In 2025, Hungary’s population totaled 9.54million. The number of inhabitants residing in Budapest decreased over the past years, measuring at **** million as of January 1, 2025. The population of other towns and villages also showed a slight decrease in recent years. Permanent internal migration increases The number of people permanently changing their place of residence within the country has increased in Hungary lately. Permanent internal migration peaked in 2021 at more than ******* individuals, while, in 2010, this figure stood at approximately *******. In 2024, the number of Hungarians migrating internally totaled *******. Budapest real estate market In addition to the country’s decreasing population, real estate prices in Budapest also contribute to the declining number of Hungarians living in the capital. As of April 2024, the lowest average monthly rental prices were recorded in districts XXIII and XVIII, at approximately ******* forints. By comparison, the highest rental prices were recorded in district V at ******* forints.

Current population data and demographics for Budapest

Historical dataset of population level and growth rate for the Budapest, Hungary metro area from 1950 to 2025.

Over the observed years, the number of women in Hungary exceeded the number of men. As of January 1, 2025, the country's female population amounted to 4,93 million, with approximately 4,61 million men living in Hungary at the same time.

3,443.2 (Inhabitants per sq. km) in 2016.

In 2022, District XI was the most populated district in Budapest, with over *** thousand inhabitants in 2022. Foreign citizens preferred District XIII and VIII, as their number reached almost ** thousand inhabitants in these areas.

In 2022, Budapest's District VI and District V had the highest share of foreign citizens, **** and **** percent, respectively. Other central districts, such as districts VII, VIII, and IX alsohad high shares of people from other nations. The least favored districts by internationals were District IV, XVII, XXII and XXIII, all recording a share below *** percent.

In 1800, the population of the area of modern-day Hungary was approximately 3.3 million, a figure which would steadily rise in the first two decades of the 19th century, as modernization driven by rising exports of cash crops resulting from the ongoing Napoleonic wars would see Hungary become a major exporter in Europe. The slowing in population growth in the 1920s can be attributed in part to the economic recession which hit Hungary in the years following Napoleon defeat, as a grain prices collapsed, and economic hardship intensified in the country. Hungary would see a small increase in population growth in the 1860s, as the country would merge with the Austria to form Austria-Hungary in 1967. As industrialization would continue to accelerate in Hungary, the country’s population rise even further, reaching just over seven million by 1900.

While Hungary had enjoyed largely uninterrupted growth throughout the 19th century, the first half of the 20th century would see several major disruptions to Hungary’s population growth. Growth would slow greatly in the First World War, as Austria-Hungary would find itself one of the largest combatants in the conflict, losing an estimated 1.8 to 2 million people to the war. Hungary’s population would flatline entirely in the 1940s, as the country would see extensive military losses in the country’s invasion of the Soviet Union alongside Germany, and further loss of civilian life in the German occupation of the country and subsequent deportation and mass-murder of several hundred thousand Hungarian Jews. As a result, Hungary’s population would remain stagnant at just over nine million until the early 1950s.

After remaining stagnant for over a decade, Hungary’s population would spike greatly in the early 1950s, as a combination of a tax on childlessness and strict contraception restrictions implemented by then-Minister of Public Welfare Anna Ratkó would lead to a dramatic expansion in births, causing Hungary’s population to rise by over half a million in just five years. However, this spike would prove only temporary, as the death of Stalin in 1953 and subsequent resignation of much of the Stalinist regime in Hungary would see an end to the pro-natalist policies driving the spike. From 1980 onward, however, Hungary’s population would begin to steadily decline, as a sharp reduction in birth rates, combined with a trend of anti-immigrant policies by the Hungarian government, both before and after the collapse of the Soviet bloc, has led Hungary’s population to fall steadily from its 10.8 million peak in 1980, and in 2020, Hungary is estimated to have a population of just over nine and a half million.

