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Credit Union Trends

Public

Quarterly metric time series for one credit union.

About this dataset

Credit Union Trends is a Public dataset in the Credit Unions group of the BankRegReports Data API. The response carries 14 documented fields, sourced from NCUA.

Endpoint

GET /api/v1/cus/{cu_number}/trends/

Parameters

Name Required Description
cu_number required NCUA charter number. e.g. 5536
quarters optional integer

Response fields

Field Type Description
brr_id string, nullable
cu_number integer, nullable NCUA Charter Number (count)
report_date string (date), nullable Report Date (date)
total_assets string (decimal), nullable Total Assets (USD thousands)
total_loans string (decimal), nullable Total Loans (USD thousands)
total_shares_deposits string (decimal), nullable Total Shares and Deposits (USD thousands). Member shares and deposits held by the credit union, in thousands of dollars.
net_worth string (decimal), nullable Net Worth (USD thousands). Credit union net worth (retained earnings and other qualifying equity), in thousands of dollars.
members integer, nullable Members (count). Number of members reported on the NCUA 5300 call report.
net_worth_ratio string (decimal), nullable Net Worth Ratio (percent). Credit union net worth as a percentage of total assets, the capital measure NCUA's prompt corrective action rules use. 7% or more is well capitalized.
loan_to_share_ratio string (decimal), nullable Loan-to-Share Ratio (percent). Total loans as a percentage of total shares and deposits, the credit union counterpart of a bank's loan-to-deposit ratio.
delinquency_ratio string (decimal), nullable Delinquency Ratio (percent). Delinquent loans as a percentage of total loans, as reported on the NCUA 5300 call report.
roa string (decimal), nullable Return on Assets (percent). Net income, annualized, as a percentage of average total assets. The single most-cited profitability metric. 1.0%+ is strong for national banks; community banks often run 0.8 to 1.2%.
roe string (decimal), nullable Return on Equity (percent)
net_interest_margin string (decimal), nullable Net Interest Margin (percent). Net interest income, annualized, as a percentage of average earning assets: the spread the bank earns between the yield on its assets and its cost of funds. The vendor (fully taxable equivalent) and computed series use different earning-asset bases, so they can differ by 10 to 15 basis points.

Example request

curl "https://api.bankregreports.com/api/v1/cus/5536/trends/" \
  -H "Authorization: Bearer $BANKREG_API_TOKEN"

Responses are JSON in a {"data": ..., "meta": {...}} envelope. Get a token on the setup page.

Example response shape

A successful call returns HTTP 200 with a JSON body. Each row in data carries the fields below, shown with their documented types rather than sample values. 

{
  "data": [
    {
      "brr_id": "string",
      "cu_number": "integer",
      "report_date": "string (date)",
      "total_assets": "string (decimal)",
      "total_loans": "string (decimal)",
      "total_shares_deposits": "string (decimal)",
      "net_worth": "string (decimal)",
      "members": "integer"
      ... 6 more fields
    }
  ],
  "meta": { ... }
}

When to use it

Credit Union Trends sits in the Credit Unions group of the Data API. Reach for it when your work needs this data straight from the API: it comes back as JSON you can load into a notebook, a spreadsheet or a scheduled pipeline, with the dataset key credit_union_trends naming it the same way in the REST path, the Python SDK and the OpenAPI document.

Access and authentication

This dataset is in the Public tier. Send your API token in the Authorization header as a Bearer token, using a GET request to the endpoint above. The Free plan is the first plan that includes it. Check the plan comparison for request limits, and the data update schedule for when new filings reach the API.

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Keep going

Browse every dataset, follow the setup walkthrough to make a first call, use the Excel add-in for formulas, or connect an assistant through the MCP server.

Python SDK

Install with pip install bankregreports and set BANKREG_API_TOKEN to your key. 

import bankregreports

brr = bankregreports.BankReg()  # reads BANKREG_API_TOKEN
cu_number = 5536  # Navy Federal Credit Union
result = brr.dataset("credit_union_trends", cu_number=cu_number)
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