Fraudulent Income Overstatement by Home Buyers During the Mortgage Credit Expansion

of 2002 to 2005

Atif Mian Princeton University

NBER

Amir Sufi University of Chicago Booth School of Business

NBER

Mian and Sufi (2009) Result in Chicago

(.51,.69](.35,.51](.23,.35][.14,.23]

Fraction subprime borrowers, 1996

Englewood

Garfield Park Downtown/MagMile

Hyde Park

Lakeview Lincoln Park

Mian and Sufi (2009) Result in Chicago Englewood,

Garfield Park Rest of Chicago

Fraction subprime 68% 37% Median household income, 2000 $24K $44K Poverty rate, 2000 35% 17% Mortgage credit growth, 02 to 05 55% 27% IRS income growth, 02 to 05 1.9% 4.0% Growth in income of home buyers, 02 to 05 7.7% 4.3% IRS income growth, 05 to 12 0.8% 2.3% Fraction of zips making top fraud list 75% 2%

Mian and Sufi (2009) Result in Chicago Englewood,

Garfield Park Rest of Chicago

Fraction subprime 68% 37% Median household income, 2000 $24K $44K Poverty rate, 2000 35% 17% Mortgage credit growth, 02 to 05 55% 27% IRS income growth, 02 to 05 1.9% 4.0% Growth in income of home buyers, 02 to 05 7.7% 4.3% IRS income growth, 05 to 12 0.8% 2.3% Fraction of zips making top fraud list 75% 2%

Mian and Sufi (2009) Result in Chicago Englewood,

Garfield Park Rest of Chicago

Fraction subprime 68% 37% Median household income, 2000 $24K $44K Poverty rate, 2000 35% 17% Mortgage credit growth, 02 to 05 55% 27% IRS income growth, 02 to 05 1.9% 4.0% Growth in income of home buyers, 02 to 05 7.7% 4.3% IRS income growth, 05 to 12 0.8% 2.3% Fraction of zips making top fraud list 75% 2%

1. What caused the unprecedented growth in mortgage credit for home purchase in low credit score zip codes? (MS09)

• This is about extensive margin, does not explain rise in total debt, use county FE

2. What caused the rise in total household debt in the United States? (MS12, MS14)

• Home equity-based borrowing, not just by low income, utilize across-county variation

Our Research: Two Separate Questions

Their summary stats show poor zips had: Higher mortgage credit growth from 02 to 05 Lower IRS income growth from 02 to 05

They do not dispute our research on: home equity borrowing and rise in aggregate

household leverage role of household debt in Great Recession

Their average mortgage size result is perfectly consistent with MS09

What Is in Dispute? Not Much

MS09 Result Shows Up in Adelino et al

MS09 Result Shows Up in Adelino et al

Any result in Adelino et al using income information from mortgage applications of home-buyers should be heavily discounted We have no problem with any result not

using mortgage application income (i.e., average mortgage size result) But, we believe MS12, MS14 already

explain aggregate rise in household debt, not cited or discussed in Adelino et al

Our Bottom Line

Visualization of Main Issue in Dispute

fraud?199820012011

2005

1

1.5

2

2.5

3

Buy

er a

pplic

atio

n in

com

e to

IRS

inco

me

ratio

0 1 2 3 4 5 6 7 8 9 10Average IRS income decile of zip codes

Buyer application incometo IRS income ratio

Dispute in a Table

Mortgage originations for home purchase growth 2002 to 2005, annualized

(1) (2) (3) (4) IRS income growth, 2002 to 2005, annualized -0.662** -0.705** (0.089) (0.087) Buyer income growth, 2002 to 2005, annualized 0.420** 0.433** (0.038) (0.038) Buyer income overstatement, 2002 to 2005 0.473**

(0.035) N 3,014 3,014 3,014 3,014 R2 0.380 0.394 0.407 0.406

Buyer income overstatement, 2002 to 2005 = Income growth according to mortgage applications – Income growth according to IRS in zip code

What neighborhoods saw high buyer income overstatement? The same subprime zip codes analyzed

extensively in Mian and Sufi (QJE 2009)! According to Adelino et al, mortgage boom

was high income individuals moving into low credit score, poor neighborhoods

Some Background

Overstatement, Credit Scores, Income

-.02

0

.02

.04

.06

Buy

er in

com

e ov

erst

atem

ent,

2002

to 2

005

Most prime 2 3 Most subprimeZip code credit scores, 1996

By 1996 credit scores

-.02

0

.02

.04

.06

Buy

er in

com

e ov

erst

atem

ent,

2002

to 2

005

Lowest income 2 3 Highest incomeAverage IRS income, 2002

By 2002 income

Overstatement Characteristics

(1) (2) (3) (4) (5) (6) Fraction

subprime, 1996

Ln[Median household income],

2000

Poverty rate, 2000

Fraction with less than high

school education,

2000

Fraction unemployed,

2000

Household debt default rate, 2000

Buyer income overstatement 0.193** -0.253** 0.178** 0.249** 0.168** 0.205** (0.026) (0.023) (0.021) (0.025) (0.019) (0.024) N 3,014 3,014 3,014 3,014 3,014 3,014

