California has the highest tax rate on gasoline in the United States. As of January 2024, the gas tax in California amounted to **** U.S. cents per gallon. California has long been known as the state with the highest tax rates – and consequently some of the highest fuel prices in the country. In October 2024, it ranked above Hawaii as the U.S. state with the highest gasoline retail price. Gas price components Out of all components making up retail gasoline prices, federal and state taxes are one of the strongest determinants of how much consumers pay for gas. These taxes are generally invested back into the construction and repair of road infrastructure. The federal government also places a tax on gasoline sold in the country, but almost every gas tax imposed by the states themselves is higher than this federal rate. Higher gasoline taxes may also affect driving habits, as those who live in states with higher gas taxes tend to drive less when other options are available. U.S. motor fuel tax revenue State taxes on fuel were first introduced in Oregon in 1919 while the rest of the states followed suit within the next decade. The amount generated through such taxes increased significantly throughout the last 40 years, with annual U.S. state and local motor fuel tax revenue climbing to over ** billion U.S. dollars.

Key comparative measures cited in the article, including price levels (RPP), electricity and natural gas averages, and income/sales/property tax references.

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 December 2018

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 (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.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. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. 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 noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

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.

This analysis, produced by the Office for National Statistics (ONS), examines how taxes and benefits redistribute income between various groups of households in the United Kingdom. It shows where different types of households and individuals are in the income distribution and looks at the changing levels of income inequality over time. The main sources of data for this study are:

• Family Expenditure Survey (FES) from 1977-2001
• Expenditure and Food Survey (EFS) from 2001-2007
• Living Costs and Food Survey (LCF) from 2008 to 2017
• Household Finances Survey (HFS) from 2018 onwards
  

Some variables have been created by combining data from the LCF (previously FES or EFS) with control totals from a variety of different government sources, including:

• United Kingdom National Accounts (ONS Blue Book)
• HM Revenue and Customs (HMRC)
• Department for Transport (DfT)
• Department of Health (DH)
• Department for Education and Employment (DfEE)
• Department for Communities and Local Government (DCLG)

For further information, see the ONS Effects of taxes and benefits on household income webpage.

Users should note that the combined ETB household (1977-2021) and person (2018-2021) datasets replace all previous individual year files, which have been withdrawn from use at the depositor's request.

Latest edition information

For the fourth edition (December 2025), replacement data and documentation for 2022 and 2023, and new data/documentation for 2024 were added to the study.

Method of Data Collection

The ETB has been produced each year since 1961 and is an annual analysis looking at how taxes and benefits affect the income of households in the UK.

Since 2018, the estimates in this analysis are based on data derived from the HFS Survey (the HCF is not currently held by the UK Data Service). The HFS is an annual survey of the expenditure and income of private households. People living in hotels, lodging houses, and in institutions such as old people's homes are excluded. Each person aged 16 and over keeps a full record of payments made during 14 consecutive days and answers questions about hire purchase and other payments; children aged 7 to 15 keep a simplified diary. The respondents also give detailed information, where appropriate, about income (including cash benefits received from the state) and payments of Income Tax. Information on age, occupation, education received, family composition and housing tenure is also obtained. The survey is continuous, interviews being spread evenly over the year to ensure that seasonal effects are covered. The Family Spending publication also includes an outline of the survey design.

The HFS data used in this analysis are grossed so that totals reflect the total population of private households in the UK. The weights are produced in two stages. First, the data are weighted to compensate for non-response (sample-based weighting). The non-response weights are then calibrated so that weighted totals match population totals for males and females in different age groups and for different regions and countries (population-based weighting). The results in the analysis are weighted so that statistics represent the total population in private households in the UK based on 2011 Census data. In 2013/14, an additional calibration to the Labour Force Survey (LFS) employment totals was also applied.

