Disclaimer: I ran most of the calculations in one long session so can’t really guarantee that all data is entirely 100% accurate and free of any accidental mistakes with filtering or whatnot. Before making any investment decisions, double-check the results and run your own calculations as well.
This is essentially what covers the risks of investing with Bondora on their official statistics page. There is also some table about profitability and recovery below it, but those don’t really show anything about risks and they have no explanations around them and no-one who actually knows what they show, would really think that this is indeed what those tables mean.
In addition, the graph highlighted above, seems to be incorrect as well. The help text under the question mark says the following:
Expected Return measures potential returns for an invested principal within a particular Bondora Rating segment. It is calculated as the difference between the interest rate and the expected loss rate. Expected Return is based on historical inventory and relies on the country mix and loan maturity structure available for the previous month. Should the country mix or the maturity structure change or appear to be substantially different in your portfolio, your expected return may vary. Higher expected return generally also means higher volatility in potential returns.
In other words, it should be based on the loans issued in January of 2016 mostly. However, if we look at the expected return of loans, then since the introduction of Bondora Rating V2 in December 2015, the E(R) for HR group has been somewhere between 8-18% and nothing even reaching the 20% figure shown here.
In fact, let’s look at all the figures based on loans that were issued and not cancelled according to loan dataset.
Not only is HR showing a lot higher number on Bondora’s statistics page than it should, the F Rating is also considerably higher. With the rest, I guess there could be a difference in the results if you weigh the values and/or look at all loans sent to market, instead of only those that received funding.
In general though, it is clear that in this situation, any investor worthy of the title, is forced to either do some necessary analyses on their own or allocate their funds to other investment opportunities.
In this post, let’s attempt to look at some of the numbers that one would perhaps want to and expect to see at a P2P-lending platform to help with assessing the risk and return of the opportunity.
The Risk – Default Rates
We are looking at a dataset as of 06.02.2016. and will start with general default rates per Rating based on the EUR amount of loans and EAD1 (exposure at default including only defaulted principal amount).
Default Rates per year and country
We can now also look at the same thing based on country and year when the loan was issued.
Interest rate per country and year
Now, while interesting and important information, the default rates alone don’t give us necessarily the whole picture, because we would also have to see the interest rates those loans were issued at.
Also, since most investors should be on average investing same or very similar amounts to each loan, then it might make more sense to look at the default rates based on counts of loans instead of amounts, so I’ve added this figure here as well.
While the actual interest rate when weighted, could be somewhat different from the unweighted result (relevant for when you compare it to default based on sum), it’s probably not too far from it. Especially for 2014 loans, where essentially most of the loans received very similar interest rates with up to a few percentage point differences on average.
Based on interest rate and default rate alone, you could make some simple calculations for rough return estimates already by assuming that defaulted loans don’t earn you interest and based on this you could probably easily identify which specific segments are performing best from these.
Although for a more accurate result, you would have to also consider the time when defaults happened and how much payments were made prior the default (you will end up with a slightly more optimistic result in case you use this count default rate, because this percentage has already been discounted by the principal proportion at default), and you would also have to account that some of the principal from performing loans will be earning interest for more than one year (while some of it is repaid also with every month). You may also want to account for some recovery, although I personally don’t since it’s relatively unpredictable and I prefer making a more conservative estimate, instead of a too optimistic one.
In other words, this sort of table is good for making quick at-a-glance conclusions about different segments of loans and judging the potential ballpark of a yearly return for those to judge which ones seem to be the best performers for your strategy (before or after taxes based on your situation).
To get a very accurate result from this, if you want the actual return figure, would take quite a lot of additional work though.
A more accurate and mainstream method to calculate your return, would be to use XIRR calculation for your selected segments. However, while theoretically possible, it would require matching up several datasets and doing a whole lot of work before you actually can come up with some solution. The main issue would be with accounting for the cash flow coming back from the specific segment of loans.
In other words, it’s way too much work to do this in excel for me so I won’t be doing that today.
