US household borrowing-cost signals (prime / card / auto rates)
US household borrowing-cost signals from Federal Reserve Board lending rates (redistributed by FRED): the bank prime loan rate (monthly, 1949 ->), the commercial-bank credit-card rate and the 48-month new-auto loan rate (quarterly, NSA), with frequency-aware momentum, change volatility, 3-sigma anomaly flags, drift forecasts, cross-series ranks, the lender risk-premium spread over prime, the unsecured-vs-secured (card-minus-auto) wedge, a 5-year borrowing-cost z-score and a high-cost flag. The price-of-credit complement to fred-mortgage-rate-signals (mortgages), us-bank-credit-cycle signals (volumes) and us-bank-lending-standards-signals (willingness to lend). All rows normalized to country_code USA. Raw series: Board of Governors of the Federal Reserve System via FRED.
Quality
Attribution
Board of Governors of the Federal Reserve System via FRED; derived signals by Frontier Data Hub
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD, monthly or quarterly). |
| country | string | |
| country_code | string | |
| series_id | string | FRED series ID: DPRIME (Bank Prime Loan Rate, Board of Governors), TERMCBCCALLNS (Commercial Bank Interest Rate on Credit Card Plans, All Accounts, Board of Governors) or TERMCBAUTO48NS (Finance Rate on Consumer Installment Loans at Commercial Banks, New Autos 48 Month Loan, Board of Governors). |
| series_label | string | Official FRED series title as published for the series. |
| value | float | Contract interest rate in percent: bank prime loan rate (monthly) or commercial-bank consumer lending rate for credit-card plans and 48-month new-auto loans (quarterly, not seasonally adjusted; Board of Governors of the Federal Reserve System, via FRED). |
| momentum_3m | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| prime_spread_pp | float | |
| card_auto_spread | float | |
| cost_z_5y | float | |
| high_cost_flag | integer |
Sample rows
| date | country | country_code | series_id | series_label | value | momentum_3m | volatility_30d | anomaly_flag | forecast_1m | rank | prime_spread_pp | card_auto_spread | cost_z_5y | high_cost_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1955-08-01 | United States | USA | DPRIME | Bank Prime Loan Rate | 3.25 | — | — | 0 | — | 1 | — | — | — | 0 |
| 1955-09-01 | United States | USA | DPRIME | Bank Prime Loan Rate | 3.25 | — | — | 0 | — | 1 | — | — | — | 0 |
| 1955-10-01 | United States | USA | DPRIME | Bank Prime Loan Rate | 3.39 | — | — | 0 | — | 1 | — | — | — | 0 |
| 1955-11-01 | United States | USA | DPRIME | Bank Prime Loan Rate | 3.5 | 0.25 | — | 0 | — | 1 | — | — | — | 0 |
| 1955-12-01 | United States | USA | DPRIME | Bank Prime Loan Rate | 3.5 | 0.25 | — | 0 | — | 1 | — | — | — | 0 |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
Use with an LLM
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/borrowcost_signals/us_household_borrowing_cost_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/borrowcost_signals/us_household_borrowing_cost_signals").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/borrowcost_signals/us_household_borrowing_cost_signals
Tip: fetch /llms.txt for the full machine-readable catalog.