This statistic shows the ten biggest cities in Switzerland, as of 2020, by number of inhabitants. In 2020, Zurich was Switzerland's most-populous city with approximately 421,878 inhabitants. See Switzerland's population figures for comparison.

Switzerland Population in Largest City data was reported at 1,356,037.000 Person in 2017. This records an increase from the previous number of 1,341,453.000 Person for 2016. Switzerland Population in Largest City data is updated yearly, averaging 951,846.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,356,037.000 Person in 2017 and a record low of 535,471.000 Person in 1960. Switzerland Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.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.; ;

About this dataset

Context

This list ranks the 2 cities in the Switzerland County, IN by Native Hawaiian and Other Pacific Islander (NHPI) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Pacific Islander Population: This column displays the rank of cities in the Switzerland County, IN by their Native Hawaiian and Other Pacific Islander (NHPI) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Pacific Islander Population: The Pacific Islander population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Pacific Islander. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Switzerland County Pacific Islander Population: This tells us how much of the entire Switzerland County, IN Pacific Islander population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

This horizontal bar chart displays population (people) by country using the aggregation sum and is filtered where the country is Switzerland. The data is about cities.

In 2023, the average rent in Swiss cities ranged between 15 Swiss franks per square meter and 36 Swiss franks per square meter. In the fourth quarter of 2023, Zurich had the highest rent, at 36.5 Swiss franks per square meter. In 2023, Switzerland was the country with the highest share of population living in rented housing in Europe.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Swiss town by race. It includes the distribution of the Non-Hispanic population of Swiss town across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Swiss town across relevant racial categories.

Key observations

With a zero Hispanic population, Swiss town is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 550 (73.14% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Swiss town
• Population: The population of the racial category (for Non-Hispanic) in the Swiss town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Swiss town total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Swiss town Population by Race & Ethnicity. You can refer the same here

Geneva was the most expensive Swiss city to buy an apartment in, with average values of approximately 15,650 euros per square meter in the first quarter of 2024. The price of an apartment in Bern was significantly lower, with values of approximately 10,400 euros per square meter.

About this dataset

Context

The dataset tabulates the Swiss town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Swiss town. The dataset can be utilized to understand the population distribution of Swiss town by age. For example, using this dataset, we can identify the largest age group in Swiss town.

Key observations

The largest age group in Swiss, Wisconsin was for the group of age 65 to 69 years years with a population of 111 (14.76%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Swiss, Wisconsin was the Under 5 years years with a population of 11 (1.46%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Swiss town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Swiss town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Swiss town Population by Age. You can refer the same here

In 2023, the share of urban population in Switzerland remained nearly unchanged at around 74.2 percent. Nevertheless, 2023 still represents a peak in the share in Switzerland. The urban population refers to the share of the total population living in urban centers. Each country has their own definition of what constitutes an urban center (based on population size, area, or space between dwellings, among others), therefore international comparisons may be inconsistent.

The data collected on members of the local elites of the three largest city-regions (Basel, Geneva and Zurich) are integrated in the more general OBELIS database on Swiss Elites. Currently, the OBELIS database includes elites from four sectors at the national level: Economic, Political, Administrative and Academic (+ national sociability associations) and covers nine dates: 1890, 1910, 1937, 1957, 1980, 2000, 2010, 2015 and 2020. The elite status of individuals is defined by the position/function held in these four spheres at the date mentioned. A description of all the different samples of the Swiss elites can be consulted on the website. The data allows researchers to understand the elites through a relational analysis (network analysis) to highlight the interrelations between these elites. The data is also suitable to conduct prosopographical analysis. As for national elites, the identification of local elites of the three largest Swiss city-regions also follows a positional approach by selecting all individuals occupying leading positions in the major local economic, political, cultural and academic institutions for the 7 benchmark years: 1890, 1910, 1937, 1957, 1980, 2000 and 2020. For the economic sphere we collected information on all the committee members of the regional chambers of commerce as well as directors of the most important companies of the three cities’ leading economic sectors. This includes the major banks and insurance companies for the financial sector; for Basel, all the major textile (until 1937) and chemical-pharmaceutical companies; for Geneva, the major watch-making companies, as well as a few other industrial companies; and for Zurich, all the major companies from the machine industry. The total number of companies varies from 49 in 1890 to 35 in 2020. The smaller sample for the recent period is due to the strong concentration process in all economic sectors, involving mergers and acquisitions as well as bankruptcies. For these companies, all CEOs/general directors and directors’ board members were taken into account. For the political sphere, we included all members of the cantonal (regional) and local (city) parliaments and governments for Geneva and Zurich, whereas in Basel, where the city’s territory fully coincides with the canton, only the members of the cantonal parliament and government were considered. For the academic sphere we include all full and extraordinary (associate) professors of the three cities’ universities until 1957, and, for the more recent dates, a selection of professors according to the occupation of institutional positions or according to their scientific reputation. Finally, the committee members of the three cities’ fine art societies are included as urban elites from the cultural sphere.

