In the presented European countries, the homeownership rate extended from 42 percent in Switzerland to as much as 96 percent in Albania. Countries with more mature rental markets, such as France, Germany, the UK and Switzerland, tended to have a lower homeownership rate compared to the frontier countries, such as Lithuania or Slovakia. The share of house owners among the population of all 27 European countries has remained relatively stable over the past few years. Average cost of housing Countries with lower homeownership rates tend to have higher house prices. In 2023, the average transaction price for a house was notably higher in Western and Northern Europe than in Eastern and Southern Europe. In Austria - one of the most expensive European countries to buy a new dwelling in - the average price was three times higher than in Greece. Looking at house price growth, however, the most expensive markets recorded slower house price growth compared to the mid-priced markets. Housing supply With population numbers rising across Europe, the need for affordable housing continues. In 2023, European countries completed between one and six housing units per 1,000 citizens, with Ireland, Poland, and Denmark responsible heading the ranking. One of the major challenges for supplying the market with more affordable homes is the rising construction costs. In 2021 and 2022, housing construction costs escalated dramatically due to soaring inflation, which has had a significant effect on new supply.

This dataset provides values for HOME OWNERSHIP RATEOWNERSHIP RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for HOME OWNERSHIP RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The mortgage prevalence among homeowners in Europe varied widely across different countries in 2023. About ** percent of the total population in Norway was a homeowner, with ** percent paying out a mortgage loan. Conversely, only *** percent of households in Romania had a mortgage, with nearly ** percent being homeowners. Meanwhile, an average of ** percent of the total population within the EU-27 was an owner-occupant with a mortgage or housing loan. Homeownership depends on multiple factors, such as housing policy, the macroeconomic situation, the state of the housing sector, and the availability of finance. Countries with more developed mortgage markets tend to have lower mortgage interest rates.

70% of White British households owned their own homes – the highest percentage out of all ethnic groups.

This dataset provides values for HOME OWNERSHIP RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This statistic shows the percentage of households owning a passenger car in 2014, with a breakdown by major economy. In 2014, more than 80 percent of Japanese households had registered at least one passenger vehicle.

Car ownership in households

Unsurprisingly, most countries with high car ownership rates in 2014 were regions with advanced economies. Americans were on the top of the list among surveyed countries, with 88 percent reporting to own a car. More common places to find a car included Germany, South Korea, France, Malaysia, and Japan, each with more than an 80 percent car ownership rate. By contrast, Vietnam and Bangladesh had the least passenger vehicles registered, with only two percent of the population reporting to own a car.

In the United States, a great share of people from affluent households reported owning or leasing a vehicle falling into the truck, SUV, and van category, followed by crossover vehicle. Toyota, Honda and Nissan were the best-selling passenger car manufacturers in the country, in terms of sales in 2015.

Two-wheelers, the more economical alternative to a car, were more often seen in South and Southeast Asia, as more than 80 percent of households in Thailand, Vietnam, Indonesia, and Malaysia owned a motorcycle or scooter. Overall, bicycles were more common around the globe than cars. Countries with the most bike owners include Germany, Indonesia, China, and India.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

This dataset provides values for HOME OWNERSHIP RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The average transaction price of new housing in Europe was the highest in Norway, whereas existing homes were the most expensive in Austria. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, not all countries are included. To compile the ranking, the source weighed the transaction prices of residential properties in the most important cities in each country based on data from their national offices. For example, in Germany, the cities included were Munich, Hamburg, Frankfurt, and Berlin. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly ** percent in Romania or around ** percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up ** percent and ** percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

Major appliances such as ovens, dishwashing machines, freezers, microwaves, refrigerators, vacuum cleaners, and washing machines are owned by many households worldwide. There are differences in the ownership rate though when comparing individual countries. Cookers/ovens for example are owned by many households in the United States, Spain, the UK, Italy, and Turkey where more than 80 percent of households own one. In contrast cookers/ovens are only part of about half of households in South Korea.

This dataset provides values for HOME OWNERSHIP RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The total number of dwellings per one thousand citizens in European countries in 2023 was the highest in Bulgaria and the lowest in Greece. There were approximately *** dwellings for every one thousand citizens in Bulgaria and in Greece, this figure amounted to ***. Germany had the largest total housing stock of **** million dwellings in the same year, of which there were *** per one thousand citizens. How prevalent is homeownership across European nations? Homeownership rates in Europe vary widely due to cultural, economic, and policy factors. Usually, countries in Southern and Eastern Europe tend to have higher rates of homeownership compared to those in Northern and Western Europe. For instance, in 2022, the homeownership rates in countries like Serbia, Romania, and Slovakia were quite high, topping ** percent. On the contrary, nations such as Germany, Switzerland, and Austria exhibited lower rates, below ** percent. New dwelling transaction prices across Europe The transaction price of a new dwelling includes the cost of the property itself, along with any additional expenses like taxes, fees, or other associated costs pertaining to the acquisition. In 2023, the average transaction price for a new dwelling in Europe was the highest in Austria, Germany, and France. Romania, Greece and Bosnia and Herzegovina had the lowest average transaction prices compared to other European countries.

