Agency MBS Archives

Surge in Cash-Out Refis Pushes VQI Sharply Higher

A sharp uptick in cash-out refinancing pushed RiskSpan’s Vintage Quality Index (VQI) to its highest level since the first quarter of 2019.

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 Rates Mean More Cash-Out Refis (and more risk)

As the following charts plotting the individual VQI components illustrate, a spike in cash-out refinance activity (as a percentage of all originations) accounted for more of the rise in overall VQI than did any other risk factor.

This comes as little surprise given the rising rate environment that has come to define the first quarter of 2022, a trend that is likely to persist for the foreseeable future.

As we demonstrated in this recent post, the quickly vanishing number of borrowers who are in the money for a rate-and-term refinance means that the action will increasingly turn to so-called “serial cash-out refinancers” who repeatedly tap into their home equity even when doing so means refinancing into a mortgage with a higher rate. The VQI can be expected to push ever higher to the extent this trend continues.

An increase in the percentage of loans with high debt-to-income ratios (over 45) and low credit scores (under 660) also contributed to the rising VQI, as did continued upticks in loans on investment and multi-unit properties as well as mortgages with only one borrower.

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.

Data Source: Fannie Mae PoolTalk®-Loan Level Disclosure

EDGE: Cash-Out Refi Speeds

Mortgage rates have risen nearly 200bp from the final quarter of 2021, squelching the most recent refinancing wave and leaving the majority of mortgage holders with rates below the prevailing rate of roughly 5% (see chart below). For most homeowners, it no longer makes sense to refinance an existing 30yr mortgage into another 30yr mortgage.

Vintage/Note Rate Distribution 30yr Conventional Mortgages

GET STARTED

But, as we noted back in February, the rapid rise in home prices has left nearly all households with significant, untapped gains in their household balance sheets. For homeowners with consumer debt at significantly higher rates than today’s mortgage rates, it can make economic sense to consolidate debt using a cash-out refi loan against their primary residence. As we saw during 2002-2003, cash-out refinancing can drive speeds on discount mortgages significantly higher than turnover alone. Homeowners can also become “serial cash-out refinancers,” tapping additional equity multiple times.

In this analysis, we review prepayment speeds on cash-out refis, focusing on discount MBS, i.e., mortgages whose note rates are equal to or below today’s prevailing rates.

The volume of cash-out refis has grown steadily but modestly since the start of the pandemic, whereas rate/term refis surged and fell dramatically in response to changing interest rates. Despite rising rates, the substantial run-up in home prices and increased staffing at originators from the recent refi boom has left the market ripe for stronger cash-out activity.

The pivot to cash-out issuance is evidenced by the chart below, illustrating how the issuance of cash-out refi loans (the black line below) in the first quarter of this year was comparable with issuance in the summer of 2021, when rates near historic lows, while rate/term refis (blue line) have plunged over the same period.

Quarterly Issuance of FN/FH Mortgages

With cash-out activity set to account for a larger share of the mortgage market, we thought it worthwhile to compare some recent cash-out activity trends. For this analysis, the graphs consist of truncated S-curves, showing only the left-hand (out-of-the-money) side of the curve to focus on discount mortgage behavior in a rising rate environment where activity is more likely to be influenced by serial cash-out activity.

This first chart compares recent performance of out-of-the money mortgages by loan purpose, comparing speeds for purchase loans (black) with both cash-out refis (blue) and rate/term refis (green). Notably, cash-out refis offer 1-2 CPR upside over rate/term refis, only converging to no cash out refis when 100bp out of the money.[1]

Next, we compare cash-out speeds by servicer type, grouping mortgages that are serviced by banks (blue) versus mortgages serviced by non-bank servicers (green). Non-bank servicers produce significantly faster prepay speeds, an advantage over bank-serviced loans for MBS priced at a discount.

Finally, we drill deeper into the faster non-bank-serviced discount speeds for cash-out refis. This chart isolates Quicken (red) from other non-bank servicers (green). While Quicken’s speeds converge with those of other non-banks at the money, Quicken-serviced cash-out refis are substantially faster when out of the money than both their non-bank counterparts and the cash-out universe as a whole.[2]

SCHEDULE A DEMO

We suspect the faster out-of-the-money speeds are being driven by serial cash-out behavior, with one servicer in particular (Quicken) encouraging current mortgage holders to tap home equity as housing prices continue to rise.

