Mortgage Moratoria: Buying Time or Delaying the …jmcollin/nyu.pdf · Mortgage Moratoria: Buying...

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ImplementationConclusions

Mortgage Moratoria: Buying Time or Delayingthe Inevitable?

J. Michael Collins1 Carly Urban2

1Assistant ProfessorDepartment of Consumer ScienceUniversity of Wisconsin-Madison

2Assistant ProfessorDepartment of Agricultural Economics and Economics

Montana State University

April 2013

J. Michael Collins, Carly Urban Moratorium

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The Mortgage Foreclosure Problem

Foreclosure starts are high, 4.4% of mortgages inforeclosure

Media cries “The Floodgates have Opened"; “ManyForeclosures, Few Listings" (NYT, 2011)

Have been many policy responsesMandatory Mediation SessionsCounseling (HOPE Hotline)Home Affordable Modification Program (HAMP)


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The Mortgage Foreclosure Problem

Foreclosure starts are high, 4.4% of mortgages inforeclosure

Media cries “The Floodgates have Opened"; “ManyForeclosures, Few Listings" (NYT, 2011)

Have been many policy responsesMandatory Mediation SessionsCounseling (HOPE Hotline)Home Affordable Modification Program (HAMP)


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Moratoria on Filings: Another Policy Response

Mortgage MoratoriumAt least 16 states (or localities within) have proposed amoratorium on mortgage foreclosuresFederal Government proposal of mortgage moratoriumMoratoria in response to Robo-Signing

Mortgage Moratoria in HistoryGreat Depression (Wheelock, 2008, FRB St. LouisReview)Farm Foreclosure (Alston 1984, AER); (Alston & Rucker,1987, AER)Katrina, Rita, Irene (CRS Report); (Davis, 2006, USDA)


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Moratoria on Filings: Another Policy Response

Mortgage MoratoriumAt least 16 states (or localities within) have proposed amoratorium on mortgage foreclosuresFederal Government proposal of mortgage moratoriumMoratoria in response to Robo-Signing

Mortgage Moratoria in HistoryGreat Depression (Wheelock, 2008, FRB St. LouisReview)Farm Foreclosure (Alston 1984, AER); (Alston & Rucker,1987, AER)Katrina, Rita, Irene (CRS Report); (Davis, 2006, USDA)


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This Paper

1 Study the outcomes associated with “delay."Zhu and Pace (2010, WP)⇒ Delay increases probability ofdefault.Gerardi et al. (2012, JUE)⇒ Those with longer timelinesno more likely to cure or renegotiate.Collins et al. (2011, JPAM)⇒ Delay increases probability ofmodifying, but no more likely to cure.

2 NJ state-wide moratorium on 6 servicers for 7 monthsgives us a case study with a natural experiment setting.Compare loans:

Before and after implementation of the policy within NJ.Servicers subject to and not subject to the moratorium.Within same MSAs that overlap NJ and a border state.Gives us a Difference-in-Difference-in-Differences (DDD)setup with which to analyze the policy.


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This Paper

1 Study the outcomes associated with “delay."Zhu and Pace (2010, WP)⇒ Delay increases probability ofdefault.Gerardi et al. (2012, JUE)⇒ Those with longer timelinesno more likely to cure or renegotiate.Collins et al. (2011, JPAM)⇒ Delay increases probability ofmodifying, but no more likely to cure.

2 NJ state-wide moratorium on 6 servicers for 7 monthsgives us a case study with a natural experiment setting.Compare loans:

Before and after implementation of the policy within NJ.Servicers subject to and not subject to the moratorium.Within same MSAs that overlap NJ and a border state.Gives us a Difference-in-Difference-in-Differences (DDD)setup with which to analyze the policy.


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Purpose of the Moratorium

Today’s actions are intended to provide greater confidence thatthe tens of thousands of residential foreclosure proceedingsunderway in New Jersey are based on reliable information.

