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Articles Tagged with: General

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.

Chart of the Month: Fed Impact on Credit ETF Performance

On March 23rd, The Fed announced that its Secondary Market Corporate Credit Facility (SMCCF) would begin purchasing investment-grade corporate bonds in the secondary market, first through ETFs and directly in a later phase. 

In June, we charted the impact of this announcement on the credit spreads of various corporate bonds. This month we are charting its impact on ETF performance.

This month’s chart plots the price of ETFs relative to their price as of March 23rd 2020 (i.e., all ETF prices are set to 1.00 as of that date). Data runs from Feb 24th to Nov 16th 2020.

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.

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RiskSpan VQI: Current Underwriting Standards Q3 2020

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

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

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

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

Risk Layers – September 20 – All Issued Loans By Count

Risk Layers – September 20 – All Issued Loans By Count

Analytical And Data Assumptions

Population assumptions:

  • Monthly data for Fannie Mae and Freddie Mac.

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

  • Loans likely to have been originated through the HARP program, as identified by LTV, MI coverage percentage, and loan purpose are also excluded. These loans do not represent credit availability in the market as they likely would not have been originated today but for the existence of HARP.                                                                                                                          

Data assumptions:

  • Freddie Mac data goes back to 12/2005. Fannie Mae only back to 12/2014.

  • Certain fields for Freddie Mac data were missing prior to 6/2008.   

GSE historical loan performance data release in support of GSE Risk Transfer activities was used to help back-fill data where it was missing.

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

EDGE: Unexplained Behavior for Idaho HFA

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

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

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

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

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

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

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

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


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


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.

The Why and How of a Successful SAS-to-Python Model Migration

A growing number of financial institutions are migrating their modeling codebases from SAS to Python. There are many reasons for this, some of which may be unique to the organization in question, but many apply universally. Because of our familiarity not only with both coding languages but with the financial models they power, my colleagues and I have had occasion to help several clients with this transition.

Here are some things we’ve learned from this experience and what we believe is driving this change.

Python Popularity

The popularity of Python has skyrocketed in recent years. Its intuitive syntax and a wide array of packages available to aid in development make it one of the most user-friendly programming languages in use today. This accessibility allows users who may not have a coding background to use Python as a gateway into the world of software development and expand their toolbox of professional qualifications.

Companies appreciate this as well. As an open-source language with tons of resources and low overhead costs, Python is also attractive from an expense perspective. A cost-conscious option that resonates with developers and analysts is a win-win when deciding on a codebase.

Note: R is another popular and powerful open-source language for data analytics. Unlike R, however, which is specifically used for statistical analysis, Python can be used for a wider range of uses, including UI design, web development, business applications, and others. This flexibility makes Python attractive to companies seeking synchronicity — the ability for developers to transition seamlessly among teams. R remains popular in academic circles where a powerful, easy-to-understand tool is needed to perform statistical analysis, but additional flexibility is not necessarily required. Hence, we are limiting our discussion here to Python.

Python is not without its drawbacks. As an open-source language, less oversight governs newly added features and packages. Consequently, while updates may be quicker, they are also more prone to error than SAS’s, which are always thoroughly tested prior to release.

Visualization Capabilities

While both codebases support data visualization, Python’s packages are generally viewed more favorably than SAS’s, which tend to be on the more basic side. More advanced visuals are available from SAS, but they require the SAS Visual Analytics platform, which comes at an added cost.

Python’s popular visualization packages — matplotlib, plotly, and seaborn, among others — can be leveraged to create powerful and detailed visualizations by simply importing the libraries into the existing codebase.


SAS is a command-driven software package used for statistical analysis and data visualization. Though available only for Windows operating systems, it remains one of the most widely used statistical software packages in both industry and academia.

It’s not hard to see why. For financial institutions with large amounts of data, SAS has been an extremely valuable tool. It is a well-documented language, with many online resources and is relatively intuitive to pick up and understand – especially when users have prior experience with SQL. SAS is also one of the few tools with a customer support line.

