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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.


EDGE: COVID Forbearance and Non-Bank Buyouts

November saw a significant jump in GNMA buyouts for loans serviced by Lakeview. Initially, we suspected that Lakeview was catching up from nearly zero buyout activity in the prior months, and that perhaps the servicer was doing this to keep in front of GNMA’s requirement to keep seriously delinquent loans below the5% of UPB threshold. [1]

Buyout rates for some major non-bank servicers.

Using EDGE to dig further, we noticed that Lakeview’s buyouts affected both multi-lender and custom pools in similar proportions and were evenly split between loans with an active COVID forbearance and loans that were “naturally” delinquent.

The month-on-month jump in Lakeview buyouts on forborne loans is notable. The graph below plots Lakeview’s buyout rate (CBR) for loans that are 90-days+ delinquent.

Further, the buyouts were skewed towards premium coupons. Given this, it is plausible that the buyouts are economically driven [2] and that Lakeview is now starting to repurchase and warehouse delinquent loans, something that non-banks have struggled with due to balance sheet and funding constraints.

Where do the current exposures lie? The table below summarizes Lakeview’s 60-day+ delinquencies for loans in GN2 multi-lender pools, for coupons and vintages where Lakeview services a significant portion of the cohort. Not surprisingly, the greatest exposure lies in recent-vintage 4s through 5s.

To lend some perspective, in June 2020 Wells serviced around one-third of 2012-13 vintage 3.5s and approximately 8% of its loans were 60-days delinquent, all non-COVID related.

This analysis does not include other non-bank servicers. As a group, non-bank servicers now service more than 80% of recent-vintage GN2 loans in multi-lender pools. The Lakeview example reflects mounting evidence that COVID forbearance is not an impediment to repurchasing delinquent loans.

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


[1] Large servicers are required to keep 90-day+ delinquencies below 5% of their overall UPB. GNMA has exempted loans that are in COVID forbearance from this tally.

[2] Servicers can repurchase GNMA loans that have missed 3 or more payments at par. If these loans cure, either naturally or due to modification, the servicer can deliver them into a new security. Given that nearly all GNMA passthroughs trade at a significant premium to par, this redelivery can create a substantial arbitrage opportunity, even after accounting for the trial period for the modification.


EDGE: Unexplained Prepayments on HFAs — An Update

In early October, we highlighted a large buyout event for FNMA pools serviced by Idaho HFA, the largest servicer of HFA loans. On October 28, FNMA officially announced that there were 544 base-pools with erroneous prepayments due to servicer reporting error. The announcement doesn’t mention the servicer of the affected pools, but when we look at pools that are single-servicer, every one of those pools is serviced by Idaho HFA.

FNMA reports the “September 2020 Impacted Principal Paydown” at $133MM. The September reported prepayment for FNMA Idaho HFA pools was 43 CPR on a total of just over $6B UPB. If we add back the principal from the impacted paydown, the speed should have been 26 CPR, which is closer to the Freddie-reported 25 CPR.

FNMA provides an announcement here and list of pools here. According to the announcement, FNMA will not be reversing the buyout but instead recommends that affected investors start a claims process. We note that Idaho HFA prepayment speeds will continue to show these erroneous buyouts in the October factor date.

Contact us to try Edge for free.



RiskSpan VQI: Current Underwriting Standards Q3 2020

RiskSpan’s Vintage Quality Index, which had declined sharply in the first half of the year, leveled off somewhat in the third quarter, falling just 2.8 points between June and September, in contrast to its 12 point drop in Q2.

This change, which reflects a relative slowdown in the tightening of underwriting standards reflects something of a return to stability in the Agency origination market.

Driven by a drop in cash-out refinances (down 2.3% in the quarter), the VQI’s gradual decline left the standard credit-related risk attributes (FICO, LTV, and DTI) largely unchanged.

The share of High-LTV loans (loans with loan-to-value ratios over 80%) which fell 1.3% in Q3, has fallen dramatically over the last year–1.7% in total. More than half of this drop (6.1%) occurred before the start of the COVID-19 crisis. This suggests that, even though the Q3 VQI reflects tightening underwriting standards, the stability of the credit-related components, coupled with huge volumes from the GSEs, reflects a measure of stability in credit availability.

Risk Layers – September 20 – All Issued Loans By Count

Risk Layers – September 20 – All Issued Loans By Count

Analytical And Data Assumptions

Population assumptions:

  • Monthly data for Fannie Mae and Freddie Mac.

