Survey Inflation Expectations

Get the inflation professional forecasters expect over the longer term, from surveys. Where get_breakeven_inflation_expectations is the bond market’s expectation, priced daily and including risk premia, this is the forecasters’ own expectation, with no inflation risk or liquidity premium in it and a longer history.

Every series comes from the official source without an API key:

• Germany: the inflation Consensus Economics’ forecasters expect over 5 and 10 years, monthly from 1989. The Bundesbank publishes the expected real interest rate, the yield on debt securities outstanding issued by German residents with a residual maturity of 5 to 6 (or 9 to 10) years minus the expected inflation, so adding that yield back gives the expected inflation.
• Euro Area: the average longer-term (five years ahead) HICP inflation forecast of the ECB’s quarterly Survey of Professional Forecasters, from 1999. With period=”monthly” each survey round is shown from the first month of its quarter until the next round.

Rates are returned as a decimal fraction (0.0223 for 2.23%), with one column per country and horizon.

See definition: https://www.ecb.europa.eu/stats/ecb_surveys/survey_of_professional_forecasters/html/index.en.html

Also known as: survey-based inflation expectations, long-term inflation expectations, Consensus Economics inflation forecasts, Survey of Professional Forecasters.

Survey Inflation Expectations in Python

Install the Finance Toolkit with:

pip install financetoolkit -U

Then call get_inflation_expectations as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2026-04-01', end_date='2026-09-30')

economics.get_inflation_expectations().xs('5Y', axis=1, level='Maturity')

Which returns:

Parameters

The function accepts the following parameters:

• countries (list[str] | str | None, optional): The countries to retrieve, from “Germany” and “Euro Area”. Defaults to None, which retrieves every country.
• period (str, optional): Whether to return the monthly or quarterly data. Quarterly data takes the last month of each quarter. Defaults to “monthly”.
• rolling (int, optional): The rolling window size to use for smoothing the data (simple moving average). Defaults to None.
• trailing (int, optional): The trailing window size to use for summing the data over trailing periods. Defaults to None.
• growth (bool, optional): Whether to return the growth data or the actual data.
• lag (int, optional): The number of periods to lag the data by.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

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