Monthly US firearm background-check demand intelligence from the FBI National Instant Criminal Background Check System (NICS), retrieved via a machine-readable parse of the FBI's official month/year-by-state PDF (verified: annual totals reproduced from the CSV match the FBI publication exactly for 2020 and 2021). Covers 55 jurisdictions (50 states, DC, Guam, Mariana Islands, Puerto Rico, Virgin Islands) with transfer-intent purchase checks (handgun + long gun + other + multiple), permit checks, and administrative/other checks separated. Includes YoY momentum, 3- and 12-month moving averages, a documented 0-100 monthly demand score with per-month state ranks, surge / easing / 12-month-record-high flags, and month-level national context. Trailing 10-year panel. Caveats: NICS counts checks initiated, not firearms sold (FBI disclaimer) — permit regimes substitute for point-of-sale checks in many states; the latest month is subject to FBI revision. FBI material is U.S. federal public domain (commercial reuse allowed); source: Federal Bureau of Investigation.

Source: Federal Bureau of Investigation, National Instant Criminal Background Check System
Rows: 6,600
Columns: 22
Source cadence: Monthly
Last refreshed: Sep 30, 2026
Theme: retail

Column	Type	Description
month	string	Reference month, YYYY-MM. The panel covers the trailing 120 months of the FBI NICS publication. (unit: ISO year-month)
country_code	string	ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3)
state_code	string	Stable geography code: USPS postal abbreviation, lowercase (tx, ca, ny, ...; dc, gu, mp, pr, vi). Primary join key with month.
state_name	string	State/territory name as published by the FBI (e.g. Texas, District of Columbia, Puerto Rico).
checks_total	integer	Total NICS background checks initiated in the state/month (all transaction types). (unit: checks)
purchase_checks	integer	Transfer-intent purchase proxy: handgun + long gun + other + multiple checks (FBI transaction-type definitions). (unit: checks)
permit_checks	integer	Permit + permit-recheck checks. Kept separate because permit regimes substitute for point-of-sale checks in many states (FBI note). (unit: checks)
admin_other_checks	integer	Administrative + pawn/redemption/return/rental/private-sale transaction checks. (unit: checks)
purchase_share_pct	float	Share of total checks that are transfer-intent purchase checks. (unit: percent)
checks_yoy_pct	float	Year-over-year percent change of total checks vs the same month 12 months earlier. (unit: percent)
purchase_yoy_pct	float	Year-over-year percent change of purchase checks vs the same month 12 months earlier. (unit: percent)
checks_3m_ma	float	Trailing 3-month moving average of total checks (minimum 3 observations). (unit: checks)
purchase_3m_ma	float	Trailing 3-month moving average of purchase checks (minimum 3 observations). (unit: checks)
checks_12m_ma	float	Trailing 12-month moving average of total checks (minimum 12 observations). (unit: checks)
demand_score	float	Documented 0-100 composite: 100 * (0.70 * min-max(winsorized purchase_yoy_pct, -40/+60) + 0.30 * min-max(winsorized checks_yoy_pct, -40/+60)), min-maxed within each month across the 55 geos. Higher = stronger demand momentum versus peers that month. (unit: 0-100 score)
demand_rank	integer	Per-month rank of demand_score across the 55 geos (1 = strongest demand). (unit: rank)
demand_surge_flag	integer	1 when purchase_yoy_pct >= +25%. (unit: binary)
demand_easing_flag	integer	1 when purchase_yoy_pct <= -25%. (unit: binary)
record_high_12m_flag	integer	1 when checks_total equals the trailing-12-month maximum (minimum 12 observations). (unit: binary)
us_checks_total	integer	Month-level context: 55-geo sum of total checks. (unit: checks)
state_share_of_us_pct	float	Month-level context: the state's share of us_checks_total. (unit: percent)
row_hash	string	Deterministic 16-hex row hash of state_code + month (idempotency).

