Definition
A delinquency rate measures the share of loans in a lender’s portfolio that are past due. It is usually expressed as a percentage: number of delinquent loans divided by total number of loans, times 100. Delinquency is different from default: delinquent means one or more missed payments; default is a legal status reached after a longer period of nonpayment.
How delinquencies are commonly defined and reported
– Lenders and credit bureaus typically begin reporting a loan as delinquent after two missed monthly payments, which shows up as “60 days late” on a credit tradeline. A tradeline is any credit account listed on a consumer’s credit report.
– Lenders may continue to update the tradeline each month the borrower remains late (e.g., 60, 90, 120 days late). Repeated late-payment cycles produce multiple delinquency marks.
– For many federal student and other federal loans, 270 days of nonpayment is treated as default under federal regulations; private loans follow state law and contract terms. After prolonged nonpayment, lenders often use collections agencies to attempt recovery.
How delinquency rates are tracked and reported
– Financial regulators and central banks publish delinquency statistics by loan type (for example: residential mortgages, credit cards, commercial loans). These series let analysts compare credit stress across sectors and over time.
– Institutions may also break delinquency rates out by borrower credit quality (investment-grade vs. non-investment-grade) to show where risk is concentrated.
– The Federal Reserve releases quarterly data on delinquency rates for many loan categories; those series are widely used for macro and sector analysis.
How to calculate a delinquency rate (formula and worked example)
Formula:
Delinquency rate (%) = (Number of delinquent loans ÷ Total number of loans) × 100
Worked numeric example:
– Total loans in portfolio = 1,000
– Loans with payments 60+ days past due = 100
– Delinquency rate = (100 ÷ 1,000) × 100 = 10.0%
Notes on measurement
– The simple formula above counts loans. Some reports instead weight by dollar balances (delinquent balance ÷ total outstanding balance); that produces a balance-weighted delinquency rate. Be sure which definition an analyst is using.
– “Delinquent” thresholds (30, 60, 90 days) and when a loan moves to default vary by loan type and jurisdiction. Always check the data provider’s definitions.
Which loan types tend to show higher delinquency rates
– Broad statistical series often find consumer categories higher than many commercial categories. For example, in the Fed’s recent data, residential mortgage delinquencies and credit-card delinquencies were higher than other bank loan categories. Student loan delinquencies have been particularly elevated historically, though policy changes and payment pauses can materially affect those rates. Conversely, some commercial loan categories have shown lower delinquency percentages in specific periods. (See sources below for the latest published figures.)
Practical checklist — what
Practical checklist — what to check when you analyze delinquency rates:
1) Confirm the definition and aging buckets
– Ask: what does the provider call “delinquent”? (common thresholds: 30, 60, 90 days past due).
– Check whether forbearances, payment deferrals, or accounts in repayment pause are counted as delinquent or excluded. These policy-driven exceptions can materially change the series.
2) Verify numerator and denominator
– Numerator: total balance (or count) of loans meeting the delinquent threshold.
– Denominator: total balance (or count) of loans in the population. Is it all originated loans, only performing loans at origination, or period-end outstanding balances?
– Example (dollar-weighted): total outstanding loans = $200,000,000; loans 30+ days delinquent = $3,000,000. Delinquency rate = 3,000,000 / 200,000,000 = 0.015 = 1.5%.
3) Decide count-weighted vs exposure-weighted
– Count-weighted rate = (number of delinquent accounts) / (number of accounts). Useful for retail-level behavioral signals.
– Exposure-weighted (dollar-weighted) = (dollar amount delinquent) / (dollar amount outstanding). Useful for balance-sheet risk.
– Example difference: 100 loans, 10 delinquent. If delinquencies are small loans, count rate = 10% but exposure-weighted rate may be 2% if delinquent balances are proportionally smaller.
4) Check seasoning and vintage effects
– Loans behave differently by age (seasoning). New-origin vintages often have elevated early delinquencies; older vintages may stabilize.
– Perform vintage analysis: group loans by origination quarter and track cumulative delinquency/default over time. This reveals deterioration patterns that aggregate rates can hide.
5) Distinguish “delinquency” vs “default” vs “charge-off”
– Delinquency = past-due status as defined by days past due.
– Default = contractual or regulatory threshold where lender treats the loan as in default (often 90+ days, but varies).
– Charge-off = the accounting write-off of a loan; charge-offs lag delinquency and may be partial or full. Don’t assume a fixed lag; it varies by product and policy.
6) Watch for reporting lags, seasonality, and policy effects
– Delinquency series can be revised, and month-to-month moves may reflect reporting timing.
– Seasonal effects: certain consumer delinquencies rise at holidays or after tax deadlines.
– Policy changes (payment pauses, stimulus, moratoria) can suppress observed delinquencies temporarily.
7) Use transition matrices and cure rates for dynamic insight
– Transition matrix: probabilities that an account moves between states (current, 30, 60, 90+ days) from one period to the next. Useful for short-term forecasting.
– Cure rate: proportion of delinquent loans that return to current status in a given period. High cure rates reduce expected losses.
8) Adjust for sample composition and concentration
– Check concentration by borrower, geographic region, loan purpose, and collateral. A steady aggregate delinquency rate can mask concentrated pockets of stress.
– For commercial portfolios, consider industry exposure and macro sensitivities.
9) Reconcile with other risk metrics
– Compare delinquency trends with non-performing loan (NPL) ratios, allowance for credit losses (ACL/loan-loss reserves), and charge-off rates. Divergences can indicate accounting or timing issues.
10) Data quality and smoothing
– Check for outliers and small-sample noise. Use moving averages (e.g., 3-month) for short series but avoid over-smoothing when detecting inflection points.
– For small portfolios, accompany rates with confidence intervals or counts so readers can judge statistical significance.
11) Visualization checklist
– Time-series chart of delinquency rate (month or quarter).
– Heatmap or cohort chart (vintage-by-age) to show where delinquencies concentrate.
– Transition matrix table and a bar chart of cure vs escalation rates.
12) Basic formulas (for clarity)
– Delinquency rate (dollar basis) = (Sum of delinquent loan balances) / (Total loan balances) × 100.
– Delinquency rate (count basis) = (Number of delinquent accounts) / (Total number of accounts) × 100.
– Charge-off rate (period) = (Charge-offs during period) / (Average loan balance during period) × 100.
Worked numeric example (brief)
– Portfolio: 10,000 accounts; total outstanding = $50,000,000.
– 30+ day delinquents: 400 accounts; delinquent balance = $1,250,000.
– Count-based delinquency = 400 / 10,000 = 4.0%.
– Dollar-based delinquency = 1,250,000 / 50,000,000 = 2.5%.
– If 200 of the 400 delinquents cure this month, cure rate = 200 / 400 = 50%.
Common pitfalls to avoid
– Comparing series with different definitions or buckets without normalization.