How Has the First “Social” RMBS Performed – And What’s So Social About It?
Now that six months have passed since Angel Oak issued AOMT 2021-2 – hailed as the first U.S. non-Agency RMBS to qualify as a social bond [1] – we can compare preliminary collateral performance to other deals. Angel Oak’s 2021-1, from the same shelf and vintage – but without the social bond distinction – provides an apt control group. To set the stage for this performance comparison, we’ll first reexamine the compositional differences – and significant overlap – between the two collateral pools. What we will show:
- The pool compositions are highly overlapping, with marginally greater risk concentrations of self-employment and alternative documentation in the social securitization, and the same WA (weighted average) coupon
- The social collateral has outperformed the benchmark credit-wise in the early going
- The social deal has exhibited some lock-in, i.e., slower refinancing, providing some very preliminary evidence that the borrowers are indeed underserved, and that investors may be rewarded if the social collateral’s credit performance holds
- However, the credit mix of the social collateral has drifted riskier – more so than the benchmark – meaning the strong early credit performance of the social deal could reverse, and ongoing surveillance is warranted
New Loans or New Label?
The Social AOMT 2021-2 Is Similar to AOMT 2021-1
Figure 1 shows AOMT 2021-1 vs. 2021-2 in the Collateral Comparison screen of Edge, RiskSpan’s data and analytics platform. Clearly, the two pools were similar at origination, with highly overlapping distributions of FICO, LTV, and DTI and many other similar metrics.
So What’s Different – And How Different Is It?
The distinguishing principle of a social bond under Angel Oak’s framework is that it provides affordable home mortgages to those who often can’t get them because they don’t qualify under the automated underwriting processes of traditional lenders because of the exceptional nature of their sources of income. [2]
Angel Oak says the specific characteristic hindering the borrowers in the AOMT 2021-2 deal is self-employment. [3] Self-employed borrowers make up 94.4% of the pool (with a median annual income of $227,803) [4], up marginally from 86.5% in the 2021-1 deal [5]. As Figure 1 shows, the proportion of low documentation by balance was up from 87.5% in 2021-1 to 97.5% in 2021-2.
Also, Figure 1 shows that 2021-2’s FICOs and LTVs are slightly worse on average with slightly more tail risk, and the cash-out proportion is slightly riskier.
Compensating marginally for 2021-2 are slightly lower ARM proportions (0 vs. 0.8% for 2021-1), lower WA. DTI, and a higher proportion of owner-occupied (90% vs. 85%), which many view as credit-positive.
In summary, RiskSpan calculates 1.83 average risk layers per loan for the social 2021-2, slightly higher than 1.78 for 2021-1.
Notably the WA coupons for the two pools are the same.
Figure 1: Edge’s Collateral Comparison Screen Showing AOMT 2021-1 (aka AOAK 2101) vs. 2021-2 (aka AOAK 2102) at Origination
Source: CoreLogic, RiskSpan
Would you like to see the tool we used to perform this analysis?
In summary, it seems that most – though perhaps not all – of the loans that qualified for AOMT 2021-2 would have qualified for AOMT 2021-1 and other non-QM deals.
Kroll’s new issue report seems to acknowledge that what is new about 2021-2 is mostly the formal emphasis on the social benefits of the loans made, and less a change in the kinds of loans made: “While many of [Angel Oak’s] lending programs overlap meaningfully with other non-QM lender’s offerings, the actions taken by AOCA generally indicate management’s attention to ESG related matters. Specifically, AOCA’s SBF puts focus on the impact that credit availability for underserved borrowers can have.” [4]
A skeptical interpretation of the overlap between 2021-1 and the social 2021-2 collateral would be that the social claim is largely hollow. Another way of looking at it is that a financial market participant is finally taking credit for good work it has been largely doing all along. Angel Oak itself seems to take this latter view, saying, “Since 2011, AOCA has been implementing ESG principles within its non-qualified mortgage (non-QM) origination and securitization program to provide access to residential credit for underserved borrowers.” [2]
Either way, logical hypotheses would be that collateral performance will be similar between 2021-2 and 2021-1, with -2 showing (a) slightly more credit trouble and (b) slightly less able to refinance. Regarding the second hypothesis, logically it should challenge the premise that the deal serves underserved borrowers if its borrowers can refinance just as readily as others.
