Housing price statistics from the real estate industry have a long history dating back to the mid-1980s.

Today's statistics have been owned by Real Estate Norway since 2011. Before that, statistics were run by Real Estate Norway and the Norwegian Association of Real Estate Agents (NEF) jointly.

The statistics have been produced by Eiendomsverdi AS since 2014, and the statistics today are based on Eiendomsverdi's data sources back to 2003.

Publishing Frequency

The housing price statistics are published on the third business day each month at 11:00 am. The press conference at which the data is presented begins at 11:00 am.

The regional reports are published every quarter and are published in medio January, April, July and October.

Holiday home price statistics are released in February for mountain cabins and in June for sea cabins.

Rental house price statistics come every quarter medio January, April, July and October.

Data Sources

The statistics are not a total count of homes sold / holiday homes.

The statistics are based on sales brokered and advertised through Finn.no.

Turnovers that are assessed as turnovers of other than residential / recreational dwellings (plots, garage, etc.) are taken out of the data base through automated routines at Eiendomsverdi AS.


Price changes are measured using index theory.

The index reports price changes for comparable homes using a starting point (set to 1.00) and where the changes are presented as percentage increases or reductions in the index level. For example, a change from an index level of 1.73 to 1.76 would represent a rise in house prices. of 1.7% (where the change of 1.76-1.73 = 0.03 relates to the previous level of 1.73 and where it amounts to 1.7%).

Property value calculates a house price index based on a further development of an internationally recognized method (SPAR, abbreviation for Sales Price Appraisal Ratio).

Our variant of "appraisal" involves estimating the coefficients in a hedonic regression model, where the model is used to estimate housing values. The program consists of two steps. In the first step, observations are made on how variations in the characteristics of a home correlate with variations in the sales prices of the homes.

We include characteristics such as type of dwelling, size, floor, plot size, year of construction, ownership of plot and dwelling, location and number and types of buildings. The regression model allows us to estimate a partial price for each characteristic, which can then be summed up to an estimated total value for the entire dwelling. In the second step, we calculate the relationship between the latest sales prices and what the regression model predicts for the observed homes given their characteristics.

For housing types and areas, we then find a typical price increase by identifying the median level for the relationship between observed prices and predicted prices. The median level indicates the price increase where fifty percent of the observations are lower and fifty percent higher. In this way, we control for composition effects and different price trends for different types of housing - and are able to say what price developments are for comparable objects.

The index for the whole of Norway is calculated as a volume-weighted upgrading of the indices for the whole of Norway by types of housing. It is not an aggregation of the indexes for sub-counties that are counties.

Seasonal adjustment

Normally, prices rise most in the spring as they fall or level off in the fall.

In the period 2003 - 2013, the rise in prices was strongest in January and weakest in December, when we consider the types of homes sold in the different months.

Even with an underlying trend in price trends, there is a tendency for prices to have seasonal variation. To find out what the underlying price trend is, we want to say something about what the price change was when the seasonal variation is controlled for.

As of 2014, we use a standard technique (x 12 ARIMA) designed to eliminate the price fluctuations that can be attributed to a repeating seasonal pattern. The difference between the change in the actual price index and the seasonally adjusted price index is attributed to seasonal variations.

Differences in index and average prices

As of 2014, we present calculated price indices and index changes for housing types and regions. In addition, we calculate average square meter prices for types and regions.

The indices best address the need to ensure that the reported price trend applies to comparable homes. Square meter prices address the need to anchor prices to a level in a given region at a given time.

However, users must be aware that the reported level of average square meter price is calculated on gross figures where composition effects can occur, ie two months contain observations from different housing stock and are therefore not completely comparable.

However, the index will control for this and set price trends for comparable homes