Search results

Logistic regression techniques for panel data are used to identify factors affecting farm credit transition probabilities. Results indicate that most farm‐specific factors do not have adequate explanatory influence on the probability of farm credit risk transition. Class upgrade probabilities are more significantly affected by changes in certain macroeconomic factors, such as economic growth signals (from changes in stock price indexes and farm real estate values) and larger money supply that relax the credit constraint. Increases in interest rates, on the other hand, negatively affect such probabilities.

The purpose of this paper is to examine the relative financial strength and endurance of several paired classes of farmers according to business maturity (beginning versus mature farm businesses), farm operators’ age/experience (young versus older, more experienced farm operators), and farm size (small vs large farm businesses) by utilizing random-effects ordered logistic techniques.

This study uses a credit migration approach to analyze the factors that impact the probability of farm credit migration rates. An ordered logit model is used to assess the influence that factors have on a farm upgrading, staying same, or downgrading in credit rating.

Results show that increasing farm size will lead to a higher probability of class upgrades. Being a young farm operator, meanwhile, decreases this probability. Positive changes in money supply and farm real estate values were found to increase the likelihood of credit upgrades. Results also show trend reversal of credit risk movement, where upgrades (downgrades) are more likely to be followed by downgrades (upgrades).

With farms being dependent on capital for growth, knowing what factors affect the ability of a farm to obtain credit lends insight in the agricultural credit markets. This paper is also the first to assess the impacts of these factors on small farms which constitute 92 percent of farms in the USA per the US Department of Agriculture.

The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector.

This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed.

Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models.

This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.

This study introduces two Markov chain time approaches, time‐homogeneous and nonhomogeneous models, for analyzing farm credit risk migration as alternatives to the traditional discrete‐time (cohort) method. The Markov chain models are found to produce more accurate, reliable transition probability rates using the 3 x 1 migration measurement method used by farm lenders. Compared to corporate bond ratings migration results, this study obtained larger mean differences in singular value decomposition between the cohort matrix and each of the Markov chain matrices. This finding suggests that the omission of transient, indirect migration activities under the cohort method is more costly when applied to farm credit analysis. This discrepancy could lead to understated transition probability estimates which, in turn, could produce misleading indicators of farm loan portfolio quality.

The purpose of this paper is to examine empirical customer account data from 2006 through 2012 to review the probability of default (PD) rating methodology implemented by a FCS association for production agricultural accounts. This analysis provides insight into the migration of accounts across the association’s currently established PD rating categories with negative migration being a precursor to potential loan default.

The data set contained 17,943 observations from the years 2006 to 2012 and consisted of various fields of data including balance sheet date, earnings statement date, and PD rating as of the statement date. The methods include analysis on the dynamics of the PD ratings and component ratios. OLS regression was used to analyze the data to see how the current period PD rating and component ratios affected the PD rating one year, three years, and five years out. OLS regression examined the statistical significance of the PD ratings and ratio components for this analysis. The dependent variable, Future PD Rating, represents the assigned PD rating for the observed farm either one, three, or five years into the future. It is expected that the initial PD rating in any given year would have a positive relationship, and be statistically significant in estimating future PD ratings. The independent variables are the current PD rating and the various component ratios of the inverse current ratio (CR), the debt to asset ratio (D/A), the gross profit to total liabilities ratio, the inverse debt coverage ratio, working capital to gross profit, and funded debt to EBITDA.

Results indicate that financial ratio information gathered today can do a good job forecasting PD ratings up to three years in the future. CR information does not forecast five years into the future very well. Thus, there is an important need to update financial information on a regular basis. The results indicate that the D/A information is very important in predicting risk ratings. As the production agriculture sector has experienced difficult financial conditions during 2014 and 2015, agricultural finance institutions need to obtain up-to-date financial information from their clientele to effectively assess the risk of and manage their financial portfolio.

Several previous works have examined and established models to assess risk in agricultural lending. This research adds to this body of work by examining the migration of an account’s risk-rating class over time using actual lender account data.

Loan migration analysis is conducted using a large data set of loan risk ratings in the Farm Credit System. We find path dependence and limited support for a trend reversal pattern. There is evidence that the magnitude of migrations reported in previous credit score proxy studies overstates trend reversal in agricultural loans rated by lenders. Our results indicate that retention rates of agricultural loan risk ratings are quite high. Small loans are less likely to migrate than medium and large‐sized loans, and unseasoned loans are more likely to migrate than seasoned farm loans

This paper aims to provide a “biography” of sorts on Agricultural Finance Review. The paper tracks the evolution of Agricultural Finance Review from its introduction in 1938 to its current status.

The paper is based on a complete review of every paper and every issue. Not all papers were read by the author, but key papers of interest that in one way or another made significant contributions to the study of agricultural finance were reviewed.

The paper shows the evolution of agricultural finance from the early days of reporting financial data in the 1930s and 1940s, to its emergence as a major and significant sub discipline of the general field of agricultural economics.

As indicated, not all papers were fully reviewed or read. It is possible that papers identified as “firsts” may have been preceded by other papers. Nonetheless the paper identifies the basic evolutionary path of the journal and defines key points in time when a paradigm shift emerged to change the direction of this discipline.

As Agricultural Finance Review transitions from the Department of Applied Economics and Management at Cornell University to Emerald Group Publishing Limited, this “biography” provides readers with a general overview of the journal's and the discipline's historical development.

This paper is simply a review of the existing literature found in Agricultural Finance Review.

The purpose of this paper is to analyze the repayment records of Farm Service Agency (FSA) borrowers in two distinct US farming regions that have been experienced serious drought conditions even as the US economy was going through a recession. The analysis will identify factors that significantly influence both the probability of FSA borrowers’ survival (capability to remain in good credit standing) and temporal endurance (or length of period of good standing with creditor).

This analysis utilizes a data set of farm borrowers of the Farm Service Agency that regular farm lenders have classified as “marginal” relative to other borrowers. The research goal is addressed by confining this study’s regional focus to the Southeast and Midwest that have both dealt with financial stress arising from abnormal natural and economic conditions prevailing during the same time period. A split population duration model is employed to separately identify determinants of the probability and duration of survival (condition of good credit standing).

This study’s results indicate that larger loan balances, declining commodity prices, and the severity of drought conditions have adversely affected both the borrowing farms’ probability of survival and temporal endurance in terms of maintaining non-delinquent borrower standing. Notably, Midwestern farms have been relatively less affected by drought conditions compared to Southeastern farms. This study’s results validate the contention that the farms’ capability to survive and the duration of their survival can be attributed to differences in regional resource endowments, farming activities, and business structures.

This study’s analytical framework departs from the basic duration model approach by considering temporal endurance, in addition to survival probability analysis. This study’s original contributions are enhanced by its specific focus on the contrasting farm business structures and operating environments in the Midwest and Southeast regions.

This study examines credit score migration rates of farm businesses, testing whether migration probabilities differ across business cycles. Results suggest that agricultural credit ratings are more likely to improve during expansions and deteriorate during recessions. The analysis also tests whether agricultural credit ratings depend on the previous period migration trends. The findings show that credit score ratings exhibit trend reversal where upgrades (downgrades) are more likely to be followed by downgrades (upgrades).

The migration approach to credit risk measurement is based on historic rates of movements of individual loans among the classes of a lender’s risk‐rating or credit‐scoring system. This article applies the migration concept to farm‐level data from Illinois to estimate migration rates for a farmer’s credit score and other performance measures under different time‐averaging approaches. Empirical results suggest greater stability in rating migrations for longer time‐averaging periods (although less stable than bond migrations), and for the credit score criterion versus ROE and repayment capacity.