Recovery, Gross Profit and ROI
There is also the method similar to what Bondora is showing under the Portfolio Profitability section on their statistics page. I personally don’t think the Principal Overdue value used by Bondora is very meaningful for making conclusions about performance of the portfolio today and I’ve previously explained why in this post within the XIRR explanation section here.
Personally, I believe it makes much more sense to compare this figure with the defaulted principal amount instead. Why? Well, a defaulted loan loses 1/3 or even more of its value relatively instantly after defaulting, simply based on its status. In reality, it’s probably losing even more, since the liquidity is even lower than for other loans (not too many investors are out there buying defaulted loans I’d imagine compared to current or few days overdue loans). Also, the entire principal is considered by the contract and legally as overdue at the moment of default so by definition that principal is overdue.
Recovery is also taking significant amount of time and while we don’t know the exact recovery amount in the end, the outcome can’t really be expected to be over roughly 60% of the principal recovered (based on historical data, that seems pretty close to the ceiling) and that’ll usually take several years to achieve at least.
So after accounting for time value of money and the drop in value and liquidity of the defaulted loans, we can assume a considerable loss on those on average already instantly after the loan has defaulted.
While considering 100% of defaulted principal as a “loss” as of today, is overly pessimistic, this is somewhat leveled out by accounting for recovery so far and not deducting any part of simply overdue loans, out of which a decent amount will default later on.
Also, not considering defaulted loans as a “loss” almost at all as of today, is overly optimistic, as it inherently assumes a considerable recovery in the future, which for a freshly defaulted loan, is extremely optimistic, and which may or may not become true, while the default and the drop of value has already happened.
In other words, again, I personally prefer to be more pessimistic in these estimations than optimistic, since this leaves room to be positively surprised if additional recovery does come in and it somewhat protects me from unexpected losses.
Based on this table, we can now calculate some other values such as:
- Recovery% – percentage of defaulted principal that has been recovered to date (as of 06.02.2016).
- Gross Profit – InterestAndPenaltiesPaid minus DefaultedPrincipal plus RecoveredPrincipal.
- ROI – simple calculation of Gross Profit divided by FundedAmount. This is relevant because simply looking at an amount of money received on a bunch of loans, is relatively meaningless without the context. A €10 000 is a positive number, but it’s still a very bad investment if it is achieved on a €5 million investment over a year. Note that this is not a yearly figure, but total over the entire period. ROI is also totally different from XIRR and any other calculation methods where time element is also taken into account and thus should not be compared to these directly. Consider it as a very simplistic “put this much in, took this much out” type of calculation.
- ROI Before Recovery – simple calculation of Gross Profit before RecoveredPrincipal divided by FundedAmount. Note that this probably still includes the interest and penalties received from recovery, if any. Note that this is not yearly figure, but total over the entire period.
Comparing the ROI Before Recovery and after at least some of it, we can see that it has historically had a relatively large effect on the end result and it’s already somewhat visible in 2014 year loans as well.
In short, while the results for more recent loans are not looking too good at the moment, it is reasonable to expect it to become at least somewhat better in the long run as the amount of new defaults starts slowing down, more interest payments will come in and additional recoveries will be secured.
Recovery, Gross Profit and ROI per country
Let’s look at the same numbers per different countries.
From this table we can see a similar pattern, in regards to the effect of recovery, emerge. We can also relatively easily spot which segments are the best performing as of 06.02.2016. at least and which ones are worst.
Ideally you would also split these segments up into specific segments or compare in some other way the proportion of risk levels within these different segments to make sure that one is not significantly higher risk than another with a lot higher interest rates to compensate for it. As in this case the higher risk segment would almost always show a lower Gross Profit initially due to higher initial default rate, but the higher interest rate would then later on compensate for this (at least in regards to pre-tax returns).
