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Articles Tagged with: General

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.

Laurie-Goodman

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/insights/ 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. Amy Cutts

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/insights/ 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. Faith Schwartz

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/insights/ 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

RMTA2021 Winner2021 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.

AICPA


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.


Overcoming Data Limitations (and Inertia) to Factor Climate into Credit Risk Modeling

With each passing year, it is becoming increasingly clear to mortgage credit investors that climate change is emerging as a non-trivial risk factor that must be accounted for. Questions around how precisely to account for this risk, however, and who should ultimately bear it, remain unanswered. 

Current market dynamics further complicate these questionsLate last year, Politico published this special report laying out the issues surrounding climate risk as it relates to mortgage finance. Even though almost everyone agrees that underinsured natural disaster risk is a problem, the Politico report outlines several forces that make it difficult for anyone to do anything about it. The massive undertaking of bringing old flood zone maps up to date is just one exampleAs Politico puts it: 

The result, many current and former federal housing officials acknowledge, is a peculiar kind of stasis — a crisis that everyone sees coming but no one feels empowered to prevent, even as banks and investors grow far savvier about assessing climate risk. 

At some point, however, we will reach a tipping point – perhaps a particularly devastating event (or series of events) triggering significant losses. As homeowners, the GSEs, and other mortgage credit investors point fingers at one another (and inevitably at the federal government) a major policy update will become necessary to identify who ultimately bears the brunt of mispriced climate risk in the marketOnce quantified and properly assigned, the GSEs will price in climate risk in the same way they bake in other contributors to credit risk — via higher guarantee fees. For non-GSE (and CRT) loans, losses will continue to be borne by whoever holds the credit risk 

Recognizing that such an event may not be far off, the GSEs, their regulator, and everyone else with credit exposure are beginning to appreciate the importance of understanding the impact of climate events on mortgage performance. This is not easily inferred from the historical data record, however. And those assessing risk need to make informed assumptions about how historically observed impacts will change in the future. 

The first step in constructing these assumptions is to compile a robust historical dataset. To this end, RIskSpan began exploring the impact of certain hurricanes a few years ago. This initial analysis revealed a significant impact on short-term mortgage delinquency rates (not surprisingly), but less of an impact on default rates. In other words, affected borrowers encountered hardship but ultimately recovered. 

This research is preliminary, however, and more data will be necessary to build scenario assumptions as climate events become more severe and widespread. As more data covering more events—including wildfires—becomes available, RiskSpan is engaged in ongoing research to tease out the impact each of these events has on mortgage performance.  

It goes without saying that climate scenario assumptions need to be grounded in reality to be useful to credit investors. Because time-series data relationships are not always detectable using conventional means, especially when data is sparse, ware beginning to see promise in leveraging various machine learning techniques to this endWe believe this historical, machine-learning-based research will provide the backbone for an approach that merges historical effects of events with inputs about the increasing frequency and severity of these events as they become better understood and more quantifiable. 

Precise forecasting of severe climate events by zip code in any given year is not here yet. But an increasingly reliable framework for gauging the likely impact of these events on mortgage performance is on the horizon.  


The NRI: An Emerging Tool for Quantifying Climate Risk in Mortgage Credit

Climate change is affecting investment across virtually every sector in a growing number of mostly secondary ways. Its impact on mortgage credit investors, however, is beginning to be felt more directly.

Mortgage credit investors are investors in housing. Because housing is subject to climate risk and borrowers whose houses are destroyed by natural disasters are unlikely to continue paying their mortgages, credit investors have a vested interest in quantifying the risk of these disasters.

To this end, RiskSpan is engaged in leveraging the National Risk Index (NRI) to assess the natural disaster and climate risk exposure of mortgage portfolios.

This post introduces the NRI data in the context of mortgage portfolio analysis (loans or mortgage-backed securities), including what the data contain and key considerations when putting together an analysis. A future post will outline an approach for integrating this data into a framework for scenario analysis that combines this data with traditional mortgage credit models.

The National Risk Index

The National Risk Index (NRI) was released in October 2020 through a collaboration led by FEMA. It provides a wealth of new geographically specific data on natural hazard risks across the country. The index and its underlying data were designed to help local governments and emergency planners to better understand these risks and to plan and prepare for the future.

The NRI provides information on both the frequency and severity of natural risk events. The level of detailed underlying data it provides is astounding. The NRI focuses on 18 natural risks (discussed below) and provides detailed underlying components for each. The severity of an event is broken out by damage to buildings, agriculture, and loss of life. This breakdown lets us focus on the severity of events relative to buildings. While the definition of building here includes all types of real estate—houses, commercial, rental, etc.—having the breakdown provides an extra level of granularity to help inform our analysis of mortgages.