There are a number of different measures of income used, the most common of which is probably household disposable income. This is the total income households receive from employment (including self-employment), income from private pensions, investments and other sources, plus cash benefits (including the state pension), minus direct taxes (including income tax, NI and council tax). Income is normally analysed at the household level as this provides a better measure of people's economic well-being; while income is usually received by individuals, it is normally shared with other household members (e.g. spouse/partner and children).

In 2018/19 a further adjustment was applied to the data to adjust for the under coverage and under-reporting of income of the richest individuals. This method is often referred to as the 'SPI adjustment' owing to its use of HM Revenue and Customs (HMRC's) Survey of Personal Incomes (SPI). For further details please see the ETB https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/methodologies/theeffectsoftaxesandbenefitsonukhouseholdincome">Quality and Methodology Information webpage and the https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/articles/theeffectsoftaxesandbenefitsonhouseholdincome/financialyearending2019">Effects of Taxes and Benefits on Household Income Technical Report.

Data Sources

The Household Finances Survey (HFS) is the source of the microdata on households from 2018 onwards. Previously, the Living Costs and Food Survey (LCF) was the data source. Derived variables are created using information from HFS and control totals from a variety of different government sources including the United Kingdom National Accounts (ONS Blue Book), HM Revenue and Customs, Department for Transport, Department of Health, Department for Education and Employment, and Department for Communities and Local Government.

Secure Access version

A Secure Access version of the ETB is available from the UK Data Archive under SN 8253, subject to stringent access conditions. The Secure Access version includes variables that are not included in the standard End User Licence (EUL) version, including case number, age and economic position of chief economic supporter, and government office region. Users are strongly advised to check whether the EUL version is sufficient for their needs before considering an application for the Secure Access version.

In 2024, the median household income in the United States was 83,730 U.S. dollars. This reflected an increase from the previous year. Household income The median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varied from state to state. In 2024, Massachusetts recorded the highest median household income in the country, at 113,900 U.S. dollars. On the other hand, Mississippi, recorded the lowest, at 55,980 U.S. dollars.Household income is also used to determine the poverty rate in the United States. In 2024, 10.6 percent of the U.S. population was living below the national poverty line. This was the lowest level since 2019. Similarly, the child poverty rate, which represents people under the age of 18 living in poverty, reached a three-decade low of 14.3 percent of the children. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.52 in 2024. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality, while a score of one indicates complete inequality.

This map shows the median household income in the United States in 2012. Information for the 2012 Median Household Income is an estimate of income for calendar year 2012. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases. The median is the value that divides the distribution of household income into two equal parts. The median household income in the United States overall was $50,157 in 2012. This map shows Esri's 2012 estimates using Census 2010 geographies. The geography depicts States at greater than 50m scale, Counties at 7.5m to 50m scale, Census Tracts at 200k to 7.5m scale, and Census Block Groups at less than 200k scale. Scale Range: 1:591,657,528 down to 1:72,224 For more information on this map, including our terms of use, visit us online at http://goto.arcgisonline.com/maps/Demographics/USA_Median_Household_Income

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.

This survey was conducted among residents of New Jersey and addresses the job performance of Governor John Corzine. A range of state political topics are covered, including school improvement, ethics, property tax, controlling state costs, and the cost of living. Other topics addressed include immigration, raising money for the state, the high tech industry, and general demographics.

