Marketplace Lending Meets Cryptographic Currency – Bitcoin Lending on BTCjam

Two of the most talked-about topics in the world of financial technology are marketplace lending and cryptographic currencies.  As we have explored in depth on this blog, marketplace lending is growing rapidly and democratizing access to capital for consumers and businesses around the globe.  Cryptographic currencies such as bitcoin offer the potential for a monetary system not controlled by central banks or governments, as well as the promise of decreased transaction costs and simplified cross-border commerce. 

Recently, some companies have endeavored to combine these two powerful concepts, the largest of which is BTCjam, a bitcoin-based lending marketplace that has processed $4 million in loans since 2012.  Using data available in the BTCjam API, we can explore the characteristics of loans offered for investment and also consider how bitcoin-based lending differs from more familiar approaches.

Data

For this analysis, we used a dataset of available listings downloaded from the BTCjam API on July 27, 2014.  At the time of retrieval, there were 201 listings, representing a total requested amount of $1,109 bitcoin.

Geography

As bitcoin is not dependent on any particular government, cross-border transactions are no more complex than those between 2 immediately adjacent neighbors.  As such, the BTCjam marketplace has attracted borrowers and investors from all over the world.  In the graph below, we see the distribution of active listings by borrower country.  While 38% of listings come from within the United States, 40 distinct nations are represented in the current dataset.

Amount

The average requested amount for listings available on the platform today is 5.5 BTC, an amount roughly equivalent to $3,282 at current exchange rates.  Below is a distribution of requested loan amounts in BTC, followed by the equivalent distribution expressed in dollars.  As we can see, the majority of loans offered on the platform are for less than $2,500, suggesting perhaps that the borrower profile, loan structure, or loan purpose differs from what we’ve seen on the larger marketplace lending platforms or, perhaps, that more performance history needs to be established before investors are comfortable funding larger loans, particularly in a currency that is not as broadly understood.

 

Currency & Exchange-Rate Risk

While we are accustomed to evaluating marketplace lending investments on the basis of credit risk, changes in the value of a currency also have the potential to affect returns.  When loans are denominated in a generally stable currency, such as the Dollar, Euro, or Pound, this risk may be considered relatively minor.  However, given the significant fluctuation in the price of bitcoin, exchange-rate risk may in this case present an even greater potential to impact returns than the underlying credit risk of the borrower!

As we can see from the graph above, based on the Coinbase API, the price of bitcoin in dollars has shifted massively over the past year, from a low of $92.13 one year ago, to a high of $1,126.82, to $595.07 today.  This volatility poses a major risk to borrowers on either side of the transaction and is one of the most common concerns in any discussion of bitcoin-denominated lending.

Fortunately, BTCjam offers 2 distinct types of loans, one of which is directly aimed at mitigating currency risk. 

  • “Bitcoin loans” are made in BTC and repaid in BTC. Both borrower and investor are exposed to changes in the value of bitcoin.  Investors who predict that bitcoin will rise in value would expect to benefit from funding these loans.  Borrowers who predict that bitcoin will decrease in value would theoretically benefit from borrowing under this structure, particularly if they receive their income in a fiat (i.e. government-issued) currency.
  • “Linked loans”, also known as “Bitstamp USD”, are made and repaid in BTC but are indexed to the bitcoin-dollar exchange rate on the date of loan origination.  For example, if a loan is made for a sum of bitcoin equal in value to $5,000, the payments on the loan will be based on the $5,000 initial value, regardless of any change in the value of bitcoin.  In this way, bitcoin serves only as the medium of exchange, and both borrower and investor are insulated from the exchange-rate risk.

In the graph below, we see the breakdown between the 2 types of loans, further segmented by the geographic location of the borrower.  As one might expect, BTCjam customers tend to opt for the dollar-linked loans, particularly those from the United States.


Purpose

A quick look at the data reveals a massive proportion of BTCjam loans to be for the stated purpose of “business”, with a relatively low number for debt consolidation.

