Housing Finance Policy Center
Lunchtime Data Talk
Mortgage Modifications Using Principal
Reduction: How Effective Are They?
Ben Keys, University of Chicago
Tess Scharlesmann, Office of Financial Research, Dept. of Treasury
Max Schmeiser, Federal Reserve Board
August 11, 2015
1
The Determinants of Subprime Mortgage Performance
Following a Loan Modification
Maximilian D. Schmeiser
Federal Reserve Board
Matthew B. Gross
University of Michigan
The opinions and conclusions expressed herein are solely my own and should not be
construed as representing the opinions or policy of the Federal Reserve Board, Federal
Reserve System, or any agency of the Federal government.
August 11, 2015
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State of the Housing Market
Percentage of Loans Delinquent (LPS)
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State of the Housing Market
Ongoing Problem of Foreclosure
As of June 2015
Nationally: 3.37% of all mortgages 90+ days delinquent or in
foreclosure (1.50% in foreclosure)
DC: 3.68% of all mortgages 90+ days delinquent or in foreclosure
(2.36% in foreclosure)
In July 2007
Nationally: 1.30% of all mortgages 90+ days delinquent or in
foreclosure (0.47% in foreclosure)
DC: 0.99% of all mortgages 90+ days delinquent or in foreclosure
(0.37% in foreclosure)
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2008
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2009
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2010
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2011
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2012
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2013
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2014
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
April 2015
Delinquent Payment: 90+ PD,Foreclosure, US, 201504*
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State of the Housing Market
Mortgage Delinquency and Foreclosure Nationwide:
June 2015
Delinquent Payment: 90+ PD,Foreclosure, US, 201506*
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Motivation
Motivation
Mortgage modifications have been the primary tool used to mitigate
foreclosure
Over 5 million mortgages had been modified as of Q1 2015 (OCC
2015; U.S. Treasury 2015)
68K MHA modifications and over 100K proprietary modifications
started in Q1 2015
Typical modification terms have changed over time
Passage of HAMP resulted in more standardized mods, particularly a
reduction in P&I to 31% of income
Still considerable variation in the exact terms of modifications
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Research Questions
Research Questions
What modification terms are most effective at reducing the
probability of a subsequent default or foreclosure?
Change in interest rate, change in P&I, capitalization of fees and
interest, forbearance of principal, reduction of principal
Further examine how loan, borrower, and servicer characteristics, and
economic conditions affect subsequent performance
Contribution: Unique dataset (Corelogic) with detailed information on
loans, modification terms, current valuations and amount of junior
liens; national focus; examine pre- and post-HAMP mods
Limitations: Have few borrower demographics or characteristics (such
as income and employment) that could affect both modification terms
and outcomes
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Background
What is a Mortgage Modification?
A mortgage modification is any change in the terms of a mortgage
Modifications relatively rare prior to recent crisis
However, magnitude of housing crisis necessitated widespread use of
modifications to help borrowers in distress
Goal of modification should be to improve affordability of mortgage
payment
Number of different options to reduce payments:
Reduce interest rate
Extend amortization period
Use principal forbearance
Reduce mortgage principal
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Background
Early Mortgage Modifications
At the start of the housing crisis, modifications were at the discretion
of the servicer and terms varied substantially
Modifications made in 2008 rarely lowered monthly payments and often
resulted in increased payments
Approximately half of all subprime and alt-a mods increased payments
(White 2009)
Among all 2008 modifications done by national banks, 32 percent
resulted in increased payments (OCC 2009)
Many modifications failed to improve affordability resulting in 12
month redefault rate over 60%
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Background
Post-HAMP Modifications
In March 2009 the HAMP program was introduced
HAMP required servicers to adjust monthly P&I on first lien to 31
percent of total monthly income
Follow ”waterfall” to meet target: reduce interest rate, extend term,
forbear part of principal interest free
Servicers can continue to offer proprietary modifications to borrowers
who don’t qualify for HAMP
Terms of proprietary modifications began to improve substantially
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Background
Trend in Number of Mortgage Modifications
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Data
Data
Use loan-level data from FirstAmerican CoreLogic’s Loan Performance
Asset Backed Securities (ABS) Data on privately securitized
mortgages
ABS data include information on subprime and alt-a loans, but does
not include information on agency backed securities or loans held in
portfolio
Data used here are only representative of privately securitized subprime
and alt-a loans, not the entire US mortgage market
Contain monthly performance history for about 20 million individual
loans
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Data
Data
CoreLogic data contain detailed static and dynamic information on
the loans and their performance
Static data include information from origination such as date of
origination, the state where the property is located, the borrower’s
FICO score, origination balance, interest rate, payment and interest
amount, and servicer
Dynamic data are updated monthly and include information on the
current interest rate, mortgage balance, payment and interest, and
loan performance
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Data
Data
CoreLogic also provides two supplemental files that are used in our
analysis:
The first contains detailed information on whether a borrower received
a loan modification, as well as some parameters of the modification
(e.g. reduction in principal, principal forbearance, change in P&I)
Other modification terms are inferred from dynamic loan data (e.g.
