This statistic shows the ten biggest cities in Norway in 2025. In 2025, approximately 0.72 million people lived in Oslo, making it the biggest city in Norway.

Population in the largest city (% of urban population) in Norway was reported at 23.43 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Norway - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Norway NO: Population in Largest City data was reported at 997,451.000 Person in 2017. This records an increase from the previous number of 982,894.000 Person for 2016. Norway NO: Population in Largest City data is updated yearly, averaging 677,489.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 997,451.000 Person in 2017 and a record low of 578,044.000 Person in 1960. Norway NO: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Population in largest city in Norway was reported at 1100868 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Norway - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Norway NO: Population in Largest City: as % of Urban Population data was reported at 23.065 % in 2017. This records an increase from the previous number of 23.044 % for 2016. Norway NO: Population in Largest City: as % of Urban Population data is updated yearly, averaging 22.957 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 32.334 % in 1960 and a record low of 22.249 % in 1981. Norway NO: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

The share of urban population in Norway saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 84 percent. Still, the share reached its highest value in the observed period in 2023. A population may be defined as urban depending on the size (population or area) or population density of the village, town, or city. The urbanization rate then refers to the share of the total population who live in an urban setting. International comparisons may be inconsistent due to differing parameters for what constitutes an urban center.Find more key insights for the share of urban population in countries like Denmark and Sweden.

There were about 3,054 residential properties for rent on FINN in the largest cities in Norway in the first quarter of 2024. Oslo had the highest number of properties (1,713), while Bergen, Trondheim, Stavanger, and Sandnes all had less than 600 properties for rent each.

Norway, officially the Kingdom of Norway, is a Nordic country in Northern Europe, the mainland territory of which comprises the western and northernmost portion of the Scandinavian Peninsula. The remote Arctic island of Jan Mayen and the archipelago of Svalbard also form part of Norway. Bouvet Island, located in the Subantarctic, is a dependency of Norway; it also lays claims to the Antarctic territories of Peter I Island and Queen Maud Land. The capital and largest city in Norway is Oslo.

Source: Objaverse 1.0 / Sketchfab

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Norway: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 32(2.50%) households where the householder is under 25 years old, 319(24.90%) households with a householder aged between 25 and 44 years, 482(37.63%) households with a householder aged between 45 and 64 years, and 448(34.97%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Norway, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Norway median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Norway: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 30(2.39%) households where the householder is under 25 years old, 318(25.30%) households with a householder aged between 25 and 44 years, 483(38.42%) households with a householder aged between 45 and 64 years, and 426(33.89%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Norway, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Norway median household income by age. You can refer the same here

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

No：最大城市人口占城市总人口的百分比在12-01-2017达23.065%，相较于12-01-2016的23.044%有所增长。No：最大城市人口占城市总人口的百分比数据按年更新，12-01-1960至12-01-2017期间平均值为22.957%，共58份观测结果。该数据的历史最高值出现于12-01-1960，达32.334%，而历史最低值则出现于12-01-1981，为22.249%。CEIC提供的No：最大城市人口占城市总人口的百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的挪威 – Table NO.World Bank.WDI：人口和城市化进程统计。

In 2023, Oslo had *** accommodation establishments, hotels and similar. This was the highest figure registered among the main Norwegian cities. Bergen was the second-largest hub for the hotel industry of this Scandinavian country, with ** of such establishments.

In 2023, the region Viken was the most populated in Norway. The region, which contains large areas surrounding the capital Oslo, had a population of nearly 1.3 million people in 2023. Oslo had the second highest number of inhabitants with around 709,000, followed by Vestland. Meanwhile, the northern region of Nordland has the lowest number of inhabitants, counting 241,000 people. At the beginning of 2023, a total of 5.5 million people lived in Norway.

No：最大城市人口在12-01-2017达997,451.000人，相较于12-01-2016的982,894.000人有所增长。No：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为677,489.500人，共58份观测结果。该数据的历史最高值出现于12-01-2017，达997,451.000人，而历史最低值则出现于12-01-1960，为578,044.000人。CEIC提供的No：最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的挪威 – Table NO.World Bank.WDI：人口和城市化进程统计。

Basic information

This dataset contains simulation results for the so-called Danmarksplass domain, an area in the city of Bergen, Norway. Danmarksplass is the major trafic conjuction point in Bergen. Danmarksplass is located in a densily built-up and populated urban district subjected to many environmental challenges among them air pollution by NOx and aerosols (particulate matter PM2.5 and PM10) are considered as significant health threats. Detailed studies of the Danmarksplass meteorological conditions, air quality, and structure of the air polution could be found in Wolf et al. (2014a,b; 2017; 2020; 2021).