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

month	country_code	state_code	state_name	checks_total	purchase_checks	permit_checks	admin_other_checks	purchase_share_pct	checks_yoy_pct	purchase_yoy_pct	checks_3m_ma	purchase_3m_ma	checks_12m_ma	demand_score	demand_rank	demand_surge_flag	demand_easing_flag	us_checks_total	state_share_of_us_pct	row_hash
2016-09	USA	ak	Alaska	6,867	6,141	306	420	89.428	10.847	9.27	6,951.333	6,284.333	7,421.25	49.744	28	0	0	1,983,596	0.346	f12818ba5ae6e33d
2016-09	USA	al	Alabama	41,336	16,265	22,014	3,057	39.348	-26.485	-47.463	44,239.333	17,193	59,534.333	4.054	55	0	1	1,983,596	2.084	88af71735ab08c7f
2016-09	USA	ar	Arkansas	18,826	11,641	3,996	3,189	61.835	-13.777	-6.498	19,965.333	12,007.667	22,651.75	31.318	51	0	0	1,983,596	0.949	9a54982b3a744d4b
2016-09	USA	az	Arizona	33,390	22,445	8,727	2,218	67.221	31.126	17.488	33,769.333	23,025.667	33,240.25	61.58	12	0	0	1,983,596	1.683	a1aa1770ccfd410d
2016-09	USA	ca	California	159,077	95,776	62,196	1,105	60.207	26.814	60.169	175,355	102,370	189,189.333	90.044	2	1	0	1,983,596	8.02	35549672e32537b7
2016-09	USA	co	Colorado	40,013	34,190	5,593	230	85.447	16.898	14.31	41,367.667	35,047.333	44,161.167	55.086	20	0	0	1,983,596	2.017	7d75b40c56d85443
2016-09	USA	ct	Connecticut	14,955	8,000	6,953	2	53.494	-31.32	-33.676	24,021	12,565	29,468.75	7.031	54	0	1	1,983,596	0.754	624715dcce681837
2016-09	USA	dc	District of Columbia	52	52	0	0	100	26.829	26.829	58	57.333	76.833	66.829	7	1	0	1,983,596	0.003	f5ca145f7b9a06e3
2016-09	USA	de	Delaware	4,160	3,813	291	56	91.659	23.81	26.678	4,274.333	3,618.333	4,857.333	65.817	9	1	0	1,983,596	0.21	dd3ea75f8d6f3584
2016-09	USA	fl	Florida	102,843	73,537	24,431	4,875	71.504	20.643	12.877	114,165.667	81,647.333	117,587.25	55.207	18	0	0	1,983,596	5.185	adf1599cbc18364a

Sep 30, 2026Current
20260930T223022Z-8cd79e42af4d · sha256 8cd79e42af4d…
6,600 rows · first snapshot

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_nics_demand_intel/us_firearm_check_demand_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/us_nics_demand_intel/us_firearm_check_demand_monthly

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

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

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Pinned to snapshot 20260930T223022Z-8cd79e42af4d and its content hash, so readers get exactly the data you used.

APA

Federal Bureau of Investigation, National Instant Criminal Background Check System. (2026). US firearm background-check demand intelligence (monthly) [Data set, snapshot 20260930T223022Z-8cd79e42af4d, sha256 8cd79e42af4d]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/us_nics_demand_intel/us_firearm_check_demand_monthly?snapshot=20260930T223022Z-8cd79e42af4d

BibTeX

@misc{dz_us_nics_demand_intel_us_firearm_check_de_8cd79e42,
  title        = {{US firearm background-check demand intelligence (monthly)}},
  author       = {{Federal Bureau of Investigation, National Instant Criminal Background Check System}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/us_nics_demand_intel/us_firearm_check_demand_monthly?snapshot=20260930T223022Z-8cd79e42af4d}},
  note         = {Snapshot 20260930T223022Z-8cd79e42af4d, sha256 8cd79e42af4d766d5debad817e5c99617b694e10f77ea8d8777297b3b7f5f4f3; accessed 2026-10-01}
}

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US firearm background-check demand intelligence (monthly)

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