Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to May 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Price to Rent Ratio in the United States increased to 134.20 in the fourth quarter of 2024 from 133.60 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

IIA04 - Average rent as a % of household disposable income. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Average rent as a % of household disposable income...

What you get:

Upvote! The database contains +40,000 records on US Gross Rent & Geo Locations. The field description of the database is documented in the attached pdf file. To access, all 325,272 records on a scale roughly equivalent to a neighborhood (census tract) see link below and make sure to upvote. Upvote right now, please. Enjoy!

Get the full free database with coupon code: FreeDatabase, See directions at the bottom of the description... And make sure to upvote :) coupon ends at 2:00 pm 8-23-2017

Gross Rent & Geographic Statistics:

• Mean Gross Rent (double)
• Median Gross Rent (double)
• Standard Deviation of Gross Rent (double)
• Number of Samples (double)
• Square area of land at location (double)
• Square area of water at location (double)

Geographic Location:

• Longitude (double)
• Latitude (double)
• State Name (character)
• State abbreviated (character)
• State_Code (character)
• County Name (character)
• City Name (character)
• Name of city, town, village or CPD (character)
• Primary, Defines if the location is a track and block group.
• Zip Code (character)
• Area Code (character)

Abstract

The data set originally developed for real estate and business investment research. Income is a vital element when determining both quality and socioeconomic features of a given geographic location. The following data was derived from over +36,000 files and covers 348,893 location records.

License

Only proper citing is required please see the documentation for details. Have Fun!!!

Golden Oak Research Group, LLC. “U.S. Income Database Kaggle”. Publication: 5, August 2017. Accessed, day, month year.

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965

please note: it is my personal number and email is preferred

Check our data's accuracy: Census Fact Checker

Access all 325,272 location for Free Database Coupon Code:

Don't settle. Go big and win big. Optimize your potential**. Access all gross rent records and more on a scale roughly equivalent to a neighborhood, see link below:

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The Housing Affordability Data System (HADS) is a set of housing unit level datasets that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The purpose of these datasets is to provide housing analysts with consistent measures of affordability and burdens over a long period. The datasets are based on the American Housing Survey (AHS) national files from 1985 through 2005 and the metropolitan files for 2002 and 2004. Users can link records in HADS files to AHS records, allowing access to all of the AHS variables. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multiunit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

Mexico Ave Qtrly HH Income: Estimate of Housing Rent data was reported at 5,247.000 MXN in 2016. Mexico Ave Qtrly HH Income: Estimate of Housing Rent data is updated yearly, averaging 5,247.000 MXN from Dec 2016 (Median) to 2016, with 1 observations. Mexico Ave Qtrly HH Income: Estimate of Housing Rent data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.H012: Average Quarterly Household Income.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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--- Original source retains full ownership of the source dataset ---

The Low-Income Housing Tax Credit (LIHTC) is the most important resource for creating affordable housing in the United States today. The LIHTC database, created by HUD and available to the public since 1997, contains information on 48,672 projects and 3.23 million housing units placed in service since 1987. Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data.

The Housing Affordability Data System (HADS), 2002, is a housing-unit level dataset that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The dataset contains selected variables from the AMERICAN HOUSING SURVEY, 2002: METROPOLITAN MICRODATA (ICPSR 4589), as well as custom, derived variables measuring monthly housing costs, housing cost burdens, assisted housing, and total salary income. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multi-unit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. These dataset give the community of housing analysts the opportunity to use a consistent set of affordability measures. The most recent year HADS is available as a Public Use File (PUF) is 2013. For 2015 and beyond, HADS is only available as an IUF and can no longer be released on a PUF. Those seeking access to more recent data should reach to the listed point of contact.

This dataset presents the Rental Affordability Index (RAI) for all dwellings. The data uses different income values for each region within the Greater Capital Cities, and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory does not form part of this dataset. National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually. It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income: RAI = (Median Income ∕ Qualifying Income) x 100 In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress. For more information on the Rental Affordability Index please refer to SGS Economics and Planning. The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia. AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.

