Competing risks models of Farm Service Agency seven‐year direct operating loans

Abstract

Purpose -The purpose of this paper is to apply duration methods to a sample of Farm Service Agency (FSA) direct, seven-year operating loans to identify those variables that influence the time to loan termination and type of termination. Variables include both those known at time of loan origination and those that characterize the changing economic environment over the life of the loan. Also, to examine the impact of various FSA programs promoting policy objectives. Design/methodology/approach -A systematic sample of 877 seven-year, FSA direct loans originated between October 1, 1993 and September 30, 1996 was collected. Cox regression, competing risks models are estimated as a function of borrower and loan characteristics observable at loan origination. Economic indicator variables emphasizing the farm economy and observed quarterly over the life of the loan are also included as explanatory variables. Findings -Loan characteristics, borrower financial characteristics and degree of borrower interaction with FSA observable at origin are significant variables in determining type of loan outcome (default or paid-in-full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant.Research limitations/implications -The sample consists only of FSA direct loans which implies borrowers are at financial margin. Application of method to agricultural loans from conventional commercial lenders could identify different significant factors. Practical implications -Using length of time to loan termination instead of just type of outcome provides for a richer analysis of loan performance. Loan performance over time is influenced by the larger economy and should be incorporated into loan performance modeling. Originality/value -The study described in the paper demonstrates use of competing risks models on intermediate agricultural loans and develops how this technique can be used to learn about dynamic aspects of loan performance. Sample consists of observations on individual FSA direct loan borrowers. The FSA direct loan program is the major source of credit for agricultural borrowers at the financial margin.

Modeling duration of FSA operating and farm ownership loan guarantees

“…These results at first appear to be somewhat different than those in Dixon et al (2011) whose results were for direct OL loans. Dixon et al found BF loans to be insignificant, while direct loans that were both beginning and socially disadvantaged (BFS) were less likely to be paid in full and, after loan restructuring, were more likely go into default.…”

“…The method is presented in more detail in Kalbfleisch and Prentice (2002), Dressler and Stokes (2010), and Dixon et al (2011). As opposed to conventional regression methods, duration models accommodate censored observations and time-varying variables routinely, which is relevant for this study.…”

“…If the outcome event is expired with loss, a positive β indicates that an increase in x increases the probability of loan default and decreases loan survival time. Different from the parametric model, the Cox proportional hazards model does not require making any assumptions about the shape of the base line hazard function (λ 0 (t)) or the distribution of the time of occurrence for some event, T. Following Dixon et al (2011), the partial likelihood of an observation ending ( J ) by ending event j out of m ending events is:…”

“…They found initial interest rates, and time-varying debt-to-asset ratios and FICO scores are significant indictors of default risk. Like Dressler and Stokes (2010), Dixon et al (2011) estimate a competing risks, Cox proportional hazards model on a sample of FSA direct, farm OLs. Proportional hazards 428 AFR 76,4 models estimate the relative probability of possible outcomes.…”

Credit NGOs' sustainability in rural financial market: a SWOT analysis on DAYBANG

“…As generally, loan characteristics and borrower financial characteristics are significant variables in determining type of loan outcome (default or paid-in-full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant (Dixon et al, 2011). The staff of DAYBANG will make the evaluation by these two kind of indexes, and highlight the project revenue assessment and other income and expenses during the loan lifetime which can suggest the changes in the economic environment and farm economy during the life of the loan.…”