These Public Use Microdata Sample (PUMS) files contain records representing a 5-percent sample of the occupied and vacant housing units in the United States and the people in the occupied units. People living in group quarters also are included. The files provide individual weights for persons and housing units, which when applied to the individual records, expand the sample to the relevant totals. Some of the items on the housing record are acreage, agricultural sales, allocation flags for housing items, bedrooms, condominium fee, contract rent, cost of utilities, family income in 1999, family, subfamily, and relationship recodes, farm residence, fire, hazard, and flood insurance, fuels used, gross rent, heating fuel, household income in 1999, household type, housing unit weight, kitchen facilities, linguistic isolation, meals included in rent, mobile home costs, mortgage payment, mortgage status, plumbing facilities, presence and age of own children, presence of subfamilies in household, real estate taxes, number of rooms, selected monthly owner costs, size of building (units in structure), state code, telephone service, tenure, vacancy status, value (of housing unit), vehicles available, year householder moved into unit, and year structure built. Some of the items on the person record are ability to speak English, age, allocation flags for population items, ancestry, citizenship, class of worker, disability status, earnings in 1999, educational attainment, grandparents as caregivers, Hispanic origin, hours worked, income in 1999 by type, industry, language spoken at home, marital status, means of transportation to work, migration Public Use Microdata Area (PUMA), migration state, mobility status, veteran period of service, years of military service, occupation, persons weight, personal care limitation, place of birth, place of work PUMA, place of work state, poverty status in 1999, race, relationship, school enrollment and type of school, time of departure for work, travel time to work, vehicle occupancy, weeks worked in 1999, work limitation status, work status in 1999, and year of entry. The Public Use Microdata Sample (PUMS) files contain geographic units known as Public Use Microdata Areas (PUMAs) and super-Public Use Microdata Areas (super-PUMAs). To maintain the confidentiality of the PUMS data, minimum population thresholds are set for PUMAs and super-PUMAs. For the 1-percent state-level files, the super-PUMAs contain a minimum population of 400,000 and are composed of a PUMA or a group of contiguous PUMAs delineated on the 5-percent state-level PUMS files. Super-PUMAs are a new geographic entity for Census 2000. The 5-percent state-level files contain PUMAs, each having a minimum population of 100,000, and corresponding super-PUMA codes. Each state is separately identified and may be comprised of one or more super-PUMAs or PUMAs. Large metropolitan areas may be subdivided into super-PUMAs and PUMAs. PUMAs and super-PUMAs do not cross state lines. Super-PUMAs and PUMAs also are defined for place of residence on April 1, 1995, and place of work.

Empire State Realty reported $15.29M in Pre-Tax Profit for its fiscal quarter ending in September of 2025. Data for Empire State Realty | ESRT - Pre Tax Profit including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

State Street reported $1.1B in Pre-Tax Profit for its fiscal quarter ending in September of 2025. Data for State Street | STT - Pre Tax Profit including historical, tables and charts were last updated by Trading Economics this last December in 2025.

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 2022-23

The impact of the coronavirus (COVID-19) pandemic on the FRS 2022-23 survey was much reduced when compared with the two previous survey years. Throughout the year, there was a gradual return to pre-pandemic fieldwork practices, with the majority of interviews being conducted in face-to-face mode. The achieved sample was just over 25,000 households. Users are advised to consult the FRS 2022-23 Background Information and Methodology document for detailed information on changes, developments and issues related to the 2022-23 FRS data set and publication. Alongside the usual topics covered, the 2022-2023 FRS also includes variables for Cost of Living support, including those on certain state benefits; energy bill support; and Council Tax support. See documentation for further details.

FRS 2021-22 and 2020-21 and the coronavirus (COVID-19) pandemic

The coronavirus (COVID-19) pandemic has impacted the FRS 2021-22 and 2020-21 data collection in the following ways:

• In 2020-21, fieldwork operations for the FRS were rapidly changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing for the whole of the 2020-21 survey year.

• This change impacted both the size and composition of the achieved sample. This shift in mode of interview has been accompanied by a substantial reduction in the number of interviews achieved: just over 10,000 interviews were achieved this year, compared with 19,000 to 20,000 in a typical FRS year. While we made every effort to address additional biases identified (e.g. by altering our weighting regime), some residual bias remains. Please see the FRS 2020-21 Background Information and Methodology document for more information.
• The FRS team have published a technical report for the 2020-21 survey, which provides a full assessment of the impact of the pandemic on the statistics. In line with the Statistics Code of Practice, this is designed to assist users with interpreting the data and to aid transparency over decisions and data quality issues.

