Visualization of’s Loan Data Part I of II – Compare and Contrast with Lending Club

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Due to the positive feedback received on this post I thought I would re-create the analysis on another peer-to-peer lending dataset, courtesy of You can access the Prosper Marketplace data via an API or by simply downloading XML files that are updated nightly

If you are going to follow the route I took and download the latest XML file,, you will find this utility helpful in converting the XML files to CSVs: Convert Prosper XML to CSV

Once you have downloaded the .jar file run the following command (changing the parameters of course!):
java -jar ProsperXMLtoCSV.jar ProsperXMLFileLocation CSVDestinationDirectory

Similar to Lending Club, Prosper provides loan-level data such as interest rate, amount funded/requested, borrower state, borrower debt to income ratio, etc. However, Prosper also provides additional information regarding their user base and loan performance history. This information includes extended credit profiles of users, groups that users belong to, social networks within the user base and even retroscores, or how a loan would be rated by Prosper under a new heuristic given macroeconomic shifts over time.

Let’s jump right into the visualizations by state:

library(ggplot2) library(maps)

## Warning: this is a very large dataset that required ~10 minutes ## to read into R on a fast 8-core Xeon server. loans <- read.csv("Loans.CSV", header=TRUE) listings <- read.csv("Listings.CSV", header=TRUE)

## Obtain the active loans from the Listings file, since it ## contains more detailed information than the Loans file listings.match <- listings[match(loans$ListingKey, listings$Key),]

listings.match$BorrowerState <- as.character(listings.match$BorrowerState) loans <- listings.match states <- map_data("state")

## Change state abbreviations to full names so we can merge our ## data frames together state.names <- unlist(sapply(loans$BorrowerState, function(x) if(length([grep(x,]) == 0) "District of Columbia" else[grep(x,]) ) loans$BorrowerState <- tolower(state.names) colnames(loans)[11] <- "region" state.counts <- data.frame(table(loans$region)) colnames(state.counts) <- c("region", "Num.Loans") result<-merge(state.counts, states, by=c("region")) result <- result[order(result$order),]

p <- ggplot(result, aes(x=long, y=lat, group=group, fill=Num.Loans)) + geom_polygon() + scale_fill_gradient(low = "yellow", high = "blue") + coord_equal(ratio=1.75) + opts(title = 'Number of Issued Loans by State') print(p)

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It comes as no surprise that a majority of issued loans originate in California. As with Lending Club, Prosper is a San Francisco-based peer-to-peer lending company.

Now we will take the log of the number of loans issued by state and compare Prosper’s market reach with Lending Club’s.

p <- ggplot(result, aes(x=long, y=lat, group=group, fill=Num.Loans)) + geom_polygon() + scale_fill_gradient(low = "yellow", high = "blue", trans="log") + coord_equal(ratio=1.75) + opts(title = 'Log Number of Issued Loans by State') print(p)


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Lending Club

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The two maps are extremely similar. Both lending companies issue the most loans in California, Texas and Florida. There are some minor differences such as Lending Club issuing more loans than Prosper in Wyoming and Montana.

Instead of the Monthly Income by State map that I created for Lending Club, we will observe Debt to Income Ratios by state for both Prosper borrowers and Lending Club borrowers.

## Aggregate median debt to income ratio by state <-aggregate(loans$DebtToIncomeRatio, by=list(loans$region), function(x) median(x, na.rm=TRUE)) colnames( <- c("region", "") result <- merge(, states, by="region") result <- result[order(result$order),]

p <- ggplot(result, aes(x=long, y=lat)) + geom_polygon(data=result, aes(x=long, y=lat, group = group, + scale_fill_gradient(low="yellow", high="purple") + coord_equal(ratio=1.75) + labs(fill="Debt to Income Ratio") + opts(title = 'Median Debt to Income Ratio of Borrowers by State')


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Lending Club

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Does anyone want to start pointing fingers for the United States debt crisis yet? The states that Prosper loans to the most are also the ones with the lowest Debt to Income Ratios. New Yorkers, in particular, have the lowest median Debt to Income Ratio. Lending Club seems to have much more homogeneous interest rates. We can compare the distributions of the two companies' Debt to Income Ratios with a call to ggplot (after a bit of pre-processing that I left out due to real estate on this page):

ggplot(combined, aes(x=DebtToIncomeRatio)) + geom_histogram() + facet_grid(Company ~ .)

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It appears as if Lending Club has a hard cut-off at a 0.30 Debt to Income Ratio for borrowers. Note that this data is taking into account all loans since the inception of both companies. Prosper implemented stricter borrowing guidelines and interest rates after 2009, which can be seen in the animation below.

issue.year <- substr(loans$StartDate, 0, 4) loans$Issued.Year <- issue.year<-aggregate(loans$BorrowerRate,by=list(loans$Issued.Year, loans$region), function(x) median(x, na.rm=TRUE)) years <- c("2006", "2007", "2008", "2009", "2010", "2011") colnames( <- c("year", "region", "interest.rate")$interest.rate <-$interest.rate * 100 result <- merge(, states, by="region") result <- result[order(result$order),]
#Calculate the lower and upper bounds for the gradient lower <- floor(summary($interest.rate)[1])[[1]] upper <- ceiling(summary($interest.rate)[6])[[1]]
states2 <- data.frame(map("state", plot=FALSE)[c("x","y")]) animateMap <- function(year){ result.year <- result[grep(year, result$year),] usamap <- ggplot(data=states2, aes(x=x, y=y)) + geom_path()+ geom_polygon(data=result.year, aes(x=long, y=lat, group = group, fill=interest.rate)) print(usamap + scale_fill_gradient(low="yellow", high="blue", limits=c(lower, upper)) + coord_equal(ratio=2.00) + opts(title = paste('Median Interest Rates for all Issued Loans by State in', year)) + labs(fill="Interest Rate (%)") + xlab("") + ylab("")) }

saveMovie(for (i in 1:length(years)) animateMap(years[i]), clean = T);

Notice the interest rates are the most varied in 2006, the year of Prosper’s inception.
It also worth noting that the median interest rates for borrowers soared after 2009, when Prosper implemented stricter guidelines for borrowers, which also resulted in lower default rates.

Stay tuned for a "social network" analysis of's member data coming up in Part II!

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