How I am Investing in Lending Club and Prosper in 2012

[Update: There is a new post detailing my Lending Club and Prosper investment strategy for 2013.]

One of the things I love about p2p lending is the transparency. By this I mean that anyone can download the entire loan history of Lending Club and Prosper and analyze the data for themselves. I am trying to bring a level of transparency to my own operations on this blog by giving you an inside look at my investments.

Last month I provided a snapshot of all my p2p lending accounts and today I will continue along on that journey by revealing exactly how I am investing in Lending Club and Prosper today. I first detailed my investment criteria nine months ago in a post that described how I was investing with Lending Club and Prosper back then. I gave you two strategies each for both companies and today I am going to expand on that.

No More Conservative Lending Strategies

The biggest change in my investing in the last nine months is that I have ditched the conservative lending strategy at Lending Club. In my main Lending Club account I had been focusing on B- and C-grade loans for quite some time. But I decided that was simply leaving money on the table so late last year I switched course and decided to focus purely on loans grades of D and below at Lending Club on all my accounts (except for my Lending Club PRIME account).

Now, this created something of challenge. As I detailed in my last post I want to invest in multiple p2p lending accounts without investing in the same note twice. So, after spending way too much time on Lendstats exploring hundreds of combinations of selection criteria I came up with these sets of filters that provide no duplication of notes. I have provided a link below each filter to the Lendstats page that shows the returns one might expect when running these filters. If you don’t know what some of these fields mean you should learn more about credit reports (just google “understand credit report” and you will find plenty of articles).

Lending Club Filter 1 – High Income

Loan Grade: D, E, F, G
Inquiries = 0
DTI% <= 23%
Open credit line >= 8
Public records = 0
Monthly income >= $7,500
Loan purpose: All except other, small business and vacation
States – exclude CA
Link to Lending Club Filter 1 on Lendstats

Lending Club Filter 2 – Medium Income

Loan Grade: D, E, F, G
Inquiries = 0
DTI% <= 25%
Open credit line >= 8
2 Yr Deliquencies = 0
Public records = 0
Monthly income >= $3,000 and < $7,500
Loan purpose: All except other, small business and vacation
States – exclude CA, GA and TX
Link to Lending Club Filter 2 on Lendstats

Lending Club Filter 3 – Inquiries 1+

Loan Grade: E, F, G
Inquiries >= 1
2 Yr Deliquencies = 0
Public records = 0
Monthly income >= $7,000
Loan purpose: All except small business
States – exclude CA
Link to Lending Club Filter 3 on Lendstats

You can see that the main difference between Filter 1 and Filter 2 is the stated monthly income. I use that field to ensure that there is no overlap between loans when I am investing in multiple accounts. You will also notice that both filter 1 and filter 2 use inquiries = 0 as a criteria so this opens up the door to use Inquiries of one or more for Filter 3. Because all three filters don’t invest in loans originated in California I could easily setup a fourth unique filter for loans just issued in that state. I haven’t done this mainly because there are not enough loans that meet my criteria.

One point I should make is that if you use the Lending Club website to invest then you will not be able to use these filters as is. The filtering capabilities on their website are not flexible enough to allow for this kind of precision and some fields such as monthly income are not even available. So what I do is download the spreadsheet of all available loans from the Browse Notes page – there is a small Download All link in the bottom right of the screen. Then I can do the filtering in Excel and invest from there.

Prosper Filter 1 – Previous Borrower 0-1 Inquiries

Loan Grade D, E, HR
Payments on previous loans >= 12
Number of late payments  <= 9%
Allow credit score drop up to 100 points
Inquiries <= 1
Current delinquencies <= 1
Link to Prosper filter 1 on Lendstats

Prosper Filter 2 – Previous Borrower 2-5 Inquiries

Loan Grade D, E, HR
Payments on previous loans >= 10
Number of late payments  <= 10%
Allow credit score drop up to 100 points
Inquiries >= 2 and <= 5
Current delinquencies <= 1
Link to Prosper filter 2 on Lendstats

Prosper Filter 3 – New Borrower 0 Inquiries

Loan Grade D, E, HR
Payments on previous loans = 0
Inquiries = 0
Current delinquencies = 0
Open credit lines >= 10
Debt-to-income ratio <= 75%
Link to Prosper filter 3 on Lendstats

Prosper Filter 4 – New Borrower 1-2 Inquiries

Loan Grade D, E, HR
Payments on previous loans = 0
Inquiries >= 1 and <=2
Current delinquencies = 0
Delinquencies in last 7 years = 0
Bankcard utilization <= 95%
Open credit lines >= 10
Debt-to-income ratio <= 75%
Public records last 10 years = 0
Employment status: exclude Unemployed, Not Available
Link to Prosper filter 4 on Lendstats

The bulk of my new investments on Prosper go towards repeat borrowers. I have found repeat borrowers to be an excellent group of borrowers and you can see by clicking on the Lendstats link with each filter that they provide excellent returns.