reduction in interest rate, change in amortization term)
The second file is the CoreLogic TrueLTV Data, which matches the
loans in the CoreLogic Loan Performance data to public records to
obtain information on the presence and amount of any other liens on
the property
Also contain a monthly estimate of the property’s value from their
automated valuation model
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Data
Data
Given size of database select a five percent random sample of
first-lien loans
Our data on modifications and loan performance cover the period
January 2008 through December 2013
Restrict our data to loans originated no earlier than January 2000 and
modifications occurring after January 2008
To provide economic context for the loan performance we merge in
state level unemployment rates obtained from the Bureau of Labor
Statistics
Yields sample of approximately 37,000 modified loans, 1.35 million
loan-month observations
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Data
Number of Modifications in Sample
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Data
Share of Modifications with Payment Increase/Decrease
2008 to 2013
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Data
Share of Modifications with Balance Increase/Decrease
2008 to 2013
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Data
Modification Performance by Year
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Data
Outcome for Modified Loans in Sample
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Methods
Empirical Methods
Use two different estimation strategies: Probit for 12 month redefault
and Proportional Hazard Model estimated using a multinomial logit
The probit model takes the form:
Pr (Yizs = 1) = f (α + βXi + γModi + δCLTVi + πHPIz + θStates + izs )
Y is an indicator for whether or not the loan becomes 60 plus days
delinquent within 12 months of modification
X is a vector of loan characteristics from origination (loan used for
purchase, FICO score, owner-occupied, low or no documentation, and
origination year)
Mod is a vector of loan characteristics at the time of modification
(loan servicer, payment status, junior lien, and modification year)
Mod also includes key mortgage modification parameters (forbearance
% principal, % change in P&I, interest rate, and principal balance, %
capitalized fees and interest, HAMP mod)
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Methods
Empirical Methods
In each period, a loan can be in one of numerous mutually exclusive
states: current, delinquent (30, 60, 90+ days), lis pendens, REO,
foreclosure sale, prepaid, short-sale, and modification
So estimate discrete time proportional hazard model using
maximum-likelihood to account for these various potential states
Similar specification to probit, but include time varying characteristics
current combined loan to value ratio (CLTV) and state
unemployment rate
Also include number of months since modification
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Results
Results: Probit 60+ Days Delinquent within 12 months
Junior lien
Loan used for purchase
FICO at Origination 580 to 649
FICO at Origination 650 to 719
FICO at Origination 720 and Above
Not owner occupied
30 to 60 Days Delinquent at Mod
90 Days Delinquent at Mod
In Foreclosure at Mod
Year on Year Change in HPI
Unemployment Rate
Base Probability of 60 Day Delinquency:
t statistics in parentheses.
Schmeiser (Federal Reserve Board)
∗
p < 0.10,
∗∗
p < 0.05,
Mortgage Modifications
(1)
0.0284∗∗∗
(5.4123)
0.0402∗∗∗
(7.7463)
-0.0429∗∗∗
(-6.5733)
-0.1281∗∗∗
(-16.9011)
-0.2164∗∗∗
(-21.4534)
0.0328∗∗∗
(2.9566)
-0.0199∗∗
(-2.5476)
0.0892∗∗∗
(14.5294)
0.1210∗∗∗
(13.8086)
-0.0033∗∗∗
(-10.0149)
0.0142∗∗∗
(8.6610)
38.9%
∗∗∗
p < 0.01
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Results
Results: Probit 60+ Days Delinquent within 12 months
(cont)
Principal Forbearance as Pct of Balance
Percent Reduction in Principal
Percent Capitalized Fees and Int
Percent Reduction in Interest Rate
Percent Reduction in P&I
Percent Increase in P&I
LTV 100 to 124 Percent
LTV 125 to 149 Percent
LTV 150 Percent and Above
Base Probability of 60 Day Delinquency:
Observations
t statistics in parentheses.
