardized Approach for Counterparty Credit Risk; Correction: Final Rule (OCC 2020-82) as of September 21, 2020. The issuance corrects errors in the standardized approach for counterparty credit risk (SA-CCR) rule:
    • Clarifying that a Bank that uses SA-CCR will be permitted to exclude the future exposure of all credit derivatives
    • Revising the number of outstanding margin disputes
    • Correcting the calculation of the hypothetical capital requirement of a qualifying central counterparty (QCCP)
  1. Agencies Finalize Amendments to Swap Margin Rule (News Release 2020-83) as of June 25, 2020:
    • Swap entities that are part of the same banking organization will no longer be required to hold a specific amount of initial margin for uncleared swaps with each other, known as inter-affiliate swaps.
    • Final rule allows swap entities to amend legacy swaps to replace the reference to LIBOR or other reference rates that are expected to end without triggering margin exchange requirements.

Operations Risk
  1. Corporate and Risk Governance. Revised and New Publications in the Director’s Toolkit (OCC 2020-97) as of November 5, 2020:
    • Defines permissible derivatives activities,
    • Allows engagement in certain tax equity finance transactions,
    • Expands the ability to choose corporate governance provisions under state law,
    • Includes OCC interpretations relating to capital stock issuances and repurchases, and
    • Applies rules relating to finder activities, indemnification, equity kickers, postal services, independent undertakings, and hours and closings to FSAs.
  1. Activities and Operations of National Banks and Federal Savings Associations: Final Rule (OCC 2020-111) as of December 23, 2020:
    • Focuses on key areas of planning, operations, and risk management,
    • Outlines directors’ responsibilities as well as management’s role,
    • Explains basic concepts and standards for safe and sound operation of banks, and
    • Delineates laws and regulations that apply to banks.
  1. Operational Risk: Sound Practices to Strengthen Operational Resilience (OCC 2020-94) as of October 10, 2020:
    • Outlines standards for operational resilience set forth in the agencies’ rules and guidance,
    • Promotes a principles-based approach for effective governance, robust scenario analysis, secure and resilient information systems, and thorough surveillance and reporting,
    • Introduces sound practices for managing cyber risk.

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