Chief Justice Rabner, NJ (December 20, 2010)


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NJ Timeline

submit paperwork for “questionable practices"BOA, JP Morgan, Citi, GMAC, OneWest, Wells

Moratorium in NJ Commencesfor 6 Servicers

Moratorium Extended

Moratorium Concludes

Sep 2010 Nov 2010 Dec 2010 June 2011 Aug 2011


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NJ Moratorium


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Data

Corporate Trust Services (CTS) Mortgage DataWells Fargo Bank serves as trustee for pools of loans inMBS issues

Monthly panel of remittance reports

Observe location (zip code) and loan terms (rate, balance,term)Only owner occupied, single family, first lien mortgagesimpacted by policies

Other DataMerge HMDA Data with CTS Data, match 80% forindividual characteristicsObtain zip code level house prices from Zillow


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Summary Statistics: Border and NJ Loans StatisticallyDifferent

Border NJ TotalDependent VariablesModification Indicator 0.0383 0.0301 0.0357

(0.1920) (0.1709) (0.1856)Begin Foreclosure 0.0586 0.0660 0.0610

(0.2349) (0.2482) (0.2393)Delinquent this month 0.2154 0.1913 0.2077

(0.4111) (0.3934) (0.4057)Control VariablesARM Indicator 0.5131 0.6081 0.5434

(0.4998) (0.4882) (0.4981)Interest Rate 5.7906 5.5905 5.7267

(1.8027) (1.8319) (1.8145)Home Value (thousands) 405.9109 343.0784 385.8365

(215.6167) (179.2444) (206.7855)Income (thousands) 125.2124 127.8955 126.0696

(71.4510) (72.3932) (71.7641)Origination Year 2005.3740 2005.3734 2005.3738

(0.8951) (0.8606) (0.8843)FICO (divided by 100) 6.9027 6.9333 6.9124

(0.6741) (0.6535) (0.6678)Minority 0.4747 0.4379 0.4630

(0.4994) (0.4961) (0.4986)Observations 146927 68311 215238

Means reported. Standard deviations in parentheses.J. Michael Collins, Carly Urban Moratorium

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Summary Statistics: Treatment and Control ServicersStatistically Different

Control Servicer Treatment Servicer TotalDependent VariablesModification Indicator 0.0400 0.0138 0.0353

(0.1959) (0.1167) (0.1846)Begin Foreclosure 0.0612 0.0426 0.0579

(0.2398) (0.2019) (0.2336)Delinquent this month 0.2186 0.1672 0.2095

(0.4133) (0.3732) (0.4069)Control Variables

ARM Indicator 0.5747 0.4845 0.5587(0.4944) (0.4998) (0.4965)

Interest Rate 5.6858 5.6653 5.6821(1.9157) (1.4927) (1.8476)

Home Value (thousands) 373.2636 460.5414 388.7815(201.8422) (213.4075) (206.6579)

Income (thousands) 121.9162 150.4314 126.9862(69.9446) (77.7567) (72.2236)

Origination Year 2005.3479 2005.4365 2005.3636(0.9017) (0.8264) (0.8894)

FICO (divided by 100) 6.8826 7.1953 6.9364(0.6478) (0.4874) (0.6342)

Minority 0.4762 0.3868 0.4604(0.4994) (0.4870) (0.4984)

Observations 147806 31659 179465

Means reported. Standard deviations in parentheses.J. Michael Collins, Carly Urban Moratorium

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Diff-in-Diff-in-Diff

Yi,s,t,j = α0 + β1TTt + β2NJs + β3TSj + γ1(TTt × TSj) + γ2(TTt × NJs)

+ γ3(NJs × TSj) + δ(TTt × NJs × TSj) + λX i + ηt + κMSA + εi,s,t,j

Y: Modification or Foreclosure Start indicator (if delinquent), Delinquentin current month

TT: Period before/after moratorium began (Before vs. After, Before vs.During, During vs. After)

TS: Loan with servicer subject to moratorium

NJ: Loan in NJ

κ: MSA fixed effects

η: Month fixed effects

X: Loan/applicant level controls


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NJ Moratorium: Filings

Difference in Total Filings Between NJ and Border MSA byDate, Servicer Type


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NJ Moratorium: Modifications

NJ Difference in Average Modification Rates Between NJ andBorder MSAs By Date, Moratorium Servicer


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NJ Moratorium: Delinquency

NJ Difference in Average Delinquency Rates Between NJ andBorder MSAs By Date, Moratorium Servicer


DDD: Probit Results

Marginal Effects Reported: What did the moratorium do?