Background: Antithrombin (AT) is one of the most important regulator of hemostasis. AT Budapest 3 (ATBp3) is a prevalent type II heparin-binding site (IIHBS) deficiency due to founder effect. Thrombosis is a complex disease including arterial (ATE) and venous thrombotic events (VTE) and the Roma population, the largest ethnic minority in Europe has increased susceptibility to these diseases partly due to their unfavorable genetic load. We aimed to calculate the age and origin of ATBp3 and to explore whether the frequency of it is higher in the Roma population as compared with the general population from the corresponding geographical area. We investigated the association of ATBp3 with thrombotic events in well-defined patients' populations in order to refine the recommendation when testing for ATBp3 is useful.Methods and Results: Prevalence of ATBp3, investigated in large samples (n = 1,000 and 1,185 for general Hungarian and Roma populations, respectively) was considerably high, almost 3%, among Roma and the founder effect was confirmed in their samples, while it was absent in the Hungarian general population. Age of ATBp3—as calculated by analysis of 8 short tandem repeat sequences surrounding SERPINC1—was dated back to XVII Century, when Roma migration in Central and Eastern Europe occurred. In our IIHBS cohort (n = 230), VTE was registered in almost all ATBp3 homozygotes (93%) and in 44% of heterozygotes. ATE occurred with lower frequency in ATBp3 (around 6%); it was rather associated with AT Basel (44%). All patients with ATE were young at the time of diagnosis. Upon investigating consecutive young (

As of January 1, 2024, Budapest had the highest number of foreign residents totaling *** thousand. At the same time, Nógrád had the lowest number of foreign citizens among its population.

Introduction

We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf

The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.

The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.

Short Description of Data Analysis and Attached Files (datasets):

Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.

Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: https://www.ksh.hu/stadat_files/nep/hu/nep0035.html, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file.

In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.

The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): https://www.nnk.gov.hu/index.php/efi (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: https://semmelweis.hu/egeszsegfejlesztes/en/ (n=1). (accessed 05 Dec. 2023.)

Geocoordinates were determined using Google Maps (N=108): https://www.google.com/maps. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file.

The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:

https://qgis.org/en/site/forusers/download.html. (accessed 04 Jan. 2024.)

The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the

Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file,

imported via .csv file.

The OpenStreetMap tileset is directly accessible from www.openstreetmap.org in QGIS. (accessed 04 Jan. 2024.)

The Hungarian county administrative boundaries were downloaded from the following website: https://data2.openstreetmap.hu/hatarok/index.php?admin=6 (accessed 04 Jan. 2024.)

HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.

Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).

A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf.

Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.

• Aggregated number of HDOs by county: Number_of_HDOs.xlsx
• Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx
• Calculation of the Lorenz curve: Lorenz_curve.xlsx
• Calculation of the Gini index: Gini_Index.xlsx
• Calculation of the LQ index: LQ_Index.xlsx
• Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx
• Calculation of the Entropy index: Entropy_Index.xlsx
• Regression and correlation analysis calculation: Regression_correlation.xlsx

Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:

• Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.
• Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.
• Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv
• Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv
• Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv
• Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv
• Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv

For easier readability, the files have been provided in both SPV and PDF formats.

The translation of these supplementary files into English was completed on 23rd Sept. 2024.

If you have any further questions regarding the dataset, please contact the corresponding author: domjan.peter@phd.semmelweis.hu

Over the observed period, the share of the Hungarian population above the age of 65 increased year over year. In 2023, people aged between 15 to 64 years old accounted for around 65 percent of the population, while Hungarians above the age of 65 years old made up a further 21 percent.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Hungary.

Analysis unit

• Household
• Individual

Universe

The WVS for Hungary covers national population aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