In 2024, Turkey, Iceland, Portugal, and Hungary had the highest house price to rent ratio index in Europe. The four countries ranked the highest, with house price to rent indices exceeding *** index points. The house price to rent ratio is an indicator of the affordability of owning housing over renting across European countries, with 2015 used as a base year. The higher the ratio, the more the gap between house prices and rental rates has widened since 2015 when the index amounted to 100. In terms of house price to income ratio, the top three countries were Portugal, Luxembourg, and Hungary Homeownership in Europe Homeownership varies widely across European countries. In some, such as Austria, Germany and Switzerland, homeownership is relatively low with less than ********** of people occupying a dwelling owned by a member of the household. In other countries (Iceland, the Netherlands, Norway, and Sweden) more than **** of people were owner-occupiers with a mortgage. A third group of countries with a high homeownership rate without a housing loan includes many Eastern and South European countries, among which were Serbia, Romania, North Macedonia, Italy, and Bulgaria. Dwellings as a non-financial asset Dwellings, along with structures, land, and intellectual property, are classed as non-financial assets and form an important part of household wealth. Through sale, refinancing or renting, they can serve as an additional source of income. In 2022, France, Germany, and Norway were the European countries with the highest value of dwellings per capita as a non-financial asset with values between ****** and ****** euros per capita.

In Europe, the ownership of care homes varies greatly from one country to the other. In 2020, the Nordic countries had the highest rates of publicly owned care homes, whereas care homes were mostly privately owned and for-profit in the United Kingdom. A significant share of care homes was privately owned in Germany and the Netherlands, although these were non-profit.

This dataset provides values for HOUSING STARTS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Small appliances such as blenders, food processors, juicers, coffee machines, and toasters are owned by many households across the world according to the Statista Global Consumer Survey 2024. There are differences in the ownership rate though when comparing individual countries. Coffee machines for example are owned by many households in Austria, Germany, France, Sweden, and Spain were more than ** percent of households own one. In contrast, coffee machines are only part of about ** percent of households in South Africa.

Abstract

The key objective of every census is to count every person (man, woman, child) resident in the country on census night, and also collect information on assorted demographic (sex, age, marital status, citizenship) and socio-economic (education/qualifications; labour force and economic activity) information, as well as data pertinent to household and housing characteristics. This count provides a complete picture of the population make-up in each village and town, of each island and region, thus allowing for an assessment of demographic change over time.

The need for a national census became obvious to the Census Office (Bureau of Statistics) during 1997 when a memo was submitted to government officials proposing the need for a national census in an attempt to update old socio-economic figures. The then Acting Director of the Bureau of Statistics and his predecessor shared a similar view: that the 'heydays' and 'prosperity' were nearing their end. This may not have been apparent, as it took until almost mid-2001 for the current Acting Government Statistician to receive instructions to prepare planning for a national census targeted for 2002. It has been repeatedly said that for adequate planning at the national level, information about the characteristics of the society is required. With such information, potential impacts can be forecast and policies can be designed for the improvement and benefit of society. Without it, the people, national planners and leaders will inevitably face uncertainties.

Geographic coverage

National coverage as the Population Census covers the whole of Nauru.

Analysis unit

• Household
• Individual (in a private household dwelling, institutions and non-private dwelling).

Universe

The Census covers all individuals living in private and non-private dwellings and institutions.

Kind of data

Census/enumeration data [cen]

Sampling deviation

There is no sampling for the population census, full coverage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was based on the Pacific Islands Model Population and Housing Census Form and the 1992 census, and comprised two parts: a set of household questions, asked only of the head of household, and an individual questionnaire, administered to each household member. Unlike the previous census, which consisted of a separate household form plus two separate individual forms for Nauruans and non-Nauruans, the 2 002 questionnaire consisted of only one form separated into different parts and sections. Instructions (and skips) were desi

