Securitization: Today’s Liquidity Engine for Online Lending

Securitization: Today’s Liquidity Engine for Online Lending

Personal loan markets are a bit imbalanced. The costs to originate loans are inflated by the need for lawyers, auditors, credit ratings agencies, brokers, and more. With so many middlemen, smaller originators are being squeezed by high expenses. Can anything be done about it? Optimally, the loans originated by peer to peer would be sold […]

Securitization: Today’s Liquidity Engine for Online Lending

Personal loan markets are a bit imbalanced. The costs to originate loans are inflated by the need for lawyers, auditors, credit ratings agencies, brokers, and more. With so many middlemen, smaller originators are being squeezed by high expenses.

Can anything be done about it?

Optimally, the loans originated by peer to peer would be sold off to retail investors. But the shift away from selling fractional loans pushed out the retail investors, leaving the hedge funds. In 2016, the hedge funds pulled out, stranding billions in loans on the originators’ balance sheets.

The only way to make it go away, is to package all of those loans into a Special Purpose Vehicle (SPV). These loans are sold as securities to institutional investors like hedge funds, pension funds, and insurance companies. In order for institutional investors to be allowed to invest, the securities must pass a rigorous legal process. The loans must be rated. The papers audited.

It all costs money.

In order for the big online lenders to pass on the debt to institutional investors, they have to incur large expenses from middle men, squeezing their margins. For the institutional investor, the ROI on these SPVs justify the increase in price. For the originators, who have few if any alternative options, it’s a cost of staying in business.

But these expenses create barriers to entry to all but the biggest online lenders.

The smaller online lenders are left out. They don’t have the means to originate enough loans to get the attention of the institutional investors. They cannot securitize so their debt stays on their books, often paralyzing their ability to originate more. Their growth is confined to their rate of return.

Why Peer to Peer Lending Didn’t Deliver

Peer to peer lending was supposed to shake things up. When online lending came on the scene, the regulators didn’t know what to make of it. They didn’t have a clue as to which laws applied. As a result, online lenders didn’t pay anyone to make sure their assets met any regulations.

Once the regulators found a way to govern, the party was over. Online lenders had to pay more for regulatory fees and rates went up. When peer to peer got started, interest rates were at their post-2008 lows. They remained at 0% for a long time. As they began to rise, the cost of capital did also.

Peer to peer lending is all about moving cash. It’s about loaning out money, and then selling the loans to investors. To keep any cash on hand went against their business model. They are not depository institutions, and cannot borrow at the Fed Funds rate, which is currently at 1.25%.

A Solution for Smaller Investors

What if an originator didn’t securitize a bundle of loans? What if an online lender went on its platform and tried to sell its loans? It could take some time. There are markets for smaller originators who don’t sell to institutional investors, but liquidity is a major issue.

The secondary markets that do exist are only to meet basic regulatory requirements. They are closed to participants outside the originating platform. If you buy a note from Lending Club and you want to sell it, you can only sell it to someone on the Lending Club platform.

Loans purchased on a peer to peer lending platform will most likely be held to maturity. If an investor wishes to sell it, it can take a long time. One investor said that he waited 18 months to sell the loans he bought on a peer to peer lending platform at the price that he wanted.

The problem is liquidity. Peer to peer loans are “trapped” within their own platform. There is no secondary market where all of the loans originated by over 200 lending platforms can come together to provide liquidity for one another.

But what if there was?

A secondary market for loans issued by small originators could enable these companies to get the loans off their balance sheet as fast as the bigger players. A marketplace of these loans can create the liquidity badly needed by both originators and investors. If loans on 200 platforms suddenly converged into one marketplace, liquidity could cease to be an issue.

A secondary market can pave the way for smaller originators to flourish. The originators will have a place to sell their loans. Investors have a place to trade them. The liquidity and stability would create opportunities for a wider array of investors, such as the crypto community, to offer new sources of lending capital, ushering in the next phase of growth for the industry.

The New Marriage

Online lending has been fueled by hedge funds, who crave high returns on low volatility. As defaults grew, the hedge funds saw too much risk relative to their investment and walked away. In short, after a short marriage the groom wanted a divorce.

The new suitor may be the crypto investing community. Holders of digital coin are invested in highly volatile assets. The same secondary markets that are too much for hedge funds can provide a safe haven to park crypto assets into something pegged to the US dollar without leaving the crypto markets.

The underlying assets they invest in on this market will be backed by a highly diversified portfolio of loans eliminating the single point of failure offered by todays “stable” coin.

This new marriage has the chance to finally make good on the original promise of peer to peer lending.

Author:

Gilad Woltsovitch is the Co-Founder and CEO at Backed Inc., responsible for designing the company’s first-class platform, UX and UI. Before Backed, Gilad co-founded iAlbums, a semantic curation engine for media players in 2010 where he served as the company’s CEO from 2011-2014. In 2013, Gilad also served as the entrepreneur in residence for Cyhawk Ventures and joined the Ethereum project, establishing the Israeli Ethereum meet-up group. Gilad holds a Masters of Art Science and Bachelors in Sonology from the Royal Conservatory of The Netherlands in The Hague, University of Leiden.

