Get Started
Category: Article

Understanding the Impact of Federal Reserve Emergency Rate Cuts

Disruptions to the U.S. and global economy brought about by COVID-19 have prompted the Federal Reserve to take a number of emergency measures. These include twice cutting the federal funds rate (to near zero), resuming its purchase of securities, and temporarily relaxing regulatory capital and liquidity requirements (among several other things).  

Although the Fed’s actions take many forms, few things capture investors’ attention in the way emergency rate cuts do. Predicting how financial markets will respond to these cuts is a complicated undertaking. To help investors analyze how these events have affected markets historically, RiskSpan has developed a tool to help investors visualize how various market indices, commodities, currencies and bond yields have reacted to emergency Fed rate cuts in the wake of various market shocks. 

Analyzing events in this way enables investors to more effectively manage their portfolio risk by monitoring market–moving events and identifying response patterns. We analyze a range of past market events to formulate scenarios for RiskSpan’s RiskDynamics market risk service. 

Every crisis is unique, of course. But the Fed’s interest rate cuts this month are specifically reminiscent of seven actions it has taken in response to past economic threats, including the Russian Ruble crisis (2014), the bursting of the dot-com bubble (2000), the September 11th attacks (2001), and the subprime mortgage/Lehman Brothers collapse (2008). 

The chart below compares the response of the S&P 500 to the Lehman collapse and COVID-19 and how long it takes the ensuing Fed rate cut to affect the market. The similarity in the shape of these two curves is quite striking. It also reflects the time required for Congress to pass stimulus following Fed action. 

federal reserve impact shown in RS Edge

The tool displays the performance of several markets across three asset classes in response to each of the seven Fed cuts. In this version we have included stocks, rates and commodities. The two interactive charts specifically help to visualize the following: 

  1. Performance of asset classes from 20 days before through 60 days following each rate cut. 
  2. Performance indexed to the event date—helping to illustrate market conditions leading up to the rate cut and its subsequent impact. 
  3. Daily returns enabling a cross-sector, cross-market comparisons to each rate cut. 

Additional patterns also emerge when looking at how markets have responded to these seven prior cuts: 

  • Equity market collapses tend to stall, but the recovery (if any) is slow. 
  • The volatility index stabilizes, but it takes time to mean revert. 
  • Treasury bonds generally perform better than other asset classes. Long–dated bonds don’t perform as well. 
  • Crude oil continues to sell off in most cases. 

We are continually expanding the list of asset classes and events covered by the tool. Our data science team is also working some interesting analytics for publication.  

We welcome your feedback and requests for additional analysis. Please contact us to discuss further. 

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: 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.

Visualizing a CMBS Portfolio’s Exposure to COVID-19

he economic impact of the Coronavirus outbreak is all but certain to be felt by CMBS investors. The only real uncertainty surrounds when missed rent payments will begin, what industries are likely to feel them most acutely, and—more to the point—how your portfolio aligns with these eventualities.

The dashboard below—created using RS Edge and Tableau—displays a stylized example compiling small random excerpts from several CMBS portfolios. While business disruptions have not (yet) lasted long enough to be reflected in CMBS default rates, visualizing portfolios in this way provides a powerful tool for zeroing in on where problems are most likely to emerge.

The maps at the top of the dashboard juxtapose the portfolio’s geographic concentration with states where COVID-19 prevalence is highest. Investors are able to drill down not only into individual states but into individual NAICS-defined industries that the loans in their deals cover.

At each level of analysis (overall, by state, or by industry) the dashboard not only reports total exposure in UPB but also important risk metrics around the portfolio’s DSCR and LTV, thus enabling investors to quickly visualize how much cushion the underlying loans have to absorb missed rent payments before the deals begin to experience losses.


The real value of visualizations like these, of course, is the limitlessness of their flexibility and their applicability to any market sector.

We sincerely desire to be helpful during these unprecedented market conditions. Our teams are actively helping clients to manage through them. Whether you are looking for historical context, market analysis or just a conversation with folks who have been through several market cycles, we are here to provide support. Please contact us to talk about what we can do for you.

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.

