Rent Inflation in the United States decreased to 3.80 percent in June from 3.90 percent in May of 2025. This dataset includes a chart with historical data for the United States Rent Inflation.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to May 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Rent Inflation in Canada increased to 4.70 percent in June from 4.50 percent in May of 2025. This dataset includes a chart with historical data for Canada Rent Inflation.

This table contains data described by the following dimensions (Not all combinations are available): Geography (247 items: Carbonear; Newfoundland and Labrador; Corner Brook; Newfoundland and Labrador; Grand Falls-Windsor; Newfoundland and Labrador; Gander; Newfoundland and Labrador ...), Type of structure (4 items: Apartment structures of three units and over; Apartment structures of six units and over; Row and apartment structures of three units and over; Row structures of three units and over ...), Type of unit (4 items: Two bedroom units; Three bedroom units; One bedroom units; Bachelor units ...).

The Dumpster Rental industry has demonstrated robust performance in recent years, bolstered by growth in the value of residential construction, a hike in consumer spending and an overall boost in corporate profit. However, challenges have surfaced in the form of rising inflation and interest rates, which have led to a drop in overall construction expenditure. Consequently, residential and nonresidential sectors have experienced substantial pressure, resulting in a shift in demand for dumpster rental services. Nonetheless, industry-wide revenue has been increasing at a CAGR of 0.5% over the past five years, including an estimated 3.6% decrease in the current year, and is expected to total $537.7 million in 2023. In the same year, profit is projected to decrease to 11.9%.Dumpster rentals are commonly utilized at temporary sites that generate substantial waste, rendering standard trash bins and pickup trucks inadequate for waste disposal. Construction projects, in particular, benefit from dumpster rentals because of their temporary nature and high volume of waste. Renting a dumpster offers customers the advantage of fewer trips to dispose of waste, thereby enhancing efficiency. Moreover, the dumpster rental industry typically experiences growth in tandem with the overall economy.Investment in manufacturing facilities, private spending on home improvement projects and growth in the value of construction will likely support revenue growth over the next five years. As a result, industry revenue is expected to increase at a CAGR of 2.2% over the next five years, reaching $598.6 million in 2028.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..In data year 2013, there were a series of changes to data collection operations that could have affected some estimates. These changes include the addition of Internet as a mode of data collection, the end of the content portion of Failed Edit Follow-Up interviewing, and the loss of one monthly panel due to the Federal Government shut down in October 2013. For more information, see: User Notes.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013 American Community Survey

In 2019, the average inflation rate in Nepal was at 4.62 percent, a slight drop compared to the previous year. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities, and raw materials (e.g. gas, oil), as well as federal fees and taxes. Political and economic turmoilThe Nepalese economy is heavily influenced by the country’s political situation. It has made only slow progress in connecting with the global economy and improving the standard of living for its inhabitants but is now finally picking up speed. Nepal’s economy is not stable yet: Inflation is decreasing but all over the place – usually a sure sign of a struggling economy – and GDP growth is also not steady and forecast to decrease again in the future. Additionally, Nepal’s trade deficit seems to be in free fall. Move to the cityA quarter of Nepal’s, mostly rural, population is living below the poverty line, but Nepal is working on improving their outlook in the future. Today, agriculture contributes about a third to the country’s GDP, and a sizeable share of commodity exports consists of agricultural products – but already the lion’s share of Nepal’s GDP is generated by the services sector, like tourism and textiles. By shifting GDP generation to services, and consequently creating jobs in the cities and attracting more people to urban areas, Nepal might finally be able to stabilize its economy and provide better living standards for its inhabitants.

The statistic shows the inflation rate in India from 1987 to 2024, with projections up until 2030. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services, on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities and raw materials (e.g. gas, oil), as well as federal fees and taxes. In 2024, the inflation rate in India was around 4.67 percent compared to the previous year. See figures on India's economic growth for additional information. India's inflation rate and economy Inflation is generally defined as the increase of prices of goods and services over a certain period of time, as opposed to deflation, which describes a decrease of these prices. Inflation is a significant economic indicator for a country. The inflation rate is the rate at which the general rise in the level of prices, goods and services in an economy occurs and how it affects the cost of living of those living in a particular country. It influences the interest rates paid on savings and mortgage rates but also has a bearing on levels of state pensions and benefits received. A 4 percent increase in the rate of inflation in 2011 for example would mean an individual would need to spend 4 percent more on the goods he was purchasing than he would have done in 2010. India’s inflation rate has been on the rise over the last decade. However, it has been decreasing slightly since 2010. India’s economy, however, has been doing quite well, with its GDP increasing steadily for years, and its national debt decreasing. The budget balance in relation to GDP is not looking too good, with the state deficit amounting to more than 9 percent of GDP.