Schmeiser (Federal Reserve Board)
∗
p < 0.10,
∗∗
p < 0.05,
Mortgage Modifications
-0.0004∗
(-1.6578)
-0.0014∗∗∗
(-4.4665)
0.0034∗∗∗
(9.0034)
-0.0019∗∗∗
(-12.0364)
-0.0021∗∗∗
(-11.2564)
0.0011∗∗∗
(3.4191)
0.0265∗∗∗
(3.3038)
0.0451∗∗∗
(4.8195)
0.0566∗∗∗
(5.6526)
38.9%
37027
∗∗∗
p < 0.01
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Results
Results: Multinomial Logit
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Results
Results: Multinomial Logit
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Results
Results: Multinomial Logit
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Results
Results: Multinomial Logit
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Results
Results: Multinomial Logit
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Results
Results: Multinomial Logit
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Conclusions
Conclusions
Any modification that improves the affordability of the loan reduces
redefault and foreclosure
Forbearance appears to reduce delinquency and foreclosure, but
magnitude is one-third that of a principal reduction
Same magnitude effect on REO
The capitalization of fees and interest significantly increases redefault
and foreclosure risk
CLTV has very large effect on subsequent redefault, foreclosure filing,
and termination in foreclosure
FICO score at origination still highly predictive of loan performance
even after a subsequent modification
Delinquency status at time of modification also highly predictive of
subsequent performance
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Conclusions
Policy Implications
Principal reductions may be quite effective as they affect redefault
and foreclosure through multiple channels
Direct effect through principal reduction coefficient
Effect on P&I
Reduces CLTV
However, this does not necessarily mean they are most cost effective
modifications from the investor’s point of view
Further analysis needed to determine relative cost of each type of
modification
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Contact Information
Contact Information
Maximilian D. Schmeiser
Senior Economist
Federal Reserve Board
Phone: 202-728-5882
Email: max.schmeiser@frb.gov
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The Effect of Negative Equity on Mortgage Default
Evidence from HAMP’s Principal Reduction Alternative
Therese Scharlemann (U.S. Office of Financial Research)
Stephen H. Shore (Georgia State University)
Views expressed in this presentation are those of the speaker(s) and not necessarily of the Office of Financial Research.
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Disclaimer: Views and opinions expressed are those of the
authors and do not necessarily represent official positions or
policy of the OFR or Treasury.
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Overview
Research Question: By how much does reducing the
amount of negative equity – by reducing the principal
balance – reduce mortgage default?
Data: Administrative data on HAMP PRA, a governmentsponsored program for delinquent borrowers.
Method: Exploit ‘quasi-experimental’ variation in amount of
principal forgiveness granted under HAMP PRA program
rules
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Preview of Results
Default ≡ program exit after 90+ days delinquency
Principal forgiveness averages 28% of principal balance
Observed quarterly default hazard: 3.1%
Counterfactual hazard absent PF: 3.8% (CI: 3.5-4.1%)
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Why might negative equity matter for default?
Neg equity → paying a price that is higher than realized
value
Vulnerable to economic shock; cannot sell home to satisfy
mortgage balance.
Holding current costs and benefits of defaulting constant
(e.g., monthly mortgage payment, home value, home price
expectations, etc).
Principal forgiveness increases future expected benefits of
not defaulting today.
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Home Affordable Modification Program (HAMP)
Government program to help delinquent borrowers
Voluntary for servicers/lenders with government subsidy
Delinquent borrowers are sent a letter about HAMP
Qualified borrowers can apply for a HAMP modification
Standard HAMP modifies mortgage terms until the
mortgage is affordable to the borrower
modification: reduce payments by reducing interest rate,
extending loan terms, and/or forbearing principal
affordability target: 31% debt-to-income (DTI) ratio
temporary: rate subsidies phase out beginning in 5 years
NPV test to ensure that modification benefits lender
Borrower completes 3 month trial period at their new
payment to receive "permanent modification".
Motivation
HAMP
Identification
Results
Conclusion
Appendix
HAMP PRA
HAMP Principal Reduction Alternative (PRA) modification
also includes principal forgiveness (PF)
same affordability target as Standard HAMP
LTV target: 115% LTV target (for 85% of loans) or a 100%
LTV target (for 15% of loans)
servicers with 100% LTV target limit PF to 30% initial UPB
Applicants may be offered Standard HAMP or HAMP PRA
Test to determine NPV to lenders. Servicers typically offer
modification (or no modification) with highest NPV.
3 month trial period at new payment
Program officially in place October 2010.
Enrollment
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Standard HAMP and HAMP PRA Waterfalls
Waterfall continues until borrower hits affordability target of
DTI=31%
Standard
HAMP
no reduction
in mortgage balance
HAMP
PRA
reduce mortgage balance
to hit LTV target
typically 115% LTV
reduce interest rate to 2%
extend loan term to 40 years
forbear principal as needed
equivalent to zero rate balloon
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Data overview
Administrative records for all new permanent HAMP
modifications enrolled 1/2011-6/2013.
45,513 loans, 244,132 loan-quarters
First cohort observable for 10 quarters.
