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.

This statistic shows the best states to make living in the United States in 2019. In 2019, Wyoming was ranked as the best state to make a living in the United States, with the cost of living index at **** value and the median income of ****** U.S. dollars.

This statistic shows the most affordable metro areas in the Unites States in 2017, by share of income spent on living expenses. In 2017, Omaha was the second most affordable metro area because ***** percent of the median blending annual household income was spent on the average cost of owning or renting a home as well the average cost of utilities and taxes.

In 2024, the annual cost for a private room in an assisted living facility in the U.S. amounted to ****** U.S. dollars - the national median price. However, cost varied greatly from one state to another. The least expensive states for a private room in assisted living were South Dakota, and Mississippi. While the most expensive states for assisted living were Hawaii and Alaska.

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

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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.

Quality of Life by State 2024

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This dataset provides insights into the quality of life across different states in the United States for the year 2024. Quality of life, encompassing aspects like comfort, health, and happiness, is evaluated through various metrics including affordability, economy, education, and safety. Dive into this dataset to understand how different states fare in terms of overall quality of life and its individual components.

Columns Description

• State: The name of the U.S. state.
• QualityOfLifeTotalScore: The total score representing the overall quality of life for the respective state. This score is calculated based on various quality of life metrics.
• QualityOfLifeQualityOfLife: The score representing the quality of life aspect for the respective state. This aspect may include subjective factors related to happiness, satisfaction, and overall well-being. Higher scores may indicate a higher level of subjective well-being, happiness, or overall satisfaction among residents. Lower scores could suggest lower levels of subjective well-being.
• QualityOfLifeAffordability: The score representing the affordability aspect of the quality of life for the respective state. This aspect evaluates factors such as cost of living, housing affordability, and income levels. Higher scores typically indicate greater affordability of housing, cost of living, and basic necessities. Lower scores may suggest that these essentials are less accessible or more expensive for residents.
• QualityOfLifeEconomy: The score representing the economic aspect of the quality of life for the respective state. This aspect assesses factors such as employment opportunities, economic growth, and income distribution. Higher scores may reflect a stronger economy with more job opportunities, higher incomes, and lower levels of poverty. Lower scores might indicate economic challenges such as unemployment or income inequality.
• QualityOfLifeEducationAndHealth: The score representing the education and health aspect of the quality of life for the respective state. This aspect considers factors such as access to quality education, healthcare facilities, and overall public health indicators. Higher scores generally signify better access to quality education, healthcare services, and overall public health. Lower scores may indicate deficiencies in these areas, such as limited access to healthcare or lower educational attainment levels.
• QualityOfLifeSafety: The score representing the safety aspect of the quality of life for the respective state. This aspect evaluates factors such as crime rates, public safety measures, and community well-being initiatives. Higher scores suggest lower crime rates, better community safety, and a higher sense of security among residents. Lower scores may indicate higher crime rates or concerns about safety.

These descriptions provide an overview of what each column represents and the specific aspects of quality of life they assess for each U.S. state.

VITAL SIGNS INDICATOR
Poverty (EQ5)

FULL MEASURE NAME
The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED
January 2023

DESCRIPTION
Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE
U.S Census Bureau: Decennial Census - http://www.nhgis.org
1980-2000

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2007-2021
Form C17002

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or non-cash benefits (such as public housing, Medicaid and food stamps).

For the national poverty level definitions by year, see: US Census Bureau Poverty Thresholds - https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.

For an explanation on how the Census Bureau measures poverty, see: How the Census Bureau Measures Poverty - https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html.

American Community Survey (ACS) 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

In 2024, the annual cost for a private room in an assisted living facility in the U.S. amounted to ****** U.S. dollars. However, costs varied greatly from one state to another. The most expensive states for a private room in assisted living was found in Hawaii, followed by Alaska and DC.

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).

Consumer Price Index CPI in the United States increased to 323.05 points in July from 322.56 points in June of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Of the most populous cities in the U.S., San Jose, California had the highest annual income requirement at ******* U.S. dollars annually for homeowners to have an affordable and comfortable life in 2024. This can be compared to Houston, Texas, where homeowners needed an annual income of ****** U.S. dollars in 2024.

