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Articles Tagged with: Agency MBS

Will a Rising VQI Materially Impact Servicing Costs and MSR Valuations?

VQI-GraphVQI-Current-Layers-September-2021

RiskSpan’s Vintage Quality Index computes and aggregates the percentage of Agency originations each month with one or more “risk factors” (low-FICO, high DTI, high LTV, cash-out refi, investment properties, etc.). Months with relatively few originations characterized by these risk factors are associated with lower VQI ratings. As the historical chart above shows, the index maxed out (i.e., had an unusually high number of loans with risk factors) leading up to the 2008 crisis.

RiskSpan uses the index principally to fine-tune its in-house credit and prepayment models by accounting for shifts in loan composition by monthly cohort.

Will a rising VQI translate into higher servicing costs?

The Vintage Quality Index continued to climb during the third quarter of 2021, reaching a value of 85.10, compared to 83.40 in the second quarter. The higher index value means that a higher percentage of loans were originated with one or more defined risk factors.

The rise in the index during Q3 was less dramatic than Q2’s increase but nevertheless continues a trend going back to the start of the pandemic. The increase continues to be driven by a subset of risk factors, notably the share of cash-out refinances and investor properties (both up significantly) and high-DTI loans (up modestly). On balance, fewer loans were characterized by the remaining risk metrics.

What might this mean for servicing costs?

Servicing costs are highly sensitive to loan performance. Performing Agency loans are comparatively inexpensive to service, while non-performing loans can cost thousands of dollars per year more — usually several times the amount a servicer can expect to earn in servicing fees and other ancillary servicing revenue.

For this reason, understanding the “vintage quality” of newly originated mortgage pools is an element to consider when forecasting servicing cash flows (and, by extension, MSR pricing).

Each of the risk layers that compose the VQI contributes to marginally higher default risk (and, therefore, a theoretically lower servicing valuation). But not all risk layers affect expected cash flows equally. It is also important to consider the VQI in relationship to its history. While the index has been rising since the pandemic, it remains relatively low by historical standards — still below a local high in early 2018 and certainly nowhere near the heights reached leading up to the 2008 financial crisis.

A look at the individual risk metrics driving the increase would also seem to reduce any cause for alarm. While the ever-increasing number of loans with high debt-to-income ratios could be a matter of some concern, the other two principal contributors to the overall VQI rise — loans on investment properties and cash-out refinances — do not appear to jeopardize servicing cash flows to the same degree as low credit scores and high DTI ratios do.

Consequently, while the gradual increase in loans with one or more risk factors bears watching, it likely should not have a significant bearing (for now) on how investors price Agency MSR assets.

VQI-Risk-Layer-All-Issued-Loans-September-2021VQI-Risk-Layers-FICO-660-September-2021

VQI-LTV-80-Shared-of-Issued-Loans-September-2021 VQI-Debt-to-Income-45-Share-of-Issued-Loans-September-2021 VQI-Adjustabel-Rate-Share-of-issued-Loans-September-2021 VQI-Loans-with-Subordinate-Financing-September-2021-1024x399.png

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.


Senior Housing Wealth Exceeds Record $9.57 Trillion

Homeowners 62 and older saw their housing wealth grow by 3.7 percent in the second quarter to a record $9.57 trillion, according to the latest quarterly release of the NRMLA/RiskSpan Reverse Mortgage Market Index.

For a comprehensive commentary, please see NRMLA’s press release.

How RiskSpan Computes the RMMI

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

A limitation of the RMMI relates to 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).


How Are Ginnie’s New RG Pools Performing?

In February of this year, the Ginnie Mae II program began guaranteeing securities backed by pools of mortgages previously bought out of Ginnie Mae securities because of delinquency. In order to qualify for these new re-performing pools (known as “RG pools”) a loan must meet two (related) conditions: 

  • Borrower has made at least six months of timely payments prior to pool issuance. 
  • Pool issue date is at least 210 days from when the mortgage was last delinquent. 

The novelty of RG pools raises questions about their composition and performance relative to other Ginnie Mae pools. While it remains too early to make many conclusive statements, a preliminary look at the prepayment data indicates speeds somewhere between those of similar vintage Ginnie Mae multi and custom pools, with typical variability from servicer to servicer.  

In this post, we discuss the prepayment behaviors we have observed over the first seven months of RG pool securitization, issuance patterns, and collateral characteristics. 

