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Treatment Effects of Subprime Mortgage Modifications

Under the Home Affordable Modification Program

Walter Scott1

March 2015

Abstract

The Home Affordable Modification Program (HAMP) was initiated in 2009 to encourage loan 

servicers to provide substantial payment relief to borrowers struggling to pay their home 

mortgages, and thereby help to reduce the high volume of defaults and foreclosures resulting 

from the subprime crisis and Great Recession. Evaluating the true net impact of HAMP is 

complicated by the likelihood of substantial selection effects: the population of delinquent 

borrowers who were able to provide hardship documentation and meet other requirements of the 

program could have differed from those who received non-HAMP modifications or whose loans 

were never modified. A selection bias correction model is constructed to measure the treatment 

effects of HAMP modifications made in 2010 and 2011, in comparison to two control groups of 

delinquent subprime loans: those that were never modified, and those receiving lenders’ 

proprietary modifications outside of HAMP. Selection effects were found to account for up to 

one third of observed modification performance, because the higher-risk borrowers did not meet 

program requirements or never responded to lender contact efforts. In the first year of HAMP’s 

implementation, modification performance slightly lagged that of non-HAMP modifications with 

the same levels of payment reduction. Following program changes in mid-2010, however, 

HAMP modifications had larger treatment effects than non-HAMPs even when controlling for 

borrower and loan characteristics, selection effects, and the modification terms. High early 

redefault rates observed for both HAMP and non-HAMP modifications were found to be driven 

by the behavior of homeowners who had accepted a modification after a delinquency period of 

one year or more; these borrowers tended to stop making payments within just two years of 

modification. This implies that longer-term modification redefault rates should stabilize, given 

that the impact of payment reduction on borrower performance does not appear to decay over 

time.

 

1 Fannie Mae, Washington, DC, USA. email: walter_l_scott@fanniemae.com

This research was conducted by the author while working for Fannie Mae in its role as program 

administrator for Making Home Affordable (MHA), a program of the United States Department of the 

Treasury and the Department of Housing and Urban Development. A number of people at Fannie Mae 

assisted in the creation of this research paper, including Matt Classick, Daniel Elghazi, Phuong Lam, 

Tonya Lauridsen, Maureen Reed and Alison Wager. Above all, the author wishes to thank Cristian Lopez, 

Noah Murrell and John Gibson for their invaluable assistance with this project, as well as Laurie 

Goodman, Therese Scharlemann, Andrew Varrieur and Hu Xiaoqiang for their helpful comments on 

earlier drafts of this paper. The views and findings in this research paper are those of the author and do 

not necessarily represent those of the author’s employer, Fannie Mae. The numbers presented herein have 

not been audited for reporting or accounting purposes.
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I. INTRODUCTION

Since the inception of the Making Home Affordable Program (MHA), more than 1.3 million 

borrowers have had their first lien mortgages permanently modified through MHA’s Home 

Affordable Modification Program (HAMP). This paper analyzes the performance of HAMP 

modifications and the key factors that most affect their performance. All data on the HAMP 

program used in this analysis was obtained from the MHA Program system of record.

In the first part of this paper, single-variable analysis and program-to-date data for all HAMP 

modifications are used to identify correlations between various HAMP modification 

characteristics and modification performance. In the second half, econometric analysis 

(regression testing) is used to isolate the key factors affecting HAMP modification performance 

and to compare the performance of loans modified through HAMP with other similarly 

delinquent loans. The innovation in this study comes from the merging of HAMP program data 

with a commercial loan performance database, which allows HAMP-modified loans to be 

compared directly with two alternate control groups consisting of loans that were modified 

outside of HAMP and loans that were similarly delinquent but never received a modification. 

Using these control groups, the average treatment effect of the modifications can be measured, 

independent of the underlying differences between the modified and unmodified loan 

populations. The HAMP loans and the two control groups were restricted to loans that are part of 

private label mortgage-backed securities (PLS).

Characteristics and Structure of HAMP Loan Modifications

In early 2009, the U.S. Department of the Treasury (Treasury) and the Department of Housing 

and Urban Development (HUD) launched MHA. MHA’s first lien modification program, 

HAMP, combines financial incentives and a standardized modification structure to encourage 

borrowers, servicers, and investors to participate in the first nationwide mortgage modification 

program.2

Prior to MHA, there were other efforts within government and the mortgage industry to develop 

more effective foreclosure prevention options for borrowers. Such efforts, however, often 

consisted of re-capitalization of missed interest payments and recalculation of the borrower’s 

monthly principal and interest payments based on the new, higher mortgage loan balance. Thus, 

most modifications prior to HAMP either raised the borrower’s monthly mortgage payment or, at 

best, kept it flat. Modifications completed in 2007 and 2008 experienced high redefault rates in 

the early months following the modification. (OCC 2009a)

As the mortgage crisis worsened throughout 2008, the movement towards modifications 

reducing a borrower’s monthly mortgage obligation gained momentum. The depth of payment 

reduction remained shallow, however, with only 39 percent of all modifications resulting in a 

monthly payment decrease of 10 percent or more by the fourth quarter of 2008. (OCC 2009b)

 

2 For more information on the creation of MHA and HAMP see (Treasury and HUD, 2012).
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HAMP both expanded upon and refined many of the principles that both public and private 

participants in the industry had begun to adopt regarding how to structure and process a 

modification. Generally, HAMP modifications share a number of key characteristics, including a 

trial period prior to permanent modification of the loan, as well as an affordability standard of 31 

percent for the borrower’s “front-end” debt-to-income (DTI) ratio (i.e., the ratio of the 

borrower’s monthly mortgage payments to gross monthly income). In addition to the standard 

HAMP structure, the HAMP Principal Reduction Alternative Program (PRA) provides financial 

incentives to investors for reducing principal owed by borrowers whose homes are worth 

significantly less than the remaining balance owed on the mortgage. HAMP also incorporated a 

Net Present Value (NPV) model so that servicers could evaluate (on behalf of investors) whether 

modifying any given loan made economic sense.

3

To date, more than 1.3 million borrowers have had their mortgages permanently modified 

through HAMP. Virtually all HAMP modifications reduce the borrower’s monthly principal and 

interest payment, with a median payment reduction of approximately $500, or over a third of the 

median monthly payment before modification. (U.S. Treasury, 2014)

Performance of HAMP and Non-HAMP Modifications

As of March 2014, over 945,000 permanent HAMP modifications were performing and 

remained in good standing within the program. More than 380,000 or 28 percent had been 

disqualified from the program because the borrower missed three consecutive monthly payments 

on the modified loan. As shown in Table 1, through February 2014, HAMP modifications were 

experiencing overall redefault rates4of 5.2 percent, 13.4 percent, 20.3 percent, and 26.1 percent 

by months 6, 12, 18, and 24, respectively. 

The data also indicate that the performance of HAMP modifications has gradually improved over 

time, with more recent vintages of modifications generally performing better than older vintages 

at any given seasoning point. For example, for modifications in effect for one year, 20.5 percent 

of modifications started in the third quarter of 2009 have disqualified, compared to 9.5 percent 

for modifications started in the first quarter of 2013.

In addition, as seen in Figure 1, the data show that the growth rate of redefaults on HAMP 

modifications is declining. Thus, while the cumulative redefault rate for each annual vintage of 

modifications grows over time, the redefault rate for each successive three-month period 

generally declines.

Studies by the Office of the Comptroller of the Currency (OCC) have found that borrowers in 

HAMP consistently exhibit lower delinquency rates (defined as 60 days or more delinquent) than 

those in non-HAMP modifications. The OCC data (Table 2 below) show that this is true across 

all vintages and seasoning points. In addition to confirming the improved performance of HAMP 

modifications over time, the OCC data also show that the performance of non-HAMP 

 

3 See (Holden, 2012) for a detailed discussion of the HAMP NPV model. 

4 Unless otherwise noted, this paper defines a redefault as a modification that becomes 90 days or more delinquent 

following modification. 
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modifications has improved over time relative to the performance of HAMP modifications. Note, 

however, that the OCC comparison data do not take into account or control for differences in 

either loan or borrower characteristics.

Table 1: Permanent HAMP Modification Redefault Rates by Vintage

Source: March 2014 MHA Program Data

Figure 1: HAMP Three-Month Conditional Redefault Rates by Modification Year5

 

5 The three-month redefault rate was calculated as the number of permanent modifications disqualified within the 

three-month period divided by the number of permanent modifications remaining active at month(T – 3) and if still 

active at month T. Permanent modifications remaining active at month(T—3) excludes those permanent 

modifications that have paid off or disqualified by month(T – 3).

# 90+ % # 90+ % # 90+ % # 90+ % # 90+ % # 90+ % # 90+ %

2009Q3 4,393 9.6% 4,631 20.5% 4,963 28.6% 5,074 33.3% 5,136 38.4% 5,179 41.6% 5,058 46.2%

2009Q4 47,396 5.6% 51,299 15.2% 54,530 21.9% 55,516 28.1% 56,647 32.6% 56,350 36.9% 56,183 40.4%

2010Q1 149,273 5.2% 160,873 15.7% 166,065 22.2% 168,118 28.6% 168,068 33.0% 166,319 37.3% 165,318 40.5%

2010Q2 156,588 6.9% 173,316 15.7% 170,552 23.8% 179,039 28.6% 177,439 33.3% 175,203 37.3% 175,433 40.1%

2010Q3 95,690 6.6% 103,944 14.1% 106,088 21.6% 106,227 26.6% 105,774 31.5% 104,535 35.1% 105,092 37.8%

2010Q4 62,311 5.4% 64,869 14.1% 66,529 20.9% 66,249 26.3% 65,940 30.8% 65,862 34.2%

2011Q1 75,650 4.5% 79,534 13.3% 81,317 19.0% 80,866 24.6% 80,098 28.9% 81,201 31.8%

2011Q2 88,878 5.3% 92,614 13.0% 91,922 19.9% 91,486 24.9% 92,103 28.5%

2011Q3 85,882 5.4% 86,878 12.2% 86,540 18.7% 85,079 23.3% 86,458 26.7%

2011Q4 67,416 4.2% 67,749 11.2% 67,876 16.7% 67,646 21.0%

2012Q1 50,837 3.9% 50,839 10.7% 50,220 15.6% 50,744 19.8%

2012Q2 44,959 4.4% 45,236 10.8% 44,782 16.0%

2012Q3 48,945 4.4% 49,680 9.9% 50,259 15.0%

2012Q4 41,207 3.8% 42,416 9.3%

2013Q1 40,893 3.4% 42,039 9.5%

2013Q2 33,018 3.7%

2013Q3 33,446 4.0%

Total 1,126,782 5.2% 1,115,917 13.4% 1,041,643 20.3% 956,044 26.1% 837,663 31.2% 654,649 36.0% 507,084 39.8%

Month 36 Month 42 Modification 

Vintage

Month 6 Month 12 Month 18 Month 24 Month 30

2009

2011 2010

2012

2013

0%

1%

2%

3%

4%

5%

6%

6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 3 Month Redefault Rate

Months After Conversion to Permanent Modification
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Table 2: Performance of HAMP Modifications Compared With Non-HAMP Modifications6 

The performance of loan modifications is a topic that is subject to increasing interest from 

oversight and regulatory bodies, lawmakers, other policy officials, and the general public. 

Modification programs have been implemented through significant effort and expense on the part 

of the entire housing finance system with the goal of stabilizing the nation’s housing market and 

helping borrowers avoid foreclosure. Since redefaults undermine this goal, it is important to 

understand the reasons that modifications succeed or fail and the types of modifications that 

maximize borrowers’ likelihood of success.

This paper seeks to address fundamental questions raised by the known data on the performance 

of HAMP and non-HAMP mortgage modifications, including:

 What factors drive modification performance? In other words, are there inherent 

characteristics specific to the borrower and/or the modification that improve the likelihood of 

the borrower’s success following modification?

 Controlling for borrower and loan characteristics, are there differences in the performance of 

HAMP and non-HAMP modifications? While there is considerable data on the likelihood of 

redefault for HAMP borrowers, what can be said about similarly situated borrowers with 

similar loans who received modifications outside of HAMP or no modification at all?

 

6

 Source: OCC (2014)

2012Q1 HAMP 37,240 4.90% 8.40% 11.30% 13.00% 13.30%

2012Q1 Non-HAMP 65,861 9.40% 17.50% 23.10% 25.50% 25.50%

2012Q2 HAMP 28,627 4.40% 7.90% 10.10% 11.00% 12.00%

2012Q2 Non-HAMP 68,088 7.50% 14.50% 17.90% 19.40% 19.40%

2012Q3 HAMP 31,745 4.30% 7.70% 9.40% 11.00% 12.30%

2012Q3 Non-HAMP 104,764 8.00% 14.60% 17.90% 21.20% 21.20%

2012Q4 HAMP 29,314 3.80% 6.20% 8.70% 10.30% 11.40%

2012Q4 Non-HAMP 114,181 8.30% 12.80% 17.50% 20.60% 20.60%

2013Q1 HAMP 28,030 3.20% 6.40% 8.90% 10.30%

2013Q1 Non-HAMP 110,519 6.50% 13.00% 17.70% 18.60%

2013Q2 HAMP 22,613 3.40% 6.90% 8.90%

2013Q2 Non-HAMP 85,582 8.30% 15.40% 18.80%

2013Q3 HAMP 23,159 3.90% 7.00%

2013Q3 Non-HAMP 76,134 10.60% 15.90%

2013Q4 HAMP 20,829 3.70%

2013Q4 Non-HAMP 51,637 8.50%

Month 12

60+ %

Month 15

60+ %

Modification 

Vintage # Mods Month 360+ %

Month 6

60+ %

Month 9

60+ %
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Section II of this paper provides a single-variable analysis of HAMP modification performance 

based on Treasury data for all HAMP modifications. Section III reviews existing published 

research on factors influencing redefault after modifications. Section IV then introduces our 

regression model of HAMP and non-HAMP treatment effects, which was constructed to test a 

series of hypotheses about the net effectiveness of modifications after correcting for borrower 

and servicer selection factors, as well as the relative performance of HAMP and non-HAMP 

modifications. Section V gives overall conclusions.

II. SINGLE-VARIABLE ANALYSIS OF FACTORS DRIVING HAMP 

MODIFICATION PERFORMANCE

Treasury data collected as part of the HAMP modification process helps shed light on the 

characteristics that most influence modification performance. Single-variable analysis of this 

data shows that payment reduction, the degree of loan delinquency at time of modification, and, 

to a slightly lesser extent, credit score at time of modification are important drivers of 

modification performance.7 Post-modification mark-to-market loan-to-value (MTMLTV) ratio 

appears to have some impact on the performance, especially within certain segments of the 

HAMP population, while other criteria, such as the post-modification “back-end” DTI (i.e., the 

ratio of a borrower’s total monthly payment obligations to gross monthly income) appear to be 

less meaningful drivers of performance.

The results of this analysis are limited both by the inherent limitations of single-variable analysis 

and by potential unobserved factors not found in the Treasury data. By definition, a singlevariable analysis can identify correlations between modification performance and modification 

characteristics (such as payment reduction, credit score, etc.), but it cannot isolate or disentangle 

the effect of one factor that may be driving performance from others that may also be driving 

performance. Similarly, there may be factors driving modification performance about which 

there is no program data. For example, while income level is collected as part of the modification 

process, post-modification fluctuations in levels of income are unknown and may have a 

significant impact on performance. 

Payment Reduction

Most HAMP modifications result in significant payment reductions, with half of the population 

receiving a monthly payment reduction of approximately $500, or over a third of the median 

monthly payment before modification. (U.S. Treasury, 2014)

As seen in Figure 2, single-variable analysis shows that the amount of payment reduction has a 

significant impact on performance. Modifications with larger payment reductions consistently 

outperform those modifications with smaller reductions. This is true across all vintages and 

seasoning points. Over time, the gap in performance among payment reduction cohorts increases 

significantly. For example, only 16 percent of borrowers with a monthly payment reduction 

 

7 These three characteristics are independent variables which are incorporated into the redefault model that is 

embedded within the HAMP Net Present Value (NPV) model.
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greater than 50 percent have redefaulted within 24 months, compared to a disqualification rate of 

41 percent for HAMP borrowers whose payment was reduced by 20 percent or less.

Figure 2: Cumulative Redefault Rate by Percent Reduction in Monthly Mortgage Payment

Delinquency at Time of Modification

The single-variable analysis also indicates that performance of the modification is influenced by 

the length of delinquency of the loan at time of modification. Borrowers who were 31 to 90 days 

delinquent at the start of the HAMP modification experienced a 24 percent redefault rate in the 

subsequent 24 months, compared to a rate of 30 percent for borrowers whose delinquency was 

between 121 and 210 days at the time of modification.8

Figure 3: Cumulative Redefault Rate by Delinquency at Time of Modification

 

8 Recognizing this correlation, both Treasury and the housing Government Sponsored Enterprises (GSEs) – Fannie 

Mae and Freddie Mac – adjusted their policies in 2011 to provide greater incentives for servicers to reach borrowers 

in the early stages of delinquency, adopting tiered incentive structures that increased incentive payments from 

$1,000 to $1,600 for servicers starting trial modifications for borrowers who are 120 days delinquent or less 

(Supplemental Directive 11-06).

0%

10%

20%

30%

40%

50%

60%

70%

12 18 24 30 36 42 48

90+ Day Delinquency Rate

Months After Conversion to Permanent Modification

<= 20%

(20%, 30%]

(30%, 40%]

(40%, 50%]

> 50%

0%

10%

20%

30%

40%

50%

60%

70%

12 18 24 30 36 42 48

90+ Day Delinquency Rate

Months After Conversion to Permanent Modification

<= 30 Days

31 - 90 Days

91 - 120 Days

121 - 210 Days

> 210 Days
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Credit Score

The single-variable analysis also indicates that credit score at the time of modification also has a 

significant impact on performance.

9

 For example, borrowers with credit scores below 580 at the 

time of modification experienced a 33 percent redefault rate in the subsequent 24 months, 

compared to a rate of 11 percent for borrowers whose credit scores were above 660. 

Figure 4: Cumulative Redefault Rate by Credit Score at Time of Modification

Other Characteristics

Single-variable analysis of HAMP data indicates that three additional characteristics also appear 

to influence modification performance, but to a lesser degree than the factors previously 

mentioned. These are MTMLTV, post-modification back-end DTI, and property location. 

Post-modification MTMLTV does appear to have an impact on redefault rates, but to a lesser 

degree than the factors discussed above. For example as seen in Figure 5, the difference in 24-

month redefault rates between borrowers with an MTMLTV less than or equal to 80 percent and 

those with an MTMLTV greater than 170 percent is about nine percentage points. Borrower 

outcomes do not vary greatly for MTMLTV between 80 and 170 percent. 

 

9 Treasury’s program data contain information on the borrower’s credit score at the time of modification. The data 

show a correlation between degree of delinquency at the time of modification and credit score at the time of 

modification. Borrowers who were more delinquent at the time of modification tended to have lower credit scores, 

which is logical since delinquency on the mortgage has a significant impact on credit score. Note that Treasury’s 

program data do not include the borrower’s credit score at the time of loan origination, making it impossible to 

analyze the potential impact of this factor on modification performance in the single-variable analysis. 

0%

10%

20%

30%

40%

50%

60%

70%

12 18 24 30 36 42 48

90+ Day Delinquency Rate

Months After Conversion to Permanent Modifiction

< 580

580 - 619

620 - 660

> 660
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Figure 5: Cumulative Redefault Rate by Post-Modification MTMLTV

As shown in Figure 6, there appears to be little correlation between post-modification back-end 

DTI and modification performance, with the percentage of loans that are 90 days or more 

delinquent at any given seasoning point being fairly consistent regardless of post-modification 

back-end DTI. 

Figure 6: Cumulative Redefault Rate by Post-Modification Back-End DTI

Finally, Treasury data show that geography appears to have some influence on modification 

performance. At 24 months after a HAMP modification, 21 of 50 states have redefault rates 

within three percentage points of the program average of 26.1 percent. As shown in Figure 7, 

among the 10 states with the most program volume, three – Georgia, New Jersey, and Texas –

have redefault rates more than three percentage points above the program average (by 3.4, 4.3

and 4.5 percentage points, respectively). California modifications, however, significantly 

outperform those in other states with a redefault rate 6.2 percentage points below the program 

0%

10%

20%

30%

40%

50%

60%

70%

12 18 24 30 36 42 48

90+ Day Delinquency Rate

Months After Conversion to Permanent Modification

<= 80%

(80%, 100%]

(100%, 120%]

(120%, 150%]

(150%, 170%]

> 170%

0%

10%

20%

30%

40%

50%

60%

70%

12 18 24 30 36 42 48

90+ Day Delinquency Rate

Months After Conversion to Permanent Modification

<= 31%

(31%, 40%]

(40%, 50%]

(50%, 60%]

(60%, 70%]

(70%, 80%]

> 80%
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average.10 The reasons for these variances are not well-understood and may merit additional 

research. 

