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Data for the CFPB's consumer credit trends visualizations

Home Page: http://www.consumerfinance.gov/data-research/consumer-credit-trends/

Python 97.99% HTML 2.01%

consumer-credit-trends-data's Introduction

Consumer Credit Trends

Explore recent developments in consumer credit markets

The data files in this repo appear on the live Consumer Credit Trends site here powering graphs for each featured credit market, as shown in this screenshot.

Screenshot of consumer-credit-trends

This site displays graphs for several consumer credit markets:

  • Auto loans
  • Credit cards
  • Mortgages
  • Student loans

For each market, credit information includes:

  • Market summaries for volume, value, and geographic changes
  • Lending levels by certain demographic breakdowns
    • Borrower risk profile (credit score)
    • Neighborhood income level
    • Borrower age
  • Inquiry activity
    • Credit inquiries
    • Credit tightness

What's in this repository

Data files

Data is organized into per-market folders. Provided data files include:

Python files

The Python 2 based script processes raw data from our Office of Research into the output data files contained in this repo. These raw data files are internal-only and are anonymized summaries produced by the Consumer Credit Panel.

  • process_incoming_data.py - Overall processing script
  • process_globals.py - Configuration settings for data processing
  • process_utils.py - Reusable utility functions, e.g. number/date conversions and reading/writing data/csv/json files

Data schema

Markets by folder or filename suffix

Data is organized into per-market folders. The suffix of each file indicates the credit market.

Market Folder File Suffix
Auto loans auto-loans AUT
Credit card credit-cards CRC
Mortgages mortgages MTG
Student loans student-loans STU

Types of data by filename prefix

The prefix of each file indicates type of aggregate data. This table lists these prefixes alphabetically.

File Prefix Description
crt_ Number of consumers who applied for credit each month and did not obtain additional credit
Values are indexed to January 2009
inq_ Number of consumers with credit inquiries
Values are indexed to January 2009
map_data_ Geographic map data containing year-over-year changes for each U.S. state
num_data_ Number of new loan originations
vol_data_ Loan volume in dollars
volume_data_Age_Group_ Loan volume in dollars, broken out by the borrower's age group demographics
volume_data_yoy_data_Income_Level_ Loan volume in dollars, broken out by the borrower's income level relative to their neighborhood's income level
volume_data_Score_Level_ Loan volume in dollars, broken out by the borrower's credit score group
yoy_data_all_ Year-over-year percentage change in new loan originations
yoy_data_Age_Group_ Year-over-year percentage change in new loan originations, broken out by the borrower's age group demographics
yoy_data_Income_Level_ Year-over-year percentage change in new loan originations, broken out by the borrower's income level relative to their neighborhood's income level
yoy_data_Score_Level_ Year-over-year percentage change in new loan originations, broken out by the borrower's credit score group

consumer-credit-trends-data's People

Contributors

anselmbradford avatar cfpbot avatar contolini avatar cwdavies avatar hillaryj avatar marcesher avatar scotchester avatar virtix avatar

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