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Too Many Documentation Types? A Data-Driven Approach to Consolidating Them

The sheer volume of different names assigned to various documentation types in the non-agency space has really gotten out of hand, especially in the last few years. As of February 2021, an active loan in the CoreLogic RMBS universe could have any of over 250 unique documentation type names, with little or no standardization from issuer to issuer. Even within a single issuer, things get complicated when every possible permutation of the same basic documentation level gets assigned its own type. One issuer in the database has 63 unique documentation names!

In order for investors to be able to understand and quantify their exposure, we need a way of consolidating and mapping all these different documentation types to a simpler, standard nomenclature. Various industry reports attempt to group all the different documentation levels into meaningful categories. But these classifications often fail to capture important distinctions in delinquency performance among different documentation levels.

There is a better way. Taking some of the consolidated group names from the various industry papers and rating agency papers as a starting point, we took another pass focusing on two main elements:

  • The delinquency performance of the group. We focused on the 60-DPD rate while also considering other drivers of loan performance (e.g., DTI, FICO, and LTV) and their correlation to the various doc type groups.
  • The size of the sub-segment. We ensured our resulting groupings were large enough to be meaningful.

What follows is how we thought about it and ultimately landed where we did. These mappings are not set in stone and will likely need to undergo revisions as 1) new documentation types are generated, and 2) additional performance data and feedback from clients on what they consider most important become available. Releasing these mappings into RiskSpan’s Edge Platform will then make it easier for users to track performance.

Data Used

We take a snapshot of all loans outstanding in non-agency RMBS issued after 2013, as of the February 2021 activity period. The data comes from CoreLogic and we exclude loans in seasoned or reperforming deals. We also exclude loans whose documentation type is not reported, some 14 percent of the population.

Approach

We are seeking to create sub-groups that generally conform to the high-level groups on which the industry seems to be converging while also identifying subdivisions with meaningfully different delinquency performance. We will rely on these designations as we re-estimate our credit model.

Steps in the process:

  1. Start with high-level groupings based on how the documentation type is currently named.
    • Full Documentation: Any name referencing ‘Agency,’ ‘Agency AUS,’ or similar.
    • Bank Statements: Any name including the term “Bank Statement[s].”
    • Investor/DSCR: Any name indicating that the underwriting relied on net cash flows to the secured property.
    • Alternative Documentation: A wide-ranging group consolidating many different types, including: asset qualifier, SISA/SIVA/NINA, CPA letters, etc.
    • Other: Any name that does not easily classify into one of the groups above, such as Foreign National Income, and any indecipherable names.

  1. We subdivided the Alternative Documentation group by some of the meaningfully sized natural groupings of the names:
    • Asset Depletion or Asset Qualifier
    • CPA and P&L statements
    • Salaried/Wage Earner: Includes anything with W2 tax return
    • Tax Returns or 1099s: Includes anything with ‘1099’ or ‘Tax Return, but not ‘W2.’
    • Alt Doc: Anything that remained, included items like ‘VIVA, ‘SISA,’ ‘NINA,’ ‘Streamlined,’ ‘WVOE,’ and ‘Alt Doc.’
  1. From there we sought to identify any sub-groups that perform differently (as measured by 60-DPD%).
    • Bank Statement: We evaluated a subdivision by the number of statements provided (less than 12 months, 12 months, and greater than 12 months). However, these distinctions did not significantly impact delinquency performance. (Also, very few loans fell into the under 12 months group.) Distinguishing ‘Business Bank Statement’ loans from the general ‘Bank Statements’ category, however, did yield meaningful performance differences.