Figure 7: Cumulative Redefault Rate by State at 24 Months After Modification

III. LITERATURE REVIEW

Published research on mortgage modification effectiveness is comparatively recent, as loan 

modifications beyond short-term workouts were uncommon prior to the subprime crisis and 

Great Recession. The literature can be roughly divided into two periods, “pre-HAMP” and “postHAMP”, depending on the time frame in which post-modification performance is observed 

(before or after mid-2009). 

A consistent finding of these studies has been the importance of affordability (monthly payment 

reduction) in achieving successful modifications. For example, an early study published by the 

New York Federal Reserve Bank (Haughwout, Okah and Tracy, 2009) examined the 

performance of modified subprime mortgages through June of 2009, using the CoreLogic 

(LoanPerformance) PLS database. The authors found that overall redefault rates for that 

population and time period were quite high, with 57% of modified loans becoming 90 days or 

more delinquent within twelve months. Many of the modifications from this period were of the 

so-called “cap and extend” type, in which amortization terms were extended and accrued interest 

was capitalized into the loan balance. These modifications usually resulted in a similar or higher 

monthly payment for the borrower, and were relatively ineffective at preventing defaults. 

Modifications with more generous payment reduction led to substantially improved performance. 

Each 10% lowering of borrowers’ monthly payments was associated with a 4.4 percentage point 

reduction in twelve-month redefault rates, which translates into an 8 percent relative 

improvement.

 

10 See Appendix Table A1 for HAMP redefault data for all 50 states, U.S. Territories and the District of Columbia.

0%

10%

20%

30%

40% California Florida Illinois New York Arizona Georgia New Jersey Maryland Michigan Texas 90+ Day Delinquency Rate

Program Average
- 11 -

Similar results were found by Agarwal, et al (2011) in their paper, “The role of securitization in 

mortgage renegotiation”, which examined loan performance from October 2007 through May 

2009. This research utilized the OCC-OTS Mortgage Metrics database, which is based on direct 

reporting from 10 high-volume loan servicers. This data source allowed the authors to compare 

the rates of modification and delinquency between PLS loans and those held in lenders’ 

portfolios. They found that delinquent, portfolio-held loans were about 40% more likely to 

receive a modification than those held in PLS. Moreover, even when controlling for loan and 

borrower characteristics, the modified portfolio loans had a 9% lower relative redefault rate than 

equivalent securitized loans. Both types of loans responded strongly to payment reduction, with a 

10% lowering leading to an 11% relative improvement in redefaults at six months. 

An advantage of using the OCC-OTS data, or similar sources such as LPS McDash that are 

based on direct servicer reporting, is that a more representative sample of the total universe of 

modified loans can be used, compared to the CoreLogic PLS data used by several modification 

studies, including this one. However, a relative weakness of the servicer-based sources is that the 

data is restricted to the largest loan servicers by volume. This can lead to selection bias, because 

these servicers sometimes transferred high-risk or non-performing loans, causing the data 

associated with those loans to be censored. By contrast, the performance histories in CoreLogic 

will always track a loan to its ultimate disposition (payoff, foreclosure, third party sale, etc.).

HAMP and Non-HAMP Comparisons

Turning now to research from the “post-HAMP” era, the same OCC-OTS database was also 

leveraged by Voicu, et al (2012), using loan performance through November 2010, allowing for 

a direct comparison of the performance of HAMP and non-HAMP modifications. This study 

population was restricted to loans originated in New York City, to allow loan records to be 

matched with property deeds, allowing a rich set of neighborhood and demographic controls to 

be included in the analysis. Once again, a strong reduction in redefaults based on payment 

reduction was found: in this case, a 16% relative improvement per 10% in payment change was 

found. Along with payment reduction, other factors found by this study to affect modification 

success were the borrower’s FICO credit score, their post-modification equity position or loanto-value ratio, and the extent of mortgage payment delinquencies prior to the modification. 

Even when controlling for all of these factors, though, the authors found that HAMP 

modifications had a 7 to 9% lower relative redefault rate than non-HAMP modifications. This 

means, for example, that a HAMP modification offering a 25% payment reduction would be 

expected to perform as well as a non-HAMP modification with a 30% payment reduction, made 

on the same loan. Without any mechanism to control for possible selection bias, however, this 

analysis could not isolate the extent to which the HAMP benefit was an actual treatment effect, 

due to the program’s design or implementation features, or because HAMP’s documentation 

requirements or eligibility criteria might be “weeding out the borrowers least likely to succeed.”

Controlling for this sort of selection bias was a major consideration for Agarwal, et al, (2012) in 

a follow-up to their earlier modification research. Re-using the Mortgage Metrics data, and 

covering the period from July 2008 through December 2010, this study sought to isolate the 

treatment effects of the HAMP program in terms of the incidence and effectiveness of 

modifications. The primary identification strategy was to use mortgages on non-owner-occupied 
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homes, which are not eligible for HAMP, as the control group in a difference-in-difference 

design. A secondary strategy was to compare “jumbo” loans which were ineligible for HAMP 

due to having balances over $729,750, with loans that were under that threshold. 

Using these difference-in-difference strategies, the authors found that the rate of non-HAMP 

modifications was unchanged by the introduction of the HAMP program. Although there was 

some “crowding out” of loans modified under HAMP that would have received non-HAMP 

modifications in the absence of the program, this effect was offset by the additional borrower 

outreach and publicity associated with HAMP. Some borrowers who otherwise never would 

have sought a modification applied for the HAMP program, were denied, and then received 

proprietary modifications. 

Curiously, the study showed that the generosity and effectiveness of non-HAMP modifications 

on HAMP-eligible loans decreased after HAMP was introduced, relative to that of the control 

group of non-HAMP eligible loans. The authors attribute this to a possible selection effect, 

where servicers may have steered borrowers with (unobserved) lower risk characteristics to the 

HAMP program, in order to maximize incentive payments associated with borrower 

performance. 

Taking all of these side effects into account, this paper concluded that the HAMP program had a 

net impact of causing an additional 1.2 million modifications to be performed, with around 

800,000 foreclosures being averted. 

LTV and Principal Reduction Effects

More recently, Shmeiser and Gross (2014) compared HAMP and non HAMP modification 

performance using the LoanPerformance data. Their study population was a 5% sample of 

subprime, PLS loans that were modified between 2008 and 2013.They found that reductions in 

monthly payments and reduction in the loan’s principal balance both improved post-modification 

outcomes. Furthermore, HAMP modifications were found to have better performance than nonHAMP modifications, even when controlling for the amount of payment reduction and other loan 

terms.

A unique advantage of the Shmeiser and Gross study is their use of CoreLogic TrueLTV data on 

second liens. This data set allowed the authors to construct a combined loan-to-value ratio 

(CLTV) for a given loan at a point in time, which should provide a more accurate picture of 

which borrowers were “underwater”. They found noticable deteriorations in loan performance as 

these CLTV values crossed the 90% (low equity) and 100% (zero/negative equity) boundaries. 

The authors concluded that modifications that reduced principal balances (and thus, CLTV) were 

more effective, independent of the amount of P&I reduction. 

Note, however, that the CoreLogic LoanPerformance data cannot distinguish between loan 

modifications that reduce a borrower’s principal balance through forbearance (which still makes 

the borrower responsible for the full balance at payoff) or forgiveness. Since the present study is 

also based on LoanPerformance, this paper does not attempt to specifically measure the effect of 

principal forgiveness on modification outcomes.
- 13 -

In a similar vein, in their paper “Modification Success – What Have We Learned?”, also based 

on CoreLogic data, Goodman et al (2011) cite principal reduction as one of the three key 

ingredients of modification success, along with payment relief and modifying a delinquent loan 

as early as possible. However, they qualify this endorsement by noting the 

forgiveness/forbearance limitation of their source. This same caveat should be taken into account 

when evaluating the results of other studies that rely on the LoanPerformance data. 

In order to isolate the specific effects of principal forgiveness on a modification’s effectivess, it 

is necessary to have performance data that can distinguish forgiveness from forbearance and 

other changes to loan terms, and to then statistically isolate the independent effects of improving 

a borrower’s equity position from the associated reductions in their payments. This strategy has 

been employed in two studies that have made use of the internal program data from HAMP to 

compare the performance of loans modified under HAMP PRA (Principal Reduction 

Alternative), in comparison to loans receiving HAMP modifications that did not reduce principal 

balances. 

An early analysis, conducted by this author (U.S. Treasury, 2012), found that principal reduction 

did have an independent effect, but that payment reduction remained the dominant factor in 

predicting post-modification performance. In one illustration, a loan modification that reduced 

payments by 30% through principal reduction lowered the risk of redefault within six months 

from 10% to 3.5%, while a modification that achieved the same payment change without 

reducing principal lowered the predicted redefault rate to 4.6%. A later study (Scharlemann and 

Shore, 2013), based on more than two years of HAMP PRA performance data and utilizing a 

regression kink design, found a similar effect. The quarterly hazard rate for 90 day delinquencies 

was 3.8% for PRA modifications that reduced principal by an average of 29%, while the hazard 

rate without principal reduction was estimated to be 4.7%. 

Servicer Heterogeneity

A recent working paper by Reid, Urban and Collins (2014) examined differences across 20 large 

servicers in the extent to which they modified delinquent mortgage loans, and how this affected 

subsequent loan cure rates. The study population was taken from a database of subprime and altA loans in private label securities for which Wells Fargo is the trustee. To be included in the 

sample, a loan must have been 60 days or more delinquent in June 2009; modification rates and 

borrower payment history was then tracked through the end of 2012. Servicers were found to 

vary quite widely in their overall modification rates – from 2 to 48% - and the extent to which 

they were willing to reduce interest rates or loan balances; these differences had a profound 

effect on borrower outcomes. 

On the other hand, the authors found that, controlling for loan and borrower characteristics such 

as credit score, there were no differences within a given servicer in the rates of modifications 

offered to low-income or minority borrowers, in comparison to the overall population. The race 

of the borrower was inferred by matching the Wells Fargo loan population with HMDA (Home 

Mortgage Disclosure Act) mortgage origination data. This finding was broadly consistent with 

that of a prior study by two of the authors (Collins and Reid, 2010), which used the same 

matching technique for an earlier time period. The earlier paper actually found that minority 
- 14 -

borrowers were slightly more likely to receive a mortgage modification, even when controlling 

for FICO, borrower income, and other factors. 

The wide variation in modification take-up rates by servicer, as seen by Reid, et al, and also 

reported on by Agarwal, et al (2012), is a key motivator for the estimation strategy used in this 

paper. Especially because servicing rights for subprime loans were often transferred after 

origination, the identity of the loan servicer at the time of a modification is essentially a random 

factor, outside of the borrower’s control and unrelated to their own behavior. This suggests that 

the identity of the loan servicer at the time of modification, and the interactions between servicer 

identity and loan attributes, can be used as instruments that are predictive of modification rates, 

but are otherwise unrelated to borrower delinquency.

IV. TREATMENT EFFECT ANALYSIS OF THE EFFECTIVENESS OF HAMP AND 

NON-HAMP MODIFICATIONS ON SUBPRIME PLS LOANS

To gain greater insight into the factors driving modification performance and the effectiveness of 

both HAMP and non-HAMP modifications, this study used a treatment effect analysis to look at 

three populations of similarly delinquent PLS loans: 

 Loans modified under HAMP; 

 Loans modified outside of HAMP (proprietary modifications); and, 

 Loans that were not modified. 

As discussed in more detail below, the study population of HAMP loans was derived by 

matching data from a commercial loan performance database for PLS loans against the subset of 

HAMP modifications of PLS. In addition, the loans from the commercial database were 

restricted to loans that became at least 30 days delinquent during the timeframe of the HAMP 

program. 

The performance of modified loans was measured in terms of their status after a fixed time 

interval (ranging from six to 36 months) from the time the modification became permanent. The 

loan was considered to have an adverse outcome (e.g., redefault) if by the end of a time interval

it had: a) become 90 days or more delinquent; b) been liquidated at a loss; or c) required an 

additional modification. Loans that became delinquent but were not modified were examined 

over the same time interval. In this control group, a loan was considered to have an adverse 

outcome if by the end of the time interval it had: a) became 90 days or more delinquent; b) been 

liquidated; or c) been subsequently modified.

11

Unlike single-variable analysis, the treatment effect regression analysis makes it possible to 

disentangle and isolate the impacts of different factors influencing the likely outcomes for loans 

in all three populations. It also allows for the separation of the true treatment effects of a 

modification from possible selection effects.

12

 Modification effectiveness is measured as the 

selection-bias-adjusted average treatment effect on the modified loan (also known as the 

 

11 See Appendix III for additional information on the construction of the study population.

12 See Appendix II for additional information on how the regression analysis controlled for selection effects. 
- 15 -

“average treatment effect on the treated” or ATET). This is estimated from an econometric 

model that simulates random assignment of loans to the modification program. The ATET is the 

average difference between a loan’s expected probability of an adverse outcome depending on 

whether the loan was modified under HAMP, modified outside of HAMP, or received no 

modification at all. For example, if the average loan has a 60 percent chance of an adverse 

outcome without modification but a 40 percent chance when modified, then the ATET is 20 

percentage points.

13

Hypotheses

Using this methodology, the analysis tested a series of hypotheses about modification 

effectiveness and the factors driving modification performance.

Hypothesis 1: Modifying a loan has a significant effect on borrower performance outcomes,

even when controlling for borrower and loan characteristics. This looks at the ATET of any 

type of modification (HAMP or non-HAMP) compared to not modifying a similar loan to a 

similar borrower.

Hypothesis 2: HAMP modifications perform better than non-HAMP modifications, even when 

controlling for borrower and loan characteristics. This is similar to Hypothesis 1 but compares 

the ATET for a HAMP-modified loan to the ATET for a loan modified outside of HAMP.

Hypothesis 3: Controlling for borrower and loan characteristics, payment reduction is the 

most significant factor driving modification performance. This hypothesis tests whether 

differences in modification outcomes are explained more strongly by variations in the amount of 

payment reduction than by other factors, such as loan and borrower characteristics, or other 

changes in loan terms such as principal reduction.

Hypothesis 4: HAMP modifications perform better than non-HAMP modifications, even when 

controlling for borrower characteristics, loan characteristics, and changes in the loan terms. 

This is a stronger statement than Hypothesis 2. It asserts that HAMP modifications will perform 

better than non-HAMP modifications even for similar borrowers whose loans are similar before 

and after the modification. Thus, what is tested is whether any non-economic factors may be 

influencing post-modification borrower behavior. If a borrower’s behavior was determined 

solely by objective economic self-interest, one would expect that modifications that made the 

same changes to loan terms, such as the amount of payment reduction, would have similar 

effects. However, if a borrower was affected by other factors, such as the process interactions 

with the servicer under a particular modification program, there might be differences between 

 

13 As with the single-variable analysis, the results may be influenced by variables that are unobservable due to 

limitations in the data set. For example, the commercial database employed by the study does not clearly indicate 

which modifications received principal forgiveness or forbearance, making it difficult to isolate their potential 

effects on modification performance. Similarly, data limitations made it impossible to test the effects of the backend DTI or the borrower’s credit score at the time of modification. Note that unlike the Treasury data used for the 

single-variable analysis, the data used for the regression analysis do include the borrower’s credit score at the time 

of loan origination.
- 16 -

HAMP and non-HAMP modifications, even after controlling for borrower characteristics and the 

modification terms.

Hypothesis 5: Modifying a loan has a significant effect, even when controlling for borrower 

characteristics, loan characteristics, and the changes in loan terms. This is a stronger statement 

than Hypothesis 1. It seeks to test whether a modification that did not reduce the borrower’s 

payments or principal balance but simply reset the borrower’s delinquency status to “current” 

will have an effect on borrower outcomes two years later. In other words, the process of being 

made current is itself effective in changing the borrower’s payment behavior.

The Study Population

The CoreLogic Loan Performance subprime database provides origination and performance 

history for approximately 90 percent of all subprime and Alt-A residential mortgages that were 

bundled into PLS and active in the 2010-2011 timeframe. By matching this data against the 

subset of HAMP PLS loans, it was then possible to perform a three-way comparison between 

subgroups of loans that received HAMP modifications, lenders’ proprietary non-HAMP 

modifications, or no modification at all. 

As a first step, the study population was restricted to non-jumbo first liens, active in the 2010-

2011 timeframe, on owner-occupied residences. This insured that the study population did not 

include loans that are categorically ineligible for HAMP.14 In addition, only loans that became 

delinquent were used in the regressions. This excluded from the study the loans that were 

modified without ever having gone delinquent. However, it helped ensure that the study 

compared like populations of loans since loans that become delinquent but are not modified are 

more similar in their characteristics to the group of modified loans than they are to loans that 

never became delinquent. 

While servicers do report some information on loan modifications to CoreLogic, this information 

is considered proprietary and is not generally provided to PLS database subscribers. As a result, 

researchers typically must infer the existence and terms of modifications indirectly. This is done 

by comparing each loan's reported payment history to expected events as defined by the 

origination terms, such as ARM reset dates or the termination of an interest-only period. 

Modifications can then be imputed when there are significant changes to a loan's interest rate or 

payment that are not consistent with the original loan terms. CoreLogic offers a list of imputed 

modifications, as do other private companies. However, for this study, having access to the 

actual HAMP modification data made it attractive to develop and validate an independent 

process for imputing modifications; this process is described in Appendix III.

Once these initial rules were applied to impute a population of modified loans, several additional 

steps were taken.

 

14 Prior to the introduction of HAMP Tier 2 in June 2012, loans where the residence was determined to be non 

owner-occupied were ineligible for a HAMP modification. 
- 17 -

First, only loans whose new loan terms resulted in the reduction of the borrower’s monthly 

payment were considered to have been “modified” for the purposes of this study. This helped to 

more effectively compare HAMP and non-HAMP modifications by eliminating those 

modifications (mostly in 2007-2008) that consisted primarily of capitalization of unpaid interest 

without any reduction in monthly payments. Restricting the population in this way also 

improved the accuracy of the imputation process, since it is more likely that a paymentincreasing change in terms in the post-2008 period will have been due to the original loan terms 

rather than due to a modification.

Second, in order to construct the sub-population of HAMP modifications, the CoreLogic loans 

with imputed, payment-reducing modifications were then matched to the subset of loans within 

HAMP that were identified as being part of a PLS (regardless of whether the loan servicer or an 

outside investor holds the security). Matches were identified using the origination terms (e.g.,

origination date, loan amount, initial rate, and property ZIP code) and information about the 

HAMP modification itself (e.g., post-modification loan balance, payment, and interest rate). 

Third, additional analysis was performed in order to align the HAMP and CoreLogic data 

elements. For example, knowing that the original appraisal values on subprime loans was often 

inflated, the MTMLTV for non-HAMP modifications was recalculated using a correction based 

on data from the HAMP program. For HAMP modifications, the HAMP database identifies the 

MTMLTV of each loan before and after the modification becomes permanent. This MTMLTV is 

based on an updated appraisal of the property performed by the servicer at the time of 

modification. Comparing these updated appraisal values of the property against the original 

appraisal values found in CoreLogic permitted a correction to be applied to the original loan-tovalue ratios of both the non-HAMP and HAMP modifications.

Figure 8 shows how, using the modification imputation process, it is possible to track how the 

study population evolved over time. Of over five million potentially HAMP-eligible subprime 

and Alt-A loans active at the beginning of 2008, 2.1 million appear to never have been modified 

and were less than 90 days delinquent or paid off as of first quarter 2014. The imputation and 

matching algorithms identified over 220,000 payment-reducing HAMP modifications, of which 

approximately 177,000 are still in good standing or had paid off as of first quarter 2014. In 

addition, over 990,000 non-HAMP modifications were identified, of which over 638,000 were in 

good standing as of first quarter 2014. 
- 18 -

Figure 8: Population of Potentially HAMP-Eligible PLS Loans from 2008 to 2014

From this point, the study sub-populations were further defined in order to set up the regression 

analysis, using the concept of a base month. The base months for the study correspond to the 

date from which the performance of the loan begins to be tracked, and range from January 2010 

through December 2011. To be included in the population for a base month T, a loan must have 

been active and unmodified as of the month prior to base month T, and it also must have been at 

least 30 days delinquent as of three months before base month T, which roughly corresponds to 

the beginning of a modification trial period. 

Within this population, the Group A subpopulation consists of those loans that did not receive a 

modification during (or before) month T, while Groups B and C consist of loans whose earliest 

modification (if any) became permanent during month T. If that modification was matched to a 

HAMP modification, then the loan is placed in Group C; otherwise it is placed in Group B. Note 

that loans whose earliest modification was after month T would still be included in Group A. To 

make the three groups more comparable in size, only a sample from Group A was included in the 

regressions; the sampling methodology is described in Appendix III.

For each base month, borrower outcomes for all of the groups were examined at an outcome 

month that is a fixed span of time M months after the base month. This allowed for a head-tohead comparison of modified and unmodified loan performance over the same time periods. 

