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).

Analysis of ‘Median of the housing cost burden distribution by degree of urbanisation - EU-SILC survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://data.europa.eu/data/datasets/aguyyc15qakxd540moipq on 29 August 2021.

--- Dataset description provided by original source is as follows ---

This indicator is defined as the median of the distribution of the share of total housing costs (net of housing allowances) in the total disposable household income (net of housing allowances) presented by degree of urbanisation.

--- Original source retains full ownership of the source dataset ---

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

Analysis of ‘Rent Burden Greater than 30%’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b00a909f-0194-420c-8595-65f4b2e539b2 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Displacement risk indicator showing how many households within the specified groups are facing housing cost burden (contributing more than 30% of monthly income toward housing costs) .

--- Original source retains full ownership of the source dataset ---

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are Housing Disadvantaged according to Justice40 Initiative criteria. "Communities are identified as disadvantaged if they are in census tracts that:Experienced historic underinvestment OR are at or above the 90th percentile for the housing cost OR lack of green space OR lack of indoor plumbing OR lead paintAND are at or above the 65th percentile for low income"Census tracts in the U.S. and its territories that meet the criteria are shaded in blue colors. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

This statistic shows the percentage of cancer survivors and persons without a history of cancer who had a high health care out-of-pocket burden in the U.S. from 2011 to 2016, by ethnicity. During this time period around 2.2 percent of white cancer survivors stated they had a high health care out-of-pocket burden, compared to 1.2 percent of white persons without a history of cancer.

This statistic shows the percentage of cancer survivors and persons without a history of cancer who had a high health care out-of-pocket burden in the U.S. from 2011 to 2016, by gender. During this time period around 2 percent of male cancer survivors stated they had a high health care out-of-pocket burden, compared to .9 percent of men without a history of cancer.

Financial burdens of the parental home through education of children.

Topics: Start of studies; length of studies; amount of money available to the student monthly; current income and burden conditions of parents; opportunities to finance studies; stay of student in semester breaks; attitude of student to work in semester breaks; readiness of parents to finance studies; degree of familiarity of the Honnef Model; detailed information on income and contributions of the student as well as the remaining children; housing situation and rent costs of respondent.

Demography: income; household income; size of household; social origins; city size; state; refugee status; possession of durable economic goods; possession of assets.

This collection automatically captures metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL OFFICE OF THE REPUBLIC OF SLOVENIA and corresponds to the source collection entitled "Share of households (%) by burden of housing costs for household, statistical regions, Slovenia, annually".

The actual data is available in PC-Axis format (.px). Among the additional links, you can access the pages of the source portal for insight and selection of data, and there is also the PX-Win program, which can be downloaded for free. Both allow you to select data for display, change the format of the printout and save it in different formats, as well as view and print tables of unlimited size and some basic statistical analyses and graphical representations.

This statistic shows the percentage of cancer survivors and persons without a history of cancer who had a high health care out-of-pocket burden in the U.S. from 2011 to 2016, by age. During this time period around 2 percent of cancer survivors aged 18 to 39 years stated they had a high health care out-of-pocket burden, compared to 0.8 percent of those in the same age group who did not have a history of cancer.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are Workforce Disadvantaged according to Justice40 Initiative criteria. "Communities are identified as disadvantaged if they are in census tracts that:ARE at or above the 90th percentile for linguistic isolation OR low median income OR poverty OR unemploymentAND fewer than 10% of people ages 25 or older have a high school education (i.e. graduated with a high school diploma)"Census tracts in the U.S. and its territories that meet the criteria are shaded in blue colors. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

BackgroundDiabetes imposes a substantial burden globally in terms of premature mortality, morbidity, and health care costs. Estimates of economic outcomes associated with diabetes are essential inputs to policy analyses aimed at prevention and treatment of diabetes. Our objective was to estimate and compare event rates, hospital utilization, and costs associated with major diabetes-related complications in high-, middle-, and low-income countries.Methods and FindingsIncidence and history of diabetes-related complications, hospital admissions, and length of stay were recorded in 11,140 patients with type 2 diabetes participating in the Action in Diabetes and Vascular Disease (ADVANCE) study (mean age at entry 66 y). The probability of hospital utilization and number of days in hospital for major events associated with coronary disease, cerebrovascular disease, congestive heart failure, peripheral vascular disease, and nephropathy were estimated for three regions (Asia, Eastern Europe, and Established Market Economies) using multiple regression analysis. The resulting estimates of days spent in hospital were multiplied by regional estimates of the costs per hospital bed-day from the World Health Organization to compute annual acute and long-term costs associated with the different types of complications. To assist, comparability, costs are reported in international dollars (Int$), which represent a hypothetical currency that allows for the same quantities of goods or services to be purchased regardless of country, standardized on purchasing power in the United States. A cost calculator accompanying this paper enables the estimation of costs for individual countries and translation of these costs into local currency units. The probability of attending a hospital following an event was highest for heart failure (93%–96% across regions) and lowest for nephropathy (15%–26%). The average numbers of days in hospital given at least one admission were greatest for stroke (17–32 d across region) and heart failure (16–31 d) and lowest for nephropathy (12–23 d). Considering regional differences, probabilities of hospitalization were lowest in Asia and highest in Established Market Economies; on the other hand, lengths of stay were highest in Asia and lowest in Established Market Economies. Overall estimated annual hospital costs for patients with none of the specified events or event histories ranged from Int$76 in Asia to Int$296 in Established Market Economies. All complications included in this analysis led to significant increases in hospital costs; coronary events, cerebrovascular events, and heart failure were the most costly, at more than Int$1,800, Int$3,000, and Int$4,000 in Asia, Eastern Europe, and Established Market Economies, respectively.ConclusionsMajor complications of diabetes significantly increase hospital use and costs across various settings and are likely to impose a high economic burden on health care systems. Please see later in the article for the Editors' Summary