This dataset presents the Rental Affordability Index (RAI) for 3 bedroom dwellings. The data uses different income values for each region within the Greater Capital Cities, and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory and Western Australia does not form part of this dataset. National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually. It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income: RAI = (Median income ∕ Qualifying Income) x 100 In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress. For more information on the Rental Affordability Index please refer to SGS Economics and Planning. The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia. AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.

This dataset presents the Rental Affordability Index (RAI) for all dwellings. The data uses a single median income value for all of Australia (enabling comparisons across regions), and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory does not form part of this dataset. National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually. It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income: RAI = (Median income ∕ Qualifying Income) x 100 In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress. For more information on the Rental Affordability Index please refer to SGS Economics and Planning. The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia. AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.

Table from the American Community Survey (ACS) 5-year series for King County and City of Seattle median values for a variety of topics including age, gross rent, monthly owner costs, family and nonfamily incomes, earnings. Includes the margin of error for the values.Table created for and used in the Neighborhood Profiles application.Vintages: 2010, 2015, 2020, 2023ACS Table(s): B01002, B25064, B25088, B19013, B19113, B19202, B20017Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer 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:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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.

AbstractTaxation Statistics 2013-14 is a continental dataset providing an overview of the income and tax status of Australian individuals, companies, partnerships, trusts and funds for the 2013-14 financial year.The dataset was compiled for the annual publication, Taxation Statistics, the ATO’s key statistical report. It provides a comprehensive statistical summary of information taxpayers report to the ATO. It includes information sourced from:The income tax returns of individuals, companies, super funds, partnerships and trusts.Annual returns for fringe benefits tax (FBT) and goods and services tax (GST).Business activity statements (BAS) and instalment activity statements (IAS).Schedules for rental properties, capital gains tax (CGT) and international dealings.Superannuation member contribution statements (MCS).Other information reported to the ATO in relation to excise, the pay as you go (PAYG) system, and charitable institutions.Previous versions of this dataset are available on the Australian Government open government data portal data.gov.auCurrencyDate Published: 10 February 2016Date Updated: 12 June 2024Modification Frequency: As neededData ExtentSpatial ExtentNorth: -9.1°South: -43.6°East: 159.1°West: 96.8°Source informationData and Metadata are available from Taxation Statistics 2013-14 - Dataset - Data.gov.auThe data was obtained from the Australian Taxation Office.Catalog Entry: Taxation Statistics 2013-14 - Dataset - Data.gov.auLineage statementThis layer was put together using two datasets. Australian taxation and income data provided by the Australian Taxation Office (ATO), was joined to the 2011 Postal Areas shapefile provided by the Australian Bureau of Statistics (ABS).Postal AreasPostal Areas (POA) are an ABS Mesh Block approximation of a general definition of postcodes. They enable comparison of ABS data with other data collected using postcodes as the geographic reference. ABS approximations of administrative boundaries do not match official legal boundaries exactly and should only be used for statistical purposes.Data and geography referencesSource data publication: Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, July 2011Further information: Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, July 2011 – Explanatory NotesSource:Australian Bureau of Statistics (ABS)Data PreparationThe CSV was joined to the POA geographies using the 4 digit postcode. For the CSV, it was exported as a file geodatabase and a new field had to be generated where the postcodes were entered as text data to maintain the leading zeroes. The new text postcode field was then joined to the ABS POA_Name field.All data manipulations, joins, and spatial operations were performed using ArcGIS Pro 3.4.3.Data dictionaryAttribute nameDescriptionAREA_SQKMThe area in square kilometres of the postcodeAverage taxable income or lossThe average taxable income of the postcodeMedian taxable income or lossThe median taxable income or loss of the postcodeAverage salary and wagesThe average salary or wages of the postcodeMedian salary and wagesThe median salary or wages of the postcodeAverage net rentThe average net rent in the postcodeMedian net rentThe median net rent in the postcodeAverage total income or lossThe average total income or loss of the postcodeMedian total income or lossThe median total income or loss of the postcodeAverage total deductionsThe average total deductions in the postcodeMedian total deductionsThe median total deductions of the postcodeAverage total business incomeThe average total business income in the postcodeMedian total business incomeThe median total business income in the postcodeAverage total business expensesThe average business expenses in the postcodeMedian total business expensesThe median business expenses in the postcodeAverage net taxThe average net tax in the postcodeMedian net taxThe median net tax in the postcodeAverage superannuation total accounts balanceThe average balance of superannuation accounts in the postcodeMedian superannuation total accounts balanceThe median balance of superannuation accounts in the postcodePostcodesThe postcode affiliated with that areaSHAPE_LengthLength of polygon outlineSHAPE_AreaArea of the polygonContactAustralian Taxation Office, taxstats@ato.gov.au

