Credit risk migration analysis of farm businesses

Credit Risk Migration Analysisof Farm BusinessesPeter J. Barry, Cesar L. Escalante, and Paul N. Ellinger

Abstract

The migration approach to credit riskmeasurement is based on historic rates ofmovements of individual loans among theclasses of a lender’s risk-rating or credit-scoring system. This article applies themigration concept to farm-level data fromIllinois to estimate migration rates for afarmer’s credit score and otherperformance measures under differenttime-averaging approaches. Empiricalresults suggest greater stability in ratingmigrations for longer time-averagingperiods (although less stable than bondmigrations), and for the credit scorecriterion versus ROE and repaymentcapacity.

Key words: credit risk, credit scoring,migration, transition probabilities

Peter J. Barry is a professor and Paul N. Ellinger isan associate professor, Center for Farm and RuralBusiness Finance, University of Illinois. Cesar L.Escalante is an assistant professor, Department ofAgricultural and Applied Economics, University ofGeorgia.

Migration analysis is a relatively recent,probability-based measurement conceptfor credit risk that is consistent with themodern approaches to economic capitalmanagement by financial institutions,including those approaches contained inthe proposed New Basel Accord (Crouhy,Galai, and Mark; Matten; Barry, 2001a). The concept considers upgrades anddowngrades in the credit quality of anentire loan portfolio as well as the potentialfor significant financial stress and/or loandefault (Saunders; Caouette, Altman, andNarayanan).

Migration analysis is based on extrapolationinto the future of historic rates ofmovement (i.e., transition probabilities) ofindividual loans among the classes of alender’s risk-rating or credit-scoringsystem. The result of utilizing themigration concept is a richer, morecomprehensive perspective on credit riskand loan losses than relying solely on themeasurement of historic default rates.

Major rating companies like Standard andPoor’s and Moody’s routinely measure andreport migration rates for bonds and otherpublicly traded securities for whichextensive historic data are available (Cartyand Fons). In addition, publicly availablecredit risk models such as CreditMetricsand Credit Portfolio View also utilizetransition probabilities in measuring creditrisks (Crouhy, Galai, and Mark).

Experiences with credit migration rateshave shown a strong tendency for ratingsto retain their current classification(especially for shorter transition periods),to decline in likelihood of moving to moredistant classes, and to show a greater

2 Credit Risk Migration Analysis of Farm Businesses

pattern of downgrading than upgrading(Saunders; Altman and Kao; Altman).

Migration rates for commercial loans,agricultural loans, and other types ofloans, however, are more difficult tocompile. Data histories tend to be shorter,underwriting histories may not be updatedon term loans, and many risk-ratingsystems are relatively new. Moreover,risk-rating systems currently in use maynot delineate well among differences incredit qualities, with a tendency for highconcentrations of rated loans in a smallnumber of classes (Brady, English, andNelson; Treacy and Carey; Barry, 2001b). Consequently, little is currently knownabout the movements over time of riskratings of agricultural loans and other farmbusiness performance measures, and theirrelationships to stress and default rates.

The purpose of this study is to apply themigration concept to the case of farmbusinesses in order to derive and evaluatemigration rates among rating andperformance classes. In the absence ofextensive and detailed loan-level data fromhomogeneous classification schemesacross lenders, this study utilizes farm-level data from the Illinois Farm BusinessFarm Management Association (FBFMA) toderive annual migration rates for farmers’calculated credit scores, rates of return onequity (ROE), and repayment capacity. Adata set over the 1985 to 1998 time periodis derived under different time-averagingapproaches in order to apply the farm dataapproach.

In the following sections, we illustratethe migration concept, describe themeasurement approaches and the data,present the results, and consider theirimplications.

Illustrating TransitionProbabilities

To illustrate migration rates, consider asimplified loan rating system with twocredit risk classes, A and B, signifying low

and high risk, respectively. An A-ratedloan can either remain in class A ormigrate to class B in the next period. Similarly, a B-rated loan can either remainin class B or migrate to class A. Thesepossibilities are portrayed in Figure 1.

The transition probabilities (Pi values) areestimated from historic migration rates. For example, historic rates of movementsamong classes might indicate class A loansremain in class A 90% of the time andmigrate to class B 10% of the time. ClassB loans remain in class B 85% of the timeand migrate to class A 15% of the time.

