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.
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.
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.
The seasonal nature of recoveries is an easy-to-spot trend in our delinquency outcome charts (loan performance 6 months after being 60 days-past-due). Viewed from a very high level, both Fannie Mae and Freddie Mac display this trend, with visible oscillations in the split between loans that end up current and those that become more delinquent (move to 90+ days past due (DPD)). This trend is also consistent both before and after the crisis – the shares of loans that stay 60 DPD and move to 30 DPD are relatively stable. You can explore the full history of the FNMA and FHLMC Historical Performance Datasets by clicking the 6-month roll links below, and then clicking the “Autoscale” button in the top-right of the graph. 
This trend is salient in April of 2019, as both Fannie Mae Connecticut Avenue Securities (CAS) and Freddie Mac Structured Agency Credit Risk (STACR) have seen 6 months of steady decreases in loans curing, and a steady increase in loans moving to 90+ DPD. While both CAS and STACR hit lows for recovery to current – similar to lows at the beginning of 2018 – it is notable that both CAS and STACR saw multi-year highs for recovery to current in October of 2018 (see Delinquency Outcome Monitoring links below). While continued US economic strength is likely responsible for the improved performance in October, it is not exactly clear why the oscillation would move the recoveries to current back to the same lows experienced in early 2018.
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).
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.
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.
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 table below 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.
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.
Please note that defaults are reported on a delay for both GSEs, and so while we have CPR numbers available for the most recent month, 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.
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.
Most asset-liability management (ALM) models contain an embedded prepayment model for residential mortgage loans. To gauge their accuracy, prepayment modelers typically run a back-test comparing model projections to the actual prepayment rates observed. A standard test is to run a portfolio of loans as of a year ago using the actual interest rates experienced during this time as well as any additional economic factors used by the model such as home price appreciation or the unemployment rate. This methodology isolates the model’s ability to estimate voluntary payoffs from its ability to forecast the economic variables.
The graph below was produced from such a back-test. The residential mortgage loans in the bank’s portfolio as of 10/31/2016 were run through the ALM model (projections) and compared with the observed speeds (actuals). It is apparent that the model did not do a particularly good job forecasting the actual CPRs, as the mean absolute error is 5.0%. Prepayment model validators typically prefer to see mean absolute error rates no higher than 1 to 2%.
Does this mean there is something unique with the bank’s loan portfolio or servicing practices that would cause prepays to deviate from expectations, or does the prepayment model require calibration?
One strategy is to compare the bank’s prepayment experience to that of the market (see below). The “market” is the universe of comparable loans, in this case residential, conventional loans. This assessment should indicate whether the bank’s portfolio is unique or if it behaves similar to the market. Although this comparison looks better, there are still some material differences, especially at the beginning and end of the time series.
Examining the portfolio composition reveals a number of differences which could be the source of the discrepancy. For example:
To compensate, the next step is to adjust the market portfolio to more closely mirror the attributes of the bank’s portfolio. Fine-tuning the “market” so that it better aligns with the bank’s channel and geographic breakout, as well as its larger average loan size, results in the following adjusted prepayment speeds.
Conclusion
Prepayments for the bank’s mortgage portfolio track the market speeds reasonably well with no adjustments. Compensating for the differences in composition related to channel, geography, and loan size tracks even better and results in a mean absolute error of only 1.1%. This indicates that there is nothing unique or idiosyncratic with the bank’s portfolio that would cause projections from a market-based prepayment model to deviate significantly from the observed speeds. Consequently, the ALM prepayment model likely needs adjustments to its tuning parameters to better capture the current environment.