  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market conditions.

  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose are also excluded. 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.

An outline of our approach to data imputation can be found in our VQI Blog Post from October 28, 2015.                                                


EDGE: Unexplained Behavior for Idaho HFA

People familiar with specified pool trading recognize pools serviced by the state housing finance authorities as an expanding sector with a rich set of behavior. The Idaho Housing Finance Authority leads all HFAs in servicing volume, with roughly $18B in Fannie, Freddie and Ginnie loans.[1]

In the October prepay report, an outsized acceleration in speeds on FNMA pools serviced by the Idaho HFA caught our attention because no similar acceleration was occurring in FHLMC or GNMA pools.

Speeds on Idaho HFA-serviced pools for GNMA (orange), FHLMC (blue), and FNMA (black)

Digging deeper, we analyzed a set of FNMA pools totaling around $3.5B current face that were serviced entirely by the Idaho HFA. These pools experienced a sharp dip in reported forbearance from factor dates August through October, dropping from nearly 6% in forbearance to zero before rebounding to 4.5% (black line). By comparison, FHLMC pools serviced by the Idaho HFA (blue line) show no such change.

Seeking to understand what was driving this mysterious dip/rebound, we noticed in the October report that 2.7% of the Fannie UPB serviced by the Idaho HFA was repurchased (involuntarily) on account of being 120 days delinquent, thus triggering a large involuntary prepayment which was borne by investors.

We suspect that in the September report, loans that were in COVID-forbearance were inadvertently reclassified as not in forbearance. In turn, this clerical error released these loans from the GSE’s moratorium on repurchasing forbearance-delinquent loans and triggered an automatic buyout of these 120+ day delinquent loans by FNMA.

We have asked FNMA for clarification on the matter and they have responded that they are looking into it. We will share information as soon as we are aware of it.


[1] Idaho HFA services other states’ housing finance authority loans, including Washington state and several others.

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


 


EDGE: An Update on Property Inspection Waivers

In June, we wrote about the significant prepay differences observed between loans with full inspection/appraisals and loans with property inspection waivers (PIW). In this short piece, we revisit these relationships to see if the speed differentials have persisted over the previous four months.

From an origination standpoint, PIWs continue to gain in popularity and are beginning to approach half of all new issuance (blue line). For refi loans this figure approaches 60% (green line).

Graph 1: Percent of loans with property inspection waivers, by balance. Source: RiskSpan Edge

Performance

Broadly speaking, PIW loans still pay significantly faster than loans with appraisals. In our June report, the differential was around 15 CPR for the wider cohort of borrowers. Since that time, the relationship has held steady. Loans with inspection waivers go up the S-curve faster than loans with appraisals, and top out around 13-18 CPR faster, depending on how deep in the money the borrower is.

Graph 2: S-curves for loans aged 6-48 months with balance >225k, waivers (black) vs inspection (blue). Source: RiskSpan Edge. 
 

The differential is smaller for purchase loans. The first chart, which reflects only purchase loans, shows PIW loans paying only 10-12 CPR faster than loans with full appraisals. In contrast, refi loans (second chart) continue to show a larger differential, ranging from 15 to 20 CPR, depending on how deep in the money the loan is.

Graph 3: Purchase loans with waivers (black) versus inspections (blue). Source: RiskSpan Edge.

Graph 4: Refi loans with waivers (black) versus inspections (blue). Source: RiskSpan Edge.

We also compared bank-serviced loans with non-bank serviced loans. The PIW speed difference was comparable between the two groups of servicers, although non-bank speeds were in general faster for both appraisal and PIW loans.

Inspection waivers have been around since 2017 but have only gained popularity in the last year. While investors disagree on what is driving the speed differential, it could be as simple as self-selection: a borrower who qualifies for an inspection waiver will also qualify upon refinancing, unless that borrower takes out a large cash-out refi which pushes the LTV above 70%[1]. In any event, the speed differential between loans with waivers and loans with full inspections continues to hold over the last four months of factor updates. Given this, appraisal loans still offer significantly better prepay profiles at all refi incentives, along with a slightly flatter S-curve, implying lower option cost, than loans with inspection waivers.

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


[1] No-cash-out refis qualify for waivers up to 90% LTV.