Managing Coronavirus-Related Risks in Aircraft Lease ABS

Last week, UK airline Flybe grounded its planes, stranded passengers and filed for bankruptcy protection, as the struggling carrier was buffeted by a Coronavirus-related slowdown in demand. Flybe’s demise is a trenchant example of the implications of Coronavirus for investors in the aircraft sector, including aircraft lease ABS investors whose cash flow depends on continued lease payments from various global carriers. Of course, the impacts of Coronavirus will vary, with some countries, servicers, credit-rating sectors and deal structures worse off than others. Using RS Edge, aircraft lease ABS investors can drill down into collateral to see country, carrier and aircraft exposures, stress test deals and learn potential fault lines for deals as Coronavirus uncertainty looms. 

The snapshot below illustrates how clients can benefit from RiskSpan and Intex data and analysis in this sector. Using its embedded Tableau functionality, RS Edge can quickly show investors the top country, carrier and aircraft exposures for each deal. Investors concerned about carriers that might be increasingly vulnerable to Coronavirus disruption (such as Italian or Asian airlines today and likely others in coming weeks) can determine the exposure to these countries using the platform. In addition, when news is announced that impacts the credit quality of carriers, investors can view exposure to these carriers and, with additional analysis, calculate the potential residual value of individual aircraft if the carrier goes bankrupt or the lease terminates. RiskSpan can also provide data on exposure to aircraft manufacturers and provide valuation of bonds backed by aircraft leases.  


Contact us to learn more about how RiskSpan helps clients manage their airline (and other risk) exposure and how we can assist with customized requests to perform further analysis requiring add-on data or calculations.

Using RS Edge to Quantify the Impact of The QM Patch Expiration

[vc_row][vc_column][vc_column_text]Using RS Edge Data to Quantify the Impact of the QM Patch Expiration

A 2014 Consumer Financial Protection Bureau (CFPB) rule established that mortgages purchased by the GSEs (Fannie Mae or Freddie Mac) can be considered “qualified” even if their debt-to-income ratio (DTI) exceeds 43 percent. This provision is known as the “qualified mortgage (QM) patch” or sometimes the “GSE patch.” It has become one of the most important holdouts of the Dodd-Frank Act and an important facilitator of U.S. lending activity under looser credit standards. The CFPB implemented the patch to encourage lenders to make loans that do not meet QM requirements, but are still “responsibly underwritten.” Because all GSE loans must pass the strict standards for conforming mortgages, they are presumed to be reasonably underwritten–notwithstanding sometimes having DTI ratios higher than 43 percent.

The QM patch is set to expire on January 10, 2021. This phaseout has spawned concern over the impact both on mortgage originators and potentially on borrowers when the patch is no longer available and GSEs are less apt to purchase loans with higher DTI ratios.[1]

We performed an analysis of GSE loan data housed in RiskSpan’s RS Edge platform to quantify this potential impact.

The Good News:

The slowdown in purchases of high-DTI loans is already occurring, which could partially mitigate the impact of the expiration of the patch.

We used RS Edge to analyze the percentage of QM loans to which the patch applies today. From 2016 through the beginning of 2019, Fannie and Freddie sharply increased their purchases of loans with DTI ratios greater than 43 percent, with these loans accounting for over 34 percent of Fannie’s purchases as recently as February 2019 and over 30 percent of Freddie’s purchases in November 2018 (see Figure 1).

Figure 1: % of GSE Acquisitions with DTI > 43 (2016 – 2019)


Our data shows, however, that Fannie and Freddie have already begun to wind down purchases of these loans. By the end of 2019, only about 23 percent of GSE loans purchased had DTI greater than 43 percent. This is illustrated more clearly in Figure 2, below.

Figure 2: % of GSE Acquisitions with DTI > 43 (2019 only)


As discussed in the December 2019 Wall Street Journal article “Fannie Mae and Freddie Mac Curb Some Loans as Regulator Reins in Risk,” the wind-down could be related to the GSE’s general efforts to hold stronger portfolios as they aim to climb out of conservatorship. However, our data suggests an equally plausible explanation for the slowdown Borrowers generally exhibit a greater willingness to stretch their incomes to buy a house than to refinance, so purchase loans are more likely than refinancings to feature higher DTI ratios. Figure 3 illustrates this phenomenon.

