In the presented European countries, the homeownership rate extended from 42.6 percent in Switzerland to as much as 95.9 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 20 euro area countries stood at 64.5 percent in 2024. Average cost of housing Countries with lower homeownership rates tend to have higher house prices. In 2024, 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 2024, European countries completed between one and six housing units per 1,000 citizens, with Ireland, Poland, and Denmark responsible for 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 RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The homeownership rate in the United States amounted to nearly ** percent in the third quarter of 2024. While there are many factors that affect people’s decision to buy a house, the recent decrease can be attributed to the higher mortgage interest rates, which make taking out a mortgage less affordable for potential buyers, especially considering the surge in house prices in recent years. Which factors affect homeownership? Age and ethnicity have a strong correlation with homeownership. Baby boomers, for example, are twice as likely to own their home than Millennials. Also, the homeownership rate among white Americans is substantially higher than among any other ethnicity. How does the U.S. homeownership rate compare with other countries? Having a home is an integral part of the “American Dream”. Compared with selected European countries, the U.S. ranks alongside the United Kingdom, Cyprus, and Ireland. Many countries in Europe, however, exceed ** percent homeownership rate.

France is one of the countries with lower homeownership rate in Europe. In 2022, approximately 63 percent of people lived in an owner-occupied home.

The mortgage prevalence among homeowners in the 30 European countries in the ranking varied widely in 2024. 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.

Home Ownership Rate in European Union decreased to 68.40 percent in 2024 from 69.10 percent in 2023. This dataset provides the latest reported value for - European Union Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Between 2008 and 2022, the homeownership rate in Germany decreased slightly. In 2022, about 46.7 percent of the population lived in an owner-occupied dwelling. This makes Germany one of the countries with the lowest homeownership rate and the biggest rental residential real estate market in Europe.

The homeownership rate in the Netherlands has increased by almost seven percent since 2005 when close to 64 percent of Dutch households lived in an owner-occupied home. In 2022, the homeownership rate stood at approximately 71 percent. Although the Netherlands has a relatively low homeownership rate compared to other European countries, it was notably higher than in other countries in Northwestern Europe, including Germany and the UK.

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.

Data description

The House Price Index (HPI) measures inflation in the residential property market. The HPI captures price changes of all types of dwellings purchased by households (flats, detached houses, terraced houses, etc.). Only transacted dwellings are considered, self-build dwellings are excluded. The land component of the dwelling is included.

The HPI is available for all European Union Member States (except Greece), the United Kingdom (only until the third quarter of 2020), Iceland, Norway, Switzerland and Turkey. In addition to the individual country series, Eurostat produces indices for the euro area and for the European Union (EU). As from the first quarter of 2020 onwards, the EU HPI aggregate no longer includes the HPI from the United Kingdom.

The national HPIs are produced by National Statistical Offices (NSIs) and the European aggregates by Eurostat, by combining the national indices. The data released quarterly on Eurostat's website include the national and European price indices, weights and their rates of change.

In order to provide a more comprehensive picture of the housing market, house sales indicators are also provided. Available house sales indicators refer to the total number and value of dwellings transactions at national level where the purchaser is a household. Eurostat publishes in its database a quarterly and annual house sales index as well as quarterly and annual rates of change.

Statistical concepts and definitions

The HPI is based on market prices of dwellings. Non-marketed prices are ruled out from the scope of this indicator. Self-build dwellings, dwellings purchased by sitting tenants at discount prices or dwellings transacted between family members are out of the scope of the indicator. It covers all monetary dwelling transactions regardless of its type (e.g., carried out through a cash purchase or financed through a mortgage loan).

The HPI measures the price developments of all dwellings purchased by households, regardless of which institutional sector they were bought from and the purpose of the purchase. As such, a dwelling bought by a household for a purpose other than owner-occupancy (e.g., for being rented out) is within the scope of the indicator. The HPI includes all purchases of new and existing dwellings, including those of dwellings transacted between households.

The number and value of house sales cover the total annual value of dwellings transactions at national level where the purchaser is a household. Transactions between households are included. Transfers in dwellings due to donations and inheritances are excluded.

The house sales value reflect the prices paid by household buyers and include both the price of land and the price of the structure of the dwelling. The prices for new dwellings include VAT. Other costs related to the acquisition of the dwelling (e.g., notary fees, registration fees, real estate agency commission, bank fees) are excluded.