Outcome variable of interest: 90-day delinquency
Borrowers disqualified from program if 90 days delinquent
HAMP database does not track final resolution
Measure default as quarterly hazard: new defaults as
share of surviving loans in each quarter.
Loans in sample have completed 3-month trial and
become permanent
Cannot reliably see mortgage characteristics of cancelled
trials.
About 10 percent of trial modifications are cancelled before
they become permanent - about 2/3rds due to nonpayment.
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Identification – Regression Kink Design (RKD)
LTV
Principal Forgiveness (PF) ≡ Initial
Final LTV − 1
= max{0, min{amount needed to hit DTI target,
amount needed to hit LTV target}}
PF needed to hit DTI target (PFDTI ):
PF needed to hit LTV target (PFLTV ):
Initial Mortgage DTI
Target Mortgage DTI
Initial LTV
Target LTV − 1
−1
Servicers with LTV target 1̄00% cap PF at 30% initial UPB.
Can control flexibly for LTV and DTI terms separately
LTV = loan-to-value (measures negative equity)
DTI = debt-to-income (measures ability to pay)
Total DTI = Mortgage DTI + Fixed DTI (taxes and insurance)
Target Mortgage DTI = 31% - Initial Fixed DTI
Motivation
HAMP
Identification
Results
Conclusion
Final LTV (red)
Identification (Moderate Initial DTI)
DTI limits PR
45º
LTV Target
PR
nt
u
o
Am
Initial LTV
Source: Authors’ analysis
Appendix
Motivation
HAMP
Identification
Results
Conclusion
Final LTV (red)
Identification (High Initial DTI)
DTI limits PR
45º
LTV Target
PR
nt
u
o
Am
Initial LTV
Source:Authors’ analysis
Appendix
Motivation
HAMP
Identification
Results
Conclusion
Cap limits PR
45º
LTV Target
PR
nt
u
o
m
A
DTI Would
Have Limited PR
Final LTV (red)
Identification (PRA-cap Kink)
Initial LTV
Source: Authors’ analysis
Appendix
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Identification: empirical model
Pr(Default) = f (α + βPF ln (1 + PF)
+ βLTV ln(1 + PFLTV )
+ βDTI ln(1 + PFDTI )
+ βX X + ε)
We are comparing the relationship between LTV and default on
either side of the kink.
RK treatment effect is calculated as the change in the slope at the
kink (βPF ) divided by the change in the first derivative of the
treatment at the kink (in this case, 1).
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Identification: Selection
PF allocation: We predict PF very well. R 2 in first-stage
regression is 0.99, with precise coefficient of 0.99.
Kink design: Difficult to argue that amount of PF is
correlated with unobservable propensity to default
(conditional on LTV, DTI, and servicer).
Selection: Do servicers select borrowers into PRA with
unobservably higher (or lower) propensity to default when
those borrowers would get higher PF amounts?
Servicer selection on unobservables is difficult for services
because they must follow rules based on observables.
Borrower selection at application on unobservables unlikely
given kink design (borrowers don’t know on which side they
will land when they apply)
Borrower selection possible between receiving trial
modification or offer and before permanent modification
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 1: Impact of Principal Forgiveness on Hazard (Logit)
Dep. Var.
ln(1+PF)
ln(1 + PFLTV )
ln(1 + PFDTI )
Dummy for LTV target = 115 percent
Quarters of observation controls
Quarter cohort and servicer dummies
DTI before modification
Interaction variable
Other controls
10-ppt LTV and DTI bins
Observations
Loans
R2
-0.85***
(0.13)
0.38***
(0.09)
-1.07***
(0.05)
YES
YES
NO
NO
NO
NO
NO
244,132
45,513
0.0289
Quarterly exit rate
-0.54***
-0.61***
-0.65***
(0.13)
(0.14)
(0.16)
0.23**
0.11
0.55***
(0.09)
(0.11)
(0.13)
0.42***
0.31***
0.26**
(0.05)
(0.08)
(0.10)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
NO
NO
YES
NO
NO
NO
244,132
244,132
211,409
45,513
45,513
39,310
0.0520
0.0520
0.0808
-0.87***
(0.25 )
-0.16
(0.67 )
1.77**
(0.73)
YES
YES
YES
YES
YES
YES
YES
210,712
39,172
0.0826
(All specifications include an LTV target control and quarters-of-observation dummies. "Other Controls": FICO, ARM dummy,
investor-owned mortgage dummy, log income, log pre-mod balance, length of trial (linear and squared), log NPV of HAMP mod over no
mod, log NPV of HAMP PRA mod over no mod, dummy for whether the standard HAMP mod had a higher NPV than the PRA mod.)
Source: Authors’ analysis.
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Context for Results
Logit coefficient for ln(PF) to predict the quarterly hazard is
-0.54 to -0.87.
