# Presentation: 268020

## Credit Booms and Lending Standards: Evidence from the Subprime Mortgage Market

- Giovanni Dell’Ariccia (IMF and CEPR)
- Deniz Igan (IMF)
- Luc Laeven (IMF, CEPR, and ECGI)

- The views expressed in this presentation are those of the author and do not necessarily represent those of the *IMF*.

## Credit Booms: Curse or Blessing?

- Financial deepening is associated with economic growth
- Booms can be good: only a minority ends in crises, and there is evidence that they contribute to long-term financial deepening
- Yet, credit booms are often seen as a recipe for financial disaster, possibly because several major banking crises have been preceded by booms
- While there are theoretical explanations linking booms to crises, empirical work is limited and primarily relies on aggregate data
- We use U.S. subprime mortgage market as a lab study for credit booms

## Credit Booms Can Be a Good Thing

- Cyclicality of credit:
- Favorable economic conditions might justify extension of credit at less stringent terms
- Wealth of profitable opportunities justify fast credit expansion
- Low interest rate environment reduces agency problems allowing sound credit growth (opposite of “flight to quality”)
- Booms promote financial deepening and widen access
- “Unfortunate tendency” to lend aggressively at the peak of a cycle (Greenspan)

## Booms and Crises

## Why Credit Booms Lead to Crises

**“*****Financial accelerators*****”** (Kiyotaki and Moore, JPE 1997): an increase in value of collateralizable goods releases credit constraints. Boom fuels further wealth effects etc. Negative shocks inverts cycle, leaving banking system overexposed
**“*****Institutional memory*****”** (Berger and Udell, JFI 2004): in periods of fast credit expansion banks find it difficult to recruit enough experienced loan officers (especially if there has not been a crisis for a while). This leads to a deterioration of loan portfolios
***“******Informational capital and adverse selection”*** (Dell’Ariccia and Marquez, JF 2006): during expansions, adverse selection is less severe and banks find it optimal to trade quality for market share, increasing crisis probability

## Subprime Market Ideal Testing Ground

- Asymmetric info relevant since subprime borrowers:
- Have poor or blemished credit histories
- Provide little or no documentation
- Have risky income profiles
- Market has grown fast and is now in a crisis
- Loan originations tripled since 2000
- Significant changes in market structure with the entry of major players
- Anecdotal evidence suggests that this trend was accompanied by a decline in credit standards and excessive risk taking by lenders (e.g., FitchRatings, 2007)
- Credit boom gone bad? Apparent relationship between delinquencies and credit growth
- Wealth of information on borrowers and lenders
- Loan application data
- Rich set of macro variables
- Significant geographical variation within country

## U.S. Subprime Mortgage Boom 

## Subprime Crisis: A Credit Boom Gone Bad?

- MSA level data

## This Paper

- How did lending standards change over the expansion?
- How did changes in local market structure affect lender behavior during the boom?
- To answer these questions, we use data from over 50 million individual mortgage applications combined with information on local and national economic variables

## Main Contribution

- Examine evolution of lending standards during subprime boom to explain origins of current crisis
- Shed light on relationship between booms and banking crises in general
- Lend empirical support to recent theories explaining cyclicality of standards and their links to financial stability

## Data Sources

- Loan application data:
- 	Home Mortgage Disclosure Act (HMDA)
- Subprime delinquency rate:
- 	LoanPerformance
- Economic and social indicators:
- 	Bureau of Economic Analysis, Bureau of Labor Statistics, Census Bureau, Office of Federal Housing Enterprise Oversight

## Data: HMDA

- Millions of loan applications / Coverage from 2000 to 2006
- Depository and non-depository institutions issuing mortgages in a metropolitan statistical area (MSA)
- Both prime and subprime loans
- Subprime lenders identified using list by Dept. of Housing and Development (HUD)
- Robustness using interest rate data after 2004

## Measuring Lending Standards

- Did banks become less choosy during the boom?
- Two measures of lending standards at MSA level:
- Denial rate (DR) = Loans denied / Applications
- Loan-to-income ratio (LIR)
- Preference for DR as more robust to measurement error

## Linking Boom and Lending Standards

- Regress measures of lending standards at MSA level on:
- Measures of credit expansion (boom)
- Measures of market structure and entry
- Macro and local variables controlling for economic conditions (including time and MSA fixed effects)
- OLS regressions; panel data of 379 MSAs and 7 years

## Measuring the Boom

- Main boom variable is the growth rate in the number of loan applications in an MSA
- For robustness we also use:
- Growth rate in the number of loan originations in an MSA
- Growth rate in the volume of originated loans in an MSA
- Preference for application measure because of greater exogeneity
- Growth in originations is obviously the result of changes in denial rates
- Exogeneity remains concern – “Neighbor effect” (more on this later)

