Agency MBS Archives

RS Edge: Loan-level Delinquencies in GNMA Pools

With the rapid rise of social distancing and a looming recession, investor thoughts are turning towards mortgage delinquencies and defaults. In GNMAs, loans that are 90+ days delinquent may be bought out of the pool by the servicer. When a servicer does this, the repurchase shows up as an involuntary prepay for the investor. GNMA servicers may buy the loan out a pool when it turns 90 days delinquent or more but must do so using their own capital. Given this, we may start to see a separation in buyout behavior between well-capitalized bank servicers and more thinly capitalized non-bank servicers, with longer liquidation timelines for some entities over others.¹

The GNMA loan–level data shows each loan’s delinquency status, listed from current to 180+ days delinquent.² In Edge, users can run either a single pool or a portfolio and separate the loans into buckets by individual servicer. Users can simultaneously overlay other filters such as loan guarantor, geography, mark-to-market LTV, and other. Doing this at portfolio level can help quantify a portfolio’s exposure to various bank and non-bank servicers segregated by different loan characteristics.

It is difficult to predict the exact repurchase differential we will see between bank and non-bank servicers, but for MBS investors, it will certainly be important to quantify the exposure at both pool and portfolio level as a first step. Most market participants expect a substantial uptick in the number of involuntary prepayments in the GNMA space. Edge lets users rapidly assess delinquency exposure across many different loan characteristics for an entire portfolio, which may matter more now than it ever has in recent history.

RS EDGE: Loan-level Delinquencies in GNMA Pools

Loan-level delinquency by servicer for 2019-vintage GN Multi-lender pools, broken out by FHA/VA. This search is simple to execute in the Edge platform. Contact us for details.

¹ In 2011-12, the market saw significant differences in buyout behavior, for example Bank of America was slow to buy out delinquent loans.

² On Bloomberg, the delinquency states 90 days onward are compressed into a single 90+ state.

RS Edge: S-curves Over Different Refi Cycles

Over the last six months, TBA speeds have progressively accelerated and are poised to print even faster given the recent lows in primary rates and near doubling in the Refi Index. But how do these speeds compare against previous refi cycles? In this short piece, we compare today’s S-curve against the 2012-13 cycle and the massive refi wave in 2002-03.

First, we start by running an S-curve on loans in recently issued Majors¹, filtering for loans that were 2019 vintage and at least 6 months seasoned. Below, we plot prepay speeds against refi incentive². In aggregate, fully in-the-money mortgages pay around 55 CPR, with actual speeds varying from pool to pool.

Next, we overlay a TBA S-curve during the 2012-13 refi wave, covering the period both pre– and post–QE3 (September 2012). Traders during that time will remember top speeds in the mid to upper 30s. Clearly the 2019-20 prepay experience has exceeded these speeds for similar refi incentive.

Finally, we compare the current refi environment against the 2002-03 environment—the gold standard for refinancing waves. Traders active during that period will remember this as a time of high cash-out refis, which drove both in– and out-of-the money prepayments higher. Out-of-the-money mortgages paid in the mid-teens as homeowners tapped their homes like an ATM, while in-the-money mortgages paid in the high 60s to low 70s in aggregate, with some sectors paying in the 80s for several months.

The 2002-03 refi wave also featured a surge driven by the “media effect.” With nearly the entire mortgage market in–the–money, a combination of aggressive advertising plus frequent news reporting reminded homeowners at every turn that they could save hundreds of dollars per month if they refinanced their loans. In early 2003, 95% of the mortgage market was 50bp or more in the money, compared with 80% today.

We conclude that with Fed easing and recession fears, 2020 could see a renewed media effect, which may help drive prepayments higher at every point on the S-curve.

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

¹ We did a similar analysis on multi-lender Giants. Please contact us for details.

² To be included, the loan had to be at least 6 months old, higher than $225k balance, LTV < 80, and FICO > 700.

RiskSpan VQI: Current Underwriting Standards – February 2020

The RiskSpan Vintage Quality Index (“VQI”) edged higher for mortgages originated during February despite remaining low (90.41) by historical, pre-crisis standards. Low-FICO and high-LTV loans continued to trend downward, while high-DTI loans, investment properties, and cash-out refinances continued to rebound after declining through much of 2019.

As the historical trend of risk layering (see below) shows, mortgages with one borrower—now accounting for more than 50 percent of originations—remain a consistent and important driver of the index. High-DTI loans today drive the index more than they did during the years immediately after the 2008 crisis but not nearly so much as they did during the years leading up to it. High-LTV loans continue to be originated in abundance, while adjustable-rate mortgages and loans with subordinate financing, in contrast, have practically vanished.

