US retail inventory-cycle intelligence (monthly)
Monthly US retail inventory-cycle intelligence from the U.S. Census Bureau's Monthly Retail Trade Survey (official 'Inventories and Inventories/Sales Ratios' workbook: 9 published industries — retail trade total, ex-motor-vehicle total, motor vehicle and parts dealers (441), furniture/home-furnishings/electronics (4423X), building materials (444), food and beverage (445), clothing (448), general merchandise (452), department stores (4522); 1992-01 ->). Each industry-month row carries published inventory levels and inventories/sales ratios (seasonally adjusted and not adjusted, millions of USD / months of supply), inventory momentum (MoM, 3-month-annualized, YoY), the ratio's YoY change and 36-month z-score, a documented 0-100 inventory pressure score (50% winsorized ratio z-score +-2.5 + 30% winsorized inventory YoY +-25pp + 20% winsorized ratio YoY change +-1.0pp, min-maxed within each month) with deterministic rank and p1-p4 tiers across the 7 peer industries, overstock/understock flags (|z| > 1.0), 36-month ratio record-high and inventory-surge flags, the exact unpublished-kinds inventory residual on the ex-441 row, plus month-level context (total and ex-441 ratios, overstock/understock breadth, the month's top pressure industry, median pressure score). An online shop joins monthly inventory pressure to its own stock planning on (date, industry_code) for overstock/understock and markdown timing; a sales team joins category pressure to pipeline value on date. Method: one polite GET of the official Census workbook; a trailing-36-month verbatim transcription is embedded for offline safety; the transform is pure pandas in map_schema with no imputation (warmup cells are honest nulls). Caveats: the latest month is preliminary and revised next release; the published kind detail is not exhaustive (the unpublished residual covers 446/447/451/453/454); the 2020-04 ratios for clothing (18.63) and department stores (48.93 SA / 51.88 NSA) reflect the COVID sales collapse, not a data error; department stores (4522) are a subset of general merchandise (452) and are excluded from the additive identity.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 3 735
- Colonnes
- 31
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 30 sept. 2026
- Thème
- economics
| Colonne | Type | Description |
|---|---|---|
| date | date | Month of observation (Census Monthly Retail Trade Survey, monthly; the latest month is preliminary and revised next release). (unit: date) |
| country | string | Country name, always United States. |
| country_code | string | ISO 3166-1 alpha-3 country code, always USA. |
| industry_code | string | Official MRTS industry code: 44000 (retail trade total), 4400A (ex motor vehicles), 441, 4423X, 444, 445, 448, 452, 4522. Primary join key with date. |
| industry_label | string | Industry label following the official MRTS industry titles. |
| inventory_sa_musd | float | Published end-of-month retail inventories, seasonally adjusted. Units: millions of US dollars. (unit: millions of USD) |
| inventory_nsa_musd | float | Published end-of-month retail inventories, not seasonally adjusted. Units: millions of US dollars. (unit: millions of USD) |
| inventory_sales_ratio_sa | float | Published inventories/sales ratio, seasonally adjusted: months of supply at the current sales pace. (unit: ratio) |
| inventory_sales_ratio_nsa | float | Published inventories/sales ratio, not seasonally adjusted (strongly seasonal — December spikes). (unit: ratio) |
| inv_mom_pct | float | Derived: month-on-month percent change of inventory_sa_musd. Connector-defined transformation. (unit: percent) |
| inv_3m_ann_pct | float | Derived: 3-month percent change of inventory_sa_musd, annualized: ((x_t / x_{t-3}) ^ 4 - 1) * 100. Connector-defined transformation. (unit: percent (annualized)) |
| inv_yoy_pct | float | Derived: year-on-year percent change of inventory_sa_musd. Connector-defined transformation. (unit: percent) |
| ratio_yoy_pp | float | Derived: inventory_sales_ratio_sa minus its 12-month lag, in ratio points. Connector-defined transformation. (unit: ratio points) |