Figure 3: Most High-DTI Loans Back Home Purchases


The Bad News:

The bad news, of course, is that one-fifth of Freddie and Fannie loans purchased with DTI>43% is still significant. Over 900,000 mortgages purchased by the GSEs in 2019 were of the High-DTI variety, accounting for over $240 billion in UPB.

In theory, these 900,000 borrowers will no longer have a way of being slotted into QM loans after the patch expires next year. While this could be good news for the non-QM market, which would potentially be poised to capture this new business, it may not be the best news for these borrowers, who likely do not fancy paying the higher interest rates generally associated with non-QM lending.

Originators, not relishing the prospect of losing QM protection for these loans, have also expressed concern about the phaseout of the patch. A group of lenders that includes Wells Fargo and Quicken Loans has petitioned the CFPB to completely eliminate the DTI requirements under ability-to-pay rules.

Figure 4: % of DTI>43 Loans Sold to GSEs by Originator


We will be closely monitoring the situation and continuing to offer tools that will help to quantify the potential impact of the expiration.

[1] Consumer Financial Protection Bureau, July 25, 2019.[/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-bankserviced 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. 

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 blazing 83 CPR for the 7-8 WALA bucket with Provident printing in the high 70s. 


Switching to SMMthe right way to examine such fast speedswe 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 newerproduction 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.

RS Edge for Loans & Structured Products: A Data Driven Approach to Pre-Trade and Pricing  

The non-agency residential-mortgage-backed-securities (RMBS) market has high expectations for increased volume in 2020. Driven largely by expected changes to the qualified mortgage (QM) patch, private-label securities (PLS) issuers and investors are preparing for a 2020 surge. The tight underwriting standards of the post-crisis era are loosening and will continue to loosen if debt-to-income restrictions are lifted with changes to the QM patch 

PLS programs can differ greatly. It’s increasingly important to understand the risks inherent in each underlying poolAt the same time, investment opportunities with substantial yield are becoming harder to find without developing a deep understanding of the riskier components of the capital structureA structured approach to pre-trade and portfolio analytics can help mitigate some of these challenges. Using a data-driven approach, portfolio managers can gain confidence in the positions they take and make data influenced pricing decisions 

Industry best practice for pre-trade analysis is to employ a holistic approach to RMBS. To do this, portfolio managers must combine analysis of loan collateral, historical data for similar cohorts of loans (within previous deals), and scenariofor projected performance. The foundation of this approach is:  

  • Historical data can ground assumptions about projected performance 
  • A consistent approach from deal to deal will illuminate shifting risks from shifting collateral 
  • Scenario analysis will inform risk assessment and investment decision  

Analytical Framework 

RiskSpan’s modeling and analytics expert, Janet Jozwik, suggests a framework for analyzing a new RMBS deal with analysis of 3 main components:  deal collateral, historical performance, and scenario forecasting. Combined, these three components give portfolio managers a present, past, and future view into the deal.  

Present: Deal Collateral Analysis 

Deal collateral analysis consists of: 1) a deep dive into the characteristics of the collateral underlying the deal itself, and 2) a comparison of the collateral characteristics of the deal being analyzed to similar deals. A comparison to recently issued deals can highlight shifts in underlying collateral risk within a particular shelf or across issuers.  

Below, RiskSpan’s RS Edge provides the portfolio manager with a dashboard highlighting key collateral characteristics that may influence deal performance. 

Example 1Deal Profile Stratification 


Example 2Deal Comparative Analysis 

Past: Historical Performance Analysis 

Historical analysis informs users of a deal’s potential performance under different scenarios by looking at how similar loan cohorts from prior deals have performedJozwik recommends analyzing historical trends both from the recent past and frohistorical stress vintages to give a sense for what the expected performance of the deal will be, and what the worst-case performance would be under stress scenarios. 

Recent Trend Analysis:  Portfolio managers can understand expected performance by looking at how similar deals have been performing over the prior 2 to 3 years. There are a significant number of recently issued PLS that can be tracked to understand recent prepayment and default trends in the market. While the performance of these recent deals doesn’t definitively determine expectations for a new deal (as things can change, such as rate environment), it provides one data point to help ground data-driven analyses. This approach allows users to capitalize on the knowledge gained from prior market trends.  

