The California Association of Realtors (C.A.R) Traditional Housing Affordability Index (HAI) measures the percentage of households that can afford to purchase the median priced home in the state and regions of California based on traditional assumptions. C.A.R. also reports its traditional and first-time buyer indexes for regions and select counties within the state. The HAI is the most fundamental measure of housing well-being for buyers in the state.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

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...

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.