The average quarterly hazard is is ∼ 3.1% for the HAMP
PRA loans in this sample.
A 10% PF at mean implies 0.2 to 0.3 percentage points
drop in the quarterly hazard rate (e.g., from 3.8% to 3.5%
or 3.6%).
The average PF of 30% implies a drop in the quarterly
hazard rate from 3.8% to 3.1%
Motivation
HAMP
Identification
Results
Conclusion
Counterfactual: Cumulative default with and without PF
Cumulative Default Rate by Quarter of Modification
0.60
‐ Average hazard with PR: 3.1%
‐ Average hazard without PR: 3.8% ‐ Confidence inverval: [3.5%,4.1%]
0.50
0.40
0.30
0.20
0.10
0.00
2011:Q12011:Q22011:Q32011:Q42012:Q12012:Q22012:Q32012:Q42013:Q12013:Q2
Quarter of Modification
Proportion of modifications that have exited through 2003:Q1 (green line)
Counterfactual proportion of modifications exiting through 2013:Q1 (blue line)
Green – Observed Rate
Blue – Counterfactual Rate Absent Principal Forgiveness
Source: Authors’ analysis
Appendix
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Figure: Estimates for 115 LTV Target Sample
0.45
0.35
Principal forgiveness increases with pre‐modification LTV. Post‐modification LTV unchanged as LTV increases.
Principal forgiveness unchanged as pre‐
modification LTV increases. Post‐modification LTV increases with pre‐modification LTV.
0.25
0.15
0.05
‐0.05
‐0.15
‐0.7 ‐0.6 ‐0.5 ‐0.4 ‐0.3 ‐0.2 ‐0.1
0
0.1
ln(PFLTV) ‐ ln(PFDTI)
Source: Authors’ analysis
0.2
0.3
0.4
0.5
0.6
0.7
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 2: Impact of Principal Forgiveness, kink analysis (logit)
Dep. Var.
Sample
ln(1 + PF )
ln(1 + PFLTV ) PR
ln(1 + PFDTI )
Observations
Loan count
R2
Source: Authors’ analysis
Full
-0.851***
(0.129)
0.380***
(0.089)
-0.539***
(0.031)
244,132
45,513
0.029
Quarterly exit rate
Uncapped
Kink ≤ 0.5
-0.748***
-0.566**
(0.145)
(0.225)
0.308***
0.112
(0.105)
(0.138)
-0.479***
-0.451***
(0.039)
(0.043)
216,407
143,851
41,339
27,641
0.0308
0.0213
Kink ≤ 0.25
-0.421
(0.519)
-0.242
(0.307)
-0.448***
(0.056)
83,913
16,200
0.0182
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 3: Placebo tests
Dep. Var.
Sample
ln(1+PF)
ln(1 + PFLTV )
ln(1 + PFDTI )
Full
-0.851***
(0.129)
0.380***
(0.089)
-1.072***
(0.045)
ln(placebo PF)
Dummy for LTV target = 115
Quarters of observation controls?
Full set of controls?
Observations
Loan count
R2
YES
YES
NO
244,132
45,513
0.029
Quarterly exit rate
Full
Full
Placebo
-0.911***
-0.920***
0.084
(0.133)
(0.264)
(0.128)
0.328***
-0.121
0.343***
(0.094)
(0.675)
(0.085)
-1.114***
1.828**
-1.658***
(0.052)
(0.737)
(0.044)
0.282*
-0.124
(0.144)
(0.252)
YES
YES
YES
YES
YES
YES
NO
YES
NO
244,132
210,712
293,962
45,513
39,172
52,862
0.039
0.083
0.042
Placebo
-0.035
(0.225)
0.351
(0.539)
0.231
(0.609)
YES
YES
YES
278,298
48,378
0.080
("Full controls": cohort and servicer dummies, DTI before mod, DTI and LTV bins, FICO, ARM dummy, investor-owned mortgage dummy,
log income, log pre-mod mortgage balance, trial mod length (linear and squared), log NPV of HAMP mod over no mod, log NPV of
HAMP PRA mod over no mod, and a dummy indicating a standard HAMP mod had a higher NPV than a HAMP PRA mod.)
Source: Authors’ analysis
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Figure: Within Estimates for 115 LTV Target Sample - Placebo
0.45
0.35
Principal forgiveness increases with pre‐modification LTV; Post‐modification LTV unchanged as LTV increases
Principal forgiveness unchanged as pre‐
modification LTV increases; Post‐modification LTV increases with pre‐modification LTV
0.25
0.15
0.05
‐0.05
‐0.15
‐0.25
‐0.35
‐0.7 ‐0.6 ‐0.5 ‐0.4 ‐0.3 ‐0.2 ‐0.1
0
0.1
ln(PFLTV) ‐ ln(PFDTI)
Source: Authors’ analysis
0.2
0.3
0.4
0.5
0.6
0.7
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Conclusion: Comparison with Existing Benchmarks
Logit coefficient for ln(1 + PF) to quarterly hazard is -0.54
to -0.87.