SAS, however, is a paid service, and at a standalone level, the costs can be quite prohibitive, particularly for smaller companies and start-ups. Complete access to the full breadth of SAS and its supporting tools tends to be available only to larger and more established organizations. These costs are likely fueling its recent drop-off in popularity. New users simply cannot access it as easily as they can Python. While an academic/university version of the software is available free of charge for individual use, its feature set is limited. Therefore, for new users and start-up companies, SAS may not be the best choice, despite being a powerful tool. Additionally, with the expansion and maturity of the variety of packages that Python offers, many of the analytical abilities of Python now rival those of SAS, making it an attractive, cost-effective option even for very large firms.

Future of tech

Many of the expected advances in data analytics and tech in general are clearly pointing toward deep learning, machine learning, and artificial intelligence in general. These are especially attractive to companies dealing with large amounts of data.

While the technology to analyze data with complete independence is still emerging, Python is better situated to support companies that have begun laying the groundwork for these developments. Python’s rapidly expanding libraries for artificial intelligence and machine learning will likely make future transitions to deep learning algorithms more seamless.

While SAS has made some strides toward adding machine learning and deep learning functionalities to its repertoire, Python remains ahead and consistently ranks as the best language for deep learning and machine learning projects. This creates a symbiotic relationship between the language and its users. Developers use Python to develop ML projects since it is currently best suited for the job, which in turn expands Python’s ML capabilities — a cycle which practically cements Python’s position as the best language for future development in the AI sphere.

Overcoming the Challenges of a SAS-to-Python Migration

SAS-to-Python migrations bring a unique set of challenges that need to be considered. These include the following.

Memory overhead

Server space is getting cheaper but it’s not free. Although Python’s data analytics capabilities rival SAS’s, Python requires more memory overhead. Companies working with extremely large datasets will likely need to factor in the cost of extra server space. These costs are not likely to alter the decision to migrate, but they also should not be overlooked.

The SAS server

All SAS commands are run on SAS’s own server. This tightly controlled ecosystem makes SAS much faster than Python, which does not have the same infrastructure out of the box. Therefore, optimizing Python code can be a significant challenge during SAS-to-Python migrations, particularly when tackling it for the first time.

SAS packages vs Python packages

Calculations performed using SAS packages vs. Python packages can result in differences, which, while generally minuscule, cannot always be ignored. Depending on the type of data, this can pose an issue. And getting an exact match between values calculated in SAS and values calculated in Python may be difficult.

For example, the true value of “0” as a float datatype in SAS is approximated to 3.552714E-150, while in Python float “0” is approximated to 3602879701896397/255. These values do not create noticeable differences in most calculations. But some financial models demand more precision than others. And over the course of multiple calculations which build upon each other, they can create differences in fractional values. These differences must be reconciled and accounted for.

Comparing large datasets

One of the most common functions when working with large datasets involves evaluating how they change over time. SAS has a built-in function (proccompare) which compares datasets swiftly and easily as required. Python has packages for this as well; however, these packages are not as robust as their SAS counterparts. 


In most cases, the benefits of migrating from SAS to Python outweigh the challenges associated with going through the process. The envisioned savings can sometimes be attractive enough to cause firms to trivialize the transition costs. This should be avoided. A successful migration requires taking full account of the obstacles and making plans to mitigate them. Involving the right people from the outset — analysts well versed in both languages who have encountered and worked through the pitfalls — is key.

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.


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.

Where Would We Be Without the Mortgage Market?

It’s bleak out there. Can you imagine how much bleaker it would be if the U.S. mortgage market weren’t doing its thing to prop up the economy?

The mortgage market is helping healthy borrowers take advantage of lower interest rates to improve their personal balance sheets. And it is helping struggling borrowers by offering generous loss mitigation options. 