This dataset presents the Rental Affordability Index (RAI) for 3 bedroom dwellings. The data uses a single median income value for all of Australia (enabling comparisons across regions), and spans the quarters Q1 2011 to Q2 2021. The RAI covers all states with available data, the Northern Territory does not form part of this dataset.

National Shelter, Bendigo Bank, The Brotherhood of St Laurence, and SGS Economics and Planning have released the RentalAffordability Index (RAI) on a biannual basis since 2015. Since 2019, the RAI has been released annually.

It is generally accepted that if housing costs exceed 30% of a low-income household's gross income, the household is experiencing housing stress (30/40 rule). That is, housing is unaffordable and housing costs consume a disproportionately high amount of household income. The RAI uses the 30 per cent of income rule. Rental affordability is calculated using the following equation, where 'qualifying income' refers to the household income required to pay rent where rent is equal to 30% of income:

RAI = (Median income ∕ Qualifying Income) x 100

In the RAI, households who are paying 30% of income on rent have a score of 100, indicating that these households are at the critical threshold for housing stress. A score of 100 or less indicates that households would pay more than 30% of income to access a rental dwelling, meaning they are at risk of experiencing housing stress.

For more information on the Rental Affordability Index please refer to SGS Economics and Planning.

The RAI is a price index for housing rental markets. It is a clear and concise indicator of rental affordability relative to household incomes, applied to geographic areas across Australia.

AURIN has spatially enabled the original data using geometries provided by SGS Economics and Planning. Values of 'NA' in the original data have been set to NULL.

This dataset contains an history of nearly all of the real estate transactions concerning a single house/appartment in France from 2014 to today. Some variables likely to have an impact on the price of real estate are also provided as time series: the households income levels per city, the average debt level of french peoples, the average amount of savings of french people, the interest rates of loans, the price of the rent per city, the number of houses and number of vacant houses per city.

This dataset is provided under a permissive licence, and is free to use for commercial uses. It has a vocation of helping research concerning the dynamics of real estate prices.

The dataset consist in extraction from several openly available datasets put together in a practical format: The DVF+ database of real estate transactions, the IRCOM dataset of household incomes and income taxes, average interest rates of real estate loans from the banque de france website, the LOVAC dataset of number of vacant and occupied housings per city, the OECD dataset of financial assets per capita, the "carte des loyers" dataset of 2018 and 2022 which list the average price of the rent per square meter, the Indice de Référence des Loyers (IRL) time series which is an index defining the maximum rent increase that can be applied to an already rented housing and is calculated every 3 months as the inflation adjusted buying power of 100€ in 1998, the TEC00104 eurostat dataset of debt levels.