This dataset contains two collections of new simulations of air quality at Danmarksplass. The simulations were performed with the PALM modeling system v23.04 with additional modules developed in the TURBAN project (Radovic et al., 2024; Resler et al. to be submitted). This two collections of the PALM runs spans two air pollution episodes :

Summer episode (2019-07-20 to 2019-07-27) of the record breaking summer heat wave in Bergen (on July 26, 2019; https://www.bt.no/nyheter/lokalt/i/QoE3rA/naa-er-334-den-nye-varmerekorden-i-bergen) when considerable problems (haze, smell) from the aerosol air pollution (PM2.5 and PM10) were noted (Esau et al., 2022). The main sources of pollution are ships in the port of Bergen and road traffic.

Winter episode (2021-02-04 to 2021-02-12) of the prolonged cold wave in Bergen (https://www.bt.no/nyheter/lokalt/i/lEOVm9/kulde-med-historisk-sus-i-bergen) when considerable problems (haze, smell) from the aerosol air pollution (PM2.5 and PM10) were noted (Esau et al., 2022). the main source of pollution is household wood combustion for heating.

Thus, the dataset presents multiple daily (24 h) runs driven by the results of model downscaling of ERA5 reanalysis with WRF model (produced by K. Eben and M. Bures). The aerosol emission sources are described in Wolf et al. (2020; 2021). The runs where combined into a single dataset in post-processing.

The observational data for these two episodes collected in the TURBAN project are avaialble in Esau et al. (2023).

For more detailed description of the experiments see the TURBAN project website at https://www.project-turban.eu/.

General organisation

The dataset organized as follows. There are four folders named as "scenario_Bergen_{episode}_PALM_set0_{domain}_domain" where

{episode} is either the selected summer scenario {episode} = summer_2019-07 or the winter scenario {episode} = winter_2021-02

{domain} is either the larger coarse resolution domain smaller fine resolution domain {domain} = child

Each scenario folder contains daily dynamic, static, chemistry drivers and the PALM configuration files in subfolders INPUT in folders designated by the day, e.g., dpc_set0_D0_D1_20210215/INPUT, to rerun all simulations if necessery. For convinience, the common static driver (set0_2021-02_static.nc) and the combined model output averaged over 3 h intervals (combined_set0_2021-02_av_xy.nc) are provided.

The simulation results for each scenario contain:

The combined PALM runs output average over 3 hours and presented on certain model levels (the files "*_av_xy*.nc"). The files are in the NetCDF4 format.

The maps in PNG format visualizing the most relevant results for stakeholds (files in folder NMAP)

In addition, we included the template of a Python script that is used to read the data and create Nmaps.

Modelled variables

Each subfolder includes 4 subfolders with variables. Variable kc_PM10 is the concentration of PM2.5 at the 3rd model level, theta_2m is the potential temperature at 2m above ground, tsurf is the surface temperature and wspeed_10m is the wind speed at 10m above ground.

File nomenclature

Each file (PNG) has the same nomenclature. An example (set0_kc_PM10_2021-02-04T0300.png) could be parsed as: domain name (set0), variable name (kc_PM10), date and time of the output (2021-02-04T0300) and averaged period (from (03:00 - 3h) to 03:00). So, the result is a map with 3 hourly averaged PM2.5 concentrations for 4 February 2021 between 00:00 and 03:00 UTC.

Important note

During the processing phase a few potentially important problems were identified and need to be analysed in detail. One of them are extremely overestimated concentrations due to stable conditions from boundary condition inputs. In certain situations it can happen that the best regional meteorological model can provide inappropriate input conditions for some episode. This needs to be checked in detail before any following interpretation.

References

Esau, I.: TURBAN – Observational datasets for studies of urban air quality hazard scenarios in Bergen, Norway, DataverseNO, V1, https://doi.org/10.18710/QHUAZ2, 2023.

