Lending Club Results, Year 1

Lending Club 2

Lending Club and other peer-to-peer lending systems have gotten a lot of press the last few years. Here’s my piece on how I got a 10% return from my first year using Lending Club and outperformed 90% of other portfolios on the platform. It’s not a debate on how good Lending Club is compared to every other investment out there, it’s just me sharing my results and the steps I took to get there.

In fact, I’ll say now I am completely biased in favor of LC. I believe what they’re doing makes the credit market a more efficient and fair place. When they went public I put my money where my mouth was and bought stock. I’m not a finance professional and am not liable for any decisions made based on my opinion or experience. If you’re interested in learning about what LC is, some links at the end of this post might be helpful.

Starting Out

One of my part time jobs during college was working as a customer service rep for a budgeting software company. I didn’t know much about personal finance, but after responding to thousands of emails and phone calls I learned that a LOT of people were paying a LOT of money when it came to consumer debt. Once someone told me they were paying off a card charging twenty-eight percent.

Twenty-eight percent! Meanwhile, my “high-interest” savings account was getting barely one.

One day I saw a quote from Renaud Laplanche, founder and CEO of Lending Club: “A very wide spread is always a signal of opportunity to an entrepreneur.” He had my attention. I had noticed this “spread,” and I didn’t like it, but had never thought of it as an opportunity. As I read and learned more about peer-to-peer (P2P) lending companies I became convinced technology was going to take the entrenched brick-and-mortar middle-men out of consumer debt and create a more efficient marketplace that benefited both savers and spenders. I wanted to be a part of it. I settled on using Lending Club to test the P2P waters. Its financiers included Google, it was making massive gains in loans issued every year, and it felt like what I wanted from this kind of system – open ended, transparent, and low maintenance.

What I didn’t want to do is what so many who bash Lending Club end up doing – scan through hundreds of notes and pick individual ones based on some feel-good criteria, then track each loan so relentlessly it becomes a personal insult when a payment goes late. There’s no time for that. If you’re looking for that kind of connection with your money, go fund your aunt’s Etsy store. I wanted the process to be automated and based on analysis I felt comfortable with so that I could set it and forget it. That involved creating a custom filter.

Lending Club makes that easy. All loan data from their start is available to download, with tons of variables that go into each loan’s assigned credit score. I then ran through most of those variables using Excel, calculating the probability that a certain condition would cause someone to default or get the loan charged off. The result was an extremely non-technical, licked-my-finger-and-held-it-up-in-the-wind analysis I made last year of LC notes. If there were any doubts about my not being a professional, let the spreadsheet settle them. My hope is someone glancing at it will get an idea of how they could do the same thing better (and just maybe share it with me). The highlighted items on the sheet are what I ended up thinking would be the players in minimizing defaults. Based on those, here is the filter I used to find loans:

The Filter

  • Open Credit Lines: 10 – >30
  • Inquiries in the last 6 months: 0 – 5
  • Min length of Employment: 3
  • Loan Purpose: Refinancing credit card, consolidate debt, Home improvement project, Major purchase
  • Revolving balance utilization: 0 – 70%
  • Term: 36-month
  • Monthly Income: 4,000 – >20000
  • Interest Rate: C, D, E, F, G
  • Home Ownership: Mortgage, Own

Using that filter, I set automatic purchasing (all done through the interface in the site, very smooth and simple) with the following somewhat arbitrary allocations for note ratings:

  • A-0%
  • B-0%
  • C-72%
  • D-22%
  • E-6%
  • F-0%
  • G-0%
  • Cash-0%.

I didn’t want to have a set allocation, but using an automatic purchasing plan requires it. No A or B type loans were purchased as I was striving for 10% or more on the return. Ideally I would have bought F or G if any were available that fit my criteria, but because that seemed to be a rare case I didn’t want money sitting around idle while the algorithm waited for something that fit.

Due to state restrictions and my net worth at the time I limited the investment to $2500. To a lot of people that’s chump change, but I saw it as a good amount for someone not fully committed and wanting to run an experiment. I set up automatic purchasing of $25 chunks of loans and let it go.


Fast forward one year. Here’s my account summary page and comparative performance page one year from when I started:

Lending Club Year 1 Performance

Lending Club Year 1 Comparative Performance

Looking at adjusted account value alone (taking out LC’s fees and expected defaults) the return is 9.96% on my principal. I believe the discrepancy between that and the projected 11% is the amount of time it took for the funds to actually be put to use. It was weeks before the entire $2500 was used to purchase chunks of 100 different loans.


First, Lending club has given me a solid return for what I see as lower risk compared to something like junk bonds. I want to quadruple my investment this next year to give it a more significant role in diversifying my investment strategy.

Second, it’s time to break out the old college stats book and calculator – my analysis needs improvement. It worked well, but could be much better. I want to look at all loans since inception which are paid off in full, charged off, or defaulted. Originally it included only four or five years of data (not including the harder times in 08-09) and looked at all loans whether or not the terms were complete.

Then, if I have time to get really fancy, I’ll take more into account than trying to avoid default or charge-off. A full analysis looks at actual returns of the different variables and would try to maximize that. Some regression analysis is in order. Where did I put that textbook…

Thanks for reading! Maybe you found something helpful here, and if you have any experiences, feedback, or resources you’d like to share, please leave a comment or message me. If you want to find out more, here are some links I found helpful:

  • An oldie, but a good, concise overview of what P2P Lending is at GetRichSlowly
  • A GREAT read on using LC by Mr. Money Mustache. This post and some of the resources/links he used are what inspired me to dig in to this a little more rather than just throw the money in, pick an allocation and go.
  • State and Financial Suitability – find out the rules for your state of investing using LC. This has gotten so much simpler in the last couple years and lending club has done a great job of getting its system legalized in more and more states, most recently Arizona and Texas.
  • For any Excel / data analysis wizards, you’ll love how LC publishes all loan data. Go forth and nerd.

For some diversity of opinion (meaning negative):

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