The combined migration patterns then areexpressed by the transition probabilitymatrix in Figure 2. In this case, potentialupgrading (downgrading) of a loan isrepresented by arrow BA (AB), and thestationary class ratings are represented byarrows AA and BB from Figure 1. Theaggregate rate of high risk (i.e., being inClass B) would be the weighted average ofAB and BB using the relative numbers orvolumes of loans A and B as the weights. Continuing the numerical example, ifclasses A and B comprise 75% and 25%,respectively, of the portfolio, then theweighted average of class B is 28.75%:0.2875 = (0.10)(0.75) + (0.85)(0.25).

These rating migrations may be measuredfor any length of period, although one yearis the most common (Saunders; Crouhy,Galai, and Mark). Generally, probabilitiesfor interclass migrations tend to increaseas the length of period increases, reflectingthe potential for greater fluctuations ineconomic conditions over longer periods(Carty and Fons). The migration rates mayalso differ across industries (e.g., Moody’sInvestor Service).

Design of the Study

This study measures and evaluatesmigration rates using three financialindicators for a set of farms in Illinois. Itis based on annual farm-level data overthe time period of 1985 to 1998 for farms

Agricultural Finance Review, Spring 2002 Barry, Escalante, and Ellinger 3

Figure 1. Migration Alternatives andProbabilities

Figure 2. Transition Probability Matrix

that consistently maintained FBFMAcertified income and balance sheetstatements for the migration horizonsconsidered here. In contrast to typicalbond applications, our approach does notrequire that each farm be in the databaseover the entire 14-year period. Fewfarms (less than 50) would meet thisrequirement, given FBFMA’s stringentdata certification procedures. Rather,two consecutive years of values,reflecting the one-year horizon, areutilized for each farm over the respectivetime-averaging periods. This approach isconsistent with the premise cited belowof homogeneous farms within a risk-rating category.

The following three classification criteriafor rating farms according to probability ofloss are employed:

1. a credit-scoring model for term loansreported by Splett, Barry, Dixon, andEllinger, based on financial ratiosrecommended by the Farm FinancialStandards Council (FFSC), representinga farm’s solvency, repayment capacity,profitability, liquidity, and financialefficiency;

2. a profitability variable measured bya farm’s rate of return on equitycapital calculated following FFSCrecommendations for market-basedasset valuation; and

3. a repayment capacity variablemeasured by the ratio of the capitaldebt repayment margin to cashavailable, excluding nonfarm incomeas a source of cash due to datalimitations.

The credit-scoring model represents anapproach lenders follow in evaluating afarm’s overall creditworthiness. The creditscore reflects the combined effects ofseveral key credit factors, includingprofitability and repayment capacity. Thus, the credit score is expected to bethe most reliable and stable indicator oftrue credit risk migration. In contrast,profitability and repayment variablesrepresent important individual attributesof creditworthiness, with profitabilityreflecting the borrower’s perspective onbusiness performance and repaymentcapacity representing a lender’sperspective on loan performance. Theselatter measures could yield greatermovements in risk-rating migrations. Measurement procedures andclassification intervals for each criterionwere based on those used by Splett et al.,and are reported in Table 1.

Use of farm record data rather than lenderdata is intended to represent a proxyapproach to actual loan performance. Inthis case, the weakest credit classification


Table 1. Credit Scoring, Profitability, and Repayment Classification Intervals

Variables (Measures)/Classes Interval Ranges Weights

LIQUIDITY (Current Ratio) 1 2 3 4 5

> 2.001.60S2.001.25S1.601.00S1.25

< 1.00 ______ × 0.10 = ______

SOLVENCY (Equity-Asset Ratio) 1 2 3 4 5

> 0.800.70S0.800.60S0.700.50S0.60

< 0.50 ______ × 0.35 = ______

PROFITABILITY (Farm Return on Equity) 1 2 3 4 5

> 0.100.06S0.080.04S0.060.01S0.04

< 0.01 ______ × 0.10 = ______

REPAYMENT CAPACITY (Capital Debt-Repayment Margin Ratio) a

1 2 3 4 5

> 0.750.50S0.750.25S0.500.05S0.25

< 0.05 ______ × 0.35 = ______

FINANCIAL EFFICIENCY (Net Farm Income from Operations Ratio) 1 2 3 4 5

> 0.400.30S0.400.20S0.300.10S0.20

< 0.10 ______ × 0.10 = ______

= TOTAL SCORE (Numeric) _____________CREDIT SCORE CLASSES Class 1 Class 2 Class 3 Class 4 Class 5

