So What is the Demand Index? Once the Demand Index algorithm has been completed and fully tested, the demand index will be a daily tracking index showing the overall demand for certain loan grades/types. It will be primarily coded in Unix Shell script. How does the Demand Index work? The Demand index will automatically download [...]

The next project to be created here at P2P Analytics is a “Demand index” for LendingClub. The premise behind a demand index is as follows: It would be interesting to see how quickly certain types/grades of loans get funded. Using “In Funding” data collected once a day and referenced against the “funded loans” data, it [...]

Ok, so lets take a look @ the state of the P2P lending scene here in the United States. In the United States, there are currently two big P2P lenders: Prosper.com and LendingClub.com Below are a few charts showing the historic rates of loan originations for these two companies: What is apparent from these graphs [...]

The first iteration of the algorithm is now bug free and fully tested for all loan grades. Here are the findings: Note: These results are based on providing the algorithm with the entire pool of 56,592 loans that Lending club has ever made that: 1. Aren’t classified as “Does not meet current credit policy”. 2. [...]

All bugs have been worked out of the algorithm so I’ve decided to do a quick post releasing the top 3 notes in funding for Grades A though C. Please follow this link to see the notes. I plan on building a portfolio over time with the algorithm’s suggestions and tracking the results of the [...]

The initial version of the algorithm that I have been working on to assist in the selection of LendingClub notes, with the intention of minimizing defaults and maximizing returns, is now complete. Here are some of the results: First, lets look at the default rates of LendingClub notes in general. The below default rate table [...]

So let’s say you hop on Lendstats.com and start looking at LendingClub data in regards to homeownership and its effect on default rates. At first glance, its apparent that people with Mortgages seem to default less on average than renters: (5.1% defaults vs 4.1% defaults). However, this isn’t the whole story. The whole story is [...]

When devising a Ranking Algorithm for notes, the best way to start is to look at all of the factors that we can derive from each individual note. LendingClub already takes the following factors into account when setting the interest rates on individual notes: FICO score of borrower, Requested loan amount, Number of recent credit [...]

I have the firm belief that Peer to Peer lending is well on its way to becoming a new and popular asset class. I also believe that the ability to lend to others as well as to small businesses via new P2P lending laws will reinvigorate the economy and will level the economic playing field [...]

P2P analytics is now launched! The goal of this website is to provide analysis of peer to peer notes such that an individual investor can make educated and informed decisions based on the performance of previous loans on the peer to peer lending service. The goal is to start with LendingClub and expand to other [...]