Technical writing
Federal Reserve Senior Loan Officer Survey: The Quarterly Credit Conditions Data the Fed Uses to Track Lending Tightening
Four times a year — in January, April, July, and October — the Federal Reserve Board surveys roughly 80 large domestic banks and 24 US branches of foreign banks about whether they have tightened or eased their lending standards and whether loan demand has risen or fallen. The Senior Loan Officer Opinion Survey on Bank Lending Practices, universally abbreviated SLOOS, is the Fed's primary quarterly instrument for tracking credit conditions across the US banking system. Where rate decisions move the price of credit, SLOOS measures whether credit is actually becoming available at any price — a distinction that matters acutely when the central bank is trying to calibrate how monetary policy is transmitting into the real economy.
What SLOOS Is and How It Works
The SLOOS is a structured opinion survey, not a statistical measurement drawn from administrative data. The Federal Reserve Board's Division of Monetary Affairs administers it to a panel of senior loan officers at the largest US commercial banks, selected to provide broad coverage of the domestic credit market. The panel typically includes the country's largest bank holding companies by asset size, ensuring that the survey captures lending practices at institutions that together hold the majority of US commercial and industrial loan balances.
The survey asks respondents about changes that have occurred during the current quarter relative to the prior quarter. It is not a forecast survey — respondents are not asked where they expect standards to go; they are asked where standards have gone. That retrospective framing distinguishes SLOOS from forward-looking credit sentiment surveys and gives it a different informational character: it tracks realized credit cycle dynamics rather than intentions.
Questions come in two varieties. The standard quarterly questions cover lending standards and loan demand across three major loan categories: commercial and industrial (C&I) loans, commercial real estate (CRE) loans, and consumer and residential mortgage loans. Special topical questions, added each quarter by Fed staff, address emerging credit themes and give the survey a forward-looking texture its standard questions lack. Results are published approximately two weeks after the quarterly FOMC meeting that follows quarter-end, typically in early to mid-February, May, August, and November.
The survey's sample size is small by the standards of federal statistical programs. About 80 domestic bank respondents plus 24 foreign bank branches means individual responses carry significant weight. The Fed does not publish individual bank responses; results are aggregated and reported as the net percentage of respondents reporting tightening or easing.
The Net Percentage Concept
The SLOOS reports its results as a “net percentage,” which is the single most important number to understand when reading any SLOOS release. Net percentage is calculated as the percentage of respondents reporting tighter standards minus the percentage reporting easier standards. Respondents who report no change contribute zero to the net. The formula is straightforward:
Net percentage = % tightening − % easing
A positive net percentage means more banks are tightening than easing — net tightening. A negative net percentage means more banks are easing than tightening — net easing. Zero means tightening and easing are exactly balanced. The measure ranges roughly from −100 (every bank is easing, none tightening) to +100 (every bank is tightening, none easing), though in practice it rarely approaches either extreme.
The net percentage framing means the absolute level of the measure reflects the balance of credit conditions at a point in time, while the direction of change reveals whether that balance is shifting. A reading of +20 sustained over four quarters means a majority of banks are persistently tightening; a move from +20 to +5 signals that tightening is becoming less widespread even if net tightening is still occurring. Both the level and the trajectory matter for macroeconomic interpretation.
The SLOOS also captures the specific dimensions along which standards are changing. For C&I loans, banks are asked separately about changes in interest rate spreads over their cost of funds, loan covenants (financial maintenance covenants, borrowing base restrictions), collateral requirements, and non-price terms such as maximum loan size and credit line commitments. For CRE loans, similar disaggregation covers loan-to-value ratios, debt service coverage requirements, and interest rate spreads. This term-level detail allows analysts to distinguish between price tightening (higher spreads) and non-price tightening (stricter covenants, lower LTVs) — a distinction with different implications for borrower behavior.
