The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.

Each week we send thousands of consumers' complaints about financial products and services to companies for response. Complaints are listed in the database after the company responds or after they’ve had the complaint for 15 calendar days, whichever comes first.

We publish the consumer’s description of what happened if the consumer opts to share it and after taking steps to remove personal information. See our Scrubbing Standard for more details

We don’t verify all the facts alleged in these complaints, but we take steps to confirm a commercial relationship. We may remove complaints if they don’t meet all of the publication criteria. Data is generally refreshed nightly. Company level information should be considered in context of company size and/or market share.

More about the Consumer Complaint Database | How we use complaint data | Technical documentation

Each week the CFPB sends thousands of consumers’ complaints about financial products and services to companies for response. Those complaints are published here after the company responds or after 15 days, whichever comes first. By adding their voice, consumers help improve the financial marketplace.

BackgroundWhere the data come fromThe Mortgage Performance Trends data come from the NMDB, a joint project we’ve undertaken with the Federal Housing Finance Agency (FHFA). For more information, visit the NMDB program page .The core data in the NMDB come from data maintained by one of the top three nationwide credit repositories. The NMDB has a nationally representative, 5 percent sample of all outstanding, closed-end, first-lien, 1–4 family residential mortgages.The data and analyses presented herein are the sole product of the CFPB. Use of information downloaded from our website, and any alteration or representation regarding such information by a party, is the responsibility of such party.Why the data matterMortgage delinquency rates reflect the health of the mortgage market, and the health of the overall economy.The 30–89 mortgage delinquency rate is a measure of early stage delinquencies. It generally captures borrowers that have missed one or two payments. This rate can be an early indicator of mortgage market health. However, this rate is seasonally volatile and sensitive to temporary economic shocks.The 90–day delinquency rate is a measure of serious delinquencies. It generally captures borrowers that have missed three or more payments. This rate measures more severe economic distress.PrivacyThe Mortgage Performance Trends data have many protections in place to protect personal identity. Before the CFPB or the FHFA receive any data for the NMDB, all records are stripped of information that might reveal a consumer’s identity, such as names, addresses, and Social Security numbers. All data shown are aggregated by state, metropolitan statistical area, or county.

The Credit Card Agreements (CCA) database includes credit card agreements from more than 600 card issuers. These agreements include general terms and conditions, pricing, and fee information and are collected quarterly pursuant to requirements in the CARD Act.

As required by the Credit CARD Act of 2009, we collect information annually from credit card issuers who have marketing agreements with universities, colleges, or affiliated organizations such as alumni associations, sororities, fraternities, and foundations.

Context

Understanding factors that support consumer financial well-being can help practitioners and policymakers empower more families to lead better financial lives to serve their own goals.

A person’s financial well-being comes from their sense of financial security and freedom of choice—both in the present and when considering the future. We measured it using our 10-item Financial Well-Being Scale.

The survey dataset includes respondents’ scores on that scale, as well as measures of individual and household characteristics that research suggests may influence adults’ financial well-being.

Content

Variables relating to question in this dataset include Income and employment, Savings and safety nets, Past financial experiences, and Financial behaviors, skills, and attitudes.

For reference on specific fields, a codebook is available online here.

Acknowledgements

This survey was originally conducted by the US Consumer Finance Protection Bureau and published online in October 2017 here.

The 2017 National Financial Well-Being in America Survey, conducted for the CFPB Offices of Financial Education and Financial Protection for Older Americans, was an online survey conducted to measure the financial well-being of adults in the United States. These data were created as a foundation for internal and external research into financial well-being and are relevant to work being done by researchers in the Office of Research who have access to the (deidentified) data.

Nationwide HMDA data, 2018-2021. Cleaned to record only accepted mortgages for primary residence, owner-occupied, single-family dwellings. Source: https://ffiec.cfpb.gov/data-browser/data/2018?category=nationwide

Code to create dataset available at https://github.com/nkacher/HMDA_age

Pursuant to the City of Chicago Municipal Code, certain banks are required to report, and the City of Chicago Comptroller is required to make public, information related to lending equity. The datasets in this series and additional information on the Department of Finance portion of the City Web site, make up that public sharing of the data. This dataset shows residential loan applications processed by responding banks. Answers in some columns are coded. Please see the "Filing Instructions Guide" section of https://ffiec.cfpb.gov for the translations. For some Number columns, nonsensical text values (e.g., values apparently answering different questions or those indicating no answer available, e.g., "N/A") have been removed in order to maintain the column type and therefore the ability to do numeric analysis of the responses. For original values, as submitted by each bank, please see the actual documents submitted at the "additional information" link above.

Python script used to examine how the marketing of properties explains neighborhood racial and income change using historical public remarks in real estate listings from Multiple Listing Services (MLS) collected and curated by CoreLogic.The primary dataset used for this research consists of 158,253 geocoded real estate listings for single-family homes in Mecklenburg County, North Carolina between 2001 and 2020. The historical MLS data which include public remarks is proprietary and can be obtained through purchase agreement with CoreLogic. The MLS is not publicly available and only available for members of the National Association of Realtors. Public remarks for homes currently listed for sale can be collected from online real estate websites such as Zillow, Trulia, Realtor.com, Redfin, and others.Since we cannot share this data, users need to, before running the script provided here, run the script provided by Nilsson and Delmelle (2023) which can be accessed here: https://doi.org/10.6084/m9.figshare.20493012.v1. This in order to get a fabricated/mock dataset of classified listings called classes_mock.csv. The article associated with Nilsson and Delmelle's (2023) script can be accessed here: https://www.tandfonline.com/doi/abs/10.1080/13658816.2023.2209803The user can then run the code together with the data provided here to estimate the threshold models together with data derived from the publicly available HMDA data. To compile a historical data set of loan/application records (LAR) for the user's own study are, the user will need to download data from the following websites:https://ffiec.cfpb.gov/data-publication/snapshot-national-loan-level-dataset/2022 (2017-forward)https://www.ffiec.gov/hmda/hmdaproducts.htm (2007-2016)https://catalog.archives.gov/search-within/2456161?limit=20&levelOfDescription=fileUnit&sort=naId:asc (for data prior to 2007)