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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.  


RiskSpan’s Edge Platform Wins 2021 Buy-Side Market Risk Management Product of the Year

RiskSpan, a leading SaaS provider of risk management, data and analytics has been awarded Buy-Side Market Risk Management Product of the Year for its Edge Platform at Risk.net’s 2021 Risk Markets Technology Awards. The honor marks Edge’s second major industry award in 2021, having also been named the winner of Chartis Research’s Risk-as-a-Service category.

Licensed by some of the largest asset managers and Insurance companies in the U.S., a significant component of the Edge Platform’s value is derived from its ability to serve as a one-stop shop for research, pre-trade analytics, pricing and risk quantification, and reporting. Edge’s cloud-native infrastructure allows RiskSpan clients to scale as needs change and is supported by RiskSpan’s unparalleled team of domain experts — seasoned practitioners who know the needs and pain points of the industry firsthand

Adjudicators cited the platform’s “strong data management and overall technology” and “best-practice quant design for MBS, structured products and loans” as key factors in the designation.

GET A DEMO

Edge’s flexible configurability enables users to create custom views of their portfolio or potential trades at any level of granularity and down to the loan level. The platform enables researchers and analysts to integrate conventional and alternative data from an impressive array of sources to identify impacts that might otherwise go overlooked.

For clients requiring a fully supported risk-analytics-as-a-service offering, the Edge Platform provides a comprehensive data analysis, predictive modeling, portfolio benchmarking and reporting solution tailored to individual client needs.

An optional studio-level tier incorporates machine learning and data scientist support in order to leverage unstructured and alternative datasets in the analysis.


Contact us to learn how Edge’s capabilities can transform your mortgage and structured product analytics. 

Learn more about Edge at https://riskspan.com/edge-platform/ 


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.


EDGE: An Update on GNMA Buyout Efficiency

In July, we examined buyouts of delinquent GNMA loans, with special focus on the buyout efficiency for bank servicers. At that time, several banks were 98% to 99% efficient at buying out delinquent loans, where efficiency is defined as the percentage of 90+ days delinquent loans that are repurchased. In this short note, we update the buyout efficiency of major bank and non-bank servicers. 

Buyout efficiency varies widely among banks. While the most efficient banks repurchase nearly 100% of eligible loansothers, including Flagstar and Citizens Bank, opt to leave virtually all the 90+ day delinquent loans they service in securities. In the table below, we show the dollar-weighted buyout efficiencies for top banks, as well as the UPB of each bank’s unpurchased 90+ day delinquent loans, as of the January 2021 factor date.

Buyout efficiency for 90+ day delinquent loans, data as of January 2021. 

Servicers listed by total UPB serviced.

The overhang of seriously delinquent loans serviced by Flagstar and Citizens is spread across several GN2 Multi-lender sectors, with concentrations of delinquent loans rising to just 1% of the total current face of 2018 4% and 2018 4.5% cohorts. If Flagstar and Citizens were to repurchase all of their delinquent loans in a single month, it would add roughly 11-12 CPR to these cohorts. This represents the upper limit in involuntary speed, and actual speeds would likely be much slower with repurchases spread over several months.

The markedly lower buyout efficiency among GNMA non-bank servicers has created involuntary prepay overhang that is potentially much more daunting. The following table summarizes top non-bank servicers, their buyout efficiency over the past two quarters, and their current overhang of 90+ day delinquent loans.

Buyout efficiency for 90+ day delinquent loans, data as of January 2021.

Servicers listed by total UPB serviced.

Both Penny Mac and Lakeview have improved their buyout efficiency over the last quarter and may continue to do so, as more investors begin to embrace the GNMA EBO trade. The multi-lender cohorts with the most exposure to 90+ day DQ loans serviced by Penny Mac or Lakeview include 2020 3.5s as well as 2017-19 production 3.5s and 4s, with each cohort ranging between 4% to 5% of its current face.

This final table, below, illustrates the impact of forbearance on buyout activity among non-banks. While forbearance status seems to pose no impediment to buyouts for banks — in fact, banks with the highest buyout efficiency seem to favor repurchasing loans that are in COVID-forbearance over loans that are “naturally” delinquent – non-bank behavior is more nuanced.

Of the top five non-bank servicers, only Lakeview has generated significant repurchases of loans in COVID forbearance, repurchasing 10% of eligible loans in Q4. In the table below, we separate the 90+ day delinquent loans by their forbearance status and then compute each servicer’s buyout efficiency across these sub-cohorts.

Buyout efficiency for 90+ day delinquent loans, data as of January 2021.

Lakeview’s buyout behavior suggests that forbearance is not an impediment to non-bank repurchases. If we see continued improvements in buyout efficiency over the next few months, involuntary speeds in GNMA securities have the potential to rise significantly.


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.


Nearly $8 Trillion in Senior Home Equity Pushes Reverse Mortgage Market Index Upward

The NRMLA/RiskSpan Reverse Mortgage Market Index (RMMI) rose to 280.99 during the third quarter of 2020, an all-time high. This reflects a 1.6% increase in senior home equity, which now stands at an estimated $7.82 trillion. Growth in senior homeowner’s wealth was largely attributable to an estimated 1.6% (or $149 billion) increase in senior housing value, offset by 1.6% (or $28 billion) increase of senior-held mortgage debt.

