Loss Severity on Residential Mortgages: Evidence from Freddie Mac’s Newest Data


Loss Severity on Residential Mortgages: Evidence from Freddie Mac’s Newest Data

February 2, 2015


In this brief, new loan-level data recently released by Freddie Mac on more than 17 million single-family mortgages are analyzed to reveal a range of new and useful insights into the ultimate financial losses associated with a loan after it experiences a credit event. Conclusions described include mortgage insurance significantly lowers loss severities and the preset severity schedule currently in place is reasonable for loans with a loan-to-value (LTV) ratio of 60–80 but too high for deals backed by higher-LTV loans. We also find that small loans have higher severity than larger loans, that real-estate-owned (REO) sales have higher severity than short sales, and that there is no stable relationship between the state of origination and severity. Finally, we review the components of loss—liquidation value and expenses—and find that the latter contributes significantly to the ultimate loss.

Full Publication

Two parameters determine a mortgage’s credit risk: probability of default and loss severity given default. While there is a growing body of research relevant to the modeling and estimation of mortgage default, there are few studies on loss severity (the percentage lost in the event of default) because of limited data. New data released by Freddie Mac, however, offer analysts the ability to assess mortgage loans made over 13 years for their loss severity. This robust database of more than 17 million loans offers a range of new and useful insights, by state, into the ultimate financial losses associated with a loan after it experiences a credit event. The addition of loan performance information beyond the credit event is a new and welcome addition to the single-family loan-level dataset.

In this brief, we review loans experiencing four distinct credit events, and for the first time track what happens to the loans. For loans that liquidate, we determine the loss severity measured by the percentage of unpaid principal balance lost at the time of default. This analysis allows us to assess both the value of mortgage insurance (MI) and the accuracy of the preset severities used in Freddie Mac’s risk-sharing deals. We find MI significantly lowers the severities. We also find that the preset severity schedule is reasonable for loans with a loan-to-value (LTV) ratio of 60–80 but too high for deals backed by higher-LTV loans.

We also analyze severity by loan size, state, and type of liquidation. We find that small loans have higher severity than larger ones, that real-estate-owned (REO) sales have higher severity than short sales, and that there is no stable relationship between the state of origination and severity. Finally, we review the components of loss—liquidation value and expenses—and find that the latter contributes significantly to the ultimate loss.

What Do the New Data Include?

Historically, data allowed market participants to track loans only through the advent of a credit event. Freddie Mac and Fannie Mae (the GSEs) define a credit event as a loan going 180 days delinquent or being liquidated through a deed-in-lieu, short sale, foreclosure sale, or REO before the 180-day delinquency point. Once a loan experienced a credit event, it was removed from the dataset and the loss was calculated by multiplying the balance affected by a predetermined severity.

In support of their risk-sharing deals, in 2013 the GSEs began releasing quarterly performance data on their 30-year full-documentation amortizing book of business. In November 2014, Freddie Mac’s quarterly release contained a broader dataset that included loan dispositions. This increased transparency may indicate that Freddie anticipates issuing a deal in which risk-sharing payments will be triggered by actual severities rather than by credit events.

Freddie’s new data allow analysts to calculate actual severities by various credit event types and timing of dispositions, and to compare actual severities to the predetermined severities on risk-sharing deals. In addition, the new data allow market participants to break down loss severity into its various components: net sale proceeds, expenses, MI recoveries, and non-MI recoveries. This is also the first time the GSEs have released loan-level loss data for mortgages. Before this, loss data had been available only on mortgages that were collateral for private-label securities.

What Happens to Loans That Experience a Credit Event?

How Common Are These Credit Events?

Among loans originated between 1999 and 2004, 2.3 percent have experienced a credit event (table 1). For 2007, the single worst issue year, 12.6 percent of originated loans have experienced a credit event. Note that the FICO/LTV distribution for 1999–2004 is very similar to that of 2007.1 The difference in default rates reflects how hard the 2007 vintage was hit by the Great Recession and the accompanying home price depreciation. The earlier vintages had some home price appreciation and, in some cases, had prepaid by the time the Great Recession hit.