The key fields that provide important information for a mortgage portfolio analysis are bulleted below. The NRI provides these data points for each of the 18 natural hazards and each geography they include in their analysis.

  • Annualized Event Frequency
  • Exposure to Buildings: Total dollar amount of exposed buildings
  • Historical Loss Ratio for Buildings (Bayesian methods to derive this estimate, such that every geography is covered for its relevant risks)
  • Expected Annual Loss for Buildings
  • Population estimates (helpful for geography weighting)

Grouping Natural Disaster Risks for Mortgage Analysis

The NRI data covers 18 natural hazards, which pose varying degrees of risk to housing. We have found the framework below to be helpful when considering which risks to include in an analysis. We group the 18 risks along two axes:

1) The extent to which an event is impacted by climate change, and

2) An event’s potential to completely destroy a home.

Earthquakes, for example, have significant destructive potential, but climate change is not a major contributor to earthquakes. Conversely, heat waves and droughts wrought by climate change generally do not pose significant risk to housing structures.

When assessing climate risk, RiskSpan typically focuses on the five natural hazard risks in the top right quadrant below.

Immediate Event Risk versus Cumulative Event Risk

Two related but distinct risks inform climate risk analysis.

  1. Immediate Event Analysis: The risk of mortgage delinquency and default resulting directly from a natural disaster eventhome severely damaged or destroyed by a hurricane, for example.  
  2. Cumulative Event Risk: Less direct than immediate event risk, this is the risk of widespread home price declines across an entire area communities because of increasing natural hazard risk brought on by climate changeThese secondary effects include: 
    • Heightened homebuyer awareness or perception of increasing natural hazard risk,
    • Property insurance premium increases or areas becoming ‘self-insured, 
    • Government policy impacts (e.g., potential flood zone remapping), and 
    • Potential policy changes related to insurance from key players in the mortgage market (i.e., Fannie Mae, Freddie Mac, FHFA, etc.). 

NRI data provides an indication of the probability of immediate event occurrence and its historic severity in terms of property losses. We can also empirically observe historical mortgage performance in the wake of previous natural disaster events. Data covering several hurricane and wildfire events are available.

Cumulative event risk is less observable. A few academic papers attempt to tease out these impacts, but the risk of broader home price declines typically needs to be incorporated into a risk assessment framework through transparent scenario overlays. Examples of such scenarios include home price declines of as much as 20% in newly flood-exposed areas of South Florida. There is also research suggesting that there are often long term impacts to consumer credit following a natural disaster 

Geography Normalization

Linking to the NRI is simple when detailed loan pool geographic data are available. Analysts can merge by census tract or county code. Census tract is the more geographically granular measure and provides a more detailed analysis.

For many capital markets participants, however, that level of geographic specific detail is not available. At best, an investor may have a 5-digit or 3-digit zip code. Zip codes do not directly match to a given county or census tract and can potentially span across those distinctions.

There is no perfect way to perform the data link when zip code is the only available geographic marker. We take an approach that leverages the other data on housing stock by census tract to weight mortgage portfolio data when multiple census tracts map to a zip code.

Other Data Limitations

The loss information available represents a simple historical average loss rate given an event. But hurricanes (and hurricane seasons) are not all created equal. The same is true of other natural disasters. Relying on averages may work over long time horizons but could significantly underpredict or overpredict loss in a particular year. Further, the frequency of events is rising so that what used to be considered 100 year event may be closer to a 10 or 20 year event. Lacking data about what losses might look like under extreme scenarios makes modeling such events problematic.

The data also make it difficult to take correlation into account. Hurricanes and coastal flooding are independent events in the dataset but are obviously highly correlated with one another. The impact of a large storm on one geographic area is likely to be correlated with that of nearby areas (such as when a hurricane makes its way up the Eastern Seaboard).

The workarounds for these limitations have limitations of their own. But one solution involves designing transparent assumptions and scenarios related to the probability, severity, and correlation of stress events. We can model outlier events by assuming that losses for a particular peril follow a normal distribution with set standard deviations. Other assumptions can be made about correlations between perils and geographies. Using these assumptions, stress scenarios can be derived by picking a particular percentile along the loss distribution.

A Promising New Credit Analysis Tool for Mortgages

Notwithstanding its limitations, the new NRI data is a rich source of information that can be leveraged to help augment credit risk analysis of mortgage and mortgage-backed security portfolios. The data holds great promise as a starting point (and perhaps more) for risk teams starting to put together climate risk and other ESG analysis frameworks.


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