This analysis illustrates how pools with the highest concentration of Quicken-serviced cash-out loans may produce substantially higher out-of-the-money speeds relative to the universe of non-spec pools. To find such pools, users can enter a list of pools into the Edge platform and simultaneously filter for both Quicken and cash-out refi. The resultant query will show each pool’s UPB for this combination of characteristics.

[1] For this analysis, we focused mortgages aged 18 to 48 months, with FICO above 700, LTV less than or equal to 80, and loan size greater than or equal to 250k. Other cuts are available. Please see us for details.

[2] For this analysis, we removed points with less than 1000 observations to reduce statistical noise.

EDGE: Recent Performance of GNMA RG Pools

In early 2021, GNMA began issuing a new class of custom pools with prefix “RG.” These pools are re-securitizations of previously delinquent loans which were repurchased from pools during the pandemic.[1] Loans in these pools are unmodified, keeping the original rate and term of the mortgage note. In the analysis below, we review the recent performance of these pools at loan-level detail. The first RG pools were issued in February 2021, growing steadily to an average rate of $2B per month from Q2 onward, with a total outstanding of $21 billion.

LEARN MORE ABOUT RISKSPAN'S EDGE PLATFORM

The majority of RG issuance has included loans that are two to seven years seasoned and represent a consistent 2-3% of the total GNMA market for those vintages, dashed line below.

Distribution of RG Loans by Age

Coupons of RG pools are primarily concentrated between 3.0s through 4.5s, with the top-10 Issuers of RG pools account for nearly 90% of the issuance.

EDGE - GNMA RG POOL PERFORMANCE

Below, we compare speeds on GNMA RG pools under various conditions. First, we compare speeds on loans in RG pools (black) versus same-age multi-lender pools (red) over the last twelve months. When out of the money, RG pools are 4-5 CPR slower than comparably aged multi-lender pools but provide a significantly flatter S-curve when in-the-money.

Next, we plot the S-curve for all GNMA RG loans with overlays for loans that are serviced by banks (green) and non-banks (blue). Bank-serviced RG loans prepay significantly slower than non-banks by an average of 9 CPR weighted across all incentives. Further, this difference is caused by voluntary prepays, with buyouts averaging a steady 4% CBR, plus or minus 1 CBR, for both banks and non-banks with no discernable difference between the two (second graph). GNMA_RG_Buyouts-graph

Finally, we analyzed the loan-level transition matrix by following each RG loan through its various delinquency states over the past year. We note that the transition rate from Current to 30-day delinquent for RG loans is 1.6%, only marginally worse than that of the entire universe of GNMA loans at 1.1%. RG loans transitioned back from 30->Current at similar rates to the wider Ginnie universe (32.3%) and the 30->60 transition rate for RG loans was marginally worse than the Ginnie universe, 30.8% versus 24.0%.[2]

Monthly Transition Rates for Loans in GNMA RG Pools: EDGE-GNMA-RG-Pool-Perform-Current-State In summary, loans in RG pools have shown a substantial level of voluntary prepayments and comparatively low buyouts, somewhat unexpected especially in light of their recent delinquency. Further, their overall transition rates to higher delinquency states, while greater than the GNMA universe, is markedly better than that of reperforming loans just prior to the outbreak of COVID.

SCHEDULE A DEMO

[1] For a more detailed description of the RG pool criteria, GNMA’s original memorandum can be found here.

[2] These transition rates have been stable over different time periods. The dollar-weighted average transition rates over the last year closely match the rates over the prior six months.

EDGE: Extension Protection in a Rising Rate Environment

With the Fed starting their tightening cycle and reducing balance sheet, mortgage rates have begun rising. Since late summer, 30-year conforming rates have risen more than 100bp, with 75bp of that occurring since the end of December. The recent flight-to-quality rally has temporarily eased that, but the overall trend remains in place for higher mortgage rates.

With this pivot, mortgage investors have switched from focusing on prepayment protection to mitigating extension risk. In this post, we offer analysis on extension risk and turnover speeds for various out-of-the-money Fannie and Freddie cohorts.[1]

In the chart below, we first focus on out-of-the-money prepays on lower loan balance loans. For this analysis, we analyzed speeds on loans that were 24 to 48 months seasoned. We further grouped the loan balance stories into meta-groups, as the traditional groupings of “85k-Max”, etc, showed little difference in out-of-the-money speeds. When compared to loans with balances above 250k, speeds on lower loan balance loans were a scant 1-2 CPR faster than borrowers with larger loan balances, when prevailing rates were 25bp to 100bp higher than the borrower’s note rate.