Statistical unit

Each published index or rate of change refers to transacted dwellings purchased at market prices by the household sector in the corresponding geographical entity. All transacted dwellings are covered, regardless of which institutional sector they were bought from and of the purchase purpose.

more: https://ec.europa.eu/eurostat/cache/metadata/en/prc_hpi_inx_esms.htm

Percentage of the population living in a household where total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances).

The total number of dwellings per one thousand citizens in European countries in 2024 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 ***. France 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.

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Real Estate Market Size 2025-2029

The real estate market size is valued to increase USD 1258.6 billion, at a CAGR of 5.6% from 2024 to 2029. Growing aggregate private investment will drive the real estate market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 64% growth during the forecast period.
By Type - Residential segment was valued at USD 1440.30 billion in 2023
By Business Segment - Rental segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 48.03 billion
Market Future Opportunities: USD 1258.60 billion
CAGR from 2024 to 2029 : 5.6%

Market Summary

In the dynamic realm of global real estate, private investment continues to surge, reaching an impressive USD 2.6 trillion in 2020. This significant influx of capital underscores the sector's enduring appeal to investors, driven by factors such as stable returns, inflation hedging, and the ongoing demand for shelter and commercial real estate space. Simultaneously, marketing initiatives have gained momentum, with digital platforms and virtual tours becoming increasingly popular.
However, regulatory uncertainty looms, posing challenges for market participants. Amidst this complex landscape, real estate remains a vital component of the global economy, continually evolving to meet the shifting needs of businesses and individuals alike.

What will be the Size of the Real Estate Market during the forecast period?

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How is the Real Estate Market Segmented ?

The real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Residential
  Commercial
  Industrial


Business Segment

  Rental
  Sales


Manufacturing Type

  New construction
  Renovation and redevelopment
  Land development


Geography

  North America

    US
    Canada


  Europe

    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The residential segment is estimated to witness significant growth during the forecast period.

Amidst the dynamic real estate landscape, the residential sector encompasses the buying and selling of various dwelling types, including single-family homes, apartments, townhouses, and more. This segment experiences continuous growth, fueled by increasing millennial homeownership rates and urbanization trends. Notably, the APAC region, specifically China, dominates the market share, driven by escalating homeownership numbers. Concurrently, the Indian real estate sector thrives due to the demand for affordable housing, with initiatives like Pradhan Mantri Awas Yojana (PMAY) spurring the development of affordable housing projects. In this evolving market, various aspects such as environmental impact studies, capital appreciation potential, title insurance coverage, building lifecycle costs, mortgage interest rates, and structural engineering analysis play crucial roles.

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The Residential segment was valued at USD 1440.30 billion in 2019 and showed a gradual increase during the forecast period.

Property tax appeals, property insurance premiums, property tax assessments, property marketing strategies, building material pricing, property management software, land surveying techniques, zoning regulations compliance, architectural design features, building code compliance, multifamily property management, rental yield calculations, construction cost estimation, energy efficiency ratings, green building certifications, tenant screening processes, investment property returns, property development plans, geotechnical site investigations, sustainable building practices, due diligence procedures, HVAC system efficiency, property renovation costs, market value appraisals, building permit acquisition, and property valuation models significantly impact the sector's progression. As of 2021, the market is projected to reach a value of USD 33.3 trillion, underscoring its substantial influence on the global economy.

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Regional Analysis

APAC is estimated to contribute 64% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Real Estate Market Demand is Rising in APAC Request Free Sample

The APAC region held the largest share of the market in 2024, driven by factors such as rapid urbanization and increasing spending capacity. This trend is expected to continue during the forecast period. The overall health of the economy signi

This indicator is defined as the percentage of the population living in a household where the total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances) presented by accommodation tenure status.

Homeowners Insurance Market Outlook

According to our latest research, the global homeowners insurance market size reached USD 245.8 billion in 2024, demonstrating robust expansion supported by rising property values and increased awareness of risk mitigation. The market is expected to grow at a CAGR of 6.1% from 2025 to 2033, reaching an estimated value of USD 419.7 billion by the end of the forecast period. This growth is primarily driven by heightened demand for comprehensive property protection solutions, the proliferation of digital distribution channels, and the increasing frequency of climate-related catastrophes worldwide.

