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Articles Tagged with: Agency MBS

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. 

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

Age-Bucket-vs-CPR

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. 

  Age-Bucket-vs-SMM

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.


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.

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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.

Age Bucket VS CPR

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.

fn3.5-major-graphfn4.0-major-graph

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

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


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

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


RiskSpan Edge & CRT Data

For participants in the credit risk transfer (CRT) market, managing the massive quantity of data to produce clear insights into deal performance can be difficult and demanding on legacy systems. Complete analysis of the deals involves bringing together historical data, predictive models, and deal cash flow logic, often leading to a complex workflow in multiple systems. RiskSpan’s Edge platform (RS Edge) solves these challenges, bringing together all aspects of CRT analysis. RiskSpan is the only vendor to bring together everything a CRT analyst needs:  

  • Normalized, clean, enhanced data across programs (STACR/CAS/ACIS/CIRT),
  • Historical Fannie/Freddie performance data normalized to a single standard,
  • Ability to load loan-level files related to private risk transfer deals,
  • An Agency-specific, loan-level, credit model,
  • Seamless Intex integration for deal and portfolio analysis,
  • Scalable scenario analysis at the deal or portfolio level, and
  • Vendor and client model integration capabilities.
  • Ability to load loan-level files related to private risk transfer deals.

Deal Comparison Table All of these features are built into RS Edge, a cloud-native, data and analytics platform for loans and securities. The RS Edge user interface is accessible via any web browser, and the processing engine is accessible via an application programming interface (API). Accessing RS Edge via the API allows access to the full functionality of the platform, with direct integration into existing workflows in legacy systems such as Excel, Python, and R. To tailor RS Edge to the specific needs of a CRT investor, RiskSpan is rolling out a series of Excel tools, built using our APIs, which allow for powerful loan-level analysis from the tool everyone knows and loves. Accessing RS Edge via our new Excel templates, users can:

  • Track deal performance,
  • Compare deal profiles,
  • Research historical performance of the full GSE population,
  • Project deal and portfolio performance with our Agency-specific credit model or with user-defined CPR/CDR/severity vectors, and
  • Analyze various macro scenarios across deals or a full portfolio

Loan Attribute Distributions

The web-based user interface allows for on-demand analytics, giving users specific insights on deals as the needs arise. The Excel template built with our API allows for a targeted view tailored to the specific needs of a CRT investor.

For teams that prefer to focus their time on outcomes rather than the build, RiskSpan’s data team can build custom templates around specific customer processes. RiskSpan offers support from premiere data scientists who work with clients to understand their unique concerns and objectives to integrate our analytics with their legacy system of choice. Loan Performance History The images are examples of a RiskSpan template for CRT deal comparison: profile comparison, loan credit score distribution, and delinquency performance for five Agency credit risk transfer deals, pulled via the RiskSpan Data API and rendered in Excel. ______________________________________________________________________________________________

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Case Study: RiskSpan Edge Platform Agency MBS Module

The Client

Multiple Agency Traders and the Research & Strategy Division of a Major Investment Bank

The Problem

RiskSpan leverages its extensive expertise to help clients rapidly access the drivers of prepayment risk and prepayment trends. Our analytical platform provides ultimate flexibility and speed to perform quickly turn securities level data into information to based decisions.

The Solution

The RiskSpan Edge Platform is used by the Agency Trading desk to slice and dice data and look for patterns among various bonds using the graphical interface. The RiskSpan Edge Platform offers users access to current and historical data on Ginnie Mae, Fannie Mae, and Freddie Mac (“Agencies”) pass-throughs as well as other data sets.

The tool provides a flexible user interface that supports analysis of prepayment data and actionable reporting. The database includes all monthly pool level data published by the Agencies dating back to 1995.  This data includes pool factors, geographic concentrations and supplemental pool level collateral information. The Prepayment Analytics tool provides a flexible user interface that supports intuitive analysis of the prepayment data and actionable reporting delivered quickly to decision‐makers. The database includes all monthly data published by the Agencies for all months back to 1995, including factors, geographic breakdowns and supplemental disclosure information.

The Deliverables

RiskSpan provides the tools for comprehensive Agency MBS analysis.

  • Visualizing data with integrated graphing and charting
  • Researching new prepayment trends
  • Creating user-defined data tables
  • Exporting customized charts and graphs for marketing purposes


CRT Deal Monitor: Understanding When Credit Becomes Risky

This analysis tracks several metrics related to deal performance and credit profile, putting them into a historical context by comparing the same metrics for recent-vintage deals against those of ‘similar’ cohorts in the time leading up to the 2008 housing crisis. You’ll see how credit metrics are trending today and understand the significance of today’s shifts in the context of historical data. Some of the charts in this post have interactive features, so click around! We’ll be tweaking the analysis and adding new metrics in subsequent months. Please shoot us an email if you have an idea for other metrics you’d like us to track.