The current book of business has a much higher percentage of high-FICO loans than either the 1999–2004 or the 2007 book of business; it would hence have fewer credit events if one applied these experiences bucket by bucket. If we applied the composition of the 2013 book of the business to the 1999–2004 experience, defaults would be 1.2 percent. If we applied the composition of the 2013 book of business to the 2007 experience, defaults would be 8.4 percent.

2.3 percent of 1999–2004 loans and 12.6 percent of 2007 loans have experienced credit events.

Exhibit 1

Do These Credit Events Really End Up in Losses?

Table 2 shows the current status of 13 years of loans that have encountered credit events, sorted by vintage and LTV. Not all loans that experience a credit event will be liquidated; some will be rehabilitated. Because of long timelines, some others will still be in process. We identify eight paths for loans that experience credit events: (1) current without a modification,2 (2) modified and current on the modification, (3) prepay without modification, (4) prepay after modification, (5) modified and not current on the modification, (6) in pipeline with no modification, (7) liquidated via a foreclosure alternative (deed-in-lieu, short sales, foreclosure sale) and (8) liquidated through REO. Under outcomes 1 through 4, the loans are rehabilitated and, thus, we assume there is no loss. Under outcomes 5 through 8, the loans have either been liquidated or will be liquidated, and, thus, have a loss expectation. To be more explicit, we assume that loans that have been modified but are not current on their modification, and those in the foreclosure pipeline that have never been modified, are likely to eventually be liquidated.

Twenty-two percent of the loans that experienced credit events from 1999 to 2013 have been rehabilitated in one of four ways and, thus, not likely to experience an eventual loss (table 2, total of columns 1–4, shown in column 5). The most common route to rehabilitation—constituting 11 percent of all loans that experienced a credit event—are those that have been modified and are now current. The next largest category are loans that later prepaid with no modification. Lower-LTV loans are more likely to prepay without a modification than higher-LTV loans. Fifteen percent of loans with LTVs of 60 or under became current again, versus 5–6 percent of loans with higher LTVs.

Among loans with credit events from 1999 to 2013, 22 percent have been rehabilitated; 78 percent have not and are likely to experience losses.

Table 2 also shows that 78 percent of loans that experienced credit events from 1999 to 2013 are likely to experience losses. Of this 78 percent, more than two-thirds (54 percent) have already been liquidated though REO or foreclosure alternatives. The remaining 24 percent are either modified and not current or not modified but in the pipeline—that is, they will eventually be liquidated.

These liquidation numbers are, not surprisingly, highly dependent on LTV and vintage year. For loans with an original LTV of 60 or under, 63 percent of the loans are expected to eventually be liquidated. In contrast, 77 percent of the loans with LTVs between 60 and 80 are expected to eventually liquidate, rising to 81 percent for loans with LTVs over 80. The higher original LTV makes a prepayment less likely (if LTV is measured correctly, a low-LTV borrower who has experienced modest home price depreciation and is behind on his payments can sell the home without a loss). It also makes rehabilitation less attractive for the borrower, as the house will likely have negative equity. Similarly, the percentage of loans that will eventually liquidate is higher during the crisis years, as home price deterioration put many mortgages into a negative equity situation. Table 3 shows the percent of loans we expect to liquidate, by LTV and FICO buckets, in a manner consistent with table 1.

Exhibit 2

Exhibit 3

23.2 cents for every dollar remaining at default was lost for 1999–2004 liquidated loans; 36–40 cents for every dollar was lost for 2005–08 loans.

How Severe Are the Actual Losses?

Table 4 shows loss severities by FICO/LTV bucket and issue years, including all loans that have already gone through REO or a foreclosure alternative. The table includes loans that have liquidated without a loss, but it does not include loans that have cured after their credit event and then prepaid. The severities at liquidation are 23.2 percent for the 1999–2004 period, rising to 36–40 percent for 2005–08, then dropping sharply thereafter.

The relationship between loss severities and LTV categories is particularly interesting. Severities for loans with LTVs over 80 are much lower than for loans with LTVs between 60 and 80. In fact, the severities for the over-80-LTV loans are even lower than severities for the 60-or-under-LTV loans. The reason is simple. Loans with LTVs over 80 are required to have mortgage insurance, which covers the first loss; this coverage is usually deep enough that Freddie is not exposed unless the market value of the home drops far more than 20 percent. For example, standard practice is to bring down an 85 LTV mortgage to 73 LTV, a 90 or 95 LTV mortgage to 65 LTV, and a 97 LTV mortgage to 63 LTV. These results would indicate that mortgage insurance is more effective at protecting the GSEs against losses than is commonly assumed.

