US state consumer-demand pressure (monthly composite)
Monthly US state consumer-demand pressure composite, 2019-12 onward (inputs from 2019-01; first 11 months are the z-score warmup), for the 50 states plus the District of Columbia: six official signals (Census MSRS total-retail YoY, BLS state employment YoY, EIA gasoline price YoY by state-or-PADD, FHFA house-price YoY forward-filled from quarterly, DOL UI initial-claims YoY, BLS JOLTS openings rate), each z-scored against its trailing 36-month state history, sign-adjusted so positive always means stronger demand, and combined into a documented weighted composite (weights renormalized over available components), scored 0-100 within each month across states with ranks, tiers and hot / weak / accelerating / cooling flags. Who joins this: Shopify shops join demand_pressure_score on year_month + state_code to weight inventory buys and geo-target promos; subscription businesses use the weak-demand flag as a consumer-strain churn feature; sales teams rank territories by demand_pressure_score and time outreach to accelerating states. One row per state x month; deterministic — identical inputs always produce byte-identical panels.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 3 978
- Colonnes
- 22
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 1 oct. 2026
- Thème
- economy
| Colonne | Type | Description |
|---|---|---|
| country | string | Country name. (unit: string) |
| country_code | string | ISO country code (always USA). (unit: string) |
| state_code | string | USPS 2-letter state code. (unit: string) |
| state_name | string | State name. (unit: string) |
| month | date | First day of the calendar month. (unit: date) |
| year_month | string | ISO year-month (YYYY-MM). (unit: string) |
| retail_yoy_pct | float | Census MSRS total-retail-sales YoY % (not seasonally adjusted). (unit: percent) |
| emp_yoy_pct | float | BLS CES total-nonfarm employment YoY % (seasonally adjusted). (unit: percent) |
| gas_yoy_pct | float | EIA retail gasoline price YoY % (state series where published, else the state's PADD). (unit: percent) |
| hpi_yoy_pct | float | FHFA all-transactions HPI YoY % (quarterly, forward-filled to months). (unit: percent) |
| ui_yoy_pct | float | DOL ETA-539 initial UI claims YoY % (monthly mean of weekly NSA claims). (unit: percent) |
| jolts_openings_rate_pct | float | BLS JOLTS job-openings rate % (seasonally adjusted). (unit: percent) |
| n_components | integer | Number of non-null z-scored components behind this row's composite. (unit: count) |
| demand_pressure_raw | float | Sign-adjusted weighted mean of the available component z-scores (weights renormalized). (unit: z-score) |
| demand_pressure_score | float | 0-100 min-max of the composite within the month across states. (unit: score 0-100) |
| demand_rank | integer | Dense rank of the composite within the month (1 = hottest demand). (unit: rank) |
| demand_tier | integer | Score tier: 4 >= 75, 3 >= 50, 2 >= 25, 1 < 25. (unit: tier 1-4) |
| hot_demand_flag | boolean | Score >= 75. (unit: boolean) |
| weak_demand_flag | boolean | Score <= 25. (unit: boolean) |
| accelerating_flag | boolean | Composite up > 0.25 sigma vs prior month. (unit: boolean) |
| cooling_flag | boolean | Composite down > 0.25 sigma vs prior month. (unit: boolean) |
| row_hash | string | Deterministic sha256[:16] of state|year_month|score|raw. (unit: hash) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| country | country_code | state_code | state_name | month | year_month | retail_yoy_pct | emp_yoy_pct | gas_yoy_pct | hpi_yoy_pct | ui_yoy_pct | jolts_openings_rate_pct | n_components | demand_pressure_raw | demand_pressure_score | demand_rank | demand_tier | hot_demand_flag | weak_demand_flag | accelerating_flag | cooling_flag | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| United States | USA | OR | Oregon | 2019-12-01 | 2019-12 | 1,9 | 1,605 | 5,275 | 5,027 | — | 5,5 | 5 | 0,384 | 100 | 1 | 4 | true | false | false | false | 8fc4c4c42d8331ba |
| United States | USA | NH | New Hampshire | 2019-12-01 | 2019-12 | 0,8 | 1,058 | 5,568 | 5,677 | — | 4,6 | 5 | 0,315 | 94 | 2 | 4 | true | false | false | false | bf6bce9d8043c189 |
| United States | USA | WA | Washington | 2019-12-01 | 2019-12 | 3 | 2,197 | 0,54 | 5,99 | — | 4,5 | 5 | 0,297 | 92,5 | 3 | 4 | true | false | false | false | 2eb96a7c47738316 |
| United States | USA | MO | Missouri | 2019-12-01 | 2019-12 | 3 | 1,115 | 13,394 | 5,309 | — | 3,6 | 5 | 0,243 | 87,8 | 4 | 4 | true | false | false | false | 45626e49e953a5f1 |
