Credit card debt in the United States has been growing at a fast pace between 2021 and 2024. In the fourth quarter of 2024, the overall amount of credit card debt reached its highest value throughout the timeline considered here. COVID-19 had a big impact on the indebtedness of Americans, as credit card debt decreased from 927 billion U.S. dollars in the last quarter of 2019 to 770 billion U.S. dollars in the first quarter of 2021. What portion of Americans use credit cards? A substantial portion of Americans had at least one credit card in 2024. That year, the penetration rate of credit cards in the United States was 67 percent. This number increased by nearly seven percentage points since 2014. The primary factors behind the high utilization of credit cards in the United States are a prevalent culture of convenience, a wide range of reward schemes, and consumer preferences for postponed payments. Which companies dominate the credit card issuing market? In 2023, the leading credit card issuers in the U.S. by volume were JPMorgan Chase & Co. and American Express. Both firms recorded transactions worth over one trillion U.S. dollars that year. Citi and Capital One were the next banks in that ranking, with the transactions made with their credit cards amounting to over half a trillion U.S. dollars that year. Those industry giants, along with other prominent brand names in the industry such as Bank of America, Synchrony Financial, Wells Fargo, and others, dominate the credit card market. Due to their extensive customer base, appealing rewards, and competitive offerings, they have gained a significant market share, making them the preferred choice for consumers.

The tables and interactive maps below allow users to explore the ratio of debt to income by state, metropolitan statistical area, and county for each year since 1999. Household debt is calculated from Federal Reserve Bank of New York (FRBNY) Consumer Credit Panel/Equifax Data, and household income is reported by the Bureau of Labor Statistics.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

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.

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

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Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

Households Debt in Canada increased to 100.39 percent of GDP in the fourth quarter of 2024 from 100.32 percent of GDP in the third quarter of 2024. This dataset provides - Canada Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Debt financing (mortgage, line of credit, term loan, credit card) terms and conditions, average interest rates and average length of term for small and medium enterprises in 2020 by region, CMA level, North American Industry Classification System (NAICS), demographics, age of business, employment size, rate of growth, etc.

Shark Tank US Dataset Description

This dataset provides comprehensive information about the American business reality television series, Shark Tank, covering seasons 1 to 14. The dataset includes 50 fields/columns and over 1260 records, capturing various details about each episode, pitch, and deal made on the show. Below is a detailed description of the columns included in the dataset:

Columns and Descriptions

• Season Number: The number of the season.
• Episode Number: The episode number within the season.
• Pitch Number: The overall pitch number.
• Original Air Date: The original or first aired date of the episode.
• Startup Name: The name of the startup company.
• Industry: The industry name or type.
• Business Description: A brief description of the business.
• Pitchers Gender: The gender of the pitchers.
• Pitchers City: The US city where the pitchers are from.
• Pitchers State: The US state or country of the pitchers, represented by a two-letter shortcut.
• Pitchers Average Age: The average age of all pitchers, categorized as <30 (young), 30-50 (middle), or >50 (old).
• Entrepreneur Names: The names of the pitchers.
• Company Website: The website of the startup or company.
• Multiple Entrepreneurs: Indicates whether there are multiple entrepreneurs (1 for yes, 0 for no).
• US Viewership: The viewership in the US, TRP rating, in millions.
• Original Ask Amount: The original ask amount in USD.
• Original Offered Equity: The original offered equity in percentages.
• Valuation Requested: The valuation requested in USD.
• Got Deal: Indicates whether the deal was secured (1 for yes, 0 for no).
• Total Deal Amount: The total deal amount in USD.
• Total Deal Equity: The total deal equity in percentages.
• Deal Valuation: The deal valuation in USD.
• Number of sharks in deal: The number of sharks involved in the deal.
• Investment Amount Per Shark: The investment amount per shark.
• Equity Per Shark: The equity received by each shark.
• Royalty Deal: Indicates whether it is a royalty deal or a deal with advisory shares.
• Loan: The loan or debt (line of credit) amount given by sharks, in USD.
• Barbara Corcoran Investment Amount: The amount invested by Barbara Corcoran.
• Barbara Corcoran Investment Equity: The equity received by Barbara Corcoran.
• Mark Cuban Investment Amount: The amount invested by Mark Cuban.
• Mark Cuban Investment Equity: The equity received by Mark Cuban.
• Lori Greiner Investment Amount: The amount invested by Lori Greiner.
• Lori Greiner Investment Equity: The equity received by Lori Greiner.
• Robert Herjavec Investment Amount: The amount invested by Robert Herjavec.
• Robert Herjavec Investment Equity: The equity received by Robert Herjavec.
• Daymond John Investment Amount: The amount invested by Daymond John.
• Daymond John Investment Equity: The equity received by Daymond John.
• Kevin O'Leary Investment Amount: The amount invested by Kevin O'Leary.
• Kevin O'Leary Investment Equity: The equity received by Kevin O'Leary.
• Guest Investment Amount: The amount invested by guest sharks.
• Guest Investment Equity: The equity received by guest sharks.
• Guest Name: The name of the guest shark.
• Barbara Corcoran Present: Indicates whether Barbara Corcoran is present in the episode.
• Mark Cuban Present: Indicates whether Mark Cuban is present in the episode.
• Lori Greiner Present: Indicates whether Lori Greiner is present in the episode.
• Robert Herjavec Present: Indicates whether Robert Herjavec is present in the episode.
• Daymond John Present: Indicates whether Daymond John is present in the episode.
• Kevin O'Leary Present: Indicates whether Kevin O'Leary is present in the episode.

