The Income Elasticity of Loan Demand

1. The Income Elasticity of Loan Demand Manthos D. Delis, Surrey Business School Iftekhar Hasan, Fordham University and Bank of Finland Chris Tsoumas, University of Piraeus Presentation prepared for the Academic Seminars Series 2014, Department of Banking and Financial Management, University of Piraeus

2. Agenda Main questions Theoretical and empirical background Data Empirical methodology Results Conclusions

3. Main questions • Q1: • How does loan demand change with people’s incomes? • Q2: • Is the income elasticity of loan demand stable over time? • Answers have important implications for: • The origination of banking and financial crises • How policy should respond to fluctuations in loan demand

4. Theoretical and empirical background • Primary reasons for the subprime crisis (e.g., Mian and Sufi, 2009) • Credit expansion • Accompanying decline in credit standards • Supply side forces • Sources behind this development • Easy monetary policy increased incentives for banks to take on higher risks in search of yield (Jimenez, Ongena, Peydro, and Saurina, 2013) • Government policy toward subprime mortgage-credit expansion (Rajan, 2010; Mian, Sufi, and Trebbi, 2014) Equally supporting evidence: (Keys, Mukherjee, Seru, and Vig, 2010; Demanyak and Van Hemert, 2011)

5. Theoretical and empirical background • Rajan (2010): • Provides anecdotal evidence for the role of household income as a determinant of loan demand and a main source behind the subprime crisis • Rising inequality: the 90-50 hourly wage differential, not the 50-10 • Argues that the political response to this in the late 1990s and early 2000s was to increase the availability of mortgage credit “Bankers responded to implicit and explicit incentives that the system created” • Demand and supply side forces

6. Theoretical and empirical background • Demand side forces may translate to: • Increased demand for credit, • Demand for riskier loans (ex ante riskier borrowers), and/or • Demand for larger credit amounts relative to income • Point 3 spans both points 1 and 2 • Only Dell’Ariccia, Igan, and Laeven (2012) provide evidence for a demand side explanation of the crisis • Exogenous increase in the demand for subprime credit partially triggered the lower denial rates in specific areas of the U.S. • They touch upon points 1 and 2 • We focus on point 3

7. Anecdotal evidence The rich demand approximately the same amount of loans with their annual income There is almost a 1:1 increase in loan demand and income in 1992-2012, quite similar for the 75%-90% and the 25%-75% income groups Decreasing gap between income and the loan amounts demanded for the latter two income groups for 2002 to 2006 The gap between loan demand and income for the 75%-90% income group is considerable, and the gap for the 25%-75% group is even larger Average income and loan demand (in nominal values and logs) for the top 10% of the income distribution, the 75%-90% income cohort, and the 25%-75% income group

8. Theoretical and empirical background • We are interested both in: • The absolute differences in loan demand due to differences in income across income groups and how they evolve over time • In the relative changes in loan demand due to differences in income between income groups • Relates to the relative-income hypothesis (Duesenberry, 1949) (“keep up with the Joneses” effect)

9. Data • Home Mortgage Disclosure Act (HMDA) database • Applicant-level data on all mortgage applications • 1992-2012 • Characteristics for each application • Loan information • Requested loan amount • Type of loan (i.e., conventional, insured by the FHA, guaranteed by the Department of Veterans Affairs or Farm Service Agency/Rural Housing Service) • Property type (one to four-family, manufactured housing, multifamily) • Purpose of the loan (i.e., home purchase, home improvement, or refinancing) • Owner’s occupancy status (e.g., house will be owner-occupied as a principal dwelling) • Loan status (originated, denied, withdrawn application, reason for denial etc.)

10. Data • Applicant information • income, sex, gender and race; co-applicant's sex, gender and race • Property location information • MSA, state, county, and census-tract codes of the property • Financial institution information • Identity; Supervisory agency • Census info for property location • Population; Minority population; Median family income; Tract-to-MSA median family income percentage; Number of owner occupied units; Number of 1- to 4-family units • Supplemented with data for • Personal income and employment summary (MSA level) (Bureau of Economic Analysis) • Median income estimates and percent of county population below the poverty line (county level) (SAIPE) • All-transactions house-price indices (MSA level) (FHFA) • Annual list of subprime lenders from HUD (Dell’ Ariccia et al., 2012)

11. Data • Use of • Both accepted and denied loans • First step for distinguishing between supply and demand • Only conventional loans for home purchase • To avoid distortions from loan guarantees and/or the presence of past mortgages • Data cleaned from missing info for variables of interest • Creation of randomized samples for • 1992-2012 period (“stacked” sample) (pseudo panel) • 657,292 obs. (the 1% of each year’s applications) • Each year (“cross-sectional samples”) • ~ 300,000 obs. each

13. Empirical analysis • Estimation of a loan-demand equation : (log) quantity of loan demanded for application i :offered lending rate : (log) income of the borrower : k-dimension vector of observed applicant’s characteristics (i.e., sex, race, occupancy status) • The coefficient of is the income elasticity of loan demand • Estimation for six different income brackets (90+ (top 10%); 70%-90%; 50%-70%; 30%-50%; 10%-30%; 10- (bottom 10%)) • Estimation using “stacked” and annual “cross-sectional” samples

