Renters in the UK spent on average 32.5 percent of their income on rent as of January 2025. Scotland and Yorkshire and Humber were the most affordable regions, with households spending less than 28 percent of their gross income on rent. Conversely, London, South West, and South East had a higher ratio. Greater London is the most expensive region for renters Greater London has a considerably higher rent than the rest of the UK regions. In 2024, the average rental cost in Greater London was more than twice higher than in the North West or West Midlands. Compared with Greater London, rent in the South East region was about 600 British pounds cheaper. London property prices continue to increase In recent years, house prices in the UK have been steadily increasing, and the period after the COVID-19 pandemic has been no exception. Prime residential property prices in Central London are forecast to continue rising until 2027. A similar trend in prime property prices is also expected in Outer London.

Since 2015, the gap between the cost of buying a home and renting has grown, with homeownership becoming increasingly less affordable. In the ***** ******* of 2024, the house price to rent ratio in the UK stood at *****. That meant that house price growth has outpaced rental growth by nearly ** percent between 2015 and 2024. The UK's house price to rent ratio was slightly below the average Euro area ratio. House price to income ratio in the UK Another indicator for housing affordability is the house price to income ratio, which is calculated by dividing nominal house prices by the nominal disposable income per head. The ratio saw an overall increase between 2015, which was the base year, and 2022. After that, the index declined, but remained close to the average for the Euro area. Is it more affordable to rent or buy? There are many things to be considered when comparing buying to renting, such as the ability to qualify for a mortgage and whether prospective homebuyers have sufficient savings for a deposit. Generally, purchasing a home is more affordable than renting one. However, the average monthly savings first-time buyers can achieve have been on the decline. In East of England, where house prices have increased rapidly over the past few years, it was cheaper to rent than to buy in 2022.

Graph and download economic data for Rental Income of Persons with Capital Consumption Adjustment (CCAdj) (RENTIN) from Q1 1947 to Q2 2025 about CCADJ, rent, personal income, personal, income, GDP, and USA.

Price to Rent Ratio in the United States increased to 134.04 in the fourth quarter of 2024 from 133.46 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

Graph and download economic data for Rental income of persons with capital consumption adjustment (A048RC1) from Jan 1959 to Aug 2025 about CCADJ, rent, personal income, personal, income, and USA.

Approximately 42.5 percent of residents in renter-occupied housing units in the United States paid gross rent which exceeded 35 percent of their income in 2023. In comparison, about 12.3 percent paid less than 15 percent of their gross household income.

Real Estate Breakeven Analysis for U.S. Home Types

Buy vs Rent Comparison Across Markets

By Zillow Data [source]

About this dataset

This dataset provides a comprehensive analysis of the current real estate situation in the United States. It includes breakeven analysis charts that compare buying vs renting across major U.S. markets. This dataset contains various metrics such as home types, housing stock, price-to-income ratio, cash buyers, mortgage affordability and rental affordability to name a few. This data has been compiled using Zillow's own data along with TransUnion financing survey data and the Freddie Mac Primary Mortgage Market Survey to provide an accurate understanding of each metro area’s market health and purchasing power for buyers and renters alike. By downloading this information you can compare different regions based on size rank and other factors to get full insights regarding their potential fit for your needs or investments strategies as well as any potential risks associated with each region's housing market health

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How to use the dataset

This dataset is for real estate professionals, owner-occupants, potential buyers and renters who are interested in understanding which U.S. markets offer the most favorable home buying or rental opportunities from a financial perspective over the long term.

The “Real Estate Breakeven Analysis for U.S Home Types” dataset contains data pulled from Zillow's current and forecasted housing market metrics across many different real estate regions in the United States including cities, counties, states, metro areas and combined statistical areas (CSAs). The data includes several measures of affordability such as median price-to-rent ratio (MedPR), median breakeven horizon (MedBE) - which refers to how long it takes to make up purchase costs when compared with renting; cash purchaser share; mortgage rate; mortgage affordability indices; rental affordability rates etc.

In order to analyze and compare buying vs renting decisions across various regions in the US this dataset provides breakeven analysis at various levels of geographies i.e., state names, region types (city/metro area/county) and show how long it will take homeowners to break even on their purchase costs when compared with renting in that region over a longer period of time using discounted cash flow methodology. This information helps people understand what type of transaction is a better fit for them by weighing short term vs long term goals accordingly by evaluating these different factors related to housing metrics carefully before making financial decisions about purchasing or renting properties in desired location(s).