1.00S1.801.81S2.702.71S3.603.61S4.504.51S5.00

Source: Splett, Barry, Dixon, and Ellinger.a New interval ranges for the repayment capacity measure were used in this study since the intervals proposed bySplett et al. resulted in the heavy concentration of observations in the first class.

for the three criteria likely includes anydefault (or delinquent) cases, and willindicate the highest probability ofapproaching default. Thus, incidences ofresiding in class 5 are considered as the“stress rate,” in that they include defaultas well as other cases of high credit risk.

Further, the use of farm record data ratherthan lender data could yield a greaterdegree of migration across risk classes forseveral reasons. First, reliance onquantitative measures alone omits theinfluence of the lender’s discretionaryjudgment, which may tend to stabilize


movements among credit ratings. Second, the farm records include somenon-borrowing farms (i.e., very low creditrisks) and some government-financedfarmers who might not qualify forborrowing from commercial lenders dueto high debt-to-asset ratios or otherdeficiencies in underwriting criteria, thusbroadening the range of risk ratings. However, no failed farms have remainedin the database. Third, lenders mayemploy various types of creditenhancements or other risk mitigationtechniques (e.g., collateral, governmentloan guarantees, co-signers, insurance) tomodify changes in the credit risks ofindividual borrowers.

The accuracy and costs of classificationerror likely experience a tradeoff as thenumber of risk-rating classes increases. A greater number of rating classesincreases the likelihood of classificationerrors, but smaller breaks between lossrates for the respective classes wouldreduce the costs of such errors.

A fixed number of five rating classesis used in this analysis. Under theschedule of interval boundaries inTable 1, the open-ended nature of classes1 and 5, versus the tighter boundaries ofclasses 2, 3, and 4, could yield largerretention tendencies at the two ends ofthe distributions. In general, this ratingapproach, which holds for any numberof rating classes, is based on theassumption that all farms (e.g.,borrowers) within the same class havethe same credit risks—i.e., they have thesame migration rates and the samestress rates.

Outlier values for the current ratio andthe repayment capacity measures werereplaced by values suggested by Novakand LaDue utilizing the outlier detectionprocedures of SAS. Current ratiosexceeding the value of 7 were assigned 7as the maximum value. The boundssuggested by Novak and LaDue for therepayment capacity variable (!4 to +15)were adjusted to correspond to the

repayment measure used in this study(i.e., to !1.25 and +0.93).1

The three classification criteria were eachevaluated using four measurementapproaches, representing differentamounts and time sequences of dataemployed in the measurement process:

1. Year-to-Year Transition (1 × 1). The year-to-year transition reflects a farm’smovement from a year n to a year n + 1classification.

2. Two-Year Moving Average (2 × 2). Thetwo-year moving average represents afarm’s possible change in theclassification of the rate of return onequity (for example, averaged for years 1and 2 to a classification determined bythe average ROEs for years 2 and 3).

3. Three-Year Moving Average (3 × 3). Thethree-year moving average represents apossible change in the classification ofROE averaged over years 1, 2, and 3, toa class determined by the average of theROEs for years 2, 3, and 4.

4. Three-Year Average to Fourth Year (3 × 1).The three-year average to fourth yearrelates the potential for changes in aROE classification based on the averageof years 1, 2, and 3, to a ROE class inyear 4.

These alternative time-measurementapproaches are intended to dampen theshort-term variations in the respectivescoring measures and thus yield measuresmore consistent with lenders’ actualpractices (Novak and LaDue). Alternative4, for example, reflects a lender’s approachto classifying a borrower based on currentperformance relative to averages of his orher three preceding years of performance.

1 Novak and LaDue used the coverage ratio of cashavailable to cash required, while Splett et al. used theratio of cash available minus cash required to cashavailable. Thus, upper and lower bounds of 15 and!4 for the coverage ratio are equivalent to 0.93 =(15 !1)/15 and !1.25 = (!4 !1)/4 for the Splett et al.ratio.


