FRED DRTSCILM — Daily CSV Download (US Bank Lending Standards)

The Senior Loan Officer Opinion Survey (SLOOS) measures the net percentage of domestic banks tightening standards on commercial and industrial loans. Positive values indicate tightening; negative values indicate easing. This indicator is a powerful leading signal: tightening above +30% has preceded every US recession since 1990 by 2–4 quarters. FRED series DRTSCILM since 1990.

Dataset: US Bank Lending Standards (1990–2026) · Updated —


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Source: FRED series DRTSCILM · Federal Reserve — Senior Loan Officer Opinion Survey (SLOOS)

Macro Takeaway

This indicator is a key component of the macro-financial monitoring framework. Its current level relative to its historical distribution — captured in the percentile and z-score above — provides immediate context for whether conditions are historically normal, stretched, or compressed.

Cross-referencing with the US GDP growth and the high yield credit spreads helps situate this indicator within the broader macro regime.

Dataset Overview

IndicatorUS Bank Lending Standards (1990–2026)
GeographyUnited States
FrequencyQuarterly
Period1990–2026
Variablesdate, net_pct_tightening
FormatCSV, Excel (XLSX)
SourcesFederal Reserve — Senior Loan Officer Opinion Survey (SLOOS)
Last updated—

Dataset Variables

The CSV and Excel files contain the following columns.

ColumnTypeDescription
dateDate (YYYY-MM-DD)Observation date (quarterly)
net_pct_tighteningFloatNet percentage of banks tightening C&I loan standards

Column names match the CSV headers exactly.

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 DRTSCILM:

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

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/us-bank-lending-standards.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/us-bank-lending-standards.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["net_pct_tightening"].describe())

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/us-bank-lending-standards.csv"
df <- read_csv(url)

head(df)
summary(df$net_pct_tightening)

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

Methodology

The data comes from the Fed’s quarterly Senior Loan Officer Opinion Survey (SLOOS), which polls ~80 large domestic banks on changes in lending terms. FRED series DRTSCILM captures net tightening on C&I loans to large and middle-market firms.

This dataset is updated quarterly (Q+1 month) via automated pull from the FRED API.

Historical Regimes

Historical regime analysis for this dataset will be added in a future update. The key stats block above provides immediate context for the current reading relative to the full historical distribution.

Related Macroeconomic Datasets

Lending standards are a transmission mechanism — they connect monetary policy (rates) to the real economy (credit availability). When banks tighten, credit growth slows, investment contracts, and eventually employment follows. The survey captures intentions before outcomes materialize in hard data.

Related Research

Lending standards sit at the intersection of monetary policy and credit conditions. The SLOOS survey has consistently led hard economic data — and credit spread widening — by 1–3 quarters.

Macroeconomic Dataset Hub

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

Explore the Eco3min Dataset Hub

Sources

  • Federal Reserve — Senior Loan Officer Opinion Survey (SLOOS)

Suggested Citation