RiskSpan introduced the VQI in 2015 as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. The idea is to provide credit modelers a way of controlling for a particular vintage’s underwriting standards, which tend to shift over time. The VQI is a function of the average number of risk layers associated with a loan originated during a given month. It is computed using:

The loan-level historical data released by the GSEs in support of Credit Risk Transfer initiatives (CRT data) for months prior to December 2005, and
Loan-level disclosure data supporting MBS issuances through today.

The value is then normalized to assign January 1, 2003 an index value of 100. The peak of the index, a value of 139 in December 2007, indicates that loans issued in that month had an average risk layer factor 39% greater (i.e., loans issued that month were 39% riskier) than loans originated during 2003. In other words, lower VQI values indicate tighter underwriting standards (and vice-versa).

Build-Up of VQI

The following chart illustrates how each of the following risk layers contributes to the overall VQI:

Loans with low credit scores (FICO scores below 660)
Loans with high loan-to-value ratios (over 80 percent)
Loans with subordinate liens
Loans with only one borrower
Cash-out refinance loans
Loans secured by multi-unit properties
Loans secured by investment properties
Loans with high debt-to-income ratios (over 45%)
Loans underwritten based on reduced documentation
Adjustable rate loans

FHFA Prepayment Monitoring Reports are Powered by RS Edge

[vc_row][vc_column][vc_column_text]To help enforce prepayment alignment across Fannie’s and Freddie’s Uniform MBS, the Federal Housing Finance Agency publishes a quarterly monitoring report comparing the prepayment speeds of UMBS issued by the two Agencies. This report helps ensure that prepayment performance remains consistent—so that market expectations of a Fannie-issued UMBS are fundamentally indistinguishable from those of a Freddie-issued UMBS and the two Agencies UMBS are both deliverable into passthrough “TBA” trades.

Last week, the FHFA released the most recent version of this report containing performance data from the fourth quarter of 2019. The charts in the FHFA’s publication, which are generated using RiskSpan’s Edge Platform, compare Fannie and Freddie UMBS prepayment rates (1-month and 3-month CPRs) across a variety of coupons and vintages.

Relying on RiskSpan’s Edge Platform for this sort of analysis is fitting in that it is precisely the type of comparative analysis for which Edge was developed.

Edge allows traders, portfolio managers, and analysts to compare performance across a virtually unlimited number of loan subgroups. Users can cohort on multiple loan characteristics, including servicer, vintage, loan size, geography, LTV, FICO, channel, or any other borrower characteristic.

Edge’s easy-to-navigate user interface makes it accessible to traders and PMs who want to be able to set up queries and tweak constraints on the fly without having to write SQL code. Edge also offers an API for users that want programmatic access to the data, useful for generating customized reporting and systematic analysis of loan sectors.

Comparing Fannie’s and Freddie’s prepay speeds only scratches the surface of Edge’s analytical capabilities. Schedule a demo to see what this tool can really do.[/vc_column_text][/vc_column][/vc_row]

RS Edge: WALA Ramps for Non-Bank Servicers

In 2019, the non-bank servicing sector continued to grow faster than traditional bank-servicers. As a group, non-bank servicers now represent nearly half of the agency MBS market, with outsized representation in newer-production mortgages. Their aggressive refinancing has driven speeds on in-the-money mortgages to post-crisis highs, and we believe this behavior will continue into 2020.

But within the non-bank sector, prepayment behavior varies widely. In this short post, we measure the fastest non-bank servicers against their cohorts and against the wider market.

We used the Edge platform to generate WALA ramps for the top 25 non-bank servicers for 30yr “generic” mortgages.¹ In the first graph, we show WALA ramps for bank-serviced and non-bank–serviced loans that were 75-125bp in the money over the last calendar year. At the peak, non-bank servicers outstripped bank servicers by roughly 8 CPR.

Graph

In the next chart, we break out performance for the two fastest non-bank servicers: United Shore and Provident Funding.² United Shore clocked in at a blazing 83 CPR for the 7-8 WALA bucket with Provident printing in the high 70s.

Switching to SMM—the right way to examine such fast speeds—we see that loans serviced by United Shore paid at 13.7 SMM, more than twice the unscheduled principal per month than the cohort of non-bank servicers in months 7 and 8.

In closing, we note that newer vintage Freddie Mac Supers consistently contain more United Shore and Provident product than similarly aged Fannie Mae Majors. Together, United Shore and Provident account for 14-18% of newer–production Freddie Supers, such as FR SD8016, SD8005, SD8001, and SD8006, but only 4-6% of Fannie Majors, such as FN MA3774 or MA3745. Most of the fast-payer Freddie Supers are 3s and 3.5s and may not show fast speeds at current rates, but in a 25-50bp rally we may see separation between Fannie and Freddie TBA speeds. As a consequence, Freddie Supers may have worse convexity than similar vintage Fannie Majors.

