US item-level retail price pressure (monthly)

Name: US item-level retail price pressure (monthly)
Creator: U.S. Bureau of Labor Statistics
License: https://www.bls.gov/opub/copyright-information.htm
Keywords: us, retail, prices, monthly, signals, inflation, grocery, cost-of-living

Commercial use OKFresh

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Monthly US item-level retail price pressure intelligence from the BLS Average Price program (keyless BLS Public Data API v2): published average retail prices for a curated staple panel (food-at-home staples plus household-energy items) in the U.S. city average and the four Census regions, with YoY / MoM percent changes, a 12-month trailing z-score of the monthly change, a documented 0-100 price-pressure score with per-month panel ranks, a regional premium gauge against the national price, and record-high / surge / deflation flags. Consistent methodology window 2018-01 onward. Caveats: AP prices are not seasonally adjusted — read single-month moves against the z-score; the latest month is preliminary and routinely revised; regional coverage is Census regions only. BLS material is public domain (commercial reuse allowed); source: U.S. Bureau of Labor Statistics.

Source: U.S. Bureau of Labor Statistics
Rows: 1,560
Columns: 20
Source cadence: Monthly
Last refreshed: Oct 1, 2026
Theme: economy

Column	Type	Description
month	string	Reference month: first day of the month, ISO date. The panel covers the consistent methodology window 2018-01 onward (the 2017 fetch year supplies the 12-month lags). (unit: ISO date)
country_code	string	ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3)
region_code	string	Stable region code: united_states (U.S. city average), northeast, midwest, south, west. Join key with month and item_slug.
region_name	string	BLS catalog Area field value (U.S. city average, Northeast, Midwest, South, West).
item_slug	string	Stable item code for the curated staple panel (e.g. bread_white, milk_whole, eggs_large). Join key with month and region_code.
item_name	string	BLS catalog Item field value (e.g. Bread, white, pan, per lb.).
category	string	Curated taxonomy: bakery, dairy, protein, produce, pantry, beverages, household_energy.
unit	string	Published price unit (e.g. USD per lb., USD per gallon, USD per dozen, USD per kWh).
price	float	Published average retail price, verbatim as published by BLS (NOT seasonally adjusted). (unit: unit as listed)
price_yoy_pct	float	Year-over-year percent change of the published price. (unit: percent)
price_mom_pct	float	Month-over-month percent change of the published price. Carries normal seasonality — read it against the z-score. (unit: percent)
price_12m_zscore	float	Z-score of the month-over-month change against its own trailing 12 months: (mom_t - mean) / std, minimum 12 observations. The seasonality-aware momentum gauge. (unit: std devs)
price_pressure_score	float	Documented 0-100 composite: 100 * (0.50 * min-max(winsorized price_yoy_pct, +/-25) + 0.50 * min-max(winsorized price_12m_zscore, +/-3)), min-maxed within each month across the full (region, item) panel. Higher = hottest price pressure versus peers this month. (unit: 0-100 score)
price_rank	integer	Per-month rank of price_pressure_score across the full panel (1 = hottest). (unit: rank)
region_premium_pct	float	Regional premium gauge: (region price / national price - 1) * 100 for the same item and month. Null on united_states rows. Positive = the region pays more than the national average. (unit: pp)
record_high_12m_flag	integer	1 when the price equals the trailing-12-month maximum (inclusive, minimum 12 observations). (unit: binary)
surge_flag	integer	1 when price_mom_pct is at least +3.0. (unit: binary)
deflation_flag	integer	1 when price_yoy_pct is negative (price falling year-over-year). (unit: binary)
bls_series_id	string	BLS series id behind the row (APU<area><item>).
row_hash	string	Deterministic 16-hex row hash of region_code + item_slug + month (idempotency).