Each regression was performed by selecting a time span M, and by pooling the three groups of 

loans from 12 base months into a base year. For the base year of 2010, the study looked at 

outcomes between six and 36 months after each base month (ranging from January 2011 through 

June 2013); similarly, outcomes between six and 24 months were examined for the 2011 base 

year. 

As seen in Figure 9, each loan, whether modified or unmodified, was assigned one of two 

outcomes: either adverse or non-adverse. When a loan was 90 days or more delinquent or had 

been liquidated at a loss at the outcome month, it was always considered to have an adverse 

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

2008Q12009Q12010Q12011Q12012Q12013Q12014Q1 Number of Loans

Modified, 90+ DLQ/Liquidated 

at a Loss

Modified, 30 DLQ/60 DLQ

Modified, Current/Paid Off

Unmodified, 90+ 

DLQ/Liquidated at a Loss

Unmodified, Current/30 

DLQ/60DLQ/Paid Off
- 19 -

outcome. Loans that received a modification in between the base month and the outcome month 

were also considered to have an adverse outcome regardless of their initial modification status.

FIGURE 9: Flowchart of the Treatment Effects Model for the Study Population

The Regression Analysis

When comparing performance between modified and unmodified loans, it is critical to separate 

the true treatment effects of the modification program from possible selection effects. Unlike 

groups in a randomized, controlled experiment, the populations that ultimately receive a 

permanent modification will differ substantially from those that do not. For example, this could 

be due to the program eligibility rules, such as a borrower failing to meet the 31 percent DTI 

threshold for HAMP, or due to borrower behavior, such as failure to provide required 

documentation. In addition, there could also be substantial differences across servicers in the 

execution of modification programs. 

Some of the factors affecting these selection processes may be related to variables that can be 

observed, such as the origination terms and prior delinquency history in the CoreLogic database 

or in the HAMP program information. However, other information, such as the borrower’s 

Delinquent at T – 3 months?

Modified at time T? Was it a HAMP mod? Yes

Was loan subsequently re- modified? 90+ DLQ or liquidated?

Group A Group B Group C No No Yes

N months later N months later

N months later Was loan subsequently modified?

No Adverse outcome

Non adverse outcome

Yes Yes

No

Yes No

Yes

Yes Examine loan at base month T Active, not modified prior to time T? Excluded

No

No
- 20 -

updated income or credit score at the point of modification, would have been visible to the loan 

servicer but is not available in the CoreLogic database for non-HAMP modifications. Also, many 

borrower-related factors affecting their willingness to participate in a modification or to make 

payments may be completely unobserved. To the extent that these unobserved selection factors 

are also related to loan performance, any regression estimates of the benefits (or lack thereof) of 

any particular type of loan modification may be incomplete or biased. 

For example, loans with high credit scores, low origination DTI, and low MTMLTV will 

generally be less likely to receive a modification, even if they become 30 days or more 

delinquent, because such borrowers are less likely to have a financial hardship that qualifies 

them for a modification. The loan will therefore have a low probit score within the selection 

equation. If such a loan ends up being modified, it logically follows that there are unobserved 

borrower, loan, or servicer characteristics that made that specific loan more likely to get a 

modification. These loans will then receive a high, positive selection correction factor. 

The correlation between such factors and loan outcomes may raise or lower the estimates of the 

effectiveness of a modification. If, controlling for all of the observable factors, the riskier loans 

also get modified more often (e.g., due to DTI eligibility thresholds), then this will cause 

modification effects to be understated. On the other hand, if it turns out modified loans have 

lower risk factors (e.g., because borrowers who cannot provide required documentation are also 

higher default risks), then this will cause modification effects to be overstated.

This analysis estimates and corrects for potential selection bias using a variant of a Roy model of 

treatment effects. Appendix II provides a more detailed description of the econometric model 

that was used. In this study, there were two probit selection equations. 

The first equation estimated the probability of a given delinquent loan receiving any kind of 

modification during its base period. The second equation, which applies only to the modified 

loans, estimated what factors influence whether the loan receives a HAMP or proprietary 

modification. Three additional probit outcome equations then estimated the probability of an 

adverse outcome for each of the population subgroups: Group A (those unmodified in the 

snapshot month), Group B (those receiving proprietary modifications), and Group C (those 

receiving HAMP modifications). 

When the outcome regressions were run using the correction factors, this allowed the observed 

outcomes to then be decomposed into selection effects and treatment effects. The total estimated 

modification effect (the ATET) was defined as the difference in the probability of default under 

two hypothetical scenarios:

[A] Selection into the modification program is random, and the loan is modified, and

[B] Selection into the modification program is random, and the loan is not modified.

The treatment effect was then determined as the difference between the actual proportion of 

adverse outcomes (in both the modified and unmodified groups) and the selection effect.
- 21 -

Overview of Results

The outcome regressions were used to evaluate each of the study hypotheses, with the following 

results. 

Hypothesis 1: Modifying a loan has a significant effect on borrower performance outcomes,

even when controlling for borrower and loan characteristics. This was strongly confirmed. 

While selection bias is a significant factor, typically accounting for 13 to 33 percent of the 

observed modification effects, there is a substantial modification treatment effect. A typical PLS 

loan receiving a HAMP modification in 2010, for example, had its likelihood of default reduced 

by 40 percentage points, 36 months after the date of modification.

Hypothesis 2: HAMP modifications perform better than non-HAMP modifications, even when 

controlling for borrower and loan characteristics. This was also confirmed, but more strongly 

for loans receiving a HAMP modification in 2011 than in 2010. Even when looking only at 

modifications that reduced borrowers’ payments, borrowers receiving a HAMP modification in 

2011 have on average a 17 percentage point lower redefault rate after 24 months than those 

receiving non-HAMP modifications. Borrowers receiving a HAMP modification in 2010 also 

perform better (seven percentage points lower redefault rate) than those receiving non-HAMP 

modifications through month 36. These differences in performance are likely the result of the 

more generous payment reductions typically offered by HAMP modifications versus non-HAMP 

modifications. 

Hypothesis 3: Controlling for borrower and loan characteristics, payment reduction is the 

most significant factor driving modification performance. The results from testing this 

hypothesis are also time-dependent. Initially, the borrower’s level of delinquency at the time of 

modification is the most important factor determining performance, with the level of payment 

reduction slightly less important. As time passes, however, payment reduction becomes 

relatively more significant, and by two years or more post-modification it becomes the most 

important performance driver.

Hypothesis 4: HAMP modifications perform better than non-HAMP modifications, even when 

controlling for borrower characteristics, loan characteristics, and changes in the loan terms. 

This hypothesis was confirmed with qualifications. For modifications made in early 2010 with 

equivalent changes in loan terms, HAMP and non-HAMP performance was roughly comparable. 

Since then, the average HAMP treatment effects have steadily improved, while the average 

effect of a proprietary modification has lagged. Loans with HAMP modifications have shown a 

stronger response to payment reduction, while non-HAMP modifications have had a relatively 

larger effect for borrowers who were extremely delinquent (12 months or more) prior to the 

modification.

Hypothesis 5: Modifying a loan has a significant effect, even when controlling for borrower

and loan characteristics and the changes in loan terms. This hypothesis is confirmed with 

qualifications. The process of resetting a delinquent borrower to current status and having the

borrower commit to making payments again appears to have a positive effect on borrower

behavior that is independent of the changes in loan terms. However, most of this performance 
- 22 -

benefit has disappeared by 18 months after the modification, and the extent of any long-term 

impact is unclear.

The remainder of this paper will show how these results were obtained from the outcome 

regressions. 

Selection and Treatment Effects

The summary statistics for the study population are shown in Tables A3 and A4 in the appendix. 

Some key differences between the modified and unmodified subgroups are shown below in 

Table 3. Not surprisingly, loans that received modifications tended to have higher credit risk 

characteristics at origination: they show higher DTI ratios, are more likely to have features such 

as balloon payments, and are more likely to have been originated as part of a cash-out 

refinancing. The HAMP-modified loans, on average, tend to have higher loan balances and are 

more underwater (higher MTMLTV) than the non-HAMP modified loans. On the other hand, 

HAMP-modified loans tend to have somewhat higher borrower credit scores at origination 

(unlike Treasury data, CoreLogic data has information on credit score at origination).

Table 3: Mean Values of Loan and Borrower Characteristics in the Study Population

Figure 10 shows the proportion of loans in the three subgroups that had an adverse outcome. The 

performance of unmodified, delinquent loans in this population was consistently poor: at any 

given time period, around 80 percent of these loans were either seriously delinquent or 

liquidated. For both groups of modified loans (HAMP and non-HAMP), performance varied 

significantly over time. The vast majority of borrowers who made it through a trial modification 

period were then able to avoid serious delinquency in the following six months. However, a 

significant proportion of modified loans became seriously delinquent within 36 months. At the 

same time, the rate of increase in the redefault rate decreased with time. Thus, the redefault 

hazard rate fell from an initial high of one to two percent per month in months zero to 18, down 

to about 0.5 percent per month by month 24.15 

 

15

 Note, this is the same trend that is depicted in Figure 1.

Average Loan Characteristics Not Modified 

[25% sample]

Non-HAMP 

Mod

HAMP 

Mod

Not Modified 

[25% sample]

Non-HAMP 

Mod

HAMP 

Mod

# of Loans in Sample 300,041 147,645 101,898 233,635 71,793 61,908

Origination FICO 640 631 633 642 629 646

Origination Loan Amount, $1000s 197 198 225 192 189 235

Origination Back End DTI 40 40 41 40 40 41

% Cash-Out Refis 48 54 58 49 56 57

% Balloon Loans 11 13 17 9 13 13

UPB at Base Date, $1000s 242 241 262 237 232 275

MTMLTV at Base Date 139 137 144 142 137 149

Modification Rate Reduction (1 = 100 

basis points) 3.5 4.4 3.7 3.8

Modification Payment % Reduction 31 39 34 39

2010 2011
- 23 -

Table 4 shows a summary of these results for the 2010 and 2011 cohorts. The observed HAMP

redefault performance was consistently better than the non-HAMP redefault performance. On 

average, modifications made in 2011 performed better in each time period for both HAMP and 

non-HAMP loans. However, these figures do not account for differences (both observed and 

unobserved) between the different populations.

Figure 10: Comparative PLS Loan Default/Redefault; by Modification Event and Year 

Table 4: Observed Performance (Percent Less Than 90 Days Delinquent) for 2010-2011 

Modifications

The sample selection model makes it possible to break down the total difference in observed 

outcomes between modified and unmodified loans into treatment and selection effects. Tables 5a 

and 5b illustrate how this breakdown is derived, using the longest term available results for each 

base year population.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6 12 18 24 30 36

Probability of 90+ Days Delinquency

Months after Base Date

2010 - Unmodified

2011 - Unmodified

2010 - Non-HAMP

2011 - Non-HAMP

2010 - HAMP

2011 - HAMP

6 12 18 24 30 36

2010 HAMP 73% 66% 60% 56% 53% 50%

2010 Non-HAMP 68% 58% 50% 45% 41% 38%

2011 HAMP 75% 70% 66% 63%

2011 Non-HAMP 70% 61% 54% 50%

Modification 

Vintage

Months After Base Date
- 24 -

The selection effects in these tables include those due to observed differences between the 

modified and unmodified loans, as well as unobserved differences that can be statistically 

inferred from the model. For example, subtracting out the selection effects yields the unbiased 

ATET, which is 40.0 percentage points for 2010 HAMP-modified loans and 33.0 percentage 

points for 2010 non-HAMP modified loans. This represents the average reduction in adverse 

outcomes after 36 months for loans modified in 2010 (both HAMP and non-HAMP) compared to 

how loans with similar observed characteristics would have performed if they had not been 

modified.16

Table 5a: Average Treatment and Selection Effects on Modified Loans, 2010 Base Date, 36 

Month Time Window 

Table 5b: Average Treatment and Selection Effects on Modified Loans, 2011 Base Date, 24 

Month Time Window 

The estimated selection effects for both HAMP and non-HAMP modifications are substantial, 

typically accounting for 13 to 33 percent of the observed differences in outcomes from 

unmodified loans.

17 But even after correcting for these selection effects, the remaining treatment

effects of modifying a loan are both statistically and economically significant. Furthermore, the 

 

16 The sample selection technique is intended to simulate the effect of a controlled trial in which loans would be 

randomly selected to receive a modification. Limitations in the sample selection model do not allow for the 

measurement of how effective the HAMP program might have been had some of the loans that fell out of the 

program been able to then receive non-HAMP modifications. This is because it is too difficult to estimate what the 

actual terms of such modifications might have been, making it impossible to estimate an average treatment effect on 

the untreated loan. 

17 A supplemental analysis of the HAMP program data supports this interpretation of modification selection effects. 

The participating servicers are required to report on borrowers who were evaluated for HAMP but did not ultimately 

receive a permanent modification. These loans are then said to have fallen out of the program. By comparing this 

program fallout data with CoreLogic derived history showing what happened to such loans afterwards, some 

additional details can be gleaned on the relationship between program selection and subsequent loan performance. 

The results of this investigation are described in Appendix IV.

Estimated Selection and Treatment of Modified Loans Unmodified HAMP Non-HAMP

Probability of 90+ DLQ Within 36 months of Modification 85.5% 35.3% 47.5%

Total Observed Effect 50.2% 38.1%

Total Selection Effects 10.2% 5.1%

Total Treatment Effects 40.0% 33.0%

Estimated Selection and Treatment of Modified Loans Unmodified HAMP Non-HAMP

Probability of 90+ DLQ Within 24 Months of Modification 83.3% 20.5% 33.2%

Total Observed Effect 62.8% 50.0%

Total Selection Effects 12.0% 16.6%

Total Treatment Effects 50.9% 33.4%
- 25 -

estimated unbiased treatment effects are more stable over time compared to the raw redefault 

rates, particularly for the HAMP-modified loans. 

These same results can be depicted graphically. Figure 11 shows how the modeled selection and 

treatment effects for delinquent loans receiving HAMP and non-HAMP modifications in 2010 

change over time following the modification. The largest component of the modeled selection 

effect (the gap between the top two curves) is due to unobserved differences between the 

observed unmodified population and what that population would have looked like had 

modification selection been random. This performance gap is more pronounced in the first 18 

months after loan modification, though it still persists 36 months after the modification. 

The gap implies that some of the loans which might have benefited from the HAMP program 

instead fell out somewhere between initial delinquency and a completed trial. For example, these

borrowers may not have been able to submit the required documentation, or they might not have 

been able to complete the mandated trial period. In addition, borrowers executing a strategic 

default may have declined to participate in HAMP or may not have qualified due to a low debtto-income ratio (high ability to pay). 

Figure 11: Breakdown of Estimated Selection and Treatment Effects of 2010 HAMP and 

Non-HAMP Loans

Selection bias also accounts for some of the observed performance differences between HAMP 

and non-HAMP modified loans. This effect is most pronounced for the loans modified in 2010, 

as shown in Figure 11, where selection effects act in opposite directions on the two loan 

populations. The regression analysis predicts that if loans had been randomly assigned to 

modification programs, expected overall HAMP performance would be worse, but non-HAMP 

performance would be better. This implies that a number of the loans that fell out of the HAMP 

pipeline but then received proprietary modifications during this time period had higher than 

average risk characteristics.

0

10

20

30

40

50

60

70

80

90

6 12 18 24 30 36 Rate of 90+ Delinquency or Liquidation

Months after Base Date

2010 HAMP Modifications

0

10

20

30

40

50

60

70

80

90

6 12 18 24 30 36 Rate of 90+ Delinquency or Liquidation

Months after Base Date

2010 Non-HAMP Modifications

Unmodified loans (observed)

Modified loans if not modified

Modified loans selection effect 

removed

Modified loans (observed)

Selection Effect

Treatment Effect
- 26 -

After accounting for all selection effects, the remaining difference between the performance of 

modified and unmodified loans is the estimated treatment effect of the modification. In 

econometric terms, this is known as a bias-adjusted or “pure” average treatment effect on the 

treated population (ATET). It compares the performance of modified loans to that of similar, 

unmodified loans, where “similar” includes not only observed data such as MTMLTV, but also 

unobserved factors, such as a borrower’s recent financial history or their attitude towards 

strategic default. Table 5c shows the estimated ATET for HAMP and non-HAMP modifications 

for all of the base year populations and time windows used in the study. 

Table 5c: Average Treatment Effect, with Sample Selection Effects Removed (Numbers 

Show Percentage Point Decrease in Likelihood of Redefault)

These results validate Hypothesis 1: even when controlling for borrower and loan 

characteristics, modifying a loan has a statistically and economically significant effect that 

persists over time. A given HAMP-eligible delinquent PLS loan is estimated to be 40 percentage 

points less likely to be seriously delinquent after 36 months than if it had not been modified. 

Hypothesis 2 is also confirmed but more strongly for modifications made in 2011 than in 2010. 

In general, HAMP modifications outperform non-HAMP modifications even when controlling 

for observed and unobserved loan characteristics. This is true across all time intervals analyzed 

and across modification vintages. At 24 months, the ATET for 2011 HAMP modifications is 18 

percentage points greater than for non-HAMP modifications and at 36 months the ATET for 

2010 HAMP modifications is seven percentage points higher than for non-HAMP modifications. 

The evolution of modification program outcomes can be seen in Figure 12, which details the 24 

month average treatment effects for cohorts of loans modified in each quarter of 2010 and 2011. 

Non-HAMP and HAMP modifications perform similarly through the early parts of 2010. This 

coincides with a period in which HAMP servicers were converting a number of loans that had 

been in lengthy trial periods to permanent modifications.

By the fourth quarter of 2010, the HAMP program rules had been updated so that servicers were 

required to verify borrower income and assets prior to initiating a trial; from this point forward, 

HAMP modifications consistently show significantly larger treatment effects than non-HAMP 

modifications. At the same time, the average selection bias effect for non-HAMP modifications 

increased substantially in the second half of 2011, while the pure treatment effects declined. This 

suggests that some lenders may have expanded their proprietary modification programs in ways 

that brought in a lower risk loan population, artificially lowering the observed redefault rates. 

6 12 18 24 30 36

2010 HAMP 56 51 47 44 42 40

2010 Non-HAMP 55 48 43 40 35 33

2011 HAMP 64 60 55 51

2011 Non-HAMP 54 43 37 33

Months Post Modification Modification Vintage
- 27 -

Figure 12: Average Treatment Effect on the Treated (Modified) Population, at 24 Months 

Post Modification, by Quarter of Permanent Modification

Some of the differences in the treatment effect of modifying a loan under HAMP versus 

modifying a loan outside of HAMP likely reflect the degree of the modification’s payment 

reduction. In general, the payment reduction in HAMP modifications is 23 percent greater than 

in non-HAMP modifications, even though modifications that did not reduce payments (almost 

entirely non-HAMP) were removed from consideration. Table 6 shows these differences, broken 

down by modification type. 

Table 6: Modification Terms of Study Population HAMP and Non-HAMP Modifications

By breaking down the population of modified loans into unpaid principal balance (UPB)-

reducing and non-UPB reducing, one can see that both rate and payment reductions were larger 

for HAMP modifications than non-HAMP modifications, even when forgiveness and 

forbearance were not included. 

Note that the CoreLogic database reporting of UPB changes has significant limitations. For 

example, the database does not properly distinguish between UPB-reducing modifications with 

forbearance and those without forbearance. Due to this and similar limitations, it is difficult to 

use the CoreLogic data to evaluate the effectiveness of principal forgiveness or forbearance.

0

1 0

2 0

3 0

4 0

5 0

6 0

2010Q1 2010Q2 2010Q3 2010Q4 2011Q1 2011Q2 2011Q3 2011Q4

Reduction in 90+ Day Delinquent Probability

Permanent Modification Quarter

HAMP

Non-HAMP

Non-HAMP HAMP Non-HAMP HAMP Non-HAMP HAMP

# of Loans 219,438 163,806 194,785 123,224 24,653 40,582

% Payment Change -31.9% -39.1% -29.2% -34.7% -53.3% -52.2%

Payment Pre-Modification $1,541 $1,711 $1,522 $1,684 $1,688 $1,793

Payment Post-Modification $1,030 $1,022 $1,062 $1,082 $780 $838

Rate Pre-Modification 7.44% 7.29% 7.5% 7.4% 7.1% 6.9%

Rate Post-Modification 3.90% 3.11% 4.0% 3.1% 3.2% 3.1%

% UPB Change 4.40% -0.9% 8.5% 7.2% -27.7% -25.4%

MTMLTV Pre-Modification 137% 146% 135% 143% 153% 157%

MTMLTV Post-Modification 141% 144% 144% 151% 117% 123%

Modification Terms

All Modifications UPB Not Reduced UPB Reduced
- 28 -

Relative Importance of Factors Driving Modification Performance

Hypothesis 3 asks whether payment reduction or some other factor is the most significant in 

determining performance after a modification. To answer this question, this study used an 

analysis of the variance from the outcome regressions for HAMP and non-HAMP modified 

loans. For example, Figure 13 shows a breakdown of the variance in post-modification 

performance at 36 months for loans modified in 2010. The performance indicator is each loan’s 

logit score (xβ) that drives its predicted chance of becoming 90 days or more delinquent after 

three years. A higher percentage indicates that a given single factor accounts for a greater 

proportion of the difference in adverse outcomes between loans. Note that the interactions 

between factors (covariances), which can be positive or negative, are included in the residual 

category of “all other factors”. 