VITAL SIGNS INDICATOR
Poverty (EQ5)

FULL MEASURE NAME
The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED
January 2023

DESCRIPTION
Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE
U.S Census Bureau: Decennial Census - http://www.nhgis.org
1980-2000

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2007-2021
Form C17002

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or non-cash benefits (such as public housing, Medicaid and food stamps).

For the national poverty level definitions by year, see: US Census Bureau Poverty Thresholds - https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.

For an explanation on how the Census Bureau measures poverty, see: How the Census Bureau Measures Poverty - https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html.

American Community Survey (ACS) 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

The Washington State University (WSU) Energy Program's Building America (BA) team conducted a case study of a high-performance affordable housing community: The Woods (Figure 1). This BA effort is part of a larger-scale study of 30 homes funded from 2013-2016 by Tacoma Public Utilities (TPU) and the Bonneville Power Administration. The Woods is a Habitat for Humanity (HFH) community of homes certified by ENERGY STAR Homes Northwest (ESHNW); the community is in the marine climate of Tacoma/Pierce County, Washington. This research report builds on an earlier preliminary draft 2014 BA report and includes significant billing analysis and cost-effectiveness research from a collaborative and ongoing DHP research effort for TPU and the Bonneville Power Administration. This final BA report focuses on the results of field testing, modeling, and monitoring of ductless mini-split heat pump hybrid heating systems in seven homes built and first occupied at various times between September 2013 and October 2014. The report also provides WSU documentation of high-performance home observations, lessons learned, and stakeholder recommendations for builders of affordable high-performance housing. The research goal of the U.S. Department of Energy's BA research team Building America Partnership for Improved Residential Construction was to compare a ductless heat pump (DHP) hybrid system (DHP in common area/electric resistance [ER] in bedrooms) to an all-electric zonal ER system in high-performance single-family affordable housing. This effort included assessing the costs and benefits of a DHP/ER hybrid system located in the main living area to offset the primary heating demand of zonal ER heaters in the bedroom zones and comparing these findings to data from of new affordable single-family housing in Washington State. This report includes: (1) Measured indoor and outdoor temperatures and relative humidity (RH) in the homes. (2) Field testing results of heating, ventilating, and air-conditioning equipment; ventilation system airflows; building envelope tightness; lighting, appliance, and other input data required for preliminary Building Energy Optimization (BEopt) modeling; and ENERGY STAR field verification (3) BEopt modeling results compared to measured energy use. (4) A comparison of the space heat energy consumption of a DHP/ER hybrid heating system and a traditional zonal ER heating system installed in the same home. This comparison is made by implementing a series of weekly "flip-flop tests" (referred to here as "switchback" tests per TPU) to compare space heating, temperature, and RH in zonal ER heating mode with a DHP/ER mode as discussed in the Building America Test Plan (Lubliner 2010a). (5) Cost data from HFH and other sources related to building efficiency measures focusing on the DHP/ER hybrid heating system. (6) An evaluation of the thermal performance and cost benefit of DHP/ER hybrid heating systems in these high-performance homes employing life cycle cost analysis for energy code policy and monthly cash flow analysis of HFH homeowners. (7) Post-monitoring occupant survey results. The report also provides the following stakeholder findings and recommendations: (1) DHP single-head systems at The Woods are cost-effective to new homebuyers of these high-performance all-electric homes. (2) Stakeholder education is needed on design, inspection, and commissioning; documentation is needed for heat recovery ventilation (HRV) and from ENERGY STAR builders, verifiers, and inspectors to help ensure that the houses meet the goal of "build tight, ventilate right." (3) A code gap in inspection and enforcement was identified that should be addressed by: (3a) Improving the fire marshal's approach to sprinkler attic piping freeze protection; (3b) Improving the maintenance of ceiling insulation continuity; and (3c) Educating the local building inspector on attic insulation inspection concerns that allow for maximizing design improvements and performance of HRV attic ducting while ensuring ceiling insulation continuity (with respect to the location of HRV) in compliance with the Washington State Energy Code. 1 - The Woods Jameson - Tacoma, WA 2 - The Woods El Jeffe - Tacoma, WA