 

As business and other are both very broad categories, they merit some more detailed examination.  In reading through the  borrower-provided loan titles and descriptions, a large number of loans appear to be for the purpose of either bitcoin mining or trading/arbitrage.  While these are not broken out as distinct categories, we can perform some text analysis of the loan information to draw our own inference.  As we can see from the graph below, a significant number of borrowers are obtaining loans in order to engage in mining – that is, to invest in the computational hardware needed to run the complex algorithms needed to “discover” new bitcoin.

In addition, many borrowers appear to be obtaining funding for the purpose of trading in the bitcoin exchange markets.

Loan Duration

While we have become accustomed to 3 or 5 year term loans on the mainstream consumer lending marketplaces, BTCjam loans tend to be of shorter and more varied duration, with terms ranging from 7 days to 1 year.    In addition, payment frequency is not necessarily monthly, with available payment cycle intervals of 1, 3, 7 or 30 days.

 

Risk and Interest Rates

As with other marketplace lending originators, BTCjam assigns borrowers an alphabetic score ranging from A+ to E.  This score is based on a variety of factors, including traditional credit data as well as identity verification and social graph information.  For this analysis, we don’t have access to performance data, so we will simply show the credit grade distribution, along with associated interest rates.


Alternative Data and Verification

One unique aspect of BTCjam is its use of alternative data for identity verification and risk-assessment purposes.    The site encourages its members to link their Facebook, LinkedIn, ebay, and PayPal accounts and then makes aspects of this data available to prospective investors.  While its hard to know exactly how these attributes affect credit ratings or interest rates, the  information itself is somewhat interesting.

 

Conclusion

Traditional economic theory teaches that money has 3 main functions: a medium of exchange, a unit of account, and a store of value.  Cryptographic currencies such as bitcoin challenge some of these notions.  For instance, earlier this year, the IRS ruled that bitcoin would be taxed like property, rather than like currency.  Fiat-linked loan options like BTCjam’s BitstampUSD also challenge traditional thinking, utilizing bitcoin as a medium of exchange and unit of account, but preserving the U.S. dollar as a stable store of value.  Clearly, cryptographic currencies still represent a bleeding-edge technology and remain the province of early adopters.  It is not yet clear if, or how, they will influence the evolution of our banking system.  Regardless of one’s feelings surrounding bitcoin as a currency, the promise of a more flexible transaction protocol that lowers costs and facilitates cross-border transactions is extremely compelling, and we are excited to see its impact on the world of marketplace lending.

Industry Profile – Kathryn Ebner of Pave

Given Kathryn’s sunny disposition, one wouldn’t guess that she is a finance industry veteran with many years of traditional banking experience spanning debt capital markets, short term credit trading, and institutional sales.   Trading in her pantsuits for a jeans and a hip tee-shirt, Kathryn seems very at home in her new role as head of business development for Pave as we meet for coffee at the charming Buvette in the West Village.

Revisiting Roll Rates using Markov Chains

In October of 2013, we did a post on Roll Rates for Lending Club.  We defined roll rates and analyzed the patterns on Lending Club data.   For the purposes of both posts, we’ll consider roll rates to be the probability of a loan going from one state of delinquency to another (e.g. 30 days past due to charge off).  Roll rates provide insight into the likelihood of future loss on a loan that has missed just one payment.  This can be used for:

1. Modeling: it is best practice to use more recent data as the current vintages are underwritten and aging during a similar period of time.  To actually charge off, an account needs at minimum 120 days (although – it usually takes at least 10 months or so.)  If you can use an early default predictor like missing 1 payment (i.e. 30 days past due) you can adjust the model with this information. 

2. Portfolio Analysis: If you know the roll rates of a population, you can then extrapolate the current performance of your portfolio earlier because you can use early indications as proxy for later credit loss.  This allows for adjustments in underwriting strategy prior to actually experiencing losses.

3. Loan Loss Reserve Calculations: If an investor keeps a reserve based on expected losses, roll rate calculations can be useful. 