Long time readers will know my love of the Number of Inquiries filter so you might be surprised by Filter 2 where I go with number of inquiries between 2 and 5 (I have long maintained that inquiries = 0 is one the best filters you can have). But I let the Lendstats ROI numbers be my guide here. And even though a previous borrower has two or more inquiries on their credit report with the additional filters in place here you can still generate an excellent ROI.

Now, I don’t want to be one dimensional and ignore new borrowers, so filters 3 and 4 provide a way to invest in new borrowers that is also likely to produce a good return. Again I am using number of inquiries as the way to separate the note selections to avoid duplication.

So, there you have it. These are the criteria I am using to invest today. It makes investing with multiple accounts a breeze or you can just as easily use these criteria on one account. Feel free to use these filters yourself if you like. Or you can always critique them and provide your own suggestions in the comments.

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Dan B
Dan B
Feb. 9, 2012 8:38 am

Again, on behalf of my fellow Californians, I must protest on our seemingly blanket exclusion from consideration. We are a great state with incredibly responsible people. You can always rest easy with the knowledge that your investment is completely safe with us. No Californian would ever consider walking away from a debt except as a second or third resort. 🙂

Danny S
Danny S
Feb. 9, 2012 8:43 am

Very interesting loan filters for LC, particularly that you are focusing on lower grade loans now. Have you determined that the higher interest rates those notes pay more than offsets the increase in defaults you experience?

I’ve also been a B and C note investor (and a few A and D here and there). My biggest concern about investing in notes that are charging 20%+ interest (E, F, G) is that these borrowers are less likely to be doing it for debt payoffs, vs things like home repairs, medical or wedding expenses, small business startups, etc… where I think they are riskier uses of the funds.

Dan B
Dan B
Feb. 9, 2012 9:17 am

As myself & others have stated here 127 times previously, the truth is that we really have no idea what the borrower ultimately does with the borrowed money. Personally, I assume that regardless of what they “say”, they will in fact spend it, rather than pay off debt. It frankly makes no difference to me as I’m not here on some misguided social do-gooder mission. I’m just here to get a high return.

I’m no longer a Prosper investor (for totally unrelated reasons) but in a sort of perverse way, those who are HEAVILY concentrating on “repeat” Prosper borrowers (like Peter) also recognize that the ultimate best borrower is one who will keep “repeat” borrowing & remain perpetually in debt while making his high interest monthly payments on time. I have no problem with that reality either. Just being honest about the dynamics & reality of the situation.

Charlie H
Charlie H
Feb. 9, 2012 12:16 pm

It should be noted that most income is just stated income and not verified income.

What the loan is used for, as Dan B points out, is 100% unverifiable. I don’t use it for filters other then to exclude people.

Danny S
Danny S
Feb. 9, 2012 2:50 pm

Peter I find some of your insights to be quite fascinating. I dont know if I can get my head aligned with G graded notes, but I am going to take a closer look a D, E, F notes.

My big concerns for those notes are monthly income (even if not verified) vs monthly payment… I try to keep to a ratio of no more than 15% of a person’s income going towards their LC loan. That has seemingly worked quite well in keeping defaults low for me.

I also dont like having borrowers which show any delinquencies within the last 4 years, so that also affects the # of loans in the pool available to me. But will see how many of these lower grade loans are available in my comfort zone.

Larry V
Larry V
Feb. 9, 2012 4:12 pm

Peter,

My continued thanks for you providing a great place to learn about and discuss P2P. Thanks for being open about your investment philosophy.

Bryce M
Bryce M
Feb. 9, 2012 8:22 pm

A few thoughts:

I see many people create filter strategies that attempt to get a handle on risk. But I have seen few people use risk in combination with reward to choose their loans. This is my approach. I predict a charge-off probability for each loan in the database (based on historical predictors), and then compare it to the interest rate to calculate an expected value for the loan. I rank by expected value, and choose the top loans in which to invest.

