Credit Builder Loan Offers Dataset

Author: Noah Gomez
Created: 1 July 2023
Published: 3 December 2023
Updated: 9 February 2024

Status: Active

Active - Continually Updated


Dataset Metadata

Product Concerned: Credit Builder Loan
File Type: Created in Microsoft Excel, Hosted Privately
Availability Status: Private
Mother Catelog: Private Datasets
Mother Catelog Description: A collection of datasets collected, processed, refined, and analyzed by Thick Credit.

Date Updated

9 February 2024


Description

This data table hosts commercial data of over 200 credit builder loans in the market. It is a private dataset not available to the public used to identify, rank, and tailor the best offers to consumers in ThickCredit's product and content.

Sample (Image)

Similar Datasets

This dataset is similar to but not the same as:

  • the credit builder card dataset,
  • the secured credit card dataset,
  • the savings-secured loan dataset, and
  • the rent reporting services dataset.

Data Fields in Table

  • Lender. The commercial name of the lender offering the credit builder loan.
  • Link. A URL to the online offer.
  • Institution Type. The lender type, such as a bank, credit union, online lender, or community development organization.
  • Max Principal. The maximum principal allowed. This is measured in United States Dollars.
  • Min Principal. The minimum principal allowed. This is measured in United States Dollars.
  • Avg Principal. The average of maximum and minimum principal allowed. This is measured in United States Dollars.
  • Duration High (mnths). The maximum duration in months allowed. This is measured in months.
  • Duration Low (mnths). The minimum duration in months allowed. This is measured in months.
  • Avg. Duration. The average of maximum and minimum monthly durations allowed. This is measured in months.
  • APR High. The maximum APR offered on the loan. This is a percentage.
  • APR Low. The minimum APR offered on the loan. This is a percentage.
  • Avg. APR. The average of maximum and minimum APR allowed. This is a percentage.
  • Credit Check. The type of credit check performed (hard or soft), if any.
  • Alternative Requirements. Alternative eligibility requirements such as income and employment.
  • CU Membership Field. The geographic or organizational requirements to join the lender, if the lender type is credit union.
  • States Available. USA States where loan is available. All 50 US states are represented in the dataset.
  • # of States Available. Total number of states where the loan is available. This is an integer.
  • Collateral Type. Whether the offer is payment-secured, fully-secured, or unsecured.
  • Interest Kickback. Whether a percent or absolute amount of interest is rebated.
  • Interest Kickback Value. The percent or absolute amount of interest rebated, if any.
  • Account Required. Whether an account is required with the lender to use the credit builder loan.
  • Can Payoff Early "in good standing" . Whether the loan allows no-penalty early payoff.
  • Savings Earn Interest?. Whether cash collateral on the loan earns interest, and how much in APY (annual percentage yield).
  • Close, Not Repay, Early. Whether the lender allows early cancellation without requiring payoff, for secured credit builder loans only.
  • Phone Number. The phone number of the lender.
  • Email. The email of the lender.
  • Minimum Credit. The minimum credit score required, if any.
  • Cost (Interest + Fees). Total cost of borrowing the loan, including interest and fees.
  • Interest. The interest rate (not APR) of the loan.

Data Quality & Validation

Relevance

Data is chosen and collected with consumer borrowers in mind. Data fields are limited to metrics that inform decision-making and risk-mitigation, as well as contact information. Most of the raw data must be analyzed by Thick Credit to drive insights, except for simple fields like phone number and email.

Collection

All data is continually collected in the field by human agents. Most data comes directly from lender websites and representatives contacted via email or phone. In some instances, automated collection techniques are used after the process has been proven and vetted by a human agent.

Accuracy

Accuracy is reinforced by dual-authentication and continual updating of evolutionary figures, such as interest rates that may evolve with the prime rate.

Completeness

In some instances, lenders refuse to communicate data points or require an application. If a reasonable substitution through peer-comparison or nearest-neighbor techniques can be used, we use these or other best practice to complete the data. Otherwise, we remove the lender from any analysis where the empty data would corrupt results. The sample is large enough to remain representative despite the removal of a small number of records.

Recency

The data is updated continually to include new data and new offers entering the market.

Cleaning

Cleaning is not required for this dataset because the data is collected raw in the field and not repurposed from other sources.

Privacy

The dataset is hosted natively offline and on hard drives inaccessible via internet channels.

Validation

Outliers do not impact raw data, only analysis. We use a conservative framework that eliminates outliers with a material impact on analytical metrics helpful to consumers. For example, fully-secured credit builder loans and their savings-secured sibling products do not usually provide maximum or minimum principal because the borrower's savings determines principal, so these lines are excluded from credit builder averages to ensure consumer borrowers have the most representative figures available.

About the Author

Noah Gomez (founder of Thick Credit) is a transatlantic professional and entrepreneur with 3+ years experience in consumer finance education. He also has 5+ years of experience in corporate finance, including debt financing, M&A, listing preparation, US GAAP and IFRS.

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