• In 2021-22, the interview mode was largely telephone, with partial return to face-to-face interviews towards end of survey year. The achieved sample was over 16,000 households. This is a return towards the number expected in a normal survey year (around 20,000 households).
• In both survey years, there remain areas where users are advised to exercise caution when making comparisons to other survey years. More details on how the results for the 2020 to 2021 and 2021-22 survey years were affected by the coronavirus (COVID-19) pandemic can be found in the FRS 2020 to 2021 Background Information and Methodology and FRS 2021 to 2022 Background Information and Methodology.

The FRS team are seeking users' feedback on the 2020-21 and 2021-22 FRS. Given the breadth of groups covered by the FRS data, it has not been possible for DWP statisticians to assess or validate every breakdown which is of interest to external researchers and users. Therefore, the FRS team are inviting users to let them know of any insights you may have relating to data quality or trends when analysing these data for your area of interest. Please send any feedback directly to the FRS Team Inbox: team.frs@dwp.gov.uk

Latest edition information

For the second edition (May 2025), the data were redeposited. The following changes have been made:

• An ONS-delivered fix to the highest level of qualification (EDUCQUAL) which for several adults had been erroneously recorded.
• For ESA (benefit 16 on the BENEFITS table) the associated VAR3 has now been populated using ESA admin data, to show whether cases are Support Group etc.
• For Pension Credit recipients (benefit 4 on the BENEFITS table) adding the low-income benefits and tax credits Cost of Living Payment as benefit 124; with its flag CLPAYIRB set on the ADULT table.

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 December 2018

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 (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.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. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. 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 noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

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 study charted Finnish people's values and attitudes. The themes of the Spring 2023 survey included the parliamentary elections of spring 2023, politics, foreign policy, taxation, and public finance. First, the respondents were presented with attitudinal statements concerning a variety of social topics, such as politics, immigration, political parties, taxation, the right to strike, the labour market and foreign policy. Next, the respondents were asked about their intentions to vote in the parliamentary elections of spring 2023, their voting behaviour, and how many political parties had candidates for whom they would vote in the upcoming parliamentary elections. Opinions on what were the most important themes (e.g. social security, taxation, public finance, national security) that impacted the respondents' choice of political party and candidate were also surveyed. Questions charted what the new government should focus on (e.g. improving employment, lowering taxation, increasing funding for education, stricter environment policy, cutting public expenditure) and what the respondents' views were on the composition of the new government (which political parties should or should not be included in the new government, and which qualities (e.g. determination, adaptability, independence) the respondents desired from political leaders in Finland. Opinions on tax policy were examined with questions concerning whether different taxes should be increased or decreased and attitudes towards taxation were investigated with a series of statements (e.g. taxation in Finland is too harsh, tax cuts should not be made if they lead to the deterioration of social security and public services, tax cuts would increase tax revenue as economic activity would increase). The respondents' views on balancing Finland's public finances were investigated, and opinions on the reformation of labour legislation were surveyed with attitudinal statements on various measures that would limit workers' right to strike. The respondents were also asked to assess which factors (e.g. climate and weather, Finnish education system, cost of living and taxation, security and stability of Finnish society) would be attractive or unattractive for potential immigrants moving to Finland. The respondents were asked which issues (e.g. relationship with Russia, relationship with USA, relationship with China, co-operation with Nordic countries, Finland's NATO membership, international crisis management) should be prioritised in Finland's foreign policy. Additionally, the respondents were asked how well-acquainted they were with issues concerning Finland's foreign policy and state security policy. Opinions were also charted on Finland's NATO membership, Finland's EU membership and the currency change to euro. Background variables included the respondent's age group, number of inhabitants in the municipality of residence, region (NUTS3), type of employer, working hours, type of employment contract, education, economic activity and occupational status, employment sector, trade union membership, what political party would vote for in parliamentary elections, self-perceived social class, mother tongue and annual gross income of the respondent's household.

This survey was conducted among residents of New Jersey and addresses the upcoming US presidential primary. The survey covers a number of issues key to the election including the war in Iraq, health care, jobs, the cost of living, and federal taxes. Additional topics include homeland security, immigration, education, and general demographics.

