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Articles Tagged with: ESG

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 OriginationSource: 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 DateSource: 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.


Prepayment Spikes in Ida’s Wake – What to Expect

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

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

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

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

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

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

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

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.

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