This post continues our series on peer-to-peer lending (and LendingClub): Peer to Peer Portfolio Returns and The Decline in Returns as Loans Age, Investing in Peer to Peer Loans. LendingClub, and other peer-to-peer lenders let you use filters to find loans that meet your criteria. So if you chose to take more, or less, risk you can use filters to find loans fitting your preferences. Those filters can also be applied to automate your lending.

There are resources online to help you understand the past results of various investing strategies (returns based on various filters). Some filter are just a trade-off of risk for return. You can invest in grade A (a LendingClub defined category) loans that have the lowest risk, and the lowest interest rates and historical returns. Or you can increase your risk and get loans with higher interest rates and also higher historical returns (after factoring in defaults).

Description of chart: This chart shows the historical performance by grade for all issued loans.

This chart includes all loans that were issued 18 months or more before the last day of the most recently completed quarter. The historical returns data in the chart is updated monthly.

Adjusted Net Annualized Return (“Adjusted NAR”) is a cumulative, annualized measure of the return on all of the money invested in loans over the life of those loans, with an adjustment for estimated future losses.

LendingClub lets you set filters to use to automatically invest in new loans as funds are available to invest (either you adding in new money or receiving payments on existing loans). This is a nice feature, there are items you can’t filter on however, such as job title. And also you can’t make trade-offs, say given x, y and z strong points and a nice interest rate in this loan I will accept a bit lower value on another factor.

So I find I have to be a bit less forgiving on the filter criteria and then manually make some judgements on other loans. For me I add a bit higher risk on my manual selections. I would imagine most people don’t bother with this, just using filters to do all the investing for them. And I think that is fine.

Practically what I do so that I can make some selections manually is to set the criteria to only be 98% invested. This will cause it to automatically invest any amount over 2% that is not invested. You can set this to whatever level you want and also is how you can make payments to yourself. I will say I think one of the lamest “features” of LendingClub is that is has no ability to send you regular monthly checks. So you have to manually deal with it.

It should be simple for them to let you set a value like send me $200 on the 15th of each month. And then it manages the re-investments knowing that and your outstanding loans. But they still don’t offer that feature.

As I said one of the factors in setting filters is managing risk v. reward but the other is really about weaknesses in the algorithm setting rates. You can just see it as risk-reward trade-off but I think it is more sensible to see 2 different things. The algorithm weaknesses are factors that will fluctuate over time as the algorithm and underwriting standards are improved. For example, loans in California had worse returns (according to every site I found accessing past results). There is no reason for this to be true. If a person with the exactly same profile is riskier in California that should be reflected in higher rates and thus bring the return into balance. My guess is this type of factor will be eliminated over time. But if not, or until it is, fixed filtering out loans to California makes sense.

Once you set your filter criteria then you select what balance you want between A, B, C, D, E and FG loans. I set mine to

A 2%
B 16%
C 50%
D 20%
E 10%

I actually have a bit over 1% in FG (but I select those myself). In 2015 the makeup of the loans given by LendingClub was A 17%, B 26%, C 28%, D 15%, E 10%, F and G 4%.

Related: Where to Invest for Yield Today (2010) – Default Rates on Loans by Credit Score – Investing in Stocks That Have Raised Dividends Consistently – Investment Risk Matters Most as Part of a Portfolio, Rather than in Isolation

Sadly Lending Club uses fragile coding practices that result in sections of the site not working sometimes. Using existing filters often fails for me – the code just does nothing (it doesn’t even bother to provide feedback to the user on what it is failing to do). Using fragile coding practices sadly is common for web sites with large budgets. Instead of using reliable code they seems to get infatuated with cute design ideas and don’t bother much making the code reliable. You can code the cute design ideas reliably but often they obviously are not concerned with the robustness of the code.