The United States home construction market is projected to grow from $XX million in 2025 to $XX million by 2033, at a CAGR of 3.00% during the forecast period. Key drivers of this growth include increasing population, rising incomes, and low interest rates. Additionally, the growing popularity of smart homes and green building technologies is creating new opportunities for home builders. The market is segmented by type (apartments & condominiums, villas, and other types), construction type (new construction and renovation), and city (New York City, Los Angeles, San Francisco, Washington DC, and Miami). The new construction segment is expected to hold the largest market share during the forecast period, driven by the increasing demand for new homes from growing families and millennials. The multi-family home builders segment is projected to grow at a higher CAGR than the single-family home builders segment during the forecast period, due to the increasing popularity of urban living and the rising demand for affordable housing. Recent developments include: June 2022 - Pulte Homes - a national brand of PulteGroup, Inc. - announced the opening of its newest Boston-area community, Woodland Hill. Offering 46 new construction single-family homes in the charming town of Grafton, the community is conveniently located near schools, dining, and entertainment, with the Massachusetts Bay Transportation Authority commuter rail less than a mile away. The collection of home designs at Woodland Hill includes three two-story floor plans, ranging in size from 3,013 to 4,019 sq. ft. with four to six bedrooms, 2.5-3.5 baths, and 2-3 car garages. These spacious home designs feature flexible living spaces, plenty of natural light, gas fireplaces, and the signature Pulte Planning Center®, a unique multi-use workstation perfect for homework or a family office., December 2022 - D.R. Horton, Inc. announced the acquisition of Riggins Custom Homes, one of the largest builders in Northwest Arkansas. The homebuilding assets of Riggins Custom Homes and related entities (Riggins) acquired include approximately 3,000 lots, 170 homes in inventory, and 173 homes in the sales order backlog. For the trailing twelve months ended November 30, 2022, Riggins closed 153 homes (USD 48 million in revenue) with an average home size of approximately 1,925 square feet and an average sales price of USD 313,600. D.R. Horton expects to pay approximately USD 107 million in cash for the purchase, and the Company plans to combine the Riggins operations with the current D.R. Horton platform in Northwest Arkansas.. Key drivers for this market are: Indonesia's Hospitality Market Shifting Preference for Local and Authentic Experiences. Potential restraints include: Difficulties in Implementing Tourism Policies. Notable trends are: High-interest Rates are Negatively Impacting the Market.

Manufactured Homes Market Outlook

The global manufactured homes market is projected to grow significantly over the forecast period, with a market size estimated at USD 28.5 billion in 2023 and expected to reach USD 47.1 billion by 2032, registering a compound annual growth rate (CAGR) of 5.6%. The growth factor is driven primarily by the increasing demand for affordable housing solutions, coupled with advancements in manufacturing technologies that make these homes more durable and aesthetically pleasing.

One of the primary growth factors for the manufactured homes market is affordability. Manufactured homes offer a cost-effective alternative to traditional site-built homes. The average cost of a manufactured home is significantly lower due to streamlined production processes and bulk purchasing of materials. This affordability makes them an attractive option for first-time homebuyers, retirees, and low-income families who may find it challenging to purchase traditional homes. Additionally, the cost of land and property taxes are often lower for manufactured homes, further enhancing their appeal.

Innovations in construction technologies and materials have also been pivotal in driving the market. Modern manufactured homes are built using high-quality materials and advanced construction techniques, making them more energy-efficient and resilient. Improvements in insulation, roofing, and HVAC systems have made these homes more sustainable and comfortable. Moreover, smart home integrations are becoming more common in manufactured homes, appealing to tech-savvy buyers looking for modern amenities at a fraction of the cost of traditional homes.

