This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

In 2023, 15 percent of households had less than one room for every three people in the household. By contrast, 85 percent had at least one room for every three people.

This Indicator measures the likelihood of individuals living in overcrowded housing, which is defined as housing units that have more than 1.5 people per room. Persons-per-room is the most common measure for overcrowding in housing, and 1.5 is a widely accepted threshold above which there are impacts on health and personal safety. (Source: https://www.huduser.gov/publications/pdf/measuring_overcrowding_in_hsg.pdf)

Global Total Housing Overcrowding by Country, 2023 Discover more data with ReportLinker!

Forecast: Total Housing Overcrowding in the US 2022 - 2026 Discover more data with ReportLinker!

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

Unequal impact of COVID-19: BAME disproportionality This slide pack covers lived experience of Black, Asian and other or mixed ethnic (BAME) communities regarding the issues they have been facing around overcrowding as a result of covid-19 from Early Help services and VCS.

Forecast: Total Housing Overcrowding in Switzerland 2024 - 2028 Discover more data with ReportLinker!

California Household Crowding

2006-2010 Risk Ratios and Percentages

By Health [source]

About this dataset

This table provides an overview of the prevalence of household overcrowding and severe overcrowding in California from 2006-2010. Data on relative Standard Error (RSE), California decimal, and California Risk Ratio (RR) are also included. Residential crowding has serious health consequences, including increased risk of infection from communicable diseases, higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. This dataset can be used to identify demographics that may be disproportionately affected by crowded housing situation such as older immigrant communities, households with low income, renter-occupied dwellings and those that engage in doubling up. Furthermore, this data can help policy makers allocate resources to improve living conditions for affected individuals. An understanding of these household characteristics is essential for creating more equitable living conditions throughout California

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How to use the dataset

This dataset provides detailed data on the populations experiencing overcrowding and severe overcrowding in California, its regions, counties, and cities/towns. It is essential to understand household crowding in order to better target governmental efforts towards the most affected communities. To use this dataset, you'll need to first become familiar with some of the key fields included and what they mean:

• ind_definition: This field provides a definition of the indicator which indicates whether we are looking at data for households experiencing overcrowding or severe overcrowding.
• reportyear: This field contains information about what year the report was published for.
• race_eth_code: This field contains a numerical code which describes race/ethnicity information for each area included in the dataset.
• race_eth_name: This field provides additional descriptive information about each area's racial/ethnic makeup based off of their race/ethnicity code in this database.
• income_level: This field displays income level measurements as specified by HUD categories such as Very Low Income (VLI) and Extremely Low Income (ELI).

• tenure: Tenure is broken down into rental households vs owner occupied households - this is an important factor when considering household crowding as renters are more likely to experience it than people who own their home outright due to cost criteria so they may be more likely living with other people or living close quarters just to save money on rent payments upfront or security deposits. - crowding cat: Describes whether we are measuring overall household crowding or severe overcrowded houses according to HUD definitions (see above). - geotype & geotypevalue : These two fields contain specific geographic data for each area that can be used for mapping analysis etc.. The geotype contains information about what type of geography we're looking at i.e., county/city etc., while geotypevalue contains ID values associated with those types allowing further analysis based off these IDs if necessary! - countyfips & regionname provide useful labels when attempting geographical analysis; regionname will describe high level geography such as state boundaries etc., while countyfips allow us more precise locations within states thus enabling precision query analysis into localized areas using tools such as ArcGIS' statistical functions etc..

    The totalhshlds column shows us exactly how many homes are present across California regions counties or cities whereas crowdedhshlds tells us
  

Research Ideas

• Analyzing and mapping regional variations in overcrowding and how it is related to regional economic conditions.
• Identifying which race/ethnicities are most likely to experience overcrowding, and why this might be the case.
• Examining how overcrowding affects housing affordability in California, and adapting public policy to address the issue where needed

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even comm...

Hungary - Overcrowding rate: Owner, no outstanding mortgage or housing loan was 13.60% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Hungary - Overcrowding rate: Owner, no outstanding mortgage or housing loan - last updated from the EUROSTAT on November of 2025. Historically, Hungary - Overcrowding rate: Owner, no outstanding mortgage or housing loan reached a record high of 43.10% in December of 2010 and a record low of 13.60% in December of 2024.

The housing problem with the highest share of people affected in Mexico was overcrowding. Around *** percent of the population in Mexico lived in overcrowded homes and were therefore considered socially vulnerable, while nearly *** percent lived in houses with dirt floors. Most of the Mexican people identified as living in inadequate housing conditions resided in the states of Mexico, Veracruz, Chiapas, Guerrero and Oaxaca.

Forecast: Total Housing Overcrowding in Italy 2024 - 2028 Discover more data with ReportLinker!

This layer identifies the areas in the city by census tract with households where the number of people living in a housing unit is considered overcrowded, typically more than 1 1/2 persons per room.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Household characteristics by occupancy rating (bedrooms), for households with usual residents, England and Wales, Census 2021. Data are available at a national, country, region, local authority district level.

Latvia - Overcrowding rate: Owner, no outstanding mortgage or housing loan was 36.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Latvia - Overcrowding rate: Owner, no outstanding mortgage or housing loan - last updated from the EUROSTAT on November of 2025. Historically, Latvia - Overcrowding rate: Owner, no outstanding mortgage or housing loan reached a record high of 52.50% in December of 2010 and a record low of 34.70% in December of 2012.

Sweden - Overcrowding rate: Owner, no outstanding mortgage or housing loan was 5.60% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Sweden - Overcrowding rate: Owner, no outstanding mortgage or housing loan - last updated from the EUROSTAT on November of 2025. Historically, Sweden - Overcrowding rate: Owner, no outstanding mortgage or housing loan reached a record high of 6.30% in December of 2011 and a record low of 3.20% in December of 2015.

The layer was compiled from the U.S. Census Bureau’s 2018 Planning Database (PDB), a database that assembles a range of housing, demographic, socioeconomic, and census operational data. The data is from the 2012 – 2016 American Community Survey 5-Year Estimates. The purpose of the data is for 2020 Census planning purposes.

Source: 2018 PDB, U.S. Census Bureau

Effective Date: June 2018

Last Update: January 2020

Update Cycle: Generally, annually as needed. 2018 PDB is vintage used for 2020 Census planning purposes by Nation and County.

Forecast: Total Housing Overcrowding in France 2024 - 2028 Discover more data with ReportLinker!