https://github.com/cfpb/consumer-credit-trends-data

Data for the CFPB's consumer credit trends visualizations
https://github.com/cfpb/consumer-credit-trends-data

Last synced: 17 days ago
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Data for the CFPB's consumer credit trends visualizations

• Host: GitHub
• URL: https://github.com/cfpb/consumer-credit-trends-data
• Owner: cfpb
• Created: 2017-01-13T20:44:32.000Z (almost 8 years ago)
• Default Branch: main
• Last Pushed: 2024-10-31T16:30:09.000Z (about 2 months ago)
• Last Synced: 2024-12-02T16:36:49.457Z (21 days ago)
• Language: Python
• Homepage: http://www.consumerfinance.gov/data-research/consumer-credit-trends/
• Size: 11.8 MB
• Stars: 7
• Watchers: 20
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://www.consumerfinance.gov/data-research/consumer-credit-trends/) powering graphs for each featured credit market, as shown in this screenshot.

![Screenshot of consumer-credit-trends](cct-screenshot.png)

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:

- CSV files of the data for download

- JSON files which are rendered on [the live site](https://www.consumerfinance.gov/data-research/consumer-credit-trends/) using [cfpb-chart-builder](https://github.com/cfpb/cfpb-chart-builder)

### 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