Figure 13: Analysis of Variance, 2010 HAMP and Non-HAMP Modifications

As shown above, the most important factors impacting post-modification performance for both 

HAMP and non-HAMP modifications were the level of payment reduction and the level of 

borrower delinquency prior to the modification. 

Table 7: Impact of Payment Change and Delinquency at Modification Over Time

Table 7 shows how the relative contributions of payment change and pre-modification

delinquency change over time. Generally, payment change becomes more significant in 

predicting performance by 12 months and beyond the base date, while the importance of premodification delinquency declines over time, thus confirming Hypothesis 3 – that payment 

change is the single most important driver of modification performance over the medium and 

36.7%

25.5%

10.2%

2.1%

10.5%

2.7%

12.2%

HAMP Performance at 36 months

36.8%

21.3%

12.6%

6.4%

6.8%

2.1% 13.9%

Non-HAMP Performance at 36 months

Payment Reduction

Pre-Mod Delinquency

MTMLTV at Mod

Origination FICO

State / MSA factors

Time Factors (Modification 

Date)

All Other Factors

Pmt DLQ Pmt DLQ Pmt DLQ Pmt DLQ Pmt DLQ Pmt DLQ

2010 HAMP 23% 34% 28% 33% 31% 31% 34% 30% 35% 27% 37% 26%

2010 Non-HAMP 28% 27% 35% 26% 36% 26% 38% 25% 36% 23% 37% 21%

2011 HAMP 27% 31% 32% 31% 35% 28% 38% 30%

2011 Non-HAMP 33% 25% 35% 22% 35% 22% 37% 20%

Modification Type & 6 Mths 12 Mths 18 Mths 24 Mths 30 Mths 36 Mths

Vintage
- 29 -

long term. Other factors, such as the borrower’s MTMLTV and credit score (for which 

CoreLogic only provides data at origination), do not account for as much of a difference in 

outcomes. 

MTMLTV Effects

The current study analyzed the degree to which a borrower’s level of (possibly negative) equity 

influenced loan performance across all three sub-populations. The MTMLTV for modified loans 

is taken to be the ratio of post-modification loan balance to the estimated home value. Appendix 

III describes in detail how MTMLTV at the base month was calculated. 

As discussed in the previous sections, a limitation of the CoreLogic data affecting MTMLTV 

measurements for modified loans is the way in which principal forgiveness and forbearance are 

reported. Servicers do not appear to have used consistent procedures in updating a loan’s 

principal balance when forgiveness or forbearance is applied. This makes it impossible to 

directly assess the impact of principal forgiveness within this population. 

Figure 14 shows the estimated sensitivity of borrower default or redefault to MTMLTV at the 

modification date (or equivalent base month). The effects were measured in terms of the change 

in probit score for the default or redefault estimation associated with a 100 percent difference in 

MTMLTV. A higher probit score indicates a higher likelihood of default or redefault associated 

with the borrower being further underwater. 

Generally, the redefault sensitivities to MTMLTV at six to 36 months after modification are 

similar in magnitude to the equivalent default sensitivity for unmodified loans, with HAMPmodified loans being more sensitive to MTMLTV than non-HAMP modified loans. Also, 

MTMLTV sensitivity is seen to increase slightly over time, although this difference could be 

partially due to changes in the economic environment. 

Figure 14: Default/Redefault Sensitivity to MTMLTV 

0

0.1

0.2

0.3

0.4

0.5

0.6

6 1 2 1 8 2 4 3 0 3 6

Relative Change in (Re)default Probit Score

Months After Base Date

2010 HAMP

2010 Non-HAMP

2010 No Mod
- 30 -

Borrower Credit Score

Although the HAMP database includes a borrower’s credit score, or FICO, at the time of 

evaluation for a modification, this figure will be greatly affected by the borrower’s recent 

mortgage delinquency. The CoreLogic data, which provides a FICO score at the time of loan 

origination, is a more independent measure of the impact of prior credit history on subsequent 

mortgage delinquency.

As shown in Table A4 in the appendix, the origination FICO was estimated in the outcome 

regression using a quadratic relationship; however, the second order term was usually fairly 

small, so the effect of FICO on subsequent default is roughly linear. Comparing two 

hypothetical, average borrowers, one with a 620 score and the other with 720, using the 

regression estimates, one can then measure the relative impact of a 100 point increase in FICO 

score on subsequent predicted default or redefault rates. 

Normally, higher credit scores are associated with lower predicted defaults, with this difference 

increasing over time. This is, in fact, confirmed for modified loans. As shown in Figure 15, for 

non-HAMP loans modified in 2010, the high FICO loans are about seven percentage points less 

likely to redefault than low FICO loans. The differences between high and low FICO loans level 

off after 18 months, suggesting that prior credit history only affects the early redefaults, and not 

the long-term performance of a modification. 

Figure 15: Estimated Impact of Credit Score at Loan Origination on Modification 

Default/Redefault 

(8.00)

(6.00)

(4.00)

(2.00)

0.00 

2.00 

4.00 

6.00 

6 1 2 1 8 2 4 3 0 3 6

Difference in Probability of (Re)default Between 

720 and 620 FICO Score

Months After Base Date

No Mod 2011

No Mod 2010

HAMP 2011

HAMP 2010

Non-HAMP 2011

Non-HAMP 2010
- 31 -

The most surprising results are those for unmodified loans. Borrowers with high FICO at 

origination who become delinquent and do not get a modification are significantly more likely to 

become or remain seriously delinquent (SDQ) six months later, by about three percentage points, 

than those who had lower FICO at origination. But after this initial period, the default rates for 

higher FICO borrowers then decline as expected. 

A possible explanation for this effect is that it stems from borrowers with negative equity who 

are defaulting strategically. Some prior studies on strategic default have shown that the typical 

profile of such a borrower is indeed someone with a high FICO who suddenly “straight rolls” 

from current to seriously delinquent.18 Such borrowers are also unlikely to consider a mortgage 

modification unless it entails significant principal forgiveness, and they may not qualify for a 

modification in any case if they cannot demonstrate financial hardship. This creates an initial 

spike in SDQ rates for high FICO borrowers with no mortgage modifications. Afterwards, 

though, there is no change in status for the strategic defaulters, but other high FICO borrowers 

who are not strategic defaulters are then slightly more likely to become current than low FICO 

borrowers, causing the FICO effect to move in the direction of better outcomes over time 

(downward), just as it does for borrowers who received modifications. 

Delinquency at Time of Modification

While the analysis confirms the proposition that payment change is the single most important 

driver of modification performance, both the raw redefault results and the variance analysis paint 

a more complex picture. In addition to the longer term effect of payment reduction, there is also 

a significant short to medium term effect stemming from the process of making the borrower 

current after a delinquency and having the borrower recommit to regular mortgage payments.19 

Isolating this “reset effect” is important in evaluating Hypotheses 4 and 5. It can be 

investigated in more detail by comparing the estimated treatment effects (again, controlling for 

selection bias) for subsets of loans that had different levels of initial delinquency. To isolate the 

reset effect as much as possible, the study population was restricted to modifications that did not 

significantly reduce the loan UPB (which would be associated with forgiveness or forbearance). 

In addition, the ATET has been adjusted to remove all of the estimated effects of payment 

reduction associated with the modification. The remaining unbiased treatment effect is thus 

intended to measure only those effects that have nothing to do with the changes in loan terms 

(aside from capitalization of accrued interest) but instead must stem from the modification 

experience itself. Figure 16 shows these pure modification treatment effects for loans that 

received HAMP modifications in 2010.

 

18 See, for example, (Fair Isaac Co., 2011).

19 As discussed earlier, a likely third factor, principal reduction, is difficult to measure with CoreLogic data. 
- 32 -

Figure 16: Treatment Effect for 2010 HAMP Modifications by Delinquency at Time of

Modification

In this graph, the magnitude of the treatment effect associated with making a borrower current 

can be seen as the vertical difference between the curves. The height of the bottommost curve 

indicates the residual treatment effect for borrowers who were only one month delinquent at time 

of modification.20

As expected, the effect of making a borrower current is strongly affected by the borrower’s 

initial degree of delinquency, at least in the early months after modification. There is nearly a 35 

percentage point difference in estimated treatment effects at six months after the modification 

between borrowers who were only one month delinquent and those who were 18 months or more 

delinquent. A borrower who stopped making payments for a long period of time (more than 18 

months), receives a modification, and makes it through a trial period initially does very well. 

However, many of these borrowers with high initial delinquency then redefault within the next 

two years. By 36 months after the HAMP modification, there is only a four percentage point 

difference in redefault rates (adjusted for modification terms and other borrower characteristics) 

between the borrowers with high (more than 18 months) and low (one month) initial 

delinquency. Given the relative slopes of treatment effects across these subgroups of loans, it is 

reasonable to project that these differences in the reset effect for borrowers with different degrees 

of delinquency at the time of modification would largely disappear approximately five years 

after the modification.

Notice that the process of receiving a HAMP modification appears to have a residual long-term 

effect on borrower performance – there is a roughly 10 percentage point improvement in 

redefault risk that is unrelated to selection effects, borrower characteristics, changes in loan 

 

20 Because of estimation issues, borrowers who were current prior to their modification are not included in the study, 

but it is reasonable to assume that their performance would be similar to those of the one-month-delinquent cohort. 

Both groups of borrowers are more likely to have proactively contacted their lender to seek a modification, rather 

than being solicited after passing a two-month delinquency threshold.

0

10

20

30

40

50

60

70

6 12 18 24 30 36

Reduction in Redefault Probability (Pctg. Points)

Months Since Modification

18+ mths DLQ

1 2-17 mths DLQ

6-11 mths DLQ

3-5 mths DLQ

2 mths DLQ

1 mth DLQ
- 33 -

terms, or initial delinquency. The reasons for this – what can be characterized as the pure 

treatment effect or “reset effect” of the HAMP program – are not clear. Whether or not this 

“reset effect” will endure beyond the 36 months for which data currently exists is also not clear 

and will merit further study. 

Significantly, the reset effect appears to persist longer for non-HAMP loans, but the pure 

treatment effect appears to persist longer for HAMP loans, as seen in Figure 17. As with HAMP 

modifications, the differences in redefault rates across groups of borrowers with different 

degrees of delinquency at trial dissipates rapidly, though it appears to still be significant at month 

30. Unlike HAMP modifications, it appears that with time, the pure treatment effects will 

disappear altogether for loans with a pre-modification delinquency of less than six months. It is 

unclear how long this effect will remain for loans with other pre-modification delinquency 

levels.

Figure 17: Treatment Effect for 2010 Non-HAMP Modifications by Delinquency at Time of 

Modification 

Going back to the original hypotheses, this analysis of the reset effect of modifications partially 

confirms Hypothesis 4. While the reset effect appears to dissipate more rapidly for HAMP 

modifications than for non-HAMP modifications, there appear to be pure treatment effects from 

HAMP modifications that non-HAMP modifications do not maintain. Similarly, this part of the 

analysis suggests a mixed result on Hypothesis 5: when controlling for changes in the loan 

terms, the pure act of resetting a loan to current has a strong short to medium term effect for both 

HAMP and non-HAMP modifications. However, for HAMP modifications this effect largely 

disappears in the long run and it is unclear whether this effect will remain for the non-HAMP 

population.

-10

0

10

20

30

40

50

60

70

6 12 18 24 30 36

Reduction in Redefault Probability (Pctg. Points)

Months Since Modification

18+ mths DLQ

1 2-17 mths DLQ

6-11 mths DLQ

3-5 mths DLQ

2 mths DLQ

1 mth DLQ
- 34 -

Payment Change Effects

To further test Hypotheses 4 and 5, the analysis also looked deeper into the presumably more 

permanent benefit of making the borrower’s loan more affordable. In particular, the analysis 

sought to determine whether or not payment changes significantly affect borrower performance 

even when controlling for selection bias, and whether there is any difference in the effect of 

payment reduction between HAMP and non-HAMP modifications.

As noted in Table 6, even when restricting to modifications that reduce payments, the average 

HAMP modification is somewhat more generous in terms of the average level of payment 

reduction (about 39 percent) compared to non-HAMP modifications (about 32 percent). 

The regressions in this study on borrower redefault controlled for the borrower’s delinquency at 

the time of modification (and thus the amount of capitalized interest), as well as the level of 

payment change. The square of the payment change amount was also included as a covariate, in 

order to capture the diminishing effects of very large payment reductions on redefault levels.21

Figure 18: Reduction in Redefault Probability 24 Months after Base Month by Payment 

Reduction Percentage

Figure 18 compares the redefault response at 24 months for different levels of payment 

reduction, for the 2010 and 2011 HAMP and non-HAMP modifications. Here, the estimated 

probit coefficients from these regressions have been applied to an average loan from the overall 

 

21 As mentioned earlier, the CoreLogic database does not track whether forbearance or forgiveness was included in a 

modification. Because of this, payment change effects of modifications may be conflated in a regression analysis 

with these other factors affecting the borrower’s principal balance. To minimize this mixing of effects in the data, a 

separate regression analysis was performed on the subset of HAMP and non-HAMP modifications in which the 

borrower’s UPB was not significantly reduced. Cross checking the HAMP-modified portion of this subpopulation to 

known HAMP modification characteristics significantly reduced the number of modifications with forgiveness or 

forbearance and reduced the possible confounding effects of forbearance and forgiveness to the order of magnitude 

of 10 percent of the reported results.

0%

5%

10%

15%

20%

25%

30%

35%

40%

5 10 15 20 25 30 35 40 45 50 55 60

% reduction in p(SDQ) in 24 months, from 45% baseline

Payment reduction %

HAMP 2010

HAMP 2011

Non-HAMP 2010

Non-HAMP 2011
- 35 -

population of modified loans (both HAMP and non-HAMP), which was assumed to have (for 

purposes of creating a comparison baseline) a 45 percent chance of an adverse outcome after 24

months if the loan were made current but the payment was not reduced. The results are then 

shown in terms of the reduction in the loan’s likelihood of redefault. 

As expected, the level of payment reduction has a very significant effect on expected redefault.

For example, for a 2011 non-HAMP loan modification with a 30 percent payment reduction, the 

estimated likelihood of redefault within 24 months has been reduced by about 20 percentage 

points – so, from 45 percent to just above 25 percent. At the average payment change level for 

HAMP modifications (39 percent), the risk of redefault is more than cut in half for the 2010 

HAMP modifications (from 45 percent to about 16 percent) due solely to the change in payments 

(i.e., without counting other effects such as making the delinquent loan current or reducing 

principal balances). 

Treatment Effects of Equivalent HAMP and Non-HAMP Modifications

Using these results for the effect of different levels of payment reduction on modification 

outcomes, Hypothesis 4 can be tested by constructing a comparison of the average treatment 

effect (the number of percentage points by which default probability was reduced) of HAMP and 

non-HAMP modifications that have identical modification terms.

The structure of this test is similar to that presented previously in Table 5c, in which selection 

bias effects were isolated from treatment effects. However, instead of measuring treatment 

effects of each type of modification on its own population, the new test measures the 

average treatment effect of both HAMP and non-HAMP modifications on a combined population 

of loans that received either type of modification. In order to control as much as possible for the 

effects of different rates of principal forgiveness, the regression is estimated only on those 

modifications that did not reduce the loan’s principal balance, measuring the effect of HAMP 

and non-HAMP modifications that offer the same amount of monthly payment reduction. This 

comparison therefore controls for loan and borrower characteristics, for selection bias, and for 

the terms of the modification. 

Tables 8a and 8b show the results of this analysis for modifications that reduce borrower 

payments by 20% and 40%, respectively.

Table 8a: Average Treatment Effect of Rate/Term Modification with 20 Percent Payment 

Reduction, by Modification Program

(Effects are expressed as the percentage point reduction in redefault probability, averaged over a population of PLS 

loans that had received either HAMP or non-HAMP modifications.)

Modification 

Type

Months Post Modification

6 12 18 24 30 36

2010 HAMP 51.0% 44.2% 38.2% 35.5% 32.9% 30.2%

2010 Non-HAMP 53.2% 45.3% 38.6% 34.5% 29.3% 26.9%

2011 HAMP 58.8% 52.6% 45.9% 42.1%

2011 Non-HAMP 55.2% 41.5% 34.7% 30.3%
- 36 -

 

Table 8b: Average Treatment Effect of Rate/Term Modification with 40 Percent Payment 

Reduction, by Modification Program

(Effects are expressed as the percentage point reduction in redefault probability, averaged over a population of 

PLS loans that had received either HAMP or non-HAMP modifications.)

Modification Type

Months Post Modification

6 12 18 24 30 36

2010 HAMP 56.7% 53.8% 50.5% 49.0% 46.9% 44.9%

2010 Non-HAMP 57.9% 54.0% 49.5% 46.4% 41.6% 39.3%

2011 HAMP 61.7% 59.2% 55.2% 52.9%

2011 Non-HAMP 58.8% 49.6% 44.4% 41.0%

These results generally validate Hypothesis 4. The short term treatment effects of non-HAMP 

modifications made in 2010 are found to be slightly better than those of HAMP modifications. 

However, treatment effects at 18 months and onward are higher for the 2010 HAMP 

modifications. The outcomes for modifications made in 2011 consistently favor the HAMP 

program. 

V. CONCLUSIONS

By combining a comprehensive subprime loan performance database with HAMP program 

administration data, HAMP-modified loans can be compared to unmodified loans and to loans 

receiving lenders’ proprietary modifications. This allows the modification treatment effects to be 

isolated from loan and borrower characteristics, as well as bias stemming from non-random 

program participation. 

The results of this study reinforce a number of prior findings on the performance of loan 

modifications, while also breaking new ground. As prior studies have indicated,22 reducing a 

borrower’s monthly mortgage payments continues to be the primary driver of long-term 

modification effectiveness. HAMP modifications continue to offer, on average, higher levels of 

payment reductions than non-HAMP modifications, which makes them more effective in 

reducing borrower redefault rates. But even when comparing modifications with the same level 

of payment reduction and other terms made to similar loans, the HAMP treatment effects 

observed in 2012 and 2013 were substantially lower than those of non-HAMP modifications.

The cause of this advantage would be a topic for future research. Possible factors that could have 

influenced outcomes, but were not incorporated into our analysis, are the HAMP program’s 

borrower and servicer incentive payments. For example, program subsidies that reward servicers 

when borrowers continue to make payments may have caused those servicers to devote more 

resources to HAMP modified loans. The timing of the improvements in HAMP performance 

seen in Tables 8a and 8b strongly suggests that the changes to the HAMP program that were 

made over the course of 2010, such as requiring a servicer single-point-of-contact (SPOC), and 

mandating that borrowers document a financial hardship prior to entering a trial modification, 

had a substantial beneficial effect on the program’s effectiveness. 

 

22 See e.g. Goodman, et al (2013).
- 37 -

This study also finds higher redefault risks associated with underwater borrowers, indicating that 

principal reduction may also have a substantial (though secondary) impact on borrower redefault 

rates, as observed in studies of the HAMP PRA program.

23 But there is also evidence that 

borrowers with negative equity who are less willing to continue making their mortgage payments 

also tend to select out of modification programs. This helps to explain why the remaining 

population of borrowers who do accept a modification are relatively less sensitive to changes in 

their MTMLTV. 

Prior studies of the HAMP program have expressed concern over the initial rates of borrower 

redefaults, as measured up to three years after modifications.24 The current analysis sheds some 

light on the drivers of these early redefault and suggests that the long-term program benefits may 

be greater than previously estimated. A substantial component of short-term modification 

performance, independent of the actual changes in loan terms, is associated with the process of 

resetting a delinquent borrower to “current” status and having the borrower commit to resuming 

monthly payments. This effect becomes more pronounced the longer the borrower was

delinquent prior to the modification. However, this reset effect diminishes rapidly over time and 

would be expected to largely disappear by about five years after the modification. In contrast, the 

economic effects of the modification (by changing loan terms) either remain constant or only 

slightly diminish over time. This implies that redefault hazard rates for both HAMP and 

proprietary modifications should decline between three and five years after modification, 

ultimately leading to a lower foreclosure rate for borrowers receiving modifications than has 

been previously estimated.

 

23 U.S. Treasury (2012), and Scarlemann and Shore (2013).

24 E.g., SIGTARP (2013).
- 38 -

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have we learned?” The Journal of Fixed Income, 21(2), 57–67.

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Office of the Comptroller of the Currency (2009a). Mortgage Metrics Report, First Quarter 2009.

___ (2009b). Mortgage Metrics Report, Second Quarter 2009.

___ (2011). Mortgage Metrics Report, Fourth Quarter 2010.

Reid, C. K., Urban, C., & Collins, J. M. (2014). “Servicer Heterogeneity: Does Servicing Matter 

for Loan Cure Rates?” 
- 39 -

Schmeiser, M. D., & Gross, M. B. (2014). “The Determinants of Subprime Mortgage 

Performance Following a Loan Modification.” The Journal of Real Estate Finance and 

Economics, 1-27.

