Auto bad credit loan maine

Abstract

This study contributes to the credit risk management literature by implementing a credit scoring model using commonly available data on small business loans made by an economic development lender based in Maine. A total of 117 variables representing loan characteristics are initially examined, and a series of practical screening methods are used to isolate the more statistically relevant variables for predicting loan default. Only the most statistically significant variables with an economically "correct" sign are then used to build a binary logistic regression model. Three ratios, the current liabilities/current assets, the sales/gross margin, and the equity/working capital are found to be highly significant in predicting loan default. The resulting model correctly predicted 87% of bad loans.

Introduction

One element supporting the foundation for strong economic growth is the efficient allocation of capital to productive small business borrowers. This allocation process depends largely on the amount of information available to lenders about the borrower, see Saunders (1994, p.166). When information is not equally shared, information asymmetries between lenders and borrowers make lending to small businesses very risky.' This paper seeks to implement practical methods in credit scoring that are designed to help lenders manage credit risk by reducing information asymmetry.

The motivation for this study is described in Section I. Section II discusses the relationship between information asymmetry and credit scoring. Section III examines the relevant literature on credit scoring models and focuses on the statistically significant variables in predicting loan defaults found in previous studies. A description of the data set used in this paper is provided in Section IV. Section V describes the methodology used and a practical screening method employed to reduce the number of variables to a manageable level. This section can benefit small business borrowers in that it shows how lenders go about evaluating credit risk. Lenders to small business will also benefit from the description of the methods used in developing a credit scoring model and the types of variables employed. Section VI employs binary logistic regression to determine the statistical importance of each variable in the reduced data set and then`to construct a final model. This insight may be important to small businesses seeking loans because it allows them to focus on managing and maintaining a few critical financial ratios necessary for obtaining credit. Section VII summarizes the paper.

Motivation for the Study

This paper was inspired by a regional economic development lender that sought help in assessing the credit quality of borrowers and requested assistance in developing a systematic and a more accurate method of making lending decisions. The portion of the activity of this lender under study is "micro" with a median loan size of approximately $64,100. This organization targets innovative, job-generating manufacturers in the state of Maine. Borrowers include small businesses in the natural-resource, telecommunications, social services facilities, manufacturing, and health-care industries. Much of their micro-lending is to businesses with limited resources, women, and low-income and minority entrepreneurs, and young businesses. This lender obtains both private and public funds with the objective of providing gap financing as a supplement to local bank loans and owner supplied equity. Public funding comes from a variety of federal, state, and local organizations who seek to create jobs in the small business community and promote harmony with the natural environment. Much, like a bank, this economic development agency has limited funds and seeks to reduce the probability of making bad credit decisions. Poor credit decisions result in less resources for future projects. Previous to this study, the lender would collect numerous financial data from prospective borrowers and then subjectively make loans with little statistical guidance.

Information Asymmetry and Credit Scoring

Information asymmetry, which affects both lenders and small business borrowers, may hinder the efficient allocation of capital. Lenders to small business need to quickly assess the creditworthiness of prospective borrowers so as to reduce the probability of issuing bad loans while attempting to maintain their own profitability. Stiglitz and Weiss (1981) argue that information asymmetry may result in adverse selection in that only the most risky small businesses will seek loans from financial institutions, especially when higher interest rates are used as a form of rationing credit. Consequently, small businesses need to gain the perspective of lenders and credit scoring models in order to increase their chances of obtaining much needed loans. The credit scoring model implemented in this paper employs binary logistic regression to estimate the probability of loan default based on historical data readily available to the lender. These data are routinely supplied to the lender by the borrower and are used in making lending decisions. Included are commonly used economic variables in managing small businesses such as financial ratios, owner characterisitcs, and loan type. The credit scoring model relies upon systematic relationships between these variables in order to estimate the probability of default.

Literature Review

According to Mester (1997), credit scoring models, as applied to small business lending, are relatively new and the literature in this area is sparse. Much of the problem lies in the availability of data concerning small business lending. This paper helps to fill this gap in the literature by using a data set from an economic development lender whose primary market is small business lending. A systematic method of constructing a credit scoring model for small business lenders is developed.

Credit scoring models are commonly used in many areas of consumer lending such as credit cards, residential mortgages, and auto loans. Despite their broad acceptance in consumer lending, credit scoring models are less commonly employed in commercial lending due to the low volume and heterogeneity of these loans.2 While credit scoring is difficult to apply to large commercial loans, it is gaining momentum in small business lending as lenders increase their activities in this area and data sets are developed for testing and implementation, see Mester (1997). Recent literature focusing on credit scoring model building for small or large commercial loans is not plentiful. Fortunately, while loan default is not the same as business failure, the predictive variables found in the loan default literature are similar to the variables found to predict business failure.

The business failure literature is extensive and hosts a bewildering list of predictive variables. Altman and Narayanan (1997), for example, compile a survey of research papers that cover data from 21 countries and focuses on predicting business failure using credit scoring models. Their findings show that a variety of variables, both qualitative and quantitative, predict business failure. Some of the quantitative variables are quite common, such as current assets/current liabilities, working capital/total assets, total debt/total assets, gross income/total assets, and gross income/current liabilities, all of which are found in Gloubos and Grammatikos (1988). Some of the variables are not so common and attaching a meaningful economic interpretation is challenging. For example, a study by Bhatia (1988), which is based on data from India, finds that (interest expense)/(value of output) and (stock of finished goods)/(sales) are predictive of loan default.

Our results show that three ratios, current liabilities/current assets, sales/gross margin, and equity/working capital are significant in predicting loan default. Current liabilities/current assets and equity/working capital represent the reciprocals of commonly used liquidity measures. The sales/gross margin ratio is the reciprocal of a profitability measure. It is difficult to find references that employ any one of these specific variables. One exception is Taffler (1981) who finds working capital/equity to be among a list of other variables that predict business failure.

In the literature, be it the scant research that employs commercial loan credit scoring tests or the business failure prediction tests, the components and/or reciprocals of the ratios that we suggest are predictive are not uncommon.3 For example, Edmister (1972) summarizes the business prediction literature and finds that working capital/total assets and net operating income/sales (i.e., net operating margin) are predictive. In his own research, Edmister (1972) finds that equity/sales, working capital/sales, current liabilities/equity, and inventory/sales are significant in predicting small business loan default.