If you are interested in seeing variations on this theme, contact us. Using RS Edge, we can examine any loan characteristic and generate a S-curve, WALA curve, or time series. [/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space][vc_empty_space][startapp_separator border_width=”1″ opacity=”25″ animation=””][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]¹For a loan to be included, it had to be securitized into a deliverable 30yr Fannie or Freddie pool and have a loan balance greater than $225,000, FICO > 700, LTV <= 80, and not in NY state. All analysis was done at loan level.

²New Residential and Home Point Financial receive an honorable mention for fast speeds. Their speeds showed more response for loans 50-100bp in the money but started to converge to average non-bank speeds when 75-125bp in the money. See RiskSpan for details.

FHFA 3Q2019 Prepayment Monitoring Report

FHFA’s 2014 Strategic Plan for the Conservatorships of Fannie Mae and Freddie Mac includes the goal of improving the overall liquidity of Fannie Mae’s and Freddie Mac’s (the Enterprises) securities through the development of a common mortgage-backed security. This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report

EDGE: Revisiting WALA-ramps on FNMA Majors

In the past few months, recent-vintage FNMA Major pools have shown significant acceleration in prepay speeds, significantly impacting TBA prices and dollar rolls. In our August report, we showed a progression of ever faster WALA ramps on FNMA Major pools¹. In this installment, we update that behavior using data from Edge, the online prepayment graphing tool.

We start with a population of recent FNMA Majors and generate WALA ramps at loan level, to capture the precise behavior of the WALA ramp. In the first chart, we show loans from Majors that are 75-125bp in the money, approximately TBA 4s, over three different time periods:

August 2018 to July 2019 (“baseline”)
August-September 2019
October 2019

In October, aggregate speeds on Majors hit a new high of 60 CPR for loans in the 9-10 WALA range. More troubling: the tail of the WALA ramp moved higher by roughly 5 CPR. This acceleration impacts carry in the 12mo+ seasoning range and is a potential negative for valuations in the TBA sector.

Graph: Speeds on loans from FN Major pools, holding refi incentive 75-125bp over three different periods.

In the next graph, we use Edge to isolate loans in Major pools that are 25-75bp in the money (approximately 3.5s). Similar to 4s, the progression in the aging curve shows the same story: a faster tail for loans 10+ months seasoned.

Graph: Speeds on loans from FN Major pools, holding refinancing incentive 25-75bp over three different periods.

We next look at the change in prepayment speeds from the Aug-Sep period to October and attribute that change to the origination channel. On average, FNMA Major pools are 50:50 Retail origination versus TPO, and we break down the speed contribution into these two groups. In the analysis below, we look at the speed change in each WALA bucket.

For Major 3.5s, the TPO loans accelerated more than the Retail origination loans. But in Major 4.0s, the speeds increased almost equally across each bucket.

In summary, the WALA ramp for TPO is more sensitive than Retail loans when refinancing incentive is small. But when loans are far enough in the money the increase in the WALA ramps are evenly distributed across origination channel.

We continue to monitor the ever-accelerating speeds on FNMA Majors and Freddie Giants, but the trend is clear – the fastest, cheapest to deliver TBA continues to be faster for longer. This makes the ongoing analysis of prepays, whether specified pools or non-spec deliverables, more important that it has been in previous rate cycles.

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

¹See RiskSpan for a similar analysis on newer WALA multi-lender Giants

FHFA 2Q2019 Prepayment Monitoring Report

Download Report

RiskSpan Credit Risk Transfer Solution

RiskSpan Managing Director, Janet Jozwik, explains how the RS Edge Platform serves as an end-to-end Credit Risk Transfer (CRT) solution designed to help investors in each stage of CRT deal analysis. The RS Edge Platform hosts historical GSE data (STACR/CAS/CIRT/ACIS) and gives users the ability to conduct historical and surveillance analysis as well as predictive and scenario analysis. Additionally, RiskSpan gives users full access to our proprietary agency-specific prepayment and credit models and is integrated with Intex for deal cash flow analysis.

FHFA 1Q2019 Prepayment Monitoring Report

This report provides insight into how FHFA monitors the consistency of prepayment rates across cohorts of the Enterprises’ TBA-eligible MBS.

Download Report

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RS Edge: Loan-level Delinquencies in GNMA Pools

RS Edge: S-curves Over Different Refi Cycles

RiskSpan VQI: Current Underwriting Standards – February 2020

Build-Up of VQI

FHFA Prepayment Monitoring Reports are Powered by RS Edge

RS Edge: WALA Ramps for Non-Bank Servicers

FHFA 3Q2019 Prepayment Monitoring Report

EDGE: Revisiting WALA-ramps on FNMA Majors

FHFA 2Q2019 Prepayment Monitoring Report

RiskSpan Credit Risk Transfer Solution

FHFA 1Q2019 Prepayment Monitoring Report

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