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.

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.

This statistic shows the rent per square foot to income ratio in selected markets in the United States in 2017. In 2017, Chicago was the most affordable rental market in the U.S. as residents spent, on average, 19.5 percent of their income on rent.

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."

Price to Rent Ratio in the United States increased to 134.20 in the fourth quarter of 2024 from 133.60 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 Other Financial Information: Estimated Monthly Rental Value of Owned Home by Deciles of Income Before Taxes: Highest 10 Percent (91st to 100th Percentile) (CXU910050LB1511M) from 2014 to 2023 about owned, information, percentile, rent, tax, financial, income, housing, estimate, and USA.

Egypt Average Household Income: Percentage Distribution: Urban: Estimated Rental Value of Dwelling data was reported at 10.200 % in 2015. This records a decrease from the previous number of 10.600 % for 2013. Egypt Average Household Income: Percentage Distribution: Urban: Estimated Rental Value of Dwelling data is updated yearly, averaging 10.200 % from Jun 2005 (Median) to 2015, with 5 observations. The data reached an all-time high of 10.800 % in 2011 and a record low of 6.600 % in 2005. Egypt Average Household Income: Percentage Distribution: Urban: Estimated Rental Value of Dwelling data remains active status in CEIC and is reported by Central Agency for Public Mobilization and Statistics. The data is categorized under Global Database’s Egypt – Table EG.H012: Average Household Income.

This dataset contains information about the percent of income households spend on rent in cities in San Mateo County. This data is for renters only, not those who live in owner-occupied homes with or without a mortgage. This data was extracted from the United States Census Bureau's American Community Survey 2014 5 year estimates.

Graph and download economic data for Expenditures: Rented Dwellings by Quintiles of Income Before Taxes: Second 20 Percent (21st to 40th Percentile) (CXURNTDWELLLB0103M) from 1984 to 2023 about percentile, rent, tax, expenditures, income, housing, and USA.

This dataset contains information about the percent of income households spend on rent in cities in San Mateo County. This data is for renters only, not those who live in owner-occupied homes with or without a mortgage. This data was extracted from the United States Census Bureau's American Community Survey 2014 5 year estimates.

Household Income By Gross Rent As A Percentage Of Household Income Report based on US Census and American Community Survey Data.

The statistic shows the median gross rent* as a share of pre-tax household income of Millennials aged 18 to 34 in the United States from 1980 to 2009. In 2009, 18-to 24-year olds spent 32 percent of their household income on rent.

Graph and download economic data for Income Before Taxes: Interest, Dividends, Rent Income, Property Income by Deciles of Income Before Taxes: Ninth 10 Percent (81st to 90th Percentile) (CXUINDIVRNTLB1510M) from 2014 to 2023 about dividends, percentile, rent, tax, interest, income, and USA.

The main objective of the Survey on Rented Households is as follows: To know the characteristics and situation of households residing in rental housing in the Basque Country and the characteristics of rental housing by type of rental. It is a basic source of information for the system of indicators for monitoring housing policy.More information can be found at https://www.euskadi.eus/survey-to-household-in-regimen-enalq-informacion/web01-s2ing/en/">departmental statistical portal.

US Census American Community Survey data table for: Housing subject area. Provides information about: MEDIAN GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME IN THE PAST 12 MONTHS (DOLLARS) for the universe of: Renter-occupied housing units paying cash rent. These data are extrapolated estimates only, based on sampling; they are not actual complete counts. The data is based on 2010 Census Tracts. Table ACS_B25071_MEDIANGROSSRENTPERCENT contains both the Estimate value in the E item for the census topic and an adjacent M item which defines the Margin of Error for the value. The Margin of Error (MOE) is the plus/minus range for the item estimate value, where the range between the Estimate minus the Margin of Error and the Estimate plus the Margin of Error defines the 90% confidence interval of the item value. Many of the Margin of Error values are significant relative to the size of the Estimate value. This table contains 1 item(s) extracted from a larger sequence table. This extracted subset represents that portion of the sequence that is considered high priority. Other portions of this sequence that are not included can be identified in the data dictionary information provided in the Supplemental Information section below. This table information is also provided as a customized layer file: B25071_AREA_MEDIANGROSSRENTPERCENT.lyr where the table information is joined to the 2010 TRACTS_AREA census geography on the GEOID item. Both the table and customized lyr file name do not contain the year descriptor (i.e. 2012-2016) for the current ACS series. This is intentional in order to maintain the same table name in each successive ACS update. The alias of each item's (E)stimate and (M)easure of Error value stores this year date information as beginning YY and ending YY, i.e., 'E1216' and 'M1216' followed by the rest of the alias description. In this way users of the data tables or lyr files that support field aliases can determine which ACS series is being represented by the current table contents.

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

In accordance with 24 CFR Part 92.252, HUD provides maximum HOME rent limits. The maximum HOME rents are the lesser of: The fair market rent for existing housing for comparable units in the area as established by HUD under 24 CFR 888.111 or A rent that does not exceed 30 percent of the adjusted income of a family whose annual income equals 65 percent of the median income for the area, as determined by HUD, with adjustments for number of bedrooms in the unit. The HOME rent limits provided by HUD will include average occupancy per unit and adjusted income assumptions.

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.

HAP20 - Rent as Percentage of Disposable Income for HAP Tenants. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Rent as Percentage of Disposable Income for HAP Tenants...

Turkey, Russia, Portugal, and Latvia were the countries with the highest house price-to-rent-ratio in the ranking in the second quarter of 2024. In all three countries, the ratio exceeded *** index points, meaning that house price growth had outpaced rents by over ** percent between 2015 and 2024. 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.