Prepayments 

Latest September prepayment prints show that RG pools’ speeds generally fell in between those of similar coupon/vintage multi and custom pools.  Below charts shows that 2015/2016 3.5% RG pools prepaid at around 37-38 CPR in September, a couple of CPR slower than similarly aged multi pools and almost 10 CPR faster than custom pools.  


Prepayments for G2 3.5% RG, Custom and Multi Pools by Vintages, September Factor Month Prepayments for G2 3.5% RG Custom and Multi Pools by vintages, Sept FactorMonthNote: Loan level data


Below, we plot S-curves for 49 to 72 wala RG loans against S-curves for similarly aged multi and other custom loans from April to September factor months Speeds for RG loans with 25 to 100 bp of rate incentives have prepaid in mid-30s CPRs (Green line in below figure).  During the same period, similar multi pools have prepaid 5 to 8 CPR faster (blue line) than RG pools while similar custom pools have prepaid around 5 CPR slower (black line) We also overlaid a s-curve for 7 to 18 wala G2 multi pools as a comparison (orange line).


S-curves for RG, Custom and Multi Pools (49 to 72 WALA) April to September Factor Months 
GNMA PoolNote: Loan level data, orange line is the s-curve for 7-18 wala G2 multi pools with one-year lookback period 


Not surprisingly, prepayment behavior differs by servicer. Wells-serviced RG pools that are seasoned 49 to 72 months with 25 to 100 bp of rate incentives appear to be prepaying in low 30s CPRs (black line in below figure).  Similar loans from Penny Mac are prepaying 5 to 10 CPR faster, which tends to be the case for non-RG loans as well. 


S-curves for RG loans by servicers, 49 to 72 WALA, April to September Factor MonthsGNMA PoolsNote: Loan level data 


While the re-performing loans that are being securitized into RG pools are already seasoned loans, prepayments have been increasing as pool seasons.  For example, one-month old RG 3.5% pools have prepaid at 27 CPR while 6- and 7-month 3.5% pools prepaid at 45-50 CPR (black line below). In addition, overall prepayment speeds for same-pool-age 3.0%, 3.5%, and 4.0% have been on top of each other. 


 Prepayments for RG 3.0%, 3.5% and 4.0% Pools by Pool Age, March to September 2021 GNMA PoolsNote: only showing data points for cohorts with more than 50 loans


Issuance Volume 

Following a brief ramp-up period in February and March, issuance of RG pools has averaged around $2 billion (and roughly 300 pools) per month for the past five months (see Issuance chart below). The outstanding UPB of these pools stands at nearly $11 billion as of the September factor month. 


GNMA PoolsNote: RiskSpan uses reporting month as a factor month. For this chart, we adjust our factor date by one month to match the collection period.


RG pools already account for a sizable share of Ginnie II custom issuance, as illustrated in the following chart, making up 18% of G2 custom issuance and 3% of all G2 issuance since April.

GNMA PoolsNote: RiskSpan uses reporting month as a factor month. For this chart, we adjust our factor date by one month to match the collection period. 


RG Pool Characteristics 

Nearly all of RG pool issuance has been in 3.0% to 4.5% coupons, with a plurality at 3.5%. As of the September factor month, almost $4 billion (37%) of the outstanding RG pools are in 3.5% coupons. The 4% coupon accounted for the next-largest share–$2.5 billion (23%)—followed by $2.3 billion in 3.0% (20.9%) and $1.3 billion in 4.5% (11.8%). 


RG Pool Outstanding Amount by Coupon — September Factor Month GNMA Pools


 The following table compares the characteristics of RG pools issued since February with those of G2 single-family custom and multi pools issued during the same period.  The table highlights some interesting differences: 

  • Issuance of RG pools seems to be concentrated in higher coupons (3% to 4%) compared to issuances for G2 custom pools (concentrated on 2.5% and 3.0%) and G2 multi-lender pools (concentrated on 2.0% and 2.5%). 
  • Loan sizes in RG pools tend to fall between those of G2 customs and smaller than G2 multis.  For example, WAOLS for 3.5% RG pools is around 245k and is around 50k smaller than multi pools and 30k larger than other custom pools. 
  • RG pools consist almost exclusively of FHA loans while G2 multis have a much higher share of VA loans.  Almost 98% of 3.5% RG loans are FHA loans. 


 G2 RG vs. G2 Custom and G2 Multi (pools issued since February), Stat as of September Factor Month GNMA Pools

Wells Fargo and Penny Mac are far and away the leaders in RG issuance, accounting collectively for 62% of outstanding RG pools.  