Scharlemann, T. C., & Shore, S. H. (2013). “Does Reducing ‘Underwaterness’ Prevent Mortgage 

Default? Evidence from HAMP PRA.”

U.S. Department of the Treasury (2012). “The Effects of Principal Reduction on HAMP Early 

Redefault Rates.” Economic Policy Research Paper Series, July 2012.

U.S. Department of the Treasury (2014). Making Home Affordable Program Performance 

Report – First Quarter 2014.

U.S. Department of the Treasury and U.S. Department of Housing and Urban Development 

(2012). “Making Contact: The Path to Improving Mortgage Industry Communication with 

Borrowers, A Report on the U.S. Department of Treasury’s Guidance on Borrower Single Point 

of Contact,” November 2012. 

U.S. Office of the Special Inspector General for the Troubled Asset Relief Program (2013).

SIGTARP Quarterly Report to Congress, Third Quarter, FY2013. July 24, 2013.

Van Praag, Bernard M.S., Van de Ven, Wynand P. M. M.. “The Demand for Deductibles in 

Private Health Insurance: A Probit Model with Sample Selection.” Journal of Econometrics 

17(2):229-252 (1981).

Voicu, Ioan, Vicki Been, Mary Weselcouch, and Andrew John Tschirart. 2012. “Performance of 

HAMP Versus Non-HAMP Loan Modifications - Evidence from New York City.” New York 

University Law and Economics Working Paper, (1-1-2012).

White, Alan M. (2009) “Deleveraging the American Homeowner: The Failure of 2008 Voluntary 

Mortgage Contract Modifications.” Connecticut Law Review 41:4.
- 40 -

Appendix I: Additional Tables and Charts

Table A1: 90 Days or More Delinquency Rate 24 Months After HAMP Modification by 

State & Territory

State 90+ Day 

Delinquency Rate

State 90+ Day 

Delinquency Rate

Alabama 34.0% Montana 22.3%

Alaska 27.7% Nebraska 33.2%

Arizona 28.0% Nevada 28.0%

Arkansas 32.1% New Hampshire 29.4%

California 19.9% New Jersey 30.4%

Colorado 22.9% New Mexico 27.6%

Connecticut 29.2% New York 24.2%

Delaware 32.9% North Carolina 31.0%

District of Columbia 26.3% North Dakota 23.5%

Florida 26.8% Ohio 28.8%

Georgia 29.5% Oklahoma 33.3%

Guam 0.0% Oregon 24.7%

Hawaii 21.3% Pennsylvania 32.0%

Idaho 26.4% Puerto Rico 17.1%

Illinois 28.2% Rhode Island 27.4%

Indiana 30.4% South Carolina 31.4%

Iowa 32.5% South Dakota 32.1%

Kansas 32.1% Tennessee 33.9%

Kentucky 32.5% Texas 30.6%

Louisiana 34.9% Utah 24.1%

Maine 32.2% Vermont 27.4%

Maryland 27.7% Virgin Islands 0.0%

Massachusetts 26.0% Virginia 24.2%

Michigan 25.3% Washington 26.8%

Minnesota 27.3% West Virginia 29.6%

Mississippi 34.8% Wisconsin 31.7%

Missouri 31.9% Wyoming 25.6%
- 41 -

Table A2: Derivation of the PLS Loan Population

1) For the purpose of this study, a jumbo loan is defined as a loan with an uncapitalized 

UPB greater than $729,750.

2) Based on the Home Price Index (HPI) developed for HAMP NPV evaluations.

All loans in CoreLogic subprime database 20,377,896

Minus:

a) Liquidated prior to Jan 2008 11,775,908

b) Not an owner occupied principal residence 1,343,158

c) Second lien 1,271,241

d) Origination date unknown 38

e) Property ZIP code unknown 9,763

f) Origination loan amount unknown 24

g) Missing or invalid property type 214

h) Jumbo loan (1) 124,157

i) Originated before 1976 (no HPI available) (2) 45

j) Missing or invalid UPB at mod 112

k) Missing or invalid delinquency at mod 10

Base population for sampling 5,853,226
- 42 -

Table A3: Origination Terms of the 2010 Study Population 

# % # % # %

Fixed Rate 108,527 36.17 56,848 38.5 37,697 36.99

ARM

Monthly 28,675 9.6 8,861 6.0 5,496 5.4

2-year hybrid 91,405 30.5 50,347 34.1 35,231 34.6

3-year hybrid 29,605 9.9 14,243 9.7 10,498 10.3

5-year hybrid 32,767 10.9 13,898 9.4 10,109 9.9

7-year hybrid 3,664 1.2 1,471 1.0 1,094 1.1

10-year hybrid 3,458 1.2 1,335 0.9 1,456 1.4

Loan Term

15 years 6,271 2.1 2,734 1.9 1,305 1.3

20 years 2,034 0.7 1,101 0.8 658 0.7

25 years 317 0.1 174 0.1 92 0.1

30 years 278,490 92.8 138,693 93.9 96,218 94.4

40 years 11,760 3.9 4,274 2.9 3,384 3.3

Negative Amortization

Yes 36,964 12.3 11,463 7.8 7,234 7.1

No 178,980 59.7 94,169 63.8 57,869 56.8

Unknown 84,087 28.0 42,009 28.5 36,795 36.1

Documentation

Full-Doc 142,897 47.6 76,694 51.9 2,418 1.6

Low-Doc 149,030 49.7 67,047 45.4 8,296 5.6

No-Doc 8,114 2.7 3,904 2.7 1,340 0.9

Loan Purpose

Purchase 120,219 40.1 51,593 34.9 31,458 30.9

Rate/Term Refi 34,622 11.5 16,478 11.2 11,122 10.9

Cash-Out Refi 145,200 48.4 79,574 53.9 59,318 58.2

2010 Not Modified Non-HAMP Mod HAMP Mod
- 43 -

Table A3: Origination Terms of the 2010 Study Population (cont’d) 

# % # % # %

Property State

California 60,123 20.0 33,264 22.5 26,925 26.4

Florida 48,311 16.1 16,004 10.8 11,955 11.7

Georgia 8,807 2.9 5,718 3.9 2,980 2.9

Illinois 12,520 4.2 6,661 4.5 4,978 4.9

Michigan 8,185 2.7 3,973 2.7 2,526 2.5

New Jersey 9,555 3.2 4,336 2.9 3,386 3.3

New York 16,143 5.4 8,005 5.4 6,052 5.9

Ohio 8,199 2.7 4,014 2.7 2,222 2.2

Pennsylvania 8,097 2.7 4,139 2.8 2,312 2.3

Texas 16,964 5.7 8,623 5.8 3,455 3.4

All Others 103,137 34.4 52,908 35.8 35,107 34.5

Origination Servicer

(or successor)

Ally/GMAC 6,207 2.1 5,057 3.4 2,151 2.1

Bank of America 28,444 9.5 6,453 4.4 3,576 3.5

Chase 34,541 11.5 14,288 9.7 15,111 14.8

Citibank 9,454 3.2 7,643 5.2 4,749 4.7

Ocwen 29,389 9.8 20,261 13.7 14,646 14.4

OneWest 9,150 3.1 1,989 1.4 5,809 5.7

SPS 4,204 1.4 1,391 0.9 4,129 4.1

Wells Fargo 16,906 5.6 15,378 10.4 3,807 3.7

Other or unknown 161,746 53.9 75,185 50.9 47,920 47.0

Balloon Type

Fixed Ballon 7,300 2.4 4,854 3.3 4,344 4.3

ARM Ballon 24,395 8.1 14,588 9.9 13,188 12.9

Non-Balloon 268,346 89.4 128,203 86.8 84,366 82.8

2010 Not Modified Non-HAMP Mod HAMP Mod
- 44 -

Table A4: Origination Terms of the 2011 Study Population 

# % # % # %

Fixed Rate 90,788 38.86 31,510 43.89 25,900 41.84

ARM

Monthly 23,085 9.9 3,963 5.5 6,274 10.1

2-year hybrid 64,857 27.8 22,413 31.2 15,431 24.9

3-year hybrid 21,998 9.4 6,775 9.4 5,299 8.6

5-year hybrid 25,413 10.9 5,437 7.6 6,667 10.8

7-year hybrid 3,097 1.3 730 1.0 921 1.5

10-year hybrid 2,843 1.2 719 1.0 1,142 1.8

Loan Term

15 years 5,471 2.3 1,662 2.3 902 1.5

20 years 1,794 0.8 746 1.0 374 0.6

25 years 246 0.1 138 0.2 59 0.1

30 years 216,009 92.5 66,817 93.1 57,667 93.2

40 years 9,265 4.0 2,045 2.9 2,771 4.5

Negative Amortization

Yes 30,049 12.9 5,254 7.3 8,509 13.7

No 138,768 59.4 45,802 63.8 34,451 55.7

Unknown 64,804 27.7 20,736 28.9 18,948 30.6

Documentation

Full-Doc 112,638 48.2 38,962 54.3 26,757 43.2

Low-Doc 114,589 49.1 31,125 43.4 33,336 53.9

No-Doc 6,408 2.7 1,706 2.4 1,815 2.9

Loan Purpose

Purchase 92,189 39.5 23,033 32.1 19,020 30.7

Rate/Term Refi 27,621 11.8 8,320 11.6 7,885 12.7

Cash-Out Refi 113,825 48.7 40,440 56.3 35,003 56.5

2011 Not Modified Non-HAMP Mod HAMP Mod
- 45 -

Table A4: Origination Terms of the 2011 Study Population (cont’d)

# % # % # %

Property State

California 42,287 18.1 14,292 19.9 18,110 29.3

Florida 38,544 16.5 7,624 10.6 7,395 12.0

Georgia 6,922 3.0 2,552 3.6 1,775 2.9

Illinois 9,819 4.2 3,166 4.4 2,837 4.6

Michigan 5,995 2.6 1,836 2.6 1,427 2.3

New Jersey 8,169 3.5 2,347 3.3 1,993 3.2

New York 13,762 5.9 4,864 6.8 3,733 6.0

Ohio 6,445 2.8 2,128 3.0 1,140 1.8

Pennsylvania 6,705 2.9 2,404 3.4 1,334 2.2

Texas 13,860 5.9 4,938 6.9 2,456 4.0

All Others 81,127 34.7 25,642 36 19,708 31.8

Origination Servicer

(or successor)

Ally/GMAC 4,096 1.8 1,477 2.1 1,084 1.8

Bank of America 24,552 10.5 3,238 4.5 5,251 8.5

Chase 28,165 12.1 6,199 8.6 11,327 18.3

Citibank 6,702 2.9 3,302 4.6 2,074 3.4

Ocwen 20,345 8.7 13,031 18.2 5,005 8.1

OneWest 6,903 3.0 464 0.7 2,729 4.4

SPS 2,710 1.2 711 1.0 754 1.2

Wells Fargo 11,719 5.0 3,823 5.3 2,229 3.6

Other or unknown 128,443 55.0 39,548 55.1 31,455 50.8

Balloon Type

Fixed Ballon 5,509 2.4 2,369 3.3 2,310 3.7

ARM Ballon 16,432 7.0 6,677 9.3 5,805 9.4

Non-Balloon 211,694 90.6 62,747 87.4 53,793 86.9

2011 Not Modified Non-HAMP Mod HAMP Mod
- 46 -

Table A5: Distribution of Origination and Modification Terms, 2010 Population

Min Median Max Mean Min Median Max Mean

Initial DLQ months 1.00 7.00 36.00 9.06 1.00 7.00 36.00 8.85

% DLQ from origination 0.01 0.40 1.00 0.43 0.01 0.38 1.00 0.42

Pre-mod MTMLTV 0.00 0.98 3.00 1.04 0.01 1.04 3.00 1.10

Post-mod MTMLTV 0.00 1.03 3.03 1.09 0.01 1.06 3.01 1.11

Origination Backend DTI 0.00 0.40 0.94 0.40 0.00 0.40 0.95 0.41

Log(Origination UPB) 9.21 12.21 14.00 12.19 9.68 12.35 13.76 12.32

Log(Payment/MSA HP) -11.21 -4.99 -2.38 -5.00 -10.71 -5.00 -2.85 -5.01

Credit Score (FICO - 680)/100 -2.92 -0.49 1.65 -0.49 -2.66 -0.48 1.48 -0.47

Origination LTV 0.02 0.80 3.29 0.80 0.06 0.80 1.15 0.80

Log(Zip code median household income) 7.82 10.91 12.43 10.93 7.82 10.92 12.24 10.93

Zip Code unemployment rate % 0.00 0.09 0.68 0.10 0.00 0.10 0.82 0.10

Zip Code % minority 0.00 0.37 1.00 0.43 0.00 0.41 1.00 0.45

Mod Payment Change % -90.00 -28.59 -2.50 -30.84 -90.00 -39.10 -2.50 -39.07

Mod UPB Change % -89.96 5.36 96.28 5.38 -87.25 4.06 95.39 1.35

Mod Rate Change % -12.70 -3.49 5.85 -3.48 -12.99 -4.62 3.25 -4.40

Pre-mod UPB ($1000s) 0.16 199.37 1,202.50 241.15 3.87 229.22 948.91 261.82

Post-mod UPB ($1000s) 0.16 207.61 1,273.23 250.66 3.86 228.99 979.53 262.09

Pre-mod Rate 0.72 7.47 16.00 7.47 0.46 7.45 15.25 7.48

Post-mod Rate 0.50 4.00 14.99 3.99 1.13 2.00 13.74 3.08

Pre-mod Payment 1.90 1,347.44 37,330.26 1,555.65 11.73 1,526.39 10,140.68 1,705.85

Post-mod Payment 1.12 895.10 6,392.66 1,052.55 6.45 896.94 4,968.79 1,018.81

Min Median Max Mean

Initial DLQ months 1.00 9.00 36.00 11.08

% DLQ from origination 0.01 0.43 1.00 0.45

Pre-mod MTMLTV 0.00 0.99 3.00 1.06

Post-mod MTMLTV 0.00 0.99 3.00 1.06

Origination Backend DTI 0.00 0.40 0.92 0.40

Log(Origination UPB) 9.02 12.22 14.10 12.19

Log(Payment/MSA HP) -8.01 -5.00 -2.64 -5.02

Credit Score (FICO - 680)/100 -2.80 -0.39 1.86 -0.40

Origination LTV 0.02 0.80 3.50 0.80

Log(Zip code median household income) 7.82 10.91 12.43 10.93

Zip Code unemployment rate % 0.00 0.09 1.00 0.10

Zip Code % minority 0.00 0.34 1.00 0.41

Mod Payment Change % -89.96 -36.14 -2.50 -37.05

Mod UPB Change % -89.76 2.80 96.68 -2.88

Mod Rate Change % -13.53 -3.88 5.38 -3.65

Pre-mod UPB ($1000s) 0.00 200.00 1,323.00 241.89

Post-mod UPB ($1000s) 0.00 200.00 1,323.00 241.89

Pre-mod Rate 0.62 7.25 17.65 7.17

Post-mod Rate 0.75 2.75 13.88 3.52

Pre-mod Payment 28.10 1,409.30 7,320.98 1,608.45

Post-mod Payment 21.79 845.66 5,804.82 986.86

Loans with HAMP Modifications Variable Loans with Non-HAMP Modifications

Variable Loans Not Modified
- 47 -

Table A6: Selection Model Probit Coefficients

Key and Notes for Table 6: Selection Model Probit Coefficients:

*pr < 0.10 

**pr < 0.05 

***pr < 0.01 

 pr < 0.0001

Base Year: 2010 2011 2010 2011

pr(mod) pr(mod) pr(HAMP) pr(HAMP)

Intercept

-4.313** **

(0.203)

-4.784 

(0.241)

-2.691** **

(0.333)

-3.235 

(0.464)

Base Month in Q1

0.099** **

(0.013)

0.129 

(0.016)

1.093** **

(0.052)

-0.550 

(0.074)

Base Month in Q2

0.115** **

(0.013)

0.157 

(0.016)

0.691** **

(0.052)

0.052

(0.074)

Base Month in Q3

0.148 

(0.013)

0.079** **

(0.016)

0.156***

(0.054)

0.182***

(0.076)

Base Month in Q4

Lambda-1 [1]*Q1 N/A N/A

0.017

(0.079)

0.585****

(0.105)

Lambda-1*Q2 N/A N/A

0.238***

(0.079)

0.429****

(0.105)

Lambda-1*Q3 N/A N/A

0.413****

(0.079)

0.357***

(0.104)

Lambda-1*Q4 N/A N/A

0.401****

(0.078)

0.402 

(0.104)

Base DLQ [2]

-0.083** **

(0.005)

-0.005

(0.006)

-0.052 

(0.011)

-0.031** *

(0.012)

Base DLQ ^2

0.000

(0.000)

-0.003****

(0.000)

0.003 

(0.001)

0.002** *

(0.001)

Prior DLQ [3]

-0.705****

(0.060)

-1.411 

(0.074)

-2.601** **

(0.145)

-3.828 

(0.210)

Prior DLQ ^2

1.347** **

(0.130)

2.400 

(0.159)

4.230** **

(0.305)

5.613 

(0.425)

Prior DLQ ^3

-0.860** **

(0.087)

-1.368 

(0.106)

-2.341** **

(0.203)

-2.879 

(0.275)

Credit Score [4]

-0.118** **

(0.010)

-0.131 

(0.012)

-0.006

(0.020)

-0.061** 

(0.027)

Credit Score ^2

0.004*

(0.003)

0.011***

(0.003) N/A N/A

Ln(Origination Amount)

0.114 

(0.010)

0.145** **

(0.012)

0.088 

(0.010)

0.134** **

(0.015)

Orig Backend DTI

0.812 

(0.059)

0.377** **

(0.068)

1.245 

(0.131)

3.877** **

(0.160)

Orig Backend DTI Missing

-0.022**

(0.011)

-0.057 

(0.014)

0.004

(0.023)

-0.033

(0.032)

Ln(Med HH Inc in Zip)

0.097** **

(0.017)

0.111 

(0.020)

-0.006

(0.013)

0.010

(0.017)

Avg Unempl % in Zip

0.886** **

(0.167)

0.621

(24295.70

0) N/A N/A

% Minority in Zip

0.189 

(0.021)

0.186** **

(0.025)

0.019

(0.037)

0.141***

(0.049)

15 year term

0.035

(0.031)

-0.154 

(0.037)

0.057** 

(0.025)

0.007

(0.031)

20 year term

0.189****

(0.051)

-0.016

(0.059)

0.110***

(0.035)

-0.017

(0.045)

25 year term

0.284***

(0.122)

0.392***

(0.114)

-0.052

(0.089)

-0.028

(0.110)

40 year term

-0.063*

(0.038)

0.040

(0.048)

-0.115****

(0.018)

-0.138 

(0.022)

Base Year: 2010 2011 2010 2011

pr(mod) pr(mod) pr(HAMP) pr(HAMP)

Monthly ARM

-0.413** **

(0.042)

-0.262 

(0.050)

-0.602** **

(0.092)

-0.171**

(0.103)

2 yr Hybrid ARM

0.010

(0.013)

-0.073****

(0.015)

0.034*

(0.025)

-0.038

(0.033)

3 yr Hybrid ARM

-0.045***

(0.017)

-0.142 

(0.020)

0.114** *

(0.034)

0.033

(0.045)

5 yr Hybrid ARM

-0.182****

(0.016)

-0.348 

(0.022)

-0.776** **

(0.039)

-0.633 

(0.060)

7 yr Hybrid ARM

-0.244** **

(0.046)

-0.219 

(0.057)

-1.525** **

(0.172)

-1.247****

(0.177)

10 yr Hybrid ARM

-0.094*

(0.068)

-0.015

(0.075)

-1.418****

(0.226)

-0.691 

(0.180)

Other Hybrid

-0.134** 

(0.073)

0.066

(0.092)

-0.905***

(0.246)

-0.338*

(0.222)

2 yr Interest Only

0.112*

(0.075)

0.050

(0.078)

0.320**

(0.140)

0.281*

(0.174)

3 yr Interest Only

-0.193***

(0.069)

-0.230***

(0.096)

-0.614***

(0.167)

-0.423**

(0.251)

5 yr Interest Only

0.006

(0.016)

-0.045**

(0.019)

0.004

(0.031)

0.101***

(0.042)

10 yr interest Only

-0.151 

(0.015)

-0.179** **

(0.020)

-0.484 

(0.033)

-0.253** **

(0.047)

Purchase

0.057 

(0.015)

0.076** **

(0.018)

0.078***

(0.031)

-0.006

(0.041)

Cash Out Refi

-0.032**

(0.015)

-0.035**

(0.019)

-0.064**

(0.032)

-0.078**

(0.042)

Rate/Term Refi

Low Doc

0.081 

(0.010)

0.117** **

(0.012)

0.052 

(0.007)

0.144** **

(0.011)

No Doc

0.155 

(0.030)

0.187** **

(0.037)

0.128 

(0.024)

0.287** **

(0.032)

Unknown Doc

-0.086**

(0.049)

-0.022

(0.056)

-0.092**

(0.047)

-0.007

(0.060)

MTMLTV 80-90%

0.150 

(0.020)

0.178** **

(0.025)

0.239 

(0.049)

0.244** *

(0.067)

MTMLTV 90-100%

0.221****

(0.020)

0.274 

(0.025)

0.412** **

(0.050)

0.405 

(0.068)

MTMLTV 100-110%

0.304** **

(0.023)

0.319 

(0.028)

0.559** **

(0.057)

0.435 

(0.074)

MTMLTV 110-120%

0.333** **

(0.027)

0.439 

(0.032)

0.599** **

(0.064)

0.625 

(0.086)

MTMLTV 120-130%

0.321** **

(0.032)

0.454 

(0.036)

0.561** **

(0.075)

0.475 

(0.094)

MTMLTV 130-140%

0.371** **

(0.035)

0.467 

(0.042)

0.583** **

(0.082)

0.604 

(0.107)

MTMLTV 140-150%

0.362** **

(0.037)

0.511 

(0.047)

0.585** **

(0.085)

0.660 

(0.118)

MTMLTV 150-160%

0.379** **

(0.041)

0.470 

(0.050)

0.692** **

(0.093)

0.642 

(0.123)

MTMLTV 160-170%

0.392** **

(0.046)

0.499 

(0.054)

0.585** **

(0.105)

0.769 

(0.134)

MTMLTV 170-180%

0.356** **

(0.059)

0.678 

(0.063)

0.810** **

(0.132)

0.667 

(0.156)

MTMLTV > 180%

0.394** **

(0.055)

0.520 

(0.056)

0.996** **

(0.120)

0.623 

(0.140)

MTMLTV < 80%
- 48 -

[1] Lambda-1 is the selection bias correction term (inverse Mills ratio) from the modification 

selection equation (see text).