The mutually exclusive filters is a poor idea in my opinion because it creates an artificial partition on the data. You could literally be choosing “the cream of the crap!” It makes much more sense to look at all loans simultaneously and choose the best ones.

Those who don’t pay attention to stated use are leaving valuable information on the table. Stated reason is a statistically meaningful predictor of charge-off. It doesn’t matter whether people use it for the intended purpose; on average it is a valid signal.

Frankie C
Frankie C
Feb. 10, 2012 1:12 am

Peter,

Thanks a lot for sharing all this valuable info. I’d like to touch on a few topics:

What’s the reasoning behind DTI% = 8 ? Did you just trial and error on lendtats until you found the magic numbers?

And what about avoiding CA? Did you try excluding each state at a time until you found the worst offender? Any idea why?

I’m very surprised to see you allow 2+ inquiries applicants. I understand your desire to come up with different criteria because of your multiple accounts. I’m not sure giving up on what has been a great predictor makes sense for those of us with a single account.

Finally, I’m surprised you don’t filter more selectively on loan purpose. It seems to be an pretty good predictor also. Do you disagree?

Thanks,
FC

Bryce M.
Bryce M.
Feb. 10, 2012 2:25 am

Partitioning the loans will never yield a profit maximizing solution if you insist on nonzero investments in all portfolios. I think I could prove it, but it seems intuitive to me anyway. Maybe at best it will match the profit maximizing allocation, but seems unlikely.

If one filter is superior to another, then you would always be better off by investing more in that space with the money from the weaker filters portfolio. My 2cp.

Moe
Moe
Feb. 10, 2012 2:11 pm

How about the P2P companies start offering balance transfers, having the the borrowed money go directly to the credit card companies owed. This would be noted on the note listing, and I’m sure these notes would get funded much quicker. Now let’s see who will jump on this first…

Chris
Chris
Feb. 10, 2012 2:37 pm

My two cents for what it’s worth:
1) Risk as a measure to whether one should choose A-G rated notes should be compared against one’s entire portfolio, which hopefully does not include just lending club or prosper products. A healthy balance of other assets (bonds, stocks, CDs, ETFs, Real Estate, whatever) will by its very nature permit you to entertain higher risk (E-G) notes if you are wanting to dabble with that asset class. I happen to agree with Peter and the other comments above that it is a class worth taking seriously. Consequently, if you find yourself curious or interested in the E-F notes, simply offset it with something else (outside of member dependent notes altogether) and you should be fine to at least experiment.
2) Speaking of diversification, I have often wondered if a P2P company will one day provide us with a platform where we can invest in credit card debt directly (funding the actual credit card’s “credit”) instead of investing indirectly in credit card debt as we do now (by providing the credit for prior credit card debt consolidation). Considering how large a market credit card use has become, one could only wonder the investment potential awaiting the first P2P platform that could service those instruments directly.

Food for thought…

Chris
Chris
Feb. 10, 2012 2:38 pm

By the way Moe, you have an EXCELLENT idea.

James
James
Feb. 12, 2012 1:20 pm

Peter,

Thanks for your ideas. I still don’t understand the rationale for excluding certain states?? And how did you choose which to eliminate?

Also the criteria you list don’t fit the filters on Lending Club. For instance you use 23% DTI in your first example and Lending Club allows selection only in 5% increments. Also you want a minimum stated income and there is no such criteria on LC? Or do you use lendstats to find the loans then link back to lending club to purchase them?

Thanks again!

Bryce M
Bryce M
Feb. 12, 2012 7:37 pm

The rationale for giving a black mark for states is simple. Certain ones default at higher rates than others. Go download the data and prove it to yourself.

Bryce M.
Bryce M.
Feb. 13, 2012 10:08 am

I don’t think your argument that you want multiple criteria is solid, Peter. For example, if what you say is true then you wouldn’t be neglecting CA loans because things might have changed since the loans used to make that negative relationship finished. You would be using CA loans because they might be stellar performers in the future. But, you do not, presumably because you also hold the belief that about all you can do is use the past to predict the future.

. The best we can do is to choose criteria that have a string theoretical basis and empirical evidence and hope they continue to perform in the future.

TJ
TJ
May. 31, 2012 12:25 pm
Reply to  Peter Renton

Peter,

When you manage multiple accounts, how do you avoid duplicate notes? Or do you not worry about that, given the vast diversification you already have?

