Population in largest city in Germany was reported at 3576873 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Germany - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950 to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continent's largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

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

This statistic shows the degree of urbanization in Germany from 2013 to 2023. Urbanization means the share of urban population in the total population of a country. In 2023, 77.77 percent of Germany's total population lived in urban areas and cities. Urbanization in Germany Currently, about three quarter of the German population live in urban areas and cities, which is more than in most nations around the world. Urbanization, as it can be seen in this graph, refers to the number of people living in an urban area and has nothing to do with the actual geographical size or footprint of an area or country. A country which is significantly bigger than Germany could have a similar degree of urbanization, just because not all areas in the country are inhabitable, for example. One example for this is Russia, where urbanization has reached comparable figures to Germany, even though its geographical size is significantly bigger. However, Germany’s level of urbanization does not make the list of the top 30 most urbanized nations in the world, where urbanization rates are higher than 83 percent. Also, while 25 percent of the population in Germany still lives in rural areas, rural livelihoods are not dependent on agriculture, as only 0.75 percent of GDP came from the agricultural sector in 2014. So while Germany's urbanization rate is growing, a significant percentage of the population is still living in rural areas. Furthermore, Germany has a number of shrinking cities which are located to the east and in older industrial regions around the country. Considering that population growth in Germany is on the decline, because of low fertility rates, and that a number of cities are shrinking, the urban population is likely shifting to bigger cities which have more economic opportunities than smaller ones.

Social and economic figures for 67 large West German cities. The data aggregated at city level have been collected for most topics over several years, but not necessarily over the entire reference time period.

Topics: 1. Situation of the city: surface area of the city; fringe location in the Federal Republic.

1. Residential population: total residential population; German and foreign residential population.

2. Population movement:live births; deaths; influx; departures; birth rate; death rate; population shifts; divorce rate; migration rate; illegitimate births.

3. Education figures: school degrees; occupational degrees; university degrees.

4. Wage and income: number of taxpayers in the various tax classes as well as municipality income tax revenue in the respective classes; calculated income figures, such as e.g. inequality of income distribution, mean income or mean wage of employees as well as standard deviation of these figures; GINI index.

5. Gross domestic product and gross product: gross product altogether; gross product organized according to area of business; gross domestic product; employees in the economic sectors.

6. Taxes and debts: debt per resident; income tax and business tax to which the municipality is entitled; municipality tax potential and indicators for municipality economic strength.

7. Debt repayment and management expenditures: debt repayment, interest expenditures, management expenditures and personnel expenditures.

8. From the ´BUNTE´ City Test of 1979 based on 100 respondents per city averages of satisfaction were calculated. satisfaction with: central location of the city, the number of green areas, historical buildings, the number of high-rises, the variety of the citizens, openness to the world, the dialect spoken, the sociability, the density of the traffic network, the OEPNV prices {local public passenger transport}, the supply of public transportation, provision with culture, the selection for consumers, the climate, clean air, noise pollution, the leisure selection, real estate prices, the supply of residences, one´s own payment, the job market selection, the distance from work, the number of one´s friends, contact opportunities, receptiveness of the neighbors, local recreational areas, sport opportunities and the selection of further education possibilities.

9. Traffic and economy: airport and Intercity connection; number of kilometers of subway available, kilometers of streetcar, and kilometers of bus lines per resident; car rate; index of traffic quality; commuters; property prices; prices for one´s own home; purchasing power.

10. Crime: recorded total crime and classification according to armed robbery, theft from living-rooms, of automobiles as well as from motor vehicles, robberies and purse snatching; classification according to young or adult suspects with these crimes; crime stress figures. 12. Welfare: welfare recipients and social expenditures; proportion of welfare recipients in the total population and classification according to German and foreign recipients; aid with livelihood; expenditures according to the youth welfare law; kindergarten openings; culture expenditures per resident. 13. Foreigners: proportion of foreigners in the residential population.

11. Students: number of German students and total number of students; proportion of students in the residential population.

12. Unemployed: unemployment rate; unemployed according to employment office districts and employment office departments.

13. Places of work: workers employed in companies, organized according to area of business.

14. Government employees: full-time, part-time and total government employees of federal government, states and municipalities as well as differentiated according to workers, employees, civil servants and judges.

15. Employees covered by social security according to education and branch of economy: proportion of various education levels in the individual branches of the economy.