We use probability sample that had been selected in multiple stages with proportional stratification. In the first stage, localities are chosen. In the second stage, the respondents were chosen from the localities with simple random sampling. In the first stage, locality strata have been created and the localities were chosen from these strata with the help of random sampling. All the regions (7) are involved into the sample. For each region 1 additional town and 2-3 villages are selected. In addition, the capital, Budapest is always included into the selected localities, because 20% of Hungarian population live there. About 70 localities are selected. Then, the number of respondents in the previously chosen localities has been defined in accordance with the proportion of the population of the given strata - regions, and different types of localities (towns and villages) within the regions. Thus, it is assured that each adult with an address in Hungary had equal probability to become a sample member. Random walking The random walking method is a quite widely used sampling method. The underlying idea of this method is that fieldworkers do not receive names and addresses, but a starting point and a route in all sample localities and a standardised procedure to select the individuals to be asked in the selected household. Choosing the localities and the streets When using the random walking method, the sample of localities is created as a first step, with the same method as it was detailed in the previous chapter. After creating the locality sample, the streets are defined with the help of a random technique using a database containing all the street names in the selected localities. The selected streets serve as starting points for the interviewers. Our interviewers receive pre-numbered questionnaires. All visited addresses are recorded on a list. This list contains the identification number of the completed questionnaires, the adapted Leslie Kish key of the sample member, his/her name, his/her address, and codes representing the success or the reason of the fallout. Choosing the house As the street is defined for the interviewers, he/she can freely choose the first house between the street numbers 1 and 4. On the side of the chosen house, the interviewer must select every fourth house/house gate as a sample house. At the end of the street, the interviewer has to proceed on the other side of the street. If one of the houses falls out, for some reason (e.g. it is inhabitable, one-flat house and the household member refuses to participate in the research), it has to be documented on the list (code of fall-out) and carry on the interviews in the next fourth house. Choosing the flat If there is more than one flat in the house, and the house has only a ground floor, than the second flat should be picked. and the house has one floor, then the second flat on the first floor should be chosen. and the house has an odd number of floors, than the second flat of the middle floor should be chosen (e.g. the house has five floors than the second flat of the third floor should be chosen). - and the house has an even number of floors, than the floor number should be halved and second flat on that floor should be included into the sample (e.g. the house has four floors than the second flat on the second floor should be chosen). The second flat is the one that is the second from the gate or the house entrance. All chosen addresses have to be administered on the list. Choosing the person answering the questionnaire The interviewer has to contact the family living in the chosen flat/house. Contact is considered successful if the interviewer could talk to one of the adults living in that flat. The person to be interviewed is chosen with the help of the key belonging to that flat. This above-mentioned key running from one to six belongs to a pre-defined table (adapted Leslie Kish keys). The rows and columns of the tables make up a matrix which makes it possible that all adult persons living in the same flat have an equal chance to become a sample member, independently from the household size. (The tables are found on the back of the covers of each questionnaire.) This page contains six numbered tables. The key number and the table number has to be matched and based on the information provided by an adult family member, the person to be interviewed is to be picked. This person is defined by the cell of the table where the rows contain the number of persons above 18 years old in the family, and the columns contain the number of males living the same family. The person to be interviewed has to be older than 18. The sample size for Hungary is N=1007.

Mode of data collection

Other [oth]

Research instrument

Questionary in hungarian. All our questionnaires are carefully designed. Experts of all kind participate in the process: experienced interviewers, instructors, researchers. The draft questionnaires are always piloted in order to reduce mistakes and ambivalence. Great emphasis is laid on trying to prevent mistakes that can be proved incorrectable later.

Response rate

A – Total issued (total sample) 2803 B – Ineligible: address vacant (79), wrong ages(275) 354 C - (=A - B) Total eligible (in scope sample) 2449 D – Total questionnaires received 1007 E - (= C - D; = F+ G + H + I ) Total non-response 1442 F – Refusals (refusing to take part) 1006 G – Non-contact (never contacted) 143 H – Respondent too sick/incapacitated to participate 26 I – Respondent away during survey period 267

Remarks about non-response: The regional instructors hand over the questionnaires to our post-fieldworkers working at the Budapest headquarters of TÁRKI. Post-fieldworkers responsibilities are: further, standardised fill-in control; coding open questions and recording data. The number of the post-fieldworkers ranges from 5 to 25 depending on the given research. Besides of this regional control, a central controller is employed in TARKI, who has not daily personal connection with the interviewers. After the results of logical control of data based on computer, and the reports of post-fieldworkers, she conducts an additional control of interviewers or regions where the mistakes or problems seem to be frequent. She controls personally or with the help of phone, or with letter of checking. She is responsible for inspect the controlling work of regional instructors as well.

La popolazione totale in Ungheria è stata stimata a 9,6 milioni di persone nel 2024, secondo gli ultimi dati del censimento e le proiezioni di Trading Economics. Questa pagina fornisce - Popolazione Ungherese - valori attuali, dati storici, previsioni, grafico, statistiche, calendario economico e notizie.

This statistic shows the biggest cities in Hungary in 2022. In 2022, approximately **** million people lived in Budapest, making it the biggest city in Hungary .