## Where was the boom?

**Notes:**
Link to next slide: the specification essentially looks at the growth in number of applications.

## Other Control Variables

- Market structure variables
- Number of competing lenders
- Entry by large (top 20) national player (market share of entrants)
- Macro variables
- Income growth, unemployment rate, population, self-employment rate, house price appreciation

## Data: Summary statistics

| Variable | Obs |  | Mean |  | Std. Dev. |  | Min |  | Max |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Loan application level |  |  |  |  |  |  |  |  |  |
| Denied | 72,119,135 |  | 0.19 |  | 0.39 |  | 0 |  | 1 |
| Subprime | 72,119,135 |  | 0.23 |  | 0.42 |  | 0 |  | 1 |
| Loan amount | 72,119,135 |  | 160.59 |  | 125.41 |  | 1 |  | 1800 |
| Applicant income | 72,119,135 |  | 82.16 |  | 50.32 |  | 16 |  | 363 |
| Loan-to-income | 72,119,135 |  | 4.25 |  | 0.56 |  | 1 |  | 6 |
| MSA level |  |  |  |  |  |  |  |  |  |
| Denial rate |  | 2,709 |  | 0.25 | 0.07 | 0.07 |  | 0.55 |  |
| Denial rate, prime |  | 2,709 |  | 0.18 | 0.07 | 0.04 |  | 0.52 |  |
| Denial rate, subprime |  | 2,703 |  | 0.50 | 0.08 | 0.00 |  | 0.73 |  |
| House price appreciation |  | 2,651 |  | 0.07 | 0.06 | -0.05 |  | 0.41 |  |
| Loan-to-income |  | 2,709 |  | 1.88 | 0.37 | 1.05 |  | 3.40 |  |
| Proportion of loans sold |  | 2,709 |  | 0.46 | 0.10 | 0.00 |  | 0.78 |  |
| Subprime delinquency rate |  | 1,137 |  | 10.49 | 3.58 | 1.70 |  | 35.80 |  |

## Loosening Subprime Lending Standards

| Dependent variable: Denial rate | All | Prime | Subprime |
| --- | --- | --- | --- |
| House price appreciation | -0.234*** | -0.150*** | -0.308*** |
| Average income | -0.002*** | -0.003*** | -0.004*** |
| Income growth | 0.003 | -0.021 | 0.100 |
| Unemployment | 0.003** | 0.002 | 0.003* |
| Self employment | 0.046 | 0.080 | -0.311** |
| Log population | -0.180*** | -0.232*** | -0.353*** |
| Log number of competitors | 0.018*** | -0.003 | -0.069*** |
| Log number of applications | -0.017*** | 0.025*** | -0.030*** |
| Constant | 2.697*** | 3.065*** | 5.749*** |
|  |  |  |  |
| R-squared | 0.69 | 0.71 | 0.44 |

## Main Finding

- Credit expansion in subprime mortgage market led to a decrease in lending standards ...
- ... as measured by a decline in application denial rates not explained by an improvement in the underlying economic fundamentals
- Result is consistent with recent theories suggesting that strategic interaction among asymmetrically informed banks may lead banks to behave more aggressively and take on more risks during booms than in tranquil times (e.g., Ruckes, 2004, Dell’Ariccia and Marquez, 2006, and Gorton and He, 2008)

## Effects of changes in applicant pool

- Estimate denial model with loan level data for 2000
- Forecast denials at loan level for 2001-2006
- Aggregate errors at MSA level and use as dependent variable

## Controlling for Applicant Pool 

| Dependent variable: Prediction error | All | Prime | Subprime |
| --- | --- | --- | --- |
| House price appreciation | -0.178*** | -0.104*** | -0.281*** |
| Average income | -0.004*** | -0.005*** | -0.003 |
| Income growth | -0.015 | 0.007 | -0.002 |
| Unemployment | -0.001 | -0.004*** | 0.003 |
| Self employment | -0.120* | -0.048 | -0.414*** |
| Log population | -0.183*** | -0.166*** | -0.335*** |
| Log number of competitors | 0.021*** | 0.008 | -0.051*** |
| Log number of applications | -0.019*** | -0.002 | -0.026*** |
| Constant | 2.660*** | 2.355*** | 5.026*** |
|  |  |  |  |
| R-squared | 0.90 | 0.87 | 0.42 |

## Endogeneity

- Instrument subprime applications with prime applications
- Lag house appreciation
- Instrument house appreciation with “Rapture Index”