    • Alternative Documentation: This group required the most iteration. We initially focused our attention on documentation types that included terms like ‘streamlined’ or ‘fast.’ This, however, did not reveal any meaningful performance differences relative to other low doc loans. We also looked at this group by issuer, hypothesizing that some programs might perform better than others. The jury is still out on this analysis and we continue to track it. The following subdivisions yielded meaningful differences:
      • Limited Documentation: This group includes any names including the terms ‘reduced,’ ‘limited,’ ‘streamlined,’ and ‘alt doc.’ This group performed substantially better than the next group.
      • No Doc/Stated: Not surprisingly, these were the worst performers in the ‘Alt Doc’ universe. The types included here are a throwback to the run-up to the housing crisis. ‘NINA,’ ‘SISA,’ ‘No Doc,’ and ‘Stated’ all make a reappearance in this group.
      • Loans with some variation of ‘WVOE’ (written verification of employment) showed very strong performance, so much so that we created an entirely separate group for them.
  • Full Documentation: Within the variations of ‘Full Documentation’ was a whole sub-group with qualifying terms attached. Examples include ‘Full Doc 12 Months’ or ‘Full w/ Asset Assist.’ These full-doc-with-qualification loans were associated with higher delinquency rates. The sub-groupings reflect this reality:
      • Full Documentation: Most of the straightforward types indicating full documentation, including anything with ‘Agency/AUS.’
      • Full with Qualifications (‘Full w/ Qual’): Everything including the term ‘Full’ followed by some sort of qualifier.
  • Investor/DSCR: The sub-groups here either were not big enough or did not demonstrate sufficient performance difference.
  • Other: Even though it’s a small group, we broke out all the ‘Foreign National’ documentation types into a separate group to conform with other industry reporting.

Among the challenges of this sort of analysis is that the combinations to explore are virtually limitless. Perhaps not surprisingly, most of the potential groupings we considered did not make it into our final mapping. Some of the cuts we are still looking at include loan purpose with respect to some of the alternative documentation types.

We continue to evaluate these and other options. We can all agree that 250 documentation types is way too many. But in order to be meaningful, the process of consolidation cannot be haphazard. Fortunately, the tools for turning sub-grouping into a truly data-driven process are available. We just need to use them.   


Value Beyond Validation: The Future of Automated Continuous Model Monitoring Has Arrived

Imagine the peace of mind that would accompany being able to hand an existing model over to the validators with complete confidence in how the outcomes analysis will turn out. Now imagine being able to do this using a fully automated process.

The industry is closer to this than you might think.

The evolution of ongoing model monitoring away from something that happens only periodically (or, worse, only at validation time) and toward a more continuous process has been underway for some time. Now, thanks to automation and advanced process design, this evolutionary process has reached an inflection point. We stand today at the threshold of a future where:

  • Manual, painful processes to generate testing results for validation are a thing of the past;
  • Models are continuously monitored for fit, and end users are empowered with the tools to fully grasp model strengths and weaknesses;
  • Modeling and MRM experts leverage machine learning to dive more deeply into the model’s underlying data, and;
  • Emerging trends and issues are identified early enough to be addressed before they have time to significantly hamper model performance.

Sound too good to be true? Beginning with its own internally developed prepayment and credit models, RiskSpan data scientists are laying out a framework for automated, ongoing performance monitoring that has the potential to transform behavioral modeling (and model validation) across the industry.

The framework involves model owners working collaboratively with model validators to create recurring processes for running previously agreed-upon tests continuously and receiving the results automatically. Testing outcomes continuously increases confidence in their reliability. Testing them automatically frees up high-cost modeling and validation resources to spend more time evaluating results and running additional, deeper analyses.

The Process:

Irrespective of the regulator, back-testing, benchmarking, and sensitivity analysis are the three pillars of model outcomes analysis. Automating the data and analytical processes that underlie these three elements is required to get to a fully comprehensive automated ongoing monitoring scheme.

In order to be useful, the process must stage testing results in a central database that can:

  • Automatically generate charts, tables, and statistical tests to populate validation reports;
  • Support dashboard reporting that allows model owners, users and validators to explore test results, and;
  • Feed advanced analytics and machine learning platforms capable of 1) helping with automated model calibration, and 2) identifying model weaknesses and blind spots (as we did with a GSE here).

Perhaps not surprisingly, achieving the back-end economies of a fully automated continuous monitoring and reporting regime requires an upfront investment of resources. This investment takes the form of time from model developers and owners as well as (potentially) some capital investment in technology necessary to host and manage the storage of results and output reports.