EDGE: GNMA Delinquencies and Non-Bank Servicers

In the past two months, investors have seen outsized buyouts of delinquent loans from GNMA pools, leading to a significant uptick in prepayment speeds. Nearly all of these buyouts were driven by bank servicers, including Wells Fargo, US Bank, Truist, and Chase. GNMA buyout speeds in July’s report were the fastest, with Wells Fargo leading the charge on their seriously delinquent loans. The August report saw lower but still above-normal buyout activity. For September, we expect a further decline in bank buyout speeds, as the 60-day delinquent bucket for banks has declined from 6.6% just prior to the July report to 2.2% today.[1]

During that same time, buyouts from non-banks were nearly non-existent. We note that the roll rate from 60-day delinquent to 90-day delinquent (buyout-eligible) is comparable between banks and non-banks.[2] So buyout-eligible delinquencies for non-banks continue to build. That pipeline, coupled with the fact that non-banks service more than 75% of GNMA’s current balance, presents a substantial risk of future GNMA buyouts.

As discussed in previous posts, the differential in buyouts between banks and their non-bank counterparts is mainly due to bank servicers being able to warehouse delinquent loans until they reperform, modified or unmodified, or until they can otherwise dispose of the loan. Non-bank servicers typically do not have the balance sheet or funding to perform such buyouts in size. If these large non-bank servicers were to team with entities with access to cheap funding or were to set up funding facilities sponsored by investors, they could start to take advantage of the upside in re-securitization. The profits from securitizing reperforming loans is substantial, so non-bank servicers can afford to share the upside with yield-starved investors in return for access to funding. In this scenario, both parties could engage in a profitable trade.

Where do delinquencies stand for non-bank servicers? In the table below, we summarize the percentage of loans that have missed 3 or more payments for the top five non-bank servicers, by coupon and vintage.[3] In this table, we show 90-day+ delinquencies, which are already eligible for buyout, as opposed to the 60 day delinquency analysis we performed for banks, where 60 day delinquencies feed the buyout-eligible bucket via a 75% to 80% roll-rate from 60-day to 90-day delinquent.

30 yr GN2 Multi-lender pools

In this table, 2017-19 vintage GN2 3.5 through 4.5s show the largest overhang of non-bank delinquencies coupled with the largest percentage of non-bank servicing for the cohort.

We summarize delinquencies for the top five non-bank servicers because they presumably have a better chance at accessing liquidity from capital markets than smaller non-bank servicers. However, we observe significant build-up of 90-day+ delinquency across all non-bank servicers, which currently stands at 7.7% of non-bank UPB, much higher than the 6.6% bank-serviced 60-day delinquency in June.

Within the top five non-bank servicers, Penny Mac tended to have the largest buildup of 90-day+ delinquencies and Quicken tended to have the lowest but results varied from cohort to cohort. In the graph below, we show the 90+ delinquency pipeline for all GN2 30yr multi-lender pools.

90+ DQ in GN2 Multi-lender Pools

While we cannot say for certain when (or if) the market will see significant buyout activity from non-bank servicers, seriously delinquent loans continue to build. This overhang of delinquent loans, coupled with the significant profits to be made from securitizing reperforming loans, poses the risk for a significant uptick in involuntary speeds in GN2 multi-lender pools.[4]

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


[1] For this analysis, we focused on the roll rate for loans in 30yr GN2 Multi-lender pools vintage 2010 onward. See RiskSpan for analysis of other GNMA cohorts.

[2] Over the past two months, 77% of bank-serviced loans that were 60-days delinquent rolled to a buyout-eligible delinquency state compared to 75% for non-banks.

[3] This analysis was performed for loans that are securitized in 30yr GN2 multi-lender pools issued 2010 onward. The top five servicers include Lakeview, Penny Mac, Freedom, Quicken, and Nationstar (Mr. Cooper).

[4] Reperforming loans could include modifications or cures without modification. Even with a six-month waiting period for securitizing non-modified reperforming loans, the time-value of borrowing at current rates should prove only a mild hinderance to repurchases given the substantial profits on pooling reperforming loans.


Edge: Potential for August Buyouts in Ginnie Mae

In the July prepayment report, many cohorts of GN2 multi-lender pools saw a substantial jump in speeds. These speeds were driven by large delinquency buyouts from banks, mostly Wells Fargo, which we summarized in our most recent analysis. Speeds on moderately seasoned GN2 3% through 4% were especially hard-hit, with increases in involuntary prepayments as high as 25 CBR.