One of the key growth factors propelling the homeowners insurance market is the increasing frequency and severity of natural disasters, such as hurricanes, floods, wildfires, and earthquakes. As climate change continues to amplify the risks associated with property ownership, both homeowners and insurers are placing greater emphasis on comprehensive coverage and risk assessment. Insurance companies are responding by developing more tailored products and leveraging advanced analytics to accurately price policies based on localized risk factors. This trend has led to a surge in demand for policies that not only cover traditional perils but also offer protection against emerging threats, fostering a more resilient and adaptive market landscape.

Another significant driver is the rapid digital transformation within the insurance industry, which is revolutionizing the way policies are marketed, sold, and serviced. Online platforms and digital distribution channels are enabling insurers to reach a broader customer base, streamline policy issuance, and enhance customer engagement through personalized offerings. The integration of artificial intelligence and machine learning in underwriting and claims management is further optimizing operational efficiency and reducing costs. These technological advancements are making homeowners insurance more accessible and appealing to tech-savvy consumers, particularly among younger demographics who prefer digital-first experiences.

The evolving regulatory landscape and increasing financial literacy among consumers are also contributing to market growth. Governments and regulatory bodies are implementing stricter guidelines to ensure transparency, fairness, and consumer protection in the insurance sector. At the same time, rising awareness about the importance of insurance coverage is encouraging more homeowners, landlords, and tenants to seek adequate protection for their properties and personal belongings. This shift is particularly evident in emerging economies, where urbanization and rising disposable incomes are creating new opportunities for market expansion and product innovation.

From a regional perspective, North America continues to dominate the homeowners insurance market, accounting for the largest share in 2024, driven by high property values, widespread insurance penetration, and a mature regulatory environment. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, increasing homeownership rates, and the expansion of digital distribution networks. Europe remains a significant market, characterized by stable growth and a strong focus on sustainability and climate resilience. Meanwhile, Latin America and the Middle East & Africa are experiencing steady growth, supported by ongoing economic development and rising awareness of property risk management.

Coverage Type Analysis

The homeowners insurance market is segmented by coverage type into dwelling coverage, personal property coverage, liability protection, and others. Dwelling coverage forms the backbone of most homeowners insurance policies, providing financial protection against damage to the physical structure of the home caused by covered perils such as fire, windstorms, and vandalism. The increasing frequency of severe weather events and rising construction costs have heightened the importance

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

This indicator is defined as the percentage of the population living in a household where the total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances) presented by household type.