Table 2. Average One-Period Transition Matrices for Credit Scores, 1985SSSS1998

Period 2 Credit Score Classes

Period 1 Credit Score Classes 1 2 3 4 5

Year-to-Year Transition !!!!!!!!!!!!!!!!!!! (%) !!!!!!!!!!!!!!!!!!! 1 2 3 4 5

75.2622.46 8.05 4.88 0.13

16.1341.8622.8018.9711.59

6.7122.3141.9429.3435.71

1.6310.1915.1928.2617.56

0.27 3.1712.0218.5535.02

Two-Year Moving Average 1 2 3 4 5

83.3917.44 4.34 1.04 0.00

11.4957.3518.3110.99 2.81

4.7318.7955.7027.6517.84

0.39 5.6115.6944.0522.64

0.00 0.81 5.9516.2756.70

Three-Year Moving Average 1 2 3 4 5

86.8714.24 1.83 0.00 0.00

11.4665.2818.43 5.14 0.00

1.6718.0761.0426.1610.24

0.00 2.4116.5556.7327.13

0.00 0.00 2.1511.9762.63

Three-Year Average to Fourth Year 1 2 3 4 5

77.1827.40 7.82 3.36 0.43

14.9040.9328.0519.78 8.51

6.6520.6239.9438.5430.29

1.14 9.2213.9722.8728.24

0.14 1.8210.2315.4632.53

Note: Source of credit-scoring model is Splett, Barry, Dixon, and Ellinger.

Alternative 3 utilizes the same three-yearperiod of history, but moves the three-yearaverage ahead through time.

The respective time series of periodicrating migrations are then measured as aweighted average over the 1985S1998period to determine the overall transitionprobability matrix for each classificationcriterion. The weight for each migrationrate in each class, for each time period, isthe proportion of each period’s number offarms to the total number of farms for thedifferent time sequences over the 14-yearperiod.

An overall stress rate is determined asthe weighted average of the transitionprobabilities to class 5 from each non-stress class using the average number offarms in each class as the weight.

The number of farms in the {1 × 1} timesequence ranged from 347 in 1985, to945 in 1997S98, with a high of 1,077 in1992S93, and a total of 10,206observations over the entire period. Longer time average sequences yieldedfewer numbers of farms—6,620observations for the {2 × 2} sequence and4,457 farms for the (3 × 3} and (3 × 1}sequences.

Migration Results

The migration rates and related measuresfor the three classification criteria and thetime sequences are reported in Tables 2S6. These results are qualitatively consistentwith findings from other migration studiesin that the frequencies are highest forremaining in the same class, the rates


Table 3. Average One-Period Transition Matrices for ROE, 1985SSSS1998

Period 2 ROE Classes

Period 1 ROE Classes 1 2 3 4 5


60.7427.4617.7814.3228.95

15.3627.9222.1320.0911.78

5.6015.9820.0215.36 6.67

6.4415.6522.5429.2614.97

11.8612.9817.5320.9737.63


75.0222.39 9.28 6.6411.71

15.7745.4926.2713.53 6.64

3.8218.0934.2920.82 6.92

2.36 9.2823.5642.9521.97

3.03 4.75 6.6016.0552.76


81.6816.99 4.69 2.18 4.48

14.5559.2823.27 9.07 4.83

1.9617.5848.1620.31 5.42

0.78 4.8120.5452.5322.49

1.03 1.34 3.3415.9162.77


62.3426.8412.9812.3820.45

15.4032.3228.2115.75 8.99

5.8015.6622.3415.66 8.31

5.7614.8922.8631.1120.24

10.7010.2913.6125.0942.01

decline for movements to more distantclasses, and the incidence of downgradingtends to exceed the occurrence ofupgrading. However, the retention ratesare generally lower than those reported forother studies, even though the number ofrating classes (five) tends to be lower.