A good rule of thumb for estimating these upfront costs is between 2 and 3 times the cost of a single annual model test performed on an ad-hoc, manual basis. Consequently, the automation process can generally be expected to pay for itself (in time savings alone) over 2 to 3 cycles of performance testing. But the benefits of automated, continuous model monitoring go far beyond time savings. They invariably result in better models.

Output Applications

Continuous model monitoring produces benefits that extend well beyond satisfying model governance requirements. Indeed, automated monitoring has significantly informed the development process for RiskSpan’s own, internally developed credit and prepayment models – specifically in helping to identify sub-populations where model fit is a problem.

Continuous monitoring also makes it possible to quickly assess the value of newly available data elements. For example, when the GSEs start releasing data on mortgages with property inspection waivers (PIWs) (as opposed to traditional appraisals) we can immediately combine that data element with the results of our automated back-testing to determine whether the PIW information can help predict model error from those results. PIW currently appears to have value in predicting our production model error, and so the PIW feature is now slated to be added to a future version of our model. Having an automated framework in place accelerates this process while also enabling us to proceed with confidence that we are only adding variables that improve model performance.

The continuous monitoring results can also be used to develop helpful dashboard reports. These provide model owners and users with deeper insights into a model’s strengths and weaknesses and can be an important tool in model tuning. They can also be shared with model validators, thus facilitating that process as well.

The dashboard below is designed to give our model developers and users a better sense of where model error is greatest. Sub-populations with the highest model error are deep red. This makes it easy for model developers to visualize that the model does not perform well when FICO and LTV data are missing, which happens often in the non-agency space. The model developers now know that they need to adjust their modeling approach when these key data elements are not available.

The dashboard also makes it easy to spot performance disparities by shelf, for example, and can be used as the basis for applying prepayment multipliers to certain shelves in order to align results with actual experience.

Continuous model monitoring is fast becoming a regulatory expectation and an increasingly vital component of model governance. But the benefits of continuous performance monitoring go far beyond satisfying auditors and regulators. Machine learning and other advanced analytics are also proving to be invaluable tools for better understanding model error within sub-spaces of the population.

Watch this space for a forthcoming post and webinar explaining how RiskSpan leverages its automated model back-testing results and machine learning platform, Edge Studio, to streamline the calibration process for its internally developed residential mortgage prepayment model.


EDGE: New Forbearance Data in Agency MBS

Over the course of 2020 and into early 2021, the mortgage market has seen significant changes driven by the COVID pandemic. Novel programs, ranging from foreclosure moratoriums to payment deferrals and forbearance of those payments, have changed the near-term landscape of the market.

In the past three months, Fannie Mae and Freddie Mac have released several new loan-level credit statistics to address these novel developments. Some of these new fields are directly related to forbearance granted during the pandemic, while others address credit performance more broadly.

We summarize these new fields in the table below. These fields are all available in the Edge Platform for users to query on.

The data on delinquencies and forbearance plans covers March 2021 only, which we summarize below, first by cohort and then by major servicer. Edge users can generate other cuts using these new filters or by running the “Expanded Output” for the March 2021 factor date.

In the first table, we show loan-level delinquency for each “Assistance Plan.” Approximately 3.5% of the outstanding GSE universe is in some kind of Assistance Plan.

In the following table, we summarize delinquency by coupon and vintage for 30yr TBA-eligible pools. Similar to delinquencies in GNMA, recent-vintage 3.5% and 4.5% carry the largest delinquency load.

Many of the loans that are 90-day and 120+-day delinquent also carry a payment forbearance. Edge users can simultaneously filter for 90+-day delinquency and forbearance status to quantify the amount of seriously delinquent loans that also carry a forbearance versus loans with no workout plan.[2] 

Finally, we summarize delinquencies by servicer. Notably, Lakeview and Wells leads major servicers with 3.5% and 3.3% of their loans 120+-day delinquent, respectively. Similar to the cohort analysis above, many of these seriously delinquent loans are also in forbearance. A summary is available on request.