| ratio_z_36m | float | Derived: z-score of inventory_sales_ratio_sa against its trailing 36-month mean/std (minimum 12 observations; null when the window std is 0). Positive = stockpiling relative to the 3-year norm. Connector-defined transformation. (unit: z-score) |
| inventory_pressure_score | float | Derived: documented 0-100 composite, min-maxed within each month across the 7 peer industries: 100 * (0.50 * min-max(winsorized ratio_z_36m, +-2.5) + 0.30 * min-max(winsorized inv_yoy_pct, +-25pp) + 0.20 * min-max(winsorized ratio_yoy_pp, +-1.0pp)). Higher = more overstock pressure. Missing components contribute 0.5 (neutral); null only when all three are missing (the first 12 panel months). Connector-defined transformation. (unit: 0-100 score) |
| pressure_rank | float | Derived: within-month rank of inventory_pressure_score across the 7 peers, 1 = highest overstock pressure (ties broken by industry_code ascending); null for the two totals and the warmup months. Connector-defined transformation. (unit: rank) |
| pressure_tier | string | Derived: tier of pressure_rank — p1 (ranks 1-2), p2 (3-4), p3 (5-6), p4 (rank 7); null for the two totals and the warmup months. Connector-defined transformation. |
| overstock_flag | float | Derived: 1 when ratio_z_36m exceeds +1.0 (inventories more than one std above the 3-year norm), else 0; null in the warmup window. Connector-defined transformation. (unit: binary) |
| understock_flag | float | Derived: 1 when ratio_z_36m is below -1.0, else 0; null in the warmup window. Connector-defined transformation. (unit: binary) |
| ratio_record_high_36m_flag | integer | Derived: 1 when inventory_sales_ratio_sa equals the trailing-36-month maximum (inclusive, minimum 12 observations), else 0. Connector-defined transformation. (unit: binary) |
| inv_surge_flag | float | Derived: 1 when inv_yoy_pct exceeds 15 (fast inventory buildup), else 0; null when YoY is unavailable. Connector-defined transformation. (unit: binary) |
| unpublished_kinds_inv_sa_musd | float | Derived (4400A rows only): the exact residual ex441 - (4423X+444+445+448+452) — seasonally adjusted inventory sitting in the unpublished kinds (446 health/personal care, 447 gasoline stations, 451 sporting goods/hobby/book/music, 453 miscellaneous, 454 nonstore), which the workbook folds into the ex-441 total but never publishes as rows. Units: millions of USD. Null on all other rows. Connector-defined transformation. (unit: millions of USD) |
| unpublished_kinds_share_pct | float | Derived (4400A rows only): unpublished_kinds_inv_sa_musd as a percent of ex-441 adjusted inventories. Connector-defined transformation. (unit: percent) |
| total_ratio_sa | float | Derived: the published 44000 (retail trade total) seasonally adjusted inventories/sales ratio for the month, broadcast to every row. Connector-defined. (unit: ratio) |
| ex441_ratio_sa | float | Derived: the published 4400A (ex motor vehicles) seasonally adjusted inventories/sales ratio for the month, broadcast to every row. Connector-defined. (unit: ratio) |
| breadth_overstock_pct | float | Derived: share of the 7 peer industries with ratio_z_36m above +1.0 that month — overstock breadth. Connector-defined transformation. (unit: percent) |
| breadth_understock_pct | float | Derived: share of the 7 peer industries with ratio_z_36m below -1.0 that month — understock breadth. Connector-defined transformation. (unit: percent) |
| top_pressure_industry | string | Derived: industry_code of the rank-1 (highest pressure) peer that month; null in the warmup window. Connector-defined transformation. |
| top_pressure_label | string | Derived: industry label of the rank-1 peer that month. Connector-defined transformation. |
| median_pressure_score | float | Derived: median of the 7 peers' inventory_pressure_score that month. Connector-defined transformation. (unit: 0-100 score) |