In order to calculate roll rates, it is important to first understand the nature of charge-offs, as the analysis requires some assumptions.  These loans amortize over time, which means if the charge off occurs later in the borrower’s tenure, the principal charged off could become de minimis.  Below is the average dollar loss and percentage of original principal lost based on when the charge-off occurs in the life of the loan.  As expected, the average dollars lost decreases as the loans age, as does the percentage of the original balance that charges off.

As opposed to Lending Club (which only designates past due loans as 31-120 days late), Prosper’s data allows for a more granular assessment of the transitions between the different past due buckets.  Prosper provides the actual days past due of every loan at every month.  In order to calculate loan movement, we need to pick a starting point.  Based on the distribution of 30 days past due (below), 50% of loans that miss one payment do so by month 12, so this will be the starting point.

We analyzed all Prosper loans that have at least 18 months tenure to see the actual change in status from 12 months to 18 months.  We used a statistical method called Markov Chains, also known as transition matrices.  These are mathematical models that calculate the probability of an object moving from one state to another.  In this case, the states are the status of the loan, and the time period we are analyzing is 6 months (from 12 to 18 months).  By applying this model to the historical changes in loan status, we are able to extrapolate the likelihood of a loan in any status moving to another status in 6 month increments from the initial 12 month status.  

Below are the results of the analysis, in which we show the probability of movement between all of the different status buckets.  The likelihood of eventual charge-off on loans that miss just one payment by 12 months is quite high.  When predicting out to 30 months, our analysis shows the likelihood to be about 85%.

The Markov Chain is a useful tool, and was easy to use for this analysis.  In the case of roll rates, this analysis could be accomplished with any starting point and ending point.  In this case, we used 12 months to start and looked out 6 months.  If an analysis required even more recent data, then starting at 3 months and looking to 6 would work.  Making this change to the analysis would bring in more recent data (including all loans with at least 6 months of tenure, instead of 18), but the tradeoff would be a model that may under predict the probability of charge off because it goes out to 9 months instead of 18 months.

Using statistical methods, both basic and complex, can help yield preliminary results for a loan portfolio.  This analysis specifically helps validate the use of early default behavior as an input into a charge-off model.  Based on what we've found, an analysis done on a more recent vintage can yield results that are worthwhile.  Of course, more data is always better, and actual performance is always the optimal input to a model.  In the case where this is not available, statistical methods such as those discussed here are useful.

Repeat Borrowers – Relationship Lending for the Modern Age

Assuming that marketplace lending offers borrowers a good deal and a positive customer experience, we would expect to see repeat business.  In a post last October, we first analyzed repeat borrowers on Prosper.  That analysis found that 10% of loans were issued to those who had previously taken out a Prosper loan.  In Oct. 2013, Prosper originated $50 million in loans. Since then, Prosper has issued an additional $726 million, including $145 million in June 2014 alone.   Given the massive growth in the marketplace, we wanted to see how much of this growth was due to repeat borrowers, how these individuals have performed, and what we can learn from borrowers’ prior experience.

Prior Borrower Market Share

The charts below show monthly loan originations on Prosper as well as the share comprised by repeat borrowers.

As we can see, Prosper’s origination volume has grown significantly over the past 8 months.  Existing borrowers, while growing in absolute number, have comprised a smaller share of monthly issued loans over time.  This pattern is not surprising given the platform’s high rate of growth.  In order for Prosper to expand, it has presumably needed to cast a broader net in acquisition marketing to source borrowers from new channels.

When Borrowers Return

In the first half of 2014, Prosper made 3,455 loans to previous borrowers, totaling $40,185,826 in originations.  Using the data made available by Prosper, we can explore the characteristics of this population.  In the chart below, we group these borrowers by the number of previous loans they had taken prior to the new loan’s date of origination.   While the vast majority had taken only 1 prior loan, 35% had 2 or greater!