Standard Questions by Loan Category
The SLOOS covers four principal loan categories, each with both a supply (standards) and a demand question, producing parallel net percentages on both sides of the credit market.
Commercial and Industrial Loans are the most closely watched category. The survey distinguishes between lending standards for large and medium-sized firms and lending standards for small firms. This split is analytically important: small businesses depend more heavily on bank credit than large firms, which can access public capital markets, so tightening in small-firm C&I standards has a more direct and rapid effect on employment and investment at the smaller end of the business distribution. FRED carries both series: DRTSCILM for large/medium firms and DRTSCIS for small firms.
Commercial Real Estate Loans are divided into three segments: construction and land development loans, multifamily residential loans, and nonfarm nonresidential loans (office, retail, industrial, hospitality). Construction and land development loans are typically the most cyclical because they finance projects whose value is contingent on future economic conditions. Nonfarm nonresidential loans have attracted particular scrutiny since 2022 because rising interest rates and declining office occupancy created simultaneous refinancing and valuation pressure.
Residential Mortgage Loans are split into four subcategories: GSE-eligible conforming mortgages, jumbo mortgages, subprime mortgages, and home equity lines of credit (HELOCs). Subprime mortgage standards are reported but receive less emphasis today than during the 2004–2008 period, when they were at the center of the housing bubble. HELOC standards have regained relevance since 2021 as rising home equity created borrowing capacity and as rising rates shifted borrowers away from cash-out refinancing.
Consumer Loans cover credit cards, auto loans, and student loans. Consumer credit standards in SLOOS are less intensively analyzed than C&I and CRE standards, partly because consumer credit markets are more commoditized and less dependent on individual bank judgment. Credit card standards, however, have become more prominent as delinquency rates rose across 2023 and 2024 and banks began tightening approval criteria for subprime card applicants.
The demand questions mirror the supply questions. For each loan category, respondents are asked whether loan demand from qualified borrowers has increased, stayed the same, or decreased. The demand net percentage is reported separately, allowing economists to distinguish between supply-driven credit contraction (banks tightening standards despite demand) and demand-driven contraction (borrowers pulling back despite available credit). The interaction of supply and demand signals is central to credit cycle diagnosis.
Historical Importance: Three Major Episodes
The SLOOS has a continuous quarterly record back to 1990, long enough to have captured every major US credit cycle of the modern era. Three episodes stand out.
The 2007–2009 Global Financial Crisis produced the most extreme SLOOS readings in the survey's history. Tightening in C&I lending standards began in the third quarter of 2007, as money market stress and early credit losses prompted banks to pull back from leveraged lending and to tighten revolving credit terms for corporate borrowers. By the fourth quarter of 2008 — following the Lehman Brothers collapse in September and the near-freeze of interbank markets — the net percentage of banks tightening C&I standards for large and medium-sized firms reached approximately +80 percent. That figure means that essentially every bank in the survey panel was simultaneously tightening credit. CRE standards followed a similar trajectory, with construction and land development lending effectively shutting down in many markets. The SLOOS data fed directly into the Fed's real-time assessment of financial conditions during the crisis and was used alongside CAMELS supervisory ratings and bank-level stress test data to inform decisions about capital injections and liquidity facilities.
The COVID-19 Shock in 2020 produced the second-largest tightening episode in SLOOS history, and it arrived with unusual speed. The Q2 2020 survey — covering the April–June period when lockdowns were most severe — showed net C&I tightening of approximately +68 percent and net CRE construction tightening approaching +74 percent. The scale was comparable to the 2008 peak even though the shock was fundamentally different in character: the 2020 tightening was driven by uncertainty about income and collateral values rather than by insolvency fears or bank capital constraints. The Fed responded with emergency credit facilities including the Main Street Lending Program and the Commercial Paper Funding Facility, and SLOOS showed standards beginning to ease by Q4 2020 as those facilities backstopped the credit market.