To use this dataset one can use either basic filters like RegionType or RegionName or more detailed filter criteria like CountyName, City name , Metro area name , State Name etc . For example if someone wanted to look at properties available for rent only then they can apply filters based on Province Type =‘Rental’ Also one can further refine searches based on filtering them with defined SampleRate , Median Price – To – Rent Ratio …..etc . This could be useful if seekers would want only specific type of property like Condominium/Coop /Multifamily 5+ Units /Duplex Triplex listing etc …and then apply other parameters like Cash Buyers percent , Mortgage Affordability Rate….etc ..in order narrow down search results while looking at Breakeven scores /horizons in their target locations . One should take advantages of all relevant parameters while searching through data before making any decision related with owning rental properties so that they can make sure best possible investment decision given

Research Ideas

• Visualizing changes in real estate trends across regions by comparing price to rent ratios, mortgage affordability indices and cash buyers over time.
• Market segmentation analysis based on region-level market characteristics such as negative equity data, rental affordability, median house values and population size.
• Predicting housing demand within a particular region based on its breakeven horizon or price to rent ratio

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: BreakEven_2017-03.csv | Column name | Description | |:----------------|:----------------------------------------------------...

Revenue for apartment lessors has expanded through the end of 2025. Apartment lessors collect rental income from rental properties, where market forces largely determine their rates. The supply of apartment rentals has grown more slowly than demand, which has elevated rental rates for lessors' benefit. As the Federal Reserve hiked interest rates 11 times between March 2022 and January 2024, homeownership was pushed beyond the reach of many, resulting in a tighter supply and increased demand for rental properties. Despite three interest rate cuts in 2024, mortgage rates have remained stubbornly high in 2025, encouraging consumers to rent. Revenue has climbed at a CAGR of 2.6% over the past five years and is expected to reach $295.3 billion by the end of 2025. This includes an anticipated 1.4% gain in 2025 alone. The increasing unaffordability of housing is caused by the steady climb of mortgage rates and high prices maintained by a low supply. Supply has been held down as buyers who locked in low rates stay put, and investment groups hold a strategic number of their properties empty as investments. Industry profit has remained elevated because of solid demand for apartment rentals. Through the end of 2030, the apartment rental industry's future performance will be shaped by varying factors. The apartment supply in the US, which hit a record in 2024, is expected to taper off, which will push rental prices and occupancy rates up to the lessors' benefit. Other factors, such as interest rate cuts, decreasing financial barriers to homeownership and a high rate of urbanization, will also significantly impact the industry. With an estimated 80.7% of the US population living in urban areas, demand for apartment rentals will strengthen, although rising rental prices could force potential renters to cheaper suburbs. Demand will continue to outpace supply growth, prompting a climb in revenue. Revenue is expected to swell at a CAGR of 1.7% over the next five years, reaching an estimated $321.9 billion in 2030.

Graph and download economic data for National income: Rental income of persons (without CCAdj) (B049RC1Q027SBEA) from Q1 1947 to Q2 2025 about national income, CCADJ, rent, personal income, persons, personal, income, GDP, and USA.

Portugal, Hungary, and Mexico were the countries with the highest house price-to-rent-ratio in the ranking in the second quarter of 2025. In all three countries, the ratio exceeded 160 index points, meaning that house price growth had outpaced rents by over 60 percent between 2015 and 2025. What does the house-price-to-rent ratio show? The house-price-to-rent-ratio measures the evolution of house prices compared to rents. It is generally calculated by dividing the median house price by the median annual rent. In this statistic, the values have been normalized with 100 equaling the 2015 ratio. Consequentially, a value under 100 means that rental rates have risen more than house prices. When all OECD countries are considered as a whole, the gap between house prices and rents was wider than in the Euro area. Measures of housing affordability The national house-price-to-rent ratio may not fully reflect the cost of housing in a particular country, as it does not capture the price variations that can exist between different regions. It also does not take into consideration the relationship between incomes and housing costs, which is measured by the house-price-to-income and household-rent-to-income ratios. Taking both these factors into account uncovers vast differences in housing affordability between different regions and different professions.

