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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 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?
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
“Rules” vs. “Discretion” “Rules” “Discretion” Loan Rates
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
Heterogeneity in Pricing Models? • Sample split regressions (by loan size) • Degryse & Ongena (JF 2005)
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
β<0 “Rules” Hypothetical Example Loan Rate Loan Size
Hypothetical Example β<0 γ<0 Loan Rate Loan Size
Relation Between β and γ β<0 γ<0 Loan Rate Loan Size
Relation Between β and γ β<0 γ<0 Loan Rate Loan Size
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
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)
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)
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
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