The Role of Soft Information in a Dynamic Contract Setting:

Transcription

1 The Role of Soft Information in a Dynamic Contract Setting: Evidence from the Home Equity Credit Market Sumit Agarwal Brent W. Ambrose Souphala Chomsisengphet Chunlin Liu Federal Reserve Bank of Chicago Penn State University Office of the Comptroller of the Currency University of Nevada-Reno The views expressed in this presentation are not necessarily those of the OCC, Federal Reserve Bank of Chicago or the Federal Reserve System. 1

2 Theoretical Motivation A growing academic literature now recognizes that information comes in two flavors: hard and soft. Hard information is easily verifiable (e.g. such as the income shown on the borrower s last several tax returns ) [Stein, 2002] Consumer credit scores Corporate bond ratings Soft information cannot be directly verified by anyone other than the agent who produces it. [Stein, 2002] Information acquired during an interview Based on bank-borrower relationships [Degryse and Cayseele (2000), Chkraborty and Hu (2006), Brick and Palia (2007)] 2

3 Theoretical Motivation Research on hard information: Role of credit scoring on loan origination and performance Roszbach (2004), DeYoung, Glennon and Nigro (2008) Deng, Quigley and Van Order (2000) Soft information is difficult to observe requiring researchers to rely on proxies to test for its presence. Proxies for soft information: Physical distance between borrower and lender: Petersen and Rajan (2002), Del Ariccia and Marquez (2004), Berger et al. (2005), Butler (2008), and Agarwal and Hauswald (2010) Banking relations at IPOs Gonzalez and James (2007) Credit scoring and borrower-lender distance DeYoung, Glennon, and Nigro (2008) 3

4 Research Questions No study has direct evidence on the actual utilization or effectiveness of soft information. Previous empirical studies rely on booked loans. One of the goals of this study is to provide such evidence using a unique dataset that tracks the dynamic contracting environment from loan application through origination. We address the following questions: How extensive is the use of soft information in loan origination? How does the outcome of the borrower-lender negotiation affect the performance (default or prepayment) of the booked loan? 4

5 Figure 1: HOME EQUITY CREDIT ORIGINATION PROCESS Step 1: Primary Screening Consumer chooses a loan contract from a menu of options: Type: Loan vs. Line LTV: 0-80 vs vs Lien: First vs. Second Step 2: Credit Rationing Lender rejects the loan contract application Step 2: Secondary Screening Lender screens for moral hazard and adverse selection and makes a counteroffer Step 2: Accepting Lender accepts the loan contract application Counteroffer 1: Lender lowers LTV and/or changes loan type (loan to line). Counteroffer 2: Lender increases LTV and/or changes loan type (line to loan). Step 3: Step 3: Step 3: Step 3: Consumer rejects the counteroffer Consumer accepts the counteroffer Consumer rejects the counteroffer Consumer accepts the counteroffer Step 4: Lender issues credit to the accepted applications 5

6 Home Equity Credit Market Home equity represents a large (and growing) segment of the consumer credit market. Market Size (2005): $702 billion Typical Home Equity Menu: Risk-based pricing according to loan-to-value Less than 80% LTV 80% to 90% LTV Greater than 90% LTV 6

7 Data Home equity contract originations from a large financial institution 108,117 consumers applying for home equity contract from lender s standardized menu (March - December 2002) 8 Northeastern states: MA, ME, CT, NH, NJ, NY, PA, RI Dataset contains information on: Borrower s initial contract choice Lender s primary screening (accept, reject, or additional screening) Lender s counteroffer Borrower s response to counteroffer Borrowers repayment behavior (origination - March 2005) Borrower s credit quality and purpose for the loan Demographics: income, debts, age, occupation 7

8 Data Count % Consumer Chooses a Credit Contract 108,117 Primary Screening: Bank Accepts Credit Contract 62, % Bank Rations Credit Contract 12, % Secondary Screening and Counteroffer 33, % Counteroffer 1: Lower LTV and/or Change Type 1 23, % Counteroffer 2: Higher LTV and/or Change Type 1 10, % Consumer Rejected Counteroffer 12, % Counteroffer 1: Lower LTV and/or Change Type 8, % Counteroffer 2: Higher LTV and/or Change Type 4, % Consumer Accepted Counteroffer 21, % Counteroffer 1: Lower LTV and/or Change Type 15, % Counteroffer 2: Higher LTV and/or Change Type 6, % Total Booked 83, % 1 Type refers to home equity loan or line. 9