The empirical dataset is derived from a survey carried out on 25 estates in 14 cities in nine different European countries: France (Lyon), Germany (Berlin), Hungary (Budapest and Nyiregyha´za), Italy (Milan), the Netherlands (Amsterdam and Utrecht), Poland (Warsaw), Slovenia (Ljubljana and Koper), Spain (Barcelona and Madrid), and Sweden (Jo¨nko¨ping and Stockholm). The survey was part of the EU RESTATE project (Musterd & Van Kempen, 2005). A similar survey was constructed for all 25 estates.

The survey was carried out between February and June 2004. In each case, a random sample was drawn, usually from the whole estate. For some estates, address lists were used as the basis for the sample; in other cases, the researchers first had to take a complete inventory of addresses themselves (for some deviations from this general trend and for an overview of response rates, see Musterd & Van Kempen, 2005). In most cities, survey teams were hired to carry out the survey. They worked under the supervision of the RESTATE partners. Briefings were organised to instruct the survey teams. In some cases (for example, in Amsterdam and Utrecht), interviewers were recruited from specific ethnic groups in order to increase the response rate among, for example, the Turkish and Moroccan residents on the estates. In other cases, family members translated questions during a face-to-face interview. The interviewers with an immigrant background were hired in those estates where this made sense. In some estates it was not necessary to do this because the number of immigrants was (close to) zero (as in most cases in CE Europe).

The questionnaire could be completed by the respondents themselves, but also by the interviewers in a face-to-face interview.

Data and Representativeness

The data file contains 4756 respondents. Nearly all respondents indicated their satisfaction with the dwelling and the estate. Originally, the data file also contained cases from the UK.

However, UK respondents were excluded from the analyses because of doubts about the reliability of the answers to the ethnic minority questions. This left 25 estates in nine countries. In general, older people and original populations are somewhat over-represented, while younger people and immigrant populations are relatively under-represented, despite the fact that in estates with a large minority population surveyors were also employed from minority ethnic groups. For younger people, this discrepancy probably derives from the extent of their activities outside the home, making them more difficult to reach. The under-representation of the immigrant population is presumably related to language and cultural differences. For more detailed information on the representation of population in each case, reference is made to the reports of the researchers in the different countries which can be downloaded from the programme website. All country reports indicate that despite these over- and under-representations, the survey results are valuable for the analyses of their own individual situation.

This dataset is the result of a team effort lead by Professor Ronald van Kempen, Utrecht University with funding from the EU Fifth Framework.

Until 2015, Budapest seemed to be the most desirable place to live in Hungary. From 2016, more and more people chose to leave the capital and move to the villages. However, in 2019, the population of towns decreased by 1.4 thousand inhabitants.

Access to healthcare, lifestyle, diet, and exercise are some of the determining factors considering life expectancy. In 2023, men and women had the highest life expectancy at birth in Budapest, the capital of Hungary, measuring at almost 75 years for men, while women at **** years, respectively. During the same year, the life expectancy for both men and women was the lowest in the county of Borsod-Abaúj-Zemplén. Are Hungarians in good health? According to the Hungarian Central Statistical Office, in 2021, ** percent of men and ** percent of women perceived their state of health as good or very good, which represented an increase compared to the preceding period. However, considering their body mass index (BMI), over a third of the country’s adult population qualified as overweight and every fourth person as obese. In addition to weight problems, the country also recorded a considerable number of alcoholics over the past decade with their number totaling *** individuals as of 2020. Chronic diseases As of 2023, ** percent of Hungarian men and ** percent of women suffered from chronic diseases while the number of chronically ill people in the country totaled *** million. Malignant neoplasms, in other words cancerous tumors became the leading cause of death over the past years, accounting for ** thousand deaths in 2023. In the same year, prostate cancer accounted for a considerable share of new cancer cases in men while a significant number of newly diagnosed women patients suffered from breast cancer.

The gini index in Hungary was forecast to remain on a similar level in 2029 as compared to 2024 with 0.3 points. According to this forecast, the gini will stay nearly the same over the forecast period. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Slovakia and Slovenia.

Between January 5, 2020 and January 4, 2021, Budapest had the highest number of robberies per 100 thousand inhabitants. The second highest number of robberies per 100 thousand population was recorded in Borsod-Abaúj-Zemplén county at 19.