Der Datensatz enthält für die gegebenen Länder jeweils zwei Zeitreihen für die Wohneigentumsquote. Die erste Zeitreihe besteht aus den Rohdatenpunkten. Die Wohneigentumsquote ist in den meisten Ländern nur zu bestimmen Volks- oder Wohnungszählungszeitpunkten erhoben worden. Deswegen liegen für die Rohdaten Messungen nur zu einzelnen Zeitpunkten vor. Die Rohdaten aller Länder können aus dem Menü ‚Beschreibung‘ (blauer Button) unter dem letzten Punkt ‚Materialien zur Studie‘ / ‚Download weiterer Texte zu dieser Studie im PDF Format (Forschungsberichte, Publikationen, Materialien zur Studie)‘ (orangener Button mit PDF-Symbol) als Excel-Datei heruntergeladen werden. Die zweite Zeitreihe geht von der gleichen Datengrundlage aus und fügt eine lineare Interpolation hinzu, damit die Variable in Panelanalysen verwendet werden kann. Die lineare Interpolation kann man damit rechtfertigen, dass die Wohneigentumsquote eine sich nur langsam verändernde Größe ist. Ferner zeigen die jüngeren jährlichen Daten aus Umfragen, dass die Reihe keine großen Sprünge macht. Die interpolierten Zeitreihen befinden sich im Datenteil der Studie (orangener Button mit der Aufschrift ‚146 Zeitreihen (1900-2015) 1 Tabelle). Hier kann die Tabelle entweder komplett downgeloadet werden, oder es können Ländergruppen nach Kontinent oder einzelne Länder ausgewählt werden. Zur Definition der Wohneigentumsquote, der Ländervergleichbarkeit und länderspezifischen Besonderheiten sollten folgende methodische Punkte berücksichtigt werden: Erstens gibt es die auf die Wohnungseinheiten basierende Definition der Wohneigentumsquote, die alle selbstgenutzten Wohn-Einheiten zählt und sie durch alle Gebäude-Einheiten teilt. Diese Definition gilt für die Daten, die auf den Wohnungszählungen der Länder basieren, und der Autor S. Kohl bezieht sich auf diese Definition für die frühesten Zeiträume der Wohneigentums-Quoten. Zweitens hängt die auf Gebäude- bzw. Wohn-Einheiten basierende Definition davon ab, was als Gebäude-Einheit zählt und was zum Wohnungsbestand gehört. Die häufigsten internationalen Vergleiche basieren auf UN (UN 1974, Doling 1997: 35: 154) oder EU-Daten, die lediglich die jeweiligen nationalen statistischen Definitionen wiederholen, die sich erheblich unterscheiden (Behring, Helbrecht und Goldrian 2002). Obwohl die Definitionen der Wohneinheit zwischen den OECD-Ländern sehr ähnlich sind (vgl. Donnison und Ungerson 1982: 42), ist die Einbeziehung von z.B. Anhängern, Saison- und Wohnmobilen in den USA eine Ausnahme (US-Census 2013), die rund 7% des Wohnungsbestandes ausmachen und zu einer deutlich überdurchschnittlichenWohneigentumsquote führen. Diese Einheiten würden, wenn sie statistisch signifikant wären, in Deutschland wahrscheinlich nicht als Wohneinheiten gelten. Der Wohnungsbestand kann sich unterscheiden je nach dem, ob Unterkünfte wie Ferienhütten, Zweitwohnsitze, Wohnwagen, Schiffe, saisonale Wohneinheiten, leerstehende oder zeitweise unbewohnte Einheiten als Wohneinheiten behandelt werden. Die deutsche Definition des Wohnungsbestandes gehört zu den konservativeren im Vergleich zu denjenigen anderer nationaler Statistikämter (Destatis 1989: 7, SE / CZR 2004). Die einheitsbasierte Definition wird durch Kriegszerstörungen verzerrt, wie in Deutschland in den 1950er Jahren, als die offizielle Wohneigentumsquote auf Einheitsbasis mit 39,1% angegeben wurde. Die Zerstörung von überwiegend städtischem Wohnungsbau durch Luftschutzbauten hatte den gesamten Wohnungsbestand reduziert. Der Autor stützt sich deshalb im Falle von Deutschland auf die realistischere Hausbesitzquote von 26,7% im Jahr 1950 stützen (Glatzer 1980: 246). Zweitens gibt es haushaltsbasierte Definitionen der Wohneigentumsquote, die alle Eigentümer-Haushalte (Wohnungs-Eigentümer und Haus-Eigentümer) in das Verhältnis setzt zur Gesamtzahl der Haushalte. Diese Definition, die auf repräsentativen Umfragedaten basiert, ersetzte die auf Wohneinheiten basierenden Daten ab den 1980er Jahren. Der Autor bezieht sich auf diese Definition für die neueren Daten seiner Wohneigentumsquoten. Umfragen berücksichtigen tendenziell Wohnungs- und Hauseigentümer aus den mittleren Klassen stärker als andere Bevölkerungsgruppen. Dies scheint vor allem bei den Eurostat-Umfragen zu gelten, die deutlich höhere Zahlen liefern als nationale Erhebungen, weil das Verhältnis von befragten Eigentümerhaushalten zu allen Befragten höher ist als wohneinheitenbasierte Berechnungen. Dadurch kommt es zu einer Verzerrung bzw. zu höheren Eigentums-Quoten. Aus diesem Grund hat sich der Autor, soweit möglich, auf Quellen außerhalb von Eurostat gestützt, um den Vergleich mit Nicht-EU-Ländern nicht zu verzerren. Eine dritte Definition ist bevölkerungsbezogen und setzt die in Eigenheimen lebende Bevölkerung in das Verhältnis zur Bevölkerung insgesamt (Braun 2004). Diese Definition führt aufgrund der statistischen Prävalenz von Familien in den Eigentümerhaushalten zu höheren Wohneigentumsquoten als die erstgenannte. Dies ist wichtig, wenn man beispielsweise nach Sozialisationseffekten von Wohneigentum sucht, spielt aber in den Vergleichsdaten dieser Studie keine Rolle. Weiterhin existiert viertens eine objektbasierte