China State reported CNY11.71B in Pre-Tax Profit for its fiscal quarter ending in September of 2025. Data for China State | 601668 - Pre Tax Profit including historical, tables and charts were last updated by Trading Economics this last December in 2025.

American States Water reported $43.92M in Pre-Tax Profit for its fiscal quarter ending in June of 2025. Data for American States Water | AWR - Pre Tax Profit including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Context

Zillow operates an industry-leading economics and analytics bureau led by Zillow’s Chief Economist, Dr. Stan Humphries. At Zillow, Dr. Humphries and his team of economists and data analysts produce extensive housing data and analysis covering more than 500 markets nationwide. Zillow Research produces various real estate, rental and mortgage-related metrics and publishes unique analyses on current topics and trends affecting the housing market.

At Zillow’s core is our living database of more than 100 million U.S. homes, featuring both public and user-generated information including number of bedrooms and bathrooms, tax assessments, home sales and listing data of homes for sale and for rent. This data allows us to calculate, among other indicators, the Zestimate, a highly accurate, automated, estimated value of almost every home in the country as well as the Zillow Home Value Index and Zillow Rent Index, leading measures of median home values and rents.

Content

The Zillow Rent Index is the median estimated monthly rental price for a given area, and covers multifamily, single family, condominium, and cooperative homes in Zillow’s database, regardless of whether they are currently listed for rent. It is expressed in dollars and is seasonally adjusted. The Zillow Rent Index is published at the national, state, metro, county, city, neighborhood, and zip code levels.

Zillow produces rent estimates (Rent Zestimates) based on proprietary statistical and machine learning models. Within each county or state, the models observe recent rental listings and learn the relative contribution of various home attributes in predicting prevailing rents. These home attributes include physical facts about the home, prior sale transactions, tax assessment information and geographic location as well as the estimated market value of the home (Zestimate). Based on the patterns learned, these models estimate rental prices on all homes, including those not presently for rent. Because of the availability of Zillow rental listing data used to train the models, Rent Zestimates are only available back to November 2010; therefore, each ZRI time series starts on the same date.

Acknowledgements

The rent index data was calculated from Zillow's proprietary Rent Zestimates and published on its website.

Inspiration

What city has the highest and lowest rental prices in the country? Which metropolitan area is the most expensive to live in? Where have rental prices increased in the past five years and where have they remained the same? What city or state has the lowest cost per square foot?

Abstract (en): This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in seven separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Journey to Work Record, Part 3, Mortgages (Owners Only), Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Mover Group Record. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. Please review the "Sample Status, Weights, Interview Status" section in the ICPSR codebook for this American Housing Survey study, as well as Appendix B in CURRENT HOUSING REPORTS, 2007, included with this collection. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Housing Units in the United States. The 2007 national data are from a sample of housing units interviewed between April and September 2007. The same basic sample of housing units is interviewed every 2 years until a new sample is selected. The United States Census Bureau updates the sample by adding newly constructed housing units and units discovered through coverage improvement efforts. For the 2007 American Housing Survey--National sample (AHS-N), approximately 60,000 sample housing units were originally selected for interview. Due to budgetary constraints, roughly 8 percent of these units were taken out of the sample and were not interviewed in 2007. These reduced units are eligible for reinstatement in future enumerations. About 2,150 of the remaining 55,000 total units included for interview were found to be ineligible because the unit no longer existed or because the units did not meet the AHS-N definition of a housing unit. Of the 52,850 eligible sample units, about 6,550 were classified (both occupied and vacant housing units), as ''Type A'' noninterviews because (a) no one was at home after repeated visits, (b) the respondent refused to be interviewed, or (c) the interviewer was unable to find the unit. This classification produced an unweighted overall response rate of 88 percent. The weighted overall response rate was 89 percent. computer-assisted personal interview (CAPI)Beginning in 1997, the methods of collecting and processing American Housing Survey (AHS) data were redesigned. All interviews are conducted using computer-assisted personal interviewing (CAPI) ...