Residential exposure to natural hazards in Europe, 2000–2024

1 Residential exposure to natural hazards in Europe, 2000–2024 (2025 update) Dominik Paprotny Institute of Marine and Environmental Sciences, University of Szczecin, Szczecin, Poland Abstract This dataset provides average national-level current gross replacement costs of the stock of residential assets (buildings and household contents) per m2 of useful floor space. The dataset includes annual time series (2000– 2024) for 33 European countries, in nominal and real prices. It is a thorough revision and update of the dataset described in Paprotny et al. (2020). Contents The dataset is provided in two formats: • An Excel file with 19 formatted tables, each containing one indicator. • A CSV file containing 18 indicators, with the following columns: o IndicatorCode – code corresponding to Excel tables; o Indicator – name of the indicator; o Unit – unit of measurement; o CountryCode – NUTS level 0 (country) code, used in Eurostat publications; o Country – name of country; o 2000…2024 – year. Significant changes compared to the 2022 dataset • Estimates for years 2021–2024 were added. • All timeseries were thoroughly revised based on new population, housing and economic data. • Majority of countries switched to COICOP 2018 classification for household final consumption expenditure, forcing an update of the methodology (see Tables 4-6) and in some cases combining COICOP 2018 and COICOP 1999 data. Items related to personal care (12.1 in COICOP 1999) were dropped in all cases as they are not considered durables anymore. Significant changes compared to the original dataset • Croatia, North Macedonia, Serbia and an aggregate for the European Union (27 countries) were added. • Estimates for years 2018–2020 were added. 2 • Underlying and auxiliary data related to residential exposure, such as the total stock of dwellings (value, number of units, useful floor space), population, gross domestic product and composition of household contents were added. • Author’s estimates of the gross stock of dwellings for some countries were replaced with new official estimates, published by Eurostat. • New sources of data on the number of dwellings/households and their floor space were identified for several countries (especially for Austria, Belgium, Slovenia and Spain), while new estimates by the author were made for several countries with limited direct information by integrating annual data on construction of new dwellings. • Majority of countries have significantly revised their national accounts data in the past year, changing estimates of household expenditure, dwelling stock and associated deflators for all available years. These changes have considerable influence on the whole timeseries of residential exposure for several countries. Acknowledgement This update was supported by the European Union’s Horizon research and innovation programme through project “Flood Adaptation under Climate Change with the European Socio-Hydrological Model” (EuroSoHo), no. 101218797. The 2022 update was supported by the German Research Foundation (DFG) through project “Decomposition of flood losses by environmental and economic drivers” (FloodDrivers), no. 449175973. The original work was supported by Climate-KIC through project “SAFERPLACES – Improved assessment of pluvial, fluvial and coastal flood hazards and risks in European cities as a mean to build safer and resilient communities”, Task ID TC2018B_4.7.3-SAFERPL_P430-1A KAVA2 4.7.3, with further funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 730381. Methods The following methodology was abridged and updated from Paprotny et al. (2020). The average current gross replacement cost of dwellings and household contents per m2 was computed based on many data sources. The basis of the calculation was total floor space of dwellings and households in a country, which was collected from national statistical institutes and Eurostat. For many countries, author’s estimates filled gaps in the data based on available proxies. All sources and adjustments to the data are listed in Table 1. Statistical institutes in most European countries are recording the stock of fixed