About the data
View the data on Urban’s Data Catalog:
This dashboard contains information derived from a 2 percent nationally representative panel of deidentified, consumer-level records from a major credit bureau from February 2020 to February 2021 and includes data on more than 5 million consumers for this period. We enriched the panel with an additional 2 percent sample of consumer-level records in August 2021 to better characterize the credit health of consumers in less populated areas.
We augment the data from February 2020 to February 2021 with information on alternative financial service loans from the consumers in our panel from a Fair Credit Reporting Act regulated agency whose unique data source is derived from various financial service providers. These providers include online small-dollar lenders; online installment lenders; storefront small-dollar lenders; and single payment, line of credit, auto title, and rent-to-own lenders.
The credit bureau data do not include information about race, so the racial and ethnic community metrics are based on the racial makeup of zip codes within the geographic area (US, state, county). We also incorporate zip code–level racial composition estimates from summary tables of the US Census Bureau’s American Community Survey (2014–18).
Specifically, communities of color are based on credit records for people who live in zip codes where more than 60 percent of residents are people of color (Black, Hispanic, Asian American or Pacific Islander, Native American, another race other than white, or multiracial). In the same way, majority-white, Black, Hispanic, and Native communities are zip codes where more than 60 percent of residents are in the respective racial or ethnic group.
We do not provide statistics on Asian Americans, Pacific Islanders, and other racial and ethnic communities because only a small number of zip codes in the US have more than 60 percent of residents identified as these respective racial or ethnic groups.
The map breaks are determined using Ckmeans.
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Share with a subprime credit score: share of people with a credit bureau record with a subprime credit score (Vantage score equal to or less than 600).
Median credit score: median Vantage score (300 to 850) of people with a credit bureau record.
Share with any debt in collections: share of people with a credit bureau record with any debt in collections.
Average credit card utilization: average credit card utilization among consumers with a credit card.
Median debt in collections: median amount of all debt in collections among those with any debt in collections.
Share with medical debt in collections: share of people with a credit bureau record who have medical debt in collections.
Median medical debt in collections: median amount of medical debt in collections among those with any medical debt in collections.
Student loan delinquency rate (60+ days): share of student loan holders with student loans 60 days or more past due or in default.
Credit card delinquency rate (30+ days): share of consumers with a credit or charge card who are 30 or more days delinquent.
Auto/retail loan delinquency rate (60+ days): share of people with an auto loan or lease or a retail installment loan who are 60 or more days delinquent.
Mortgage delinquency rate (30+ days): share of mortgage holders with a mortgage 30 days or more past due.
Share with AFS loan: share of people with a credit bureau record with an alternative financial sector loan. Alternative financial services are provided outside traditional banking institutions. These typically include short-term unsecure loans (such as payday loans), loans where personal property is used as collateral (such as pawn shop and auto title loans), and transactions under which property is leased in exchange for a weekly or monthly payment with the option to purchase (rent-to-own).
AFS loan delinquency rate (30+ days): share of AFS loan holders who are 30 days or more past due on their AFS loan.
Dive into a deeper analysis of the financial health of eight major US cities.
This feature was funded by the Annie E. Casey Foundation. We are grateful to them and all our funders, who make it possible for Urban to advance its mission. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of our experts. More information on our funding principles is available here. Read our terms of service here.
JoElla Carman and Luis Melgar
View the project on GitHub