In addition to delinquency, the Enterprises provide other novel performance data, including a loan’s total payment deferral amount. The GSEs started providing this data in December, and we now have sufficient data to start to observing prepayment behavior for different levels of deferral amounts. Not surprisingly, loans with a payment deferral prepay more slowly than loans with no deferral, after controlling for age, loan balance, LTV, and FICO. When fully in the money, loans with a deferral paid 10-13 CPR slower than comparable loans.

Next, we separate loans by the amount of payment deferral they have. After grouping loans by their percentage deferral amount, we observe that deferral amount produces a non-linear response to prepayment behavior, holding other borrower attributes constant.

Loans with deferral amounts less than 2% of their UPB showed almost no prepayment protection when deep in-the-money.[3] Loans between 2% and 4% deferral offered 10-15 CPR protection, and loans with 4-6% of UPB in deferral offered a 40 CPR slowdown.

Note that as deferral amount increases, the data points with lower refi incentive disappear. Since deferral data has existed for only the past few months, when 30yr primary rates were in a tight range near 2.75%, that implies that higher-deferral loans also have higher note rates. In this analysis, we filtered for loans that were no older than 48 months, meaning that loans with the biggest slowdown were typically 2017-2018 vintage 3.5s through 4.5s.

Many of the loans with P&I deferral are also in a forbearance plan. Once in forbearance, these large deferrals may act to limit refinancings, as interest does not accrue on the forborne amount. Refinancing would require this amount to be repaid and rolled into the new loan amount, thus increasing the amount on which the borrower is incurring interest charges. A significantly lower interest rate may make refinancing advantageous to the borrower anyway, but the extra interest on the previously forborne amount will be a drag on the refi savings.

Deferral and forbearance rates vary widely from servicer to servicer. For example, about a third of seriously delinquent loans serviced by New Residential and Matrix had no forbearance plan, whereas more than 95% of such loans serviced by Quicken loans were in a forbearance plan. This matters because loans without a forbearance plan may ultimately be more subject to repurchase and modification, leading to a rise in involuntary prepayments on this subset of loans.

As the economy recovers and borrowers increasingly resolve deferred payments, tracking behavior due to forbearance and other workout programs will help investors better estimate prepayment risk, both due to slower prepays as well as possible future upticks in buyouts of delinquent loans.


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


[1] A link to the Deferral Amount announcement can be found here, and a link to the Forbearance and Delinquency announcement can be found here. Freddie Mac offers a helpful FAQ here on the programs.

[2] Contact RiskSpan for details on how to run this query.

[3] For context, a payment deferral of 2% represents roughly 5 months of missed P&I payments on a 3% 30yr mortgage.


A Mentor’s Advice: Work Hard on Things You Can Control; Learn to Live with Things You Cannot

March is Women’s History Month and RiskSpan is marking the occasion by sharing a short series of posts featuring advice from women leaders in our industry.

Today’s contributor is Dr. Laurie Goodman, vice president at the Urban Institute and codirector of Urban’s Housing Finance Policy Center. Laurie helped break barriers as one of the first women to work on Wall Street and built her own brand as a go-to researcher for the housing and mortgage industry.

Laurie serves on the board of directors of MFA Financial, Arch Capital Group, Home Point Capital and DBRS. In 2009, she was inducted into the Fixed Income Analysts Hall of Fame following a series of successful research leadership and portfolio management positions at several Wall Street firms.


Laurie offers this guidance to young women (though it is applicable to everyone):

#1 – Figure out the balance that works for you between your personal life and your work life, realizing that you can’t be all things to all people all the time. There are times when you will spend more time on your work life and times when you will spend more time on your home life and other non-work related activities. You can’t be a super-performer at both all the time. Don’t beat yourself up for that part of your life where you feel you are underperforming.

#2 – Develop a thick skin and don’t take things personally. This will make you a much better colleague. Many times, colleagues and others in your organization make comments that can be interpreted either as personal affronts or general statements on the project. Always look for the non-personal interpretation (even if you suspect it is personal). For example, “Gee, these results aren’t very useful” can be interpreted personally as “It’s your fault — if you had done it differently it would have been better” or non-personally, as in “The material just didn’t give us any new insights.” Assume it was meant non-personally.  