assets, including dwellings, for purposes of national accounting. Annual time series of the gross stock of dwellings is available for 24 EU countries from Eurostat, though the data for three countries – Latvia, Poland and Romania – couldn’t be used due to major methodological differences which are discussed in Table 2. The value of dwellings is provided from the aforementioned resource in nominal and previous year’s prices. A deflator to obtain real (2015) prices was constructed based on the two timeseries. Finally, the value of all dwellings was divided by the total floor space area in a country to obtain average value per m2. The method does not consider building types or quality, but this 3 information is scarcely available from open datasets on buildings. Information on specific data sources on dwelling values is provided in Table 1. Dwelling stock for Iceland, Malta, Norway, Sweden and Switzerland required more data collection efforts. The Perpetual Inventory Method (PIM) was applied instead. Here, we use a simplified formula for PIM due to data limitations discussed in Paprotny et al. (2020), which is as follows: 𝑆𝑡= ∑ 𝐼𝑡−𝑗 𝐿𝑚𝑒𝑎𝑛 𝑗=0 (1) where: 𝑆 denotes stock of an asset; 𝑡 is the calendar year; 𝑗 is an annual increment; 𝐼 is investment in year 𝑡−𝑗 ; 𝐿𝑚𝑒𝑎𝑛 is the average service life of an asset in years; Two quantities are needed to obtain the stock of dwellings 𝑆 : investment in housing and an estimate of the dwellings’ average service life. Investment (gross fixed capital formation for asset type ‘dwellings’) is available from Eurostat, national statistical institutes or country-specific research estimates. However, sufficiently long investment series were only identified for Sweden, while for other countries had to be extrapolated using total investment or gross domestic product (GDP), a method which is also applied by national statistical institutes when necessary. 𝐿𝑚𝑒𝑎𝑛 for each country was taken from national methodologies collected in a survey by Eurostat and OECD (2014), except for Switzerland, which was taken from Bundesamt für Statistik (2006). For further four countries, where data on investment is limited, but the balances of the number of buildings and their floor space is available, a modified PIM was applied. In those cases, we computed an initial estimate of the stock of dwellings (Bulgaria in 1999, Latvia and Romania in 2000, Poland in 1995) based on national construction costs in the base year and then used annual data on investments in, and retirement of, dwellings in the country to arrive to a timeseries of the gross stock. In this case eq. (2) becomes: 𝑆𝑡=𝑆𝑡−1(1−𝐺𝑡)+𝐼𝑡 (2) where 𝐺𝑡 is the fraction of the stock retired during year 𝑡. In this way, service life assumption and long data series are not needed, with the drawback of assuming uniformity of the existing stock of dwellings and that all investment goes into building new dwellings rather than also into renovation of dwellings. Finally, the calculation for the remaining country, Croatia, was not possible due to the lack of investment data needed for the computation. We therefore used national annual estimates of the construction costs of new 4 dwellings. Data sources and assumptions for individual countries are provided in Table 1, and a summary of methods used is shown in Table 3. Data availability for the stock of household contents is much lower than for dwellings, as discussed in Paprotny et al. (2020). In order to estimate the stock of household contents, the PIM method is applied again. However, the contents consist of various durables of different service lives, therefore eq. (1) has to be rewritten as: 𝑆𝑡= ∑∑𝐼𝑡,𝑎−𝑗 𝐿𝑎 𝑗=0 𝐴 𝑎=1 (3) where the stock of household contents equals the sum of stocks for items 𝑎 = (1,...,𝐴), each with service life 𝐿𝑎. No retirement pattern was assumed, hence all items are included in the stock until reaching their average service life. The data on annual investment was gathered from final consumption expenditure of households split according to the Classification of Individual Consumption by Purpose (COICOP). The relevant durables are a set of twelve items at COICOP 4-digit level, i.e. all durables less items under code 07.1 “Purchase of vehicles”. However, only Sweden publishes annual data with such level of detail; data disaggregated at COICOP 3-digit level are disseminated for 31 countries, at COICOP 2-digit level for Switzerland. We therefore computed the average share of spending on durables within COICOP 3-digit categories using 5-yearly household survey data from Eurostat on detailed consumption expenditure patterns per country. Assumptions about service life of durable items (aggregated to COICOP 3-digit items) were calculated from German estimates presented by Schmalwasser et al. (2011). We averaged 1991 and 2009 estimates of service lives from that study and weighted the COICOP 4-digit items according to their share in spending. A list of durable items and assumptions on their service life is shown in Table 4, and the share of spending on durables per COICOP 3-digit items is shown in Tables 5. For Iceland detailed consumption expenditure surveys are not available, hence average share in 15 EU members states was used instead. Final consumption expenditure data were collected from Eurostat, OECD and national statistical institutes. Due to the very long estimated service life of durables in the ‘personal effects’ (COICOP code 12.3.1) category (45 years), the spending on those items had to be extrapolated using data on total private consumption expenditure, or GDP. Detailed sources of data are shown in Table 6. The calculation in eq. (3) was carried out with expenditure time series in real (2015) prices, and then converted to nominal prices using countryand item-specific deflators. Additionally, country-specific deflators of household contents were devised from the time series of the stock of consumer durables in real and nominal prices. Lastly, the stock of consumer durables was divided by the total floor space area in a country to obtain average value per m2, as for residential buildings. However, in most 5 countries there is certain share of unoccupied dwellings, hence only the floor space area of occupied dwellings or the number of households was used in this calculation, if such information was available. Instances of using different floor space area estimates to obtain average building and contents values are indicated in Table 1. 6 Table 1. Sources of data for estimating residential building value. Country Gross stock of dwellings (current and constant prices) Total floor space area and number of dwellings/households (stock) Austria Eurostat 2004–2024: annual number of dwellings multiplied by average floor space; 2000–2003: number of dwellings multiplied by average floor space interpolated from 1994 and 2004 microcensus data (Statistics Austria); 2000– 2021 and 2024 dwellings computed by revising upwards the number of households by a percentage interpolated from decennial 19912021 censuses and 2023 estimate; 2022–2023 actual number of dwellings from building register (Statistics Austria / Eurostat)* Belgium Eurostat Annual time series of dwellings (STATBEL) and households (IBSA 2025) multiplied by average floor space extrapolated from 2012 estimate (Eurostat) using change in structure of dwellings by area (STATBEL)* Bulgaria Eurostat (2023-24 value extrapolated from 2022 using change in dwelling total floor space and deflator ‘Construction cost, new residential buildings’) Annual time series (BNSI); number of households (interpolated from 1992, 2001, 2011, 2014, 2016 and 2018 and 2021-2024 data by BNSI and Eurostat) multiplied by annual average dwelling floor space (BNSI)* Croatia Annual average construction cost of new dwellings per m2 for 2000–2024 (DZS) Extrapolated from 2001, 2011 censuses and 2024 estimate (all dwellings) or 2001, 2011 and 2021 censuses (occupied dwellings) using annual data on construction and demolition of dwellings (number and floor space) from DZS, adjusted to match census numbers* Cyprus Eurostat (2023-24 value extrapolated from 2022 using change in number of dwellings and deflator ‘Construction cost, new residential buildings’) Annual number of all dwellings and households multiplied by average floor space in 2012 (CYSTAT), with dwellings and households for 2024 extrapolated from 2023 using the change in population* Czechia