US shelter inflation signals (rent/OER momentum, affordability wedge, anomalies)
Monthly signals derived from BLS shelter CPI series (redistributed by FRED): 30-month annualized change volatility, 3-month momentum, year-over-year change, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-month cross-series volatility rank, the shelter premium (shelter YoY minus headline CPI YoY — the affordability wedge) and the OER-minus-rent divergence gauge. Covers rent of primary residence from 1915 and owners' equivalent rent from 1983. All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: U.S. Bureau of Labor Statistics.
Qualité
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Labor Statistics; derived signals by Frontier Data Hub)
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD). |
| country | string | |
| country_code | string | |
| series_id | string | FRED series ID (CUUR0000SEHA for CPI rent of primary residence, CUSR0000SEHC for CPI owners' equivalent rent of residences); IDs resolve to the series page at https://fred.stlouisfed.org/series/<id>. |
| series_label | string | Official FRED series title as published for the series (U.S. Bureau of Labor Statistics Consumer Price Index shelter components). |
| value | float | Index value, monthly, not seasonally adjusted; see the series notes for index base periods and methodology. |
| volatility_30d | float | |
| momentum_3m | float | |
| yoy_change_pct | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| shelter_premium | float | |
| oer_rent_gap | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | yoy_change_pct | anomaly_flag | forecast_1m | rank | shelter_premium | oer_rent_gap |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1914-12-01 | United States | USA | CUUR0000SEHA | Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average | 21 | — | — | — | 0 | — | — | — | — |
| 1915-12-01 | United States | USA | CUUR0000SEHA | Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average | 21.2 | — | — | — | 0 | — | — | — | — |
| 1916-12-01 | United States | USA | CUUR0000SEHA | Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average | 21.4 | — | — | — | 0 | — | — | — | — |
| 1917-12-01 | United States | USA | CUUR0000SEHA | Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average | 21 | — | 0 | — | 0 | — | — | — | — |
| 1918-12-01 | United States | USA | CUUR0000SEHA | Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average | 22 | — | 3.7735849056603765 | — | 0 | — | — | — | — |
Télécharger un échantillon
Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).
Utiliser avec un LLM
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
curl "https://datazimuts.com/v1/datasets/shelter_inflation_signals/us_shelter_inflation_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/shelter_inflation_signals/us_shelter_inflation_signals").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/shelter_inflation_signals/us_shelter_inflation_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.