In 2024, the average annual inflation rate in China ranged at around 0.2 percent compared to the previous year. For 2025, projections by the IMF expect slightly negative inflation. The monthly inflation rate in China dropped to negative values in the first quarter of 2025. Calculation of inflation The inflation rate is calculated based on the Consumer Price Index (CPI) for China. The CPI is computed using a product basket that contains a predefined range of products and services on which the average consumer spends money throughout the year. Included are expenses for groceries, clothes, rent, power, telecommunications, recreational activities, and raw materials (e.g. gas, oil), as well as federal fees and taxes. The product basked is adjusted every five years to reflect changes in consumer preference and has been updated in 2020 for the last time. The inflation rate is then calculated using changes in the CPI. As the inflation of a country is seen as a key economic indicator, it is frequently used for international comparison. China's inflation in comparison Among the main industrialized and emerging economies worldwide, China displayed comparatively low inflation in 2023 and 2024. In previous years, China's inflation ranged marginally above the inflation rates of established industrialized powerhouses such as the United States or the European Union. However, this changed in 2021, as inflation rates in developed countries rose quickly, while prices in China only increased moderately. According to IMF estimates for 2024, Zimbabwe was expected to be the country with the highest inflation rate, with a consumer price increase of about 561 percent compared to 2023. In 2023, Turkmenistan had the lowest price increase worldwide with prices actually decreasing by about 1.7 percent.

Companies operating in the third-party real estate industry have had to navigate numerous economic headwinds in recent years, notably rising interest rates, spiralling inflation and muted economic growth. Revenue is projected to sink at a compound annual rate of 0.6% over the five years through 2025, including an estimated jump of 1.2% in 2025 to €207.6 billion, while the average industry profit margin is forecast to reach 35.1%. Amid spiralling inflation, central banks across Europe ratcheted up interest rates, resulting in borrowing costs skyrocketing over the two years through 2023. In residential markets, elevated mortgage rates combined with tightening credit conditions eventually ate into demand, inciting a drop in house prices. Rental markets performed well when house prices were elevated (2021-2023), being the cheaper alternative for cash-strapped buyers. However, even lessors felt the pinch of rising mortgage rates, forcing them to hoist rent prices to cover costs and pricing out potential buyers. This led to a slowdown in rental markets in 2023, weighing on revenue growth. However, this has started to turn around in 2025 as interest rates have been falling across Europe in the two years through 2025, reducing borrowing costs for buyers and boosting property transactions. This has helped revenue to rebound slightly in 2025 as estate agents earn commission from property transactions. Revenue is forecast to swell at a compound annual rate of 3.7% over the five years through 2030 to €249.5 billion. Housing prices are recovering in 2025 as fixed-rate mortgages begin to drop and economic uncertainty subsides, aiding revenue growth in the short term. Over the coming years, PropTech—technology-driven innovations designed to improve and streamline the real estate industry—will force estate agents to adapt, shaking up the traditional real estate sector. A notable application of PropTech is the use of AI and data analytics to predict a home’s future value and speed up the process of retrofitting properties to become more sustainable.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