About this dataset

Context

The dataset tabulates the data for the Swiss, Wisconsin population pyramid, which represents the Swiss town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Swiss, Wisconsin, is 16.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Swiss, Wisconsin, is 71.5.
• Total dependency ratio for Swiss, Wisconsin is 88.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Swiss, Wisconsin is 1.4.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Swiss town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Swiss town for the selected age group is shown in the following column.
• Population (Female): The female population in the Swiss town for the selected age group is shown in the following column.
• Total Population: The total population of the Swiss town for the selected age group is shown in the following column.

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 Swiss town Population by Age. You can refer the same here

The Luxury Residential Real Estate Market in Switzerland is segmented by type (villas and landed houses & apartments and condominiums) and by city (Bern, Zurich, Geneva, Basel, Geneva, Lausanne, and other cities). The report offers the market sizes and forecasts for the Switzerland Luxury Residential Real Estate Market in value (USD) for all the above segments.

About this dataset

Context

The dataset tabulates the population of Swiss town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Swiss town. The dataset can be utilized to understand the population distribution of Swiss town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Swiss town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Swiss town.

Key observations

Largest age group (population): Male # 65-69 years (48) | Female # 60-64 years (68). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Swiss town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Swiss town is shown in the following column.
• Population (Female): The female population in the Swiss town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Swiss town for each age group.

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 Swiss town Population by Gender. You can refer the same here

Based on a wide variety of categories, the top major global smart cities were ranked using an index score, where a top index score of 10 was possible. Scores were based on various different categories including transport and mobility, sustainability, governance, innovation economy, digitalization, living standard, and expert perception. In more detail, the index also includes provision of smart parking and mobility, recycling rates, and blockchain ecosystem among other factors that can improve the standard of living. In 2019, Zurich, Switzerland was ranked first, achieving an overall index score of 7.75. Spending on smart city technology is projected to increase in the future.

Smart city applications Smart cities use data and digital technology to improve the quality of life, while changing the nature and economics of infrastructure. However, the definition of smart cities can vary widely and is based on the dynamic needs of a cities’ citizens. Mobility seems to be the most important smart city application for many cities, especially in European cities. For example, e-hailing services are available in most leading smart cities. The deployment of smart technologies that will incorporate mobility, utilities, health, security, and housing and community engagement will be important priorities in the future of smart cities.

The Swiss cities of Geneva and Zurich had some of the highest construction costs in Europe, with a price of well over 5,000 U.S. dollars per square meter built as of 2024. London was the third city at 4,473 U.S. dollars per square meter, closely followed by Munich and Dublin. When it comes to the construction cost of education buildings in the UK, Glasgow was more expensive than London. However, this is an exception, as generally, London is the most expensive city to build in the UK.

CH：最大城市人口：占城镇人口百分比在12-01-2017达20.309%，相较于12-01-2016的20.328%有所下降。CH：最大城市人口：占城镇人口百分比数据按年更新，12-01-1960至12-01-2017期间平均值为20.220%，共58份观测结果。该数据的历史最高值出现于12-01-2007，达20.747%，而历史最低值则出现于12-01-1963，为19.215%。CEIC提供的CH：最大城市人口：占城镇人口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Global Database的瑞士 – 表 CH.世界银行：人口和城市化进程统计。

The dataset contains the population of the resident population at the end of the corresponding month. The resident population includes:- the permanent resident population at the main residence: all persons who are registered with their main residence in the city of St.Gallen and have Swiss citizenship or a foreign citizenship with a residence or settlement permit - the non-permanent foreign resident population: foreign nationals with a short-stay permit, temporarily admitted persons, persons in need of protection and applicants for asylum as far as they are registered with the municipal population control - persons with a secondary residence (so-called "weekly residents"): registered residents in the city of St.Gallen with a main residence elsewhere in Switzerland or abroad. A secondary residence is usually established in connection with a job or a visit to a training institution in the city of St.Gallen. It is based on data from the Population Services of the City of St.Gallen (processed under the name "STADTSGPOP" by the Statistical Office).

In 2023, the average rent for apartments and houses in Switzerland peaked at 23 Swiss francs per square meter. That was an increase from 21.57 Swiss francs per square meter in 2022 and the largest rise since 2017. In 2023, Zurich and Geneva were the Swiss cities with the highest rents.

Singapore and New York were ranked as the most expensive cities worldwide with an index of 100 out of a possible 100. Three of the 11 most expensive cities were in the United States, whereas two were in Switzerland.

According to the European Backpacker Price Index for 2025, Zurich in Switzerland was the most expensive destination for budget travelers. The average daily cost in that city – based on prices for a cheap hostel, budget meals, public transport, and a limited budget for entertainment – amounted to almost 164 U.S. dollars as of January 2025. In comparison, the same trip to Budapest, one of the most affordable cities for backpacking in Europe, would have cost less than 50 U.S. dollars per day.