RG Pools by Servicer, September Factor Month GNMA Pools


 How to Run RG Pools in Edge Perspective 

Subscribers to Edge Perspective can run these comparisons (and countless others) themselves using the “GN RG” pool type filter. The “Custom/Multi-lender” filter can likewise be applied to separate those pools in G2SF. 


Contact Us

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


EDGE: QM vs Non-QM Prepayments

Prepayment speeds for qualified mortgages (QM loans) have anecdotally been faster than non-QM loans. For various reasons, the data necessary to analyze interest rate incentive response has not been readily available for these categories of mortgages.

In order to facilitate the generation of traditional refinancing curves (S-curves) over the last year, we have normalized data to improve the differentiation of QM versus non-QM loans within non-agency securities.

Additionally, we isolated the population to remove prepay impact from loan balance and seasoning.

The analysis below was performed on securitized loans with 9 to 36 months of seasoning and an original balance between 200k and 500k. S-curves were generated for observation periods from January 2016 through July 2021.

Results are shown in the table and chart below.

Edge-QM-vs-Non-QM-Refi-Incentive


Edge-QM-vs-Non-QM-Refi-Incentive

For this analysis, refinance incentive was calculated as the difference between mortgage note rate and the 6-week lagged Freddie Mac primary mortgage market survey (PMMS) rate. Non-QM borrowers would not be able to easily refi into a conventional mortgage. We further analyzed the data by examining prepayments speeds for QM and non-QM loans at different level of SATO. SATO, the spread at origination, is calculated as the difference between mortgage note rate and the prevailing PMMS rate at time of loan’s origination.

Edge-QM-vs-Non-QM-Refi-Incentive

Using empirical data maintained by RiskSpan, it can be seen the refinance response for QM loans remains significantly faster than Non-QM loans.

Using Edge, RiskSpan’s data analytics platform, we can examine any loan characteristic and generate S-curves, aging curves, and time series. If you are interested in performing historical analysis on securitized loan data, please contact us for a free demonstration.


RiskSpan VQI: Agency Mortgage Risk Layers for Q2 2021

RiskSpan’s Vintage Quality Index computes and aggregates the percentage of Agency originations each month with one or more “risk factors” (low-FICO, high DTI, high LTV, cash-out refi, investment properties, etc.). Months with relatively few originations characterized by these risk factors are associated with lower VQI ratings. As the historical chart above shows, the index maxed out (i.e., had an unusually high number of loans with risk factors) leading up to the 2008 crisis.

RiskSpan uses the index principally to fine-tune its in-house credit and prepayment models by accounting for shifts in loan composition by monthly cohort.

Rising Home Prices Contribute to More High-DTI Loans and Cash-out Refis

The Vintage Quality Index rose noticeably during the second quarter of 2021 — up to a value of 83.40, compared to 76.68 in the first quarter.

Unlike last quarter, when a precipitous drop in high-LTV loans effectively masked and counterbalanced more modest increases in the remaining risk metrics, this quarter’s sizeable VQI jump is attributable to a more across-the-board increase in risk layers.

A sharp rebound in the percentage of high-LTV loans, a metric that had been in steady decline since the middle of 2019, was accompanied by modest increases in borrowers with low credit scores (FICO below 660) and high debt-to-income ratios (greater than 45%).

The spike in home prices across the country that likely accounts for the rise in high-LTV mortgages also appears to be prompting an increasing number of borrowers to seek cash-out refinancings. More than 22 percent of originations had LTVs in excess of 80 percent at the end of Q2, compared to just 17 percent at the end of Q1. Similarly, nearly 25 percent of mortgages were cash-out refis in June, compared to 22 percent in March.

Modest declines were observed in the percentages of loans on investment and multi-unit properties. All other risk metrics were up for the quarter, as the plots below illustrate.

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 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

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: Extended Delinquencies in Loan Balance Stories

In June, we highlighted Fannie Mae’s and Freddie Mac’s new “expanded delinquency” states. The Enterprises are now reporting delinquency states from 1 to 24 months to better account for loans that are seriously delinquent and not repurchased under the extended timeframe for repurchase of delinquent loans announced in 2020.

This new data reveals a strong correlation between loan balance and “chronically delinquent” loans. In the graph below, we chart loan balance on the x-axis and 180+Day delinquency on the y-axis, for 2017-18 production 30yr 3.5s through 4.5 “generic” borrowers.[1]

As the graph shows, within a given coupon, loans with larger original balances also tended to have higher “chronic delinquencies.