#3 – Develop confidence and advocate for yourself. Speak up in meetings, particularly if you have points to add, or can steer the conversation back on track. If you are not feeling confident, fake it until you realize that you have as much (or more) to contribute than anyone else. And use that confidence to advocate for yourself — your success is more important to you than it is to anyone else. Have the confidence to own your mistakes; we all make mistakes. if you own them, you will do everything you can to correct them.

We also asked Laurie what, if anything, she might have done differently. Her response:

Early in my career, when things went off track for any reason, I got very frustrated. I was unable or unwilling to distinguish between those aspects of my work that were under my control, and those aspects of my work environment that I could not control. As a result, in the early years of my career, I changed jobs frequently. As the years have gone on, I have learned to do the best work I can on issues that are under my control and accept and live with what is not. It has made my work life much more enjoyable and productive.


Our thanks to Laurie for her valuable perspective!

Keep an eye on https://riskspan.com/resources/ throughout March for insights from other women we admire in mortgage and structured finance.


RiskSpan is proud to sponsor POWER OF VOICE BENEFIT. Girls Leadership teaches girls to exercise the power of their voice. #powerofvoice2021


A Mentor’s Advice: Go Where Your Heart Leads and Learn to Say Yes (and No)

March is Women’s History Month and RiskSpan is marking the occasion by sharing a short series of posts featuring advice from women leaders in our industry.

Today’s contributor is Dr. Amy Crews CuttsPresident and Chief Economist of AC Cutts and Associates, an economics and strategy consulting firm based in Reston, VA. She started her professional career as academic and she used that experience to build her network, soon landing at Freddie Mac. There she honed her professional skills and reputation as an economist, writer, and speaker. Amy was engaged by Equifax in 2011 to create the office of the chief economist. She has been recruited to serve on corporate and nonprofit advisory boards and elected to serve on boards of directors of leading economics associations. Amy has become an internationally recognized expert on consumer credit and economic policy and is a sought-after speaker and advisor. She is a participant in the Wall Street Journal Survey of Leading Economics, and her work has been cited in federal regulations and in cases before the U.S. Supreme Court.


Amy offers this guidance to women embarking on their careers (though it is applicable to everyone): 

#1 – Look deep for your talents and passion. We tend to think of jobs as titles rather than the small things that make up the role. The best day of my career came when I embraced the joy in the small parts of the job, and from that I was able to move mountains within the company and create the role that suited me.  

#2 – Build your networks always. When invited to join others for lunch, go! There are always deadlines, but this is just as important. You never know when you will learn a critically valuable piece of information from a casual conversation. When given the opportunity to present (internally or externally), take it, and know that they respect you enough to have asked and they care about what you have to say. Even a small opportunity may be the start of something big, so jump in with both feet.   

#3 – Speak up and, when you have a strong opinion, add your voice to the discussion (but never, ever, make it personal). In a corporate setting, you get one chance to speak critically of a plan, a policy, or problem. After that you need to move on because you were heard, even if they chose to go against your advice. Good counsel is valuable in every organization. Be someone others want to get advice from. 

We also asked Amy what, if anything, she might have done differently. Her response: 

My biggest regrets have come from not being openminded enough. I didn’t know at the time that the days of secretaries were numbered, but I rejected the suggestion of taking a typing class in high school because I had bigger plans (who knew we would spend our days typing?). I never learned how to code well because, at the time I was in college, computer science courses were mainly for mainframe applications (unfortunately, I did take APL programming, an already dying language in the 1980s). I have rejected job opportunities because I did not fit 100 percent of the job description but would later see someone much less capable in that role, with the prestige or promotion I should have tried for.


Our thanks to Amy for her valuable perspective!

Keep an eye on https://riskspan.com/resources/ this month for insights from other women we admire in mortgage and structured finance.


