280156
/en/priser-og-prisindekser/statistikker/lmu/aar
280156
Highest rents in Oslo
statistikk
2016-12-12T08:00:00.000Z
Prices and price indices;Construction, housing and property
en
lmu, Rental market survey, letting, rent, rents per square metre, dwelling types, lessor categories (for example family, local authority, employer), dwelling sizeDwelling and housing conditions , House prices and house price indices , Construction, housing and property, Prices and price indices
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Rental market survey

Updated

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Key figures

32 %

higher yearly rents per sqm for a two-room dwelling in Oslo and Bærum compared to Norwegian average in 2016

Average monthly rents and annual rents per sqm, for dwellings with two rooms. Selected cities and the whole country.
2016
Average monthly rentsAverage annual rents per sqm
The whole country8 1302 050
Oslo and Bærum municipality10 2002 700
Bergen municipality8 4602 260
Trondheim municipality8 6702 380
Stavanger municipality8 4401 860

See more tables on this subject

Table 1 
Average monthly rents and annual rents per sqm, by price zone and number of rooms. NOK

Average monthly rents and annual rents per sqm, by price zone and number of rooms. NOK
2016
Average monthly rentsAverage annual rents per sqm
1Excluding Oslo, Bergen, Trondheim, Stavanger, and Akershus.
2Excluding Akershus
The whole country
1 room6 8302 870
2 rooms8 1302 050
3 rooms9 6101 750
4 rooms10 4801 530
5 rooms or more11 4701 270
 
Oslo and Bærum municipality
1 room7 7403 360
2 rooms10 2002 700
3 rooms12 8802 410
4 rooms15 1502 270
5 rooms or more18 2602 140
 
Akershus county except Bærum municipality
1 room6 920:
2 rooms8 1302 050
3 rooms9 3501 640
4 rooms10 5901 510
5 rooms or more11 640:
 
Bergen municipality
1 room6 3602 750
2 rooms8 4602 260
3 rooms9 9601 890
4 rooms12 0901 870
5 rooms or more15 010:
 
Trondheim municipality
1 room6 3702 820
2 rooms8 6702 380
3 rooms10 4201 950
4 rooms12 7302 010
5 rooms or more16 4701 910
 
Stavanger municipality
1 room::
2 rooms8 4401 860
3 rooms9 5601 720
4 rooms11 9601 660
5 rooms or more:1 400
 
Urban settlements with more than 20,000 inhabitants1
1 room5 9702 710
2 rooms7 0201 680
3 rooms8 1801 470
4 rooms9 0101 310
5 rooms or more9 6401 160
 
Urban settlements with between 2,000-19,999 inhabitants2
1 room4 980:
2 rooms6 1301 410
3 rooms7 2001 270
4 rooms7 9301 070
5 rooms or more8 940940
 
Urban settlements with between 200 and 1,999 inhabitants and sparsely populated areas2
1 room::
2 rooms5 4001 200
3 rooms6 0701 000
4 rooms6 130830
5 rooms or more6 750640

Table 2 
Average monthly rents and annual rents per sqm, by number of rooms (NOK). The whole country.

Average monthly rents and annual rents per sqm, by number of rooms (NOK). The whole country.
2016
Average monthly rentsAverage annual rents per sqm
1 room6 8302 870
2 rooms8 1302 050
3 rooms9 6101 750
4 rooms10 4801 530
5 rooms or more11 4701 270

About the statistics

Definitions

Definitions of the main concepts and variables

Rents - The actual rent of the rental object. Monthly rents are selected. No ajustments are made for rents that include electricity and/or heating except for the predicted monthly rents (table 09897), where the markup for electricity and or heating are excluded.

Rent allowance - Economic housing benefit administered by the State and the municipalities, provided to cover all or part of rental charges.

SSB-Matrikkelen - Ground Property, Address and Building Register owned by the Norwegian Mapping Authority.

Population and housing census 2011 - In 2011, Statistics Norway carried out a Population and Housing Census in Norway (Census 2011). The purpose of this nationwide census is to illustrate how people live in Norway, provide information on the composition of the population and describe living conditions in the Norwegian society. In the 2011 census, all data are for the first time collected from administrative and statistical registers. Last census was conducted in 2001. Census information is used as weight information in the rental market survey.

Number of rooms - The rooms that are used in the calculations are the number of bedrooms and living rooms excluding kitchens, bathrooms and storage rooms. Rooms beyond 8 are omitted.

Regression model - A statistical method where a dependent variable (here: rent) is explained by a set of explanatory variables (here: dwelling characteristics). Based on actual observations in the main survey a mathematic function gives a connection between the rent and its characteristics.

Predicted monthly rents – Rents estimated by the regression model and the "price" of the different explanatory variables.

Standard classifications

A variant of standard classification of urban settlements is used.

Administrative information

Name and topic

Name: Rental market survey
Topic: Prices and price indices

Next release

Responsible division

Division for Price Statistics

Regional level

Regional level

Frequency and timeliness

Annual survey of rent levels. The annual statistics is published around a month after the current period.

International reporting

Not relevant

Microdata

Anonymous data at micro level are stored in SAS datasets.

Background

Background and purpose

The purpose of the survey is to measure rent levels in Norway grouped into different segments of the rental market. The rental survey was first carried out in 2005 as an external commission and was based on the need for more detailed and improved rental statistics. The statistics was further established as official statistics in 2006. In 2012 the survey is expanded with more detailed figures.

