Fraudulent Income Overstatement by Home Buyers During the Mortgage Credit Expansion of 2002 to 2005

Transcription

1 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

2 Mian and Sufi (2009) Result in Chicago Fraction subprime borrowers, 1996 Lakeview Lincoln Park Garfield Park Downtown/MagMile Englewood Hyde Park (.51,.69] (.35,.51] (.23,.35] [.14,.23]

3 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, % 17% Mortgage credit growth, 02 to 05 55% 27% IRS income growth, 02 to % 4.0% Growth in income of home buyers, 02 to % 4.3% IRS income growth, 05 to % 2.3% Fraction of zips making top fraud list 75% 2%

4 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, % 17% Mortgage credit growth, 02 to 05 55% 27% IRS income growth, 02 to % 4.0% Growth in income of home buyers, 02 to % 4.3% IRS income growth, 05 to % 2.3% Fraction of zips making top fraud list 75% 2%

5 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, % 17% Mortgage credit growth, 02 to 05 55% 27% IRS income growth, 02 to % 4.0% Growth in income of home buyers, 02 to % 4.3% IRS income growth, 05 to % 2.3% Fraction of zips making top fraud list 75% 2%

6 Our Research: Two Separate Questions 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

7 What Is in Dispute? Not Much 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

8 MS09 Result Shows Up in Adelino et al

9 MS09 Result Shows Up in Adelino et al

10 Our Bottom Line 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

11 Visualization of Main Issue in Dispute Buyer application income to IRS income ratio Buyer application income to IRS income ratio fraud? Average IRS income decile of zip codes

12 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 ** ** (0.089) (0.087) Buyer income growth, 2002 to 2005, annualized 0.420** 0.433** (0.038) (0.038) Buyer income overstatement, 2002 to ** (0.035) N 3,014 3,014 3,014 3,014 R Buyer income overstatement, 2002 to 2005 = Income growth according to mortgage applications Income growth according to IRS in zip code

13 Some Background 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

14 Overstatement, Credit Scores, Income.06 By 1996 credit scores.06 By 2002 income Buyer income overstatement, 2002 to Buyer income overstatement, 2002 to Most prime 2 3 Most subprime Zip code credit scores, Lowest income 2 3 Highest income Average IRS income, 2002

15 Overstatement Characteristics (1) (2) (3) (4) (5) (6) Ln[Median Poverty rate, Fraction household 2000 unemployed, income], Fraction subprime, 1996 Fraction with less than high school education, 2000 Household debt default rate, 2000 Buyer income overstatement 0.193** ** 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

16 Application Income is Not True Income 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

17 1. Aggregate Evidence

18 Credit Boom and Rampant Fraud 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%

19 Michael Hudson, WSJ Reporter 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.

20 Academic Research 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

21 Easy to See Fraud in Aggregate Data HMDA versus ACS/Census HMDA versus AHS Annualized real income growth Annualized real income growth HMDA, 2000 to 2005 ACS/Census, 2000 to 2005 HMDA, 2002 to 2005 AHS, 2002 to 2005 HMDA shows strong increase in homebuyer income from 02 to 05, ACS/Census and AHS show decline

22 Avery, et al (2013) 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 lowerincome borrowers, as measured in the HMDA data, may be attenuated around the peak of the housing market

23 2. Direct Evidence of Fraud

24 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, Change in low-doc share of mortgages, Zip code makes top mortgage fraud list, 2010 Zip code makes top mortgage fraud list, Misreported non-owneroccupant 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 R

25 3. No Gentrification

26 Negative Gentrification Period: x = 1991 y = 1998 Growth in IRS Income from time x to time y (1) (2) (3) (4) (5) x = 1998 x = 2002 x = 2004 y = 2002 y = 2004 y = 2005 x = 2005 y = 2006 Ln(Buyer income) Ln(IRS income), at time x 0.004* 0.043** ** ** ** (0.002) (0.002) (0.002) (0.005) (0.003) N 2,590 3,013 3,014 3,014 3,014 R

27 High Overstatement Zips Fall Apart (1) (2) (3) (4) (5) (6) (7) IRS wage Census Change in Change in Change in growth, income poverty rate, unemp rate, mortgage 2005 to growth 2000 to 2000 to default rate, to to IRS income growth, 2005 to 2012 Change in mortgage default rate, 2005 to 2010 Buyer income overstatement ** ** ** 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 R

28 4. Correlation Breakdown

29 Breakdown in Low GSE Share Zips Buyer income growth from mortgage applications, annualized (1) (2) (3) (4) Time period 1991 to to to to 2007 IRS Income growth*quartile 1 GSE share 0.481** 0.550** ** (0.039) (0.136) (0.064) (0.052) IRS Income growth*quartile 2 GSE share 0.298** 0.422* * (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** ** 0.307** (0.081) (0.181) (0.107) (0.085) N 2,590 3,013 3,014 3,014 R

30 Arguments in Adelino et al

31 Fraud Too Small to Explain Gap 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

32 GSE versus Non-GSE Results 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

33 Average Mortgage Size Matters 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

34 Mortgage Size Result and MS09 Low Credit Score Zip High Credit Score Zip Individual: Subprime Prime Prime Prime Mortgage size, Mortgage size, Mortgage origination growth Growth in average mortgage size, conditional on origination ( )/100 = 50% ( )/200 = 0% (75 100)/100= -25% ( )/100= 0%

35 Other Issues

36 Aggregate Increase in Household Debt 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

37 Aggregate Share of Mortgages, Defaults Fraction of all originations, 2002 and 2005 Fraction of all defaults, 2007, 2008, Most prime 2 3 Most subprime 0 Most prime 2 3 Most subprime

38 Robust to Full Sample? 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

39 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** * ** ** ** ** (0.049) (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 R

40 Conclusion

41 No Change in Lending Technology? 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

42 Academic Research 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 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.

43 Final Thoughts 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