| row_hash | string | Deterministic 16-hex sha256 of (source_id, dataset_id, date, industry_code, inventory_sa_musd) — stable row identity. Connector-defined. |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| date | country | country_code | industry_code | industry_label | inventory_sa_musd | inventory_nsa_musd | inventory_sales_ratio_sa | inventory_sales_ratio_nsa | inv_mom_pct | inv_3m_ann_pct | inv_yoy_pct | ratio_yoy_pp | ratio_z_36m | inventory_pressure_score | pressure_rank | pressure_tier | overstock_flag | understock_flag | ratio_record_high_36m_flag | inv_surge_flag | unpublished_kinds_inv_sa_musd | unpublished_kinds_share_pct | total_ratio_sa | ex441_ratio_sa | breadth_overstock_pct | breadth_understock_pct | top_pressure_industry | top_pressure_label | median_pressure_score | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1992-01-01 | United States | USA | 44000 | Retail trade, total | 234 641 | 228 960 | 1,65 | 1,81 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | 5cfc53cafbef6fbe |
| 1992-01-01 | United States | USA | 4400A | Retail trade excluding motor vehicle and parts dealers | 175 050 | 167 847 | 1,6 | 1,72 | — | — | — | — | — | — | — | — | — | — | 0 | — | 42 577 | 24,32 | 1,65 | 1,6 | — | — | — | — | — | 188055ca870c3b5a |
| 1992-01-01 | United States | USA | 441 | Motor vehicle and parts dealers | 59 591 | 61 113 | 1,82 | 2,11 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | a18fc878405d2cbc |
| 1992-01-01 | United States | USA | 4423X | Furniture, home furnishings, electronics, and appliance stores | 15 039 | 14 738 | 1,92 | 2,03 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | e5b174aa9575d9ff |
| 1992-01-01 | United States | USA | 444 | Building material and garden equipment and supplies dealers | 19 584 | 19 290 | 1,83 | 2,18 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | 5889c40a68866a45 |
| 1992-01-01 | United States | USA | 445 | Food and beverage stores | 26 370 | 26 458 | 0,88 | 0,91 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | e19cb4e39e28f5be |
| 1992-01-01 | United States | USA | 448 | Clothing and clothing accessories stores | 24 226 | 21 852 | 2,56 | 3,25 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | 47b233d48b062941 |
| 1992-01-01 | United States | USA | 452 | General merchandise stores | 47 254 | 43 558 | 2,38 | 2,92 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | aca0392eb57b3af4 |
| 1992-01-01 | United States | USA | 4522 | Department stores | 36 737 | 33 798 | 2,6 | 3,29 | — | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,65 | 1,6 | — | — | — | — | — | 831c5ba850f0b93d |
| 1992-02-01 | United States | USA | 44000 | Retail trade, total | 236 321 | 232 613 | 1,66 | 1,83 | 0,72 | — | — | — | — | — | — | — | — | — | 0 | — | — | — | 1,66 | 1,6 | — | — | — | — | — | 481b3c1b3d167fd1 |
- Actuelle
20260930T142135Z-382c866f6190 · sha256 382c866f6190…
3 735 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/census_retail_inventory_intel/us_retail_inventory_cycle_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/census_retail_inventory_intel/us_retail_inventory_cycle_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)Point d’accès API : https://datazimuts.com/v1/datasets/census_retail_inventory_intel/us_retail_inventory_cycle_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
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Épinglé à l’instantané 20260930T142135Z-382c866f6190 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
US Retail Inventory-Cycle Intelligence (derived). (2026). US retail inventory-cycle intelligence (monthly) [Data set, snapshot 20260930T142135Z-382c866f6190, sha256 382c866f6190]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/fr/datasets/census_retail_inventory_intel/us_retail_inventory_cycle_monthly?snapshot=20260930T142135Z-382c866f6190
@misc{dz_census_retail_inventory_intel_us_retail__382c866f,
title = {{US retail inventory-cycle intelligence (monthly)}},
author = {{US Retail Inventory-Cycle Intelligence (derived)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/census_retail_inventory_intel/us_retail_inventory_cycle_monthly?snapshot=20260930T142135Z-382c866f6190}},
note = {Snapshot 20260930T142135Z-382c866f6190, sha256 382c866f61909bff2402e0825506619adf8fc1639fafa45b94050a8391ebf345; accessed 2026-09-30}
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<iframe src="https://datazimuts.com/embed/chart?dataset=census_retail_inventory_intel%2Fus_retail_inventory_cycle_monthly&lang=fr&theme=auto&snapshot=20260930T142135Z-382c866f6190&x=date&y=inventory_sa_musd&agg=avg" title="US retail inventory-cycle intelligence (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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