For the borrowers with one prior loan, one has to wonder how much time had passed between the issuance of the prior loan and the new one.  In the histogram below, we  show the distribution of time between loans, divided into 3-month intervals and broken out by the term of the previous loan.

Interestingly, a very large proportion of new loans were granted to borrowers who had already received Prosper loans under a year ago.  The vast majority of repeat loans were granted within 2 years of the prior origination, perhaps a surprising finding given that Prosper’s minimum loan term is now 3 years (a small number of 1-year loans were issued in the past, but this practice stopped in early 2013).   If so many borrowers are getting new loans prior to the initial maturity date of an existing loan on the same platform, there are various potential reasons to consider.

  1. The borrower is current on the existing loan and has sufficiently good credit to qualify for an additional loan (potentially to refinance the first one)
  2. The borrower has paid off the existing loan and now qualifies for an additional loan
  3. The borrower defaulted on  the existing loan (note – this scenario would not occur within Prosper’s underwriting policy, as prior defaulters are  restricted from obtaining new credit)

Understanding the frequency and details of the above scenarios is an analysis unto itself and perhaps will be the subject of a future post.

Creditworthiness of Prior Borrowers

Presumably, if Prosper has decided to give an additional loan to a prior borrower, its own credit rating system has favorably evaluated this individual.  Therefore, to get an externally consistent view of the creditworthiness of repeat vs. first-time borrowers, we must use an externally-developed model: in this case, FICO.  In the graph below, we show the distribution of loans by FICO score, split out by the number of prior Prosper loans at the time of origination.  The graph  reveals a relatively stable distribution between new and prior borrowers, with some rightward-skew for those with a previous loan.  Perhaps the higher scores for these individuals reflect good performance on the earlier loan.

Conclusion – Relationship Lending

The concept of “relationship banking” has existed for decades in traditional financial services.  The idea was that customers who were already doing business with a given institution would prefer to come back to that same institution as repeat customers and for additional services.  Embedded in this concept was the assumption that this would allow a bank to make more accurate credit decisions, aided by a wealth of information on existing customers and the ability to consider a customer’s lifetime value.  With this idea in mind, retail banks across the country expanded into a wide variety of financial services, such as checking, savings, credit cards, mortgages, auto loans, student loans, and personal loans.  For a time, this was quite successful.  However, the internet has essentially driven switching costs to zero, and consumers increasingly decide to do business with whichever firm is able to provide the best cost for the best service.  Clearly, this unbundling has been to the benefit of marketplace lenders, who have been able to deliver a positive customer experience without the burden of legacy technologies, and whose operating efficiency has allowed them to offer favorable rates.  As this new wave of loan origination platforms grows to comprise a larger share of overall lending, it will be interesting to observe if they are able to use relationships and data to their advantage to make better decisions and win the loyalty that had been so highly sought by traditional banks.

Vacation Loans on Prosper & LendingClub

As many of us were enjoying a 4th of July weekend vacation, at Orchard we used it as an opportunity to review “vacation” loans on both Prosper and LendingClub.  Both of these lenders offer this as a loan purpose option.  Vacation loans are of interest to us because there has always been some discussion on the general idea of a vacation loan and the performance of these loans on the different discussion boards such as Lend Academy.  Also, if you Google “vacation loans,” LendingClub comes up in the paid adwords section. 

These loans get attention, because they represent an interesting idea: if people need to borrow money to take a vacation, should they take it at all?  However, people have always financed vacations.  Before Prosper and LendingClub existed, the only option was to put the trip on a credit card and pay it off over time.  The interest rates on credit cards are generally higher than those on a marketplace lending term loan.  Given this, is there a difference between consolidating debt (which was potentially incurred on a vacation) and using a loan to fund the vacation in the first place?  For the borrower, a marketplace lending loan is a great new alternative.  For the investor, we’ll have to look at the data to find out if the investment is worthwhile.

Prosper started tracking vacation loans as a separate category in December 2011; LendingClub has been tracking them all along.  For the analysis in this post, we will look at December 2011 data and forward for both Prosper and LendingClub.