Early Performance of the Social Bonds
Let’s see how AOMT’s social 2021-2 has performed as benchmarked to 2021-1 during the first six and seven months, respectively, of available data.
Better Delinquency Trend Than the Benchmark
As Figure 2 shows, delinquencies opened higher for the social 2021-2 but have mostly cured. By contrast, delinquencies have trended up for 2021-1. So far, Angel Oak’s social origination is outperforming its non-social contemporary from a credit standpoint.
Figure 2: AOMT 2021-2 Delinquencies Began Higher, Have Mostly Cured; AOMT 2021-1’s Delinquencies Have Trended Up 60 day-plus delinquency share over time, AOMT 2021-2 vs AOMT 2021-1
Source: CoreLogic, RiskSpan
Significantly Better Credit Performance by the Social DSCR Investor Loans
A small slice of the deals driving outsized delinquencies in 2021-1 are the DSCR-based investor loans (Figure 3). In the social 2021-2, delinquencies among this cohort are zero. We plot the spreads at origination (SATO) of this cohort alongside delinquencies to show that the DSCR loans in 2021-2 had lower credit spreads by about 20bps. Perhaps the investor loans pooled into 2021-2 were managed to higher standards for DSCR, rent rolls or other attributes (their LTVs and ages are similar).
Figure 3: Delinquencies – and SATOs – Are Lower Among DSCR-Based Investor Loans in AOMT 2021-2 60 day-plus delinquency share and WA SATOs over time, AOMT 2021-2 vs. AOMT 2021-1, includes Detailed Doc Type = DSCR Investor Cash Flow.
Source: CoreLogic, RiskSpan
Ironically, The Full Doc Loans Are the Social Deal’s Blemish
The slice of full doc loans in the social 2021-2 have a much lower WA FICO than the low doc loans in the same deal or either the low or full doc loans in 2021-1 (see the green dotted line in Figure 4). Correspondingly, these full doc loans have the highest delinquent share among the four cohorts in Figure 4 (green solid line). If this pattern holds, it highlights the viability of using tradeoffs to manage down the overall credit risk represented by loans with risky attributes.
Figure 4: AOMT 2021-2’s Full Doc Loans Are the Most Delinquent Doc Cohort from Either Deal 60 day-plus delinquency share and WA FICOs over time, AOMT 2021-2 vs. AOMT 2021-1 and Full Doc vs. Low Doc
Source: CoreLogic, RiskSpan
Slower Refinances Than the Benchmark
While credit performance has been better for the social deal than we might expect, voluntary prepays so far (Figure 5) support our hypothesis that the social deal should prepay slower. Note that we plot voluntary prepays over loan age, and that all loans from this recent non-QM vintage have similar (and highly positive) refinance incentive. If the social deal’s refinances remain slower, that accomplishes two significant things: 1) it supports the claim that the social borrowers are indeed underserved; 2) if combined with sustained credit performance, it provides support in terms of financial risk and return for the price premiums that social bonds tend to command.
Figure 5: AOMT 2021-2 Is Refinancing Slower CRR over loan age, AOMT 2021-2 vs. AOMT 2021-1, July 2021-January 2022
Source: CoreLogic, RiskSpan
The Relative Refinance Slowness Is From the Large Balance Loans
The overall slowness of the social collateral in Figure 5 is driven by large loans. Figure 6 shows that, among loans <$417K, the prepay patterns of 2021-1 and 2021-2 are similar, while among loans > $417K, the prepays of 2021-2 are consistently slower. This may suggest that large loans with complex sources of income are particularly hard to underwrite.
Figure 6: The Social Deal’s Low-Balance Loans Refi Similar to Benchmark, But Large Balances Have Been Slower CRR over loan age, AOMT 2021-2 vs. AOMT 2021-1, bucketed by loan size, July 2021-January 2022 
Source: CoreLogic, RiskSpan
Updated Collateral Mix
The Social Deal’s Credit Mix Has Drifted Riskier, Warranting Ongoing Monitoring
While the early performance of the social collateral is positive, Figure 7 provides reason for concern and ongoing watchfulness. Since origination, the composition of the social 2021-2 has drifted riskier in all respects except slight improvements in WA DTI and WA LTV. Its LTV tails, WA FICO, and FICO tails; proportions of cash-out, low doc, non-owner-occupied; and average overall risk layers are all somewhat riskier.