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

month	country_code	region_code	region_name	item_slug	item_name	category	unit	price	price_yoy_pct	price_mom_pct	price_12m_zscore	price_pressure_score	price_rank	region_premium_pct	bls_series_id	row_hash
2018-01-01	USA	united_states	U.S. city average	ground_beef	Ground beef, 100% beef, per lb.	meat	USD per lb.	3.641	—	—	—	—	—	—	APU0000703112	57ecb1340221ca8e
2018-01-01	USA	united_states	U.S. city average	chicken_breast	Chicken breast, boneless, per lb.	meat	USD per lb.	3.073	—	—	—	—	—	—	APU0000FF1101	60adb56e9b784d78
2018-01-01	USA	united_states	U.S. city average	gasoline	Gasoline, unleaded regular, per gallon	fuel	USD per gallon	2.539	—	—	—	—	—	—	APU000074714	35fd827376ac5af5
2018-01-01	USA	northeast	Northeast	ground_beef	Ground beef, 100% beef, per lb.	meat	USD per lb.	3.568	—	—	—	—	—	-2.005	APU0100703112	780c01a569ea8f76
2018-01-01	USA	northeast	Northeast	chicken_breast	Chicken breast, boneless, per lb.	meat	USD per lb.	2.974	—	—	—	—	—	-3.222	APU0100FF1101	d7e4a23384212cc2
2018-01-01	USA	northeast	Northeast	gasoline	Gasoline, unleaded regular, per gallon	fuel	USD per gallon	2.626	—	—	—	—	—	3.427	APU010074714	378866ba570e657d
2018-01-01	USA	midwest	Midwest	ground_beef	Ground beef, 100% beef, per lb.	meat	USD per lb.	3.688	—	—	—	—	—	1.291	APU0200703112	ec82842601cb9933
2018-01-01	USA	midwest	Midwest	chicken_breast	Chicken breast, boneless, per lb.	meat	USD per lb.	3.291	—	—	—	—	—	7.094	APU0200FF1101	db84bc793b9e0788
2018-01-01	USA	midwest	Midwest	gasoline	Gasoline, unleaded regular, per gallon	fuel	USD per gallon	2.548	—	—	—	—	—	0.354	APU020074714	798237eba339b85d
2018-01-01	USA	south	South	ground_beef	Ground beef, 100% beef, per lb.	meat	USD per lb.	3.239	—	—	—	—	—	-11.041	APU0300703112	e28f4a4bced0db58

Profiled Oct 1, 2026 from snapshot 20261001T135944Z-369e2af9999f

Measured

Completeness: 95.8%
Rows: 1,560
Columns: 20
Columns with gaps: 7

Rows per yearmonth · 2018–2026 · 9 years

20182026

Column	Missing	Distinct	Range	Distribution
monthvarchar	0%	84	—	2018-01-0115 2018-02-0115 2018-03-0115
country_codevarchar	0%	1	—	USA1,560
region_codevarchar	0%	5	—	midwest312 northeast312 south312
region_namevarchar	0%	4	—	Midwest312 Northeast312 South312
item_slugvarchar	0%	3	—	chicken_breast520 gasoline520 ground_beef520
item_namevarchar	0%	3	—	Chicken breast, boneless, per lb.520 Gasoline, unleaded regular, per gallon520 Ground beef, 100% beef, per lb.520
categoryvarchar	0%	2	—	meat1,040 fuel520
unitvarchar	0%	2	—	USD per lb.1,040 USD per gallon520
pricedouble	1%	1,252	1.57 → 7.44median 3.81	32 outside 1st–99th percentile
price_yoy_pctdouble	12.9%	1,417	-43.41 → 76.91median 3.68	28 outside 1st–99th percentile
price_mom_pctdouble	2.8%	1,456	-24.29 → 29.26median 0.3447	32 outside 1st–99th percentile
price_12m_zscoredouble	21.2%	853	-2.72 → 3.08median -0.006	26 outside 1st–99th percentile
price_pressure_scoredouble	12.9%	1,072	0 → 100median 50.4	14 outside 1st–99th percentile
price_rankbigint	12.9%	17	1 → 15median 8
region_premium_pctdouble	20.9%	1,232	-18.45 → 36.06median -1.52	26 outside 1st–99th percentile
record_high_12m_flagbigint	0%	2	0 → 1median 0
surge_flagbigint	0%	2	0 → 1median 0
deflation_flagbigint	0%	2	0 → 1median 0
bls_series_idvarchar	0%	13	—	APU0000703112104 APU000074714104 APU0000FF1101104
row_hashvarchar	0%	1,763	—	001aabff06c3aeeb1 003ade9c6d58a9201 00b9c91986c95ea31

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/bls_retail_price_intel/item_price_pressure_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/bls_retail_price_intel/item_price_pressure_monthly

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

Cite this snapshot

Pinned to snapshot 20261001T135944Z-369e2af9999f and its content hash, so readers get exactly the data you used.

APA

U.S. Bureau of Labor Statistics. (2026). US item-level retail price pressure (monthly) [Data set, snapshot 20261001T135944Z-369e2af9999f, sha256 369e2af9999f]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/bls_retail_price_intel/item_price_pressure_monthly?snapshot=20261001T135944Z-369e2af9999f

BibTeX

@misc{dz_bls_retail_price_intel_item_price_pressu_369e2af9,
  title        = {{US item-level retail price pressure (monthly)}},
  author       = {{U.S. Bureau of Labor Statistics}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/bls_retail_price_intel/item_price_pressure_monthly?snapshot=20261001T135944Z-369e2af9999f}},
  note         = {Snapshot 20261001T135944Z-369e2af9999f, sha256 369e2af9999fcb8cbd948a869b3de7723c266dec6a15cb8179e659a313b784ec; accessed 2026-10-02}
}

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US item-level retail price pressure (monthly)

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