Below, we show Prosper’s loan distribution by loans for vacation, and those that are not.  As expected, vacation loans make up a small percentage of overall loans (~1% of listings.)

The actual volume of listings ranges from ~30-120 listings per month.  There are a couple periods of larger volume, but given the relatively small numbers, this is probably due to natural fluctuations and not telling of anything significant.

Some listings become loans, while others do not, as we discussed in this blog post.  Of the vacation loan listings, the percentage that fully fund has been increasing in recent months.  On average, about 55% are funded.  This is lower than the overall average funding rate of 61%.  The difference in funded loans is mainly due to a larger volume of loan requests withdrawn by the borrower.  This could be because the rates are higher than a borrower would like to pay to fund a vacation – or not any better than the rates on a credit card.  Because we are not provided the reason a borrower withdraws a loan, we will not be able to know.

For LendingClub, we see similar patterns.  The percentage of loans is slightly lower at 0.5% on average.  Keep in mind that LendingClub’s data includes only loans that have funded (i.e. listings that do not become loans are not included in the available data), so the percentages below include only funded loans.

The number of loans funded for vacation purposes on LendingClub ranges from ~20 – 140 loans per month.  It appears that there is a bump in the number of loans for vacations during the summertime.  Similar to Prosper, given the relatively low volumes here, it is difficult to know if this represents a real pattern.

Prosper's distribution by credit grade for vacation loans skews to the less risky credit grades in the more recent months.  In fact, there are very few vacation loans in the E or HR credit grades.  It is unclear how much of this is due to a change in applicant mix versus Prosper's underwriting strategy.  What it does mean is that the average interest rate for a vacation loan on Prosper has gone down over time.  

LendingClub vacation loans are skewing in the other direction.  It appears that around December 2012, there may have been a change to the underwriting criteria used by LendingClub, because the distribution changes fairly dramatically.  There is no way to know for certain, but the more recent vacation loans are in riskier credit grades on average.

With the understanding of the relative size and distribution of these loans, we will now assess the default rates (in this case, default is defined as any loan 30+ days past due).  For this analysis, we only look at loans originated from December 2011 – December 2012, excluding more recent vintages.  Prosper vacation loans have a slightly lower default rate than other types of loans; LendingClub vacation loans have a slightly higher default rate than other types of loans.  Overall, the pattern is not clear given the difference between the two originators.  The difference could have to do with either applicant mix or underwriting.

Given that the distribution in credit grades was fairly different for the two originators, we need to look at these default rates by credit grade.  However, it is important to keep in mind that the volumes are small, so these results may not be significant.

For Prosper, it appears that the AA credit grade vacation loans have a higher rate of default than other types of loans (this accounts for only 12 loans total – so this finding could easily be an anomaly.)  Vacation loans outperform the other loan types in all of the other credit grades.

For LendingClub, we see a similarly high default rate on the vacation A grade loans (which accounts for 107 loans).  For the rest of the credit grades, the vacation loans are generally worse.

In conclusion, the number of vacation loans issued by Prosper and LendingClub is small, and therefore it is difficult to say if the patterns and trends we see in the data are due to sample size or the behavior associated with these loans. 

Overall, when compared to the alternatives, it might make sense for a borrower to take out a loan to fund a vacation and, according to the data, the performance may be in line with certain investors' strategies.  According to the numbers in this analysis, these loans could be both profitable to investors and an interesting new way for those who are cash strapped to avoid costly credit card debt in the first place.  Given how new this asset class is, it makes sense to continue assessing the performance and default rates to see how the data plays out before making an assumption about the quality of these loans. 

We hope everyone had a great holiday weekend!

June 2014 Marketplace Lending Monthly Recap

Month and Year-to-Date Asset Class Returns 

For the month, the Orchard US Consumer Marketplace Lending Index outperformed many of the comparable US Fixed-Income asset classes.  As a reminder, the Orchard Index is designed to measure the performance of direct online lending to US consumers. The Orchard Index tracks the performance of the aggregate amount of loans to consumers originated and funded on eligible US-based online lending platforms. Today those platforms include Lending Club and Prosper with more to be added in the future.