For example, Altman and Kao reportretention rates ranging from 86.1% to100% for their analysis of one-year ratingmigrations for bonds and corporate loans,while Carty and Fons report retentionrates ranging from 75.7% to 89.6%. Thelow retention rates for the agriculturalmeasures could indicate relatively highmovement among the rating classes,especially the higher risk classes, perhapsreflecting high intertemporal variability offarm performance, even when using thetime-averaging approaches. Alternatively,the low retention rates could reflect the

characteristics of farm data versus loandata cited earlier.

Across Tables 2, 3, and 4, the retentionrates ranged from a high of 86.87% forclass 1 in the {3 × 3} moving average creditscore to a low of 3.94% for class 4 in the{3 × 1} time sequence for the repaymentcapacity criterion. In general, retentionrates are higher for the lower risk classesand for the credit score versus the otherclassification criteria. The latter result isconsistent with the broader scope of thecredit-scoring approach in accountingfor multiple factors contributing tocreditworthiness. The {3 × 3} timesequence also appears to yield the highestretention rates.

Table 5 summarizes the transition ratesin terms of average rates of retention,upgrading, and downgrading for each


Table 4. Average One-Period Transition Matrices for Repayment Capacity, 1985SSSS1998

Period 2 Repayment Classes

Period 1 Repayment Classes 1 2 3 4 5


70.7434.7325.1817.3526.18

15.3029.8428.7729.7119.07

3.7912.9215.5016.9211.01

1.35 4.54 5.45 6.43 6.14

8.8217.9825.1129.5837.59


78.8623.0611.57 9.1512.27

12.1549.5428.1211.4812.59

2.5912.6335.3226.71 6.27

0.67 3.49 8.7922.37 4.00

5.7411.2816.2130.2964.88


81.8420.44 8.0813.81 0.00

11.7258.3120.44 8.97 9.74

2.5111.6750.34 8.50 9.01

3.73 4.38 9.3139.3113.51

0.20 5.1911.8329.4067.74


72.4039.3731.8238.0618.54

15.2832.1530.0724.4526.15

3.7510.3214.7511.5414.78

1.43 2.93 3.99 3.94 4.34

7.1315.2319.3722.0236.18

classification criterion and time sequence. Using higher average retention rates asthe evaluative criterion, the highestperformance for the transition matrices(70.16%) again occurred for the {3 × 3}time sequence of the credit score. Moreover, the highest retention rates forthe ROE and the repayment criteria alsooccurred under the {3 × 3} time sequence. The {2 × 2} time sequence consistentlyyielded the second highest set of averageretention rates.

The incidence of downgrading also tendedto predominate with 10 of the 12 pairwisecomparisons between down- andupgrading yielding higher values fordowngrading. This result is consistentwith the greater concentration ofobservations in the lower risk intervals,thus yielding a greater potential fordowngrades than upgrades.

The results for the alternative timesequences are also consistent with theobservations by Novak and LaDue thataveraging farm business performancemeasures over multiple years yields morerealistic values and avoids excessivemovements among rating classes. Clearly,the {1 × 1} sequence is highly volatile. Conversely, the {3 × 3} moving averageapproach yields more stable migrationpatterns than the {3 × 1} approach likelyfollowed by many lenders. Specifically,comparing two three-year moving averageswill yield greater stability than comparinga single year’s observation to the averageof the three preceding years.

Stress rates (including possible default)reported in Table 6 represent the overallfrequencies of migrating to (or remainingin) class 5. The stress rates are calculatedas weighted averages of the frequencies for


Table 5. Summary Transition Rates for Illinois Farms, 1985SSSS1998

Credit Score ROE Repayment Capacity

Time Sequence % No. Farms % No. Farms % No. Farms

Retention Rates 1 year × 1 year 2 years × 2 years 3 years × 3 years 3 years × 1 year

51.2463.9670.1650.03

5,2304,2343,1272,230

41.5857.1967.2045.05

4,2443,7862,9952,008

51.4063.9767.8547.14

5,2464,2353,0242,101

Upgrades 1 year × 1 year 2 years × 2 years 3 years × 3 years 3 years × 1 year

22.2216.9014.2226.50

2,2681,119 6341,181

25.1318.8813.9120.57

2,5651,250 620 917

21.1615.7814.1129.12

2,1601,045 6291,298

Downgrades 1 year × 1 year 2 years × 2 years 3 years × 3 years 3 years × 1 year

26.5319.1415.6223.47

2,7081,267 6961,046

33.2823.9318.8934.37

3,3971,584 8421,532

27.4320.2418.0423.74

2,8001,340 8041,058

Table 6. Average Stress Rates, 1985SSSS1998

Classification Criteria/ Measurement Approaches

Avg. StressRate (%)