This dataset provides a rich source of information for analyzing the trends, investments, and outcomes of pitches on Shark Tank.

Households Debt in Canada decreased to 173.07 percent of gross income in 2024 from 174.22 percent in 2024. This dataset provides - Canada Households Debt To Income- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table shows the indicators of the macroeconomic scoreboard. Furthermore, some additional indicators are shown. To identify in a timely manner existing and potential imbalances and possible macroeconomic risks within the countries of the European Union in an early stage, the European Commission has drawn up a scoreboard with fourteen indicators. This scoreboard is part of the Macroeconomic Imbalance Procedure (MIP). This table contains quarterly and annual figures for both these fourteen indicators and nine additional indicators for the Netherlands. The fourteen indicators in the macroeconomic scoreboard are: - Current account balance as % of GDP, 3 year moving average - Net international investment position, % of GDP - Real effective exchange rate, % change on three years previously - Share of world exports, % change on five years previously - Nominal unit labour costs, % change on three years previously - Deflated house prices, % change on one year previously - Private sector credit flow as % of GDP - Private sector debt as % of GDP - Government debt as % of GDP - Unemployment rate, three year moving average - Total financial sector liabilities, % change on one year previously - Activity rate, % of total population aged 15-64, change in percentage points on three years previously - Long-term unemployment rate, % of active population aged 15-74, change in percentage points on three years previously - Youth unemployment rate, % of active population aged 15-24, change in percentage points on three years previously The additional indicators are: - Real effective exchange rate, index - Share of world exports, % - Nominal unit labour costs, index - Households credit flow as % of GDP - Non-financial corporations credit flow as % of GDP - Household debt as % of GDP - Non-financial corporations debt as % of GDP - Activity rate, % of total population aged 15-64 - Youth unemployment rate, % of active population aged 15-24 Data available from: first quarter of 2006. Status of the figures: Annual and quarterly data are provisional. Changes as of April 16th, 2025: The figures for the fourth quarter of 2025 have been added for all indicators. In addition, due to revisions in the sources, several indicators have also been updated for past periods. Adjustment as of July 17th 2024: Data of the private sector’s credit flow and debt were not correct. They have been adjusted in this version. When will new figures be published? New data are published within 120 days after the end of each quarter. The first quarter may be revised in October, the second quarter in January. Quarterly data for the previous three quarters are adjusted along when the fourth quarter figures are published in April. This corresponds with the first estimate of the annual data for the previous year. The annual and quarterly data for the last three years are revised together with the publication of the first quarter in July.