I’m going to start a new series where I will periodically discus a filter, it’s potential impact on ROI and the rationale. This is the first on many to come in my new series, Featured Filter!

Introduction to Revolving Credit Balance

Many people already know what this, but for those who may not, revolving credit balance is the total amount on your credit card(s) that is unpaid.  The reasons for the balance size varies greatly depending on spending habits, income, unexpected expenses or any number of other factors. There are a lot of components that will contribute to this value. As a result it can vary greatly from month to month. In my own case, I recently bought a new bedroom set which set my credit utilization at about 10%. I monitor my credit score details using a free service called Credit Karma which gives you your TransRisk score. Once this bedroom purchase settled my TransRisk score dropped 40 points. The credit card has just been paid off and I can reasonably expect the score to return to it’s previous level next month.  As you can see revolving credit balance and percent can be quite volatile!  But in most cases, people with unpaid balances carry those from month to month. Revolving credit balance can be a good indication of the level of debt someone is able to accommodate.  

Motivation for Analyzing Revolving Credit Balance

I have a few notes that have defaulted. I try to analyze why the notes might have died (defaulted) and if there is a pattern I can integrate into my filters going forward. For a number of my defaults I noticed people are borrowing money far in excess of their revolving credit balance for loans labeled as debt consolidation. Why would someone with 10K of credit card debt need 35k for a debt consolidation loan? It’s a valid question. In addition, do these types of loans have a higher rate of default?

I decided to create a new metric on the NSR platform which is simply a ratio of the revolving credit card balance to the loan amount. I took an in depth look at the results and determined there is an interesting correlation.

1. People who request loans for roughly 100% of their revolving credit balance have the highest ROIs.
2. People with zero credit card debt perform poorer than people with as little 2K + in credit card debt.  Simply selecting people with 4K or more in credit card debt can add almost 1% more to your ROI.

The to and from values are a result of taking the loan amount and dividing by the credit balance

Take Away

Perhaps people with no credit card debt don’t have to skills/discipline to budget for a monthly payments in excess of their typically fixed costs. Or potentially people requested loans for the amount of outstanding credit balance are those who are most honest with themselves about their debt situation and getting on the path to prosperity. Regardless, buying notes for loans that are same size as the revolving credit balance outperform their peers.  Obviously this will reduce the number of loans significantly, so I would recommend to simply avoid borrowers that have zero credit card debt.

The data I used in screen shots above is from Lending Club, but this filter performs in the same behavior on Prosper.

If you are thinking about using this filter on Lending Club, you’ll be disappointed to find that you can not.  They only support a less than filtering.

Lack of options will make using filters here impossible

For those of you who blindly invest based on your filter, you’re going to have to look at the loan it order to see how the revolving credit balance stacks up against the requested loan amount. Prosper handles their revolving balance filter in a different, but much more flexible manner:

Discrete filtering options allow for augmented functionality. NSR Approves!

Prior to digging into the numbers I assumed people with zero credit card debt would outperform everyone. It’s also worth pointing out that credit balance is not a direct factor in Lending Club’s underwriting process. I encourage you to play with the numbers yourself. You can always check out my newest project P2PXML to get direct access to both platform’s raw data and confirm these findings yourself!

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