The drift for 2021-1 has been more mixed. Like 2021-2, it is safer with respect to WA DTI and WA LTV. Unlike 2021-2, it is also safer with respect to LTV tails, FICO tails, and cash-out proportion. Like 2021-2, it is riskier with respect to WA FICO; proportions of low doc and non-owner-occupied; and average overall risk layers.
We will continue to monitor whether this composition drift drives differential performance going forward.
Figure 7: Edge’s Collateral Comparison Screen Showing AOMT 2021-1 (aka AOAK 2101) vs. 2021-2 (aka AOAK 2102) updated to the Current Factor Date
Source: CoreLogic, RiskSpan
Using Edge, you can examine prepay or credit performance of loan subsets defined by any characteristics, and generate aging curves, time series, or S-curves.
[1] https://angeloakhomeloans.com/angel-oak-capital-advisors-delivers-first-to-market-non-agency-social-bond-securitization/
[2] https://www.issgovernance.com/file/spo/spo-20210408-angel-oak.pdf
[3] https://www.businesswire.com/news/home/20210517005728/en/Angel-Oak-Capital-Advisors-Delivers-First-to-Market-Non-Agency-Social-Bond-Securitization
[4] https://www.kbra.com/documents/report/48093/angel-oak-mortgage-trust-2021-2-aomt-2021-2-new-issue-report
[5] https://www.fitchratings.com/research/structured-finance/angel-oak-mortgage-trust-2021-1-us-rmbs-02-03-2021
Improving MSR Pricing Using Cloud-Based Loan-Level Analytics — Part II: Addressing Climate Risk
Modeling Climate Risk and Property Valuation Stability
Part I of this white paper series
introduced the case for why loan-level (as opposed to rep-line level) analytics are increasingly indispensable when it comes to effectively pricing an MSR portfolio. Rep-lines are an effective means for classifying loans across many important categories. But certain loan, borrower, and property characteristics simply cannot be “rolled up” to the rep-line level as easily as UPB, loan age, interest rate, LTV, credit score, and other factors. This is especially true when it comes to modeling based on available information about a mortgage’s subject property.
Assume for the sake of simplicity that human and automated appraisers do a perfect job of assigning property values for the purpose of computing origination and updated LTVs (they do not, of course, but let’s assume they do). Prudent MSR investors should be less interested in a property’s current value than in what is likely to happen to that value over the expected life of their investment. In other words, how stable is the valuation? How likely are property values within a given zip code, or neighborhood, or street to hold?
The stability of any given property’s value is tied to the macroeconomic prospects of its surrounding community. Historical and forecast trends of the local unemployment rate can be used as a rough proxy for this and are already built into existing credit and prepayment models. But increasingly, a second category of factors is emerging as an important predictor of home price stability, the property’s exposure to climate risk and natural hazard events.
Climate exposure is becoming increasingly difficult to ignore when it comes to property valuation. And accounting for it is more complicated than simply applying a premium to coastal properties. Climate risk is not just about hurricanes and storm surges anymore. A growing number of inland properties are being identified as at risk not just to wind and water hazards, but to wildfire and other perils as well. The diversity of climate risks means that the problem of quantifying and understanding them will not be solved simply by fixing out-of-date flood plain maps.
MSR investors are exposed to climate risk in ways that whole loan or securities investors are not. When climate events force borrowers into forbearance or other repayment plans, MSR investors not only forego the cash flows associated with missed interest payments that will never be made, but also incur the additional costs of administering the loss mitigation programs and making necessary P&I and escrow advances.
Overlaying climate scenario analysis on top of traditional credit modeling is unquestionably the future of quantifying mortgage asset exposure. And in many respects, the future is already here. Regulatory guidance is forthcoming requiring public companies to quantify their exposure to climate risk across three categories: acute physical risk, chronic physical risk, and economic transition risk.
