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.

Cost of Living Index (Excl. Rent) is a relative indicator of consumer goods prices, including groceries, restaurants, transportation and utilities. Cost of Living Index does not include accommodation expenses such as rent or mortgage. If a city has a Cost of Living Index of 120, it means Numbeo has estimated it is 20% more expensive than New York (excluding rent).

Please refer further to: https://www.numbeo.com/cost-of-living/cpi_explained.jsp for motivation and methodology.

All credits to https://www.numbeo.com .

This dataset would surely help socio-economic researchers to analyse and get deeper insights regarding the life of people country-wise.

Thanks to @andradaolteanu for the motivation! Upwards and onwards...

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.

Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April 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.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

Russia Living Cost: Average per Month data was reported at 10,213.000 RUB in Dec 2018. This records a decrease from the previous number of 10,451.000 RUB for Sep 2018. Russia Living Cost: Average per Month data is updated quarterly, averaging 3,050.000 RUB from Mar 1992 (Median) to Dec 2018, with 108 observations. The data reached an all-time high of 10,451.000 RUB in Sep 2018 and a record low of 1.423 RUB in Jun 1992. Russia Living Cost: Average per Month data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from http://ukdataservice.ac.uk/media/178323/secure_frs_application_guidance.pdf" style="background-color: rgb(255, 255, 255);">Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

FRS 2023-24

Alongside the usual topics covered, the 2023-2024 FRS includes new variables on veterans (ex-armed forces, former regulars and reserves); care leavers (where young adults were previously living in care, during their teenage years); and, for the self-employed, length of time in that occupation. For doctors, we add clarifying variables for NHS vs private earnings streams. There are new variables on food support from friends/relatives, which complement the existing food bank and household food security set. 2023-2024 also includes Cost of Living Payment variables, including those on certain state benefits and the Warm Homes Discount scheme.

The achieved sample was over 16,500 households (28,500+ adults). A large majority of interviews were face-to-face with a minority being by telephone.

The BENUNIT table contains a raft of variables on the new material deprivation question set; see GOV.UK for background.

This version of the dataset (End User Licence) adds the DEBT table for the first time this year. The table contains responses on credit card debt, loan debt, hire purchase debt and store card debt.

Please send any feedback directly to the FRS Team Inbox: team.frs@dwp.gov.uk

Documentation

Many variables in the data files are fully labelled, but additional details can be found in the frs2324_variable_listing_eul.xlsx document.

The Consumer Expenditure Estimates dataset was created by SimplyAnalytics using small area estimation techniques. The Consumer Expenditure (CE) Public Use Microdata (PUMD) samples thousands of respondents (referred to as consumer units, or "CUs") across Texas. Each CU is assigned a weight that reflects the relative proportion of all American CUs that they represent. To estimate expenditures at the Census block group and ZCTA5 levels, we use data from the American Community Survey 5-Year Estimates as a proxy for how CUs are distributed over small areas, and use this information to derive expenditure estimates for all CE spending categories. Due to limitations on the PUMD sample size, and to account for national-level weighting of all CUs, the estimates are further adjusted to account for regional fluctuations in cost of living.

The Consumer Expenditure Estimates dataset was created by SimplyAnalytics using small area estimation techniques. The Consumer Expenditure (CE) Public Use Microdata (PUMD) samples thousands of respondents (referred to as consumer units, or "CUs") across Texas. Each CU is assigned a weight that reflects the relative proportion of all American CUs that they represent. To estimate expenditures at the Census block group and ZCTA5 levels, we use data from the American Community Survey 5-Year Estimates as a proxy for how CUs are distributed over small areas, and use this information to derive expenditure estimates for all CE spending categories. Due to limitations on the PUMD sample size, and to account for national-level weighting of all CUs, the estimates are further adjusted to account for regional fluctuations in cost of living.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

Living Cost: Average per Month: FE: Kamchatka Territory data was reported at 21,524.000 RUB in Dec 2020. This records a decrease from the previous number of 21,797.000 RUB for Sep 2020. Living Cost: Average per Month: FE: Kamchatka Territory data is updated quarterly, averaging 12,478.000 RUB from Sep 2001 (Median) to Dec 2020, with 78 observations. The data reached an all-time high of 21,797.000 RUB in Sep 2020 and a record low of 3,014.000 RUB in Sep 2001. Living Cost: Average per Month: FE: Kamchatka Territory data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Living Cost: Average per Month: CF: Ryazan Region data was reported at 10,785.000 RUB in Dec 2020. This stayed constant from the previous number of 10,785.000 RUB for Sep 2020. Living Cost: Average per Month: CF: Ryazan Region data is updated quarterly, averaging 5,785.000 RUB from Mar 2001 (Median) to Dec 2020, with 80 observations. The data reached an all-time high of 10,785.000 RUB in Dec 2020 and a record low of 1,187.000 RUB in Mar 2001. Living Cost: Average per Month: CF: Ryazan Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Living Cost: Average per Month: NW: Nenetsky Area data was reported at 21,757.000 RUB in Dec 2020. This records a decrease from the previous number of 22,219.000 RUB for Sep 2020. Living Cost: Average per Month: NW: Nenetsky Area data is updated quarterly, averaging 11,611.500 RUB from Mar 2001 (Median) to Dec 2020, with 80 observations. The data reached an all-time high of 22,219.000 RUB in Sep 2020 and a record low of 2,408.000 RUB in Mar 2001. Living Cost: Average per Month: NW: Nenetsky Area data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Living Cost: Average per Month: NW: Novgorod Region data was reported at 11,352.000 RUB in Dec 2020. This records a decrease from the previous number of 11,624.000 RUB for Sep 2020. Living Cost: Average per Month: NW: Novgorod Region data is updated quarterly, averaging 5,888.000 RUB from Mar 2002 (Median) to Dec 2020, with 76 observations. The data reached an all-time high of 11,624.000 RUB in Sep 2020 and a record low of 1,668.000 RUB in Mar 2002. Living Cost: Average per Month: NW: Novgorod Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Living Cost: Average per Month: CF: Kostroma Region data was reported at 11,010.000 RUB in Dec 2020. This records a decrease from the previous number of 11,241.000 RUB for Sep 2020. Living Cost: Average per Month: CF: Kostroma Region data is updated quarterly, averaging 6,021.500 RUB from Sep 2001 (Median) to Dec 2020, with 78 observations. The data reached an all-time high of 11,241.000 RUB in Sep 2020 and a record low of 1,267.000 RUB in Sep 2001. Living Cost: Average per Month: CF: Kostroma Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Living Cost: Average per Month: NW: Pskov Region data was reported at 11,441.000 RUB in Dec 2020. This records a decrease from the previous number of 11,807.000 RUB for Sep 2020. Living Cost: Average per Month: NW: Pskov Region data is updated quarterly, averaging 5,457.500 RUB from Mar 2001 (Median) to Dec 2020, with 80 observations. The data reached an all-time high of 11,807.000 RUB in Sep 2020 and a record low of 1,229.000 RUB in Mar 2001. Living Cost: Average per Month: NW: Pskov Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Living Cost: Average per Month: NW: Vologda Region data was reported at 11,428.000 RUB in Dec 2020. This records a decrease from the previous number of 11,811.000 RUB for Sep 2020. Living Cost: Average per Month: NW: Vologda Region data is updated quarterly, averaging 6,434.500 RUB from Mar 2002 (Median) to Dec 2020, with 76 observations. The data reached an all-time high of 11,811.000 RUB in Sep 2020 and a record low of 1,777.000 RUB in Mar 2002. Living Cost: Average per Month: NW: Vologda Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.