Exhibit 4

Comparison of Loss Severities to Preset Severities in the STACR Deals

This analysis allows us to compare the severities actually experienced with the preset severities that Freddie Mac assumes in its credit risk transfer program, known as the Structured Agency Credit Risk (STACR) deals. Based on our assessment, the preset severity schedule in the STACR deals produces results on historical data that are very close to the actual severity levels for the 60-to-80-LTV loans, but the preset severity schedule is much higher than the actual levels for the over-80 LTV loans.

To assess the expected severity for loans that have experienced credit events, we multiply the percentage of loans that we expect to liquidate by the severity on the loans that have already liquidated. For the 1999–2004 book of business, for loans with 60–80 LTV, we expect 72 percent of the loans to liquidate, at an average severity of 30.4 percent. Thus, the expected severity from all loans of this vintage that have experienced a credit event is 22 percent (0.72 x 0.304).

The STACR deals apply a preset severity schedule to the percent of balances affected by credit events. This schedule is a weighted average of a step function and differs for the 60-to-80-LTV deals and the over-80-LTV deals. For the 60-to-80-LTV deals, the schedule is 15 percent severity for the first 1 percent of credit events, 25 percent for credit events between 1 and 2 percent, and 40 percent for credit events over 2 percent. If we look at the 1999–2004 book of business for loans with 60–80 LTV (table 1), the cumulative credit events are 2.0 percent. Thus, the loss severity would be 20 percent ([1 x 0.15 + 1 x 0.25]/2). If we compare this number to the actual loss severity, 22 percent, the two are very similar. Overall, for vintages through 2008, Freddie’s preset severity schedule produces results very close to the actual severities (table 5). The comparison for 2009 and later vintages is less valid. We would expect the preset numbers to be lower than the actual severities, as the credit events are still ramping up.

The preset severity schedule is reasonable for loans with LTVs of 60–80 but too high for deals backed by higher-LTV loans.

To calculate losses on over-80-LTV loans, the preset severity schedule is 10 percent for the first 1 percent of credit events, 20 percent for credit events between 1 and 3 percent, 25 percent for credit events between 3 and 5 percent, and 40 percent for credit events over 5 percent. For example, 4.3 percent of the 1999–2004 book of business experienced a credit event. Thus, the overall loss severity for that book would be 19 percent ([1 x 0.10 + 2 x 0.20 + 1.3 x 0.25]/4.3).The actual severity was 12 percent. In general, when using the preset severity schedule on the over-80-LTV mortgages the derived severities are very close to the derived severities on the 60-to-80-LTV mortgages (table 5). However, the actual severities on the over-80-LTV mortgages are much lower than the preset severity schedule. 8 LOSS SEVERITY ON RESIDENTIAL MORTGAGES

Some readers may note that we have not included lost interest in our calculation of severity.3 Would our conclusion be sensitive to the inclusion of lost interest in the severity calculations? It would definitely bring the over-80-LTV actual severities closer to the preset severity schedule. But then the actual severities on the 60-to-80-LTV loans would be above the preset severity schedule, so the qualitative conclusion would remain the same. Note that Fannie Mae uses severities very similar to Freddie Mac’s on deals backed by 60-to-80-LTV collateral, but substantially lower severities than Freddie Mac’s on deals backed by over-80-LTV collateral.4

Exhibit 5

A Deeper Analysis of Loss Severities

Loss severities depend on different loan characteristics. For example, they vary by origination year of the loan and by LTV bucket. The results of this analysis are intuitive—loans originated in the 1999–2004 period had more home price appreciation, and hence lower severities. Similarly, lower-LTV loans (60 and under) have more equity and hence lower severities than the 60-to-80-LTV bucket. However, the over-80-LTV bucket had severities not too different from, and in many cases less than, the 60-and-under-LTV bucket, owing to the presence of mortgage insurance.