The average quarterly hazard is ∼ 3.1% for HAMP PRA.
∼ 30% PF reduces average hazard from 3.8% to 3.1%.
∼ 30% PF reduces cumulative default rate from 49% to
39% in earliest cohort.
Impact of PF may not remain constant over time.
Modifications with rate reduction get less generous after 5
years, so a spike in default for lower-PF (higher rate
reduction) modifications is possible.
Improvements in the housing market may make principal
forgiveness less important.
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Conclusion: A simplified discussion of costs vs. benefits of PF
Cost of PF: 30% × (1 − lifetime re-default rate)
Benefit of PF: (lifetime change in re-default rate) ×
(difference between modified & recovery values)
Cost-effectiveness of PF will increase/decrease if:
default-reducing benefits of PF grow/narrow over time
and/or
Counterfactual default rate increases/decreases.
First cohort: change in default rate implies $877k in
writedowns per avoided foreclosure.
Ten loans written down for every avoided foreclosure.
Government subsidy of $262k per avoided foreclosure.
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Evidence of bunching: Loans whose servicers do not apply cap
Distribution of PRA Recipients Not Subject to Cap
0
.2
.4
Density
.6
.8
1
by Distance from Kink
-1
0
1
Distance from Kink
2
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Evidence of bunching: Loans whose servicers apply cap
Distribution of PRA Recipients Subject to Cap
0
.5
Density
1
1.5
2
by Distance Between Uncapped and Capped PRA Amounts
-.4
-.2
0
.2
.4
Distance Between Uncapped and Capped PRA
.6
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Context for Results
Cost of PF:
(PF amount) ×(1 − permanent default rate)
Benefit of PF: (permanent change in default rate) ×
(severity of loss in default, e.g. 50% of mortgage)
∼ 30% PF reduces average hazard from 3.8% to 3.1%.
∼ 30% PF reduces cumulative default rate from 49% to
39% in earliest cohort.
To be cost effective absent subsidies or externalities
default-reducing benefits of PF would have to grow over
time relative to the baseline default rate and/or
baseline lifetime default rate would have to be very high
absent PF
return
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Figure 2: HAMP Enrollment
Cumulative enrollment of non‐GSE loans from Jan 2011 ‐ Dec 2012 Thousands
300
HAMP with Principal Reduction
Standard HAMP
250
200
150
100
50
0
Jan
Mar
May
Jul
2011
return
Sep
Nov
Jan
Mar
May
Jul
2012
Sep
Nov
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 1: Summary Statistics About Loans and Borrowers
Balance pre-mod (0 000s)
Home value (0 000s)
Gross monthly income (0 000s)
Total mortgage payment (0 000s)
Principal & interest payments (0 000s)
FICO
N
(0 000s)
45.5
45.5
45.5
45.5
45.5
41.8
Med.
Mean
SD
Min
Max
$292
$180
$4.5
$2.0
$1.6
559
$324
$205
$4.9
$2.2
$1.8
568
$173
$118
$2.39
$1.05
$0.90
69
$18
$10
$0.6
$0.3
$0.2
250
$1,279
$856
$22.2
$9.5
$9.0
839
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 2: Summary Statistics About Modifications
Total DTI pre-mod
Total DTI post-mod
Total payment reduction
LTV before modification
LTV after modification
Principal balance reduction
Rate pre-mod
Rate post-mod
Term pre-mod (months)
Term post-mod (months)
N
(0 000s)
45.5
45.5
45.5
45.5
45.5
45.5
45.5
45.5
45.5
45.5
Med.
Mean
SD
Min
Max
44.2%
31.0%
29.8%
158.8%
115.0%
28.2%
6.4%
3.0%
305
301
47.0%
31.0%
30.0%
164.2%
121.2%
28.9%
6.3%
3.9%
317
333
12.3%
0.4%
16.0%
33.5%
16.1%
17.4%
2.0%
2.1%
60
76
24.0%
12.4%
0.0%
109.8%
100.0%
0.0%
0.8%
1.0%
1
12
100.0%
33.0%
69.0%
240.0%
237.7%
73.5%
15.1%
15.0%
541
541
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Figure: Treatment for 115 LTV Target Sample
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 4: Impact of PF, Between and Within Estimates
Dep. Var.