14. 1st Identification challenge • Available only for a small fraction of originated loans and only after 2004 • Solution proposed: • Assume that applicants observe the same lending rates within a particular census tract, given that we estimate equation (1) separately for each income group. • Thus, include census tract dummies to control, inter alia, for • The error term structure becomes • For cross-sectional samples estimation: • For stacked sample estimation: • Offered lending rate is unobserved until the loan is originated

15. Results with census tract fixed effects – Elasticities The middle class demands larger amounts of loans relative to its income Loan demand gradually shrinks for middle-income and especially lower-middle-income people relative to their income during the 2000s Convergence towards the rich until 2006 Findings in line with Mian and Sufi (2009) who show that increases in subprime borrowers incomes do not justify larger loan originations

16. 2nd identification challenge • Distinguishing loan supply from loan demand • Banks may influence borrowers’ decisions through their marketing strategies and/or their loan terms • Thus, supply side (bank) unobserved effects • Solution proposed • Use of all applications – not only originated loans, and • Add bank fixed effects

17. Results with bank and census tract fixed effects - Elasticities Same picture Elasticities about 10% smaller Thus, it seems that the system became riskier not because of increased loan demand of individual applicant, but…

18. 3rd identification challenge • Potential endogeneity of due to unobservable characteristics of individuals affecting loan demand • Wealth, age, education, expected income in future years, other • Solution proposed: • Grouping individuals based on a large number of their observed characteristics (income bracket, sex, race, co-applicant’s sex and race, occupancy, loan status, reason for denial (if present), bank, census tract) • Large number of singleton groups (about 65% to 85% each year)

19. 3rd identification challenge • Keep only groups with two “identical” individuals • Calculate differences on (log) loan demand and (log) income • Aim to difference out any unobservable characteristics • Estimate equation:

20. Results when individuals are grouped based on observed characteristics Same picture once more Upward trend after 2007, more intense for the 70%-90% and 30%-50% income brackets

21. Results when individuals are grouped based on observed characteristics – Obs. Fast accumulating burden on the system because of a very large number of loans

22. A "Keeping up with the Joneses" Effect? • Two possible forms of the effect (non-mutually exclusive) • Individuals demand bigger loans relative to their incomes so as to keep up with their richer neighbors “first order” effect • Individuals apply for loans to keep up with their neighbors whereas they would not apply at all otherwise “second order” effect • We examine the “first order” effect

23. A "Keeping up with the Joneses" Effect? • Solution proposed: • Take averages at year t-1 by income bracket and census tract for the applicant’s loan amount and income • For each application at year t, calculate the difference between the loan amount and income for applicant i minus the average loan amount and income, respectively, in the income bracket just above that of i in the same census tract at year t-1. • Aim to identify mimicking behavior among loan applicants stemming from the observable loan decisions of their richer neighbors one year earlier. • Higher value on the differenced coefficient of income supports an enhanced “keeping up with the Joneses” effect. • Bank and census tract dummies included

24. Results – "Keeping up with the Joneses" Effect • Roughly similar behavior of loan-demand elasticities for the middle-class income brackets with that in previous specifications • Exception for the 70%-90% bracket • (quite lower, more unstable) • Short-term upward spikes for the 50%-70% and • 30%-50% income groups In general, not definitive support for “first-order “ “keeping up with the Joneses” effect

25. Sensitivity analyses • Results driven by subprime lending applications? • Solution proposed: • Following Dell’Ariccia et al. (2012), use the annual list of subprime lenders to identify financial institutions that specialize in subprime mortgage lending for 1993-2005 • Repeat the analysis with bank and census tract dummies

26. Sensitivity analyses Higher poverty rate counties Lower poverty rate counties • Results driven by local inequality conditions? • Solution proposed: • Only the counties at the top or bottom 20% centile of the poverty line measure, i.e., the percent of county population that lie below the poverty line, estimated by the Census Bureau, are included in each year.

27. Sensitivity analyses • C. Are the elasticities different or the originated loans? • Solution proposed: • Replicate the analysis with grouped individuals based on observed characteristics, but only for loans that were actually originated

28. Conclusions • Income elasticity of mortgage demand for all middle-class income groups is: • Higher than that of the rich • Falling and converging with that of the rich before the eruption of the subprime crisis • Thus, proponents of income inequality idea are right, but: • Middle-class income levels affect the loan amounts less and less before the crisis • Impact through the number of applications • Behavioral decisions of households can be systemically risky at the aggregate level even if they could be prudent from a microeconomic perspective • Implications • “Systemic view” of loan policies from financial institutions • Foreclosure policies

29. Possible caveats and extensions • But… • What if income data are not quite reliable for the period before the crisis due to the advent of ‘low doc/no doc’ loans? • Solution proposed (to be conducted): • Use aggregate data on income (census data) and loans at the census tract level • Other? • Does the economic structure of counties/MSAs affects the results through its impact on expected income? • Other?