Highlights

  • Performance metrics signal steadily increasing credit risk, but no cause for alarm.
    • We’re starting to see the hurricane-related (2017 Harvey and Irma) delinquency spikes subside in the deal data. Investors should expect a similar trend in 2019 due to Hurricane Florence.
    • The overall percentage of delinquent loans is increasing steadily due to the natural age ramp of delinquency rates and the ramp-up of the program over the last 5 years.
    • Overall delinquency levels are still far lower than historical rates.
    • While the share of delinquency is increasing, loans that go delinquent are ending up in default at a lower rate than before.
  • Deal Profiles are becoming riskier as new GSE acquisitions include higher-DTI business.
    • It’s no secret that both GSEs started acquiring a lot of high-DTI loans (for Fannie this moved from around 16% of MBS issuance in Q2 2017 to 30% of issuance as of Q2 this year). We’re starting to see a shift in CRT deal profiles as these loans are making their way into CRT issuance.
    • The credit profile chart toward the end of this post compares the credit profiles of recently issued deals with those of the most recent three months of MBS issuance data to give you a sense of the deal profiles we’re likely to see over the next 3 to 9 months. We also compare these recently issued deals to a similar cohort from 2006 to give some perspective on how much the credit profile has improved since the housing crisis.
    • RiskSpan’s Vintage Quality Index reflects an overall loosening of credit standards–reminiscent of 2003 levels–driven by this increase in high-DTI originations.
  • Fannie and Freddie have fundamental differences in their data disclosures for CAS and STACR.
    • Delinquency rates and loan performance all appear slightly worse for Fannie Mae in both the deal and historical data.
    • Obvious differences in reporting (e.g., STACR reporting a delinquent status in a terminal month) have been corrected in this analysis, but some less obvious differences in reporting between the GSEs may persist.
    • We suspect there is something fundamentally different about how Freddie Mac reports delinquency status—perhaps related to cleaning servicing reporting errors, cleaning hurricane delinquencies, or the way servicing transfers are handled in the data. We are continuing our research on this front and hope to follow up with another post to explain these anomalies.

The exceptionally low rate of delinquency, default, and loss among CRT deals at the moment makes analyzing their credit-risk characteristics relatively boring. Loans in any newly issued deal have already seen between 6 and 12 months of home price growth, and so if the economy remains steady for the first 6 to 12 months after issuance, then that deal is pretty much in the clear from a risk perspective. The danger comes if home prices drift downward right after deal issuance. Our aim with this analysis is to signal when a shift may be occurring in the credit risk inherent in CRT deals. Many data points related to the overall economy and home prices are available to investors seeking to answer this question. This analysis focuses on what the Agency CRT data—both the deal data and the historical performance datasets—can tell us about the health of the housing market and the potential risks associated with the next deals that are issued.

Current Performance and Credit Metrics

Delinquency Trends

The simplest metric we track is the share of loans across all deals that is 60+ days past due (DPD). The charts below compare STACR (Freddie) vs. CAS (Fannie), with separate charts for high-LTV deals (G2 for CAS and HQA for STACR) vs. low-LTV deals (G1 for CAS and DNA for STACR). Both time series show a steadily increasing share of delinquent loans. This slight upward trend is related to the natural aging curve of delinquency and the ramp-up of the CRT program. Both time series show a significant spike in delinquency around January of this year due to the 2017 hurricane season. Most of these delinquent loans are expected to eventually cure or prepay. For comparative purposes, we include a historical time series of the share of loans 60+ DPD for each LTV group. These charts are derived from the Fannie Mae and Freddie Mac loan-level performance datasets. Comparatively, today’s deal performance is much better than even the pre-2006 era. You’ll note the systematically higher delinquency rates of CAS deals. We suspect this is due to reporting differences rather than actual differences in deal performance. We’ll continue to investigate and report back on our findings.

Delinquency Outcome Monitoring

While delinquency rates might be trending up, loans that are rolling to 60-DPD are ultimately defaulting at lower and lower rates. The tables below track the status of loans that were 60+ DPD. Each bar in the chart represents the population of loans that were 60+ DPD exactly 6 months prior to the x-axis date. Over time, we see growing 60-DPD and 60+ DPD groups, and a shrinking Default group. This indicates that a majority of delinquent loans wind up curing or prepaying, rather than proceeding to default. The choppiness and high default rates in the first few observations of the data are related to the very low counts of delinquent loans as the CRT program ramped up. The following table repeats the 60-DPD delinquency analysis for the Freddie Mac Loan Level Performance dataset leading up to and following the housing crisis. (The Fannie Mae loan level performance set yields a nearly identical chart.) Note how many more loans in these cohorts remained delinquent (rather than curing or defaulting) relative to the more recent CRT loans. https://plot.ly/~dataprep/30.embed