Radović, J., Belda, M., Resler, J., Eben, K., Bureš, M., Geletič, J., Krč, P., Řezníček, H., Fuka, V., 2024. Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM. Geosci. Model Dev. 17, 2901–2927. https://doi.org/10.5194/gmd-17-2901-2024

Wolf, T., Esau, I., Reuder, J., 2014a. Analysis of the vertical temperature structure in the Bergen valley, Norway, and its connection to pollution episodes. J. Geophys. Res. 119. https://doi.org/10.1002/2014JD022085

Wolf, T., Esau, I., 2014b. A proxy for air quality hazards under present and future climate conditions in Bergen, Norway. Urban Clim. 10, 801–814. https://doi.org/10.1016/j.uclim.2014.10.006

Wolf-Grosse, T., Esau, I., Reuder, J., 2017. The large-scale circulation during air quality hazards in Bergen, Norway. Tellus A Dyn. Meteorol. Oceanogr. 69, 1406265. https://doi.org/10.1080/16000870.2017.1406265

Wolf, T., Pettersson, L.H., Esau, I., 2020. A very high-resolution assessment and modelling of urban air quality. Atmos. Chem. Phys. 20, 625–647. https://doi.org/10.5194/acp-20-625-2020

Wolf, T., Pettersson, L.H., Esau, I., 2021. Dispersion of particulate matter (PM2.5) from wood combustion for residential heating: optimization of mitigation actions based on large-eddy simulations. Atmos. Chem. Phys. 21, 12463–12477. https://doi.org/10.5194/acp-21-12463-2021

Acknowledgements

The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Norwegian SIGMA2 facilities. The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.

The Turbulent-resolving urban modeling of air quality and thermal comfort (TURBAN) project aims to develop an integrated urban modeling system on basis of the large-eddy simulation code PALM. Urban simulations require a large number of datasets representing urban environment. This dataset is a collection of available observations for the model development and testing. The dataset has a value beyond the technical model development. It represent two observed periods of extreme deterioration of the urban air quality in Bergen, Norway. The observational dataset for studies of urban air quality hazard scenarios combines a selection of meteorological, air quality, and geospatial (urban features) data. The data are sampled for two distinct periods: • Summer – covers 2019-07-20 to 2019-07-27 when one of the strongest historical heatwaves with maximum air temperatures exceeding +30C affected Bergen. • Winter – covers 2021-02-04 to 2021-02-15 when one of the strongest historical coldwaves with temperatures below -10C and ice covering some the fjords (not observed since 1986) affected Bergen The dataset combines the observations and surface features within the central urban area – the Bergen valley and Byfjorden where (1) air quality observations are available and (2) the most of available data records are found.

The annual number of passengers of the Oslo streetcar network (Trikken) remained almost stable between 2016 to 2019. However, amid the COVID-19 pandemic, the ridership dropped by 58.4 percent, compared with 2019 levels. Oslo is the capital and largest city in Norway, counting approximately 700,000 inhabitants.

The annual number of passengers of the Oslo urban bus system (Unibuss) reported an overall increase between 2016 and 2019. Amid the COVID-19 pandemic, the ridership of the Unibuss dropped by 39 percent compared with 2019 levels. Oslo is the capital and largest city in Norway, counting approximately 700,000 inhabitants.

Detached houses were the most common dwelling type in Norway. In 2023, there were roughly 1.3 million detached houses in the country, while the number of multi-dwellings was approximately 686,000. Multi-dwelling homes is the property type that has experienced the fastest price growth since 2015. How many new buildings were started? As of 2019, nearly 32 thousand new residential buildings had been started in Norway. This was a decrease, when compared to the year before, but still an increase from 2008, when the corresponding figures were approximately 26 thousand new buildings started. The number peaked in 2016, when over 26 thousand new dwelling buildings had been started.  How much does it cost to buy a dwelling in Norwegian cities?  A report from 2022 investigated dwelling prices in some major Norwegian cities. The average price per square meter turned out to be the highest in the capital, Oslo, amounting to over 90,000 Norwegian kroner. The Northern city of Tromsø was second in the ranking, followed by Bergen and Trondheim.