RiskSpan is proud to sponsor POWER OF VOICE BENEFIT. Girls Leadership teaches girls to exercise the power of their voice. #powerofvoice2021


A Mentor’s Advice: Raise Your Hand, Be Inquisitive, and Find Your Niche

March is Women’s History Month and RiskSpan is marking the occasion by sharing a short series of posts featuring advice from women leaders in our industry.

Today’s contributor is Faith Schwartz, President of Housing Finance Strategies, a strategic advisory services firm. She achieved a significant industry renown for having developed and led the HOPE NOW Alliance to unite the industry throughout the housing crisis. Faith was named among the “Most Powerful Women in Mortgage Banking” by National Mortgage Professional Magazine in 2018. She is also a HousingWire Vanguard Award winner.


Faith offers three core pieces of advice to young women (though it is applicable to everyone):

#1 – The business world is complex and there is much to learn.  Raise your hand often, be inquisitive, and always seek to understand the economics of every last detail of the business.

#2 Whatever your line of work, develop your unique expertise, develop a way to measure and report it, and share it often with those who are less knowledgeable. You will become a “go-to” resource for your colleagues.

#3 Establish yourself as an inclusive leader. So many people forget it is your peers, staff and leaders who make up the ecosystem of your company. Listen to many ideas and then come to your own conclusion. This will help you downstream as you lead new and exciting initiatives.

We also asked Faith what, if anything, she might have done differently. Her response:

My lessons learned are many: Be a better listener; think about how to most effectively communicate internally and externally – and to know the  difference. Understand the full corporate culture where you work to best adapt your own style and stay effective. Over my career, I have learned how to adapt, how to evolve in a matrixed leadership role and how to continue to be an influencer, regardless of title. To this day, I much prefer the title of “senior advisor” in most any role I play.


Our thanks to Faith for her valuable perspective!

Keep an eye on https://riskspan.com/resources/ this month for insights from other women we admire in mortgage and structured finance.


RiskSpan is proud to sponsor POWER OF VOICE BENEFIT. Girls Leadership teaches girls to exercise the power of their voice. #powerofvoice2021


Edge Enhancements: Spotlight AGENCY EDGE

2021 is off to a great start, but the Edge Team is not resting on its laurels.

On the heels of a year that saw more than a 30 percent increase in Edge subscribers, including a doubling of Agency Module users, we continue to add more of the Ginnie and GSE data you need.

Edge’s enhanced datasets make customizing S-curves even easier.

For example:

Loans with a principal deferral pay more slowly than loans without them when faced with the same refinancing incentive.

But how much more slowly?

Edge lets you quantify the difference, so you can adjust your models accordingly.

7 of the 10 largest U.S. broker/dealers use Edge to analyze Agency prepays.
Find out why.


RiskSpan a Winner of HousingWire’s RiskTech100 Award

For the third consecutive year, RiskSpan is a winner of HousingWire’s prestigious annual HW Tech100 Mortgage award, recognizing the most innovative technology companies in the housing economy.

The recognition is the latest in a parade of 2021 wins for the data and analytics firm whose unique blend of tech and talent enables traders and portfolio managers to transact quickly and intelligently to find opportunities. RiskSpan’s comprehensive solution also provides risk managers access to modeling capabilities and seamless access to the timely data they need to do their jobs effectively.

“I’ve been involved in choosing Tech100 winners since we started the program in 2014, and every year it manages to get more competitive,” HousingWire Editor and Chief Sarah Wheeler said. “These companies are truly leading the way to a more innovative housing market!”

Other major awards collected by RiskSpan and its flagship Edge Platform in 2021 include winning Chartis Research’s “Risk as a Service” category and being named “Buy-side Market Risk Management Product of the Year” by Risk.net.

RiskSpan’s cloud-native Edge platform is valued by users seeking to run structured products analytics fast and granularly. It provides a one-stop shop for models and analytics that previously had to be purchased from multiple vendors. The platform is supported by a first-rate team, most of whom come from industry and have walked in the shoes of our clients.

