P2P Lending Data Visualisation with Python — Prosper Marketplace
By: Evelyn Lin
Tags: Seaborn, Matplotlib, Python, Financial Data, Data Visualisation

Peer-to-peer (P2P) lending, or crowdlending, has been an emerging alternative financial service in the global market. As it gains popularity, the industry could have a long-term impact on more and more people’s financial decisions. With this in mind, this project aims to use Prosper’s real-world data to visualise and understand what contributes to differences in loan interest rates among different borrower groups.

Dataset Overview

There are 113,937 loans with 81 features in the dataset. (44,384 data points were removed due to missing values and outliers before the investigation started). Variables consist of both numeric and categorical types. The attributes included variables that indicate both the borrowers’ characteristics (such as whether or not they are homeowners), and the loans’ features (such as terms, status).

A Glance at the Distribution of Borrower Rates

My main focus is on finding predictor variables for interest rates ( BorrowerRate ). So I will first take a look at this column with the below graph. The interest rate is normally distributed between 0 to 0.4, with most loans ranging from 0.08 to 0.35, the data has a mode of 0.17. The Borrower APR column is distributed almost identically to the former variable; thus the following discussion will focus on only one, i.e., the BorrowerRate .

Interest Rate vs. Prosper Scores/Terms/Loan Status

With some initial explorations, I identified 3 potential parameters that can contribute to higher interest rates - a borrower’s Prosper score, the term, and status of a loan.

The graph shows that:

The higher the Prosper score, the lower the interest rates.

The longer a loan takes to be repaid, the higher the interest rates.

The longer a loan passes its due date, the higher the interest rates.

With this result, we can now take a closer look at each variable, starting with Prosper Scores.

What Do Prosper Scores Measure?

Since the interest rate is negatively correlated with Prosper scores, I went ahead to identify how the score is determined in the first place. From the below graph, as Prosper Score increases, the percentage of borrowers in the higher-income range also increases. The same trend goes for the binary variable of whether a borrower is also a homeowner or not, the higher the score, the more likely the borrower has a home.

So we can conclude that the score is correlated to:

Stated monthly income

Homeowner or not

Interest Rates across ProsperScore, by Homeowner or Not

Homeowners pay a slightly lower interest rate (by around 0.05%) than non-homeowners in general. ProsperScore of between 4 and 7 are the most common. The interest rates vary within this range, the lowest and highest end of scores has a narrower range of interest rate.

For all scores except for 1.0, homeowners can usually expect to get lower interest rates than non-homeowner counterparts.

Interest Rates across Terms, by Income Ranges

From the below graph on the left, we can see that within each income range group, the rate increases as the amount of terms increases; overall, for the same amount of terms, people with higher income are generally granted lower interest rates.