We next compare borrowers in low FICO pools, high LTV pools, and 100% investor pools. Speeds on low-FICO pools (blue) offer some extension protection due to higher involuntary speeds. At the other end, loans in 100% investor pools were dramatically slower than non-spec pools when out-of-the money.

Finally, we look at the behavior of borrowers in non-spec pools segregated by loan purpose, again controlling for loan age. Borrowers with refi loans pay significantly faster than purchase loans when only slightly out-of-the money. As rates continue to rise, refi speeds converge to purchase loans at 75bp out of the money and pay slower when 75-100bp out of the money, presumably due to a stronger lock-in effect.

REQUEST A DEMO OF EDGE

We also separated these non-spec borrowers by originators, grouping the largest banks and non-bank originators together. Out-of-the-money speeds on refi loans were significantly faster for loans originated by non-bank originators (blue and green) versus those originated by banks (red and orange). Speeds on purchase loans were only 1-2 CPR faster for non-banks versus banks and were omitted from this graph for readability.

In the current geopolitical climate, rates may continue to drop over the short term. But given the Fed’s tightening bias, it’s prudent to consider extension risk when looking at MBS pools, in both specified and non-specified pools.

[1] For investors interested in GNMA analysis, please contact RiskSpan

EDGE: The Fed’s MBS, Distribution and Prepayments

Since the Great Financial Crisis of 2008, the Federal Reserve Bank of New York has been the largest and most influential participant in the mortgage-backed securities market. In the past 14 years, the Fed’s holdings of conventional and GNMA pools has grown from zero to $2.7 trillion, representing roughly a third of the outstanding market. With inflation spiking, the Fed has announced an end to MBS purchases and will shift into balance-sheet-reduction mode. In this short post, we review the Fed’s holdings, their distribution across coupon and vintage, and their potential paydowns as rates rise.

The New York Fed publishes its pool holdings here. The pools are updated weekly and have been loaded into RiskSpan’s Edge Platform. The chart below summarizes the Fed’s 30yr Fannie/Freddie holdings by vintage and net coupon.

LEARN MORE ABOUT A DEMO OR TRIAL

We further categorize the Fed’s holdings by vintage and borrower note rate (gross WAC) at the loan level. Using loan-level data (rather than weighted-average statistics published on Fed-held Supers or their constituent pools [1]) provides a more accurate view of the Fed’s distribution of note rates and hence prepayment exposure.

Not surprisingly, the recent and largest quantitative easing has left the Fed holding MBS with gross WACs below the current mortgage rate. Roughly 85% of the mortgages held by the Fed are out-of-the-money, and the remaining in-the-money mortgages are several years seasoned. These older pools are beginning to exhibit burnout, with the sizable refinancing wave over the last two years having limited these moderately seasoned loans mainly to borrowers who are less reactive to savings from refinancing.

With most of the Fed’s portfolio at below-market rates and the remaining MBS moderately burned out, market participants expect the Fed’s MBS runoff to continue to slow. At current rates, we estimate that Fed paydowns will continue to decline and stabilize around $25B per month in the second quarter, just shy of 1% of its current MBS holdings.

With these low levels of paydowns, we anticipate the Fed will need to sell MBS if they want to make any sizable reduction in their balance sheet. Whether the Fed feels compelled to do this, or in what manner sales will occur, is an unsettled question. But paydowns alone will not significantly reduce the Fed’s holdings of MBS over the near term.

[1] FNMA publishes loan-level data for pools securitized in 2013 onward. For Fed holdings that were securitized before 2013, we used FNMA pool data.

EDGE: Measuring the Potential for Another Cash-out Refi Wave

With significant home price gains over the last two years, U.S. homeowners are sitting on vast, mostly untapped wealth. Nationally, home prices are up an aggregate of 28% over the last two years, with some regions performing even better. But unlike other periods of strong home price gains, cash-out refinancings lagged overall refinancings during the pandemic rate-rally. In this short article, we look at cash-out refinancings over time, and their potential impact on prepayments, especially on discount cohorts.