Abstract copyright UK Data Service and data collection copyright owner.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. Safe Room Access FRS data In addition to the standard End User Licence (EUL) version, Safe Room access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 7196, where the extra contents are listed. The Safe Room version also includes secure access versions of the Households Below Average Income (HBAI) and Pensioners' Incomes (PI) datasets. The Safe Room access data are currently only available to UK HE/FE applicants and for access at the UK Data Archive's Safe Room at the University of Essex, Colchester. Prospective users of the Safe Room access version of the FRS/HBAI/PI will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.FRS, HBAI and PIThe 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 within the Safe Room FRS study under SN 7196 (see above). The FRS aims to: support the monitoring of the social security programme; support the costing and modelling of changes to national insurance contributions and social security benefits; provide better information for the forecasting of benefit expenditure. From April 2002, the FRS was extended to include Northern Ireland. Detailed information regarding anonymisation within the FRS can be found in User Guide 2 of the dataset documentation. For the second edition (October 2014) the data have been re-grossed following revision of the FRS grossing methodology to take account of the 2011 Census mid-year population estimates. New variable GROSS4 has been added to the dataset. Main Topics: Household characteristics (composition, tenure type); tenure and housing costs including Council Tax, mortgages, insurance, water and sewage rates; welfare/school milk and meals; educational grants and loans; children in education; informal care (given and received); childcare; occupation and employment; health restrictions on work; children's health; National Health Service treatment; wage details; self-employed earnings; personal and occupational pension schemes; income and benefit receipt; income from pensions and trusts, royalties and allowances, maintenance and other sources; income tax payments and refunds; National Insurance contributions; earnings from odd jobs; children's earnings; interest and dividends; investments; National Savings products; assets; vehicle ownership. Standard Measures Standard Occupational Classification Multi-stage stratified random sample Face-to-face interview Computer Assisted Personal Interviewing 2005 2006 ABSENTEEISM ACADEMIC ACHIEVEMENT ADMINISTRATIVE AREAS AGE APARTMENTS APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BANK ACCOUNTS BEDROOMS BONDS BONUS PAYMENTS BUILDING SOCIETY AC... BUSES BUSINESS RECORDS CARE OF DEPENDANTS CARE OF THE DISABLED CARE OF THE ELDERLY CARS CHARITABLE ORGANIZA... CHILD BENEFITS CHILD CARE CHILD DAY CARE CHILD MINDERS CHILD MINDING CHILD SUPPORT PAYMENTS CHILD WORKERS CHILDREN CHRONIC ILLNESS COHABITATION COLOUR TELEVISION R... COMMERCIAL BUILDINGS CONCESSIONARY TELEV... CONSUMPTION CONTACT LENSES COUNCIL TAX CREDIT UNIONS Consumption and con... DAY NURSERIES DEBILITATIVE ILLNESS DEBTS DENTISTS DISABILITIES DISABILITY DISCRIMI... DISABLED CHILDREN DISABLED PERSONS DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATION EDUCATIONAL BACKGROUND EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL INSTITU... EDUCATIONAL VOUCHERS ELDERLY EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ETHNIC GROUPS EXPENDITURE EYESIGHT TESTS FAMILIES FAMILY MEMBERS FINANCIAL DIFFICULTIES FINANCIAL INSTITUTIONS FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FREE SCHOOL MEALS FRIENDS FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION Family life and mar... GENDER GIFTS GRANDPARENTS GRANTS HEADS OF HOUSEHOLD HEALTH HEALTH SERVICES HEARING IMPAIRED PE... HEARING IMPAIRMENTS HIGHER EDUCATION HOLIDAY LEAVE HOME BASED WORK HOME OWNERSHIP HOME SHARING HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLD HEAD S OC... HOUSEHOLDS HOUSING HOUSING FACILITIES HOUSING FINANCE HOUSING TENURE INCOME INCOME TAX INDUSTRIES INSURANCE INSURANCE PREMIUMS INTEREST FINANCE INVESTMENT INVESTMENT RETURN Income JOB DESCRIPTION JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEAVE LOANS LODGERS MANAGERS MARITAL STATUS MARRIED WOMEN MARRIED WOMEN WORKERS MATERNITY LEAVE MATERNITY PAY MEDICAL PRESCRIPTIONS MORTGAGE PROTECTION... MORTGAGES MOTORCYCLES NEIGHBOURS Northern Ireland OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS ONE PARENT FAMILIES OVERTIME PARENTS PART TIME COURSES PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PASSENGERS PATERNITY LEAVE PENSION CONTRIBUTIONS PENSIONS PHYSICALLY DISABLED... PHYSICIANS POVERTY PRIVATE EDUCATION PRIVATE PERSONAL PE... PRIVATE SCHOOLS PROFITS QUALIFICATIONS RATES REBATES REDUNDANCY REDUNDANCY PAY RENTED ACCOMMODATION RENTS RESIDENTIAL MOBILITY RETIREMENT ROOM SHARING ROOMS ROYALTIES SAVINGS SAVINGS ACCOUNTS AN... SCHOLARSHIPS SCHOOL MILK PROVISION SCHOOLCHILDREN SCHOOLS SEASONAL EMPLOYMENT SECONDARY EDUCATION SECONDARY SCHOOLS SELF EMPLOYED SEWAGE DISPOSAL AND... SHARES SHIFT WORK SICK LEAVE SICK PAY SICK PERSONS SOCIAL CLASS SOCIAL HOUSING SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIAL SECURITY CON... SOCIAL SERVICES SOCIAL SUPPORT SOCIO ECONOMIC STATUS SPECIAL EDUCATION SPECTACLES SPOUSES STATE EDUCATION STATE HEALTH SERVICES STATE RETIREMENT PE... STUDENT HOUSING STUDENT LOANS STUDENTS STUDY SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS Social stratificati... TAXATION TELEVISION LICENCES TELEVISION RECEIVERS TEMPORARY EMPLOYMENT TENANCY AGREEMENTS TENANTS HOME PURCHA... TERMINATION OF SERVICE TIED HOUSING TIME TOP MANAGEMENT TRAINING UNEARNED INCOME UNEMPLOYED UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS VEHICLES VISION IMPAIRMENTS VISUALLY IMPAIRED P... VOCATIONAL EDUCATIO... VOLUNTARY WORK WAGES WIDOWED WORKING MOTHERS WORKING WOMEN property and invest...