The mortgage market plays a unique role in the U.S. economy. It is a hybrid consortium of originators, guarantors, investors, and policymakers intent on offering competitive rates in a transparent market structure—a structure that is the beneficiary of both good government policy and a robust, competitive private marketplace. 

The mortgage market’s pro-cyclical role in the U.S. economy allocates credit and interest rate risk among borrowers, investors and the federal government. When the government’s interest rates go down, so do mortgage rates.

March 2020

COVID-19 turned the world’s economies on their heads. Once strong growing economies ground to a stop. By mid-March, the negative effect of the pandemic in the U.S. was clear, with sharply rising unemployment claims and a declining Q1 GDP. COVID-19 did not spare the mortgage market. Fear of borrower defaults led to a freezing up of the credit market, which in turn fueled anxiety among mortgage servicers, guarantors, investors, and originators.

The U.S. government and Federal Reserve responded quickly. Applying lessons learned from the 2008, they initiated housing relief programs early. Congress immediately passed legislation enabling forbearance and eviction protection programs to borrowers and renters. The Federal Reserve promptly cut interest rates to near zero while using its balance sheet to quell market concerns and ensure liquidity.

The FHFA’s Credit Risk Transfer program worked as intended, sharing with willing investors the credit risk uncertainty and, in due course, the resulting credit losses. By April, the mortgage market’s guarantors—Ginnie Mae, Fannie Mae and Freddie Mac—imposed P&I advance programs on servicers and investors, thus ensuring the continuation of the mortgage servicing market.

Rallying the Troops

Boy, it was a tough spring for the industry. But now all the pieces were in place:

  1. New legislation to aid borrowers
  2. Lower rates and market liquidity from the Fed
  3. P&I advance solutions and underwriting guidance from the Agencies

The U.S. mortgage market was finally in a position to play its role in steadying the economy. Mortgages help the economy by lowering debt burden ratios and increasing available spendable income and investible assets. Both of these conditions contribute to the stabilization and recovery of the economy. 

This relief is provided through:

  • Rate-and-term refinances, which lower borrowers’ monthly mortgage payments,
  • Purchase loans, which help borrowers capitalize on low interest rates to buy new houses, and
  • Cash-out refinances, which enable borrowers to convert home equity into spendable and investable cash.

Mortgage origination volume in 2020 is now projected to reach $2.8 trillion—a 30% increase over 2019—despite 11% unemployment and more than 4 million loans in forbearance.

But near-term issues remain

It would be a misstatement to say all things are great for the U.S. mortgage market. While mortgage rates are at 50-year lows, they are not as low as they could be. The dramatic increase in volume has forced originators to raise rates in order to manage their production surges. Mortgage servicing rights values have plunged on new originations, which also leads to higher borrower rates. In other words, a good portion of the pro-cyclical benefit of lower interest rates is not actually making its way into the hands of mortgage borrowers.

In addition, the current high rate of unemployment and forbearance will ultimately come home to roost in the form of elevated default rates as the economy’s recovery from COVID-19 continues to look more U-shaped than the originally hoped for V-shape. Any increases in default rates will certainly be met with new rounds of government intervention. This almost always results in higher costs to servicers.  

Long-term uncertainties

The pandemic continues to wreak havoc on people and economies. Its duration and cumulative impacts are still unknown but are certain to reshape the U.S. mortgage market. Still unanswered are the growing questions around how the following will affect local real estate values, defaults, and future business volumes:

  • The emerging work-from-home economy
  • Permanent employment dislocations from the loss of travel, entertainment, and retail jobs
  • Loss of future rate-and-term refinance business because of today’s low rates
  • Muted future purchase volumes due to high unemployment

Notwithstanding these uncertainties, the U.S. mortgage market will play a vital role in the economy’s rebuilding. Its resiliency and willingness to learn from past mistakes, combined with an activist role of government and its guarantors, not only ensure the market’s long-term viability and success. These qualities also position it as a mooring point for an economy otherwise tossed about in a turbulent storm of uncertainty. 

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