1. Aggregate evidence from extant research 2. Direct measures of fraud higher in zip

codes with high overstatement 3. Zip codes in question see terrible

contemporaneous and ex post outcomes – no evidence of gentrification

4. Break down in correlation between IRS and mortgage application income growth

Application Income is Not True Income

1. Aggregate Evidence

FCIC: “about $1 trillion of the loans made during the [2005 to 2007] period were fraudulent” State of Illinois: “60% of the [stated loan]

income amounts were inflated by more than 50%”

Credit Boom and Rampant Fraud

“At the downtown L.A. branch [of mortgage lender Ameriquest], some of Glover's coworkers had a flair for creative documentation. They used scissors, tape, Wite-Out, and a photocopier to fabricate W-2s, the tax forms that indicate how much a wage earner makes each year. It was easy: Paste the name of a low-earning borrower onto a W-2 belonging to a higher-earning borrower and, like magic, a bad loan prospect suddenly looked much better. Workers in the branch equipped the office's break room with all the tools they needed to manufacture and manipulate official documents. They dubbed it the ‘Art Department.’”

Michael Hudson, WSJ Reporter

On fraudulent income overstatement: Avery, Bhutta, Brevoort, and Canner (2013) Blackburn and Vermilyea (2012) Jiang, Nelson, and Vytlacil (2014)

Other types of fraud: Griffin and Maturana; Piskorski, Seru, and Witkin; Ben David; Garmaise One lesson: fraud concentrated in non-

GSE private label securitization market

Academic Research

Easy to See Fraud in Aggregate Data

-.01

0

.01

.02

.03

Ann

ualiz

ed re

al in

com

e gr

owth

HMDA, 2000 to 2005 ACS/Census, 2000 to 2005

HMDA versus ACS/Census

-.03

-.02

-.01

0

.01

.02

Ann

ualiz

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al in

com

e gr

owth

HMDA, 2002 to 2005 AHS, 2002 to 2005

HMDA versus AHS

HMDA shows strong increase in homebuyer income from 02 to 05, ACS/Census and AHS show decline

“users of the HMDA data should be aware that borrower income was likely significantly overstated during the peak of the housing boom, particularly in some areas of the country. One potential implication of this finding is that lending to lower-income borrowers, as measured in the HMDA data, may be attenuated around the peak of the housing market”

Avery, et al (2013)

2. Direct Evidence of Fraud

Fraud in High Overstatement Zip Codes

(1) (2) (3) (4) (5) (6) (7) InterThinx Measures of

Fraud Piskorski, Seru, and Witkins (2014)

Measures of Fraud Change in

non-agency share of

mortgages, 2002-2005

Change in low-doc share of

mortgages, 2002-2005

Zip code makes top mortgage fraud list,

2010

Zip code makes top mortgage fraud list, 2010-2014

Misreported non-owner-

occupant

Misreported second lien

Either misreported

Buyer income overstatement 0.121** 0.100** 0.051** 0.123** 0.030** 0.034** 0.051** (0.020) (0.016) (0.018) (0.045) (0.009) (0.009) (0.009) N 2,981 2,981 3,014 3,014 2,969 2,969 2,969 R2 0.483 0.598 0.071 0.067 0.271 0.321 0.245

3. No Gentrification

Negative Gentrification

Growth in IRS Income from time x to time y (1) (2) (3) (4) (5)

Period: x = 1991 y = 1998

x = 1998 y = 2002

x = 2002 y = 2004

x = 2004 y = 2005

x = 2005 y = 2006

Ln(Buyer income) – Ln(IRS income), at time x 0.004* 0.043** -0.014** -0.040** -0.023** (0.002) (0.002) (0.002) (0.005) (0.003) N 2,590 3,013 3,014 3,014 3,014 R2 0.226 0.405 0.226 0.213 0.185

High Overstatement Zips Fall Apart

(1) (2) (3) (4) (5) (6) (7) IRS income

growth, 2005 to

2012

IRS wage growth, 2005 to

2012

Census income growth 2000 to

2010

Change in poverty rate,

2000 to 2010

Change in unemp rate,

2000 to 2010

Change in mortgage

default rate, 2005 to

2007

Change in mortgage

default rate, 2005 to

2010 Buyer income overstatement -0.094** -0.149** -0.121** 0.041** 0.024** 0.059** 0.126** (0.020) (0.018) (0.022) (0.007) (0.006) (0.007) (0.012) N 3,011 3,011 3,011 3,011 3,011 3,014 3,014 R2 0.325 0.372 0.412 0.333 0.252 0.331 0.529

4. Correlation Breakdown

Breakdown in Low GSE Share Zips

Buyer income growth from mortgage applications, annualized (1) (2) (3) (4)

Time period 1991 to 1998 1998 to 2002 2002 to 2005 2005 to 2007 IRS Income growth*Quartile 1 GSE share 0.481** 0.550** -0.075 0.269** (0.039) (0.136) (0.064) (0.052) IRS Income growth*Quartile 2 GSE share 0.298** 0.422* 0.134 0.172* (0.056) (0.174) (0.094) (0.075) IRS Income growth*Quartile 3 GSE share 0.297** 0.555** 0.272** 0.279** (0.060) (0.165) (0.087) (0.083) IRS Income growth*Quartile 4 GSE share 0.238** 0.330 0.314** 0.307** (0.081) (0.181) (0.107) (0.085) N 2,590 3,013 3,014 3,014 R2 0.535 0.210 0.367 0.188