9 Initial Contract Choice Three contract choices borrower risk sorting mechanism through collateral 1) LTV 80 pledging at least 20 cents per dollar loan (j=1) 2) 80 < LTV < 90 pledging cents per dollar loan (j=2) 3) LTV 90 pledging 10 cents or less per dollar loan (j=3) Test higher (lower) credit quality borrowers offer more (less) collateral Pr LTV i e j 3 e k 1 X j X k j i k W i j i W k i W = borrower credit quality X = control variables (demographics, prop type, loan purpose, etc...) 10

10 Initial Contract Choice (Table 3) Less credit-worthy borrowers (lower FICO) are more likely to apply for higher LTV home equity contracts (pledging less collateral per dollar). For example, (a) (b) A borrower with FICO score of 700 is 21.4% more likely to select an 90+ LTV contract than a 800 FICO score borrower. A borrower with FICO score of 700 is 18.9% more likely to apply for a LTV than a 800 FICO score borrower. Results clearly indicate an inverse relationship between borrower credit quality and collateral pledged. 11

11 The Use of Soft Information in Underwriting Example of acquisition of soft information: Assume that a borrower initially submits an application requesting a 90 percent LTV loan for the stated purpose of making a home improvement. Based on hard information contained in the loan application, the automated underwriting system refers the application to a loan officer for secondary screening. During the secondary review, the applicant and loan officer discuss the loan request. The applicant reveals a more extended description of the planned home improvement. In this context, the actual intended home improvement is soft information not captured on the loan application. 12

12 The Use of Soft Information in Underwriting Example (continued): Based on local knowledge of the market, the loan officer may realize that the loan amount requested far exceeds the usual costs for such an improvement. The loan officer suggests a lower loan amount as her objective is to reduce credit losses by lowering the debt service burden and curtailing the borrower s ability to consume the excess credit on non-home improvement projects. If the consumer insists on the requested loan amount and the loan officer realizes (again through the collection of soft information) that the consumer does not need the funds immediately, then the loan officer could suggest a switch in products from a loan to a line-of-credit. Under both these scenarios, the counteroffer has a lower APR. We classify the contracts based on whether the loan officer proposed an increase or decrease in the contract interest rate. 13

13 The Impact of Dynamic Contracting We evaluate the ex post repayment performance of all the 83,411 borrowers who were booked during both the primary screening and secondary screening using a standard competing risks model. 916 (1.1%) defaulted 32,860 (39.4%) prepaid Focus on the effects of counteroffer 1 (lower APR) versus counteroffer 2 (higher APR). 14

14 The Impact of Dynamic Contracting Overall, we find that the lender s use of soft information successfully reduced the risks associated with ex post credit losses. Counteroffer 1: Relative to loans that did not receive additional screening, loans that the lender ex ante required additional collateral and/or switched the product type from HEL to HELOC are 11.1% less likely to default and 10% more likely to prepay ex post. Counteroffer 2: Relative to loans that did not receive additional screening, loans with a higher APR counteroffer are 4.1% more likely to default and 2.7% more likely to prepay. 15

15 The Impact of Dynamic Contracting Economic implications of using soft information: We estimate the impact on the $700 billion home equity credit portfolio that existed in 2005, assuming an average default rate of 1%. The 11.1 percent net reduction in defaults arising from counteroffer 1 could have saved approximately $777 million in direct default costs. The 4.1 percent higher default rate resulting from counteroffer 2 would have increased default costs by approximately $294 million. The higher default costs associated with counteroffer 2 are offset by the higher APR. For example, the increase in APR by counteroffer 2 is about 180 basis points for an average duration of 18 months on a loan amount of $40,

16 Conclusions Our empirical analysis suggests that a borrower s choice of credit contract reveals information about his risk level. Specifically, we find that a less credit-worthy borrower is more likely to select a contract that requires him to pledge less collateral. We find that a lender s efforts ex ante to mitigate contract frictions by using soft information can be effective in reducing overall portfolio credit losses ex post. Results show that a counteroffer that lowers the APR reduces the default risk ex post by 11 percent, while a counteroffer that raises the APR increases the default risk ex post by 4 percent. 17

17 Conclusions Our analysis clearly indicates that borrower lender contract negotiations can impact ex post default risk and thus should impact ex ante loan pricing. The analysis clearly shows that, in a market with readily available credit scoring and automated underwriting technology, samples of originated loans will contain loans originated solely through the use of hard information as well as loans that were originated based on soft information. As a result, empirical studies of the effect of soft information that rely on observations of originated loans will be biased. 18