The impact of local predatory lending laws north carolina and beyond not all laws are created equal
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The Impact of Local Predatory Lending Laws: North Carolina and Beyond (not all laws are created equal). Giang Ho & Anthony Pennington-Cross Federal Reserve Bank of St. Louis Disclaimer

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The impact of local predatory lending laws north carolina and beyond not all laws are created equal

The Impact of Local Predatory Lending Laws: North Carolina and Beyond (not all laws are created equal)

Giang Ho

&

Anthony Pennington-Cross

Federal Reserve Bank of St. Louis

Disclaimer

The views expressed in this research are those of the individual author(s) and do not necessarily reflect the official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, and the Board of Governors.


Outline
Outline

  • Introduction

    • Spread of local laws

    • HOEPA style

  • Why Do the Laws Focus on Subprime?

  • Research Questions

  • Measuring the Strength of the Laws

  • Empirical Approach

  • Does Law Strength Matter?

  • Conclusions


Introduction spread of local laws
IntroductionSpread of Local Laws


Introduction federal regulations
IntroductionFederal Regulations

  • Home Ownership and Equity Protection Act (HOEPA)

    • Regulation Z

      • Refinance and 2nd mortgages only

      • Coverage Triggers

        • APR

          • 8% 1st lien

          • 10% 2nd lien

        • Points and fees

          • Indexed to inflation

      • Restrictions

        • Short-term balloon notes

        • Prepayment penalties greater than 5-years

        • Non-amortizing schedules

        • Refinance HOEPA to HOEPA in 1st 12 months

        • Impose higher interest rate upon default

        • No-documentation loans


Introduction local predatory lending laws
IntroductionLocal Predatory Lending Laws

  • State, county, and city

  • States most successful in surviving legal challenges

  • At least 24 states w/ HOEPA style (end of 2004)

    • Arkansas, California, Colorado, Connecticut, Florida,

    • Georgia, Illinois, Kentucky, Maine, Maryland,

    • Massachusetts, Nevada, New Jersey, New Mexico,

    • New York, North Carolina, Ohio, Oklahoma, Pennsylvania,

    • South Carolina, Texas, Utah, and Wisconsin

  • Typically extends coverage

    • Purchase loans

    • Lower triggers

      • APR & Fees

  • Typically extends restrictions

    • Prepayment penalties

    • Balloons

    • Require counseling

    • For details see http://research.stlouisfed.org/wp/2005/2005-049.pdf

      • Appendix A

      • Butera and Andrews WDC law firm

      • www.butera-andrews.com


Why do the laws focus on subprime
Why Do The Laws Focus On Subprime?



Source: Freddie Mac’s Primary Mortgage Market Survey for Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


Prime 90 day delinquency rates mbaa not seasonally adjusted
Prime 90-Day Delinquency Rates Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).(MBAA,not seasonally adjusted)


Subprime 90 day delinquency rates mbaa not seasonally adjusted
Subprime 90-Day Delinquency Rates Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).(MBAA, not seasonally adjusted)


Foreclosure in progress rate
Foreclosure In-Progress Rate Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


Why focus on subprime
Why Focus on Subprime? Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

  • Interviews

    • HUD,

    • Treasury, and

    • the Federal Reserve Board

  • Some, perhaps many,

    • borrowers using high-cost loans may not have understood

      • rights and the terms of the mortgage

  • Makes it possible to

    • take advantage of the borrower


Why Focus on Subprime? Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

  • Great Promise & Great Peril

    • Opportunity for Homeownership

      • Asset Building

        • Typical household holds no corporate equity (Tracy, et al 1999)

        • Positive Effects

          • Neighborhood & Children

    • Risky Loans

      • High default & high prepayment (Pennington-Cross, 2003)

      • Costly

      • Higher than prime loss severity (Capozza and Thomson, 2005)

    • Vulnerable Population

      • Less educated (Courchane, Surette, and Zorn, 2004)

      • Less knowledgeable about mortgages (Courchane, Surette, and Zorn, 2004)

    • Geographically Concentrated

      • Low income and minority areas (Calem, Gillen, and Wachter, 2004)

      • Economically challenged areas (Pennington-Cross, 2002)

  • What is a Socially Acceptable Failure Rate?

    • Predatory Lending Laws


Research questions
Research Questions Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


  • Did the North Carolina Law Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).(Ernst, Farris, and Stein 2002; Quercia, Stegman, and Davis 2003 & 2004; Harvey and Nigro 2004; and Elliehausen and Staten 2004)

    • Reduce originations of subprime loans?

      • Yes

    • Reduce applications for subprime loans?

      • Yes

    • Increase or decrease rejections?

      • No impact / mixed

    • Similar findings for Chicago and Philadelphia(Harvey and Nigro 2003)

  • Research Questions

    • Do the findings in NC apply to other laws?

    • Does the strength of the law matter?

      • Restrictions and coverage

    • Is there a regulatory cost?