Arguments in Adelino et al

“In 2005, buyer income 75% more than IRS income – too large to be fraud” Incorrect comparison: fraud buyers

overstate 20-30% relative to regular buyers, not to average IRS income HMDA 2005 home buyer income: $99K ACS 2005 home buyer income: $83K Average IRS income: $53K

Fraud easily explains difference

“Fraud Too Small to Explain Gap”

“Buyer income positively related to credit growth even within high GSE share zip codes where fraud was less likely” We agree, but this is because there was

no fraud and no big credit supply expansion in these zip codes We would expect positive correlation between

true income growth and credit growth where credit supply expansion less important

“GSE versus Non-GSE Results”

“Change in average mortgage size from 2002 to 2005 conditional on origination positively related to IRS income” Yes, we agree, but this is perfectly

consistent with MS09 Marginal individuals in subprime zip codes

getting mortgages obtain smaller mortgages Even if mortgage amounts constant in prime

zip codes, we would expect this result

“Average Mortgage Size Matters”

Mortgage Size Result and MS09

Low Credit Score Zip High Credit Score Zip

Individual: Subprime Prime Prime Prime

Mortgage size, 2002 - 100 100 100

Mortgage size, 2005 50 100 100 100

Mortgage origination growth

(150-100)/100 = 50% (200-200)/200 = 0%

Growth in average mortgage size, conditional on origination

(75 – 100)/100= -25% (100 – 100)/100= 0%

Other Issues

We agree: expansion to low income extensive margin cannot explain rise in total debt (MS09 never claimed this) Home equity-based borrowing much more

important, was present even among middle to upper-middle households: MS12, MS14 (not really addressed here) But, aggregate results using fraudulent

application income are incorrect

Aggregate Increase in Household Debt

Aggregate Share of Mortgages, Defaults

0

.1

.2

.3

.4

Most prime 2 3 Most subprime

Fraction of all originations, 2002 and 2005

2002 2005

0

.1

.2

.3

Most prime 2 3 Most subprime

Fraction of all defaults, 2007, 2008, 2009

2007 2008 2009

In MS09, we used sample of zip codes with house price data available (45% of 2002 debt outstanding) Adelino et al claim negative correlation

between credit growth and income growth not robust when using full sample Major flaw: when moving to full sample,

one must weight by zip code population because of extremely small zip codes

Robust to Full Sample?

MS09 Result Robust to Full Sample

Mortgage originations for home purchase growth, 2002 to 2005, annualized (1) (2) (3) (4) (5) (6) (7) IRS Income growth, 02 to 05, annualized 0.143** 0.005 -0.124* -0.203** -0.123** -0.304** -0.662** (0.049) (0.051) (0.051) (0.052) (0.046) (0.065) (0.089) Sample Full Full Full Full Full >5000 House

Price Winsorized 1% 5% 10% Population weights No No No No Yes No No Fraction of 2000 population 100% 100% 100% 100% 100% 80% 29% Fraction of 2000 mortgage debt outstanding

100% 100% 100% 100% 100% 85% 45%

N 18,336 18,336 18,336 18,336 18,336 7,622 3,014 R2 0.356 0.407 0.428 0.432 0.378 0.452 0.380

Conclusion

Main argument in Adelino et al is: “lending technology in early 2000s did not change fundamentally – no real credit supply shift” There are more than 10 published

academic articles (excluding us) contradicting this argument Top regulators also disagree – Bernanke

and Dudley on record saying housing boom mainly due to lending change

No Change in Lending Technology?

Levitin and Wachter (2013) : “the bubble was, in fact, a supply-side phenomenon, meaning that it was caused by excessive supply of housing finance … it was the result of a fundamental shift in the structure of the mortgage finance market from regulated to unregulated securitization.”

Justiniano, Primiceri, and Tambalotti (2014): “the housing boom that preceded the Great Recession was due to an increase in credit supply driven by looser lending constraints in the mortgage market.”

Landvoigt, Piazzesi, and Schneider (2014): “cheaper credit for poor households was a major driver of prices, especially at the low end of the market.”

Demyanyk and Van Hemert (2011) show that “loan quality – adjusted for observed characteristics and macroeconomic circumstances – deteriorated monotonically between 2001 and 2007.”

Mayer, Pence, and Sherlund (2009): “lending to risky borrowers grew rapidly in the 2000s. We find that underwriting deteriorated along several dimensions: more loans were originated to borrowers with very small down payments and little or no documentation of their income or assets, in particular.”

Academic Research

MS09 main result confirmed: expansion of mortgage credit to low credit score zip codes was unrelated to fundamental improvements in income Credit supply shift, driven by private label

securitization, was the culprit Maybe fraud exploded in subprime zip

codes because brokers responding to same credit supply shift? Future research

Final Thoughts