EDGE-Orig-Loan-Size

The graph above also illustrates a clear correlation between higher chronic delinquencies and higher coupons. This phenomenon is most likely due to SATO. While each of these queries excluded low-FICO, high-LTV, and NY loans, the 2017-18 30yr 3.5 cohort was mostly at-the-money origination, whereas 4.0s and 4.5s had an average SATO of 30bp and 67bp respectively. The higher SATO indicates a residual credit quality issue. As one would expect, and we demonstrated in our June analysis, lower-credit-quality loans tend also to have higher chronic delinquencies.

The first effect – higher chronic delinquencies among larger loans within a coupon – is more challenging to understand. We posit that this effect is likely due to survivor bias. The large refi wave over the last 18 months has factored-down higher-balance cohorts significantly more than lower-balance cohorts.

EDGE-Factors

Higher-credit-quality borrowers tend to refinance more readily than lower-credit-quality borrowers, and because the larger-loan-balance cohorts have seen higher total prepayments, these same cohorts are left with a larger residue of lower-quality credits. The impact of natural credit migration (which is observed in all cohorts) tends to leave behind a larger proportion of credit-impaired borrowers in faster-paying cohorts versus the slower-paying, lower-loan-balance cohorts.

The higher chronic delinquencies in larger-loan-balance cohorts may ultimately lead to higher buyouts, depending on the resolution path taken. As loan balance decreases, the lower balance cohorts will have reduced risk to these potential buyouts, leaving them better protected to any uptick in involuntary speeds.


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] We filtered for borrowers with LTV<=80, FICO>=700, and ex-NY. We chose 2017-18 production to analyze, to give sufficient time for loans to go chronically delinquent. We see a similar relationship in 2019 production, see RiskSpan for details.


EDGE: Extended Delinquencies in FNMA and FHLMC Loans

In June, the market got its first look at Fannie Mae and Freddie Mac “expanded delinquency” states. The Enterprises are now reporting delinquency states out to 24 months to better account for loans that are seriously delinquent and not repurchased under the extended timeframe for repurchase of delinquent loans announced in 2020. In this short post, we analyze those pipelines and what they could mean for buyouts in certain spec pool stories. 

First, we look at the extended pipeline for some recent non-spec cohorts. The table below summarizes some major 30yr cohorts and their months delinquent. We aggregate the delinquencies that are more than 6 months delinquent[1] for ease of exposition. 

Recent-vintage GSE loans with higher coupons show a higher level of “chronically delinquent” loans, similar to the trends we see in GNMA loans. 

Digging deeper, we filtered for loans with FICO scores below 680. Chronically delinquent loan buckets in this cohort are marginally more prevalent relative to non-spec borrowers. Not unexpectedly, this suggests a credit component to these delinquencies.

Finally, we filtered for loans with high LTVs at origination. The chronically delinquent buckets are lower than the low FICO sector but still present an overhang of potential GSE repurchases in spec pools.

It remains to be seen whether some of these borrowers will be able to resume their original payments —  in which case they can remain in the pool with a forbearance payment due at payoff — or if the loans will be repurchased by the GSEs at 24 months delinquent for modification or other workout. If the higher delinquencies lead to the second outcome, the market could see an uptick in involuntary speeds on some spec pool categories in the next 6-12 months.


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] The individual delinquency states are available for each bucket, contact us for details.


Non-Agency Delinquencies Fall Again – Still Room for Improvement

Serious delinquencies among non-Agency residential mortgages continue marching downward during the first half of 2021 but remain elevated relative to their pre-pandemic levels.

Our analysis of more than two million loans held in private-label mortgage-backed securities found that the percentage of loans at least 60 days past due fell again in May across vintages and FICO bands. While performance differences across FICO bands were largely as expected, comparing pre-crisis vintages with mortgages originated after 2009 revealed some interesting distinctions.

The chart below plots serious delinquency rates (60+ DPD) by FICO band for post-2009 vintages. Not surprisingly, these rates begin trending upward in May and June of 2020 (two months after the economic effects of the pandemic began to be felt) with the most significant spikes coming in July and August – approaching 20 percent at the low end of the credit box and less than 5 percent among prime borrowers.