The upcoming August prepayment report, due out August 7th, should be substantially better. Delinquencies for banks with the highest buyout efficiency are significant lower than they were last month, which will contribute to a decrease in involuntary speeds by 5 to 15 CBR, depending on the cohort. In the table below, we show potential bank buyout speeds for some large GN2 multi-lender cohorts. These speeds assume an 80% roll-rate from 60DQ to 90DQ and 100% buyouts from the banks mentioned above. The analysis does not include buyouts from non-banks, whose delinquencies continue to build.July prepay report

We have details on other coupon and vintage cohorts as well as buyout analysis at an individual pool level. Please ask for details.

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


RiskSpan Vintage Quality Index (VQI): Q2 2020

The RiskSpan Vintage Quality Index (“VQI”) is a monthly index designed to quantify the underwriting environment of a monthly vintage of mortgage originations and help credit modelers control for prevailing underwriting conditions at various times. Published quarterly by RiskSpan, the VQI generally trends slowly, with interesting monthly changes found primarily in the individual risk layers. (Assumptions used to construct the VQI can be found at the end of this post.) The VQI has reacted dramatically to the economic tumult caused by COVID-19, however, and in this post we explore how the VQI’s reaction to the current crisis compares to the start of the Great Recession. We examine the periods leading up to the start of each crisis and dive deep into the differences between individual risk layers.

Reacting to a Crisis

In contrast with its typically more gradual movements, the VQI’s reaction to a crisis is often swift. Because the VQI captures the average riskiness of loans issued in a given month, crises that lower lender (and MBS investor) confidence can quickly drive the VQI down as lending standards are tightened. For this comparison, we will define the start of the COVID-19 crisis as February 2020 (the end of the most recent economic expansion, according to the National Bureau of Economic Research), and the start of the Great Recession as December 2007 (the first official month of that recession). As you might expect, the VQI reacted by moving sharply down immediately after the start of each crisis.[1]

riskspan-VQI-report

Though the reaction appears similar, with each four-month period shedding roughly 15% of the index, the charts show two key differences. The first difference is the absolute level of the VQI at the start of the crisis. The vertical axis on the graphs above displays the same spread (to display the slope of the changes consistently), but the range is shifted by a full 40 points. The VQI maxed out at 139.0 in December 2007, while at the start of the COVID-19 crisis, the VQI stood at just 90.4.

A second difference surrounds the general trend of the VQI in the months leading up to the start of each crisis. The VQI was trending up in the 18 months leading up the Great Recession, signaling an increasing riskiness in the loans being originated and issued. (As we discuss later, this “last push” in the second half of 2007 was driven by an increase in loans with high loan-to-value ratios.) Conversely, 2019 saw the VQI trend downward, signaling a tightening of lending standards.

Different Layers of Risk

Because the VQI simply indexes the average number of risk layers associated with the loans issued by the Agencies in a given month, a closer look at the individual risk layers provides insights that can be masked when analyzing the VQI as a whole.

The risk layer that most clearly depicts the difference between the two crises is the share of loans with low FICO scores (below 660).

riskspan-VQI-report

The absolute difference is striking: 27.9% of loans issued in December 2007 had a low FICO score, compared with just 7.1% of loans in February 2020. That 20.8% difference perfectly captures the underwriting philosophies of the two periods and pretty much sums up the differing quality of the two loan cohorts.

FICO trends before the crisis are also clearly different. In the 12 months leading up to the Great Recession the share of low-FICO loans rose from 24.4% to 27.9% (+3.2%). In contrast, the 12 months before the COVID-19 crisis saw the share of low-FICO loans fall from 11.5% to 7.2% (-4.3%).

The low-FICO risk layer’s reaction to the crisis also differs dramatically. Falling 27.9% to 15.4% in 4 months (on its way to 3.3% in May 2009), the share of low-FICO loans cratered following the start of the recession. In contrast, the risk layer has been largely unimpacted by the current crisis, simply continuing its downward trend mostly uninterrupted.