Chris F
Chris F
Feb. 14, 2012 12:36 pm

Peter,

I’m curious if you and others are doing any manual filtering based off answers and written descriptions. I use similar filters as above and then base my final decision off seeing some written answers. If someone wants a loan for $35k and they can’t be bothered to write anything anything beyond, “creidt consold” then I skip it. However, this can really limit the number of loans available for me to invest in since a fair amount of borrowers don’t write anything. I am curious if I’m biased against these loans for no good reason and what others take on it is.

I remember reading articles a while back that showed a positive correlation between word count and ROI, but that was a while back and I really haven’t seen too much discussion about it since.

Larry V
Larry V
Feb. 14, 2012 1:21 pm

I’m the same way. I have a variety of filters, but I read every loan description I’m considering. I definitely run away if they are asked specifics and answer with generalities. I’ve also noticed that many of my defaults literally contained some “desperate” language that I used to ignore. On a side note, in case LC is watching, it would be great if I could look at a loan, then hide it or mark it somehow so i don’t keep reading the description.

Chris
Chris
Feb. 14, 2012 3:00 pm

@ Chris F:
Above you write:

“I remember reading articles a while back that showed a positive correlation between word count and ROI, but that was a while back and I really haven’t seen too much discussion about it since.”

Would you happen to remember where you read this material/article? I would love to look at that data as I have often wondered the same thing with regard to possible correlation between description and default rates.

Thanks for sharing,
Chris S

Roy S
Roy S
Feb. 14, 2012 6:06 pm

@Chris, I had to go back through many, many posts to find this…https://lendingclubmodeling.wordpress.com/2011/04/25/why-loan-descriptions-and-qa-matter/

There is also another post somewhere that also has a list with the 10 worst and 10 best words in the description section. I think the ten worst mainly included familial relationships, like child and children. I think another of the worst was “help.” That article might take me a little more time to find.

Roy S
Roy S
Feb. 14, 2012 6:07 pm

Oh look! Peter beat me to posting that article! Oh well…

Frankie C
Frankie C
Feb. 14, 2012 6:09 pm

On the topic of descriptions, I wonder if there is a correlation between the quality of the spelling (@Chris’ “creidt consold”) and the default rate. It would be interesting to massively feed descriptions into a spell checker, come up with a metric (errors/100 words?) and then correlate that to default rate over time. Or am I taking this too far? 😉

Bryce M.
Bryce M.
Feb. 15, 2012 9:24 am

Whoops! I posted a couple nice graphs about loan description size but in the wrong thread. They are in the How I Run Multiple accounts thread.

Bryce M
Bryce M
Feb. 15, 2012 8:44 pm

Just a followup, people should look at both graphics. The first is a histogram of the distribution of the lengths. The second is the money graphic, showing the relationship to charge-off. I note this because the second graphic only had 1 view where the first had 10. People may have thought that the link was just one continuous thing.

Bryce M
Bryce M
Feb. 16, 2012 1:03 am

Had some fun with words:

Loans with the word “bills” in the description were 6% more likely to charge off. “Bills” was in the top 50 words used.

Loans with “bible,” “God,” or “pray” defaulted at a 35% rate compared to a population 22% charge off rate, but the result was only borderline significant because there were just 25 such loans among the first 3200 in LC’s history.

Chris F
Chris F
Feb. 16, 2012 3:54 pm

@Bryce Very interesting, thanks for passing those graphs along. Just curious, on your loan description axis it goes from 0-8 characters, but have you looked at beyond 8 characters?

The lendingclubmodeling link and lendingtuber where the two articles I remember reading. However, I thought lendingtuber’s findings (on Prosper, not Lending Club) that the shorter the description the greater chance of getting paid was surprising and looking for other research to back that up.

https://lendingtuber.blogspot.com/2011/09/loan-description-length-by-credit-grade.html

Bryce M.
Bryce M.
Feb. 16, 2012 4:00 pm

Chris,

Per the histogram, there are hardly any loans with descriptions beyond 8 characters. The x scale is in natural logged characters. So, 8 is really e^8 ~ 3000 characters.

Bryce

Dan B
Dan B
Feb. 16, 2012 6:56 pm

Whatever did happen to the “Wordman”? :

Bryce M
Bryce M
Feb. 18, 2012 12:30 am

Inspired by the keyword analysis, I did the same using Lending Club loan titles and found 8 keywords that I will be keeping in my model going forward. There is definitely value to be gained there.

Bryce M.
Bryce M.
Feb. 19, 2012 7:44 pm

I wouldn’t waste everyone’s time reporting statistically insignificant results. These are the patterns over all of LCs completed loan history. Some words may not have enough appearances to say much, and that’s why I looked at the top 50 commonly used words only (and many of those were boring and not bother to check like articles the and a).

