A New Standard for P2P Lending Data Analysis Launches

Many investors like to do their own analysis of the raw data from Lending Club and Prosper. To do that you have to go to the download area of each site and access the data separately. Then you have to figure out what all the fields mean because both companies store their data in completely different ways.

Michael from Nickel Steamroller thought there had to be a better way. So, he has created an open standard and a central location where investors can go to download the data from Lending Club and Prosper. Best of all he has made the data available in exactly the same format for both companies. It is called P2PXML and it launched last week.

Why Use P2PXML?

Of course, investors can just go to Lendstats or Nickel Steamroller to do analysis on the p2p lending data. But these sites do not give users access to the raw data. The biggest benefit of accessing data through P2PXML is standardization which makes it much easier to use. The data is presented in exactly the same way for both companies.

Downloading the raw data has always been a time consuming and complex process. Although at Lending Club it is relatively straight forward – the loan history is a simple CSV file that anyone can download and import directly into Excel.

At Prosper, however, they provide a much richer data set and consequently it is more complicated to analyze. Their full data download contains over four gigabytes of information and is in XML format. I have never bothered looking at the raw Prosper data simply because I am on a Mac and Excel for Mac can’t open XML files.

Now, I just go to the download area of P2PXML to access the loan history of both companies – this data is updated daily.

Introducing P2P Lending Rate Groups

Rate groups is a new concept that is part of the P2PXML specification and download. This is a new development I am very excited about. It is an idea I have been mulling around for several months and when Michael discussed creating a p2p lending data export standard I thought it would be a great addition.

As we all know Lending Club and Prosper have different loan grades that represent different interest rates. Prosper targets, on average, a higher risk borrower than Lending Club and consequently has a higher average interest rate. What I have been looking for is an apples to apples comparison. How does a bucket of 12-14% loans at Lending Club compare with a bucket of 12-14% loans at Prosper? There has never been an easy way to figure this out … until now.

Now, we can ignore the loan grades and look at the underlying interest rates of the loans. Every loan is assigned a rate group by P2PXML in a 2% range starting at 0% (although there has never been any loans issued below 4% and likely never will be). For example, all loans with an interest rate between 6% and 7.99% fall into rate group R-4, 8 – 9.99% loans are R-5 and so on. Look for some interesting analysis based on rate groups in a future blog post.

As the name suggests the standard format for downloading data from p2pxml.com is XML format. But I asked Michael to also include a CSV download for people like me who don’t want to work with XML data. You can download a sample set of 1,000 records or the full data set of both companies. I should point out that this is still a work in progress and some fields don’t have any data – that is because both Lending Club and Prosper only make certain fields available. But even as it is right now it still provides useful information for investors.

Let me know what you think of this attempt to bring a standard to p2p lending data exports. And I am sure Michael will read the comments here so feel free to make suggestions as well.

  • Peter Renton

    Peter Renton is the chairman and co-founder of Fintech Nexus, the world’s first and largest digital media and events company focused on fintech. Peter has been writing about fintech since 2010 and he is the author and creator of the Fintech One-on-One Podcast, the first and longest-running fintech interview series. Peter has been interviewed by the Wall Street Journal, Bloomberg, The New York Times, CNBC, CNN, Fortune, NPR, Fox Business News, the Financial Times, and dozens of other publications.