Since last August’s peak, serious delinquency rates have fallen most precipitously (nearly 8 percentage points) in the 620 – 680 FICO bucket, compared with a 5-percentage point decline in the 680 – 740 bucket and a 4 percentage point drop in the sub-620 bucket. Delinquency rates have come down the least among prime (FICO > 740) mortgages (just over 2 percentage points) but, having never cracked 5 percent, these loans also had the shortest distance to go.

Serious delinquency rates remain above January 2020 levels across all four credit buckets – approximately 7 percentage points higher in the two sub-680 FICO buckets, compared with the 680 – 740 bucket (5 percentage points higher than in January 2020) and over-740 bucket (2 percentage points higher).

So-called “legacy” vintages (consisting of mortgage originated before the 2008-2009 crisis) reflect a somewhat different performance profile, though they follow a similar pattern.

The following chart plots serious delinquency rates by FICO band for these older vintages. Probably because these rates were starting from a relatively elevated point in January 2020, their pandemic-related spike were somewhat less pronounced, particularly in the low-FICO buckets. These vintages also appear to have felt the spike about a month earlier than did the newer issue loans.

Serious delinquency rates among these “legacy” loans are considerably closer to their pre-pandemic levels than are their new-issue counterparts. This is especially true in the sub-prime buckets. Serious delinquencies in the sub-620 FICO bucket actually were 3 percentage points lower last month than they were in January 2020 (and nearly 5 percentage points lower than their peak in July 2020). These differences are less pronounced in the higher-FICO buckets but are still there.

Comparing the two graphs reveals that the pandemic had the effect of causing new-issue low-FICO loans to perform similarly to legacy low-FICO loans, while a significant gap remains between the new-issue prime buckets and their high-FICO pre-2009 counterparts. This is not surprising given the tightening that underwriting standards (beyond credit score) underwent after 2009.

Interested in cutting non-Agency performance across any of several dozen loan-level characteristics? Contact us for a quick, no-pressure demo.


RiskSpan VQI: Current Underwriting Standards Q1 2021

VQI-Risk-Layers-Calc-March-2021

RiskSpan’s Vintage Quality Index estimates the relative “tightness” of credit standards by computing and aggregating the percentage of Agency originations each month with one or more “risk factors” (low-FICO, high DTI, high LTV, cash-out refi, investment properties, etc.). Months with relatively few originations characterized by these risk factors are associated with lower VQI ratings. As the historical chart above shows, the index maxed out (i.e., had an unusually high number of loans with risk factors) leading up to the 2008 crisis.

Vintage Quality Index Stability Masks Purchase Credit Contraction

The first quarter of 2021 provides a stark example of why it is important to consider the individual components of RiskSpan’s Vintage Quality Index and not just the overall value. 

The Index overall dropped by just 0.37 points to 76.68 in the first quarter of 2021. On the surface, this seems to suggest a minimal change to credit availability and credit quality over the period. But the Index’s net stability masks a significant change in one key metric offset by more modest counterbalancing changes in the remaining eight. The percentage of high-LTV mortgages fell to 16.7% (down from 21% at the end of 2020) during the first quarter.  

While this continues a trend in falling rates of high-LTV loans (down 8.7% since Q1 of 2020 and almost 12% from Q1 2019) it coincides with a steady increase in house prices. From December 2020 to February 2021, the Monthly FHFA House Price Index® (US, Purchase Only, Seasonally Adjusted) rose 1.9%. More striking is the year-over-year change from February 2020 to 2021, during which the same rose by 11.1%. Taken together, the 10% increase in home prices combined with a 10% reduction in the share of high-LTV loans paints a sobering picture for marginal borrowers seeking to purchase a home.  

Some of the reduction in high-LTV share is obviously attributable to the growing percentage of refinance activity (including cash-out refinancing, which counterbalances the effect the falling high-LTV rate has on the index). But these refis does not impact the purchase-only HPI. As a result, even though the overall Index did not change materially, higher required down payments (owing to higher home prices) combined with fewer high-LTV loans reflects a credit box that effectively shrank in Q1.

 

VQI-Risk-Layers-Historical-Trend-March-2021

VQI-RISK-LAYERS-HEADER-MARCH-2021

VQI-March-2021-FICO-660

VQI-March-2021-LTV-80

VQI-March-2021-Adjust-Rate

VQI-March-2021-Loans-W-SF

VQI-March-2021-Cash-Refi

VQI-RISK-LAYERS-HEADER-MARCH-2021

VQI-March-2021

VQI-March-2021

VQI-March-2021-OBL

VQI-Analytic-and-Data-Assumptions-Header

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: New Forbearance Data in Agency MBS

Over the course of 2020 and into early 2021, the mortgage market has seen significant changes driven by the COVID pandemic. Novel programs, ranging from foreclosure moratoriums to payment deferrals and forbearance of those payments, have changed the near-term landscape of the market.

