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 84.8 - well below the national benchmark of 100. Nevada - which had an index value of 100.1 - 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 427,000 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 200,000 U.S. dollars. That makes living costs in these states significantly lower than in states such as Hawaii and California, where housing is much more expensive. 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 500 U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

In 2024, the western island state of Hawaii offered the most affordable homeowners insurance for homeowners in the United States. Homeowners in Hawaii paid annually 515 U.S. dollars in order to have insurance coverage for their homes. In the meantime, homeowners in the northeastern state of Delaware had to pay 870 U.S. dollars on an annual basis for the same sort of insurance.

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 981,000 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 167,000 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 nine percent in 2023.

This map uses a two-color thematic shading to emphasize where areas experience the least to the most affordable housing across the US. This web map is part of the How Affordable is the American Dream story map.

Esri’s Housing Affordability Index (HAI) is a powerful tool to analyze local real estate markets. Esri’s housing affordability index measures the financial ability of a typical household to purchase an existing home in an area. A HAI of 100 represents an area that on average has sufficient household income to qualify for a loan on a home valued at the median home price. An index greater than 100 suggests homes are easily afforded by the average area resident. A HAI less than 100 suggests that homes are less affordable. The housing affordability index is not applicable in areas with no households or in predominantly rental markets . Esri’s home value estimates cover owner-occupied homes only. For a full demographic analysis of US growth refer to Esri's Trending in 2017: The Selectivity of Growth.

The pop-up is configured to show the following 2017 demographics for each County and ZIP Code:

Total Households 2010-17 Annual Pop Change Median Age Percent Owner-Occupied Housing Units Median Household Income Median Home Value Housing Affordability Index Share of Income to Mortgage

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

New Orleans was the most affordable city for backpackers in the United States as of January 2025. According to the source, backpackers could expect to spend around 94.93 U.S. dollars per day in the city. This figure includes a dorm bed at a cheap hostel, three budget meals, two public transportation rides, one paid cultural attraction, and three cheap beers (as an “entertainment fund”).

In 2019, the state of California had the least affordable child care.The cost of care is presented as a percentage of state median income for a two-parent family. About 18 percent of the median income of a two-parent family had to be spent for full-time care for an infant in a child care center.

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

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

This 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 90.5 value and the median income of 40,240 U.S. dollars.

The U.S. housing market has slowed, after 13 consecutive years of rising home prices. In 2021, house prices surged by an unprecedented 18 percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by 4.2 percent. That was lower than the long-term average of 4.4 percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of 2.96 percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over 389,000 U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show comparison of housing ownership costs and rental costs to income by State of Georgia in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

HUM_SMOCAPI_e

# Housing units with a mortgage, costs as a percentage of income computed, 2017

HUM_SMOCAPI_m

# Housing units with a mortgage, costs as a percentage of income computed, 2017 (MOE)

MSMOCAPI30PctPlus_e

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017

MSMOCAPI30PctPlus_m

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pMSMOCAPI30PctPlus_e

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017

pMSMOCAPI30PctPlus_m

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

HUNM_SMOCAPI_e

# Housing units without a mortgage, costs as a percentage of income computed, 2017

HUNM_SMOCAPI_m

# Housing units without a mortgage, costs as a percentage of income computed, 2017 (MOE)

NMSMOCAPI30PctPlus_e

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017

NMSMOCAPI30PctPlus_m

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pNMSMOCAPI30PctPlus_e

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017

pNMSMOCAPI30PctPlus_m

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

OccGRAPI_e

# Occupied units for which rent as a percentage of income can be computed, 2017

OccGRAPI_m

# Occupied units for which rent as a percentage of income can be computed, 2017 (MOE)

GRAPI30PctPlus_e

# Gross rent 30.0 percent of income or greater, 2017

GRAPI30PctPlus_m

# Gross rent 30.0 percent of income or greater, 2017 (MOE)

pGRAPI30PctPlus_e

% Gross rent 30.0 percent of income or greater, 2017

pGRAPI30PctPlus_m

% Gross rent 30.0 percent of income or greater, 2017 (MOE)

HousingCost30PctPlus_e

# All occupied units for which costs exceed 30 percent of income, 2017

HousingCost30PctPlus_m

# All occupied units for which costs exceed 30 percent of income, 2017 (MOE)

PayingForHousing_e

# Total households paying for housing (rent or owner costs), 2017

PayingForHousing_m

# Total households paying for housing (rent or owner costs), 2017 (MOE)

pHousingCost30PctPlus_e

% Occupied units for which costs exceed 30 percent of income, 2017

pHousingCost30PctPlus_m

% Occupied units for which costs exceed 30 percent of income, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

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.