Companies operating in the third-party real estate industry have had to navigate numerous economic headwinds in recent years, notably rising interest rates, spiralling inflation and muted economic growth. Revenue is projected to sink at a compound annual rate of 0.6% over the five years through 2025, including an estimated jump of 1.2% in 2025 to €207.6 billion, while the average industry profit margin is forecast to reach 35.1%. Amid spiralling inflation, central banks across Europe ratcheted up interest rates, resulting in borrowing costs skyrocketing over the two years through 2023. In residential markets, elevated mortgage rates combined with tightening credit conditions eventually ate into demand, inciting a drop in house prices. Rental markets performed well when house prices were elevated (2021-2023), being the cheaper alternative for cash-strapped buyers. However, even lessors felt the pinch of rising mortgage rates, forcing them to hoist rent prices to cover costs and pricing out potential buyers. This led to a slowdown in rental markets in 2023, weighing on revenue growth. However, this has started to turn around in 2025 as interest rates have been falling across Europe in the two years through 2025, reducing borrowing costs for buyers and boosting property transactions. This has helped revenue to rebound slightly in 2025 as estate agents earn commission from property transactions. Revenue is forecast to swell at a compound annual rate of 3.7% over the five years through 2030 to €249.5 billion. Housing prices are recovering in 2025 as fixed-rate mortgages begin to drop and economic uncertainty subsides, aiding revenue growth in the short term. Over the coming years, PropTech—technology-driven innovations designed to improve and streamline the real estate industry—will force estate agents to adapt, shaking up the traditional real estate sector. A notable application of PropTech is the use of AI and data analytics to predict a home’s future value and speed up the process of retrofitting properties to become more sustainable.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2016 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2016 American Community Survey 1-Year Estimates

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2014 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2014 American Community Survey 1-Year Estimates

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.

The statistic shows the average inflation rate in Canada from 1987 to 2024, with projections up until 2030. The inflation rate is calculated using the price increase of a defined product basket. This product basket contains products and services, on which the average consumer spends money throughout the year. They include expenses for groceries, clothes, rent, power, telecommunications, recreational activities and raw materials (e.g. gas, oil), as well as federal fees and taxes. In 2022, the average inflation rate in Canada was approximately 6.8 percent compared to the previous year. For comparison, inflation in India amounted to 5.56 percent that same year. Inflation in Canada In general, the inflation rate in Canada follows a global trend of decreasing inflation rates since 2011, with the lowest slump expected to occur during 2015, but forecasts show an increase over the following few years. Additionally, Canada's inflation rate is in quite good shape compared to the rest of the world. While oil and gas prices have dropped in Canada much like they have around the world, food and housing prices in Canada have been increasing. This has helped to offset some of the impact of dropping oil and gas prices and the effect this has had on Canada´s inflation rate. The annual consumer price index of food and non-alcoholic beverages in Canada has been steadily increasing over the last decade. The same is true for housing and other price indexes for the country. In general there is some confidence that the inflation rate will not stay this low for long, it is expected to return to a comfortable 2 percent by 2017 if estimates are correct.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Companies operating in the third-party real estate industry have had to navigate numerous economic headwinds in recent years, notably rising interest rates, spiralling inflation and muted economic growth. Revenue is projected to sink at a compound annual rate of 0.6% over the five years through 2025, including an estimated jump of 1.2% in 2025 to €207.6 billion, while the average industry profit margin is forecast to reach 35.1%. Amid spiralling inflation, central banks across Europe ratcheted up interest rates, resulting in borrowing costs skyrocketing over the two years through 2023. In residential markets, elevated mortgage rates combined with tightening credit conditions eventually ate into demand, inciting a drop in house prices. Rental markets performed well when house prices were elevated (2021-2023), being the cheaper alternative for cash-strapped buyers. However, even lessors felt the pinch of rising mortgage rates, forcing them to hoist rent prices to cover costs and pricing out potential buyers. This led to a slowdown in rental markets in 2023, weighing on revenue growth. However, this has started to turn around in 2025 as interest rates have been falling across Europe in the two years through 2025, reducing borrowing costs for buyers and boosting property transactions. This has helped revenue to rebound slightly in 2025 as estate agents earn commission from property transactions. Revenue is forecast to swell at a compound annual rate of 3.7% over the five years through 2030 to €249.5 billion. Housing prices are recovering in 2025 as fixed-rate mortgages begin to drop and economic uncertainty subsides, aiding revenue growth in the short term. Over the coming years, PropTech—technology-driven innovations designed to improve and streamline the real estate industry—will force estate agents to adapt, shaking up the traditional real estate sector. A notable application of PropTech is the use of AI and data analytics to predict a home’s future value and speed up the process of retrofitting properties to become more sustainable.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.