Eurostat Number of households (interpolated from 2001 and 2011 census and 2017–2024 data) multiplied by average floor space from 2011 and 2021 censuses; revised upwards by a percentage interpolated from 2001, 2011 and 2021 censuses to account for unoccupied dwellings (CZSO)* Denmark Eurostat Annual time series for all dwellings (DST); annual average dwelling size multiplied by number of households (DST)* 7 Estonia Eurostat (2023-24 value extrapolated from 2022 using change in dwelling floor space area and deflator ‘Construction cost, new residential buildings’) Dwellings: 2000–2011: Annual time series of dwelling floor space, 2012–2020: annual number of dwellings (except 2012–2015 interpolated from 2011 and 2016) multiplied by average floor space interpolated from 2011 and 2021 censuses and 2017-18 estimates, 20232024: extrapolated with number of households (Statistics Estonia); Households: annual number households multiplied by average floor space of dwellings (Statistics Estonia)* Finland Eurostat Annual number of households multiplied by average annual floor space (Statistics Finland); 2000–2024 revised upwards by a percentage interpolated from 2000, 2010, 2017, 2018, 2020 and 2024 censuses to account for unoccupied dwellings (Statistics Finland)* France Eurostat Annual number of all dwellings/households multiplied by average floor space interpolated from 1996, 2001, 2006 and 2013 estimates and extrapolated from 2013 based on the area of newly started dwellings (INSEE); Number of households taken from principal dwelling statistics; Data covers France without Mayotte* Germany Eurostat Annual time series of dwellings in residential buildings (DESTATIS) Greece Eurostat (2023-24 value extrapolated from 2022 using change in number of households and deflator ‘Construction cost, new residential buildings’) Number of households interpolated from 1991, 2001, 2011 and 2021 censuses (ELSTAT) and extrapolated from 2021 using annual survey estimate of the number of households from Eurostat, multiplied by average floor space interpolated from 2001 and 2012 data (Eurostat/Federcasa 2006); revised upwards by a percentage interpolated from 1991, 2001, 2011 and 2021 censuses to account for unoccupied dwellings (ELSTAT/Eurostat)* Hungary Eurostat (2024 value extrapolated from 2023 using change in number of dwellings and deflator ‘Construction cost, new residential buildings’) Annual number of dwellings (KSH) multiplied by average floor space interpolated from 2001 and 2012 data (Eurostat/Federcasa 2006) and extrapolated from 2012 based on the area of newly built dwellings (KSH); Number of households interpolated from 1991, 2001, 2011 and 2022 censuses (KSH) and extrapolated from 2022 using annual survey estimate of the number of households from Eurostat, multiplied by average floor space* 8 Iceland Statistics Iceland Annual number of households in 2004–2022 multiplied by average floor space interpolated from 2011 and 2021 censuses (Statistics Iceland); 2000–2003 and 2023–2024 extrapolated with the number of nuclear families (Statistics Iceland) Ireland CSO Number of dwellings/households interpolated from 1996, 2002, 2006, 2011, 2016 and 2022 data and extrapolated from 2022 based on the number of newly built dwellings (CSO), multiplied by average floor space in 2012 (Eurostat)* Italy Eurostat Number of households for 2018–2023 from ISTAT extrapolated with data from Eurostat for 2004–2017 and 2024, and interpolated with 1991 and 2001 censuses (ISTAT); revised upwards by a percentage interpolated from 1991, 2001, 2011, 2019 and 2021 censuses to account for unoccupied dwellings (ISTAT); households and dwellings multiplied by average size interpolated from 2001 and 2011 data (ISTAT/Federcasa 2006)* Latvia Eurostat (2023-24 value extrapolated from 2022 using change in dwelling floor space area and deflator ‘Construction cost, new residential buildings’) Dwellings: 2000–2020: annual floor space in 2010–2020 extrapolated back to 2000 using earlier annual time series before a series break; number of dwellings for 2002–2009 and 2011 census (used for year 2010) extrapolated using annual number of dwellings completed, with 2011–2019 adjusted to match 2021 census (used for year 2020) (CSP); 2021–2024: extrapolated with number and size of newly completed dwellings (CSP). Households: annual number of households multiplied by annual average floor space of dwellings (CSP)* Lithuania Eurostat (2023-24 value extrapolated from 2022 using change in dwelling floor space area and deflator ‘Construction cost, new residential buildings’) Annual time series (Statistics Lithuania), with number of dwellings for 2000–2003 extrapolated from 2004 based on the total floor space Luxembourg Eurostat (2024 value extrapolated from 2023 using change in number of households and deflator ‘Construction cost, new residential buildings’) Number of households (interpolated from 1991, 2001 and 2006–2024 data by STATEC/Eurostat) multiplied by average floor space interpolated from 2001 and 2012 data (Eurostat/Federcasa 2006) and extrapolated to 2000 and 2021 based on the floor space of newly constructed dwellings (STATEC) 9 Malta PIM (service life: 80 years**) based on GFCF of dwellings for 2000–2024 (Eurostat), total GFCF for 1970-1999 from PWT 10.0, GDP for 1950-1969 from MPD 2018) and 1921–1949 interpolated from 1921, 1930 and 1938 estimates by Apostolides (2010) and 1950 estimate from MPD 2018 Number of dwellings and households for 2000– 2021 interpolated from 1995, 2005, 2011 and 2021 censuses (National Statistics Office), number of households for 2021–2024 from Eurostat and number of dwellings estimated from 1995, 2005, 2011 and 2021 census ratio of dwellings to households; both dwellings and households multiplied by average floor space in 2002 (Federcasa 2006) Netherlands Eurostat (2024 value extrapolated from 2023 using change in dwelling floor space area and deflator ‘Construction cost, new residential buildings’) 2011–2024: annual number of dwellings multiplied by average floor space; 2000–2010: annual number of dwellings multiplied by average floor space extrapolated from 2011 based on floor space by year of construction and number of newly-built dwellings (CBS) North Macedonia Average building expenses per floor space of residential buildings in 2021 (MakStat) deflated using ‘Construction cost, new residential buildings’ (Eurostat), except 2000–2004 using GDP deflator (Eurostat) Dwellings: 2002 census extrapolated to 2000– 2024 using number and floor space of newly built dwellings and adjusted for 2003–2020 to match 2021 census (MakStat); Household: 2000–2021 number of households interpolated from 1994, 2002 and 2021 censuses, 2022–2024 extrapolated with the number of newly built dwellings multiplied by average dwelling size in each year (MakStat)* Norway PIM (service life: 80 years) based on GFCF of dwellings for 1970-2024 (SSB/Eurostat) and total gross investment for 1921-1969 (Grytten 2004) Dwellings: annual number for 2006–2024 extrapolated back to 2000 based on number of newly built dwellings (SSB) multiplied by average floor space in 2012 (Eurostat) extrapolated based on floor space area of newly built dwellings (SSB). Households: interpolated from 2000 and 2004– 2023 data (SSB), with 2024 extrapolated with the number of dwellings, and multiplied by annual average floor space of dwellings* Poland PIM using starting stock in 1995 (average GFCF of dwellings per m2 of completed dwellings multiplied by total floor space of dwellings), adding annual GFCF of dwellings (1996–2024) and removing annual apparent retirement of dwellings (value per m2 from the average value of previous year’s stock); deflator of GFCF of dwellings deflator until 2000 and deflator ‘Construction cost, new residential buildings’ used afterwards; data from Eurostat (GFCF and deflator) and GUS (dwelling stock and construction) Annual time series of dwellings and their floor area (GUS) 16 Table 5. Assumptions on the share of durables in final consumption expenditure COICOP 3-digit categories per country (based on expenditure surveys listed in Table 6). COICOP 1999 Country Spending on durables as % of total spending per COICOP 3-digit category 05.1 05.3 05.5 06.1 08.2 09.1 09.2 12.3 Finland 92.4 84.0 36.8 25.6 100.0 84.0 92.0 49.2 Iceland 98.1 70.8 21.7 30.8 100.0 73.3 87.3 57.5 Lithuania 99.5 80.9 41.9 8.5 100.0 92.5 92.9 25.3 Luxembourg 99.3 