The 2022–2023 Rate Hike Cycle produced a more gradual and sustained tightening episode. As the Federal Reserve raised the federal funds rate from near zero in early 2022 to over 5 percent by mid-2023, banks progressively tightened lending standards across all major loan categories. Net C&I tightening for large and medium firms climbed steadily from near zero in Q1 2022 to approximately +40 to +45 percent by early 2023. The tightening reflected both deliberate credit risk management as banks anticipated slower growth and rising defaults, and the mechanical effect of higher funding costs on loan economics. The March 2023 failure of Silicon Valley Bank and the associated stress at First Republic and Signature Bank caused a temporary spike in reported tightening, particularly at mid-sized banks, as concerns about deposit outflows and funding stability prompted defensive credit postures. By Q3 2023 the pace of new tightening had slowed, and through 2024 gradual easing became evident as the Fed began cutting rates and credit conditions normalized.
Special Questions
Each SLOOS release includes a section of special topical questions developed by Federal Reserve staff to address credit market conditions that the standard quarterly questions cannot fully capture. These questions are announced quarterly and vary with the macroeconomic and financial environment.
In 2023, following the March banking stress, the special questions asked respondents how changes in deposit flows had affected their lending standards and capacity. Banks that had experienced significant deposit outflows — primarily those with high concentrations of uninsured corporate deposits — reported tightening credit more aggressively than peers. This causal channel, from funding pressure to credit supply, is a classic bank lending channel mechanism and the special questions provided real-time evidence of its operation.
Other recurring special question themes include the outlook for commercial real estate loan quality over the next twelve months (asked annually since 2020), changes in credit card delinquency rates and approval criteria for new accounts, the extent to which deteriorating collateral values have prompted tightening in CRE standards, and the sources of changes in loan demand (asking banks to attribute demand shifts to factors like interest rate levels, customer investment plans, or reduced customer confidence). The special questions give SLOOS a genuinely forward-looking dimension — asking about outlooks and attributions rather than only about backward-looking quarterly changes.
The Fed sometimes uses special SLOOS questions as a substitute for formal surveys during rapidly evolving episodes. In early 2020, a mid-quarter special SLOOS was fielded in April — between the regular survey cycles — to capture real-time credit conditions during the acute phase of the COVID shock. That supplemental survey, unprecedented in the program's recent history, illustrated how the standard quarterly rhythm can be compressed when conditions require more timely information.
SLOOS and the Macroeconomy
The relationship between SLOOS tightening and subsequent economic activity is one of the better-documented empirical regularities in US business cycle research. Credit supply constraints reduce business investment, hiring, and consumer spending through several mechanisms: directly, by denying credit to borrowers who would otherwise invest; and indirectly, by raising the effective cost of credit through non-price tightening (covenants, collateral requirements) that imposes real constraints even when rates are unchanged.
Historically, net C&I tightening above +50 percent — meaning a majority of banks are net tighteners — has predicted recession within approximately four quarters with reasonable consistency across post-1990 cycles. The lag reflects the time required for tighter credit standards to work through the pipeline: existing credit lines continue to fund activity for several quarters before their expiration or renegotiation forces adjustments. Small businesses, which have fewer alternative financing sources, tend to feel tightening faster than large firms.
The Federal Reserve incorporates SLOOS data extensively in its internal and public communications. Each quarterly Beige Book report from the twelve regional Federal Reserve Banks includes anecdotal credit conditions information that is cross-referenced against SLOOS results. The semiannual Monetary Policy Report to Congress, which the Fed Chair presents to the House and Senate banking committees, typically includes a SLOOS chart in the financial conditions section. FOMC minutes regularly cite SLOOS readings when characterizing credit supply conditions, particularly during tightening episodes when the difference between monetary policy tightness and bank-level credit tightness is material to policy assessment.