This layer is intended for researchers, students, policy makers, and the general public for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production. This layer will provide a basemap for layers related to socio-political analysis, statistical enumeration and analysis, or to support graphical overlays and analysis with other spatial data. More advanced user applications may focus on demographics, urban and rural land use planning, socio-economic analysis and related areas (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output.)

This statistic shows a ranking of the most affordable colleges in the United States as of 2012. To calculate the ranking the Daily Beast considered average student debt, total cost for tuition and general living expenses, average amount of financial aid received by students and average income earned by graduates in their future careers. In this graphic the average in-state attendance cost is depicted. At Massachusetts Institute of Technology, the university ranked as the most affordable, total attendance cost is on average 55,270 U.S. dollars.

The number of manufactured home shipments in the United States has been on the rise since 2009, despite remaining substantially lower than in the 1990s. In 2022, there were about 113,000 mobile homes shipments, down from over 373,000 in 1998 - the year with the most homes shipped. Texas was the largest mobile home market and the state with the most mobile homes manufacturing plants.

This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Housing Index in the United States increased to 436.50 points in January from 435.80 points in December of 2024. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This layer is published from the Department of Community Affairs to show Federally designated Opportunity Zones.The U.S. Department of the Treasury and the Internal Revenue Service (IRS) have designated Opportunity Zones in 18 States, including 260 census tracts in the State of Georgia. Economic investment in these areas, which are some of the most distressed communities in the country, may now be eligible for preferential tax treatment. These new Federal Opportunity Zones are intended to facilitate investment in areas where poverty rates are greater than 20 percent.“This designation will enable some of our state’s struggling communities to attract much-needed private sector investment,” said DCA Commissioner Christopher Nunn. “By giving an economic ‘shot in the arm’ to these communities, the goal is to boost investment where it’s most urgently needed.”Georgia’s 260 zones, located in 83 counties, represent some of the most concentrated poverty in the state and are found in both rural and metropolitan areas, with approximately 60% rural and 40% urban. Qualified Opportunity Zones retain this designation for 10 years. Investors can defer tax on any prior gains until no later than December 31, 2026, so long as the gain is reinvested in a Qualified Opportunity Fund, an investment vehicle organized to make investments in Qualified Opportunity Zones. In addition, if the investor holds the investment in the Opportunity Fund for at least ten years, the investor would be eligible for an increase in its basis equal to the fair market value of the investment on the date that it is sold.Treasury and the IRS plan to issue additional information on Qualified Opportunity Funds to address the certification of Opportunity Funds, which are required to have at least 90 percent of fund assets invested in Opportunity Zones. DCA will communicate additional information about the specifics of the program as it is released by Treasury. Interactive map of designated Opportunity Zones.Additional information on Opportunity Zones.View a full list of Georgia’s designated census tracts, by county.Click here for FAQs.About the Georgia Department of Community AffairsThe Georgia Department of Community Affairs (DCA) partners with communities to create a climate of success for Georgia’s families and businesses through community and economic development, local government assistance, and safe and affordable housing. Using state and federal resources, DCA helps communities spur private job creation, implement planning, develop downtowns, generate affordable housing solutions, and promote volunteerism. DCA also helps qualified low- and moderate-income Georgians buy homes, rent housing, and prevent foreclosure and homelessness. For more information, visit www.dca.ga.gov.

Among the largest cities in the United States, renting an apartment was most affordable in Wichita, KS, in 2024. On average, renters in Wichita could rent an 1,359 square foot apartment for 1,500 U.S. dollars. The average apartment rent varies widely across different metros and states, with Hawaii, California, and Washington D.C. fetching the most expensive rents.

College Affordability and Transparency List Explanation Form 2016–17 (CATEF 2016–17) is a cross-sectional data collection that collects information on the major areas of institutions’ budgets with the greatest cost increases, the explanations for these increases, and the steps institutions have been or will be taking towards reducing these costs. The data collection is conducted on the subset of institutions that appear on the tuition and fees and/or net price increase lists for being in the five percent of institutions in their institutional sector that have the highest increases, expressed as a percentage change, over the three-year time period. This data collection is mandatory and expects a 100 percent response rate. Key statistics produced from CATEF 2016–17 are a description of the major areas in the institution's budget with the greatest cost increases; an explanation of the cost increases; a description of the steps the institution will take toward the goal of reducing costs in the areas described; an explanation of the extent to which the institution participates in determining such cost increase; the identification of the agency or instrumentality of state government responsible for determining such cost increase; and any other information the institution considers relevant to the report.