Classification Criteria/ Measurement Approaches

Avg. StressRate (%)

CREDIT SCORE REPAYMENT CAPACITY Year-to-Year Transition Two-Year Moving Average Three-Year Moving Average Three-Year Average to Fourth Year

6.76 6.13 4.64 6.17

Year-to-Year Transition Two-Year Moving Average Three-Year Moving Average Three-Year Average to Fourth Year

15.4817.5412.4514.99

PROFITABILITY (ROE) Year-to-Year Transition Two-Year Moving Average Three-Year Moving Average Three-Year Average to Fourth Year

16.79 9.73 6.7514.54

Note: Stress rate is the probability of migrating to class 5 for the respective criteria and measurement approaches.

class 5 ratings using the average annualproportion of farm numbers in therespective classes as the weights. Underthe {3 × 3} time sequence, for example,these stress rates averaged 4.64% for thecredit score, 6.75% for ROE, and 12.45%for repayment capacity. Again, the loweststress rate for the credit-scoring criterionis consistent with its more general andconservative representation of credit risk.

These stress rates reflect measures of theoverall quality of the loan portfolio, whichare consistent with the credit risk inputs

of modern capital management models. The stress rates may also be tracked overtime to indicate possible patterns of change.Figure 3 presents time paths of stress ratesfor the {3 × 3} moving averages over the 1985to 1998 period for the three classificationcriteria. Again, the credit-score criterionconsistently yields the most stable andlowest stress rates (except for twoinstances when the ROE criterion yieldedlower values). The upward drift in thetime paths could represent the potentialfor greater stress over time, or the need toupdate the lenders’ risk-rating systems.


0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

1985-1988

1986-1989

1987-1990

1988-1991

1989-1992

1990-1993

1991-1994

1992-1995

1993-1996

1994-1997

1995-1998

Period

Percent

Credit Score Profitability (ROE) Repayment Capacity

Figure 3. Stress Rates for Illinois Farms, 1985SSSS1998, Under Three-Year Moving Average Approach

Concluding Comments

Our goal in this article was to demonstratethe applicability of credit migrationconcepts to the evaluation of farmers’credit risks. The migration concept ishighlighted by the broad scope ofinformation it provides about the riskstability of loan portfolios, and includesbut does not rely solely on measuringstress or default rates. The concept alsocontributes directly to the mark-to-marketmethod of measuring credit risk in whichthe effects of changes in credit quality onthe market values of loans can beevaluated using statistically based valuesat risk (VaR) (Saunders; CreditMetrics;Caouette, Altman, and Narayanan).

Deriving transition probabilities is anatural use of the output of institutionalrisk-rating systems. These systems arewidely used by lenders, and in the futurewill form the basis for credit riskmeasurement in determining regulatoryrequirements for economic capital held bymany banks and other types of financialinstitutions (Basel Committee on BankingSupervision).

However, similar to the estimation oftraditional default rates, the migrationapproach utilizes extensive historical data. Such data histories are seldom available,but the recent emphasis on measuringcredit risk is stimulating improved datacollection. Many of the Farm CreditSystem institutions, for example, havebegun to report credit migration rates,although the data histories tend to be lessthan five years in length. Redesigningdata systems to maintain and retrievelonger histories of risk ratings willcontribute to more effective use ofmigration rates in analyzing creditqualities of loan portfolios and incomplying with economic capitalrequirements.

Further empirical work could also addressthe uniqueness of migration rates foragricultural loans relative to other typesof loans, how migration varies acrossdifferent risk-rating and credit-scoringsystems of agricultural lenders, and howmigration rates are conditioned byeconomic cycles and structuralcharacteristics. As this study shows, farmrecord data can be utilized in long-term


migration analysis, just as they areemployed in lenders’ credit analyses offarm loans; however, accounting for thejudgmental contributions of lenders is animportant attribute of lenders’ own risk-rating systems.

References

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Credit risk migration analysis of farm businesses

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