SLOOS is one of several key credit and labor market indicators that Fed staff aggregate into their assessment of economic conditions. Alongside the Job Openings and Labor Turnover Survey (JOLTS), the Employment Cost Index (ECI), and the Consumer Price Index (CPI), SLOOS provides the credit supply dimension of the Fed's quarterly data review. When SLOOS tightening, JOLTS quit rates, and ECI wage growth are all moving in the same direction — each pointing toward a cooling economy — the Fed has high confidence in its assessment of labor and credit market dynamics. When the signals diverge, SLOOS helps diagnose whether credit supply constraints are an independent drag on activity or merely a lagging reflection of deteriorating demand.
Data Structure and Access
The SLOOS release is published at federalreserve.gov/data/sloos. Each release includes a summary document, a full set of tables in PDF format, and downloadable Excel files containing the historical data series. Historical coverage begins in 1990 for most standard questions, with some series starting later as questions were added or modified. The data files are organized into four principal tables: domestic C&I loan results, domestic CRE loan results, domestic residential and consumer loan results, and foreign bank C&I and CRE results (reported separately from domestic banks because foreign branches operate under different regulatory and funding structures).
FRED, the Federal Reserve Bank of St. Louis data portal, carries the most widely used SLOOS series with standardized mnemonics for programmatic access. The key C&I series are DRTSCILM (C&I loan standards for large and medium-sized firms, net percentage tightening, quarterly, not seasonally adjusted) and DRTSCIS (C&I loan standards for small firms). Credit card standards are available as DRTSCLCC. A composite summary of special questions results, where it has been aggregated historically, appears in DRTSSP.
For recession dating in conjunction with SLOOS analysis, FRED's USREC series (US recession indicator, monthly, binary) provides NBER business cycle expansion and contraction dates back to the 1850s, allowing recession shading to be added to any SLOOS time series chart. The combination of SLOOS quarterly data and monthly USREC shading is the standard visualization approach in both academic research and Fed staff presentations.
Python Example: C&I Standards Analysis
The example below pulls the two core C&I loan standards series from FRED — large/medium firms (DRTSCILM) and small firms (DRTSCIS) — aligns them on a common quarterly index, computes their correlation, identifies quarters where small-firm tightening led large-firm tightening by more than 10 percentage points (a signal that credit stress is hitting smaller borrowers first), adds NBER recession shading, and produces an annotated dual-line chart. The +50 percent threshold line marks the level historically associated with recession prediction within four quarters.
import pandas as pd
from fredapi import Fred
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# Set your FRED API key here
fred = Fred(api_key="YOUR_FRED_API_KEY")
# --- Pull C&I loan standards: large/medium firms and small firms ---
# DRTSCILM: Net percentage tightening C&I standards for large/medium firms (quarterly)
# DRTSCIS: Net percentage tightening C&I standards for small firms (quarterly)
ci_large = fred.get_series("DRTSCILM", observation_start="2000-01-01")
ci_small = fred.get_series("DRTSCIS", observation_start="2000-01-01")
# Align both series to a common quarterly index
combined = pd.DataFrame({
"large_medium": ci_large,
"small": ci_small,
}).dropna()
# Correlation between the two series
corr = combined["large_medium"].corr(combined["small"])
print("Correlation (large/medium vs small firm C&I standards):", round(corr, 3))