Acute Risk
Acute climate risk describes a property’s exposure to individual catastrophic events. As a result of climate change, these events are expected to increase in frequency and severity. The property insurance space already has analytical tools in place to quantify property damage to hazard risks such as:
- Hurricane, including wind, storm surge, and precipitation-induced flooding
- Flooding, including “fluvial” and “pluvial” – on- and off-plan flooding
- Wildfire
- Severe thunderstorm, including exposure to tornadoes, hail, and straight-line wind, and
- Earthquake – though not tied to climate change, earthquakes remain a massively underinsured risk that can impact MSR holders
Acute risks are of particular concern for MSR holders as disaster events have proven to increase both mortgage delinquency and prepayment. The chart below illustrates these impacts after hurricane Katrina.
Chronic Risk
Chronic risk characterizes a property’s exposure to adverse conditions brought on by longer-term concerns. These include frequent flooding, sea level rise, drought hazards, heat stress, and water shortages. These effects could erode home values or put entire communities at risk over a longer period. Models currently in use forecast these risks over 20- and 25-year periods.
Transition Risk
Transition risk describes exposure to changing policies, practices or technologies that arise from a broader societal move to reduce its carbon footprint. These include increases in the direct cost of homeownership (e.g., taxes, insurance, code compliance, etc.), increased energy and other utility costs, and localized employment shocks as businesses and industry leave high-risk areas. Changing property insurance requirements (by the GSEs, for example) could further impact property valuations in affected neighborhoods.
———–
Converting acute, chronic and transition risks into mortgage modeling scenarios can only be done effectively at the loan level. Rep-lines cannot adequately capture them. As with most prepayment and credit modeling, accounting for climate risk is an exercise in scenario analysis. Building realistic scenarios involves taking several factors into account.
Scenario Analysis
Quantifying physical risks (whether acute or chronic) entails identifying:
- Which physical hazard types the property is exposed to
- How each hazard type threatens the property[1]
- The materiality of each hazard; and
- The most likely timeframes over which these hazards could manifest
Factoring climate risk into MSR pricing requires translating the answers to the questions above into mortgage modeling scenarios that function as credit and prepayment model inputs. The following table is an example of how RiskSpan overlays the impact of an acute event – specifically a category 5 hurricane in South Florida — on home price, delinquency, turnover and macroeconomic conditions.
Applying this framework to an MSR portfolio requires integration with an MSR cash flow engine. MSR cash flows and the resulting valuation are driven by the manner in which the underlying delinquency and prepayment models are affected. However, at least two other factors affect servicing cash flows beyond simply the probability of the asset remaining on the books. Both of these are likely impacted by climate risk.
- Servicing Costs: Rising delinquency rates are always accompanied by corresponding increases in the cost of servicing. An example of the extent to which delinquencies can affect servicing costs was presented in our previous paper. MSR pricing models take this into account by applying a different cost of servicing to delinquent loans. Some believe, however, that servicing loans that enter delinquency in response to a natural disaster can be even more expensive (all else equal) than servicing a loan that enters delinquency for other reasons. Reasons for this range from the inherent difficulty of reaching displaced persons to the layering impact of multiple hardships such events tend to bring upon households at once.[2]
- Recapture Rate: The data show that prepayment rates consistently spike in the wake of natural disasters. What is less clear is whether there is a meaningful difference in the recapture rate for these prepayments. Anecdotally, recapture appears lower in the case of natural disaster, but we do not have concrete data on which to base assumptions. This is clearly only relevant to MSR investors that also have an origination arm with which to capture loans that refinance.
Climate risk encompasses a wide range of perils, each of which affects MSR values in a unique way. Hurricanes, wildfires, and droughts differ not only in their geography but in the specific type of risk they pose to individual properties. Even if there were a way of assigning every property in an MSR portfolio a one-size-fits-all quantitative score, computing a “weighted average climate risk” value and applying it to a rep-line would be problematic. Such an average would be denuded of any nuance specific to individual perils. Peril-specific data is critical to being able to make the LTV, delinquency, turnover and macroeconomic assumption adjustments outlined above.
And there is no way around it. Doing all this requires a loan-by-loan analysis. RiskSpan’s Edge Platform was purpose built to analyze mortgage portfolios at the loan level and is becoming the industry’s go-to solution for measuring and managing exposures to market, credit and climate events.
Contact us to learn more.
[1] Insurability of hazards varies widely, even before insurance requirements are considered.