“After the uncertainty and unpredictability of last year, we expected a greater adoption of technology. However, these 100 real estate and mortgage companies took digital disruption to a whole new level and propelled a complete digital revolution, leaving a digital legacy that will impact borrowers, clients and companies for years to come,” said Brena Nath, HousingWire’s HW+ Managing Editor. ”Knowing what these companies were able to navigate and overcome, we’re excited to announce this year’s list of the most innovative technology companies serving the mortgage and real estate industries.”


Get in touch with us to explore why RiskSpan is a best-in-class partner for data and analytics in mortgage and structured finance. 

HousingWire is the most influential source of news and information for the U.S. mortgage and housing markets. Built on a foundation of independent and original journalism, HousingWire reaches over 60,000 newsletter subscribers daily and over 1.0 million unique visitors each month. Our audience of mortgage, real estate and fintech professionals rely on us to Move Markets Forward. Visit www.housingwire.com or www.solutions.housingwire.com to learn more


Flood Insurance Changes: What Mortgage Investors Need to Know

Major changes are coming to FEMA’s National Flood Insurance Program on April 1st2021, the impacts of which will reverberate throughout real estate, mortgage, and structured finance markets in a variety of ways. 

For years, the way the NFIP has managed flood insurance in the United States has been the subject of intense scrutiny and debateCompounding the underlying moral hazard issues raised by the fact that taxpayers are subsidizing homeowners who knowingly move into flood-prone areas is the reality that the insurance premiums paid by these homeowners collectively are nowhere near sufficient to cover the actual risks faced by properties in existing flood zones. 

Climate change is only exacerbating the gap between risk and premiums. According to research released this week by First Street Foundation, the true economic risk is 3.7 times higher than the level at which the NFIP is currently pricing flood insurance. And premiums would need to increase by 7 times to cover the expected economic risk in 2051. 

New York Times article this week addresses some of the challenges (political and otherwise) a sudden increase in flood insurance premiums would create. These include existing homeowners no longer being able to afford the higher monthly payments as well as a potential drop in property values in high-risk areas as the cost of appropriately priced flood insurance is priced in. These risks are also of concern to mortgage investors who obviously have little interest in seeing sudden declines in the value of properties that secure the mortgages they own. 

Notwithstanding these risks, the NFIP recognizes that the disparity between true risk and actual premiums cannot continue to go unaddressed. The resulting adjustment to the way in which the NFIP will calculate premiums – called Risk Rating 2.0  will reflect a policy of phasing out subsidies (wherein lower-risk dwellings absorb the cost of those in the highest-risk areas) and tying premiums to thactual flood risk of a given structure. 

Phase-In 

The specific changes to be announced on April 1st will go into effect on October 1st, 2021. But the resulting premium increases will not happen all at once. Annual limits currently restrict how fast premiums can increase for primary residences, ranging from 5%-18% per year. (Non-primary residences have a cap of 25%). FEMA has not provided much guidance on how these caps will apply under Risk Rating 2.0 other than to say that all properties will be on a glide path to actuarial rates.” The caps, however, are statutory and would require an act of Congress to change. And Members of Congress have shown reluctance in the past to saddle their constituents with premium spikes. 

Phasing in premium increases helps address the issue of affordability for current homeowners. This is equally important to investors who hold these existing homeowners’ mortgages. It does not however, address the specter of significant property value declines because the sale of the home has historically caused the new, fully priced premium to take effect for the next homeowner. It has been suggested that FEMA could blunt this problem by tying insurance premiums to properties rather than to homeowners. This would enable the annual limits on price increases to remain in effect even if the house is sold. 

Flood Zones & Premiums 

Despite a widely held belief that flood zone maps are out of date and that climate change is hastening the need to redraw them, Risk Rating 2.0 will reportedly apply only to homes located in floodplains as currently defined. Premium calculations, however, will focus on the geographical and structural features of a particular home, including foundation type and replacement cost, rather than on a property’s location within a flood zone.  