Source: Fixed-income asset class returns based on Barclays Benchmark Indices

Source: Fixed-income asset class returns based on Barclays Benchmark Indices

The Orchard Index serves as a catalyst in promoting marketplace lending as a major investable asset class. Like other asset classes, investors will now have the ability to benchmark their returns against an index. For this reason, we will begin providing monthly recaps of the month-to-date and year-to-date returns of the Orchard Index against five comparable US fixed-income asset classes:

1.     3-5 year Treasuries,

2.     Intermediate High Yield Corporates,

3.     3-5 year Investment Grade Corporates,

4.     5 year Municipals, and

5.     Securitized Products.

US fixed-income returns are sourced from Barclays benchmark indices.        

Source: Fixed-income asset class returns based on Barclays Benchmark Indices

Source: Fixed-income asset class returns based on Barclays Benchmark Indices

Similar to the month-to-date returns, the year-to date returns for the Orchard US Consumer Marketplace Lending Index outperformed several of the comparable US Fixed-Income asset classes.

 

Orchard Research & Blog

Industry Profile – John Birge of CommonBond

Understanding Loan Statuses

Cash Drag and Marketplace Lending

Borrower Impact on Consumer Marketplace Lending

Understanding Debt-to-Income Ratios

Cash Drag and Marketplace Lending

As institutional investors deploy more capital into marketplace lending, one issue that continues to arise for investors is cash drag. Cash drag refers to the effect of cash holdings potentially reducing the performance of an investment account. It can be computed as the difference between a fund's investment returns without cash and the total fund returns including cash. Regardless of the asset class, cash drag is a concern for investors who are trying to maximize returns.

Borrower Impact of Consumer Marketplace Lending

Marketplace lending is rapidly growing into one of the largest sources of consumer lending in the United States.  While much of the news headlines focus on dollar volume and investor yield, we should not lose sight of the hundreds of thousands of individuals, families, and businesses who have successfully borrowed money through online lending marketplaces.  In Q1 of 2014 alone, 72,848 unique loans were issued on LendingClub and Prosper.  In today’s analysis, we explore these borrowers – who they are, why they borrow, and the impact that marketplace lending has on their financial lives.

High-Level Metrics – Q1 2014

In the first quarter of 2014, LendingClub originated 56,557 loans, totaling $791MM in initial principal.  During the same period, Prosper issued 16,291 loans totaling $196MM in initial principal.  Given that Prosper has somewhat more granular data, we will focus on their numbers for the remainder of the post.

Loan Purpose – Why Do They Borrow

The graph below shows a distribution of loans by stated loan purpose.  As we can see, the most prevalent loan purpose by a significant margin is Debt Consolidation, followed by ‘other’, Home Improvement, and Business.  To think that nearly thirteen-thousand people in just 3 months were able to refinance their debt at a lower rate is already a testament to the impact of marketplace lending, and we will delve into the numbers in more detail later on.  In the meantime, we shouldn’t ignore all of the other stated uses of Prosper loans.  These loans have helped people improve their homes, pay medical/dental bills, purchase a car motorcycle, boat, or RV, and maybe even use their personal credit to start a business.


Interest Rates

The graph below shows the number of Prosper loans issued in Q1 2014 by borrower interest rate in 1.5% increments.  The median interest rate was 14.6%, and the middle 50% of borrowers had rates between 11.4% and 18.4%. To put these rates in perspective, let’s consider the typical rates for other forms of unsecured consumer credit.  The Chase Freedom card, one of the most popular cards in the United States, is currently offered with an interest rate of 13.99% to 22.99%, increasing to 29.99% in the case of late payment, exceeding the credit limit, or having a returned payment.  Note: these are variable rates based on the prime rate and are therefore subject to change.  Rates we were able to find for unsecured term loans offered by major retail banks were somewhat lower, around 7-10% (“assuming excellent borrower credit history”), but most of these loans require the borrower to have an existing deposit relationship with the bank and require an extensive application process that cannot be performed online.