81.7 36.3 54.1 100.0 85.4 95.6 60.5 North Macedonia 97.7 83.3 18.0 10.0 100.0 92.3 100.0 41.9 United Kingdom 99.9 72.5 19.4 48.8 100.0 73.0 93.4 56.3 COICOP 2018 Country Spending on durables as % of total spending per COICOP 3-digit category 05.1 05.3 05.5 06.1 08.1 09.1 13.2 Austria 99.5 77.1 34.9 44.1 100.0 100.0 44.9 Belgium 99.2 76.2 31.8 30.7 100.0 100.0 43.6 Bulgaria 99.2 79.1 25.8 4.6 100.0 100.0 38.4 Croatia 99.1 88.1 40.5 12.6 100.0 100.0 17.4 Cyprus 99.1 86.1 43.9 10.6 100.0 100.0 43.6 Czechia 99.1 76.8 45.7 19.9 100.0 100.0 42.2 Denmark 98.0 81.8 32.8 33.6 100.0 100.0 58.0 Estonia 99.6 84.8 41.2 12.6 100.0 100.0 33.1 France 99.1 84.7 33.5 40.4 100.0 100.0 47.0 Germany 94.8 69.5 36.9 37.1 100.0 100.0 64.3 Greece 95.3 85.3 17.7 18.8 100.0 100.0 24.5 Hungary 97.4 86.2 29.8 11.5 100.0 100.0 29.2 Ireland 98.8 62.7 27.4 15.0 100.0 100.0 62.8 Italy 95.3 56.6 15.4 17.9 100.0 100.0 49.9 Latvia 98.2 79.1 49.1 9.9 100.0 100.0 26.1 Malta 99.5 73.8 16.9 20.1 100.0 100.0 55.6 Netherlands 98.5 73.3 14.6 49.8 100.0 100.0 60.2 Norway 99.6 81.1 11.0 29.9 100.0 100.0 53.1 Poland 99.5 79.9 24.9 8.1 100.0 100.0 17.2 Portugal 97.2 81.3 25.1 14.2 100.0 100.0 45.4 Romania 97.5 72.8 41.8 2.5 100.0 100.0 47.6 Serbia 99.4 83.5 14.7 13.5 100.0 100.0 11.3 Slovakia 99.3 79.6 31.5 9.3 100.0 100.0 27.5 Slovenia 98.8 86.1 66.2 37.2 100.0 100.0 28.0 Spain 97.3 74.6 12.9 41.7 100.0 100.0 54.3 Sweden 99.0 83.1 29.5 34.3 100.0 100.0 55.7 Switzerland* 35.7 14.7 12.8 28.0 29.1 3.4 14.7 100.0 70.7 19.0 13.4 100.0 100.0 58.1 Notes: * upper row is the share of COICOP 3-digit category in respective COICOP 2-digit categories. 17 Table 6. Availability of household final consumption expenditure data by country. Country Annual final consumption expenditure data (COICOP 3-digit) – sources by year Detailed consumption expenditure data (COICOP 4-digit) – surveys used (from Eurostat unless otherwise noted) Austria 1995-2024: Eurostat; 1976-1994: OECD (COICOP 1999); 1956-1975: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Belgium 1995-2024: Eurostat; 1956-1994: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Bulgaria 1995-2024: Eurostat; 1970-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1969: extrapolated using GDP from MPD 2018 2005, 2010, 2015, 2020 Croatia 1995-2024: Eurostat; 1990-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1989: extrapolated using GDP from MPD 2018 2005, 2010, 2015, 2020 Cyprus 1995-2024: Eurostat; 1956-1994: extrapolated using total household consumption expenditure from PWT 10.0 2005, 2010, 2015, 2020 Czechia 1990-2024: CZSO; 1956-1989: extrapolated using GDP from MPD 2018 (Czechoslovakian GDP before 1970) 2005, 2010, 2015, 2020 Denmark 1966-2024: DST; 1956-1965: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Estonia 1995-2024: Eurostat; 1990-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1950-1989: extrapolated using GDP from MPD 2018 (Soviet GDP per capita before 1980) 2005, 2010, 2015, 2020 Finland 1975-2024: Statistics Finland (COICOP 1999); 19561974: extrapolated using total private consumption expenditure from Statistics Finland 1994, 1999, 2005, 2010, 2015, 2020 France 1975-2024: Eurostat; 1959-1974: OECD; 1956-1958: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Germany 1991-2024: DESTATIS; 1956-1990: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Greece 1995-2024: Eurostat; 1956-1994: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 18 Hungary 1995-2024: Eurostat; 1970-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1969: extrapolated using GDP from MPD 2018 2005, 2010, 2015, 2020 Iceland 1990-2024: Statistics Iceland (COICOP 1999); 19571989: extrapolated using household expenditure on certain items, in local classification, roughly comparable with COICOP (Statistics Iceland); 1956: extrapolated using total private consumption expenditure (Statistics Iceland) COICOP items 05.3.1, 09.1.1-3 and 12.3.1: average consumer price index weights during 1995–2025 (Statistics Iceland); other COICOP items - average shares for the European Union (15 member states) from 1994, 1999, 2005, 2010 surveys Ireland 1995-2024: Eurostat; 1956-1994: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Italy 1995-2024: Eurostat; 1970-1994: OECD, with deflator per COICOP item for 1970-1991 extrapolated from 1992 using deflator for all durables from ISTAT; 1956-1969: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2010, 2015, 2020 Latvia 1995-2024: Eurostat; 1990-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1950-1989: extrapolated using GDP from MPD 2018 (Soviet GDP per capita before 1980) 2005, 2010, 2015, 2020 Lithuania 2024: extrapolated from 2023 using total household consumption expenditure on durables in current and constant prices (Eurostat); 1995-2023: Eurostat (COICOP 1999); 1990-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1950-1989: extrapolated using GDP from MPD 2018 (Soviet GDP per capita before 1980) 2005, 2010, 2015, 2020 Luxembourg 1995-2024: Eurostat (COICOP 1999); 1956-1994: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Malta 2000-2024: Eurostat; 1970-1999: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1969: extrapolated using GDP from MPD 2018 2005, 2010, 2015, 2020 Netherlands 1995-2024: Eurostat; 1980-1994: OECD; 1956-1979: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2015, 2020 North Macedonia 2000-2024: MakStat (COICOP 1999); 1990-1999: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1989: extrapolated using GDP from MPD 2018 2005, 2010, 2015 19 Norway 2024: extrapolated from 2023 using total household consumption expenditure on certain aggregated COICOP categories in current and constant prices (SSB); 1970-2023: SSB; 1956-1969: extrapolated using total private consumption expenditure from Grytten (2004) 2005, 2010, 2015 Poland 1995-2024: Eurostat; 1970-1994: extrapolated using total private consumption expenditure from PWT 10.0; 1956-1969: extrapolated using total consumption expenditure from GUS 2005, 2010, 2015, 2020 Portugal 1995-2024: Eurostat; 1956-1994: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015 Romania 1995-2024: Eurostat; 1960-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1959: extrapolated using GDP from MPD 2018 2005, 2010, 2015 Serbia 1995-2024: Eurostat; 1990-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1989: extrapolated using GDP from MPD 2018 2015, 2020 Slovakia 1995-2024: Eurostat; 1990-1994: extrapolated using total household consumption expenditure from PWT 9.1; 1956-1989: extrapolated using GDP from MPD 2018 (Czechoslovakian GDP before 1985) 2005, 2010, 2015, 2020 Slovenia 1995-2024: Eurostat; 1990-1994: extrapolated using total household consumption expenditure from PWT 10.0; 1956-1989: extrapolated using GDP from MPD 2018 2005, 2010, 2015, 2020 Spain 1995-2024: Eurostat; 1956-1994: extrapolated using total household consumption expenditure from PWT 10.0 1994, 1999, 2005, 2010, 2015, 2020 Sweden 2024: Eurostat; 1980-2023: SCB; 1956-1979: extrapolated using total private consumption expenditure from Schön and Krantz (2017) 1994, 1999, 2005, 2010, 2015 Switzerland 1995-2024: COICOP 2-digit level data from BFS; 1956-1994: extrapolated using household consumption expenditure by five broad categories from BFS (1986-1995) and HSSO (1948-1986) Swiss surveys for 2006–2008, 2009–2011, 2012–2014, 2015– 2017, 2018–2019 and 2020–2021 (BFS) United Kingdom 1985-2024: ONS (COICOP 1999); 1956-1984: extrapolated using total household consumption expenditure from Blue Book 2025 (ONS) 1994, 1999, 2005, 2010, 2015 Notes: PWT 10.0 = Penn World Table 10.0 (Feenstra et al. 2015), https://www.rug.nl/ggdc/productivity/pwt/?lang=en MPD 2018 = Maddison Project Database 2018 (Bolt et al. 2018) 20 References Apostolides, A.: Economic Growth or Continuing Stagnation? Estimating the GDP of Cyprus and Malta, 19211938, PhD Thesis, The London School of Economics and Political Science, London, 2010. BFS: Federal Office for Statistics, https://www.bfs.admin.ch/ BNSI: Republic of Bulgaria National Statistical Institute, http://www.nsi.bg/ Bolt, J., Inklaar, R., de Jong, H., and van Zanden, J. L.: Rebasing ‘Maddison’: new income comparisons and the shape of long-run economic development, Maddison Project Working paper 10, 2018. Bundesamt für Statistik: Nichtfinanzieller Kapitalstock: Methodenbericht. Arbeitsdokument, Bern, Switzerland, 2006. CBS: Statistics Netherlands, https://www.cbs.nl/ CSO: Central Statistics Office, https://www.cso.ie/ CSP: Central Statistical Bureau of Latvia, https://www.csb.gov.lv/lv/sakums CYSTAT: Republic of Cyprus Statistical Service, https://www.mof.gov.cy/mof/cystat/statistics.nsf/index_en/index_en?OpenDocument CZSO: Czech Statistical Office, https://www.czso.cz/ DESTATIS: Federal Statistical Office, https://www.destatis.de/ DST: Statistics Denmark, https://www.dst.dk/ DZS: Croatian Bureau of Statistics, https://www.dzs.hr/default_e.htm Edvinsson, R.: Growth, Accumulation, Crisis: With New Macroeconomic Data for Sweden. Almqvist & Wiksell International; Stockholm, 2005. ELSTAT: Hellenic Statistical Authority, http://www.statistics.gr/ Eurostat and OECD: Report on Survey of National Practices in Estimating Net Stocks of Structures, Eurostat/OECD, https://ec.europa.eu/eurostat/documents/24987/4253483/Eurostat-OECD-survey-ofnational-practices-estimating-net-stocks-structures.pdf, 2014. Eurostat: European system of accounts ESA 2010, Publications Office of the European Union, Luxembourg, 2013. Eurostat: https://ec.europa.eu/eurostat/home Federcasa (Italian Housing Federation): Housing Statistics in the European Union 2005/2006, Ministry of Infrastructure of the Italian Republic, Rome, 2006. Feenstra, R. C., Inklaar, R. and Timmer, M. P.: The Next Generation of the Penn World Table, Am. Econ. Rev., 105, 3150-3182, 2015. 21 Fundación BBVA e Ivie: El stock de capital en viviendas en España y su distribución territorial (1990-2007). https://www.ivie.es/es_ES/ptproyecto/el-stock-de-capital-en-viviendas-en-espana-y-su-distribucionterritorial-1900-2007/, 2009. Grytten, O. H.: The Gross Domestic Product for Norway, 1830-2003, in: Eitrheim, Ø., Klovland, T., and Qvigstad, J. F. (Eds.), Historical Monetary Statistics for Norway 1819‐2003, Norges Bank Occasional Papers 35, 241-288, 2004. GUS: Statistics Poland, https://stat.gov.pl/ HSSO: Historical Statistics of Switzerland, University of Zurich, https://hsso.ch/en IBSA: Ménages, https://ibsa.brussels/themes/population/menages, L’Institut Bruxellois de Statistique et d’Analyse, 2025. INE: National Institute of Statistics (Spain), http://www.ine.es/ INSEE: National Institute of Statistics and Economic Studies, https://insee.fr/ INSSE: National Institute of Statistics (Romania), http://www.insse.ro/ ISTAT: Italian National Institute of Statistics, https://www.istat.it/ KSH: Hungarian Central Statistical Office, https://www.ksh.hu/ MakStat: State Statistical Office of the Republic of Macedonia, https://www.stat.gov.mk/Default_en.aspx Ministerio de Fomento: Estimación del parque de viviendas, https://apps.fomento.gob.es/BoletinOnline2/?nivel=2&orden=33000000 National Statistics Office, http://www.nso.gov.mt OECD: Organisation for Economic Co-operation and Development, https://stats.oecd.org/ ONS: Office for National Statistics, https://www.ons.gov.uk/ Paprotny, D., Kreibich, H., Morales-Nápoles, O., Terefenko, P., and Schröter, K.: Estimating exposure of residential assets to natural hazards in Europe using open data, Nat. Hazards Earth Syst. Sci., 20, 323–343, https://doi.org/10.5194/nhess-20-323-2020, 2020. SCB: Statistics Sweden, https://www.scb.se/ Schmalwasser, O., Müller, A., and Weber, N.: Gebrauchsvermögen privater Haushalte in Deutschland, Wirtschaft und Statistik, 06/2011, 565–578, 2011. Schön, L., and Krantz, O. (2017) Swedish Historical National Accounts 1300―2010, https://www.ekh.lu.se/en/research/economic-history-data/shna SIStat: Statistical Office of the Republic of Slovenia, https://www.stat.si/ SORS: Statistical Office of the Republic of Serbia, stat.gov.rs/en-us/ 22 SOSR: Statistical Office of the Slovak Republic, https://slovak.statistics.sk/ SSB: Statistics Norway, https://www.ssb.no/ STATBEL: Belgian Statistical Office, https://statbel.fgov.be/en STATEC: National Institute of Statistics and Economic Studies of the Grand Duchy of Luxembourg, https://statistiques.public.lu/en/index.html Statistics Austria: http://statistik.at/ Statistics Estonia: https://www.stat.ee/ Statistics Finland: https://www.stat.fi/ Statistics Iceland: https://statice.is/ Statistics Lithuania: https://osp.stat.gov.lt/ Statistics Portugal: https://www.ine.pt/