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Articles Tagged with: Agency MBS

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

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

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

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

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

Note on the Limitations of RMMI

To calculate the RMMI, an econometric tool is developed to estimate senior housing value, senior mortgage level, and senior equity using data gathered from various public resources such as American Community Survey (ACS), Federal Reserve Flow of Funds (Z.1), and FHFA housing price indexes (HPI). The RMMI is simply the senior equity level at time of measure relative to that of the base quarter in 2000.[1]  The main limitation of RMMI is non-consecutive data, such as census population. We use a smoothing approach to estimate data in between the observable periods and continue to look for ways to improve our methodology and find more robust data to improve the precision of the results. Until then, the RMMI and its relative metrics (values, mortgages, home equities) are best analyzed at a trending macro level, rather than at more granular levels, such as MSA.


[1] There was a change in RMMI methodology in Q3 2015 mainly to calibrate senior homeowner population and senior housing values observed in 2013 American Community Survey (ACS).


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

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

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

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

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

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

Population assumptions:

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

Data assumptions:

  • Freddie Mac data goes back to 12/2005. Fannie Mae only back to 12/2014.
  • Certain fields for Freddie Mac data were missing prior to 6/2008.
  • GSE historical loan performance data release in support of GSE Risk Transfer activities was used to help back-fill data where it was missing.

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


EDGE: GNMA Forbearance End Date Distribution

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

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

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

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

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

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

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

 

 

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

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

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

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


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

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

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


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

Sept 2020 Vintage Quality Index

Riskspan VQI Historical Trend

Riskspan VQI Historical Trend

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 Historical Trend

Risk Layers – September 20 – All Issued Loans By Count

FICO < 660 - Share Issued Loans

Loan to Value > 80 - Share of Issued Loans

Debt-to-Income > 45 - Share of Issued Loans

Ajustable-Rate-Share-of-Issued-Loans

Loans-w-Subordinate-Financing-Sept-2020

Cashout-Refinance

Risk Layers – September 20 – All Issued Loans By Count

Loan-Occupancy

Multi-Unit-Share-of-Issued-Loans

One-Borrower-Loans

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.

FactorDate vs CPR
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.

FactorDate vs ForbearancePercent

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.

FactorDate vs InvoluntaryPurchase

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.

 

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.


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