Loan Amount

People who take out loans on Prosper borrow in amounts ranging from $2,000 to $35,000, with the average loan being just over $12,000. 

From the graph above, we can see that borrowers tend to be clustered around particular loan amounts, so let’s take a look at the most common amounts borrowed for some of the most common loan purposes.  The graph below shows that debt consolidation loans tend to be clustered around 3, 10, 15, 20, or 25-thousand dollars.  On the contrary, loans for Home Improvement or Other are much less tightly clustered around any particular amount.  Business loans do seem to somewhat favor “round” numbers such as $10,000 or $15,000, though they seem to be less concentrated than those for consolidation.

Geography

Where are all these borrowers coming from?  The graph below shows Q1 2014 loan volume by U.S. state.  No major surprises here, with California, Texas, New York, and Florida leading the numbers.

Credit Quality

Given all of the focus on the safety or risk of marketplace lending as an investment class, it is natural to wonder about the credit quality of the borrowers.  We are often asked whether these loans are being made to subprime borrowers who cannot obtain credit anywhere else or to prime borrowers who prefer the convenience, rates, and experience of online lending over that of a traditional bank.  While there are many measures of credit quality, let’s focus here on FICO score, as it is a broadly-used and externally-validated metric.  While we don’t see too many loans over 760 (such scores are less prevalent in the U.S. population overall and generally represent borrowers who are less likely to be seeking credit), the population does appear to comprise a relatively prime range of scores, with 71% of borrowers having FICOs over 680.

Financial Impact

Of course, some of the most compelling statistics around marketplace lending have to do with the impact on the financial lives of its borrowers.  Let’s run the numbers for an archetypical borrower, someone borrowing for the purpose of debt consolidation. A typical loan of this type has an amount of $12,500, a duration of 36 months, and an interest rate of 15.2%.  If the borrower pays this Prosper loan at $434.54/month according to the schedule, he should pay total interest of $3,143.51 over the 3 years of the loan. 

If this same borrower were to leave the balance on a credit card, the total interest paid would of course be a function of interest rate and the time taken to repay the loan.  Let’s assume this loan had an interest rate of 22.99% as discussed in the interest rate section above.  If the borrower were to pay off the credit card in the same 3-year time frame, this would lead to cumulative interest payments of $4,918.  If the borrower were to pay $434/month to the credit card (i.e. the same as the monthly payment due on the putative Prosper loan), the loan would be paid off in 43 months, resulting in cumulative interest of $5,854.  Either way, refinancing the debt through marketplace lending makes a substantial financial impact.

Debt To Income

Debt to Income (DTI) is a metric that many active investors in Marketplace Lending are familiar with and potentially use in their investment strategies.  It represents the total amount of debt payments the borrower owes each month excluding mortgage (e.g. credit cards, student loans, car loans) divided by the stated monthly income.  

Industry Profile: David Haber

 

As the country celebrated National Small Business Week last week, we were reminded of the significant role small businesses play in the growth and health of our economy.  While small business owners employ roughly half of the nation’s workforce and help create nearly two-thirds of new jobs each year, one of their biggest challenges remains access to affordable capital.

Investing in High Yield Corporate Bonds vs. Consumer Loans

The consumer loans currently issued by Prosper and Lending Club are, in some ways, another U.S. fixed-income asset class investment.  Here we compare these loans to a more traditional asset class, High Yield Corporate Bonds.  We chose High Yield, because they have been able to generate returns comparable to the consumer loans originated by Lending Club and Prosper in 2013.

Anatomy of an Auction

When new borrower loan applications are processed and approved by LendingClub or Prosper, the loan listings are not immediately released to prospective investors.  Rather, new listings are released in batches – often referred to as “auctions” – at specific times during the day. Using the data available from these platforms, we can understand the flow…