In most cases, the nominal house price index covers the sales of newly-built and existing dwellings, following the recommendations from the RPPI (Residential Property Prices Indices) manual.

The real house price index is given by the ratio of the nominal house price index to the consumers’ expenditure deflator in each country from the OECD national accounts database. Both indices are seasonally adjusted.

Both are an index based on 2015 = 100.

See definition: https://data.oecd.org/price/housing-prices.htm

It is also possible to get the data from the Global Macro Database (GMDB) by setting the gmdb_source to True.

Also known as: real estate prices, property prices, housing index.

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Calculate the House Prices in Python

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

pip install financetoolkit -U

Then call get_house_prices as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2015-01-01', end_date='2023-12-31')

economics.get_house_prices(
    countries=['Japan', 'Netherlands', 'Ireland'],
    quarterly=False,
    inflation_adjusted=True
)

Which returns:

  Japan Netherlands Ireland
2015 100 100 100
2016 102.559 104.557 106.626
2017 104.76 110.834 116.945
2018 106.053 118.68 127.047
2019 107.254 124.372 127.837
2020 106.994 131.653 128.345
2021 112.714 144.382 135.141
2022 118.739 152.287 141.162
2023 118.74 139.601 134.022

Parameters

get_house_prices accepts the following parameters:

  • countries (list[str] | str | None, optional): The countries to include in the data. Defaults to None.
  • quarterly (bool | None, optional): Whether to return the quarterly data or the annual data.
  • inflation_adjusted (bool, optional): Whether to return the inflation adjusted data or the nominal data.
  • gmdb_source (bool | None, optional): Whether to get the data from the Global Macro Database (GMDB).
  • 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 (e.g. a trailing-4-quarter sum). 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.
Consumer Price Index Inflation Rate Consumer Confidence Index Business Confidence Index Composite Leading Indicator Rent Prices Share Prices Exchange Rates Producer Price Index Real Effective Exchange Rate Household Savings Rate Household Debt to Income Ratio

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

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