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Rules versus Discretion in Loan Rate Setting

Rules versus Discretion in Loan Rate Setting. Geraldo Cerqueiro‍, Hans Degryse and Steven Ongena CentER, Tilburg University Small Business Banking and Financing: A Global Perspective May 26, 2007. Man or Machine Behind the Desk? Is this the End of the Loan Officer?.

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Rules versus Discretion in Loan Rate Setting

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  1. Rules versus Discretion in Loan Rate Setting Geraldo Cerqueiro‍, Hans Degryse and Steven Ongena CentER, Tilburg University Small Business Banking and Financing: A Global Perspective May 26, 2007

  2. Man or Machine Behind the Desk?Is this the End of the Loan Officer?

  3. The Role of Technology in Banking «The solution (LiquidCredit Bank2Business) also provides a risk-based pricing matrix. Having an objective, suggested price is very helpful» Tina Reisedge*, 2003 *Small Business Product Manager of First Tennessee Bank

  4. “Rules” vs. “Discretion” “Rules” “Discretion” Loan Rates

  5. Methodology and Main Results • Our methodological approach: • Variance analysis of unexplained component of loan rates (heteroscedastic regression model) • Our main findings: • The importance of “discretion” decreases with: • Loan size • Strength of the firm bank relationship • And increases with: • Borrower opaqueness • Distance between bank and firm

  6. Loan Pricing Models and R2

  7. Heterogeneity in Pricing Models? • Sample split regressions (by loan size) • Degryse & Ongena (JF 2005)

  8. Econometric Model • Heteroscedastic regression model: Mean equation: yi = β'Xi + ui Variance equation: Var[ui] = exp(γ‘Zi) • Extreme cases: • 100% “Rules”: R2 of mean equation → 1 • 100% “Discretion”: R2 of mean equation → 0

  9. β<0 “Rules” Hypothetical Example Loan Rate Loan Size

  10. Hypothetical Example β<0 γ<0 Loan Rate Loan Size

  11. Relation Between β and γ β<0 γ<0 Loan Rate Loan Size

  12. Relation Between β and γ β<0 γ<0 Loan Rate Loan Size

  13. Data and Variables in Mean Equation • Datasets: • 1993, 1998 and 2003 (N)SSBF • Belgian sample in Degryse & Ongena (JF 2005) • In the loan-pricing equation we control for: • Underlying cost of capital • Loan characteristics • Firm/Owner characteristics • Relationship characteristics • Competition / Location measures • Type of lender

  14. Results Mean Equation • Number of predictors: 60 • R2 of mean equation: 25% • Relevance of information: • Predicts 91% of observed loan approval outcomes • Linearity assumption: • Inclusion of higher order terms • Semi-parametric approach (single-index model)

  15. Variables in Variance Equation • In the variance equation we include variables proxying for market imperfections: • Information asymmetries • Firm opaqueness Petersen & Rajan (QJE 1995) • Characteristics of loan contract • Strength of firm-bank relationsip Petersen & Rajan (JF 1994), Berger & Udell (JB 1995) • Competitive structure of banking markets • Market concentration Hannan (JBF 1991, RIO 1997) • Firm-bank distance Hauswald & Marquez (RFS, 2005)

  16. Results of Variance Equation

  17. Results of Variance Equation

  18. Results of Variance Equation

  19. Economic Significance

  20. Has “Discretion” Varied Over Time? • Standard deviation of loan rates increases from 2.1 in 1993 to 2.5 in 2003 • Empirical Test: • include in variance equation a time trend and interaction terms • Results: • Discretion has not decreased over time • Changes in market conditions explain increase in variance Berger, Frame & Miller, (JMCB 2005) • Evidence of risk-shifting behavior

  21. Conclusions • Heteroscedastic model identifies determinants of unexplained dispersion of loan rates (“discretion”) • “Discretion” increases with... • Borrower opaqueness • Distance between bank and firm • and decreases with... • Loan size • Strength of firm-bank relationship • Evidence that the use of “discretion” has not decreased over the last 15 years

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