In 2024, 40-59-year-olds made up the largest age group in Germany, at around 22.3 million people. The most recent figures confirm that the next-largest age group was 65 years and older, at roughly 19 million. Aging population With the number of people belonging to older age groups visibly outstripping younger ones, in recent years it has become clear that Germany’s population is aging. In fact, figures on age structure in Germany depict a constant trend of a slowly increasing population share aged over 65 since 2012. Meanwhile, the share of population members aged 0 to 14 years has been falling, which was also reflected in the fluctuating national birth rate in recent years. A look at the future Germany’s current total population is around 83.6 million. While this number is predicted to increase, the same goes for the age group of 65 years and older. This means that the national population will continue to age.

Chart and table of population level and growth rate for the Berlin, Germany metro area from 1950 to 2025.

Among the four largest cities in Germany, Cologne scored the highest for availability of shared bicycles and e-scooters, indicating that it had the largest number of shared micro-mobility vehicles in relation to its population size. Hamburg ranked second among the largest cities in the country.

Chart and table of population level and growth rate for the Frankfurt am Main, Germany metro area from 1950 to 2025.

Among all 16 German federal states, the city-state Hamburg in the north has the highest share of population members aged 15 to 64 years, with a share of 67.7 percent. Berlin, also a city-state, followed and the southern German state of Bavaria took the third place. This indicator provides information on the development of the proportion of the working-age population in the total population.

The author statistically depicts the population growth of the largest German cities between 1871 and 1910. He uses the official statistics. He breaks down the cities into zones in order to make their development comparable.In order to do this, he used the term agglomeration, with which he refers to the contiguous built-up area, with developed infrastructure as well as traffic routes and rail traffic, and their population. In order to find as uniform a demarcation as possible, he delimits the areas of the respective cities by drawing circles whose radius starts from the center of the city and is 10 km long. This is the entire agglomeration, which finally differentiates it into the inner agglomeration and outer agglomeration. The inner agglomeration is located within 5 km of the city center. The outer agglomeration refers to the areas at a distance of 5 to 10 km around the city center. The following cities are being investigated by him:Aachen; Augsburg; Berlin; Braunschweig; Bremen; Breslau (Wroclaw); Kassel (in the data old spelling: Cassel); Chemnitz; Cologne (in the data old spelling: Cöln); Krefeld (in the data old spelling: Crefeld); Danzig; Dortmund; Dresden; Dusseldorf; Duisburg; Elberfeld; Erfurt; Essen; Frankfurt a. Main; Halle; Hannover; Karlsruhe; Kiel; Koenigsberg; Leipzig; Magdeburg; Mainz; Mannheim; Munich; Nuremberg; Plauen; Posen; Saarbrücken; Stettin; Strasbourg; Stuttgart. Variables are the city area, the inhabitants of the city as a whole, the population as an index on the basis of 1971 = 100, the inhabitants of the territories incorporated into the cities between 1871 and 1910, and the population density of the city and the incorporated areas. Reporting years are 1871, 1880, 1890,1900, and 1910. The data has been classified in the online database histat (https://histat.gesis.org/histat/) under the topic ´population´.

About this dataset

Context

The dataset tabulates the German town household income by gender. The dataset can be utilized to understand the gender-based income distribution of German town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• German, New York annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• German, New York annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of German town income distribution by gender. You can refer the same here

About this dataset

Context

The dataset tabulates the German Flatts town median household income by race. The dataset can be utilized to understand the racial distribution of German Flatts town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• German Flatts, New York median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in German Flatts, New York (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of German Flatts town median household income by race. You can refer the same here

Content

A dataset of counties that are representative for Germany with regard to

• the average disposable income,
• the quota of divorces,
• the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,
• the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.
• population density, if the benchmark is defined as the national average density weighted by county population. This value is calculated by averaging the population-per-square-kilometer figures of all German counties, weighted by the population size of each county. In contrast to a simple arithmetic mean of county-level densities, which would disproportionately reflect sparsely populated areas, this method reflects the actual density of the environments where people live. The resulting value for the selected counties falls within the 95% confidence interval of the national weighted average.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

🇩🇪 독일 English Inhabitants of Constance German - non-German Resident population (residents with main residence) by first citizenship German and first citizenship non-German for the years from 2010 in the districts and for the city of Konstanz as a whole from 1975. Source is the population statistics of the city of Konstanz (own population update). It shows the resident population (residents with main residence) by first nationality in the city of Konstanz and its 15 districts.

