The Housing Affordability Index value in the United States plummeted in 2022, surpassing the historical record of ***** index points in 2006. In 2024, the housing affordability index measured **** index points, making it the second-worst year for homebuyers since the start of the observation period. What does the Housing Affordability Index mean? The Housing Affordability Index uses data provided by the National Association of Realtors (NAR). It measures whether a family earning the national median income can afford the monthly mortgage payments on a median-priced existing single-family home. An index value of 100 means that a family has exactly enough income to qualify for a mortgage on a home. The higher the index value, the more affordable a house is to a family. Key factors that drive the real estate market Income, house prices, and mortgage rates are some of the most important factors influencing homebuyer sentiment. When incomes increase, consumer power also increases. The median household income in the United States declined in 2022, affecting affordability. Additionally, mortgage interest rates have soared, adding to the financial burden of homebuyers. The sales price of existing single-family homes in the U.S. has increased year-on-year since 2011 and reached ******* U.S. dollars in 2023.

Graph and download economic data for Housing Affordability Index (Fixed) (FIXHAI) from Sep 2024 to Sep 2025 about fixed, housing, indexes, and USA.

The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. These dataset give the community of housing analysts the opportunity to use a consistent set of affordability measures. The most recent year HADS is available as a Public Use File (PUF) is 2013. For 2015 and beyond, HADS is only available as an IUF and can no longer be released on a PUF. Those seeking access to more recent data should reach to the listed point of contact.

The percentage of households that pay more than 30% of their total household income on mortgage and other housing-related expenses.Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

United States Housing Affordability Index: Fixed data was reported at 146.900 NA in Oct 2018. This records a decrease from the previous number of 147.400 NA for Sep 2018. United States Housing Affordability Index: Fixed data is updated monthly, averaging 127.900 NA from Jan 1989 (Median) to Oct 2018, with 357 observations. The data reached an all-time high of 212.800 NA in Jan 2013 and a record low of 97.600 NA in May 1989. United States Housing Affordability Index: Fixed data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

United States Housing Affordability Index: Mortgage Rate data was reported at 4.770 % in Sep 2018. This records a decrease from the previous number of 4.780 % for Aug 2018. United States Housing Affordability Index: Mortgage Rate data is updated monthly, averaging 6.470 % from Jan 1989 (Median) to Sep 2018, with 357 observations. The data reached an all-time high of 10.590 % in Jun 1989 and a record low of 3.430 % in Dec 2012. United States Housing Affordability Index: Mortgage Rate data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

As of the first quarter of 2024, Seoul's housing affordability index was the highest among all cities and provinces in South Korea at ***. It was followed by Sejong and Gyeonggi at around 100 and **, respectively.

View monthly updates and historical trends for US Fixed Housing Affordability Index. from United States. Source: National Association of Realtors. Track e…

As of the third quarter of 2024, the housing affordability index in South Korea stood at around ** points. The third quarter of 2022 saw the first drop in figures since 2020. The lower the index value, the more affordable a home is for a median-income household. Apartments in South Korea Average apartment prices in South Korea had risen for almost a decade before 2022. The country's popularity worldwide helped it become an economic powerhouse, attracting young workers from the countryside to large cities in hopes of taking part in or benefiting from this growth. As such, apartments are an attractive option for cities, optimizing space as they become more crowded. In terms of financing a new home, the leasehold deposit system exists as an alternative to traditional monthly rentals in Korea. Jeonse and leasehold deposits Jeonse is a leasehold deposit system in Korea where, instead of paying rent monthly, people pay a large deposit equivalent to a share of a property's value. In exchange, the person receives the right to reside in the property as a tenant for a limited amount of time. Granted, the up-front cost is high, and it is common to receive loans from banks to pay for the leasehold deposit. It had traditionally been a popular method as you could live in a housing unit without worrying about rent for *** or two years. However, this system is slowly being phased out as interest rates, the large up-front cost, and cases of jeonse fraud have turned people away.

Affordability ratios calculated by dividing house prices by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

United States Housing Affordability Index: Median Family Income data was reported at 77,021.000 USD in Oct 2018. This records an increase from the previous number of 76,754.000 USD for Sep 2018. United States Housing Affordability Index: Median Family Income data is updated monthly, averaging 53,251.500 USD from Jan 1989 (Median) to Oct 2018, with 358 observations. The data reached an all-time high of 77,021.000 USD in Oct 2018 and a record low of 33,287.000 USD in Jan 1989. United States Housing Affordability Index: Median Family Income data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

First launched by the U.S. Department of Housing and Urban Development (HUD) and Department of Transportation (DOT) in November 2013, the Location Affordability Index (LAI) provides ubiquitous, standardized household housing and transportation cost estimates for all 50 states and the District of Columbia. Because what is affordable is different for everyone, users can choose among eight household profiles—which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.