Historical Vintage Proxy Analysis:  Portfolio managers can understand stressed performance of the deal by looking at performance of similar loans from vintages that experienced the stress environment of the housing crisisThough potentially cumbersome to execute, this approach leverages the rich set of historical performance data available in the mortgage space 

For a new RMBS Dealportfolio managers can review the distribution of key features, such as FICO, LTV, and documentation typeThey can calculate performance metrics, such as cumulative loss and default rates, from a wide set of historical performance data on RMBS, cut by vintage. When pulling these historical numbers, portfolio managers can adjust the population of loans to better align with the distribution of key loan features in the deal they are analyzing. So, they can get a view into how a similar loans pool originated in historical vintages, like 2007, performed. There are certainly underwriting changes that have occurred in the post-crisis era that would likely make this analysis ultraconservative. These ‘proxy cohorts’ from historical vintages can provide an alternative insight into what could happen in a worst-case scenario.  

Future: Forecasting Scenario Analysis 

Forecasting analysis should come in two flavors. First, very straightforward scenarios that are explicitly transparent about assumptions for CPR, CDR, and severity. These assumptions-based scenarios can be informed with outputs from the Historical Performance Analysis above.  

Second, forecasting analysis can leverage statistical models that consider both loan features and macroeconomic inputs. Scenarios can be built around macroeconomic inputs to the model to better understand how collateral and bond performance will change with changing economic conditions.  Macroeconomic inputs, such as mortgage rates and home prices, can be specified to create particular scenario runs. 

How RiskSpan Can Help 

Pulling the required data and models together is typically a burdenRiskSpan’s RS Edge has solved these issues and now offers one integrated solution for:  

  • Historical Data: Loan-level performance and collateral data on historical and pre-issue RMBS deals 
  • Predictive Models: Credit and Prepayment models for non-agency collateral types 
  • Deal Cashflow Engine: Intex is the leading source for an RMBS deal cashflow library 

There is a rich source of data, models, and analytics that can support decision making in the RMBS market. The challenge for a portfolio manager is piecing these often-disparate pieces of information together to a cohesive analysis that can provide a consistent view from deal to dealFurther, there is a massive amount of historical data in the mortgage space, containing a vast wealth of insight to help inform investment decisions. However, these datasets are notoriously unwieldy. Users of RS Edge cut through the complications of large, disparate datasets for clear, informative analysis, without the need for custom-built technology or analysts with advanced coding skills.

Introducing: RS Edge for Loans and Structured Products

RiskSpan Introduces RS Edge for Loans and Structured Products  

RiskSpan, the leading mortgage data and analytics provider, is excited to announce the release of RS Edge for Loans and Structured Products. 

RS Edge is the next generation of RiskSpan’s data, modeling, and analytics platform that manages portfolio risk and delivers powerful analysis for loans and structured products.  Users can derive insights from historical trends and powerful predictive forecasts under a range of economic scenarios on our cloud-native solution. RS Edge streamlines analysis by bringing together key industry data and integrations with leading 3rd party vendors. 

An on-demand team of data scientists, quants, and technologists with fixed-income portfolio expertise support the integration, calibration, and operation across all RS Edge modules 

RMBS Analytics in Action 

RiskSpan has developed a holistic approach to RMBS analysis that combines loan collateral, historical, and scenario analysis with deal comparison tools to more accurately predict future performance. Asset managers can define an acceptable level of risk and ground pricing decisions with data-driven analysis. This approach illuminates risk from shifting collateral and provides investors with confidence in their positions. 

Loan Analytics in Action 

Whole loan asset managers and investors use RiskSpan’s Loan Analytics to enhance and automate partnerships with Non-Qualified Mortgage originators and servicers. The product enhances the on-boarding, pricing analytics, forecasting, and storage of loan data for historical trend analytics. RS Edge forecasting analytics support ratesheet validation and loan pricing 

About RiskSpan 

RiskSpan provides innovative technology and services to the financial services industry. Our mission is to eliminate inefficiencies in loans and structured finance markets to improve investors’ bottom line through incremental cost savings, improved return on investment, and mitigated risk.  

RiskSpan is holding a webinar on November 6 to show how RS Edge pulls together past, present, and future for insights into new RMBS deals. Click below to register.

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:

  1. August 2018 to July 2019 (“baseline”)
  2. August-September 2019
  3. 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.

WALA Curve and Prepayment Speeds Graph

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

Get Started