# --- Identify quarters where small-firm tightening led large-firm tightening ---
# "Led" defined as: small firm net pct crossed above 20 one or more quarters
# before large/medium crossed above 20 in the same tightening episode.
# Simple implementation: compute quarter-over-quarter change in spread.
combined["spread"] = combined["small"] - combined["large_medium"]
combined["small_leads"] = (combined["spread"] > 10) & (combined["large_medium"] < combined["small"])
leading_quarters = combined[combined["small_leads"]].index
print("Quarters where small-firm tightening led by >10pp:")
for q in leading_quarters:
print(" ", q.strftime("%Y-Q") + str(q.quarter),
" large/med:", round(combined.loc[q, "large_medium"], 1),
" small:", round(combined.loc[q, "small"], 1))
# --- Pull NBER recession indicator for shading ---
rec = fred.get_series("USREC", observation_start="2000-01-01")
def add_recession_shading(ax):
in_recession = False
start = None
for date, val in rec.items():
if val == 1 and not in_recession:
in_recession = True
start = date
elif val == 0 and in_recession:
in_recession = False
ax.axvspan(start, date, alpha=0.12, color="#6b7280", zorder=0)
if in_recession:
ax.axvspan(start, rec.index[-1], alpha=0.12, color="#6b7280", zorder=0)
# --- Plot: dual-line chart with recession shading ---
fig, ax = plt.subplots(figsize=(13, 6))
ax.plot(combined.index, combined["large_medium"],
color="#0b4a8f", linewidth=2, label="Large/medium firms")
ax.plot(combined.index, combined["small"],
color="#b45309", linewidth=2, linestyle="--", label="Small firms")
add_recession_shading(ax)
# Reference lines
ax.axhline(0, color="#1a1a1a", linewidth=0.8, linestyle="-")
ax.axhline(50, color="#dc2626", linewidth=0.8, linestyle=":",
label="+50% threshold (historically predicts recession within 4Q)")
# Annotate peak tightening events
annotations = [
("2008-Q4", "2008-10-01", 82, "GFC peak"),
("2020-Q2", "2020-04-01", 70, "COVID"),
("2023-Q1", "2023-01-01", 45, "SVB stress"),
]
for label, date_str, y_offset, text in annotations:
date = pd.Timestamp(date_str)
if date in combined.index:
val = combined.loc[date, "large_medium"]
ax.annotate(text,
xy=(date, val),
xytext=(date, val + 8),
fontsize=8,
ha="center",
arrowprops=dict(arrowstyle="-", color="#555", lw=0.8))
ax.set_ylabel("Net percentage tightening (positive = tighter, negative = easier)")
ax.set_title("SLOOS C&I Loan Standards: Large/Medium vs. Small Firms (Net % Tightening)")
ax.legend(fontsize=9, loc="upper left")
ax.grid(axis="y", alpha=0.35)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y"))
ax.xaxis.set_major_locator(mdates.YearLocator(2))
plt.setp(ax.xaxis.get_majorticklabels(), rotation=30, ha="right")
plt.tight_layout()
plt.savefig("sloos_ci_standards.png", dpi=150, bbox_inches="tight")
plt.show()
print("Chart saved to sloos_ci_standards.png")
The correlation between large/medium-firm and small-firm C&I standards is typically high — above 0.90 across the full post-2000 sample — because the same macroeconomic and credit cycle forces tend to drive bank behavior broadly. The spread between the two series is more revealing than the absolute levels: when small-firm standards tighten faster or earlier than large-firm standards, it often signals bank concern about credit quality in the more opaque, less liquid portion of the business credit market where alternative financing sources are unavailable.
For access to CRE and residential mortgage SLOOS series that are not individually listed in FRED, the full historical Excel files from federalreserve.gov/data/sloos remain the primary source. The FRED DDP (Data Download Program) at fred.stlouisfed.org/categories/32993 allows bulk download of all available SLOOS mnemonics with a single API call, which is useful for building comprehensive credit conditions dashboards that combine multiple SLOOS categories with complementary bank balance sheet and delinquency series.
Related writing
Federal Reserve Z.1: The Complete Quarterly Accounting of Every Dollar in the US Financial System — the Z.1 Financial Accounts provide the sectoral balance sheet context in which SLOOS credit tightening cycles play out, including household net worth, corporate leverage, and financial sector size.
FDIC Call Report Data: The Quarterly Financial Statements Every US Bank Files with Regulators — the Call Report is the bank-level administrative data counterpart to SLOOS, showing the realized loan balances, charge-off rates, and capital ratios that emerge from the credit conditions SLOOS measures in advance.