[2] Base delinquency as of three months prior to the permanent modification date or equivalent 

base date.

[3] Prior delinquency is the percent of months borrower was at least 30 days delinquent, between 

origination and base months.

[4] Credit score has been normalized to (FICO – 680) / 100.

In addition to the factors shown in Table A6, the selection model also included instruments 

(factors that were found not to affect outcomes other than through modification selection) and 

other controls: 

 Current servicer;

 Property state;

 Origination LTV;

 Missing origination credit score;

 Missing property ZIP demographics;

 Negative amortization flag;

 Balloon code;

 TPO Code;

 Flag if loan was transferred from a prior servicer; and,

 Base month delinquency interaction w/ MTMLTV.

In addition, the following interacted with current servicer: 

 Mod/base date (year and quarter);

 Number of quarters after servicing transfer;

 Property state;

 Origination back-end DTI;

 Origination DTI missing flag;

 Borrower FICO;

 Mod/base date delinquency;

 Log of origination UPB;

 Origination year;

 Prior delinquency percent;

 Log of median household income in property ZIP;

 Percent minority in property ZIP;

 Balloon code;

 Loan purpose;

 ARM type;

 Interest-only term;

 Negative amortization flag;

 Origination term; and,

 Documentation level.
- 49 -

Table A7: Outcome Model Probit Coefficients (2010 Population)

Post Modification Period:

2010 Base Date HAMP Non-HAMP No Mod HAMP Non-HAMP No Mod

Intercept

-0.861** **

(0.174)

-1.015 

(0.118)

-1.834** **

(0.090)

-1.046 

(0.160)

-0.949** **

(0.114)

-1.729 

(0.091)

Modified in Q1

0.230** *

(0.072)

0.008

(0.046)

0.114****

(0.017)

0.388 

(0.066)

0.095** 

(0.044)

0.104****

(0.018)

Modified in Q2

0.282****

(0.076)

-0.066*

(0.047)

0.006

(0.018)

0.335****

(0.069)

-0.047

(0.046)

-0.010

(0.018)

Modified in Q3

0.150**

(0.083)

-0.077*

(0.048)

-0.006

(0.018)

0.164**

(0.076)

-0.022

(0.046)

-0.022

(0.019)

Modified in Q4

Lambda-1 / Q1 [1]

-0.162 

(0.030)

0.011

(0.023)

-1.180** **

(0.039)

-0.169 

(0.027)

-0.008

(0.022)

-0.980** **

(0.041)

Lambda-1 / Q2

-0.275 

(0.034)

0.099** **

(0.024)

-1.273 

(0.042)

-0.223** **

(0.032)

0.087***

(0.024)

-1.081 

(0.043)

Lambda-1 / Q3

-0.202** **

(0.042)

0.106 

(0.025)

-1.222** **

(0.047)

-0.168 

(0.039)

0.077** *

(0.025)

-1.012****

(0.048)

Lambda-1 / Q4

-0.226 

(0.042)

-0.031

(0.025)

-1.279** **

(0.051)

-0.129***

(0.038)

-0.026

(0.024)

-1.022 

(0.052)

Lambda-2 / Q1 [1]

-0.030

(0.026)

-0.105** **

(0.020) N/A

-0.022

(0.024)

-0.080 

(0.020) N/A

Lambda-2 / Q2

0.063** 

(0.029)

0.016

(0.025) N/A

0.052**

(0.027)

0.008

(0.024) N/A

Lambda-2 / Q3

0.015

(0.033)

0.173 

(0.033) N/A

0.056** 

(0.030)

0.227****

(0.031) N/A

Lambda-2 / Q4

0.108***

(0.034)

0.210 

(0.032) N/A

0.095** *

(0.031)

0.196****

(0.030) N/A

Pre Mod Delinquency [3]

0.214 

(0.006)

0.154** **

(0.005)

0.494 

(0.004)

0.208** **

(0.006)

0.144 

(0.004)

0.419** **

(0.005)

Pre Mod Delinquency ^ 2

-0.010 

(0.001)

-0.007** **

(0.001)

-0.028 

(0.001)

-0.011** **

(0.001)

-0.006 

(0.001)

-0.024** **

(0.001)

Payment Change [4]

0.258 

(0.010)

0.198** **

(0.007) N/A

0.251 

(0.009)

0.187** **

(0.007) N/A

Payment Change ^ 2

0.014 

(0.001)

0.007** **

(0.001) N/A

0.010 

(0.001)

0.005** **

(0.001) N/A

No UPB reduction [5]

-0.005

(0.017)

-0.003

(0.016) N/A

0.012

(0.015)

0.028**

(0.015) N/A

Credit Score [2]

-0.194 

(0.010)

-0.196** **

(0.008)

0.113 

(0.007)

-0.188** **

(0.009)

-0.184 

(0.007)

0.079** **

(0.007)

18 months 36 months
- 50 -

Table A7: Outcome Model Probit Coefficients (2010 Population, cont’d)

Post Modification Period:

2010 Base Date HAMP Non-HAMP No Mod HAMP Non-HAMP No Mod

MTMLTV < 80%

MTMLTV 80-90%

0.076 

(0.020)

0.059** **

(0.014)

0.151 

(0.011)

0.073** **

(0.018)

0.085 

(0.013)

0.185** **

(0.011)

MTMLTV 90-100%

0.128 

(0.020)

0.111** **

(0.014)

0.220 

(0.012)

0.131** **

(0.018)

0.157 

(0.013)

0.272** **

(0.012)

MTMLTV 100-110%

0.162 

(0.020)

0.165** **

(0.015)

0.263 

(0.014)

0.189** **

(0.018)

0.212 

(0.014)

0.335** **

(0.014)

MTMLTV 110-120%

0.220 

(0.022)

0.209** **

(0.016)

0.334 

(0.017)

0.246** **

(0.020)

0.287 

(0.016)

0.429** **

(0.017)

MTMLTV 120-130%

0.251 

(0.024)

0.275** **

(0.018)

0.374 

(0.020)

0.293** **

(0.022)

0.349 

(0.017)

0.480** **

(0.020)

MTMLTV 130-140%

0.325 

(0.027)

0.309** **

(0.020)

0.425 

(0.022)

0.366** **

(0.025)

0.372 

(0.020)

0.527** **

(0.022)

MTMLTV 140-150%

0.383 

(0.029)

0.333** **

(0.022)

0.468 

(0.024)

0.423** **

(0.026)

0.411 

(0.022)

0.570** **

(0.025)

MTMLTV 150-160%

0.413 

(0.030)

0.326** **

(0.024)

0.500 

(0.026)

0.444** **

(0.028)

0.426 

(0.024)

0.562** **

(0.027)

MTMLTV 160-170%

0.426 

(0.032)

0.391** **

(0.026)

0.483 

(0.029)

0.462** **

(0.030)

0.480 

(0.025)

0.607** **

(0.031)

MTMLTV 170-180%

0.481 

(0.035)

0.382** **

(0.028)

0.607 

(0.036)

0.547** **

(0.033)

0.487 

(0.027)

0.710** **

(0.039)

MTMLTV > 180%

0.582 

(0.031)

0.490** **

(0.024)

0.594 

(0.034)

0.635** **

(0.029)

0.624 

(0.024)

0.722** **

(0.037)

Insufficient Docs N/A

-0.088 

(0.018)

0.146** **

(0.023) N/A

-0.134 

(0.017)

0.120** **

(0.024)

Trial Not Accepted N/A

0.092***

(0.033)

0.370 

(0.043) N/A

0.100** *

(0.032)

0.303****

(0.044)

Trial Failure N/A

0.020

(0.024)

0.152 

(0.032) N/A

0.018

(0.024)

0.182** **

(0.033)

DTI at Eval <31% N/A

-0.193 

(0.027)

-0.417** **

(0.028) N/A

-0.210 

(0.026)

-0.328** **

(0.029)

NPV Negative N/A

-0.255 

(0.028)

0.004

(0.036) N/A

-0.204** **

(0.026)

-0.011

(0.036)

Excessive Forbearance N/A

-0.189 

(0.040)

0.322** **

(0.051) N/A

-0.190 

(0.037)

0.254** **

(0.052)

18 months 36 months
- 51 -

Table A8: Outcome Model Probit Coefficients (2011 Population)

Post Modification Period:

2011 Base Date HAMP Non-HAMP No Mod HAMP Non-HAMP No Mod

Intercept

-1.131****

(0.260)

-0.259*

(0.187)

-0.868****

(0.104)

-0.439**

(0.226)

-0.175

(0.172)

-0.859 

(0.102)

Modified in Q1

0.079

(0.116)

0.045

(0.071)

0.010

(0.020)

0.000

(0.100)

0.118** 

(0.066)

0.025*

(0.019)

Modified in Q2

-0.005

(0.108)

0.048

(0.079)

-0.103****

(0.021)

0.028

(0.092)

0.083

(0.073)

-0.076****

(0.020)

Modified in Q3

0.065

(0.106)

0.104

(0.082)

-0.022

(0.020)

0.039

(0.091)

0.107*

(0.076)

-0.002

(0.020)

Modified in Q4

Lambda-1 / Q1 [1]

-0.082*

(0.051)

-0.009

(0.032)

-1.853****

(0.054)

-0.103***

(0.044)

-0.046*

(0.029)

-1.617****

(0.054)

Lambda-1 / Q2

-0.088**

(0.052)

-0.023

(0.039)

-2.117 

(0.061)

-0.166** **

(0.045)

-0.016

(0.036)

-1.864 

(0.061)

Lambda-1 / Q3

-0.212** **

(0.054)

-0.079**

(0.039)

-1.933 

(0.065)

-0.234** **

(0.046)

-0.098***

(0.036)

-1.635 

(0.065)

Lambda-1 / Q4

-0.143** *

(0.056)

-0.038

(0.040)

-1.851****

(0.066)

-0.198 

(0.048)

-0.063** 

(0.036)

-1.616****

(0.066)

Lambda-2 / Q1 [1]

0.131***

(0.046)

0.192 

(0.040) N/A

0.124** *

(0.040)

0.149****

(0.036) N/A

Lambda-2 / Q2

0.213 

(0.047)

0.200** **

(0.038) N/A

0.173 

(0.041)

0.175** **

(0.034) N/A

Lambda-2 / Q3

0.329 

(0.054)

0.161** **

(0.041) N/A

0.216 

(0.048)

0.120** *

(0.037) N/A

Lambda-2 / Q4

0.230***

(0.065)

0.173***

(0.047) N/A

0.172***

(0.056)

0.139***

(0.042) N/A

Pre Mod Delinquency [3]

0.116 

(0.008)

0.052** **

(0.006)

0.531 

(0.005)

0.122** **

(0.007)

0.054 

(0.006)

0.445** **

(0.005)

Pre Mod Delinquency ^ 2

-0.004 

(0.001)

0.000

(0.001)

-0.028** **

(0.000)

-0.004 

(0.001)

0.000

(0.001)

-0.023** **

(0.000)

Payment Change [4]

0.187 

(0.015)

0.189** **

(0.011) N/A

0.189 

(0.013)

0.191** **

(0.010) N/A

Payment Change ^ 2

0.006***

(0.002)

0.009 

(0.001) N/A

0.003** 

(0.002)

0.008****

(0.001) N/A

No UPB reduction [5]

-0.013

(0.019)

-0.016

(0.019) N/A

-0.025*

(0.016)

0.000

(0.017) N/A

Credit Score [2]

-0.209****

(0.015)

-0.183 

(0.011)

0.145** **

(0.008)

-0.207 

(0.013)

-0.192** **

(0.010)

0.107****

(0.007)

12 months 24 months
- 52 -

Table A8: Outcome Model Probit Coefficients (2011 Population, cont’d)

Key and Notes for Table A7 and Table A8: Outcome Model Probit Coefficients:

*pr < 0.10 

**pr < 0.05 

***pr < 0.01 

 pr < 0.0001

[1] Lambda-1 is the selection bias correction term (inverse Mills ratio) from the modification 

selection equation (see text); Lambda-2 is the corresponding term from the HAMP selection 

equation. Both terms are interacted with the (quarterly) date of permanent modification.

Post Modification Period:

2011 Base Date HAMP Non-HAMP No Mod HAMP Non-HAMP No Mod

MTMLTV < 80%

MTMLTV 80-90%

-0.022

(0.028)

0.033** 

(0.020)

0.105****

(0.013)

0.016

(0.024)

0.061***

(0.018)

0.131 

(0.013)

MTMLTV 90-100%

0.016

(0.028)

0.061** *

(0.021)

0.166****

(0.014)

0.070***

(0.024)

0.095 

(0.019)

0.211** **

(0.014)

MTMLTV 100-110%

0.068***

(0.029)

0.104 

(0.022)

0.170** **

(0.016)

0.100 

(0.025)

0.136** **

(0.020)

0.239 

(0.015)

MTMLTV 110-120%

0.115** *

(0.031)

0.158****

(0.024)

0.254 

(0.019)

0.174** **

(0.027)

0.192 

(0.022)

0.331** **

(0.018)

MTMLTV 120-130%

0.163 

(0.034)

0.230** **

(0.027)

0.315 

(0.022)

0.200** **

(0.030)

0.279 

(0.025)

0.378** **

(0.021)

MTMLTV 130-140%

0.203 

(0.038)

0.253** **

(0.031)

0.335 

(0.025)

0.291** **

(0.033)

0.309 

(0.028)

0.440** **

(0.025)

MTMLTV 140-150%

0.290 

(0.041)

0.283** **

(0.034)

0.374 

(0.028)

0.314** **

(0.036)

0.377 

(0.032)

0.466** **

(0.028)

MTMLTV 150-160%

0.294 

(0.047)

0.380** **

(0.037)

0.389 

(0.030)

0.325** **

(0.041)

0.428 

(0.035)

0.481** **

(0.029)

MTMLTV 160-170%

0.320 

(0.051)

0.355** **

(0.042)

0.358 

(0.032)

0.402** **

(0.044)

0.390 

(0.039)

0.484** **

(0.032)

MTMLTV 170-180%

0.403 

(0.055)

0.394** **

(0.046)

0.422 

(0.038)

0.439** **

(0.049)

0.432 

(0.043)

0.516** **

(0.038)

MTMLTV > 180%

0.453 

(0.045)

0.525** **

(0.035)

0.494 

(0.033)

0.539** **

(0.039)

0.632 

(0.033)

0.601** **

(0.033)

Insufficient Docs N/A

-0.070***

(0.029)

-0.061**

(0.030) N/A

-0.134****

(0.027)

-0.041*

(0.030)

Trial Not Accepted N/A

0.056

(0.048)

0.301****

(0.054) N/A

0.002

(0.045)

0.245 

(0.052)

Trial Failure N/A

0.157** **

(0.034)

0.074**

(0.041) N/A

0.131 

(0.033)

0.033

(0.040)

DTI at Eval <31% N/A

-0.218** **

(0.049)

-0.254 

(0.040) N/A

-0.197** **

(0.044)

-0.180 

(0.040)

NPV Negative N/A

-0.191** **

(0.047)

-0.073*

(0.049) N/A

-0.223****

(0.042)

-0.063*

(0.047)

Excessive Forbearance N/A

-0.183***

(0.059)

0.174***

(0.062) N/A

-0.146***

(0.051)

0.278****

(0.064)

12 months 24 months
- 53 -

[2] Credit score has been normalized to (FICO – 680) / 100.

[3] Initial delinquency (normalized to units of 90 days), as of three months prior to the 

permanent modification date or equivalent base date.

[4] Modification payment change, normalized (e.g. -1.0 = 10 percent payment reduction).

[5] See text. Modifications with no reported substantial reduction in UPB are more likely to be 

rate/term changes only, with no principal forgiveness or forbearance.

In addition to the factors shown in Table A7, the outcome regressions also include the following 

controls: 

 Log of origination UPB;

 Origination year;

 Percent minority in property ZIP;

 Unemployment rate in property ZIP;;

 Hybrid ARM period;

 Interest-only period;

 Loan purpose; and,

 Current servicer.
- 54 -

Appendix II: Econometric Model Details

The econometric theory of program evaluation describes two major factors that make it difficult 

to estimate the true effect of an economic program on its participants. The first is selection bias: 

because participants in a program are not randomly selected, and indeed may to some extent be 

self-selected, some of the apparent modification program benefits may simply be due to 

unobserved characteristics of the participating population that may make them less (or more) 

likely to default. The second is out-of-sample estimation: even when differences between the 

modified and unmodified population can be captured in covariates, such as MTMLTV, FICO 

scores, or initial delinquency status, a regression model fit to unmodified loans may not be 

accurate for a typical modified loan, and vice versa. 

In this study, the main out-of-sample estimation issue is the difference in initial delinquency 

status. The populations of modified and unmodified loans do not differ from each other much 

along dimensions of credit score, MTMLTV, or loan origination features. However, the 

populations are skewed in that loans that become even 30 days delinquent are far more likely to 

receive a modification than loans that always remain current. It is also reasonable to assume that 

the subset of current borrowers who receive modifications (due to some change in hardship 

status that they proactively report to their servicer) differ substantially in unobserved ways from 

those not receiving modifications. As a result, a model based on unmodified default expectations 

for current borrowers will not accurately reflect the no-modification expectation for a current 

borrower who does receive a modification. For this reason, borrowers who were current prior to 

modification were excluded from this regression analysis.

The selection bias problem was approached using Heckman sample selection correction factors. 

Alternative methods were considered but rejected for a variety of reasons:

 Because there are significant unobserved characteristics that affect both selection and 

outcome (such as the borrower’s current financial situation), propensity scoring, matching, 

and similar methods that rely on an unconfoundedness assumption have been ruled out. 

 A difference-in-difference design was also considered, based on time frames before or after 

adoption of loan modification programs by servicers. This approach was abandoned because 

servicers’ modification rates changed slowly over time and early HAMP modifications were 

not representative of the program as a whole.

 Various eligibility criteria for HAMP were considered for a regression discontinuity or 

instrumental variables analysis but were largely rejected. For example, investment properties 

are not eligible for Tier 1 HAMP modifications, but these loans also have very different 

performance characteristics than loans on a borrower’s primary residence. Borrowers are also 

ineligible for HAMP if their DTI ratio is below 31 percent, but the borrower’s current DTI is 

generally not known for loans that did not receive a HAMP modification. 

 The available information on loans that were evaluated for HAMP NPV, while incomplete, 

does allow for the construction of experiments that are not possible for the broader loan 
- 55 -

population. Appendix V describes a regression discontinuity analysis of a subset of these 

loans, based on the NPV score. 

In order to effectively identify the effects of selection bias on outcomes using the Heckman 

approach, it is important to isolate any factors that influence whether a delinquent loan gets 

modified, but do not affect loan’s chance of progressing to a serious delinquency, other than 

through that selection effect. Such factors, analogous to the instruments in an instrumental 

variable regression, are then excluded from the outcome regression. Identifying these factors can 

be a challenge, as most characteristics of a loan or borrower that a servicer will use for selecting 

the borrower for modification are, in fact, the risk factors that predict future delinquency and 

default.