The City of Toronto monitors the affordability of food annually using the Nutritious Food Basket (NFB) costing tool. Food prices increased considerably in 2022. People with low incomes do not have enough money to cover the cost of basic expenses, including food. As such, NFB data is best viewed in relation to income, alongside other local basic expenses. The dataset describes the affordability of food and other basic expenses relative to income for nine household scenarios. Scenarios were selected to reflect household characteristics that increase the risk of being food insecure, including reliance on social assistance as the main source of income, single-parent households, and rental housing. A median income scenario has also been included as a comparator. Income, including federal and provincial tax benefits, and the cost of four basic living expenses - shelter, food, childcare, and transportation - are estimated for each scenario. Results show the amount of money remaining at the end of the month for each household. Three versions of the scenarios were created to describe: Income scenarios with subsidies: Subsidies can substantially reduce a households’ monthly expenses. Local subsidies for rent (Rent-Geared-to-Income), childcare (Childcare Fee Subsidy), and transit (Fair Pass) are accounted for in this file. Income scenarios without subsidies + average rent: In this file, rental costs are based on average rent, as measured by the Canadian Mortgage and Housing Corporation (CMHC). Income scenarios without subsidies + market rent: Rental costs are based on average market rent (as of June 2022), as measured by the Toronto Regional Real Estate Board (TRREB). Limitations Scenarios describe estimated values only, rounded to the nearest dollar. Income is estimated using a May/June 2022 reference period to align with Nutritious Food Basket data collection. Thus, tax year 2020 has been utilized in calculations. Income amounts include all entitlements available to Ontario residents; therefore, they are maximum amounts. Actual income amounts may be lower if residents do not file their income tax and/or do not apply for all available tax credits.

