Get the quarterly Commercial Real Estate Price Index for the United States from FRED (sourced from the IMF’s Financial Soundness Indicators).

This tracks commercial (office, retail, industrial, apartment) property prices, as distinct from residential house prices (see get_house_prices, which tracks a completely different asset class/market). It is a transaction-based index rather than the appraisal-smoothed methodology used by institutional benchmarks like the NCREIF Property Index – which is not freely available anywhere – so expect more volatility and less autocorrelation than an appraisal-based series would show.

Requires a free FRED API key, see the fred_api_key parameter of the Economics class.

See definition: https://fred.stlouisfed.org/series/COMREPUSQ159N

Also known as: commercial property price index, CRE price index.

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the Commercial Real Estate Prices for you. Just ask in plain English.

Calculate the Commercial Real Estate Prices in Python

The Commercial Real Estate Prices is available in the Economics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_commercial_real_estate_prices as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2015-01-01', fred_api_key='FRED_API_KEY')

economics.get_commercial_real_estate_prices()

Which returns:

Date United States
2024-04-01 -0.1067
2024-07-01 -0.1058
2024-10-01 -0.0273
2025-01-01 -0.0301
2025-04-01 -0.0701

Parameters

get_commercial_real_estate_prices accepts the following parameters:

  • 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.
Exchange Rates Producer Price Index Real Effective Exchange Rate Household Savings Rate Household Debt to Income Ratio Misery Index Retail Sales Industrial Production Index Housing Starts Real Personal Income Commodity Forward Curve Recession Indicator

The Economics module page introduces the module, and the sidebar lists all of its functions.

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