SELECTED HOUSING CHARACTERISTICS GROSS RENT AS PERCENTAGE OF INCOME - DP04 Universe - Occupied units paying rent Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Gross rent as a percentage of household income is a computed ratio of monthly gross rent to monthly household income (total household income divided by 12). The ratio is computed separately for each unit and is rounded to the nearest tenth. Units for which no rent is paid and units occupied by households that reported no income or a net loss comprise the category “Not computed."

Description: This metric examines how affordable the average First Time Buyer (FTB) priced property would be for a couple earning the average FTB disposable income by NUTS 2 Region, NUTS 3 Region and County, for each year between 2016 and 2021, with this ratio expressed as a percentage (i.e. average monthly mortgage repayment due on the average FTB priced property as a percentage of the average monthly disposable income of an FTB couple). This percentage should be compared relative to the standard affordability mark of 30% (i.e. housing costs should be below 30% of a household’s disposable income). For example, in the attached excel file, the data shows that the Border recorded an Average Mortgage Repayment to Disposable Income Ratio for First Time Buyers of 17.1% in 2021, which was below the standard affordability mark of 30%. This implies that a FTB couple from the Border – on average disposable income levels in the Border and adjusted to reflect incomes of people aged 40 or below in the Border – would only typically have to pay 17.1% of their joint monthly disposable income on their mortgage instalments on the average priced FTB property in the Border. In contrast, the corresponding ratio for Dublin and the Mid-East is 35% and 31.5%, which are both above the standard affordability mark and show that housing for FTBs – on average – is relatively unaffordable in these areas.Basic Calculations = (Average mortgage repayment on average FTB priced property / Average disposable income of a couple under the age of 40).For full detail on the methodology for the development of this ratio please see the RDM FAQ section.This ratio has been developed by the Regional Economist at the three Regional Assemblies and is primarily based on the CSO County Income and Regional GDP as well as the CSO Regional Property Price Index.Geography available in RDM: State, Regional Assembly and Strategic Planning Area (SPA), County (26).Source: Regional AssembliesWeblink: n/aDate of last source data update: April 2023Update Schedule: Annual

Price to Rent Ratio in the United Kingdom decreased to 111.37 in the second quarter of 2025 from 113.72 in the first quarter of 2025. This dataset includes a chart with historical data for the United Kingdom Price to Rent Ratio.

Price to Rent Ratio in Iceland decreased to 147.64 in the third quarter of 2025 from 151.02 in the second quarter of 2025. This dataset includes a chart with historical data for Iceland Price to Rent Ratio.

Price to Rent Ratio in Canada decreased to 125.50 in the third quarter of 2025 from 128.87 in the second quarter of 2025. This dataset includes a chart with historical data for Canada Price to Rent Ratio.

Price to Rent Ratio in South Korea increased to 102.42 in the first quarter of 2025 from 102.15 in the fourth quarter of 2024. This dataset includes a chart with historical data for South Korea Price to Rent Ratio.

Context

This dataset is created via OECD datasource which is consisted of 2000 between 2020. https://data.oecd.org/price/housing-prices.htm

Content

The housing prices indicator shows indices of residential property prices over time. Included are rent prices, real and nominal house prices, and ratios of price to rent and price to income; the main elements of housing costs. In most cases, the nominal house price covers the sale of newly-built and existing dwellings, following the recommendations from RPPI (Residential Property Prices Indices) manual. The real house price is given by the ratio of nominal price to the consumers’ expenditure deflator in each country, both seasonally adjusted, from the OECD national accounts database. The price to income ratio is the nominal house price divided by the nominal disposable income per head and can be considered as a measure of affordability. The price to rent ratio is the nominal house price divided by the rent price and can be considered as a measure of the profitability of house ownership. This indicator is an index with base year 2015.

Price to Rent Ratio in Italy decreased to 102.89 in the second quarter of 2025 from 102.91 in the first quarter of 2025. This dataset includes a chart with historical data for Italy Price to Rent Ratio.

Price to Rent Ratio in Germany decreased to 129.81 in the second quarter of 2025 from 130.53 in the first quarter of 2025. This dataset includes a chart with historical data for Germany Price to Rent Ratio.

This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census: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 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.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.