I suppose one way to validate it would be to predict delinquency on the currently in repayment loans population. I have been thinking of making more use of that data to see if my modeling holds on more current data anyway.

Bryce M.
Bryce M.
Feb. 19, 2012 9:58 pm

The problem is that defaulting is not the same outcome as charging off. I have done all my modeling with chargeoff or paid in full as the outcomes. Obviously many loans that default wind up curing themselves, so things that predict default may or may not be the same or of the same strength as for chargeoff.

I do two things to test my model. First When I built my model I put a portion of the data aside and then tested the model’s performance on it. Once since I’ve completed it, I tested the model on all the loans that completed since the last loan used on the original model. That is, as time passes, more loans complete to study. But the problem is that they are always three years behind the loans to fund today.

The hope is to build a set of leading indicators to keep an eye out and ensure that the predictors are continuing to perform as expected.

Best wishes!

Bryce M.
Bryce M.
Feb. 19, 2012 10:35 pm

I definitely knew that LC tweaked their eligibility criteria, and when I built the model I used a technique with an indicator random variable on that set of loans to see if, after all of the other variable I was using, there was still any basic difference for those loans. After all of the work that I did, I no longer had to treat them any differently. It was clear that LC selected better borrowers on criteria that they were disclosing to lenders. So, in the end, we can use those loans to model with also.

I’d be happy to have a conversation with you any time, Peter. You have my email.

Dan B
Dan B
Feb. 19, 2012 11:21 pm

Bryce…………I’m sorry, but defaults & charge offs are in fact virtually the same thing. Almost no loans that default “cure themselves:” as you put it. I have no idea how you could come to that conclusion.

Bryce M
Bryce M
Feb. 19, 2012 11:31 pm

Perhaps it is a definition difference, but many people do miss a payment deadline and subsequently fix their situation. The outcomes for loans in repayment are essentially four: (1) paid in full, (2) late of varying degrees, (3) charged off, and (4) current. I am equating “late” to default.

I do not like the word “default” because I have not seen LC define it precisely. It is a meaningless marketing term to me that I cannot associate to any of their public data. I’m sorry if I’m just ignorant of a commonly accepted definition.

Bryce M
Bryce M
Feb. 19, 2012 11:34 pm

The main point is that it is hard to analyze loans in repayment in a way that is meaningful and to get a set of leading indicators because we simply don’t know how the outcome of “late” is going to shake out. LC doesn’t give us the whole payment history so we can model that. That’s why I just simply skipped the middle steps and modeled the end state of charge off vs. full payment. That’s what matters in the end anyway.

Dan B
Dan B
Feb. 19, 2012 11:44 pm

Ok I see. So when YOU say “default”, it could actually be someone who is late by a few days. If that is the way you’re defining it then we have no argument here.

Bryce M
Bryce M
Feb. 19, 2012 11:48 pm

As further proof that “default” and “charge-off” cannot be the same, LC consistently states its default rate is something like 3%. The facts are that the charge-off rate for all loans that have completed is around 22%. These are so different as to be comical to assert they mean the same thing.

I tried to reproduce this number many ways, but about all I could come up with was that it is the instantaneous proportion of late loans. That is, at any given time, there are about 3% of loans that are delinquent. These risks accrue over time, some cure, some charge off, and we wind up with about a 22% charge-off rate.

Chris S
Feb. 20, 2012 12:00 am

Bryce, you bring up an interesting point. I had always assumed that:

Default = Payer has missed too many payments; so now LC/Prosper is going to send off to a third party collections agency with hopes of getting some portion of the balance recouped.

Written-Off = Hopeless possibility of getting any money due to various reasons (borrower has died, filed bankruptcy, etc)

This leads me to wonder – does anyone reading this thread have a more formal/official explanation from LC/Prosper as to how they define these two classifications?

Bryce M
Bryce M
Feb. 20, 2012 12:06 am

As a mathematician, it is extremely frustrating (but not at all surprising) to see people throw around words without precise definitions. One of the first things I did with the public data file was to try and reproduce LC’s numbers, such as the 3% “default” rate. I assumed that they meant only 3% of their loans went bad. Imagine my shock when I looked at the loans that had run their 3-year course and found almost 23% had charged off.

A 3% instantaneous delinquency rate sounds much more appealing than 1 in 4 of our loans charges off. I can see why they went that route. But of course all investors do plenty of due diligence to know this, right? =)