A special experiment was conducted to help identify the selection instruments. A 10 percent 

sample was taken of all subprime loans that met the basic HAMP eligibility criteria (owneroccupied, originated before March of 2009, etc.), had not previously been modified, and which 

were exactly 30 days delinquent at some base date in the first half of 2007. A probit regression 

was then performed on the rate of serious delinquencies six months after the base date. This time 

period was chosen because it reflects the first wave of subprime delinquencies, before servicers 

had started to implement modification programs. While the overall rate of serious delinquency 

outcome in this sample was 27 percent, less than half of one percent of the loans in this sample 

received a modification within six months of the base date. This experiment allows the factors 

affecting serious delinquency to be examined independently of modifications.

A number of borrower and loan characteristics were found that fit the exclusion criteria of 

having a significant influence on modification selection, but no significant effect (using a Wald 

Chi-Square test at the five percent level) on the outcomes of the 2007 sample. In particular, the 

interactions between the current servicer and most borrower and loan characteristics fit this 

pattern. The borrower’s back-end DTI at origination was also excluded as it had a p-value of just 

0.078 in this experiment. Because these variables are not guaranteed to influence selection 

outcomes for all borrowers in the same direction, they cannot be employed for a local average 

treatment effects (LATE) design, but they can aid in the identification of treatment effects in the 

Heckman framework, by being included in the 2010 and 2011 selection models but not in the 

outcome model.

The specific Heckman correction method used is a series of probit regressions with correction 

terms constructed from selection equations into outcome equations. This technique produces

correct, unbiased beta coefficients but with incorrect standard errors (confidence levels). As a 

check on the confidence levels, sensitivity tests were also done to execute the same model on a 

subsample of data but using a full information maximum likelihood algorithm (FIML) 

implemented in proc qlim of SAS/ETS. This algorithm takes much longer to run and is 

prohibitive for the full data sample, but it produces correct standard errors. In general, the data 

sample is large enough such that a beta (regression coefficient) that is significant has a p value of 

< 0.0001, so that even with “incorrect” standard errors, there is extremely low risk that a value 

reported as significant is actually insignificant, or vice versa.
- 56 -

The study model consists of two selection probit equations (selection of loans into modification, 

and selection of modified loans into HAMP), and three outcome equations, one for each of the 

three subgroups (HAMP-modified, non-HAMP modified, and no modification). The outcome is 

a binary variable defined as “adverse” if, as of the outcome month, the loan is either 90 days or 

more delinquent or has been modified after the base month (which is seen here as an 

impairment). The outcome month may be 6, 12, 18, 24, 30, or 36 months past the base month. A 

loan that is liquidated via foreclosure or short sale prior to the outcome month is considered to 

have an adverse outcome. A loan that is paid off fully is considered to have a non-adverse

outcome.

Because the study views modifications as impairments, the definition of what constitutes a 

modification will affect outcome estimates. For this study, ”modifications” have been restricted 

to those that reduce the borrower’s payments. This means that loan workouts and capitalizationonly modifications (which will be exclusively non-HAMP) are not given the status of ‘real’ 

modifications. This has several implications for how the study regressions are constructed:

 A loan is considered modified for initial selection purposes only if the HAMP or non-HAMP 

modification under consideration is the first payment-reducing imputed modification 

observed during the study period. The study ignores any imputed payment-reducing 

modifications prior to January 1, 2008, which were very rare. 

 The study also ignores the fairly large volume of capitalization-only modifications performed 

on this population in 2008 and early 2009, which have been demonstrated in other studies to 

be ineffective (White, 2009). This has the effect of giving non-HAMP modifications more of 

a head-to-head comparison with HAMP.

 Also, a step-up of an initial modification that increases the payment was ignored and had no 

effect on the outcome variables. This can potentially affect quite a number of non-HAMP 

modifications, which may have earlier step-ups than HAMP.

 Hopefully, the payment-reducing restriction caused a modified loan to be considered to have 

an adverse outcome only if it gets disqualified through SDQ or otherwise has to be given a 

new modification that involves a further concession from the investor.

No single source in the literature has been found with a model structure precisely like this 

(binary outcome variable with a nesting of two selection equations). However, the correct 

methodology can be deduced from consulting a number of sources on selection correction, such 

as Maddala (1983). Pages 265-266 of Maddala briefly discuss the problem of a nested series of 

selection factors. This corresponds to this study’s treatment of selection-into-modification and 

selection-into-HAMP. There are several ways in which this selection process could be modeled. 

Because HAMP modifications are at the top of a servicer’s waterfall, then a strictly 

chronological ordering of selection processes would produce a sequence as follows:

[A] Selection of delinquent loan into HAMP vs. fallout

[B] Selection of fallout loans into non-HAMP modification vs. no modification.
- 57 -

There are some problems with this ordering. First, the population of HAMP modifications in 

proportion to an entire delinquent population is fairly small, making the estimation more 

difficult. Second, this ordering does not map well to the hypotheses being considered, which are 

to measure modification effectiveness vs. non-modification, and then HAMP modifications vs. 

non-HAMP modifications.

For this reason, the selection steps have been reversed so that selection-into-modification is 

estimated first, and then selection-into-HAMP, conditional on a loan having been modified, is 

estimated in a second stage. This thus generates the following two equations:

[A]

( ) [ ] pr Mod   1  1X1

[B]

( | ) [ ] pr HAMP Mod    2   2X2  1,21

When the outcome equation is estimated, both inverse Mills ratios from the two selection 

equations must be carried forward. This is because the unobserved factors that may influence a 

loan’s selection into modification (lambda-1) and its selection into HAMP vs. non-HAMP 

modification (lambda-2) may be correlated with redefault rates. 

[A]

( | ) [ ] pr Adverse NoMod    A   A X A  1,A1A

[B]

( | ) [ ] pr Adverse NonHampMod    B   B XB  1,B1B   2,B2B

[C]

( | ) [ ] pr Adverse HampMod    C   C X C  1,C 1C   2,C 2C

The correction terms are derived from the selection equation estimates as indicated in the table 

below. The ‘beta’ variables are the estimated probit regression coefficients from the selection 

equations; each ‘X’ represents an observable characteristic of each loan. Each resulting ‘lambda’ 

factor is the expected value of the probit error term, which represents unobserved factors that 

influence whether a loan receives a HAMP modification, a non-HAMP modification, or no 

modification at all.
- 58 -

Table A9: Modification and HAMP Selection and Correction Factors 

Modification selection 

correction factor

HAMP selection

correction factor

Unmodified 

Loans

( )/[1 ( )] 1A   1X1   1X1 2A  0

Loans with 

non-HAMP 

modifications

( )/ ( ) 1B  1X1  1X1( )/[1 ( )] 2B   2 X2    2 X2

Loans with 

HAMP 

modifications

( )/ ( ) 1C   1X1  1X1( )/ ( ) 2C    2X2   2X2

In each case, there are two kinds of correction factors, one based on a loan not being selected, in 

which case the correction will be downward, and one based on a loan being selected, so that the 

correction will be positive. The ultimate effects of these ‘lambda’ factors on the outcomes 

depends on the sign of the correlation variable ‘rho’ that is calculated in the course of the 

outcome probit estimation.

The use of Heckman selection correction with a binary rather than a continuous outcome is 

common enough that Stata has a built-in command called heckprob that performs this kind of 

two-stage probit estimation automatically. The theory is fairly straightforward: in a two-stage 

estimator, one can carry forward the inverse Mills ratio term from the selection equation into the 

second stage probit equation, just as one would with a second stage OLS. A standard reference 

paper for this approach is Van de Ven and Van Praag (1981). When the outcome is binary, there 

is a slight change in the interpretation of average treatment effects. Any treatment effects 

equation that is based on a continuous outcome variable can be applied instead to the hidden 

predicted probability of default. 

To separate the selection and treatment effects, five probabilities were calculated from the 

outcome probit equations:

P1 is the estimated probability of an adverse outcome using the model coefficients for a HAMP 

or non-HAMP modification, conditional on that loan having received that modification type. In 

other words, this is the in-sample outcome propensity score for modified loans. On average, it 

will be very close to the actual proportion of adverse outcomes in each modified population.

P2 is the pure treatment effect probability of an adverse outcome conditional on the loan’s 

observable variables only. Here, the probit score component attributable to the influence of 

unobserved variables on the outcome was subtracted out. This then simulated what outcomes 

would happen if borrowers with a given set of observable characteristics could be randomly 

assigned to modification or not. 

P3 is the estimated probability of an adverse outcome using the out-of-sample coefficients for an 

unmodified loan, as applied to a loan that actually did get a modification. Then, the bias effects 

of selection-into-modification were removed so that the study simulates a loan with a set of 
- 59 -

observed characteristics being randomly assigned to not get a modification and estimating what

would have happened to that loan.

P4 is the probability of an adverse outcome for an unmodified loan, conditional on the 

observable characteristics only. It is the no-modification equivalent of P2.

P5 is the raw estimated probability of an adverse outcome for an unmodified loan, without any 

correction. It is the no-modification equivalent of P1.

The ATET is calculated as the average difference between P2 and P3 over a given population of 

loans, either HAMP-modified or non-HAMP modified. Thus, it measures the expected change in 

probability of an adverse outcome due to the modification for a given population of loans with 

the distribution of observable characteristics matching that of the actually modified loans if one

could have done a controlled experiment on those loans.

Going further than this and trying to calculate an average treatment effect on a larger population 

presents some difficulties. It is not practical to try to extrapolate an out-of-sample outcome “if 

treated” for an unmodified loan since it is not clear what the terms of such a modification would 

be. However, the difference between P3 and P4 will show, at least, how much of a difference in 

no-modification outcomes is due to the difference in observable factors between the modified 

and unmodified populations. This difference in outcomes can be computed by multiplying the 

no-modification betas by the average population difference in covariances.

The variance calculations in the study are made directly from the xbeta probit scores for HAMP

modifications and non-HAMP modifications [equations B and C]. These scores determine the 

predicted probability of a given loan having an adverse outcome after some period of time. 

Differences in this score between two modified loans could be due to differences between 

modification terms, or they could stem from characteristics of the loans or borrowers. 

Mathematically, the variance of the total xbeta statistic can be decomposed into variances and 

covariances of each loan and modification attribute:

var( | ) var[ ] var( ) cov( , )

2

iC j C ic j c iC ic

Adverse HampMod   C   C X C   X    X X

The variance decomposition in this report looks at the variance subcomponents associated with 

key attributes, such as the level of payment reduction and borrower FICO score. The remaining 

variance associated with other attributes, as well as from covariances (effects stemming from 

combinations of variables) or selection effects, is referenced as “all other factors.”
- 60 -

Appendix III: Data Transformations

The source data for the study was comprised of:

 A Fannie Mae internal database with loan-level data and monthly performance data which is 

derived from a subset of about 85 percent of the CoreLogic Loan Performance subprime 

database, corresponding to all loans for which Fannie Mae owns or has owned an interest in a 

related collateralized debt obligation (CDO). The HAMP program administration database 

which contains data on NPV calculator submissions and loan modifications under the HAMP 

program;

 A time series of MSA-level historic and projected home price indices used within the HAMP 

program, as well as a ZIP code to MSA lookup table;

 ZIP code-level demographics from the US Census Bureau’s American Community Survey of 

2007-2011. 

The data transformations that were used to set up the probit regressions were:

 Selection of initial study population; 

 Imputation of loan modifications in the CoreLogic database;

 Matching of imputed modifications to known HAMP modifications on PLS loans;

 Data sampling for a given base year and post-modification time span; and,

 Derivation of additional regressors including MTMLTV.

Selection of Initial Population

The filtering of the loan population was done in two stages. The first set of filters generated a 

broad set of potentially HAMP-eligible loans. This population was used for the modification 

imputation and HAMP matching stages. After this, some secondary filters were applied to 

eliminate loans that were useful to the matching algorithm but for which not all of the covariates 

needed for the regressions were properly populated.

The first stage filters determining HAMP eligibility, in order, were:

 Loan must not have a liquidation date prior to January 1, 2008 (i.e., it must have been active 

during the study period);

 Loan must have an occupancy status of owner-occupied principal residence (not a vacation 

home or investor-owned property);

 Loan must be a first lien;

 The origination date must be populated (for matching purposes) – if the origination date is 

not populated, it is taken to be two months prior to the first payment due date, if this is 

known;

 Five-digit ZIP code must be populated (for the matching algorithm);

 Origination UPB must be populated (for the matching algorithm); and,

 Loan must be for one to four units and must be within the conforming loan limits to be 

HAMP eligible.
- 61 -

The second stage, post-matching filters were:

 Origination date must be after January 1, 1976 so that MTMLTV can be calculated from the 

home price index series that was used;

 Origination LTV and/or property value at origination must be populated. Origination LTV 

must have a reasonable value (<= 500 percent); and,

 For modified loans:

o Pre- and post-mod payment must be $1 to $100,000;

o Pre- and post-mod UPB must be $1 to $2,000,000;

o Percent change in UPB must be -99 percent to +99 percent;

o Delinquency before modification must be no more than 10 years;

o Pre- and post-mod interest rate must be > 0 percent and <= 99 percent;

o Percent change in payment must be -99 percent to +99 percent; and,

o Change in interest rate must be -2000 to +2000 basis points.

Modification Imputation

The CoreLogic database identifies, for each month, the loan’s scheduled principal and interest 

payment, the current interest rate, the remaining unpaid principal balance, as well as delinquency 

status. Each loan’s payment history was compared to its origination loan terms to identify when 

and how many times the loan had been modified. 

For a regular, fixed-rate mortgage, the interest rate and payment should remain unchanged from 

month-to-month while the principal balance will slowly decline. If there is an interest-only 

period, then the principal balance will remain fixed during that period, and then the payment will 

jump up at to an amortizing level, possibly accompanied by a rate change. Subprime ARMs 

typically take the form of a hybrid loan with a fixed-rate period of two to 10 years, followed by 

an initial rate reset and then periodic resets every six months. Subsets of subprime ARMs have 

rates that reset every month over the life of the loan. Modifications to an ARM loan can usually 

be readily identified because the expected pattern of periodic rate or payment changes comes to 

an end, and no recurring changes occur afterwards. Conversely, some rate changes can be 

immediately identified as a modification if they violate the origination terms of the loan, such as 

a periodic or lifetime rate floor. The margin of note rate over an ARM index can also effectively 

be used as a lifetime floor because the underlying index, such as the six month LIBOR, cannot 

go below zero. 

Therefore, a cutoff point was established for each of these loans at the date of the last observed 

expected rate change that was not preceded by an illegal rate change. Changes in loan terms after 

the cutoff date were then considered to be potential modifications. The rate change occurring 

exactly at the cutoff date is handled as a special case, as described below.

For example, a 2/28 hybrid ARM whose first payment was in April of 2007 would have an initial 

rate reset in March 2009, and expected subsequent rate changes in following March or 

September periods. Slight timing variations were allowed for: if the initial rate reset is one month 

earlier or later than expected, and the subsequent rate changes all follow the same pattern, those 
- 62 -

rate changes are considered to be expected rate changes. Suppose that for such a loan, rate 

changes were observed in February 2009, then again in August 2009 and February 2010, but the 

only rate change after this is in December 2010. The December rate change (and any other 

change in loan terms after February 2010) would then be considered as a possible imputed 

modification.

For a subset of subprime ARMs, the note rate is scheduled to reset continuously over the life of 

the loan on a monthly basis, once any hybrid (fixed rate) period has ended. The borrower’s 

monthly payment is then recast at 12-month intervals. For these loans, the last sequence of three 

or more consecutive monthly rate changes was observed, and a check was made to see if a 

payment recast occurred at the next scheduled anniversary date. The cutoff was set either to the 

final payment recast, or to the final rate change if no recast was observed.

The rate reset periods of subprime ARMs are usually either monthly, or every six or 12 months. 

If a loan coded as a 12 month reset showed patterns of rate changes every six months, these rate 

changes were treated as expected, and the origination data was assumed to be incorrect.

As the first step in the imputation process, the CoreLogic payment history was scrubbed by 

removing stated changes to loan terms that were entirely reversed in the following month. These 

data anomalies were presumed to be due to servicer reporting errors. Following this, expected 

events were flagged, including any change to an ARM loan taking place before the cutoff points 

described above. Any payment change of more than one percent and any UPB drop of five 

percent or more was provisionally marked as an unexpected event. However, changes in loan 

terms occurring within one month of a loan’s interest-only reset date were flagged as expected 

unless they involved an illegal rate change.

An actual modification may end up being recorded in CoreLogic over a two- to three-month 

span. For this reason, unexpected changes in loan terms that took place over two or three 

consecutive months were grouped together as one event for the purposes of imputation. For 

example, if a loan were recorded as having gone from six months delinquent to current, with the 

accrued interest being added to the loan balance in June, and in July the interest rate and payment 

were reduced, then this was considered to be one modification event. 

Once the unexpected events in consecutive months were merged together in this manner, each 

unexpected event was identified as a modification if:

 A delinquent loan was made current (or had its delinquency reduced to one month) and 

had a payment change (either up or down) of greater than one percent, or

 The loan remained current while having a payment reduction of 2.5 percent or more.

Occasionally, a borrower will become 30 days delinquent in the very first month after their 

modification becomes permanent. Because of this, a loan whose delinquency status drops from 

two months or more down to one month was also considered to have been made current.

Rate change events occurring just at the cutoff point (i.e., the last observed rate change at the 

expected periodic interval) are considered ambiguous, and are therefore classified as 
- 63 -

modifications only if the loan was also made current. Also, a payment reduction stemming from 

a drop in the principal balance on a current loan was interpreted as a curtailment by the borrower 

rather than a modification. 

HAMP Matching

The imputed modifications in CoreLogic were matched to known HAMP modifications by 

comparing data elements associated with loan origination and modification terms present in both 

databases. A HAMP match was identified if the imputed modification and the HAMP terms 

showed:

 The same five-digit property ZIP code and

 Origination date within 45 days, and

 Modification date within 75 days, and

 Some combination of correspondences from the table below totaling seven points or 

more:

Table A10: Derivation of CoreLogic to HAMP Matching Score 

Loan Origination / Modification Terms Closeness of Match Point Score

Origination Terms

Loan origination date 15 days 1

Principal balance at origination 0.5 % 5

Principal balance at origination 1.0 % 4

Principal balance at origination 2.5% 3

Principal balance at origination 5.0% 2

Principal balance at origination 7.5% 1

Origination note rate 1 basis point 4

Origination note rate 6.25 basis points 3

Origination note rate 31.25 bp 2

Modification Terms

Loan modification effective date / change 

in official loan terms

15 days 1

Post-mod interest rate 6.25 basis points 1

Pre-mod interest rate 6.25 basis points 2

Pre-mod interest rate 20 basis points 1

Post-mod payment Exact match 5

Post-mod payment 0.6% 4

Post-mod payment 1.2% 3

Post-mod payment 2.5% 2

Post-mod payment 5.0% 1

Post-mod principal balance Exact match 7

Post-mod principal balance 0.5% 4

Post-mod principal balance 1.0% 3

Post-mod principal balance 2.0% 2
- 64 -

Post-mod principal balance 4.0% 1

The point scores were assigned so that differences in terms with similar frequencies in the 

population of potential matched pairs would be given the same weight. This calibration was 

performed by creating all possible matches between HAMP and CoreLogic modifications that 

met the base criteria. For example, there is a three percent chance that a HAMP modification and 

a randomly matched imputed modification, sharing the same ZIP code and with a modification 

date within 75 days, will have post-modification payments within 2.5 percent of each other. 

Similarly, there is a three percent chance that the pair will have post-modification principal 

balances agreeing within two percent. Each of these correspondences is given a point score of 

two. A one point score increase roughly corresponds to a decrease in the chance of a random 

pairing by a factor of two. In practice, the calibration of point scores for matching requires some 

judgment because different attributes are subject to data errors in both the HAMP and CoreLogic 

database.

In some cases, potential HAMP matches were disqualified if there were two or more HAMP 

modifications with a high matching score to a CoreLogic loan modification, or two or more 

CoreLogic modifications matching the same HAMP. Each high scoring match was compared to 

the next runner-up in point score. A disqualification occurred if this runner-up exceeded a 

threshold score, as indicated below:

Table A11: HAMP Matching Uniqueness Conditions 

High Match Score (S) 7-10 11 12 13 14 15 16+

Runner Up Threshold 7 8 8 9 9 10 S - 5

The HAMP database also identifies origination data for some loans that were evaluated for the 

program but did not ever receive permanent modifications. The most common reasons for a 

disqualification are either, eligibility-related (failure to meet the 31 percent DTI threshold or an 

NPV negative result), failure of the homeowner to complete documentation requirements, or to 

respond to the trial offer, or failure of the homeowner to make the required trial payments. 

CoreLogic loans that received a point score of 10 or greater when matching on the subset of 

origination terms were thereby matched to these HAMP fallout cases. 

