Welcome to Interest Radar!
In this article I’ll try to give you some tips to get you acquainted to Interest Radar, and start using our tools to find the loans with the best chances of providing high yield returns.
First of all, Interest Radar is for people that already understand the basics of peer-to-peer lending. If you’re new to this world, here is some recommendations of where to start:
- Interest Radar focuses on loans provided by LendingClub. If you don’t have an account set up there yet, I suggest you read their Prospectus and decide whether this type of investment is for you
- Social Lending Network is a very complete site about P2P lending. They also maintain a forum for the community
- Interest Radar focuses on providing you with information for your investments. If you’re looking for data regarding the P2P lending industry and overall statistics, I recommend Nickel Steamroller charts or LendStats summaries
- Many sites also provider news related to P2P lending. You can start looking into P2P Lending News or the weekly digest provided by Social Lending Network
So, what is Interest Radar trying to accomplish?
At least once a month, if not almost daily, P2P lenders are faced with the tough decision of which loan listings to put money in. For conservative investors, expecting 5 to 8% in annualized returns, the decisions are easier: low-risk notes with grades A and B are safe bets with low exposition to defaults. But if you’re more aggressive, the challenges are more interesting. You have to pick the right high-interest loans, and you must expect a higher default rate, but find a balance where the losses due to default don’t decrease your returns too much. If you pick the wrong borrowers, your returns could be even less than the conservative A and B notes are yielding.
There is a lot of talk in the internet about credit worthiness and how to find the right loans to invest. Unfortunately, they’re mostly based in intuition and common sense. In Interest Radar we don’t rely in this type of advice to find the best loans. The background of the creators of this site is with the data analysis teams in the financial industry. The tools we provide are designed to:
- Test an hypothesis through iteration
- Find where the money is, i.e., where the loss is minimal
- Find the current loans to put your money in
- Effortlessly repeat step 3 every time you have cash to reinvest
And our principles are:
- Pragmatism and a practical approach. In the course of your investor life you’ll be picking hundreds, if not thousands of loans to invest. You have to spend your energy in what matters
- The numbers. Statistical analysis as opposed to “common sense”. Past behavior as an indication of future performance
- Integration. You must be able to track back your performance with the same instruments you picked your loans to begin with
If this sounds too abstract, let me walk you through a real world example of what all that means.
- Let’s start with an anecdotal “common sense” belief and try our hypothesis. For this exercise, let’s try to prove that homeowners default less than those paying rent or a bank mortgage
- We also want to be making at least 10% in returns, so we will rule out loans with grades A and B
- With Interest Radar’s analysis tools, we will set up a filter: only notes C, D, E, F and G, and we will select all Home Ownership filters (Own, Mortgage, Rent)
- (If you’re used to other tools in the internet that allow you to filter loans, you’re asking why can’t you go more granular and select, let’s say, C4 through G1. The answer is in our “pragmatism” principle: if you had all 35 credit grades to choose from, you’d be spending too much energy (picking, analyzing, filtering out, reading in the screen) and the reward for that wouldn’t be material)
- You may additionally want to specify the loan term (36 or 60 months), based on your investment needs. For the purpose of this exercise, we’ll select only 36 months
- We will select the Filter Breakdown option, so that we can see the distribution of loans and the summary of the two things we need to test our hypothesis: how many loans, and the average loss rate
- When we run our analysis, Interest Radar will present us with the breakdown tables and some small charts for a quick visual representation of the distribution
- By a quick look at the charts we can see an important trend: most of the loans in our analysis are C grade, then D and so on, and as the number of loans reduces, the loss rate increases. That’s something we expected, as we know C-grade notes should be better than G-grade notes
- But the second chart, related to the filter we’re studying (home ownership), is where we get our main answer. For the universe of loans we’re analyzing, as the time of this post, loans for people that Own their homes default at a rate of 4.6%, while the loss rate for borrowers that rent or have a mortgage is 4.2% and 3.1% respectively
This is a good example of a situation where statistical analysis defeats intuitive knowledge. You must be asking yourself now, why people that don’t have to pay rent or a mortgage installment every month stop paying a loan more often than those who have such big burden in their financial lives? That’s the wrong question to ask, because that’s not what the numbers are saying. They are simply saying that when LendingClub report a borrower as “Owner” of their houses as opposed to “Rent” or “Mortgage” you can expect a higher risk of default. Why? Interest Radar can’t answer that. Maybe people lie when they enter that information, and a mortgage is easier to verify (it must be in their credit bureau report) than home ownership. Maybe relatives living as a favor report they own the house they live (as they’re not renting or paying mortgage, right?), and they’re not the best borrowers. Maybe there are people that couldn’t qualify for a mortgage and had to buy their places all cash, and they’re failing to pay back the loans. Maybe people living in a trailer default too much. The fact is that it’s beside the point for you as an investor of $25 for each loan. You have to focus your energy in what you can control, and right now, you’re going to start looking at those Rent and Mortgage loans with a little bit more respect.
I hope this example illustrated the key principles behind Interest Radar, and gets you started with the tools.
Are you interested so far? If so, I’m guessing you’ll enjoy the more advanced tools in Interest Radar, such as the Cash Flow generator, saving and retrieving Strategies, monitoring Strategy performance, the Loan Description word frequency analyzer, the alerts etc.
To read more about the features of the site, look for the context help icon in the site. They are links to blog posts about the tools and lending tips. I’m looking forward to read your comments about the site and my suggestions!