DRCLACBS: US Consumer Loan Delinquency Rate Quarterly from FRED (1987–2026)

The US Consumer Loan Delinquency Rate covers all consumer lending at commercial banks — credit cards, auto loans, and other personal credit — quarterly since 1987, from the Federal Reserve Board. It is the broadest bank-side gauge of household repayment stress, spanning four decades and five recessions: from 4.85% at the 2009 peak to the 1.52% stimulus floor of 2021, with the rate at 2.64% in early 2026 after a post-pandemic normalization.

Dataset: US Consumer Loan Delinquency Rate (1987–2026) · Updated 2026-01-01

Source: Board of Governors of the Federal Reserve System · FRED series DRCLACBS

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns.

A value of 2.64 means 2.64% of consumer loan balances at commercial banks were at least 30 days past due.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats.

FRED Direct CSV Access

The underlying data is available from FRED under series code DRCLACBS:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=DRCLACBS

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/consumer-loan-delinquency.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/consumer-loan-delinquency.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(f"Latest: {df['consumer_delinquency_rate'].iloc[-1]:.2f}% of balances 30+ days past due")
print(df.tail())

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/consumer-loan-delinquency.csv"
df <- read_csv(url)

tail(df)
summary(df$consumer_delinquency_rate)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

The Federal Reserve Board computes the rate from quarterly call reports filed by all US commercial banks: consumer loan balances 30+ days past due (or nonaccrual) over total consumer loan balances, seasonally adjusted. Consumer loans comprise credit cards and “other consumer loans” — auto loans, personal loans, and other non-mortgage household credit.

The universe is bank-held loans. Credit unions, captive auto finance arms, fintech lenders, and securitized pools are excluded — a scope caveat that has grown as non-bank consumer lending expanded.

This dataset is updated daily (Mon–Sat, 08:00 UTC) via automated pull from the FRED API; new observations appear with each source release.

Data Quality & Provider Notes

• Release latency. ~2 months after quarter end with the Board’s delinquency release.
• Revisions policy. Minor call-report corrections only.
• Universe. Commercial banks only; the non-bank share of consumer credit (notably subprime auto and fintech personal loans) is invisible here.
• Composition. Cards and auto dominate; the mix shifts over decades, so very long comparisons partly reflect portfolio composition.
• Known gaps. None; continuous since Q1 1987.

Common Pitfalls When Using Consumer Delinquency Data

1. Using it as an auto-loan proxy. Auto delinquency is not separately published on FRED; this aggregate dilutes auto stress with card and personal loans. Segment-level claims require the NY Fed panel data.
2. Ignoring the universe shift. Consumer credit migrated toward non-banks over 30 years; a stable bank-held rate is compatible with rising stress elsewhere in the system.
3. Level comparisons without the card spread. Movements can be card-driven; subtracting the card series clarifies whether the broad aggregate or one component is moving.
4. Reading quarterly upticks as cycle turns. The series moves in slow, multi-quarter trends; single-quarter changes of ±0.1pp carry little signal.

Historical Regimes

1987–1992 — The first modern peak. Delinquency climbed to 4.21% (Q2 1991) through the S&L-era downturn — the pre-GFC record for the series.

1993–2007 — The 3–4% band. Fifteen years of range-bound delinquency across two expansions and the 2001 recession — consumer credit stress as a chronic, stable feature.

2008–2009 — The GFC record. The rate reached 4.85% (Q2 2009), the series high, as unemployment doubled and card losses surged.

2010–2019 — Structural decline. Post-crisis underwriting and deleveraging cut the rate to a 2.34% average by 2019 — a regime shift, not merely a cyclical recovery.

2020–2021 — The stimulus floor. Transfers and forbearance drove the all-time low of 1.52% (Q3 2021) — repayment performance improved during the deepest recession in the record.

2022–2026 — Normalization. A climb back to the mid-2s as pandemic supports faded, stabilizing near 2.64% — above the 2019 base, below every pre-2010 decade.

Related Macroeconomic Datasets

This aggregate spans the consumer book; its components and drivers are below.

Macroeconomic Dataset Hub

This dataset is part of the Eco3min macro-financial data repository.

Sources

• Board of Governors of the Federal Reserve System — Charge-Off and Delinquency Rates on Loans at Commercial Banks
• Federal Reserve Bank of St. Louis — FRED series DRCLACBS

Dataset Reference