The National Reverse Mortgage Lenders Association (NRMLA) and RiskSpan have published the Reverse Mortgage Market Index (RMMI) since the beginning of 2000. The RMMI provides a trending measure of home equity among U.S. homeowners age 62 and older.

The RMMI defines senior home equity as the difference between the aggregate value of homes owned and occupied by seniors and the aggregate mortgage balance secured by those homes. This measure enables NRMLA to help gauge the potential market size of those who may be qualified for a reverse mortgage product. The chart above illustrates the steady increase in this index since the end of the 2008 recession.

Increasing house prices drive the index’s upward trend, mitigated to some extent by a corresponding modest increase in mortgage debt held by seniors. The most recent RMMI report (reflecting data as of the end of Q3 20202) was published last week on NRMLA’s website.

Note on the Limitations of RMMI

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


Cash-out Refis, Investment Properties Contribute to Uptick in Agency Mortgage Risk Profile

RiskSpan’s Vintage Quality Index is a monthly measure of the relative risk profile of Agency mortgages. Higher VQI levels are associated with mortgage vintages containing higher-than-average percentages of loans with one or more “risk layers.”

These risk layers, summarized below, reflect the percentage of loans with low FICO scores (below 660), high loan-to-value ratios (above 80%), high debt-to-income ratios (above 45%), adjustable rate features, subordinate financing, cash-out refis, investment properties, multi-unit properties, and loans with only one borrower.

The RiskSpan VQI rose 4.2 points at the end of 2020, reflecting a modest increase in the risk profile of loans originated during the fourth quarter relative to the early stages of the pandemic.

The first rise in the index since February was driven by modest increases across several risk layers. These included cash-out refinances (up 2.5% to a 20.2% share in December), single borrower loans (up 1.8% to 52.0%) and investor loans (up 1.4% to 6.0%). Still, the December VQI sits more than 13 points below its local high in February 2020, and more than 28 points below a peak seen in January 2019.

While the share of cash-out refinances has risen some from these highs, the risk layers that have driven most of the downward trend in the overall VQI – percentage of loans with low FICO scores and high LTV and DTI ratios – remain relatively low. These layers have been trending downward for a number of years now, reflecting a tighter credit box, and the pandemic has only exacerbated tightening.

Population assumptions:

  • Monthly data for Fannie Mae and Freddie
  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market
  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose, are also 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.

This analysis is developed using RiskSpan’s Edge Platform. To learn more or see a free, no-obligation demo of Edge’s unique data and modeling capabilities, please contact us.


EDGE: GNMA Forbearance End Date Distribution

With 2021 underway and the first wave of pandemic-related FHA forbearances set to begin hitting their 12-month caps as early as March, now seems like a good time to summarize where things stand. Forbearance in mortgages backing GNMA securities continues to significantly outpace forbearance in GSE-backed loans, with 7.6% of GNMA loans in forbearance compared to 3.5% for Fannie and Freddie borrowers.[1] Both statistics have slowly declined over the past few months.

Notably, the share of forbearance varies greatly amongst GNMA cohorts, with some cohorts having more than 15% of their loans in forbearance. In the table below, we show the percentage of loans in forbearance for significant cohorts of GN2 30yr Multi-lender pools.

Percent of Loans in Forbearance for GNMA2 30yr Multi-lender Pools:

Cohorts larger than $25 billion. Forbearance as of December 2020 factor date.

Not surprisingly, newer production tends to experience much lower levels of forbearance. Those cohorts are dominated by newly refinanced loans and are comprised mostly of borrowers that have not struggled to make mortgage payments. Conversely, 2017-2019 vintage 3s through 4.5s show much higher forbearance, most likely due to survivor bias – loans in forbearance tend not to refinance and are left behind in the pool. The survivor bias also becomes apparent when you move up the coupon stack within a vintage. Higher coupons tend to see more refinancing activity, and that activity leaves behind a higher proportion of borrowers who cannot refinance due to the very same economic hardships that are requiring their loans to be in forbearance.

GNMA also reports the forbearance end date and length of the forbearance period for each loan. The table below summarizes the distribution of forbearance end dates across all GNMA production. This date is the last month of the currently requested forbearance period.[2]

For loans with forbearance ending in December 2020 (last month), half have taken a total of 9 months of forbearance, with most of the remaining loans taking either three or six months of forbearance.

 

 

For loans whose forbearance period rolls in January and February 2021, the total months of forbearance is evenly distributed between 3, 6, and 9 months. Among loans with a forbearance end date of March 2021, more than half will have taken their maximum twelve months of forbearance.[3]

In the chart below, we illustrate how things would look if every Ginnie Mae loan currently in forbearance extended to its full twelve-month maximum. As this analysis shows, a plurality of these mortgages – more than 25 percent — would have a forbearance end date of March 2021, with the remaining forbearance periods expiring later in 2021.

A successful vaccination program is expected to stabilize the economy and (hopefully) end the need for wide-scale forbearance programs. The timing of this economic normalization is unclear, however, and the distribution of current end dates, as illustrated above, suggests that the existing forbearance period may need to be extended for some borrowers in order to forestall a potentially catastrophic credit-driven prepayment spike in GNMA securities.

Contact us if you 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] As of the December 2020 factor date, using the data reported by the GSEs and GNMA. This data may differ marginally from the Mortgage Bankers Association survey, which is a weekly survey of mortgage servicers.

[2] Data as of the December 2020 factor date.

[3] Charts of January, February and March 2021 rolls are omitted for brevity. See RiskSpan for a copy of these charts.


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