Sampling Methodology

Each regression defined three population groups (A: unmodified loans; B: non-HAMP

modifications; C: HAMP modifications) for a collection of 12 base months. Given a choice of 

base month, loans were classified by their modification status:

 If the loan was never modified during the study period (January 2008 to March 2013), or if 

its first payment-reducing modification occurred after the base month, then for econometric 

purposes the loan is classified as not modified (Group A). 

 If the loan received a payment-reducing modification that became permanent on the base 

month, then it is counted as non-HAMP or HAMP-modified (Groups B and C) depending on 

whether the modification was matched to the HAMP database or not.
- 65 -

 If the loan received a payment-reducing modification during the study period before the base 

month then it was excluded from the study population for that base month.

An additional filter was applied based on the loan delinquency status. The study sought to 

exclude loans that were initially current prior to their modification. As noted in the body of the 

paper, loans that remained current are very different from those that ever became even 30 days 

delinquent, so it is difficult to include the two populations in a single model. However, it would 

not be appropriate to simply look at the period just prior to the modification to assess loan 

delinquency, because some servicers may report a loan as being delinquent while it is in trial and 

the borrower is making timely (but reduced) payments. Because of this, the borrower

delinquency level was tested three months prior to the base month. Loans that were current at 

this point in time were excluded from the study population for that given base month.

Finally, some downsampling was performed on the unmodified loan population, since otherwise 

the modified loan population would be relatively small. The downsampling was performed as 

follows:

 For a given loan month, all modified loans (groups B and C) were included, but only 1/48 of 

the not modified loans (group A) were selected.

 Each regression was based on a base year; that is, the sampled populations for 12 base 

months within the base year were combined together. 

 Note that it is possible for a single loan to appear more than once in a regression sample but 

under different base months; this is an expected aspect of the sampling methodology. The 

occurrence of duplicate loan instances was low enough that clustering of the standard errors 

was not thought to be necessary.

 The non-modified loans were given a sampling weight of 4 in the selection regressions, 

compared to 1 for the modified loans. This was based on the fact that for a set of N loans that 

were never modified in the base year, a subset of N/4 of them, in expectation, were included 

in the sample for a given base year. However, for a set of M loans that were modified in the 

base year, exactly all M of them appeared in the sample as modified loans. (An additional 

subset of about M/8 of them also appeared as unmodified loans in base months prior to their 

modification becoming permanent.) The weighting factor put the unmodified and modified 

loans on equal footing.

Calculation of Covariates

Many of the covariates used in the selection and outcome regressions were taken directly from 

the CoreLogic database and have straightforward interpretation. Regressors taken from other 

sources or computed are described below. The internal HAMP database loan modification 

attributes were not used once a match to an imputed CoreLogic modification was detected; 

instead, the corresponding fields from CoreLogic were used. This ensures that HAMP and nonHAMP modifications were compared as fairly as possible.
- 66 -

ZIP Code

Three demographic variables were taken from the 2007-2011 American Community Survey of 

the US Census. Each variable represents an average value over the five year survey period for 

each property ZIP code. These variables are the log of median household income, the mean 

unemployment rate, and the percentage of the population reported as belonging to an historically 

underserved minority group, here defined as any race/ethnicity code other than Asian or nonHispanic white.

Delinquency Status

The borrower’s delinquency status was used for three different variables. First, the base 

delinquency corresponds to the delinquency at the start of modification trial period in the single 

variable analysis. Since the length of the trial period is unknown for non-HAMP modifications, 

and has no meaning for the unmodified loans, this variable was defined consistently for all loans 

as the delinquency status three months prior to the base date.

An additional covariate used in the selection model was the prior delinquency. This is the 

percentage of months between origination and the base date that a loan was at least 30 days 

delinquent. This covariate allowed some of the prior delinquency history provided by CoreLogic 

and not available in HAMP to be used. Finally, the borrower delinquency level at the outcome 

month affects their outcome status variable.

Servicer

To effectively control for differences in servicer policies or practices that could affect 

modification rates, it was critical to identify which servicer was handling a delinquent loan in 

some particular base month. Although this information is not included in the Loan Performance 

loan history table, it can be extracted from monthly historic snapshots of the loan’s current state. 

Acquisitions of servicing portfolios from defunct institutions and other transfers can be observed 

in this way. 

Since the study period only starts in January 2010, a great number of subprime loans in the study 

population had already been transferred at least once due to industry consolidations. For 

example, all CountryWide originated loans were grouped with Bank of America, and Wachovia 

loans with Wells Fargo. In some cases when a servicer’s portfolio in 2010 or 2011 had 

significant sub-populations associated with specific prior transfers or acquisitions, these subportfolios were tagged separately, as follows:
- 67 -

Table A12: Servicer Sub-Portfolio Groupings

Prior Servicer Post-transfer Servicer Servicer SubPortfolio

Citigroup American Home 

Mortgage (AHMSI)

Citi/AHMSI

All others AHMSI Other/AHMSI

Washington Mutual JP Morgan Chase WAMU/Chase

EMC (Bear Stearns) JP Morgan Chase EMC/Chase

All others JP Morgan Chase Other/Chase

Litton (Including Avelo 

and Equity One)

Ocwen Litton/Ocwen25

Saxon Ocwen Saxon/Ocwen

HomEq Ocwen HomEq/Ocwen

All others Ocwen Other/Ocwen

MTMLTV 

Some adjustments were made to MTMLTV-related fields when the CoreLogic data was 

incomplete or inconsistent. On a purchase loan, the origination LTV (which is usually a round 

number such as 80 percent) was taken as a reference point, and the property appraised value was

recalculated as the ratio of origination UPB to origination LTV. If the origination LTV was

missing, however, the stated appraised value was used to back-calculate the origination LTV. If 

the appraised value and origination LTV were both missing, then the sale price was used as an 

approximation of appraised value. On a refinance, the appraised value at the time of refinance

was used as the reference point and the origination LTV was recalculated from that value and the 

origination UPB. If the appraised value was missing then it was reconstructed from the stated 

origination LTV and UPB.

The MTMLTV for modified loans was taken to be the ratio of post-modification loan balance to 

the estimated home value in that month. The home value was estimated from the original 

appraised value, as calculated above, and adjusted for changes in MSA-level home prices from 

the origination date through the date of modification. The home price index used was the same 

Treasury proprietary index that is used for the HAMP NPV model calculations. The MTMLTV 

for unmodified loans was calculated in the same way, using the appropriate base month. 

These CoreLogic-based calculations were found to result in a higher average home value (and, 

therefore, a lower MTMLTV) at the time of modification than the value reported by servicers at 

the time of HAMP modification. This may be due to inflated appraisals at subprime origination 

or because of actual deterioration of the property when the loan is in default. A regression 

comparing the LTVs derived by both methods was performed, resulting in a linear rescaling of:

 

25 Note that the transfer of Litton’s portfolio to Ocwen occurred in the middle of 2011, so that depending on the 

month of evaluation, a loan’s servicer might be tagged either as “Litton” or “Litton/Ocwen.”
- 68 -

HAMP-estimated MTMLTV percent = 11 percent + 1.21 * [CoreLogic-estimated MTMLTV]

Because the HAMP data was taken to be more accurate, all of the CoreLogic-derived MTMLTV 

figures were rescaled for all loans in the sample, regardless of their modification status. Because 

all loans were rescaled by the same linear factor, this did not materially affect the regression 

results or hypothesis tests, but it does place results within a reasonable and consistent metric for 

comparison to other studies.
- 69 -

Appendix IV: HAMP Trial Fallout Effect

Loans that were initially evaluated by a servicer for a possible HAMP modification but did not 

ultimately receive one are considered to have fallen out of the HAMP modification pipeline. 

These loans may have then be evaluated for a servicer’s non-HAMP proprietary modifications. 

Some (but not all) of this fall-out population was reported by servicers to the HAMP program 

administrator. In general, the further along a loan makes it in the pipeline before being rejected 

for a modification, the more likely its status will have been recorded. The most common reasons 

for such fall-out, in order of evaluation, are:

 Loan or borrower was categorically ineligible for the program (not owner-occupied, etc.);

 Borrower did not provide necessary documentation such as verification of income;

 Loan or borrower was found to be currently ineligible for the program in view of data 

collected at the time of evaluation (e.g., borrower income was too high, borrower was not in 

hardship);

o In particular, a borrower whose current mortgage payment to income ratio was 

already below the program target of 31 percent was deemed ineligible, and

o A borrower with a very high ratio may have been denied a modification because an 

excessive amount of principal forbearance would have had to be applied in order to 

bring their ratio down to the 31 percent target;

 Loan did not pass HAMP NPV test;

 Borrower did not respond to trial modification offer; and,

 Borrower initiated a trial but failed to make timely payments.

The origination data provided by servicers about these loans is limited, particularly if the loan 

was disqualified prior to an NPV test being run. Nevertheless, it was possible to match some of 

these loans to those in the CoreLogic database, including loans imputed to have received nonHAMP modifications. This made it possible to at least partially classify these loans in terms of 

the type of fall-out and subsequent modification. 

Table A13 shows the result of this partial classification. The “no modification” group consists of

PLS loans that became delinquent but were not modified in the period from 2010 to 2011. The 

“non-HAMP” group consists of delinquent loans that received proprietary modifications over 

this time frame. Within each group, loans for which one of the common HAMP fallout reasons 

(listed above) was indicated by the servicer are broken out for comparison. With a few 

exceptions, the loan characteristics did not show strong differences across categories. Notably, 

loans that had reduced documentation requirements at origination were more likely to be 

ineligible for HAMP due to excessive forbearance (which is associated with very high DTI), and 

are less likely to be disqualified due to low DTI. Also, loans with chronically late payments, as

measured in the percentage of delinquent periods between origination and the NPV test, were 

more likely to fail to complete a HAMP trial.
- 70 -

Table A13: Characteristics of Loans, by HAMP Fallout Type and Subsequent Modification 

Status

Table A14 then compares the raw outcomes for these groups, in terms of whether the loans were 

90 days or more delinquent, as of 24 months after the HAMP NPV evaluation (or, alternatively, 

the submission of a report by the servicer that the loan was ineligible for HAMP). For example, 

the average SDQ rate after 24 months for a non-HAMP modified loan is 37 percent overall, but 

just 31.5 percent for those loans that were denied a HAMP due to the borrower submitting 

insufficient documentation. Similarly, the overall 24 month default rate for delinquent but 

unmodified loans is 84 percent, but it is over 90 percent for those with insufficient 

documentation for HAMP. This suggests that lenders may have been able to perform successful 

modification by relaxing documentation requirements, or by giving a borrower another chance to 

submit documents. 

HAMP Fallout 

Reason

Got NonHAMP # of 

Loans 

 Backend DTI FICO 

 Low/ 

No Doc 

% 

Cash 

out Refi 

%

 % of 

time 

DLQ 

 MTM 

LTV 

 % 6-11 

months 

DLQ 

 % 12+ 

months 

DLQ 

Insufficient Docs N o 12,178 4 0 632 5 1 5 3 50% 107 2 3 5 0

DTI at eval < 31% N o 4,400 4 0 622 3 2 5 2 48% 101 2 5 3 1

Excess Forbearance N o 2,976 4 1 651 7 1 5 3 46% 104 2 3 4 8

NPV Negative N o 5,006 4 1 647 6 3 5 2 49% 109 2 2 5 5

Trial Not Accepted N o 5,194 4 1 635 4 8 5 1 50% 112 1 9 5 8

Trial Failure N o 8,516 4 1 622 4 8 5 3 56% 110 2 0 6 3

All 30+ dlq loans N o 515,150 4 0 641 5 1 4 9 46% 107 1 8 4 5

Insufficient Docs Yes 10,434 4 1 627 4 6 5 7 43% 105 3 2 3 6

DTI at eval < 31% Yes 3,578 4 0 615 2 9 5 6 46% 101 3 3 2 7

Excess Forbearance Yes 2,287 4 1 628 6 7 5 6 54% 108 3 3 4 3

NPV Negative Yes 4,250 4 1 651 6 8 5 4 44% 108 2 9 5 0

Trial Not Accepted Yes 2,716 4 2 622 4 3 5 9 48% 108 3 4 4 1

Trial Failure Yes 5,053 4 1 615 4 7 5 8 54% 109 2 9 5 7

All 30+ dlq loans Yes 218,072 4 0 631 4 7 5 5 44% 104 2 8 3 3

At Origination: At HAMP Eval:
- 71 -

Table A14 HAMP Fallout Category Marginal Effects on Subsequent Borrower 

Performance 24 Months after NPV Fallout Date

Figure A1: HAMP Fallout Category Influence on Subsequent Borrower Performance 24 

Months after NPV Fallout Date

Keep in mind, however, that differences in raw outcomes can be due to a number of factors, 

including differences in borrower and loan characteristics, selection bias, and the relative levels 

of payment reduction offered to the different groups (which is also shown in the table). To adjust 

for these factors, the common HAMP fallout categories were also included in the outcome 

regressions for the 2010 and 2011 populations. This allows the effect of a borrower being in each 

category to be statistically isolated, and measured as a marginal effect on an average loan. This 

marginal effect (taken as a weighted average of the 2010 and 2011 measurements, and using a 

time span of 24 months) is shown in Table A14 and Figure A1 for both the non-HAMP modified 

and unmodified loans. The difference between these two marginal effects (if positive) represents 

a further improvement in the probability of default, when a borrower in the given category gets a 

non-HAMP modification, and can be directly compared to the raw improvement. 

HAMP Fallout 

Reason

Non-HAMP pmt 

% reduction

Marginal 

Effect, 

Non-HAMP

Marginal 

Effect, 

No Mod

Difference in 

Marginal 

Effect

Insufficient Docs 37 -2.6 0.7 3.3

DTI at eval < 31% 22 -4.1 -4.4 -0.3

Excess Forbearance 41 -5.1 -0.4 4.7

NPV Negative 41 -4.7 3.4 8.0

Trial Not Accepted 39 1.3 3.7 2.5

Trial Failure 33 1.4 1.1 -0.3

All 30+ dlq loans 32

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

Raw Improvement (Pctg. Points)

HAMP Fallout Reason

All Loans
- 72 -

Note that while the isolated effects are smaller than the raw effects in magnitude, the relative 

rankings of each fallout category are roughly equivalent. Loans receiving a non-HAMP 

modification that had previously failed to get a HAMP due to a negative NPV result or high DTI 

show a greater treatment effect than other non-HAMP modified loans. Loans that are rejected 

from HAMP due to a low DTI are relatively low risk and do not show as high a treatment effect 

as other loans when they get a proprietary mod. Loans that fail their HAMP trial period are 

significantly higher risk than other loans, but in terms of non-HAMP treatment effect are no 

better or worse than average. 

 

Generally, loans that fell out of the HAMP modification process tend to have riskier 

characteristics, such as lower FICO scores and higher MTMLTV, than those for which no fallout 

data was recorded. These loans also generally had worse subsequent performance, particularly if 

they did not subsequently receive a non-HAMP modification. Also, loans that fell out due to 

borrower behavior, and particularly those that fail during trial, were riskier and have worse 

performance than those that were found ineligible by the servicer.

It is interesting to note that loans that fell out of the HAMP pipeline prior to trial and then 

received a non-HAMP modification performed better than other non-HAMP modifications on 

average, while such loans that received no modification at all perform worse than average. This 

implies that some of the loans that were disqualified from HAMP but found to be eligible for 

non- HAMP modifications have better than average unobserved risk factors. Some of these loans 

could possibly belong to borrowers whose DTI at evaluation fell below the HAMP threshold of 

31 percent, but who have a high MTMLTV or interest rates that made them eligible for 

alternative modification offers.

These fallout effects help explain why the observed performance of delinquent, unmodified loans 

is so high: the pool of these loans is over weighted with many borrowers who fell out of the 

modification process due to factors that also put them at a high risk for default. 
- 73 -

Appendix V: HAMP Fallout Analysis to Check on Results of Overall Study

At each of the steps in the HAMP evaluation process, some combination of borrower and 

servicer selection effects will be present. However, for certain steps, there is a potential for a 

kind of natural experiment in which two groups of borrowers, who are very close to the threshold 

of being selected, can be thought of as randomly assigned:

 Borrowers whose front-end DTI ratio is just above 31 percent (making them HAMP 

eligible) or just below (making them ineligible);

 Borrowers whose DTI is sufficiently high that they become ineligible due to excessive 

forbearance, or those who just barely pass this test; and,

 Borrowers who are ineligible for HAMP because their modification NPV is a small 

negative amount compared to those getting HAMPs with a small positive NPV.

Unfortunately, the first two comparisons cannot be made because the available data from 

servicers is incomplete. If a borrower is found to be ineligible via a HAMP waterfall test 

according to the servicer’s own calculations, then an NPV test is not required to be run, and so 

information about such a loan may or may not be present in the HAMP database. However, 

whenever a servicer runs an NPV test, they are required to submit the results (whether positive or 

negative) to the HAMP system of record. This allows an experiment to be constructed using an 

instrumental variables design known as local average treatment effect (LATE).

Figure A2 shows the position of the NPV test in a sequence of possible outcomes for a 

delinquent subprime loan. Borrowers who were never evaluated for HAMP or were deemed 

ineligible prior to the NPV test are excluded from the population. Loans with an NPV negative 

result are divided into two groups based on whether they were matched to an imputed nonHAMP modification that occurred within 12 months of the NPV evaluation. A loan that is NPV 

positive can have three possible outcomes, depending on whether the HAMP modification offer 

is accepted by the borrower and a trial is completed, or if instead a non-HAMP modification is 

made, or no modification at all.
- 74 -

Figure A2. Waterfall for NPV Test Treatment Effects Experiment

Borrower is 30+ dlq Contact w/ servicer for HAMP Submits docs, is HAMP eligible

NPV Test Positive Accepts and completes HAMP trial Eligible for non-HAMP

Offered & accepts non- HAMP NPV Pos, Non-HAMP

NPV Pos, No Mod NPV Pos, HAMP

Yes Yes

Yes

No No

Not included

Yes

No Eligible for non-HAMP

Offered & accepts non- HAMP

Yes

NPV Neg, Non-HAMP

NPV Neg, No Mod

Yes No

No

No

Yes Yes No No

The population for the experiment was restricted to those subprime, securitized loans that had a 

HAMP NPV evaluation between October 1, 2009 and October 1, 2011, and received a score 

between negative ten thousand and positive ten thousand dollars. HAMP modifications within 

this sample become permanent, on average, about four months after the NPV test, while 

proprietary modifications did so after about six months, so that this time frame roughly 

corresponds to the 2010-2011 study period. The outcome variable is the SDQ status of each loan 

27 months after the NPV evaluation date, or roughly two years after a permanent HAMP 

modification.

Although a regression discontinuity design was considered for this experiment, the LATE

method was chosen instead because it can still be used to measure a pure treatment effect with 

selection bias removed, even though there are selection biases affecting the loan’s handling after 

the NPV test takes place. For example, borrower selection effects will affect whomever

completes a trial successfully, and servicer selection effects will affect whomever receives a nonHAMP modification. 

The LATE method can sidestep these issues because the NPV test has a monotonic effect on 

selection into a HAMP modification: a positive NPV result will move some subset of loans from 

either a no modification or non-HAMP modification outcome to a HAMP modification outcome. 

This is called the ‘complier’ group. It is reasonable to assume that no loans are moved from a 

HAMP modification outcome to a non-HAMP modification or no modification outcome as a 

result of getting an NPV positive result; that is, there is no possibility of a ‘defier’ population. 

Therefore, the pure treatment effect of a HAMP modification can be calculated as:
- 75 -

LATE =

[pr(SDQ |NPV neg) − pr(SDQ|NPV pos)]

pr(HAMP|NPV pos)

That is, the treatment effect is the change in a loan’s default probability based on its having come 

out slightly NPV positive rather than slightly negative (which is taken to be more or less 

random), divided by the induced change in probability of the loan getting a HAMP modification 

(from zero). 

This treatment effect is shown in table A15 below, broken out by three month cohorts, and 

compared with the corresponding Heckman treatment effect calculation at 24 months after 

modification.

Table A15: Local Average Treatment Effect of HAMP modifications, for NPV Test 

subsample 

NPV Test 

Date

# of loans Treatment 

effect, local

Treatment effect, full 

population (Heckman)

2009 Q4 14,537 37.4 42.3

2010 Q1 13,240 30.0 41.0

2010 Q2 6,225 38.2 44.5

2010 Q3 7,066 32.8 46.1

2010 Q4 4,283 30.1 50.5

2011 Q1 4,506 55.1 47.6

2011 Q2 3,067 51.2 50.0

2011 Q3 1,814 38.1 53.5

The LATE test confirms that the treatment effects of HAMP modifications are substantial, 

ranging from 30 to 55 percent, and it parallels the Heckman results in that HAMP performance 

improves over time. However, the local results show a somewhat lower overall treatment effect, 

with greater variation over time, compared to the Heckman results which range from 41 to 53 

percent. These differences in results could be due to a number of factors. For example, the 

population with NPV results reported may not be representative of the larger group, and the 

HAMP modifications that had a low NPV score might have lower treatment effects than other 

HAMP modifications.