Sample
Identification
ln(1+PF)
ln(1 + PFuncapped )
ln(uncapped PF) ln(capped PF)
ln(1 + PFLTV )
ln(1 + PFDTI )
ln(LTV initial)
LTV bin x PRA from DTI bin
Observations
Loan count
R2
Quarterly default rate
Full
LTV Target of Sample
Sample
115%
100%
Full
Between
Within
Within
-0.851***
-1.130***
-0.615***
(0.129)
(0.224)
(0.163)
-0.925**
(0.387)
-0.486
(0.481)
0.380***
0.182
0.688***
(0.089)
(0.124)
(0.183)
-1.072***
0.577
-1.184***
-0.777***
(0.045)
(0.659)
(0.058)
(0.115)
0.502
(0.670)
No
Yes
No
No
244,132
244,132
200,146
43,876
45,513
45,513
38,513
7,000
0.0289
0.0332
0.0261
0.0209
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 7:Estimates in subsamples (logit)
Dep. Var.
Sample
ln(1+PF)
ln(1 + PFLTV )
ln(1 + PFDTI )
Dummy for LTV target = 115
Quarters of observation controls?
Observations
Loan count
R2
Full
-0.851***
(0.129)
0.380***
(0.089)
-1.072***
(0.045)
YES
YES
244,132
45,513
0.0289
Quarterly exit rate
ARM
FRM
PLS
-0.945***
-0.771***
-0.796***
(0.210)
(0.167)
(0.165)
0.504***
0.352***
0.518***
(0.145)
(0.115)
(0.108)
-0.842***
-1.146***
-1.053***
(0.071)
(0.063)
(0.056)
YES
YES
YES
YES
YES
YES
105,265
138,867
146,271
18,867
26,646
29,412
0.0225
0.0322
0.0336
Portfolio
-0.728***
(0.218)
0.144
(0.161)
-1.099***
(0.079)
YES
YES
97,861
16,101
0.0247
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Table 6: Variation in estimates (logit)
Dep. Var.
ln(1+PF)
ln(1+PF)
x ln(initial TDTI)
ln(1+PF)
x quarters since mod
quarter of mod
-0.851***
(0.129)
-0.228
(0.325)
0.487
(0.349)
Quarterly exit rate
-0.817*** -0.924***
(0.187)
(0.203)
-0.010
(0.039)
-0.095***
(0.013)
ln(1+PF)
0.039
x quarter of mod
(0.045)
Dummy for LTV target = 115
YES
YES
YES
YES
Quarters of observation controls?
YES
YES
YES
YES
Observations
244,132
244,132
244,132
244,132
Loan count
45,513
45,513
45,513
45,513
R2
0.029
0.039
0.029
0.031
NB: ln(Predicted PF)≡ ln(Pre-Mod LTV)− ln(Predicted Post-Mod LTV)
-0.950***
(0.284)
0.006
(0.041)
-0.096***
(0.014)
0.041
(0.047)
YES
YES
244,132
45,513
0.031
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Counterfactual: hazard by modification duration, with and without
PF
Hazard by Quarters Since Modification
(Quarterly rate of exit from program)
0 06
0.06
0.05
0.04
0.03
0.02
0.01
0.00
1
2
3
4
5
6
7
Quarters since Modification
8
Observed hazard (green)
Counterfactual hazard absent principal reduction (blue)
9
10
Motivation
HAMP
Identification
Results
Conclusion
Appendix
Counterfactual: hazard by calendar quarter, with and without PF
Hazard by Calendar Quarter
(Quarterly rate of exit from program)
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
2011:Q2 2011:Q3 2011:Q4 2012:Q1 2012:Q2 2012:Q3 2012:Q4 2013:Q1 2013:Q2 2013:Q3
Calendar Quarter
Observed hazard (green line)
Counterfactual hazard absent principal reduction (blue line)
Mortgage Modifications using Principal
Reduction: How Effective Are They?
Ben Keys
Harris School of Public Policy
University of Chicago
August 11, 2015
What we talk about when we talk about
mortgage modification
• Goals:
• Help struggling homeowners continue to maintain consumption
• Effect of losing $5 trillion (!) in home equity
• Avoid foreclosure by “curing” delinquent borrowers
• Prevent neighborhood-level spillovers from neglect/abandonment
• Campbell, Giglio, and Pathak (2011)
• Why do homeowners default?
• Income disruptions or shocks to liquidity
• Bad underwriting or contract-related shocks
• Strategic incentives to “walk away” when sufficiently underwater
• Bhutta, Dokko, and Shan (2010) suggest relevant only when LTV>150%
Key Research Questions
• How do we help homeowners stop the transition from
default to foreclosure?
• These papers unpack the mechanisms of modification effectiveness
• Are modifications cost-effective?
• Huge costs to the government to facilitate modifications
• Depends on which features are used (e.g. principal reduction)
• Depends on how we estimate the benefits
• Are principal reductions the solution if temporary income
disruptions are the problem?