| United States | USA | AZ | Arizona | 2019-12-01 | 2019-12 | 2,9 | 3,17 | 5,275 | 6,657 | — | 4,8 | 5 | 0,2 | 84,1 | 5 | 4 | true | false | false | false | 90aceef11ac83408 |
| United States | USA | NV | Nevada | 2019-12-01 | 2019-12 | 2,6 | 2,857 | 5,275 | 3,784 | — | 4,6 | 5 | 0,14 | 78,9 | 6 | 4 | true | false | false | false | 6f602bbbc046390e |
| United States | USA | NM | New Mexico | 2019-12-01 | 2019-12 | 1,8 | 1,904 | 9,724 | 5,608 | — | 5,1 | 5 | 0,115 | 76,7 | 7 | 4 | true | false | false | false | af91d9eec99d7efe |
| United States | USA | RI | Rhode Island | 2019-12-01 | 2019-12 | 2,5 | 0,617 | 5,568 | 5,347 | — | 4,2 | 5 | 0,057 | 71,7 | 8 | 3 | false | false | false | false | 59290cee298a88f7 |
| United States | USA | KY | Kentucky | 2019-12-01 | 2019-12 | 2,7 | 0,973 | 13,394 | 4,894 | — | 4 | 5 | 0,048 | 70,9 | 9 | 3 | false | false | false | false | 36f04b41863ea609 |
| United States | USA | AK | Alaska | 2019-12-01 | 2019-12 | 1,8 | 0,275 | 5,275 | 3,032 | — | 6,5 | 5 | 0,048 | 70,9 | 10 | 3 | false | false | false | false | fae33dd98b23fd9c |
Profilé le 1 oct. 2026 à partir de l’instantané 20261001T194208Z-527128c3b697
Mesuré- Complétude
- 97,8 %
- Lignes
- 3 978
- Colonnes
- 22
- Colonnes incomplètes
- 2
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| countryvarchar | 0 % | 1 | — |
|
| country_codevarchar | 0 % | 1 | — |
|
| state_codevarchar | 0 % | 55 | — |
|
| state_namevarchar | 0 % | 59 | — |
|
| monthdate | 0 % | 76 | 1 déc. 2019 → 1 mai 2026 | — |
| year_monthvarchar | 0 % | 79 | — |
|
| retail_yoy_pctdouble | 0 % | 531 | -70,4 → 353,9médiane 3,2 | 79 hors du 1er–99e centile |
| emp_yoy_pctdouble | 0 % | 4 466 | -23,6 → 22,5médiane 1,23 | 80 hors du 1er–99e centile |
| gas_yoy_pctdouble | 0 % | 1 005 | -43,98 → 87,59médiane -1,88 | 86 hors du 1er–99e centile |
| hpi_yoy_pctdouble | 0 % | 1 193 | -5,85 → 32,09médiane 6 | 84 hors du 1er–99e centile |
| ui_yoy_pctdouble | 42,3 % | 2 906 | -97,15 → 228,51médiane 0,9149 | 46 hors du 1er–99e centile |
| jolts_openings_rate_pctdouble | 6,4 % | 63 | 2,1 → 11,4médiane 5,3 | 66 hors du 1er–99e centile |
| n_componentsbigint | 0 % | 2 | 5 → 6médiane 5 | |
| demand_pressure_rawdouble | 0 % | 3 709 | -2,36 → 2,59médiane -0,1897 | 80 hors du 1er–99e centile |
| demand_pressure_scoredouble | 0 % | 1 045 | 0 → 100médiane 49,7 | |
| demand_rankbigint | 0 % | 45 | 1 → 51médiane 26 | |
| demand_tierbigint | 0 % | 4 | 1 → 4médiane 2 | |
| hot_demand_flagboolean | 0 % | 2 | — |
|
| weak_demand_flagboolean | 0 % | 2 | — |
|
| accelerating_flagboolean | 0 % | 2 | — |
|
| cooling_flagboolean | 0 % | 2 | — |
|
| row_hashvarchar | 0 % | 3 453 | — |
|
- Actuelle
20261001T194208Z-527128c3b697 · sha256 527128c3b697…
3 978 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/us_state_demand_pressure_intel/us_state_demand_pressure_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/us_state_demand_pressure_intel/us_state_demand_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)Point d’accès API : https://datazimuts.com/v1/datasets/us_state_demand_pressure_intel/us_state_demand_pressure_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20261001T194208Z-527128c3b697 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
US State Consumer-Demand Pressure (agent composite, keyless). (2026). US state consumer-demand pressure (monthly composite) [Data set, snapshot 20261001T194208Z-527128c3b697, sha256 527128c3b697]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/fr/datasets/us_state_demand_pressure_intel/us_state_demand_pressure_monthly?snapshot=20261001T194208Z-527128c3b697
@misc{dz_us_state_demand_pressure_intel_us_state__527128c3,
title = {{US state consumer-demand pressure (monthly composite)}},
author = {{US State Consumer-Demand Pressure (agent composite, keyless)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/us_state_demand_pressure_intel/us_state_demand_pressure_monthly?snapshot=20261001T194208Z-527128c3b697}},
note = {Snapshot 20261001T194208Z-527128c3b697, sha256 527128c3b697484da7f990405756e901e3f2057f6cdafa15f65e46acbc3273fa; accessed 2026-10-02}
}Intégrer un tableau ou un graphique
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<iframe src="https://datazimuts.com/embed/chart?dataset=us_state_demand_pressure_intel%2Fus_state_demand_pressure_monthly&lang=fr&theme=auto&snapshot=20261001T194208Z-527128c3b697&x=year_month&y=retail_yoy_pct&agg=avg" title="US state consumer-demand pressure (monthly composite)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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