      • Passed onto consumers (future work)


Measuring the strength of the laws
Measuring the Strength of the Laws Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


Coverage index
Coverage Index Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

  • Loan Purpose

    • HOEPA equivalent=0

    • all loans except -- no government loans=1

    • all loans except -- no reverse, or no open ended loans=2

    • all loans except -- no reverse, business, or construction loans =3

    • all loans with no exceptions=4

  • APR Trigger 1st Lien

    • 8%, HOEPA equivalent =0,

    • 7%=1,

    • 6%=2, and

    • no trigger=3

  • APR Trigger Higher Lien

    • 10%, HOEPA equivalent =0,

    • 9%=1,

    • 8%=2,

    • 7%=3, and

    • no trigger=4

  • Points and Fees Trigger

    • 8%,HOEPA equivalent =0,

    • 6%-7%=1,

    • 5%=2 ,

    • <5%=3, and

    • no trigger=4


Restrictions index
Restrictions Index Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

  • Prepayment Penalty Prohibitions

    • No restriction=0

    • prohibition or percent limits after 60 months=1

    • prohibition or percent limits after 36 months=2

    • prohibition or percent limits after 24 months=3

    • no penalties allowed=4

  • Balloon Prohibitions

    • No restriction =0

    • no balloon if term<7 years (all term restrictions) =1

    • no balloon in first 10 years of mortgage =2

    • no balloon in first 15 years of mortgage and Cleveland=3

    • no balloons allowed=4

  • Counseling Requirements

    • Not required=0

    • Required=1

  • Mandatory Arbitration Limiting Judicial Relief

    • Allowed=0

    • partially restricted=1

    • prohibited =2


Describing the laws
Describing the Laws Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


Scaled index
Scaled Index Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


Empirical approach
Empirical Approach Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


Natural experiment
Natural Experiment Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

  • Neighbor A in state A has law introduced and it becomes in effect or effective

  • Neighbor B, across the street in state B, does not have a law introduced

  • Compare market conditions

    • Pre-law

      • Neighborhood A

      • Neighborhood B

    • Post-law

      • Neighborhood A (in effect)

      • Neighborhood B


Natural experiment1
Natural Experiment Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

  • Border counties

    • One state introduces a law and the other doesn’t

    • “nearest neighbor” approach

  • HMDA (applications, rejections, & originations)

    • Year before and year after law become effective

    • Subprime loans defined by HUD lender list

    • All border county loans (not a matched sample)


Estimation approach
Estimation Approach Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

  • Estimate probit specification

    • Each law sample & each outcome (30 models & 300 coefficients)

  • Probability of outcome

    • Applying for a subprime loan

    • Being rejected on a subprime application

    • Originating a subprime loan

  • Identifying the impact

    • Law Dummy

      • 1 if a location has a law at some point, 0 otherwise

    • Postlaw Dummy

      • 1 if the post-legislation time period, 0 otherwise

    • Ineffect = Law*Postlaw

      • Interaction variable

      • The borrower/applicant is from a location with a law in effect

    • Law Index

      • Restrictions & Coverage

  • Control for

    • Location & Borrower/Loan characteristics

      • Missing credit scores


  • Control variables
    Control Variables Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Mean of outcome variables
    Mean of Outcome Variables Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Marginal effects ineffect variable
    Marginal Effects – Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).Ineffect Variable


    Does law strength matter
    Does Law Strength Matter? Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Heterogeneity of market response correlation of law strength change of outcome
    Heterogeneity of Market Response Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).Correlation of Law Strength & %Change of Outcome


    Pooled sample
    Pooled Sample Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

    • Include law strength measures

      • Scaled indexes

        • Full

        • Coverage

        • Restrictions

    • Outcome & Law Dummy (treatment location) Jointly Determined?

      • Political propensity

        • Dem/Rep ratio in state legislatures 2000

      • HUD-Treasury (2000) report

        • Urban areas

          • State percent of population urban (2000 census)

        • In subprime lending

          • State mkt share subprime, t-1 (HMDA)

        • Nonwhite populations

          • State percent non-white (2000 census)

    • Estimate bivariate probit


    Descriptive statistics dependent and control variables
    Descriptive Statistics Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).Dependent and Control Variables


    A lot of identification variables law postlaw ineffect law postlaw
    A Lot of Identification Variables Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).Law, Postlaw, Ineffect (Law*Postlaw)

    • Need to control for

      • Law sample location

      • Treatment location

      • Pre/post law time period

  • LawSample dummies

    • ca, ct, fl, ga, ma, md, oh, pa, tx

  • Law dummy for each Law Sample

    • Law*ca, Law*ct, Law*ga, Law*ma, Law*md, Law*oh, Law*pa, Law*tx

  • Postlaw dummy for Law Sample

    • Postlaw*ca, Postlaw*ct, Postlaw*ga, Postlaw*ma, Postlaw*md, Postlaw*oh, Postlaw*pa, Post law*tx

  • Pooled Ineffect dummy


  • Results treatment law equation
    Results – Treatment (Law) Equation Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Results outcome equations control variables
    Results – Outcome Equations Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).(control variables)


    Results outcome equations control variables1
    Results – Outcome Equations Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).(control variables)


    Results outcome equations
    Results – Outcome Equations Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Results scaled law index
    Results – Scaled Law Index Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Impact of Law Strength Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Results scaled law index1
    Results – Scaled Law Index Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Impact of Law Restrictions Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Impact of Law Coverage Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).


    Conclusions
    Conclusions Prime loans and author’s calculations using LoanPerformance ABS data set for Subprime loans (fixed rate loans only).

    • Typical Laws have little impact

    • Possible to design a local predatory lending law to

      • Increase the flow of high cost credit

        • coverage

      • Reduce the flow of high cost credit

        • restrictions

      • No impact on the flow of credit

        • typical law

      • Typically reduce rejection rates

      • Is there an impact on cost of credit?


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