Differences by Loan Size

Loss severities are also affected by loan size (table 6). For all vintage years, the smallest loans exhibit the highest severity, and the severities decline monotonically with loan size. For example, for 1999–2004, loans with a balance of $60K or less had a loss severity of 47 percent, while those with a balance of $60–100K had a severity of 31.3 percent and those with a balance over $100K had a severity of 18 percent. One possible reason is that loan size may influence liquidation costs. A smaller loan is more likely to trade at a higher foreclosure discount. In addition, liquidation costs are likely larger on smaller loans as a percentage of loan unpaid principal balance (UPB).

Exhibit 6

Differences by State of Origination

The data also afford us the opportunity to look at loss severity by state by vintages (table 7). This relationship is not constant through time. The sand states (California, Florida, Arizona, and Nevada) had very high severities for loans originated in 2005–07, but their severities were around or below the national average both pre-crisis and post-crisis. By contrast, some of the Rust Belt states (Michigan, Ohio, Illinois, and Indiana) were above the national average for both pre-crisis and crisis origination years.

Differences by Liquidation Type

Severity may also vary by liquidation types, whether REO or foreclosure alternative (table 8). REO liquidation produces much higher severities than foreclosure alternatives. For example, for the 2007 book of business, REO liquidations had a severity of 42 percent, versus other foreclosure alternatives at 35 percent. This suggests that though the GSEs have put into place incentives to encourage foreclosure alternatives, the use of stronger incentives merits further study.

REO sales have a higher severity than short sales.

Exhibit 8

It is important to realize that severities are based on the loans that actually liquidate. The last column of table 7 shows this percentage state by state. As noted earlier, the share of loans that actually liquidate is 54 percent for the entire portfolio. However, it varies considerably by state. In Tennessee, California, and Texas, about 60–64 percent of loans that have experienced credit events have liquidated, well above the national average. In contrast, in New York and New Jersey, the share of loans that liquidated is 14–15 percent, well below the national average. The share is also below the national average in DC (21 percent) and in Massachusetts (36 percent). This low percentage of liquidation in certain states is the basis of the FHFA’s recent decision to suspend compensatory fees in five areas (New York State, New York City, New Jersey, Washington, DC, and Massachusetts), as the information to set timelines was too spotty.5 Note that these areas do not have significantly more successful modifications than California, a state with a far higher percentage of liquidations; they just have more modified and non-modified loans in the pipeline.

An Analysis of the Components of Loss

Table 9 shows the components of loss, among loans that have experienced a loss, broken down by vintage and LTV buckets. Loss is defined as the unpaid principal balance of the loan less proceeds from the sale of the home, less any recoveries from mortgage insurance or other items, plus expenses. For the entire period, for all loans that experienced losses, the average severity was 40.3 percent. However, 18.3 percent of the loans that liquidated did not experience a loss.6 Thus, the overall severity was 33.9 percent, a fact we first saw in table 4.

Loss on the sale of the properties, calculated as [1 - (net sale proceeds/defaulted UPB)], averages 40 percent and is higher with over-80 LTV loans. For example, for 2007, the sale loss for over-80 LTV loans is 49 (100-51) percent, while the sale proceeds per defaulted UPB for 60–80 LTV loans is 40 (100-60) percent. Mortgage insurance will offset some of these losses. Thus, for 2007, the total severity for over-80 LTV loans is 36 percent, lower than 44 percent for loans with 60–80 LTV. For the entire universe of loans liquidated at a loss, mortgage insurance adds 6.1 percent to total recoveries; it adds 19–22 percent for above-80 LTV loans. Other non-MI recoveries average less than 2 percent. Note that expenses can be sizeable, adding 8.6 percent to severity. These are direct expenses only; they do not include any expenses from lost interest.

Exhibit 9


In this brief, we focus on loss severity, using Freddie Mac’s enhanced data; these are the first loan-level loss severity data available to the public for loans outside private-label securities. Loans with higher LTVs and mortgage insurance have a significantly lower loss severity than loans with lower LTVs and no mortgage insurance. Loss severities assigned for the risk-sharing deals are very reasonable for loans with 60–80 LTV. The preset severities are higher than the actual severities for loans with above-80 LTV. Besides LTV, FICO, and origination vintages, state, loan size and liquidation types will also affect loss severity. And. expenses are a significant portion of liquidation costs.