European Union - Housing cost overburden rate: Cities was 10.60% in December of 2023, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for European Union - Housing cost overburden rate: Cities - last updated from the EUROSTAT on March of 2025. Historically, European Union - Housing cost overburden rate: Cities reached a record high of 13.40% in December of 2016 and a record low of 9.90% in December of 2020.

Background information of the study participant, (n = 302).

EBIC provides academics, researchers, decision-makers, and policy-makers with objective and comparable information on the magnitude of the cost of illness and injury in Canada. EBIC provides a comprehensive overview of the distribution of direct and indirect costs of illness and injury in Canada.

BackgroundNeedlestick injury (NSI) is one of the most burdensome professional hazards in any medical setting; it can lead to transmission of fatal infectious diseases, such as hepatitis B, hepatitis C and human immunodeficiency virus. In the United States, the annual cost burden was estimated as somewhere between $118 million to $591 million; in the United Kingdom it is approximated to be £500,000 (US$919,117.65) per the National Health Service.MethodThis is the first published paper on the national cost burden of NSIs in Japan. A systematic literature review was conducted to review previous study design in global studies and to extract parameter values from Japanese studies. We conducted abstract searches through PubMed and the Japan Medical Abstracts Society (Ichushi), together with grey literature and snowball searches. A simple economic model was developed to calculate cost burden of NSIs from a societal perspective over a one-year time horizon. We assumed all NSIs are reported and perfect adherence in post NSI management that presented in the labour compensation scheme. Local guidelines were also referenced to extract resource utilization. Lastly, a deterministic sensitivity analysis was conducted and a scenario analysis which considered a payer perspective was also included.Result and conclusionThe national cost burden of in-hospital NSIs is estimated as ¥33.4 billion (US$302 million) annually, based on an average cost per NSI of ¥63,711 (US$577) and number of NSIs at 525,000/year. 70% of the cost is due to initial laboratory tests, followed by productivity loss, estimated at 20% of the total cost. Cost of contaminated NSIs remains at 5% of the total cost. Change in number of NSIs significantly influences outcomes. Variation in post-exposure management practices suggests a need for NSI specific National guidelines and holistic labour compensation scheme development in Japan.

Medicaid and the Children's Health Insurance Program (CHIP) provide health insurance coverage to approximately 85 million Americans as of late 2023. There is substantial variation in eligibility criteria, application procedures, premiums, and other programmatic characteristics across states and over time. Analyzing changes in Medicaid policies is important for state and federal agencies and other stakeholders, but such analysis requires data on historical programmatic characteristics that are often not available in a form ready for quantitative analysis. Our objective is to fill this gap by synthesizing existing qualitative policy data to create a new data resource that facilitates Medicaid policy research. Our source data were the 50-state surveys of Medicaid and CHIP eligibility, enrollment, and cost-sharing policies conducted near annually by KFF since 2000, which we originally coded through 2020. These reports are a rich source of point-in-time information but not operationalized for quantitative analysis. Through a review of the measures captured in the KFF surveys, we developed five Medicaid policy domains with 122 measures in total, with each coded by state-quarter—1) eligibility (28 measures), 2) enrollment and renewal processes (39), 3) premiums (16), 4) cost-sharing (26), and 5) managed care (13). 1 (June 28, 2023) – original version 2 (March 14, 2024) – re-reviewed, corrected (where necessary), and extended five income eligibility measures (inc_inf, inc_child_1_5, inc_child_6_18, inc_parents, and inc_preg) through January 2023

Duration of illness, number of caregivers, lost workdays, loss of income and indirect costs per VL episode in US$ (1US$ = 28.91 ET Birr in 2019).

Supply chain disruptions are an economic hardship, costing organizations around the world an average of 184 million U.S. dollars per year according to a 2021 survey. On a regional distribution, the financial burden is highest in the United States, where the estimated average annual cost of respondents' organizations amounted to 228 million U.S. dollars.

Inputs for cost-effectiveness analysis.