The growing trend toward sustainable living is another critical growth driver. As consumers become more environmentally conscious, the demand for eco-friendly housing solutions is rising. Manufactured homes can be designed with sustainable materials and energy-efficient systems, reducing their environmental footprint. Furthermore, the manufacturing process itself tends to generate less waste compared to traditional construction methods. This sustainable aspect aligns well with global efforts to combat climate change and reduce carbon emissions.

Regionally, North America dominates the manufactured homes market, driven by high demand in the United States, where manufactured housing is a popular option for affordable living. The market in Europe is also expanding, particularly in countries with stringent housing regulations and high real estate prices, such as the UK and Germany. The Asia Pacific region is anticipated to witness the highest growth rate, owing to urbanization and the need for affordable housing solutions in countries like India and China.

Product Type Analysis

The manufactured homes market can be segmented by product type into single-section and multi-section homes. Single-section homes, often referred to as "single-wides," are more compact and typically cover less than 1,000 square feet. These homes are easier to transport and set up, making them a popular choice for individuals or small families. Single-section homes tend to be more affordable due to their smaller size and simpler design, which makes them an attractive option for budget-conscious buyers.

Multi-section homes, also known as "double-wides" or "triple-wides," offer more space and can cover up to 3,000 square feet or more. These homes are designed with multiple sections that are assembled on-site. The extra space in multi-section homes allows for more customization and the inclusion of additional amenities such as larger kitchens, multiple bathrooms, and extra bedrooms. This makes them suitable for larger families or individuals looking for more spacious living accommodations.

The market for multi-section homes is growing faster than single-section homes due to their resemblance to traditional site-built homes. They offer a higher level of comfort and luxury while still being more affordable than conventional housing. The flexibility in design and increased living space make multi-section homes an appealing option for a broader range of consumers. Additionally, advancements in construction technology have made it easier to manufacture and assemble these larger units, further boosting their popularity.

In terms of market share, multi-section homes hold a larger portion due to the high demand for more spacious living solutions. However, single-section homes continue to maintain a significant presence, particularly in rural areas where land is

In the United States, Hawaii was the state with the most expensive housing, with the typical value of single-family homes in the 35th to 65th percentile range exceeding ******* U.S. dollars. Unsurprisingly, Hawaii also ranked top as the state with the highest cost of living. Meanwhile, a property was the least expensive in West Virginia, where it cost under ******* U.S. dollars to buy the typical single-family home. Single-family home prices increased across most states in the United States between December 2023 and December 2024, except in Louisiana, Florida, and the District of Colombia. According to the Federal Housing Association, house appreciation in 13 states exceeded **** percent in 2023.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2022 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Counties are ranked within their state on both health outcomes and health factors. Counties with a lower (better) health outcomes ranking than health factors ranking may see the health of their county decline in the future, as factors today can result in outcomes later. Conversely, counties with a lower (better) factors ranking than outcomes ranking may see the health of their county improve in the future.Some new variables in the 2022 Rankings data compared to previous versions:COVID-19 age-adjusted mortalitySchool segregationSchool funding adequacyGender pay gapChildcare cost burdenChildcare centersLiving wage (while the Living wage measure was introduced to the CHRR dataset in 2022 from the Living Wage Calculator, it is not available in the Living Atlas dataset and user’s interested in the most up to date living wage data can look that up on the Living Wage Calculator website).Data Processing Notes:Data downloaded April 2022Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.Ratios were set to null if negative to make them easier to work with in the map.For demographic variables, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.2010 US boundaries were used as the data contain 2010 US census geographies, for a total of 3,142 counties.

In 2019, the state of California had the least affordable child care for school-aged children. The cost of care is presented as a percentage of state median income for a two-parent family. A two-parent family, living in the state, spent 19 percent of their median income for full-time care of a school-aged child in a child care center.

This statistic illustrates the most popular social networks among Millennials for finding the most relevant content on the cost of living crisis in the United States in 2023. According to a survey by We Are Social and Statista Q, 61 percent of Millennials who use TikTok find the most relevant content over there, followed by another 59 percent of the consumers who use YouTube.