The questionnaire cover recorded various identifiers: district name, enumeration area, house number, number of households (family units) residing, total number of residents, gender, and whether siblings of the head of the house were also recorded. The second page, representing a summary page, listed every individual residing within the house. This list was taken by the enumerator on the first visit, on the eve of census night. The first part of the census questionnaire focused on housing-related questions. It was administered only once in each household, with questions usually asked of the household head. The household form asked the same range of questions as those covered in the 1992 census, relating to type of housing, structure of outer walls, water supply sources and storage, toilet and cooking facilities, lighting, construction materials and subsistence-type activities. The second part of the census questionnaire focused on individual questions covering all household members. This section was based on the 1992 questions, with notable differences being the exclusion of income-level questions and the expansion of fertility and mortality questions. As in 1992, a problem emerged during questionnaire design regarding the question of who or what should determine a ‘Nauruan’. Unlike the 1992 census, where the emphasis was on blood ties, the issue of naturalisation and citizenship through the sale of passports seriously complicated matters in 2 002. To resolve this issue, it was decided to apply two filtering processes: Stage 1 identified persons with tribal heritage through manual editing, and Stage 2 identified persons of Nauruan nationality and citizenship through designed skips in the questionnaire that were incorporated in the data-processing programming.

The topics of questions for each of the parts include: - Person Particulars: - name - relationship - sex - ethnicity - religion - educational attainment - Economic Activity (to all persons 15 years and above): - economic activity - economic inactive - employment status - Fertility: - Fertility - Mortality - Labour Force Activity: - production of cash crops - fishing - own account businesses - handicrafts. - Disability: - type of disability - nature of disability - Household and housing: - electricity - water - tenure - lighting - cooking - sanitation - wealth ownerships

Cleaning operations

Coding, data entry and editing Coding took longer than expected when the Census Office found that more quality-control checks were required before coding could take place and that a large number of forms still required attention. While these quality-control checks were supposed to have been done by the supervisors in the field, the Census Office decided to review all census forms before commencing the coding. This process took approximately three months, before actual data processing could begin. The amount of additional time required to recheck the quality of every census form meant that data processing fell behind schedule. The Census Office had to improvise, with a little pressure from external stakeholders, and coding, in conjunction with data entry, began after recruiting two additional data entry personnel. All four Census Office staff became actively involved with coding, with one staff member alternating between coding and data entry, depending on which process was dropping behind schedule. In the end, the whole process took almost two months to complete. Prior to commencing data entry, the Census Office had to familiarise itself with the data entry processing system. For this purpose, SPC’s Demography/Population Programme was invited to lend assistance. Two office staff were appointed to work with Mr Arthur Jorari, SPC Population Specialist, who began by revising their skills for the data processing software that had been introduced by Dr McMurray. This training attachment took two weeks to complete. Data entry was undertaken using the 2 .3 version of the US Census Bureau’s census and surveying processing software, or CSPro2.3. This version was later updated to CSPro2.4, and all data were transferred accordingly. Technical assistance for data editing was provided by Mr Jorari over a two-week period. While most edits were completed during this period, it was discovered that some batches of questionnaires had not been entered during the initial data capturing. Therefore, batch-edit application had to be regenerated. This process was frequently interrupted by power outages prevailing at the time, which delayed data processing considerably and also required much longer periods of technical support to the two Nauru data processing staff via phone or email (when available).

Data appraisal

Data was compared with Administrative records after the Census to review the quality and reliability of the data.

This statistic illustrates the share of the total population living in houses (detached and semi-detached) in Europe as of 2016, broken down by country. It can be seen that a share of almost three quarters (72.9 percent) of the population of Macedonia were living in detached houses as of 2016, a share of 67.7 percent more than for the population of Malta (5.2 percent). The average share of people living in detached houses for the EU as a whole stood at 33.5 percent at that time.

Abstract

The 2011 Population and Housing Census is the third national Census to be conducted in Namibia after independence. The first was conducted 1991 followed by the 2001 Census. Namibia is therefore one of the countries in sub-Saharan Africa that has participated in the 2010 Round of Censuses and followed the international best practice of conducting decennial Censuses, each of which attempts to count and enumerate every person and household in a country every ten years. Surveys, by contrast, collect data from samples of people and/or households.

Censuses provide reliable and critical data on the socio-economic and demographic status of any country. In Namibia, Census data has provided crucial information for development planning and programme implementation. Specifically, the information has assisted in setting benchmarks, formulating policy and the evaluation and monitoring of national development programmes including NDP4, Vision 2030 and several sector programmes. The information has also been used to update the national sampling frame which is used to select samples for household-based surveys, including labour force surveys, demographic and health surveys, household income and expenditure surveys. In addition, Census information will be used to guide the demarcation of Namibia's administrative boundaries where necessary.

At the international level, Census information has been used extensively in monitoring progress towards Namibia's achievement of international targets, particularly the Millennium Development Goals (MDGs).

