House Prices
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.
By default the data comes from the Global Macro Database (GMDB), annual with 2015 = 100. Set gmdb_source=False to retrieve it from the OECD instead, which is the only source with quarterly data: asking for quarterly data takes it from the OECD unless gmdb_source=True is given.
In the OECD data, 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
Also known as: real estate prices, property prices, housing index.
House Prices in Python or via the MCP
Install the Finance Toolkit 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
)
Or, after setting up the Finance Toolkit MCP, ask:
How did inflation-adjusted house prices in Japan, the Netherlands and Ireland develop each year from 2015 to 2023?
Which returns:
| Japan | Netherlands | Ireland | |
|---|---|---|---|
| 2015 | 100 | 100 | 100 |
| 2016 | 102.645 | 106.044 | 108.503 |
| 2017 | 104.009 | 113.945 | 120.785 |
| 2018 | 105.604 | 122.047 | 128.926 |
| 2019 | 106.087 | 126.665 | 128.744 |
| 2020 | 107.773 | 135.898 | 130.088 |
| 2021 | 115.407 | 156.892 | 144.681 |
| 2022 | 121.069 | 150.691 | 145.71 |
| 2023 | 119.632 | 145.341 | 141.362 |
Parameters
get_house_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"] |
quarterly | bool | None | Whether to return the quarterly data (True) or the annual data (False). Defaults to None, which returns the quarterly data, the highest frequency the OECD publishes. | True |
inflation_adjusted | bool | Whether to return the inflation adjusted data or the nominal data. Defaults to False. | True |
gmdb_source | bool | None | Whether to get the data from the Global Macro Database (GMDB), which is annual. Defaults to None, which uses the OECD for quarterly data and the GMDB for annual data; asking for the GMDB together with quarterly data logs a warning. | False |
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 (e.g. a trailing-4-quarter sum). 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 |
gmdb_forecasts | bool | Whether to include the years the Global Macro Database projects, from the IMF’s World Economic Outlook, up to five years ahead. Only applies to data from the Global Macro Database. Defaults to False, which ends a series with its last observation. | True |