Global house price signals (BIS real residential property prices)
Quarterly housing-market signals derived from the BIS selected residential property price indices: real (inflation-deflated) house-price indices (2010 = 100) for ~60 economies and BIS aggregates, with year-on-year and quarter-on-quarter changes, 30-quarter annualized change volatility, 1-quarter momentum, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-country volatility rank, a 10-year overvaluation z-score (froth gauge) and drawdown-from-decade-peak (correction gauge). Country codes are normalized to ISO alpha-3 (BIS aggregates keep stable codes) so rows join cleanly with other country-keyed datasets. Raw series: Bank for International Settlements (WS_SPP).
Qualité
Attribution
Bank for International Settlements (derived signals by Frontier Data Hub)
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | First day of the reference quarter (BIS SDMX TIME_PERIOD, e.g. 2026-Q1 -> 2026-01-01). |
| country | string | |
| country_code | string | |
| series_id | string | BIS REF_AREA code for the economy or aggregate (official CL_AREA codelist, e.g. US, DE, XM for the euro area, XW for the world aggregate). |
| series_label | string | BIS CL_AREA English name plus the series definition: real residential property prices (CL_VALUE code R = Real, i.e. deflated). |
| value | float | Real residential property price index (BIS UNIT_MEASURE code 628 = Index, 2010 = 100) as published in the BIS WS_SPP dataflow. |
| yoy_change_pct | float | |
| qoq_change_pct | float | |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| overvaluation_z_10y | float | |
| drawdown_from_peak_pct | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | yoy_change_pct | qoq_change_pct | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank | overvaluation_z_10y | drawdown_from_peak_pct |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1947-01-01 | Italy | ITA | IT | Italy — Real residential property prices | 32.0416 | — | — | — | — | 0 | — | — | — | — |
| 1947-04-01 | Italy | ITA | IT | Italy — Real residential property prices | 29.9313 | — | -6.586125536802168 | — | -2.1103000000000023 | 0 | — | — | — | — |
| 1947-07-01 | Italy | ITA | IT | Italy — Real residential property prices | 27.9126 | — | -6.744444778542857 | — | -2.018699999999999 | 0 | — | — | — | — |
| 1947-10-01 | Italy | ITA | IT | Italy — Real residential property prices | 29.1209 | — | 4.328869399482671 | — | 1.2082999999999977 | 0 | — | — | — | — |
| 1948-01-01 | Italy | ITA | IT | Italy — Real residential property prices | 33.8255 | 5.5674498152401775 | 16.155407284802315 | — | 4.704599999999999 | 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/bis_property_signals/global_house_price_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/bis_property_signals/global_house_price_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/bis_property_signals/global_house_price_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.