In the past three months, Fannie Mae and Freddie Mac have released several new loan-level credit statistics to address these novel developments. Some of these new fields are directly related to forbearance granted during the pandemic, while others address credit performance more broadly.

We summarize these new fields in the table below. These fields are all available in the Edge Platform for users to query on.

The data on delinquencies and forbearance plans covers March 2021 only, which we summarize below, first by cohort and then by major servicer. Edge users can generate other cuts using these new filters or by running the “Expanded Output” for the March 2021 factor date.

In the first table, we show loan-level delinquency for each “Assistance Plan.” Approximately 3.5% of the outstanding GSE universe is in some kind of Assistance Plan.

In the following table, we summarize delinquency by coupon and vintage for 30yr TBA-eligible pools. Similar to delinquencies in GNMA, recent-vintage 3.5% and 4.5% carry the largest delinquency load.

Many of the loans that are 90-day and 120+-day delinquent also carry a payment forbearance. Edge users can simultaneously filter for 90+-day delinquency and forbearance status to quantify the amount of seriously delinquent loans that also carry a forbearance versus loans with no workout plan.[2]  Finally, we summarize delinquencies by servicer. Notably, Lakeview and Wells leads major servicers with 3.5% and 3.3% of their loans 120+-day delinquent, respectively. Similar to the cohort analysis above, many of these seriously delinquent loans are also in forbearance. A summary is available on request.

In addition to delinquency, the Enterprises provide other novel performance data, including a loan’s total payment deferral amount. The GSEs started providing this data in December, and we now have sufficient data to start to observing prepayment behavior for different levels of deferral amounts. Not surprisingly, loans with a payment deferral prepay more slowly than loans with no deferral, after controlling for age, loan balance, LTV, and FICO. When fully in the money, loans with a deferral paid 10-13 CPR slower than comparable loans.

Next, we separate loans by the amount of payment deferral they have. After grouping loans by their percentage deferral amount, we observe that deferral amount produces a non-linear response to prepayment behavior, holding other borrower attributes constant.

Loans with deferral amounts less than 2% of their UPB showed almost no prepayment protection when deep in-the-money.[3] Loans between 2% and 4% deferral offered 10-15 CPR protection, and loans with 4-6% of UPB in deferral offered a 40 CPR slowdown.

Note that as deferral amount increases, the data points with lower refi incentive disappear. Since deferral data has existed for only the past few months, when 30yr primary rates were in a tight range near 2.75%, that implies that higher-deferral loans also have higher note rates. In this analysis, we filtered for loans that were no older than 48 months, meaning that loans with the biggest slowdown were typically 2017-2018 vintage 3.5s through 4.5s.

Many of the loans with P&I deferral are also in a forbearance plan. Once in forbearance, these large deferrals may act to limit refinancings, as interest does not accrue on the forborne amount. Refinancing would require this amount to be repaid and rolled into the new loan amount, thus increasing the amount on which the borrower is incurring interest charges. A significantly lower interest rate may make refinancing advantageous to the borrower anyway, but the extra interest on the previously forborne amount will be a drag on the refi savings.

Deferral and forbearance rates vary widely from servicer to servicer. For example, about a third of seriously delinquent loans serviced by New Residential and Matrix had no forbearance plan, whereas more than 95% of such loans serviced by Quicken loans were in a forbearance plan. This matters because loans without a forbearance plan may ultimately be more subject to repurchase and modification, leading to a rise in involuntary prepayments on this subset of loans.

As the economy recovers and borrowers increasingly resolve deferred payments, tracking behavior due to forbearance and other workout programs will help investors better estimate prepayment risk, both due to slower prepays as well as possible future upticks in buyouts of delinquent loans.


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




[1] A link to the Deferral Amount announcement can be found here, and a link to the Forbearance and Delinquency announcement can be found here. Freddie Mac offers a helpful FAQ here on the programs.

[2] Contact RiskSpan for details on how to run this query.

[3] For context, a payment deferral of 2% represents roughly 5 months of missed P&I payments on a 3% 30yr mortgage.


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