Three other large drivers of the difference between the VQI in December 2007 and in February 2020 are the share of cash-out refinances, the share of loans for second homes, and the share of loans with debt-to-income (DTI) ratios above 45%. What makes these risk layers different from FICO is their reaction to the crisis itself. While their absolute levels in the months leading up to the Great Recession were well above those seen at the beginning of 2020 (similar to low-FICO), none of these three risk layers appear to react to either crisis but rather continue along the same general trajectory they were on in the months leading up to each crisis. Cash-out refinances, following a seasonal cycle are mostly unimpacted by the start of the crises, holding a steady spread between the two time-periods:

riskspan-vqi-report

Loans for second homes were already becoming more rare in the runup to December 2007 (the only risk layer to show a reaction to the tumult of the fall of 2007) and mostly held in the low teens immediately following the start of the recession:

Finally, loans with high DTIs (over 45%) have simply followed their slow trend down since the start of the COVID-19 crisis, while they actually became slightly more common following the start of the Great Recession:

riskspan-VQI-report

The outlier, both pre- and post-crisis, is the high loan-to-value risk layer. For most of the 24 months leading up to the start of the Great Recession the share of loans with LTVs above 80% was well below the same period leading up to the COVID-19 crisis. The pre-Great Recession max of 33.2% is below the 24-month average of 33.3% at the start of the COVID-19 crisis. The share of high-LTV loans also reacted to the crisis in 2008, falling sharply after the start of the recession. In contrast, the current downward trend in high-LTV loans started well before the COVID-19 crisis and was seemingly unimpacted by the start of the crisis.

RiskSpan-VQI-report

Though the current downward trend is likely due to increased refinance activity as mortgage rates continue to crater, the chart seems upside down relative to what you might have predicted.

The COVID-19 Crisis is Different

What can the VQI tell us about the similarities and differences between December 2007 and February 2020? When you look closely, quite a bit.

  1. The loans experiencing the crisis in 2020 are less risky.

By almost all measures, the loans that entered the downturn beginning in December 2007 were riskier than the loans outstanding in February 2020. There are fewer low-FICO loans, fewer loans with high debt-to-income ratios, fewer loans for second homes, and fewer cash-out refinances. Trends aside, the absolute level of these risky characteristics—characteristics that are classically considered in mortgage credit and loss models—is significantly lower. While that is no guarantee the loans will fare better through this current crisis and recovery, we can reasonably expect better outcomes this time around.

  1. The 2020 crisis did not immediately change underwriting / lending.

One of the more surprising VQI trends is the non-reaction of many of the risk layers to the start of the COVID-19 crisis. FICO, LTV, and DTI all seem to be continuing a downward trend that began well before the first coronavirus diagnosis. The VQI is merely continuing a trend started back in January 2019. (The current “drop” has brought the VQI back to the trendline.) Because the crisis was not born of the mortgage sector and has not yet stifled demand for mortgage-backed assets, we have yet to see any dramatic shifts in lending practices (a stark contrast with 2007-2008). Dramatic tightening of lending standards can lead to reduced home buying demand, which can put downward pressure on home prices. The already-tight lending standards in place before the COVID-19 crisis, coupled with the apparent non-reaction by lenders, may help to stabilize the housing market.

The VQI was not designed to gauge the unknowns of a public health crisis. It does not directly address the lessons learned from the Great Recession, including the value of modification and forbearance in maintaining stability in the market. It does not account for the role of government and the willingness of policy makers to intervene in the economy (and in the housing markets specifically). Despite not being a crystal ball, the VQI nevertheless remains a valuable tool for credit modelers seeking to view mortgage originations from different times in their proper perspective.

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Analytical and Data Assumptions

Population assumptions:

  • Issuance Data for Fannie Mae and Freddie Mac.
  • Loans originated more than three months prior to issuance are excluded because the index is meant to reflect current market conditions.
  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose are also excluded. These loans do not represent credit availability in the market, as they likely would not have been originated today if not for the existence of HARP.

Data Assumptions:

  • Freddie Mac data goes back to December 2005. Fannie Mae data only goes back to December 2014.
  • Certain Freddie Mac data fields were missing prior to June 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.

 

 

[1] Note that the VQI’s baseline of 100 reflects underwriting standards as of January 2003.

 


Edge: Bank Buyouts in Ginnie Mae Pools

Ginnie Mae prepayment speeds saw a substantial uptick in July, with speeds in some cohorts more than doubling. Much of this uptick was due to repurchases of delinquent loans. In this short post, we examine those buyouts for bank and non-bank servicers. We also offer a method for quantifying buyout risk going forward.

