ECB Dataset · Inflation

Euro Area HICP Index Level (1996–2026)

Name: Euro Area HICP Index Level (1996–2026)
Creator: Eco3min Research
Published: 2026-03-23
License: https://creativecommons.org/licenses/by/4.0/

The HICP index level (base 2015=100) — the building block for computing inflation rates over any custom period, deflating nominal European data, and constructing real (inflation-adjusted) time series for euro area assets.

Source

ECB / Eurostat

Frequency

Monthly

Coverage

1996–Present

Unit

Index (2015=100)

Download CSV
Python Code ↓
← All ECB Datasets

Using the HICP index for real-value calculations

While the annual rate of change (headline HICP) captures the flow of inflation, the index level captures the stock — the cumulative effect of price changes since the base year (2015=100). As of late 2025, the index stands near 130, meaning the euro area price level has risen approximately 30% since 2015.

For researchers and analysts, the index level is the more useful series: divide any nominal euro-denominated time series by this index (and multiply by 100) to obtain real, inflation-adjusted values. This is how Eco3min computes datasets like the Real Deposit Facility Rate (DFR minus HICP).

Methodological note

Eurostat began a major methodological revision of the HICP in February 2026, including changes to weights, classifications, and seasonal adjustment. The ECB is migrating from the legacy ICP dataset to a new HICP dataset. Users should monitor for potential series breaks around this transition.

CSV Data Dictionary

Column	Type	Description
date	YYYY-MM-DD	First day of the reference month
hicp_index	float	HICP all-items index level (2015=100)

Python Code Example

import pandas as pd
from io import StringIO
import requests

url = "https://data-api.ecb.europa.eu/service/data/ICP/M.U2.N.000000.4.INX"
resp = requests.get(url, params={"format": "csvdata"})
raw = pd.read_csv(StringIO(resp.text))

df = raw[["TIME_PERIOD", "OBS_VALUE"]].copy()
df.columns = ["date", "hicp_index"]
df["date"] = pd.to_datetime(df["date"] + "-01")
df = df.sort_values("date").set_index("date")

df.plot(title="Euro Area HICP Index (2015=100)", figsize=(12, 5))