AbstractTaxation Statistics 2014-15 is a continental dataset providing an overview of the income and tax status of Australian individuals, companies, partnerships, trusts and funds for the 2014-15 financial year.The dataset was compiled for the annual publication, Taxation Statistics, the ATO’s key statistical report. It provides a comprehensive statistical summary of information taxpayers report to the ATO.The income tax returns of individuals, companies, super funds, partnerships and trusts.Annual returns for fringe benefits tax (FBT) and goods and services tax (GST).Business activity statements (BAS) and instalment activity statements (IAS).Schedules for rental properties, capital gains tax (CGT) and international dealings.Superannuation member contribution statements (MCS).Other information reported to the ATO in relation to excise, the pay as you go (PAYG) system, and charitable institutions.Previous versions of this dataset are available on the Australian Government open government data portal data.gov.auCurrencyDate Published: 28 March 2017Date Updated: 12 June 2024Modification Frequency: As neededData ExtentGeocentric Datum of Australia 1994 (GDA94)Spatial ExtentNorth: -9.1°South: -43.6°East: 159.1°West: 96.8°Source InformationData and Metadata are available from Taxation Statistics 2014-15 - Dataset - data.gov.auThe data was obtained from the Australian Taxation Office.Catalog Entry: Taxation Statistics 2014-15 - Dataset - Data.gov.auLineage StatementThis layer was put together using two datasets. Australian taxation and income data provided by the Australian Taxation Office (ATO), was joined to the 2011 Postal Areas shapefile provided by the Australian Bureau of Statistics (ABS).Postal AreasPostal Areas (POA) are an ABS Mesh Block approximation of a general definition of postcodes. They enable comparison of ABS data with other data collected using postcodes as the geographic reference. ABS approximations of administrative boundaries do not match official legal boundaries exactly and should only be used for statistical purposes.Data and geography referencesSource data publication:Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, July 2011Further information: Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, July 2011 – Explanatory NotesSource: Australian Bureau of Statistics (ABS)Data PreparationThe CSV was joined to the POA geographies using the 4 digit postcode. For the CSV, it was exported as a file geodatabase and a new field assigned where postcodes were entered as text data to maintain the leading zeroes. The new text postcode field was then joined to the ABS POA_Name field.All data manipulations, joins, and spatial operations were performed using ArcGIS Pro 3.4.3.Data dictionaryAttribute nameDescriptionAREA_SQKMThe area in square kilometres of the postcodeCount taxable income or lossThe number of individuals reporting taxable income or loss in the postcodeAverage taxable income or lossThe average taxable income or loss of the postcodeMedian taxable income or lossThe median taxable income or loss of the postcodeCount of salary or wagesThe number of individuals reporting salary or wages in the postcodeAverage salary and wagesThe average salary or wages of the postcodeMedian salary and wagesThe median salary or wages of the postcodeCount net rentThe number of individuals reporting net rent in the postcodeAverage net rentThe average net rent in the postcodeMedian net rentThe median net rent in the postcodeCount total income or lossThe number of individuals reporting total income or loss in the postcodeAverage total income or lossThe average total income or loss of the postcodeMedian total income or lossThe median total income or loss of the postcodeCount total deductionsCount of individuals reporting total deductions in the postcodeAverage total deductionsThe average total deductions of the postcodeMedian total deductionsThe median total deductions of the postcodeCount total business incomeThe number of individuals reporting business income in the postcodeAverage total business incomeThe average total business income in the postcodeMedian total business incomeThe median total business income in the postcodeCount total business expensesThe number of individuals reporting business expenses in the postcodeAverage total business expensesThe average business expenses in the postcodeMedian total business expensesThe median business expenses in the postcodeCount net taxThe number of individuals with net tax in the postcodeAverage net taxThe average net tax in the postcodeMedian net taxThe median net tax in the postcodeCount super total accounts balanceThe total number of super accounts in the postcodeAverage super total accounts balanceThe average balance of super accounts in the postcodeMedian super total accounts balanceThe median balance of super accounts in the postcodePostcodesThe postcode affiliated with that areaSHAPE_LengthLength of polygon outlineSHAPE_AreaArea of the polygon.ContactAustralian Taxation Office, taxstats@ato.gov.au