Vintage Quality Index

RiskSpan’s Vintage Quality Index (VQI) reflects a reversion to the looser underwriting standards of the early 2000s as a result of the GSEs’ expansion of high-DTI lending. RiskSpan introduced the VQI in 2015 as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. We use the metric as an empirically grounded way to control for vintage differences within our credit model. VQI-History While both GSEs increased high-DTI lending in 2017, it’s worth noting that Fannie Mae saw a relatively larger surge in loans with DTIs greater than 43%. The chart below shows the share of loans backing MBS with DTI > 43. We use the loan-level MBS issuance data to track what’s being originated and acquired by the GSEs because it is the timeliest data source available. CRT deals are issued with loans that are between 6 and 20 months seasoned, and so tracking MBS issuance provides a preview of what will end up in the next cohort of deals. High DTI Share

Deal Profile Comparison

The tables below compare the credit profiles of recently issued deals. We focus on the key drivers of credit risk, highlighting the comparatively riskier features of a deal. Each table separates the high-LTV (80%+) deals from the low-LTV deals (60%-80%). We add two additional columns for comparison purposes. The first is the ‘Coming Cohort,’ which is meant to give an indication of what upcoming deal profiles will look like. The data in this column is derived from the most recent three months of MBS issuance loan-level data, controlling for the LTV group. These are newly originated and acquired by the GSEs—considering that CRT deals are generally issued with an average loan age between 6 and 15 months, these are the loans that will most likely wind up in future CRT transactions. The second comparison cohort consists of 2006 originations in the historical performance datasets (Fannie and Freddie combined), controlling for the LTV group. We supply this comparison as context for the level of risk that was associated with one of the worst-performing cohorts. The latest CAS deals—both high- and low-LTV—show the impact of increased >43% DTI loan acquisitions. Until recently, STACR deals typically had a higher share of high-DTI loans, but the latest CAS deals have surpassed STACR in this measure, with nearly 30% of their loans having DTI ratios in excess of 43%. CAS high-LTV deals carry more risk in LTV metrics, such as the percentage of loans with a CLTV > 90 or CLTV > 95. However, STACR includes a greater share of loans with a less-than-standard level of mortgage insurance, which would provide less loss protection to investors in the event of a default. Credit Profile Low-LTV deals generally appear more evenly matched in terms of risk factors when comparing STACR and CAS. STACR does display the same DTI imbalance as seen in the high-LTV deals, but that may change as the high-DTI group makes its way into deals. Low-LTV-Deal-Credit-Profile-Most-Recent-Deals

Deal Tracking Reports

Please note that defaults are reported on a delay for both GSEs, and so while we have CPR numbers available for August, CDR numbers are not provided because they are not fully populated yet. Fannie Mae CAS default data is delayed an additional month relative to STACR. We’ve left loss and severity metrics blank for fixed-loss deals. STACR-Deals-over-the-past-3-months CAS-Deals-from-the-past-3-months.

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RiskSpan Adds Home Equity Conversion Mortgage Data to Edge Platform

ARLINGTON, VA, September 12, 2018 — Leading mortgage data analytics provider RiskSpan added Home Equity Conversion Mortgage (HECM) Data to the library of datasets available through its RS Edge Platform. The dataset includes over half a billion records from Ginnie Mae that will expand the RS Edge Platform’s critical applications in Reverse-Mortgage Analysis. RS Edge is a SaaS platform that integrates normalized data, predictive models and complex scenario analytics for customers in the capital markets, commercial banking, and insurance industries. The Edge Platform solves the hardest data management and analytical problem – affordable off-the-shelf integration of clean data and reliable models.

The HECM dataset is the latest in a series of recent additions to the RS Edge data libraries. The platform now holds over five billion records across decades of collection and is the solution of choice for whole loan and securities analytics. RiskSpan’s data strategy is simple. Provide our customers with normalized, tested, analysis-ready data that their enterprise modeling and analytics teams can leverage for faster, more reliable insight. We do the grunt work so that you don’t have to, said Patrick Doherty, RiskSpan’s Chief Operating Officer.  The HECM dataset has been subjected to RiskSpan’s comprehensive data normalization process for simpler analysis in RS Edge. Edge users will be able to drill down to snapshot and historical data available through the UI. Users will also be able to benchmark the HECM data against their own portfolio and leverage it to develop and deploy more sophisticated credit models.  RiskSpan’s Edge API also makes it easier-than-ever to access large datasets for analytics, model development and benchmarking. Major quant teams that prefer APIs now have access to normalized and validated data to run scenario analytics, stress testing or shock analysis. RiskSpan makes data available through its proprietary instance of RStudio and Python.

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