The latest and most comprehensive Census was conducted in August 2011. Preparations for the Census started in the 2007/2008 financial year under the auspices of the then Central Bureau of Statistics (CBS) which was later transformed into the Namibia Statistics Agency (NSA). The NSA was established under the Statistics Act No. 9 of 2011, with the legal mandate and authority to conduct population Censuses every 10 years. The Census was implemented in three broad phases; pre-enumeration, enumeration and post enumeration.

During the first pre-enumeration phase, activities accomplished including the preparation of a project document, establishing Census management and technical committees, and establishing the Census cartography unit which demarcated the Enumeration Areas (EAs). Other activities included the development of Census instruments and tools, such as the questionnaires, manuals and field control forms.

Field staff were recruited, trained and deployed during the initial stages of the enumeration phase. The actual enumeration exercise was undertaken over a period of about three weeks from 28 August to 15 September 2011, while 28 August 2011 was marked as the reference period or 'Census Day'.

Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultat.The post-enumeration phase started with the sending of completed questionnaires to Head Office and the preparation of summaries for the preliminary report, which was published in April 2012. Processing of the Census data began with manual editing and coding, which focused on the household identification section and un-coded parts of the questionnaire. This was followed by the capturing of data through scanning. Finally, the data were verified and errors corrected where necessary. This took longer than planned due to inadequate technical skills.

Geographic coverage

National coverage

Analysis unit

Households and persons

Universe

The sampling universe is defined as all households (private and institutions) from 2011 Census dataset.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample Design

The stratified random sample was applied on the constituency and urban/rural variables of households list from Namibia 2011 Population and Housing Census for the Public Use Microdata Sample (PUMS) file. The sampling universe is defined as all households (private and institutions) from 2011 Census dataset. Since urban and rural are very important factor in the Namibia situation, it was then decided to take the stratum at the constituency and urban/rural levels. Some constituencies have very lower households in the urban or rural, the office therefore decided for a threshold (low boundary) for sampling within stratum. Based on data analysis, the threshold for stratum of PUMS file is 250 households. Thus, constituency and urban/rural areas with less than 250 households in total were included in the PUMS file. Otherwise, a simple random sampling (SRS) at a 20% sample rate was applied for each stratum. The sampled households include 93,674 housing units and 418,362 people.

Sample Selection

The PUMS sample is selected from households. The PUMS sample of persons in households is selected by keeping all persons in PUMS households. Sample selection process is performed using Census and Survey Processing System (CSPro).

The sample selection program first identifies the 7 census strata with less than 250 households and the households (private and institutions) with more than 50 people. The households in these areas and with this large size are all included in the sample. For the other households, the program randomly generates a number n from 0 to 4. Out of every 5 households, the program selects the nth household to export to the PUMS data file, creating a 20 percent sample of households. Private households and institutions are equally sampled in the PUMS data file.

Note: The 7 census strata with less than 250 households are: Arandis Constituency Rural, Rehoboth East Urban Constituency Rural, Walvis Bay Rural Constituency Rural, Mpungu Constituency Urban, Etayi Constituency Urban, Kalahari Constituency Urban, and Ondobe Constituency Urban.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaire instruments were used for the Namibia 2011 Population and and Housing Census:

Form A (Long Form): For conventional households and residential institutions

Form B1 (Short Form): For special population groups such as persons in transit (travellers), police cells, homeless and off-shore populations

Form B2 (Short Form): For hotels/guesthouses

Form B3 (Short Form): For foreign missions/diplomatic corps

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) During data collection in the field b) Manual editing and coding in the office c) During data entry (Primary validation/editing) Structure checking and completeness using Structured Query Language (SQL) program d) Secondary editing: i. Imputations of variables ii. Structural checking in Census and Survey Processing System (CSPro) program

Sampling error estimates

Sampling Error The standard errors of survey estimates are needed to evaluate the precision of the survey estimation. The statistical software package such as SPSS or SAS can accurately estimate the mean and variance of estimates from the survey. SPSS or SAS software package makes use of the Taylor series approach in computing the variance.

Data appraisal

Data quality Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultation with government ministries, university expertise and international partners; the preparation of detailed supervisors' and enumerators' instruction manuals to guide field staff during enumeration; the undertaking of comprehensive publicity and advocacy programmes to ensure full Government support and cooperation from the general public; the testing of questionnaires and other procedures; the provision of adequate training and undertaking of intensive supervision using four supervisory layers; the editing of questionnaires at field level; establishing proper mechanisms which ensured that all completed questionnaires were properly accounted for; ensuring intensive verification, validating all information and error corrections; and developing capacity in data processing with support from the international community.