## A Rather Exogenous Instrument

**Notes:**
Link to next slide: the specification essentially looks at the growth in number of applications.

## No obvious time-series pattern ...

**Notes:**
Link to next slide: the specification essentially looks at the growth in number of applications.

## Little overlap with boom areas

**Notes:**
Link to next slide: the specification essentially looks at the growth in number of applications.

## Controlling for Endogeneity

| Dependent variable: Denial rate | APPL_S | IV: APPL_P | IV: Rapt |
| --- | --- | --- | --- |
| House price appreciation | -0.329*** | -0.334*** | -0.576*** |
| House price apprec., lagged |  |  |  |
| Average income | -0.004** | -0.003* | -0.004* |
| Income growth | 0.108 | 0.051 | 0.189*** |
| Unemployment | 0.003* | 0.003 | 0.000 |
| Self employment | -0.271** | -0.263** | -0.289** |
| Log population | -0.385*** | -0.266*** | -0.304*** |
| Log number of competitors | -0.074*** | -0.035*** | -0.057*** |
| Log number of subprime appl. | -0.013** | -0.074*** | -0.014*** |
| Constant | 5.996*** | 4.679*** | 4.918*** |
|  |  |  |  |
| R-squared | 0.43 | 0.40 | 0.40 |

## Alternative measures of lending standards and credit boom

- Loan-to-income ratio
- Loan originations and volumes

## Market Structure

- Effects of changes in market structure
- Focus on role of entry of new institutions
- Threat of competition may induce incumbents to cut standards
- Augment model with measure of entrants’ market share
- Focus on incumbents denial rates
- Nonlinearities in boom and market size
- Focus on larger MSA markets
- Focus on MSA with more pronounced booms

## Effect of New Entry

| Dependent variable: Incumb. denial rate | All | Prime | Subprime |
| --- | --- | --- | --- |
| House price appreciation | -0.205*** | -0.096*** | -0.297*** |
| Average income | -0.004*** | -0.007*** | -0.001 |
| Income growth | 0.009 | 0.041 | 0.031 |
| Unemployment | 0.001 | -0.001 | 0.006** |
| Self employment | -0.087 | -0.074 | -0.291** |
| Log population | -0.164*** | -0.224*** | -0.348*** |
| Log number of competitors | 0.006 | 0.011** | -0.063*** |
| Log number of applications | -0.052*** | -0.031*** | -0.022*** |
| Market share of entrants | 0.024 |  |  |
| MS of entrants to prime |  | -0.023* |  |
| MS of entrants to subprime |  |  | -0.149*** |
| Constant | 2.990*** | 3.568*** | 5.572*** |
|  |  |  |  |
| R-squared | 0.76 | 0.74 | 0.34 |

## Asset Securitization

- Recent work shows that asset securitization (e.g., Keys et al., 2007; Mian and Sufi, 2007) helped fuel the subprime crisis
- Effects of increased recourse to securitization
- Decreased incentives to monitor
- Augment model with proportion of loans sold within 1 year
- Distinguish between earlier and later periods as securitization became more relevant in the second half of the sample
- While increased recourse to securitization contributed to the decline in denial rates, our main results are robust

## Effect of Loan Sales

| Dependent variable: Denial rate | Subprime |
| --- | --- |
| House price appreciation | -0.269*** |
| Average income | -0.002 |
| Income growth | 0.096 |
| Unemployment | 0.004* |
| Self employment | -0.271** |
| Log population | -0.256*** |
| Log number of competitors | -0.057*** |
| Log number of applications | -0.032*** |
| Proportion of loans sold | -0.123*** |
| Prop. loans sold * Year≥2004 | -0.110*** |
| Constant | 4.444*** |
|  |  |
| R-squared | 0.45 |

## Summary of Findings (I)

- Credit expansion in subprime mortgage market led to a decrease in lending standards
- As measured by a decline in application denial rates and a significant increase in loan-to-income ratios not explained by changes in underlying economic fundamentals
- Denial rates declined more and loan-to-income ratios rose more in areas where the number of loan applications rose faster
- These areas subsequently experienced a sharper increase in delinquency rates, in a pattern reminiscent of boom-bust cycles in emerging markets (e.g., Sachs, Tornell, and Velasco, 2006; Caballero and Krishnamurthy, 2006)
- Results shed light on relationship between booms and crises
- Lend support to recent asymmetric information based theories

## Summary of Findings (II)

- Changes in market structure affected lending standards
- Denial rates declined more in areas with more competitors: incumbents’ lending standards were negatively affected by the entry into local markets of new financial institutions
- We interpret this as evidence that local lenders were “forced” to cut lending standards when facing competition from new entrants
- Results are robust across several specifications:
- Lending standard measures
- Credit boom measures
- Controlling for pool quality
- Endogeneity of boom variables
- Endogeneity in house prices
- Market size effects

## Discussion

- The paper sheds light on the origins of the current U.S. financial crisis by
- establishing a link between credit growth and lending standards in the subprime mortgage market, and
- identifying entry into local credit markets as a factor amplifying this decline in lending standards
- The paper highlights that there is a credit quality element in lending cycles
- This has consequences for financial stability
- A case for cyclical capital regulation?
- Booms can still be optimal