Filtering Bondora Loans for Fewer Defaults
As some of you may know, Bondora is from Estonia, and Estonia has a wonderfully digital approach to almost everything. When you file your taxes in Estonia, you do so online, and within a few days you get money back if the government owes you, for example. Other nice features include the possibility to digitally sign documents in a legally binding way, and precise control and monitoring of how and when the government uses your personal data.
So it’s not surprising that the main P2P lender from Estonia is ambitious. As you may know, Bondora wants to become a pan-European lending service, catering to the biggest economy in the world with its 500 million potential customers. In order to allow such a lending system to function on such a huge market, where the national requirements for every country may be different, you need a really good system, and that’s what Bondora is working on.
For those interested in the actual loan data: you can download a CSV (currently about 6 MB in size) of all the loans on Bondora to analyse yourself. I’ve done this, and I’ve used Excel’s pivot table feature to analyse the dataset. If you want to know how pivot tables in Excel are used, check out this video on Youtube. The same features are also available in Google Spreadsheets, in case you don’t have Excel. Basically, pivot tables allow you to compare different parameters to others by grouping the data from a huge table like Bondora’s CSV file into different categories that you can customise.
So what are the results of my analysis?
First, I’ve only been looking at Estonian and Finnish loans in my analysis, because Spanish and Slovakian loans seem to be 1) less numerous, so the analysis isn’t as meaningful right now and 2) somewhat more risky, so I want to restrict my investing to the two countries for now. The following analyses are therefore applicable to loans from Finland and Estonia only.
Next, what do we look for in our analysis? The main problem with P2P investing are the default rates. If borrowers stop paying their debt, we don’t earn any return, so we’d like to reduce the amount of defaults as much as possible. For individual borrowers, it’s impossible to say with any kind of certainty whether or not they will default at some point or not. So we look at probability of default by searching for groups of borrowers that have default rates below average.
The parameter used to indicate default was the ‘In Debt 60 days’ column, which indicates whether or not (‘1’ or ‘0’) the loan has, at some point, been 60 days overdue, no matter what happens after the default. In many cases, defaulted loans are eventually paid back with a delay, but we want to know how to avoid borrowers with a higher probability of default altogether.
1) HOME OWNERSHIP TYPE
The first things I looked at was the home ownership. The categories ‘tenant, unfurnished’ and ‘mortgage’ had the lowest percentage of defaulted loans. The mortgage makes intuitive sense for me, because when a borrower has a mortgage, he 1. is used to paying back a loan and 2. has something to lose if he doesn’t consistently repay his debts, so his overall discipline might be higher. The ‘tenant, unfurnished’ category could make sense in so far as people who live in apartments that are unfurnished probably have some cash on hand to spend on new furniture, so they might be overall more financially healthy than people who do not have any cash for such expenses. These explanations of why these two categories seem to be better are pure conjecture, I do not have any evidence for their validity. I only have evidence for the lower default rate, but not for my explanation of why this is. In the end, it doesn’t matter if we can explain it, as long as we can take advantage of it.
2) OCCUPATION AREA
Same thing as before, the numbers in the leftmost column represent a code for different occupational areas that can be looked up on Bondora’s website. The low default rates highlighted here belong to the categories: Utilities, Real Estate, Research and Administrative.
Unfortunately, these two first aspects we’ve looked at cannot be easily filtered for when looking for new loans to invest in. You can select one type of home ownership to search for, and one occupational area, but you can’t select a group of values that are OK for your search. So to filter based on the results for occupational area, we’d have to execute 4 searches. Other parameters are however easier to use when searching for loans. Here they are:
As you can see on this chart, the default rate goes down the older the borrower gets. And there’s a quite visible drop at age 26, so borrowers younger than 26 have a higher probability of defaulting. It seems intuitively correct that as people get older, they take fewer risks and may take on a loan only if they are sure to be able to repay it, whereas younger borrowers may be less cautious and therefore more risky overall.
4) CREDIT RATING
The next parameter is the credit rating on Bondora (500 to 1000). we can see in the chart below that the default rate is very high for 500 and then quickly drops. Personally, I focus on loans with a credit rating of 800 or higher.
5) LOAN DURATION
According to the chart below, loans with a longer duration seem less likely to default. Does that make intuitive sense? I don’t think so. I think this chart is not reliable as a source of information because Bondora is not that old yet, so the amount of loans that are repaid over 60 months / 5 years is still very low. Therefore, I will not use the information in this chart for any practical investment purposes myself.
6) EMPLOYMENT STATUS
Here we see that it’s better to focus on fully employed borrowers than partially employed ones, and entrepreneurs and retirees seem to be an even better bet: entrepreneurs are on average more financially robust than employees, and retirees are generally older, so they take fewer risks than younger people would.
7) TOTAL INCOME
Your financial wealth depends on where you live. But Bondora operates in Europe, and the living standards, although quite diverse overall, are not as different as those of Switzerland and Nigeria. Even though we are looking at income data from Estonia and Finland together, we can clearly see that there’s a somewhat smooth slope in the chart: the more people earn, the easier it is for them to pay back their loans and not default. So who should we exclude from our investments? In order to still find enough opportunities, I focus on borrowers with 900 euros in income or more. This cuts out most of the low-earners.
Finally, the amount funded. As you can see in the chart, the only real outlier seems to be loans for 0-1000 euros, which default in almost 30% of cases. Why? 1000 euros is not a lot of money, so if people need to borrow 1000 euros, this means they don’t have any cash or savings (and probably no job either, since a monthly net salary is about 800 euros in Estonia and 2300 euros in Finland), so they are not very healthy financially. It’s best to not invest in those kinds of loans.
The ideal loan to invest in, therefore, would be a loan to a borrower older than 26, with a credit rating at or above 800, who is fully employed, earns more than 900 euros a month and wants to borrow more than 1000 euros.
This is what I now use to filter the investments on Bondora before I invest. You can now easily save searches when looking for new investments, and you can invest in bulk to more quickly invest your money on Bondora.
What do you think about these criteria? If you have any other interesting conclusions, or a better explanation for the results I’m seeing here, let me know in the comments!