For background, GNMA servicers have the right (but not the obligation) to buy delinquent loans out of a pool if they have missed three or more payments. The servicer buys these loans at par and can later re-securitize them if they start reperforming. Re-securitization rules vary based on whether the loan is naturally delinquent or in a forbearance program. But the reperforming loan will be delivered into a pool with its original coupon, which almost always results in a premium-priced pool. This delivery option provides a substantial profit for the servicer that purchased the loan at par.

To purchase the loan out of the pool, the servicer must have both cash and sufficient balance sheet liquidity. Differences in access to funding can drive substantial differences buyout behavior between well-capitalized bank servicers and more thinly capitalized non-bank servicers. Below, we compare recent buyout speeds between banks and non-banks and highlight some entities whose behavior differs substantially from that of their peer group.[1]

In July, Wells Fargo’s GNMA buyouts had an outsized impact on total CPR in GNMA securities. Wells, the largest GNMA bank servicer, exhibits extraordinary buyout efficiency relative to other servicers, buying out 99 percent of eligible loans. Wells’ size and efficiency, coupled with a large 60-day delinquency in June (8.6%), caused a large increase in “involuntary prepayments” and drove total overall CPR substantially higher in July. This effect was especially apparent in some moderately seasoned multi-lender pools. For example, speeds on 2012-13 GN2 3.5 multi-lender pools accelerated from low 20s CPR in June to mid-40s in July, nearly converging to the cheapest-to-deliver 2018-19 production GN2 3.5 and wiping out any carry advantage in the sector.


Figure 1: Prepayment speeds in GN2 3.5 multi-lender pools: 2012-13 vintage in blue, 2018-19 vintage in black.

This CPR acceleration in 2012-13 GN2 3.5s was due entirely to buyouts, with the sector buyouts rising for 5 CBR to 29 CBR.[2] In turn, this increase was driven almost entirely by Wells, which accounted for 25% of the servicing in some pools.


Figure 2: Buyout speeds in GN2 3.5 multi-lender pools. 2012-13 vintage in blue, 2018-19 vintage in black

In the next table, we summarize performance for the top ten GNMA bank servicers. The table shows loan-level roll rates from June to July for loans that started June 60-days delinquent. Loans that rolled to the DQ90+ bucket were not bought out of the pool by the servicer, despite being eligible for it. We use this 90+ delinquency bucket to calculate each servicer’s buyout efficiency, defined as the percentage of delinquent loans eligible for buyout that a servicer actually repurchases.

Roll Rates for Bank Servicers, for July 2020 Reporting Date

roll rates

Surprisingly, many banks exhibit very low buyout efficiencies, including Flagstar, Citizens, and Fifth Third. Navy Federal and USAA (next table) show muted buyout performance due to their high VA concentration.

Next, we summarize roll rates and buyout efficiency for the top ten GNMA non-bank servicers.

Roll Rates for Ginnie Mae Non-bank Servicers, for July 2020 Reporting Date

roll rates

Not surprisingly, non-banks as a group are much less efficient at buying out eligible loans, but Carrington stands out.

Looking forward, how can investors quantify the potential CBR exposure in a sector? Investors can use Edge to estimate the upcoming August buyouts within a sector by running a servicer query to separate a set of pools or cohort into its servicer-specific delinquencies.[3] Investors can then apply that servicer’s 60DQ->90DQ roll rate plus the servicer’s buyout efficiency to estimate a CBR.[4] This CBR will contribute to the overall CBR for a pool or set of pools.

Given the significant premium at which GNMA passthroughs are trading, the profits from repurchase and re-securitization are substantial. While we expect repurchases will continue to play an outsized role in GNMA speeds, this analysis illustrates the extent to which this behavior can vary from servicer to servicer, even within the bank and non-bank sectors. Investors can mitigate this risk by quantifying the servicer-specific 60-day delinquency within their portfolio to get a clearer view of the potential impact from buyouts.

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


[1] This post builds on our March 24 write-up on bank versus non-bank delinquencies, link here. For this analysis, we limited our analysis to loans in 3% pools and higher, issued 2010-2020. Please see RiskSpan for other data cohorts.

[2] CBR is the Conditional Buyout Rate, the buyout analogue of CPR.

[3] In Edge, select the “Expanded Output” to generate servicer-by-servicer delinquencies.

[4] RiskSpan now offers loan-level delinquency transition matrices. Please email techsupport@riskspan.com for details.


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