Baseline With Controls(1) (2) (3) (4) (5) (6)

Modification Fcl Start Delinq Mod Fcl Start DelBefore vs. After the Moratorium

Treatment 0.0437 0.0587** -0.0104 0.0287 0.0906*** -0.0090(0.0346) (0.0243) (0.0093) (0.0273) (0.0253) (0.0105)

Observations 71938 71938 281625 51088 51088 207328

Before vs. During the Moratorium

Treatment 0.0135 0.0192 0.0214* 0.0152 0.0155 0.0216*(0.0321) (0.0301) (0.0116) (0.0324) (0.0301) (0.0115)

Observations 62460 62460 257688 61778 61778 253967

During vs. After the Moratorium

Treatment 0.0348 0.0310 -0.0339*** 0.0250 0.0804*** -0.0319***(0.0348) (0.0281) (0.0109) (0.0334) (0.0292) (0.0120)

Observations 62245 62245 235313 42652 42652 165479

Controls IncludedMSA Fixed Effects - - - X X XMonth Fixed Effects - - - X X XLog(Home Value) - - - X X X

DD: Probit Results | TREATED Servicer

Loan-Level Controls Loan and Applicant-Level Controls(1) (2) (3) (4) (5) (6)

Mod Fcl Del Mod Fcl DelBefore vs. After the Moratorium

Treatment 0.0232*** 0.0206* -0.0063 0.0322*** 0.0475*** -0.0018(0.0073) (0.0124) (0.0050) (0.0067) (0.0145) (0.0057)

Observations 29092 29092 122743 16006 16183 69940

Before vs. During the Moratorium

Treatment 0.0048 -0.0463*** -0.0069 0.0140** -0.0441*** -0.0002(0.0069) (0.0140) (0.0052) (0.0064) (0.0151) (0.0052)

Observations 18016 18016 94054 15117 15296 74132

During vs. After the Moratorium

Treatment 0.0213*** 0.0755*** 0.0001 0.0273*** 0.1020*** -0.0018(0.0078) (0.0143) (0.0059) (0.0084) (0.0168) (0.0066)

Observations 25558 25558 101916 13533 13653 55252

Controls IncludedMSA Fixed Effects X X X X X XMonth Fixed Effects X X X X X XLog(Home Value) X X X X X XARM X X X X X XLog(Loan Amount) X X X X X XInterest Rate X X X X X XLog(income) - - - X X XMinority - - - X X XFICO - - - X X X

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Recap of Results

Modifications increased due to the moratoriumDelinquency rates up slightly in some modelsHowever, after the moratorium concluded, those subject tothe moratorium caught up on payments and reduceddelinquency


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Robustness

Results are Robust to:Including applicant-level controls such as income, race,FICO from HMDA (though this decreases sample size andincreases standard errors).Other functional form: results from LPM consistent.Including servicer level fixed effects or drop each servicerone by one.


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Limitations

Measuring off of a potentially low-start of foreclosure filings(in the pre-period).Do not know who “should" receive a modification or cure.What is happening on the supply side?

Hard to say if foreclosure is good or bad for either partyBut modification suggests both seek to continue contract

Generalizable?


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Conclusions

Did the moratorium work in NJ?It certainly changed borrower and lender incentives.People modified behavior to deal with the policy.

Is the policy generalizable to other states?Other judicial states with similar housing characteristicslikely to see similar effects.

Is the policy generalizable to other servicers?Both borrowers and courts seem in tune with policies aimedat any servicer.Unique to robo signing issue?


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Contact

J. Michael CollinsCenter for Financial Securityjmcollins @ wisc.eduTwitter: @jmcollinswisc

cfs.wisc.edu


cfs.wisc.edu

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