*This blog post is part 3 of a “Beyond the default rate” series, which investigates what the default rate actually means, how it affects you and why you should know more than simply the number you see on some report.*

*If you haven’t yet read the previous posts in the series, please do that before reading this one. This post will be uilding upon the foundation we set in previously.*

In case you are simply looking at the number of loans that defaulted, then it’s rather easy to use the AD number from the dataset (some use the 60 days overdue instead to have more data, although 60 days doesn’t necessarily mean that a loan has defaulted and the full collection process has started).

However, when looking at the amounts, things get a bit more complicated.

## Should you use EAD1, EAD2 or Exposure_at_Default for your calculations?

There are three different exposure at default measures in the loan dataset provided by Bondora.

Before we talk about which one you should be using, let’s first start with what each of these means.

**EAD1**

Exposure at default in general is meant to show the amount of funds that are at risk at the moment of default, meaning that this is the amount you may be losing if the default doesn’t recover.

In case of EAD1, the exposure is considered as the outstanding principal only.

Example:

You invested €100. Borrower has repaid €25 in principal and €30 in interests before he defaults. In this case:

EAD1 = 100 – 25 = €75.

This ignores all the interests and late charges received and is probably the easiest figure to use in case you have to pay taxes on received interests and late charges irregardless of whether the loan actually is in profit or not (like individuals in Estonia).

This means that calculating default based on this figure, you are likely to end up with a rather pessimistic figure (unless all loans default with 0 payments, which is mostly not the case so far).

If you want to get more investment opportunities, then you may want to also include the interest payments, but adjust these for taxes to find a bit riskier loan groups that are still profitable due to the defaults happening after a few payments and you gaining some interest income.

**EAD2**

EAD2 is similar to EAD1, but in this case, all interest and late charge payments are also counted as if they were reducing your exposure.

Example:

You invested €100. Borrower has repaid €25 in principal and €30 in interests before he defaults. In this case:

EAD2 = 100 – 25 – 30 = €45

This shows you the actual amount of money you are risking, because you got some back as interest and some as principal payments. If you have 0% taxation (or close to this), then this is the case for you as in total, it doesn’t really matter whether the repayment was interest or principal. Your account balance doesn’t care.

In case you have to pay taxes on these interests, then you’ll get an overly optimistic result here.

**Exposure_at_Default**

Another column in the dataset that you can use, is Exposure_at_Default. This column also considers all outstanding principal as exposure, but additionally adds any accrued interests (seems to include late charges as well, but can’t confirm this) to the exposure as well.

Example:

You invested €100. Borrower has repaid €25 in principal and €30 in interests before he defaults. In this case:

Exposure_at_Default = 100 – 25 + accrued_interests = €75 + accrued_interests

If you are an individual investor, then using this in your calculations will likely exaggerate the default rates of every loan group and thus reduce the amount of suitable investment opportunities for you considerably.

Only use if you want to be extremely pessimistic in your forecasts or the accounting of your business requires this approach.

## Calculating your returns

Now that you have used all the knowledge to actually come to the default rate with the most suitable method for your needs, you can finally use it for the purpose we actually calculate it for.

We want to know what the return would be for a certain loan group at such a default rate.

In other words, if we come back to the question I asked in the first part of this series, you won’t really know if you want to invest into a 20% default rate loan group or not.

By now you hopefully know how the 20% has been calculated and whether this result is meaningful for your situation or not, but you still have no idea whether it would be profitable to invest into this.

For this I find it convenient to use a table I modified based on the return calculator table that was sent in a Bondora newsletter several months ago. You can download my modified table from here.

To calculate a potential return of a certain loan group, you need to have the following info:

- Average interest rate

You can either use the average in the dataset or in case it has changed dramatically due to some changes (e.g. risk-based pricing) you might want to use the average that’ll be likely used today. Otherwise if you were to calculate the default rate and average interest of the AA group based on historical data, you would get an overly optimistic result as the interest rates before were a lot higher. And likewise, you’d get an overly pessimistic result for the higher risk loans. - Default rate

This you have hopefully learned how to calculate based on this series of posts. - Estimated Recovery

Calculating the recovery is a whole another topic and includes a ton of nuances to account for, which we are not going to cover in this post series. However, you can use the average recovery announced in some Bondora newsletters months ago or if you want to be very conservative, you can use recovery as 0%. Keep in mind though that this is a simplified return calculation so it’s probably best to use max 1 year recovery rate here. - Count

I have also included the count of loans in here to make sure that a result is somewhat trustworthy. In other words, if you calculate a default rate for a group that has 3 loans, the result is almost certainly random and you shouldn’t base your investment decisions on this. If you use proper statistical tools to do these analyses, they will do the required calculations for you and tell you if the result is statistically meaningful. When using excel, there’s no such luxury, so I’ve used a somewhat arbitrary rule here that if count is <100, then I consider the result to be rather random, 100-200 as somewhat meaningful and >200 as significant enough to consider it as basis for investment decisions. Of course, this doesn’t guarantee anything, but is still a lot better than trusting every result.

You enter your results into the table and will get the potential return as an output.

Taking into consideration that the default rates with such calculations are likely not entirely constant and your personal default rate will likely somewhat differ from your calculations as you cannot participate in each of the opportunities in the loan group, you could just as well play around with the default rate by subtracting and adding a few % and getting a range of potential return to see if it would still be profitable for you if the situation got a little bit worse.

## Conclusion

Hopefully after reading through this post series, you’ll understand that although almost all investors are staring at the default rate and panicking if it goes up, it doesn’t really matter that much.

What matters is what the return after the default is going to be and if it is worth the risk associated with it.

The goal is not to figure out what the default rate will be, but the default rate is simply a means to figure out what the return will be.

As a conclusion to this series of posts, I’ll finish with the question that we started with and you should now have enough knowledge to answer to:

**Would you invest into a 20% default rate loan group?**

Post your answers in the comments below.