Version 3 updates the constituent data sets with 2012-2016 American Community Survey data and makes several methodological tweaks, most notably moving to modeling at the Census tract level rather at the block group. As with Version 2, the inputs to the simultaneous equation model (SEM) include six endogenous variables—housing costs, car ownership, and transit usage for both owners and renters—and 18 exogenous variables, with vehicle miles traveled still modeled separately due to data limitations.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2012-2016 Data Dictionary: DD_Location Affordability Indev v.3.0LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation

This is the unadjusted median house priced for residential property sales (transactions) in the area for a 12 month period with April in the middle (year-ending September). These figures have been produced by the ONS (Office for National Statistics) using the Land Registry (LR) Price Paid data on residential dwelling transactions.

The LR Price Paid data are comprehensive in that they capture changes of ownership for individual residential properties which have sold for full market value and covers both cash sales and those involving a mortgage.

The median is the value determined by putting all the house sales for a given year, area and type in order of price and then selecting the price of the house sale which falls in the middle. The median is less susceptible to distortion by the presence of extreme values than is the mean. It is the most appropriate average to use because it best takes account of the skewed distribution of house prices.

Note that a transaction occurs when a change of freeholder or leaseholder takes place regardless of the amount of money involved and a property can transact more than once in the time period.

The LR records the actual price for which the property changed hands. This will usually be an accurate reflection of the market value for the individual property, but it is not always the case. In order to generate statistics that more accurately reflect market values, the LR has excluded records of houses that were not sold at market value from the dataset. The remaining data are considered a good reflection of market values at the time of the transaction. For full details of exclusions and more information on the methodology used to produce these statistics please see http://www.ons.gov.uk/peoplepopulationandcommunity/housing/qmis/housepricestatisticsforsmallareasqmi

The LR Price Paid data are not adjusted to reflect the mix of houses in a given area. Fluctuations in the types of house that are sold in that area can cause differences between the median transactional value of houses and the overall market value of houses. Therefore these statistics differ to the new UK House Price Index (HPI) which reports mix-adjusted average house prices and house price indices.

If, for a given year, for house type and area there were fewer than 5 sales records in the LR Price Paid data, the house price statistics are not reported. Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Mortgage repayments as a percentage of monthly equivalised disposable household income, throughout the house price and income distribution.

In many metros in the United States, the median household income was insufficient to qualify for the median-priced home. Among the ** largest metros in the U.S., San Jose-Sunnyvale-Santa Clara, CA was the least affordable one in 2022, with the housing affordability index at **** index points. This means that the median household income, when accounting for monthly housing expenses, was less than ** percent of the necessary income to qualify for a mortgage. An index value over 100, on the other hand, shows that the median income is sufficient for a mortgage. Metros, such as Cleveland-Elyria, OH, and St. Louis, MO-IL had a median household income much higher than the income needed to buy the median-priced home.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The Housing Affordability Data System (HADS) is a set of housing unit level datasets that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The purpose of these datasets is to provide housing analysts with consistent measures of affordability and burdens over a long period. The datasets are based on the American Housing Survey (AHS) national files from 1985 through 2005 and the metropolitan files for 2002 and 2004. Users can link records in HADS files to AHS records, allowing access to all of the AHS variables. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multiunit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

A commonly accepted threshold for affordable housing costs at the household level is 30% of a household's income. Accordingly, a household is considered cost burdened if it pays more than 30% of its income on housing. Households paying more than 50% are considered severely cost burdened. These thresholds apply to both homeowners and renters.

The Housing Affordability indicator only measures cost burden among the region's households, and not the supply of affordable housing. The directionality of cost burden trends can be impacted by changes in both income and housing supply. If lower income households are priced out of a county or the region, it would create a downward trend in cost burden, but would not reflect a positive trend for an inclusive housing market.

United States Housing Affordability Index: Monthly Principal and Interest Payment data was reported at 1,092.000 USD in Oct 2018. This records an increase from the previous number of 1,085.000 USD for Sep 2018. United States Housing Affordability Index: Monthly Principal and Interest Payment data is updated monthly, averaging 783.000 USD from Jan 1989 (Median) to Oct 2018, with 358 observations. The data reached an all-time high of 1,207.000 USD in Jul 2006 and a record low of 568.000 USD in Feb 1994. United States Housing Affordability Index: Monthly Principal and Interest Payment data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

The Housing Affordability Index, calculated by the Runstad Center for Real Estate Studies, measures the ability of a middle-income family to carry the mortgage payments on a median-price home. When the index is 100 there is a balance between the family’s ability to pay and the cost. Higher indexes indicate housing is more affordable.

For example, an index of 126 means that a median-income family has 26 percent more income than the bare minimum required to qualify for a mortgage on a median-price home. An index of 80 means that a median-income family has less income than the minimum required.