US industrial production: business equipment (monthly)

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Monthly real output of US business equipment (industrial machinery, information processing, transit equipment and the like), index 2017 = 100, seasonally adjusted, from the Federal Reserve Board's G.17 release via keyless FRED. The capex-cycle pulse: when businesses buy equipment, B2B order books follow. Derived columns: month-over-month and year-over-year change, deviation from the trailing-12-month mean, and 12-month record-high/low flags. Caveats: seasonally adjusted; the latest month is preliminary and routinely revised. Board of Governors of the Federal Reserve System (US) — U.S. federal public domain; cite "Board of Governors of the Federal Reserve System (US), Industrial Production: Equipment: Business Equipment [IPBUSEQ], retrieved from FRED".

Source: US Manufacturing Intelligence Pack (FRED, keyless)
Rows: 956
Columns: 11
Source cadence: Monthly
Last refreshed: Oct 2, 2026
Theme: economy

Column	Type	Description
date	string	Observation month as an ISO date (first of the month); G.17 publishes mid-month for the prior month.
year_month	string	Observation month as YYYY-MM — the join key for persona models (sales territories, shop orders, subscription cohorts all join on year_month).
country	string	Country name, 'United States' (all series are US national).
country_code	string	ISO 3166-1 alpha-3 code 'USA' — the join key for the 'Enrich with public data' feature.
ip_index	float	Industrial production index (2017 = 100): real output of the covered establishments, seasonally adjusted. The primary level series of this dataset. (unit: Index 2017=100, Seasonally Adjusted)
mom_pct	float	Month-over-month change of the level series, in percent. Null for the first observation — never back-filled. (unit: percent)
yoy_pct	float	Year-over-year change of the level series, in percent. Null for the first 12 observations — never back-filled. (unit: percent)
vs_12m_avg_pp	float	Deviation of the level series from its trailing-12-month mean, in index points for production series and percentage points for capacity utilization — the momentum-vs-trend gauge. (unit: index points)
record_high_12m_flag	integer	1 when the print equals the trailing-12-month maximum (inclusive, requires >= 12 observations), 0 otherwise.
record_low_12m_flag	integer	1 when the print equals the trailing-12-month minimum (inclusive, requires >= 12 observations), 0 otherwise.
row_hash	string	Deterministic 16-hex row identity (date + value) for change detection.

First 10 sample rows — a preview, not the complete dataset.

date	year_month	country	country_code	ip_index	mom_pct	yoy_pct	vs_12m_avg_pp	row_hash
1947-01-01	1947-01	United States	USA	5.438	—	—	—	6b25406a8ba33726
1947-02-01	1947-02	United States	USA	5.497	1.079	—	—	cac204618aa447d0
1947-03-01	1947-03	United States	USA	5.526	0.533	—	—	67f9149c8b4c60ba
1947-04-01	1947-04	United States	USA	5.605	1.417	—	—	f25e9100da3304e7
1947-05-01	1947-05	United States	USA	5.634	0.523	—	—	55b609192ec05017
1947-06-01	1947-06	United States	USA	5.673	0.694	—	—	5432b0887b42ce68
1947-07-01	1947-07	United States	USA	5.575	-1.724	—	—	089dcb6a4c93f25b
1947-08-01	1947-08	United States	USA	5.673	1.754	—	—	bcd7ebbfb22f1ab8
1947-09-01	1947-09	United States	USA	5.751	1.38	—	—	9916597c6ff9b6e4
1947-10-01	1947-10	United States	USA	5.791	0.68	—	—	345163e619faf2e4

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/fred_manufacturing_pack/us_ip_business_equipment_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/fred_manufacturing_pack/us_ip_business_equipment_monthly").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/fred_manufacturing_pack/us_ip_business_equipment_monthly

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261002T052838Z-c6a62f88f755 and its content hash, so readers get exactly the data you used.

APA

US Manufacturing Intelligence Pack (FRED, keyless). (2026). US industrial production: business equipment (monthly) [Data set, snapshot 20261002T052838Z-c6a62f88f755, sha256 c6a62f88f755]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/fred_manufacturing_pack/us_ip_business_equipment_monthly?snapshot=20261002T052838Z-c6a62f88f755

BibTeX

@misc{dz_fred_manufacturing_pack_us_ip_business_e_c6a62f88,
  title        = {{US industrial production: business equipment (monthly)}},
  author       = {{US Manufacturing Intelligence Pack (FRED, keyless)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/fred_manufacturing_pack/us_ip_business_equipment_monthly?snapshot=20261002T052838Z-c6a62f88f755}},
  note         = {Snapshot 20261002T052838Z-c6a62f88f755, sha256 c6a62f88f755b6b682a84302336fe54a273f86016251dcec3d6d63aff63dc0d7; accessed 2026-10-02}
}

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US industrial production: business equipment (monthly)

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