In this case however, we can simply look at the average interest rate, which is also highest for EST for every year, with the exception of Spanish loans in 2015, where average interest rate is over 50% compared to 31% for EST. In other words, if there is no sudden unexpected large increase in defaults for Estonian loans so that it exceeds those of other countries significantly, Estonian loans will continue to show a lot better ROI and Gross Profit on average than other countries (prior to 2015 at minimum).
Another aspect to consider in this context would be to look at the average loan duration. In other words, with everything else being equal, the longer the duration, the larger the earned interest amount should be by now, since initial payments in long duration loans are majorly consisting of interests and the more interest can be earned by the end of the loan schedule as a whole.
In most cases Estonian loans are somewhere in the middle of the bunch compared to other countries and the interest amount relative to issued amount is significantly higher in close to every case, with the exception of 2015, where interest received for Spanish loans amount to 11.5% of funded amount compared to 10.8% for EST so far.
The following graph sums up where the Gross Profit is coming from on platform level up nicely.
ROI and Gross Profit after taxes
This simplistic calculation doesn’t tell us much about the actual annual return and the effect of time on return (for example, if 80% of loans from 2014 were issued in the end of the year, then the actual return would be higher than compared to issuing those loans early on in the year and if most of the interest had arrived early on in 2014 already, then also the return would be higher than when it is paid later on in the 2015 instead).
However, since it’s such a simplistic money in vs money out type of calculation, and we know the interest amounts received, we can very easily calculate the Gross Profit and the ROI after taxes by deducting the tax rate from interest earned.
So, considering that I’m an Estonian investor who has to pay 20% tax on earned interest (actually it was 21% for the earlier years, but let’s keep it simple), we can easily calculate the Gross Profit and ROI after taxes for same formula.
I have to admit that the effect of taxes on this broad level seems larger than I had anticipated.
What you probably would want to look at when considering taxes though, is the split between risk levels because usually the higher the risk level, the bigger the effect of taxes since at least in Estonia we’re not allowed to deduct losses from taxable interest income. The higher the risk level, the bigger the portion of the return coming from interest payments that cover the defaulted loans.
Ideally you would also split them up by year for example and then see the performance more clearly, but since the table is also quite huge and the numbers in many segments are relatively small, then I’ll skip that part here.
So the Gross Profit and ROI after taxes has been better in general for the lower risk loans, compared to HR and others. It tends to even out a bit after the risk-based pricing was introduced in 2015 so the difference is not that huge anymore, but currently it’s still there. With the new V2 model where HR has a lower Expected Return, the difference will likely increase again.
Gross Profit and ROI based on Bondora’s statistics
Now, while I personally find this Principal Overdue concept a relatively impractical and in this context even misleading since contractually a defaulted loan is fully overdue, then I know that some people like this concept.
So here’s a small table on the Gross Profit and ROI after taxes based on the same Bondora’s Statistics page table as of 10.02.2016. Since the interest received is there, you can easily calculate these figures for yourself any time you want with your own tax rates.
Unfortunately we can’t see this data split by country here so we can’t really look into why some figure is where it is today, but I guess you can use the tables provided previously in this post to make some assumptions and guesstimates on it anyway.
The statistics shown on Bondora’s official page don’t offer much practical value for making daily investment decisions since they don’t cover almost any aspect of risks nor allow any more granular look into the actual causes of why the outcome is as it is. Also, they don’t seem to be accurate in some cases for some reason, as seen from the image at the top of the post.
I also think it is pretty clear from these tables, that doing your own analyses has at least historically meant that, based on data, you can make a lot better quality decisions about your investments, understand better what’s going on at the platform as a whole and end up with a significantly better outcome than average.
While the new Bondora Rating V2 model is supposed to be a lot more accurate than its predecessors, and I’d expect it to be also, we will start getting data on the actual results perhaps in about 6-12 months on. I opted to wait for statistical evidence about the performance to come in when model V1 was introduced and stuck with my own analyses, and it seems to have paid off nicely, but in the end, it’s up to everyone to decide themselves how and where they will invest their money and what information they need to make those decisions.
What additional analyses or statistics do you use when making your investment decisions?