SBA 7(a) and 504 loan data (FOIA)

SBA 7(a) and 504 loan data (FOIA) is a set of loan-level files released by the U.S. Small Business Administration under FOIA. Each record is one approved loan and includes borrower name and address, lender name and FDIC/NCUA number, gross approval amount, SBA-guaranteed amount, approval date and fiscal year, initial interest rate, fixed-or-variable indicator, term in months, NAICS code and description, project county and state, SBA district office, congressional district, and jobs supported. The files cover the two main SBA credit programs: the 7(a) loan-guarantee program and the 504/CDC program. Used in, for example, Johnston-Ross, Ma & Puri for the number, amount, interest rate, and average size of small-business loans by county.

• Cost: free, public domain.
• API key: none required.
• Coverage: 7(a) approvals from FY1991 and 504 approvals from FY1991, split into FY-range files, refreshed periodically. Each filename encodes an “asof” snapshot date.
• Home: https://data.sba.gov/dataset/7-a-504-foia

Access

The portal is a CKAN instance. You can query it programmatically to list all resources, then download any CSV directly without authentication.

Step 1: list available resources

curl -s "https://data.sba.gov/api/3/action/package_show?id=7-a-504-foia" \
  | python3 -c "
import json, sys
pkg = json.load(sys.stdin)
for r in pkg['result']['resources']:
    print(r['name'], r['url'])
"

This prints each resource name (e.g. 7(a) FY2010-FY2019 (asof 230930)) and its direct download URL.

Step 2: download a CSV file

Pick the URL for the file you need and fetch it directly:

# Example: 7(a) FY2010-FY2019 snapshot
curl -L -o sba_7a_2010_2019.csv \
  "https://data.sba.gov/dataset/7-a-504-foia/resource/<resource-id>/download/<filename>.csv"

Replace <resource-id> and <filename> with values from the package listing above; the exact URL is stable for a given snapshot.

Step 3: load in Python

import pandas as pd

df = pd.read_csv(
    "sba_7a_2010_2019.csv",
    dtype=str,           # read everything as str first; cast after schema alignment
    low_memory=False,
)

# Cast key numeric fields after inspection
df["GrossApproval"] = pd.to_numeric(df["GrossApproval"], errors="coerce")
df["ApprovalDate"]  = pd.to_datetime(df["ApprovalDate"], errors="coerce")

A separate data-dictionary XLSX is listed alongside the CSV resources in the package; download it to map coded fields and confirm column names for the vintage you are using.

Gotchas (the ones that bite pipelines)

• Approvals, not disbursements or outcomes. Every record means a loan was approved. The file carries no information on whether the loan was fully drawn, repaid, or defaulted. Default and charge-off information lives in a separate SBA purchase/charge-off dataset; join on the loan number if you need it.
• Multiple FY-range CSVs that must be concatenated; schemas drift. The data is split into files by program and FY range. Column names and coded values change across vintages: a field named NaicsCode in one file may appear as NAICS in another. Inspect and align schemas before stacking; do not assume a union of columns is safe.
• Pin the “asof” snapshot date. Each filename encodes a snapshot date (e.g. asof230930 for the September 30, 2023 cut). Records can be retroactively revised or added in later snapshots. To reproduce a prior result, use the same snapshot, not the latest file.
• Geography is recorded at approval. The project county and state reflect where the project was located at the time of approval. There is no update if the borrower relocates.
• NAICS definitions change across years. NAICS codes are revised on a five-year cycle. A code present in FY1997 data may map to a different industry description in FY2022 data. Use the NAICS revision year to cross-walk codes before aggregating across long panels.
• 7(a) and 504 have different structures; do not pool blindly. The 7(a) program is a direct guarantee to a participating lender. The 504 program involves a Certified Development Company (CDC) and a separate third-party lender, so the “lender” field means different things. The guaranteed percentage and term conventions also differ. Keep the programs separate unless you have a specific reason to combine them, and document that choice.

Citation

Cite the program, the FY-range file(s), and the specific “asof” snapshot date used, for example: U.S. Small Business Administration, 7(a) Loan Data (FOIA), FY2010-FY2019 (as of 2023-09-30), retrieved from https://data.sba.gov/dataset/7-a-504-foia, accessed YYYY-MM-DD. The snapshot date is in the filename; record it so the pull is reproducible.