Commercial Real Estate Prices
Get the quarterly growth of commercial real estate prices: the change in the price of commercial property (offices, retail, industrial and apartment buildings) on the same quarter a year earlier, for around twenty economies.
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.
No API key is needed: the prices come from the commercial property price indices the Bank for International Settlements collects from national sources, for the euro area, the United States (from 1945), Japan, Denmark, the Netherlands, Germany, France, Spain and around ten more, taking per country a nominal index of commercial property, preferably all commercial property in the whole country. When a FRED API key is set (see the fred_api_key parameter of the Economics class), the United States instead comes from FRED’s series sourced from the IMF’s Financial Soundness Indicators.
See definitions: https://data.bis.org/topics/CPP and https://fred.stlouisfed.org/series/COMREPUSQ159N
Also known as: commercial property price index, CRE price index.
Commercial Real Estate Prices in Python or via the MCP
Install the Finance Toolkit with:
pip install financetoolkit -U
Then call get_commercial_real_estate_prices as shown below.
from financetoolkit import Economics
economics = Economics(start_date='2025-01-01', end_date="2025-12-31")
economics.get_commercial_real_estate_prices(countries=['United States', 'Euro Area', 'Japan', 'Germany'])
Or, after setting up the Finance Toolkit MCP, ask:
How did commercial property prices in the United States, the euro area, Japan and Germany change year on year in each quarter of 2025?
Which returns:
| United States | Euro Area | Japan | Germany | |
|---|---|---|---|---|
| 2025Q1 | -0.027 | 0.0232 | 0.0289 | 0.0221 |
| 2025Q2 | -0.0494 | 0.0331 | 0.0197 | 0.0266 |
| 2025Q3 | 0.0209 | 0.0173 | 0.0222 | 0.0264 |
| 2025Q4 | 0.0109 | 0.0189 | 0.0104 | 0.0207 |
Parameters
get_commercial_real_estate_prices accepts the following parameters.
| Parameter | Type | Description | Example |
|---|---|---|---|
countries | list[str] | str | None | The countries to include in the data. Defaults to None. | ["United States", "Germany"] |
rolling | int | The rolling window size to use for smoothing the data (simple moving average). Defaults to None. | 4 |
trailing | int | The trailing window size to use for summing the data over trailing periods. Defaults to None. | 4 |
growth | bool | Whether to return the growth data or the actual data. Defaults to False. | True |
lag | int | The number of periods to lag the data by. Defaults to 1. | 4 |
standardize | bool | Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False. | True |
rounding | int | None | The number of decimals to round the results to. Defaults to None. | 2 |