Schmeiser and Gross (2015)
• Data from CoreLogic on subprime and Alt-A loans
• Modifications between 2008 and 2013 (HAMP and non-HAMP)
• Many mods increase principal balance by rolling in fees/interest
• Almost 20% of mods increase payments (!) through post-due capitalization
• Hazard model approach to estimate impact of modification
features on re-default
• Baseline characteristics at time of modification matter a lot
• Delinquency status, FICO and especially CLTV
• Forbearance effect is 1/3 that of principal reduction
• Principal reduction helps through multiple channels
• Reduces CLTV (strategic incentive)
• Reduces monthly payment at same time
Challenges for Studying Modification
• Size and elements of modification are endogenous to pre-
modification characteristics of borrower and housing market
• e.g. why is initial CLTV so predictive of subsequent default?
• Strategic default, or
• Homeowners who reached high CLTV did so through low down payments,
bad contracts (e.g. NegAm), or aggressive extraction combined with
negative regional shocks
• Servicers may be strategically selecting those loans most
likely to succeed
• Results are conditional on choice to modify
• Servicers can design their own program or opt into HAMP program
Scharlemann and Shore (2015)
• Use data from HAMP PRA program and kinks in program design
used to determine amount of principal forgiveness
• Non-GSE loans enrolled in 2011-2013: $3.8b in total forgiveness
• Average loan receives 28% reduction in principal
• Kinks in design affect different borrowers in terms of the amount of
principal reduction
• Based on distance from kink in principal reduction schedule
• Avoids endogeneity problems
• Requires assumptions about similar borrowers on either side of the kink,
and relatively farther away as well to estimate slopes
• Useful placebo test where default increases monotonically in pre-mod LTV
• Cost estimate: $877,000 per foreclosure avoided (!)
• Cost to government: $262,000 per foreclosure avoided
Placebo test figure nails effect of PRA:
Consensus View:
Larger Payment Reduction, Fewer Defaults
• This is true almost by construction
• Need a better understanding of why people default in order
to target modifications and modification features
• If people are liquidity constrained, reducing principal with no change
in payment should not matter on the margin
• Only affects wealth, not liquidity
• Holy grail of data: 1)Debts, 2)Assets, 3)Income, 4)Consumption
• If we knew more about income shocks, we could better design
temporary assistance programs
• If we knew more about consumption, we can see whether programs
had their intended effect outside of the mortgage contract alone
Efficient Credit Policies in a Housing Debt Crisis
• Eberly and Krishnamurthy (BPEA 2014) model:
• Government’s priority should be helping cash-strapped homeowners
• 1) Maintain spending in a weak economy
• 2) Avoid foreclosures
• Some role for principal reduction to reduce strategic default motives
• But costly and spreads benefits over much longer period of time
• Much larger role for temporary efforts to lower monthly payments:
• Defer/forbear mortgage payments
• Reduce interest rates
• Extend mortgage terms
• Propose redesigning mortgages with automatic adjustments
• “Allow for disproportionately lower payments when borrowing constraints bind”
Concluding Thoughts
• Two fantastic examples of careful empirical work to answer
hugely important policy question
• Building consensus around view that principal forgiveness
received limited bang for its buck
• Alternatives
• Large-scale forbearance and refinancing programs
• Automatic refinancing contracts
• Letting some homeowners fail in a low-cost way (own-to-rent?)
• Final note that mortgage programs missed some of the neediest
households
• e.g. NPV-negative because unemployed, failed trial mod, failing to take-
up HARP
More notes on Schmeiser and Gross
• Unemployment rate coefficient is negative in paper (positive in slides)
• MNL results on unemployment and HPI flip sign depending out mortgage
outcome, this is puzzling
• How to think about static vs. time-varying characteristics
• Most importantly CLTV, HPI, and unemployment rate
• Why are HAMP modifications more effective even after conditioning
on terms?
• Has to be something about selection, right?
• Or omitted variables?
• Can be more up-front about endogeneity concerns
More notes on Scharlemann and Shore
• Introduction undersells the result
• Doesn’t fully explain how important the finding is or put it in a broader context of the literature, and
varies across too much detail and too little
• Identification could use a clearer simpler example
• Terms of forbearance: How long does it last? 5 years?
• Why does PRA have the “reverse waterfall” relative to HAMP? How does 2MP fit in?
• Median FICO at time of modification is only 560: Should we expect any of these
people to cure?
• Why are the signs different on PF from LTV and PF from DTI?
• Shouldn’t a dollar in PF be a dollar regardless of where it comes from? This is still unclear
• Is counterfactual exercise of no principal forgiveness but same payment reduction
way out of sample?
• Is this all just substituting forgiveness for forbearance?