Definition, die sich auf die Anzahl der Haushalte, die Immobilien besitzen, konzentriert. Die Wohneigentumsquote nach dieser Definition kann höher als die wohneinheitenbasierte Definition sein, weil Mieter mit Immobilienbesitz hier auch als Eigentümer zählen. Diese Definition findet in der Studie allerdings keine Anwendung. Eine fünfte Definition umfasst alle Wohnimmobilien, die in Privatbesitz sind (Privateigentum), im Gegensatz zu denen, die dem Staat oder den Unternehmen gehören (Jenkis 2010). Diese Definition ist wichtig im Kontext der kommunistischen Länder, aber auch in den westlichen Ländern, wo Genossenschaften oder Unternehmen einen großen Anteil am gesamten Immobilienbesitz hatten. Der Autor bezieht sich auf diese Zahl als Proxy für die Eigennutzung im Fall einiger kommunistischer Länder, in denen das verbleibende Privateigentum stark mit dem Besitz eines Einfamilienhausbesitzers korreliert.“ (Sebastian Kohl) Die Datentabellen zu dieser Studie kann in Online-Datenbank Histat unter dem Thema ‚Bauen‘ downgeloadet werden. Der Download für die Rohdaten wird über die Studienbeschreibung unter ‚Materialien zur Studie‘ angeboten. Die interpolierten Zeitreihen befinden sich im Datenteil der Studie (orangener Button mit der Aufschrift ‚146 Zeitreihen (1900-2015) 1 Tabelle). Anmerkungen:„Methodological note about home ownership statistics: There are five different measures that one can distinguish. First, there is the unit-based definition which counts all owner-occupied units and divides them by all units. This definition prevails for the data based on the countries’ housing censuses and I rely on it for the earliest periods. First, it depends on what counts as “owning” in critical cases where the bundle of rights of owner-occupiers is restricted (they cannot freely sell the underlying land or unit, for instance) or entirely unregulated. I followed the existing definition – which counts many owner-occupiers in the Global South in spite of unclear property rights. I decided to count “cooperative ownership” in the Scandinavian countries as “owner occupation”. For even though the bundle of rights was restricted in the early days, cooperative owners had to put money down for housing, which is essentially different from renting. Second, the unit-based definition depends on what counts as a unit and on what belongs to the housing stock. Most common international comparisons are based on UN (UN 1974, Doling 1997: 35: 154) or EU collected data that merely repeat the respective national statistical definitions which differ quite considerably (Behring, Helbrecht, and Goldrian 2002). Though OECD countries adopt quite similar definition of housing unit (cf. Donnison and Ungerson 1982: 42) the US’ inclusion of trailers, seasonal and mobile homes is an exception (US-Census 2013), constituting around 7% of the housing stock with significantly above-average homeownership rate. These units, were they statistically significant, would probably not count as housing units in Germany, for instance. The housing stock can differ as to whether one includes recreational housing units such as tourist cabins, secondary residences, trailers, ships, seasonal housing units, vacant or temporarily unoccupied units. An intra-European comparison of what various national statistical institutes count in the housing stock of the homeownership rate reveals the German definition to be among the most conservative (Destatis 1989: 7, SE/CZR 2004), i.e. were other countries to adopt the German definition, their homeownership rate would be even higher. This observation holds also for the US-German comparison: as the US Census definition of homeownership rate includes seasonal and other mobile units, it tends to be lower than it would be according to the German definition. The unit-based definition is distorted by war-time destructions such as in Germany in the 1950s, when the official unit-based homeownership rate is given as 39,1%. Air-raid destructions of predominantly urban tenement housing had reduced the overall housing stock and two million people still lived in barracks with many others doubling up, 35,6% of households subleasing and the secretary of housing estimating a housing deficit of 4,8 million units, mostly rental (Schulz 1994: 32-35). I will therefore rely on the more realistic household-based homeownership rate of 26,7% in 1950 (Glatzer 1980: 246). Second, there is household-based definitions which counts all owner-occupying households divided by the overall number of households. This definition, based on representative survey data, began to replace the unit-based data from the 1980s onwards and I rely

Analysis of ‘Housing cost overburden rate by household type - EU-SILC survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://data.europa.eu/data/datasets/9h7bs2nfebyfmvnnl6qcq on 30 September 2021.

--- Dataset description provided by original source is as follows ---

This indicator is defined as the percentage of the population living in a household where the total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances) presented by household type.

--- Original source retains full ownership of the source dataset ---