What The FHFA’s Forbearance Announcement Means for Agency Prepayments

VIENNA, Va., March 9, 2017 – RiskSpan, the data management, data applications, and predictive analytics firm that specializes in risk solutions for the mortgage, capital markets, and banking industries, announced that it has been selected for HousingWire’s 2017 HW TECH100™ award. This year saw the highest number of nominees in the history of HW TECH100™, which recognizes leading companies that bring tech innovation to the U.S. housing economy. Among this year’s winners are other industry-leading firms such as Accenture, CoreLogic, and Freddie Mac.

FHFA forbearance report

RS Edge: Loan-level Delinquencies in GNMA Pools

VIENNA, Va., March 9, 2017 – RiskSpan, the data management, data applications, and predictive analytics firm that specializes in risk solutions for the mortgage, capital markets, and banking industries, announced that it has been selected for HousingWire’s 2017 HW TECH100™ award. This year saw the highest number of nominees in the history of HW TECH100™, which recognizes leading companies that bring tech innovation to the U.S. housing economy. Among this year’s winners are other industry-leading firms such as Accenture, CoreLogic, and Freddie Mac.

RS Edge: S-curves Over Different Refi Cycles

VIENNA, Va., March 9, 2017 – RiskSpan, the data management, data applications, and predictive analytics firm that specializes in risk solutions for the mortgage, capital markets, and banking industries, announced that it has been selected for HousingWire’s 2017 HW TECH100™ award. This year saw the highest number of nominees in the history of HW TECH100™, which recognizes leading companies that bring tech innovation to the U.S. housing economy. Among this year’s winners are other industry-leading firms such as Accenture, CoreLogic, and Freddie Mac.

s-curve-in-rs-edge

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...

Age-bucket-vs-CPR

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...

WALA-ramps

LTV, Low and Slow — RS Edge Analysis

In agency MBS, the specified pool market prices high-LTV loans at a pay-up over TBA for the prepayment protection they offer. This relationship has been well established for both high-LTV purchase loans as well as MHA (Making Home Affordable–i.e., modification and refi programs for troubled loans) production. But what about low-LTV loans? In this post,…

Loans Under $200K Prepay Slowly—But Not in Every State

In agency pools, loans with balances below $200,000 offer prepayment protection (i.e., they prepay more slowly) relative to loans with higher balances. Servicers typically segregate these loans into specified pools that trade at a premium over TBA-deliverable pools. But the prepayment protection isn’t homogenous and varies significantly by state.1 The following chart compares the S-curve…

Fed MBS Runoff Portends More Negative Vega for the Broader Market

With much anticipation and fanfare, the Federal Reserve is finally on track to reduce its MBS holdings. Guidance from the September FOMC meeting reveals that the Fed will allow its MBS holdings to “run off,” reducing its position via prepayments as opposed to selling it off. What does this mean for the market? In the long-term, it means a large increase in net supply of Agency MBS and with it an increase in overall implied and realized volatility.

Machine Learning and Portfolio Performance Analysis

Attribution analysis of portfolios typically aims to discover the impact that a portfolio manager’s investment choices and strategies had on overall profitability. They can help determine whether success was the result of an educated choice or simply good luck. Usually a benchmark is chosen and the portfolio’s performance is assessed relative to it. This post, however, considers the question of whether a non-referential assessment is possible. That is, can we deconstruct and assess a portfolio’s performance without employing a benchmark? Such an analysis would require access to historical return as well as the portfolio’s weights and perhaps the volatility of interest rates, if some of the components exhibit a dependence on them. This list of required variables is by no means exhaustive.

Managing Risk Data: Financial Instrument Terms and Conditions

An instrument’s terms and conditions lie at the heart of cash flow generation and valuation. Not surprisingly, errors in terms and conditions can drive errors in valuation. Fortunately, fixing these errors is often straightforward, provided the terms and conditions data is readily available, which is not always the case for private placement instruments.