Users and applications

The rental survey is aimed at lessors, tenants and various professional and industrial bodies, as well as users in the public sector (such as ministries) and others with an interest in the rental market. Within Statistics Norway, the National Accounts , the Consumer Price Index and the Household Budget Survey are important users. Primary data is also used in analysis and research within Statistics Norway.

Equal treatment of users

Not relevant

Coherence with other statistics

The data and results from the main survey (not register data) are used in the National Accounts , the Consumer Price Index and the Household Budget Survey . The Rental Market Survey sample is also the sample of the monthly rent survey in the CPI.

Legal authority

None

EEA reference

None

Production

Population

The population is defined as all rental units inhabited by private tenants in Norway.

Data sources and sampling

Rents are mainly collected by web questionnaires directly with households. Electronic register data from municipalities and student organisations are also collected.

As registers of rental units and of tenants are incomplete, a potential population of rental units/tenants is therefore established by connecting different registers. To remove homeowners, persons/addresses from the Central Population Register (DSF) are connected to the Ground Property, Address and Building Register which is called SSB-Matrikkelen. In order to remove homeowners in cooperative dwellings and institutions, information from the Statistics Norway's Business Register is also connected. The sample is established through random selection of persons/addresses from this population of potential rental units.

The size of the gross sample in the fourth quarter of 2016 is 35 286 persons/addresses including an overrepresentation of the largest municipalities of about 6 700 persons/addresses. The size of the net sample (the share of responded questionnaires) is about 9 727.  Each year a new sample is selected without overlapping previous samples.

The sample is limited to rents between NOK 1000 and NOK 40 000, units between 10 and 300 sqm, and number of rooms less than 9.

Collection of data, editing and estimations

The data collection period was carried out in the period from 28. September to 21. October 2016. Rents are collected by web questionnaires.

Questionnaires with missing values for rents and inconsistent answers are deleted. The prices are subsequently checked in order to identify mistakes and observations with major deviations from average levels stratified by different segments of the rental market. Households are not contacted during the revision process.

Average monthly rents and average annual rents per square metre are calculated for different segments of the market such as geographical areas, letting status, size (such as number of rooms) and period of tenancy. Average rents are weighted together into more aggregated levels and at national level by using weights based on geographical areas, letting status and to a certain degree number of rooms. Most average rent levels are entirely based on rents from the main survey, i.e. the questionnaires, while some average levels are also based on register data. The number of rental units within each average level stratum will therefore vary immensely.

Detailed predicted monthly rents for different geographical areas and size (number of rooms and utility floor space) are estimated based on a regression model.

Seasonal adjustment

Not relevant

Confidentiality

Data collected from households and firms are subject to confidentiality and are to be kept or destroyed in a secure manner. Any use of the data must be in accordance with the rules set out by the Data Inspectorate.

Comparability over time and space

The rental market survey is a price level survey. The survey is not conducted for price development. The average rent levels from different years are not directly comparable since the survey is based on unique samples each year that can differ according to variables that are important for the rent level. Unlike 2006, no register data are collected from major private letting agencies. As of 2012 detailed predicted monthly rents are estimated by hedonic techniques. A more detailed stratification is introduced and the rents are, unlike previous figures, rounded off.

Accuracy and reliability

Sources of error and uncertainty

The data collection is carried out by web questionnaires. Measurement errors can occur if the household provides wrong answers due to the difficulty in estimating the values for, for instance, the utility floor space.

Non-response: The non-response is high. The non-response is primarily caused by difficulties reaching the respondents; refusal is only a minor problem in the survey. The non-response increased as a consequence of changing the data collection method in 2013 (mainly web qustionnaires). In 2013 only about 10 per cent of the net sample was contacted by telephone. The share of elderly in the sample is somewhat lower in 2013 compared to earlier years.

Total and partial non-responses are not imputed.

Almost all contacted municipalities and student organisations deliver electronic register data.

Skewness: The lack of registers of rental units and tenants makes it difficult to define the population and to control how representative the sample is in proportion to the population.

The share of tenants among the younger population between the ages 20-29 is high. One criterion for the selection of the sample is that persons are actually living at their registered residences. A high percentage of the younger population, especially students, fails to report changing residences. This results in the omission of an important tenant group. In the Rental Market Survey 2005, this part of the population was underrepresented. To avoid this in 2006 and 2007, an overrepresentation is made among the younger population.

The largest municipalities are overrepresented in the sample in order to be able to publish more detailed figures. Geographical skewness of the sample is corrected with population shares from the Population and housing census 2011.

The survey shows wide variation in rents and several estimated average rent levels are not published due to high uncertainty in the estimates. The estimated rents are published under the condition that with 95 per cent certainty the actual average rent lies within 10 per cent from the published estimate.

Predicted rents based on a regression model will never be without uncertainty. A model will never capture all the factors affecting the rents. The explanatory power of the model, measured by the R 2 , is 61-63 per cent. In other words, the regression model manages to explain well over 60 per cent of the variation in the rents. This is regarded as quite high given the heterogeneity of the Norwegian rental market. The explanatory variables behave with expected signs and with reasonable magnitudes.

The survey measures the actual rent of the rental object, excluding rent allowance and other housing benefits (i.e. the gross rent). Due to different rent allowances, the actual rents of the rental unit and the rent actually paid by the tenants might deviate (gross versus net rents). The degree of reported net rents is unknown. Rent allowances are assumed to be most relevant for local authority housing tenants, and in these cases this has been controlled for to a certain degree.

No adjustments are made for rents that include electricity and/or heating.

Revision

Not relevant

Contact