A historical perspective

In the early 2000s, mortgage rates fell nearly 200bp, triggering a massive refinancing wave as well as a rally in home prices that lasted well into 2005.

Edge Housing Gains and Cash out Refis

During this early millennium rally, the market saw significant cash-out refi activity with homeowners borrowing at then-historically low rates to free up cash. The market even saw refinancing activity in mortgages with note rates below the prevailing market rate. In 2002, CPRs on some discount cohorts hit the low to middle teens, which many participants attributed to cash-out refinancing. Resetting a mortgage 50 basis points higher can nevertheless often lead to overall lower debt servicing when borrowers use cash-out refis to consolidate auto loans, credit cards and other higher-rate unsecured borrowings.[1] In the early 2000s, this cash-out refinancing activity led to overall faster speeds and a higher S-curve for out-of-the-money cohorts. How does 2002-03 cash-out refi activity compare to today? In the early 2000s, issuance of cash-out mortgages, as a percentage of the total market, varied between 1% and 2.5% of the outstanding mortgage universe each month.

LEARN MORE ABOUT RISKSPAN's EDGE PLATFORM

Since the onset of the pandemic, that figure has not experienced the same kind of spike, hovering around just 0.9%.[2]

EDGE-Housing Gains and Cash-out Refis-Graph 2-2-7-2022

In 2002-03, most of these cash-out borrowers refinanced into lower rates, but a sufficient number took out mortgages at same or higher rates to drive prepayments on discount MBS into the low teens CPR (see black s-curve below). By comparison, out-of-the money speeds today (the blue s-curve) are approximately 4 CPR slower.

The nearly 30% rally in home prices during the pandemic has further strengthened a solid housing market. Today’s borrowers have substantial equity in their homes, leaving many homeowners with untapped borrowing power, shown in the market-implied LTVs below. From an origination standpoint, mortgage lenders have sufficient capacity to support any uptick in cash-out refinancing as rate-term refinancing volumes decline.

Any growth in cash-out refi issuance is likely to come on loans with note rates close to the prevailing mortgage rate. If a homeowner needs to generate cash for a large purchase, it can make economic sense to refinance an existing loan into a new loan with rates as much as 25bp or 50bp higher, rather than incur even higher (and shorter-term) interest rates on credit cards or personal loans. Therefore, any uptick in cash-out refinancing will likely have a larger effect on prepayment speeds for MBS that are either at-the-money or slightly out-of-the-money. This uptick may mitigate some of the extension risk in near-discount mortgages, especially in non-spec cohorts where refinancing frictions are lower. While the past two years have seen substantial changes, positive and negative, in overall refinancings, cash-out refis have largely not followed suit. But a significant home price rally, coupled with strong economic activity and excess originator capacity, could change that trend in the upcoming year.

[1] See RiskSpan for examples.

[2] To compute this statistic, we summed the originations of cash-out refi mortgages and divided by the total size of mortgages extant.

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

VQI-Graph VQI-Current-Layers-September-2021

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-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-LTV-80-Shared-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.

RMMI Q2 2021–Changes in Senior Home Equity

The NRMLA/RiskSpan Reverse Mortgage Market Index (RMMI) rose in Q2 2021 to 334.71, another all-time high since the index was first published in 2000. The increase in older homeowners’ wealth was mainly driven by an estimated 3.4 percent increase in home values, offset by a 2.2 percent increase in senior-held mortgage debt.

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 FactorMonth Note: 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 Pool Note: Loan level data, orange line is the s-curve for 7-18 wala G2 multi pools with a 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 Months GNMA Pools Note: 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 Pools Note: 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 Pools Note: 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 Pools Note: 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

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.

‹ Prev 1 234 5 6 Next ›Last »

Surge in Cash-Out Refis Pushes VQI Sharply Higher

EDGE: Cash-Out Refi Speeds

EDGE: Recent Performance of GNMA RG Pools

EDGE: Extension Protection in a Rising Rate Environment

EDGE: The Fed’s MBS, Distribution and Prepayments

EDGE: Measuring the Potential for Another Cash-out Refi Wave

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

Senior Housing Wealth Exceeds Record $9.57 Trillion

How RiskSpan Computes the RMMI

How Are Ginnie’s New RG Pools Performing?

Contact Us

EDGE: QM vs Non-QM Prepayments

Company

Products

Security & Compliance