1. This issue was discussed in Laurie Goodman and Jun Zhu, “The GSE Reform Debate: How Much Capital Is Enough?” Journal of Structured Finance Spring, 2014.
2. Current is defined as current for the past three months.
3. We calculate severity as the unpaid principal balance of the loan plus expenses, less the net sales proceeds, less recoveries from MI and other sources as a percentage of the unpaid principal balance. Many researchers would argue that we should have included lost interest expense, calculated as [the note rate less the servicing fee] times the amount of time the loan is delinquent or in foreclosure. In fact, the mortgage insurers do reimburse the GSEs for lost interest expense up to a certain maximum. We deliberately elected not to do so. The GSEs’ real cost of carrying these loans is not [the note rate less the servicing fee], but rather their short-term funding cost, which is near zero. We ignored these funding costs, which would very marginally raise severities, for the purposes of our calculations, as we figured they would be offset by two items that marginally lower severities: compensatory fees paid by the lenders to Freddie are not included in the recovery amounts, and recoveries include pool policies that Freddie no longer uses.
4. On the 60- to-80-LTV deals, the Fannie and Freddie preset severity schedules are very similar: the only difference is that Fannie uses a 10 percent loss rate (rather than Freddie’s 15 percent loss rate) for the first 1 percent of credit events. For the over-80-LTV deals, Fannie uses a 25 percent loss rate (rather than Freddie’s 40 percent) where credit events exceed 5 percent.
5. For a fuller discussion of this point, see Laurie Goodman, “Servicing Is an Underappreciated Constraint on Credit Access” (Washington, DC: Urban Institute, 2014).
6. This occurrence was most common in the 60-and-under LTV bucket and for loans with mortgage insurance.

About the Authors

Laurie Goodman is the director of the Housing Finance Policy Center at the Urban Institute. The center is dedicated to providing policymakers with data-driven analysis of housing finance policy issues that they can depend on for relevance, accuracy, and independence.

Before joining Urban in 2013, Goodman spent 30 years as an analyst and research department manager at a number of Wall Street firms. From 2008 to 2013, she was a senior managing director at Amherst Securities Group, LP, a boutique broker/dealer specializing in securitized products, where her strategy effort became known for its analysis of housing policy issues. From 1993 to 2008, Goodman was head of Global Fixed Income Research and Manager of US Securitized Products Research at UBS and predecessor firms, which was ranked first by Institutional Investor for 11 straight years. She has also held positions as a senior fixed income analyst, a mortgage portfolio manager, and a senior economist at the Federal Reserve Bank of New York.

Goodman was inducted into the Fixed Income Analysts Hall of Fame in 2009. She serves on the board of directors of MFA Financial and is a member of the Bipartisan Policy Center’s Housing Commission, the Federal Reserve Bank of New York’s Financial Advisory Roundtable, and the New York State Mortgage Relief Incentive Fund Advisory Committee. She has published more than 200 articles in professional and academic journals, and has coauthored and coedited five books. Goodman has a BA in mathematics from the University of Pennsylvania and a MA and PhD in economics from Stanford University.

Jun Zhu is a senior financial methodologist at The Urban Institute. She designs and conducts quantitative studies of housing finance trends, challenges, and policy issues. Previously she s as a senior economist in the Office of the Chief Economist at Freddie Mac where she conducted research on the mortgage and housing markets, including default and prepayment modeling. While at Freddie Mac, she also served as a consultant to the US Treasury on housing and mortgage modification issues. She obtained her PhD in real estate from the University of Wisconsin–Madison in 2011.


The nonprofit Urban Institute is dedicated to elevating the debate on social and economic policy. For nearly five decades, Urban scholars have conducted research and offered evidence-based solutions that improve lives and strengthen communities across a rapidly urbanizing world. Their objective research helps expand opportunities for all, reduce hardship among the most vulnerable, and strengthen the effectiveness of the public sector.

The Urban Institute’s Housing Finance Policy Center (HFPC) was launched with generous support at the leadership level from the Citi Foundation and John D. and Catherine T. MacArthur Foundation. Additional support was provided by The Ford Foundation and The Open Society Foundations.

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Copyright © February 2015. Urban Institute. Permission is granted for reproduction of this file, with attribution to the Urban Institute. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders.

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