[2] In addition, because servicers normally staff for business-as-usual levels of delinquencies, a large acute event will create a significant spike in the demand for servicer personnel. If a servicer’s book is heavily concentrated in the Southeast, for example, a devastating storm could result in having to triple the number of people actively servicing the portfolio.
Climate Terms the Housing Market Needs to Understand
The impacts of climate change on housing and holders of mortgage risk are very real and growing. As the frequency and severity of perils increases, so does the associated cost – estimated to have grown from $100B in 2000 to $450B 2020 (see chart below). Many of these costs are not covered by property insurance, leaving homeowners and potential mortgage investors holding the bag. Even after adjusting for inflation and appreciation, the loss to both investors and consumers is staggering.
Properly understanding this data might require adding some new terms to your personal lexicon. As the housing market begins to get its arms around the impact of climate change to housing, here are a few terms you will want to incorporate into your vocabulary.
- Natural Hazard
In partnership with climate modeling experts, RiskSpan has identified 21 different natural hazards that impact housing in the U.S. These include familiar hazards such as floods and earthquakes, along with lesser-known perils, such as drought, extreme temperatures, and other hydrological perils including mudslides and coastal erosion. The housing industry is beginning to work through how best to identify and quantify exposure and incorporate the impact of perils into risk management practices more broadly. Legacy thinking and risk management would classify these risks as covered by property insurance with little to no downstream risk to investors. However, as the frequency and severity increase, it is becoming more evident that risks are not completely covered by property & casualty insurance.
We will address some of these “hidden risks” of climate to housing in a forthcoming post.
- Wildland Urban Interface
The U.S. Fire Administration defines Wildland Urban Interface as “the zone of transition between unoccupied land and human development. It is the line, area, or zone where structures and other human development meet or intermingle with undeveloped wildland or vegetative fuels.” An estimated 46 million residences in 70,000 communities in the United States are at risk for WUI fires. Wildfires in California garner most of the press attention. But fire risk to WUIs is not just a west coast problem — Florida, North Carolina and Pennsylvania are among the top five states at risk. Communities adjacent to and surrounded by wildland are at varying degrees of risk from wildfires and it is important to assess these risks properly. Many of these exposed homes do not have sufficient insurance coverage to cover for losses due to wildfire.
- National Flood Insurance Program (NFIP) and Special Flood Hazard Area (SFHA)
The National Flood Insurance Program provides flood insurance to property owners and is managed by the Federal Emergency Management Agency (FEMA). Anyone living in a participating NFIP community may purchase flood insurance. But those in specifically designated high-risk SFPAs must obtain flood insurance to obtain a government-backed mortgage. SFHAs as currently defined, however, are widely believed to be outdated and not fully inclusive of areas that face significant flood risk. Changes are coming to the NFIP (see our recent blog post on the topic) but these may not be sufficient to cover future flood losses.
- Transition Risk
Transition risk refers to risks resulting from changing policies, practices or technologies that arise from a societal move to reduce its carbon footprint. While the physical risks from climate change have been discussed for many years, transition risks are a relatively new category. In the housing space, policy changes could increase the direct cost of homeownership (e.g., taxes, insurance, code compliance, etc.), increase energy and other utility costs, or cause localized employment shocks (i.e., the energy industry in Houston). Policy changes by the GSEs related to property insurance requirements could have big impacts on affected neighborhoods.
- Physical Risk
In housing, physical risks include the risk of loss to physical property or loss of land or land use. The risk of property loss can be the result of a discrete catastrophic event (hurricane) or of sustained negative climate trends in a given area, such as rising temperatures that could make certain areas uninhabitable or undesirable for human housing. Both pose risks to investors and homeowners with the latter posing systemic risk to home values across entire communities.
- Livability Risk
We define livability risk as the risk of declining home prices due to the desirability of a neighborhood. Although no standard definition of “livability” exists, it is generally understood to be the extent to which a community provides safe and affordable access to quality education, healthcare, and transportation options. In addition to these measures, homeowners also take temperature and weather into account when choosing where to live. Finding a direct correlation between livability and home prices is challenging; however, an increased frequency of extreme weather events clearly poses a risk to long-term livability and home prices.