The present study is an exemplary analysis of the development and the changes of living in a big city of the 19th century. The study tries to develop indicators for the evaluation of the urban housing provision during the process of urbanization that relate occurrence and extent of housing shortage with quantifiable standards of housing provision looking at the example of Hamburg in the second part of the 19th century. Thereby a long term analysis of the material circumstances of housing conditions will be shown and at the same time the concept of housing shortage in its historical relativity through a comparison with the living conditions in the Federal Republic of Germany will be clarified. Based on this, in the second step standards and social disparities of housing in their social stratification and in its urban structure change will be examined. In how far the example of Hamburg in the late 19th century can be generalized will be reviewed through a comparison of housing types and housing provision levels of German cities using a cross-sectional comparison of 1905. “Based on a comparison of metropolitan living situations it will be attempted to show the level and extend of disparities in the housing provision in the late 19th century using a typology of metropolitan housing structures. We assume that the housing structure of a city can only be represented in a very imperfect way using a more or less arbitrary chosen random variable. Much more information can be derived from the correlation of many different characteristics of the basic structure of urban housing, that can be understood as quasi-independent variables and that build the basis of the comparison. For a hierarchical cluster analysis with 27 variables 30 cities were considered for statistical classification and an empirical typecast. The used data is based on statistical housing surveys of German cities that were made in the context of the population census from the first December of 1905 and on the information if the profession and establishment census from July 1907. Three fourth of the cities (with more than 100.000 inhabitants) of the German Empire were selected” (Wischermann, a. cit., p. 401).“The excellent public statistics from Hamburg were, especially in the turn of the century, adjusted with many private surveys. Therefor the state of the quantitative sources for Hamburg is probably the best one of the German Empire… The situation of the sources for the investigation of the development of the housing circumstances in the 19th century in Hamburg is better than in other parts of Germany. This data basis enabled this study going back until the beginning of the industrialization; it enabled the investigation of one of the most important German case studies of changes in urban housing in the 19th century in a city that was at the frontier of English, French and Berlin zones of influence on the development of living circumstances in Germany. It is also a paradigm for the investigation of structural changes of housing within a city under the impact of economic transitions (extension of the harbor), hygienic innovation (since the cholera epidemic) and several urban development projects. Especially in the German urbanization period quality and structure of housing in the developing cities are connected with the urban area and the socio-spatial differentiation of housing in an extent that has not been known so far.” (Wischermann, a. cit., p. 13, p. 15). Data tables in HISTAT:(In addition, the cross-sectional data for 1905 for cluster analysis of German cities (30 cities, 27 variables for the year 1905) can be ordered at the GESIS data archive number ZA8474). A. Tables from the appendixA.01 Development of the population in the inner city and the suburbs of Hamburg (1817-1866)A.02 Housing stock in the inner city and the suburbs of Hamburg (1817-1866)A.03 Present local population of Hamburg and the districts (1867-1910)A.04 Movement of the population in Hamburg (1864-1913)A.05 The population density of Hamburg’s districts (1871-1910)A.06 Housing stock density of Hamburg’s districts(1867-1910)A.07 The provision level of housing in Hamburg (1867-1912)A.08 Internal density of Hamburg and its districts A: residents per room without kitchens (1885-1910)A.09 Internal density of Hamburg and its districts B: inhabitants per heated room (1885-1910)A.10 Occupancy of Hamburg and its districts: residents per apartment (1867-1910)A.11 Empty rooms and their rental value in Hamburg (1866-1913)A.12 Empty rooms in Hamburg and its districts (1867-1910) A.13 New construction, alteration and demolition statistics of the city of Hamburg (1885-1912)A.14 Small housing production in Hamburg (1896-1912)A.15 Landowner relations in Hamburg (1875-1910)A.16 Development of floor living in Hamburg (1867-1910)A.17 Residents after number of floors on Hamburg (1867-1910)A.18 Basement apartments in the districts of Hamburg (1867-1910)A.19 Apartments in the back building in Ham...

About this dataset

Context

The dataset tabulates the median household income in German town. It can be utilized to understand the trend in median household income and to analyze the income distribution in German town by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• German, New York Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in German, New York: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in German, New York
• German, New York households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of German town median household income. You can refer the same here

In 2023, **** percent of Berlin's population were foreigners. Therefore, among all German federal states, Berlin had the highest foreigner share, followed by Bremen and Hamburg. On the other side of the spectrum, only ***** percent of Mecklenburg-Western Pomerania were non-Germans.

Out of the four largest cities in Germany, Hamburg was scored the safest for walking and cycling in 2022. The northern German city scored ** percent in the safe roads category, which indicates how many pedestrian and cyclist fatalities occurred in the city in relation to its population.

🇩🇪 독일 English How has the number of inhabitants in the city of Constance developed since 2010 in the districts and districts? The dataset contains the population (residential population = main residence) by district, district, nationality (German, non-German), sex (women) and age group. Base is the own population update at 31.12. of each year. The city of Constance is divided into 51 districts. The district 100 2 in Egg comprises the University of Konstanz, here no residents are registered. Source: City of Konstanz, Office for Digitalization and IT, Department of Data Management and Statistics (own population update)