The Congressional Research Service’s paper detailing Risk Rating 2.0 acknowledges that premiums are likely to go up for many properties that are currently benefiting from subsidies. The paper emphasizes that it is not in FEMA’s authority to provide affordability programs and that this is a job for Congress as they consider changes to the NFIP. 

“FEMA does not currently have the authority to implement an affordability program, nor does FEMA’s current rate structure provide the funding required to support an affordability program. However, affordability provisions were included in the three bills which were introduced in the 116th Congress for long-term reauthorization of the NFIP: the National Flood Insurance Program Reauthorization Act of 2019 (H.R. 3167), and the National Flood Insurance Program Reauthorization and Reform Act of 2019 (S. 2187) and its companion bill in the House, H.R. 3872. As Congress considers a long-term reauthorization of the NFIP, a central question may be who should bear the costs of floodplain occupancy in the future and how to address the concerns of constituents facing increases in flood insurance premiums.” 

Implications for Homeowners and Mortgage Investors 

FEMA is clearly signaling that NFIP premium increases are coming. Any increases to insurance premiums will impact the value of affected homes in much the same way as rising interest rates. Both drive prices down by increasing monthly payments and thus reducing the purchasing power of would-be buyers. The difference, however, is that while interest rates affect the entire housing market, this change will be felt most acutely by owners of properties in FEMA’s Special Flood Hazard Areas that require insurance. The severity of these impacts will clearly be related to the magnitude of the premium increases, whether increase caps will be applied to properties as well as owners, and the manner in which these premiums get baked into sales prices. 

Mortgage risk holders need to be ready to assess their exposure to these flood zone properties and the areas that see the biggest rate jumps. The simplest way to do this is through HPI scenarios based on a consistent view of the ‘affordability’ of the house  i.e., by adjusting the maximum mortgage payment for a property downward to compensate for the premium increase and then solving for the drag on home price.


Get in touch with us for a no-obligation discussion on how to measure the impact of these forthcoming changes on your portfolio. We’d be interested in hearing your insights as well. 


EDGE: An Update on GNMA Delinquencies

In this short post, we update the state of delinquencies for GNMA multi-lender cohorts, by vintage and coupon. As the Ginnie market has shifted away from bank servicers, non-bank servicers now account for more than 75% of GNMA servicing, and even higher percentages in recent-vintage cohorts.  

The table below summarizes delinquencies for GN2 cohorts where outstanding balance is greater than $10 billion. The table also highlights, in red, cohorts where delinquencies are more than 85% attributable to non-bank servicersThat non-banks are servicing so many delinquencies is not surprising given the historical reluctance (or inability)of these servicers to repurchase delinquent mortgages out of pools (see our recent analysis on this here). This is contributing to an extreme overhang of non-bankserviced delinquencies in recent-vintage GNMA cohorts. 

The 60-day+ delinquencies for 2018 GN2 3.5s get honorable mention, with the non-bank delinquencies totaling 84% of all delinquencies, just below our 85% threshold. At the upper end, delinquencies in 2017 30yr 4s were 93% attributable to non-bank servicers, and they serviced nearly 90% of 2019 delinquencies across all coupons.

The delinquencies in this analysis are predominantly loans that are six-months or more delinquent and in COVID forbearance.[1] Current guidance from GNMA gives servicers the latitude to leave these loans in pools without exceeding their seriously delinquent threshold.[2] However, as noted in our previous research, several non-bank servicers have started to increase their buyout activity, driven by joint-ventures with GNMA EBO investors and combined with a premium bid for reperforming GNMA RG pools. While we saw a modest pullback in recent buyout activity from Lakeview,[3] which has been at the vanguard of the activity, the positive economics of the trade indicates that we will likely see continued increases in repurchases, with 2018-19 production premiums bearing the brunt of involuntary speed increases.


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


[1] Breakdown of delinquencies available on request.

[2] GNMA APM 2020-17 extended to July 31st the exemption of counting post-COVID delinquencies as part of the servicer’s Seriously Delinquent count.

[3] Lakeview repurchased 15% of seriously delinquent loans in January, down from 22% in December. Penny Mac and Carrington continued their repurchases at their recent pace.


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