Data and toolsets designed explicitly to measure and monitor climate related risk and its impact on the housing market are developing rapidly. RiskSpan is at the forefront of developing these tools and is working to help mortgage credit investors better understand their exposure and assess the value at risk within their businesses.
Contact us to learn more.
Hurricane Season a Double-Whammy for Mortgage Prepayments
As hurricane (and wildfire) season ramps up, don’t sleep on the increase in prepayment speeds after a natural disaster event. The increase in delinquencies might get top billing, but prepays also increase after events—especially for homes that were fully insured against the risk they experienced. For a mortgage servicer with concentrated geographic exposure to the event area, this can be a double-whammy impacting their balance sheet—delinquencies increase servicing advances, prepays rolling loans off the book. Hurricane Katrina loan performance is a classic example of this dynamic.
In ESG Policy, ‘E’ Should Not Come at the Expense of ‘S’
ESG—it is the hottest topic in our space. No conference or webinar is complete without a panel touting the latest ESG bond or the latest advance in reporting and certification. What a lot of these pieces neglect to address is the complicated relationship between the “E” and the “S” of ESG. In particular, that climate-risk exposed properties are also often properties in underserved communities, providing much-needed affordable housing to the country.
Last week, the White House issued an Executive Order of Climate-Related Financial Risk. The focus of the order was to direct government agencies toward both disclosure and mitigation of climate-related financial risk. The order reinforces the already relentless focus on ESG initiatives within our industry. The order specifically calls on the USDA, HUD, and the VA to ‘consider approaches to better integrate climate-related financial risk into underwriting standards, loan terms and conditions, and asset management and servicing procedures, as related to their Federal lending policies and programs.” Changes here will likely presage changes by the GSEs.
In mortgage finance, some of the key considerations related to disclosure and mitigation are as follows:
Disclosure of Climate-Related Financial Risk:
- Homes exposed to increasing occurrence to natural hazards due to climate changes.
- Homes exposed to the risk of decreasing home prices due to climate change, because of either increasing property insurance costs (or un-insurability) or localized transition risks of industry-exposed areas (e.g., Houston to the oil and gas industry).
Mitigation of Climate-Related Financial Risk:
- Reducing the housing industry’s contribution to greenhouse gas emissions in alignment with the president’s goal of a net-zero emissions economy by 2050. For example, loan programs that support retrofitting existing housing stock to reduce energy consumption.
- Considering a building location’s exposure to climate-related physical risk. Directing investment away for areas exposed to the increasing frequency and severity of natural disasters.
But products and programs that aim to support the goal of increased disclosure and mitigation of climate-related financial risk can create situations in which underserved communities disproportionately bear the costs of our nation’s pivot toward climate resiliency. The table below connects the FEMA’s National Risk Index data to HUD’s list of census tracts that qualify for low-income housing tax credits, which HUD defines as tracts that have ‘50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more.’ Census tracts with the highest risk of annual loss from natural disaster events are disproportionally made of HUD’s Qualified Tracts.
As an industry, it’s important to remember that actions taken to mitigate exposure to increasing climate-related events will always have a cost to someone. These costs could be in the form of increased insurance premiums, decreasing home prices, or even loss of affordable housing options altogether. All this is not to say that action should not be taken, only that balancing social ESG goals should also be considered when ambitious environmental ESG goals come at their expense.
The White House identified this issue right at the top of the order by indicating that any action on the order would need to account for ‘disparate impacts on disadvantaged communities and communities of color.’
“It is therefore the policy of my Administration to advance consistent, clear, intelligible, comparable, and accurate disclosure of climate-related financial risk (consistent with Executive Order 13707 of September 15, 2015 (Using Behavioral Science Insights to Better Serve the American People), including both physical and transition risks; act to mitigate that risk and its drivers, while accounting for and addressing disparate impacts on disadvantaged communities and communities of color (consistent with Executive Order 13985 of January 20, 2021 (Advancing Racial Equity and Support for Underserved Communities Through the Federal Government)) and spurring the creation of well-paying jobs; and achieve our target of a net-zero emissions economy by no later than 2050.”
The social impacts of any environmental initiative need to be considered. Steps should be taken to avoid having the cost of changes to underwriting processes and credit policies be disproportionately borne by underserved and vulnerable communities. To this end, a balanced ESG policy will ultimately require input from stakeholders across the mortgage industry.