The Broken Promises of Peer to Peer Lending

The Broken Promises of Peer to Peer Lending

About a decade ago, peer to peer lending came on the scene. It was designed as a way for people to borrow money not just from banks or other lending institutions, but from individual investors. Anyone could go onto a platform like Lending Club or Prosper, and fund any of the loans on its platform. […]

The Broken Promises of Peer to Peer Lending

About a decade ago, peer to peer lending came on the scene. It was designed as a way for people to borrow money not just from banks or other lending institutions, but from individual investors. Anyone could go onto a platform like Lending Club or Prosper, and fund any of the loans on its platform. They could fund entire loans, or invest in fractions.

This new technology promised to cut out the middle man and enable the everyday depositor to make a bank’s return on their money instead of it sitting in a savings account generating 1%.

Peer to peer promised new methods of assigning risk by utilizing technology to gain more information on borrowers. New data points gave underserved markets like millennials another chance to access new capital.

By streamlining the process, fewer expenses to originate these loans would result in better margins for originators, smaller barriers to entry, more competition, and ultimately more innovation. With more transparency, individual investors would have all the information they needed to buy loans that would be paid back.

Everybody was supposed to win.

But it didn’t work out that way. What went wrong?

In their zeal to revolutionize lending, the peer to peers fell short. They were unable to execute working solutions to overcome the following challenges:

Too many middlemen.

Online lenders were able to streamline some of the steps and automate others, reducing costs. They still need lawyers. They still need credit rating services. They still need accountants, auditors, brokers, salespeople, and more middlemen to make their platform work.

Even though onboarding and underwriting are now more efficient, there are still huge expenses in acquisition, cost of capital, and servicing. The added value in the new lending model only brought down costs marginally, and not significantly.

Failure to Diversify.

A new breed of lender was hatched when platforms empowered ordinary customers to lend their savings out at 7-20% rather than parking in a standard checking account. This brought a diversity of investors and interests to this new form of lending.
It fizzled out too soon. To raise $100 million, a lending platform would have to encourage 10,000 customers to transfer $10,000 from their personal accounts to their platform. That requires an army of salespeople, all earning commissions. A bank, or large financial institution could fund $100 million in loans with a single order.

Which is exactly what they did.

They began to wield disproportionate power over the lending platforms. Peer to peer was supposed to offer everyone the same opportunity. The banks were able to muscle their way into control by scaling up the platform companies in a way no retail investor could. The banks began to demand to see the loans before everyone else. They were able to cherry pick the most attractive loans, leaving the pits for everyone else.

As lending platforms grew, hedge funds rushed in to provide lending capital. They saw the opportunity while interest rates for low. They could raise money at low rates, while personal loans still demanded higher interest, creating high margins. This created a single flow of capital to the online lenders, thus creating a concentration risk where all the money was coming from one place.

Hedge funds don’t originate personal loans, so they funded online originators to get into this market. This drove peer to peer away from lending to the people, and more towards lending for the hedge funds. The biggest sign was when lending platforms stopped offering fractional loans, which was a great opportunity for smaller investors.

Market Share over Monetization

Most lending platforms started with venture capital funding. The marriage between finance and technology, is complex. The traditional strategy of early stage tech companies is to prioritize gaining market share over profitability. With financial institutions, the bottom line is always, the bottom line: profitability comes first, then scale up.

Peer to peer lenders applied the tech model to a financial industry. As a result, standards for accepting new borrowers were relaxed, loaning money to less qualified people. By the first quarter of 2016, default rates skyrocketed. This resulted in a higher risk premium on everyone. The hedge funds pulled back all at once.

Supply kept on coming in, but they were unable to unload it to enough investors once the hedge funds pulled out. They had to pivot to securitization. The small investor gradually got edge out of the buy side.

Lack of Transparency

In giving retail investors the chance to loan money to people, they were supposed to have access to all the details involved in pricing the loan. As costs to maintain this lending model continued to rise, online lenders were only giving out enough information on potential borrowers to satisfy regulators. Too much uncertainty increased the level of risk for potential lenders within the platform.

Conclusion

Despite the innovations in user experience, underwriting, and measuring risk, online lenders are still struggling to fulfill their vision to revolutionize lending by increasing inclusion and reducing costs. Since mid-2016, the barriers of entry have risen dramatically for originators, as well as smaller investors. With less diversity in the market, liquidity remains a barrier to growth. The inability of both platforms and investors to offload loans from their balance sheet has caused a stagnation in the industry.

The winner takes all momentum has reasserted itself, and the big players have retained their title.

Authors:

Gilad Woltsovitch is the Co-Founder and CEO at Backed Inc., responsible for designing the company’s first-class platform, UX and UI. Before Backed, Gilad co-founded iAlbums, a semantic curation engine for media players in 2010 where he served as the company’s CEO from 2011-2014. In 2013, Gilad also served as the entrepreneur in residence for Cyhawk Ventures and joined the Ethereum project, establishing the Israeli Ethereum meet-up group. Gilad holds a Masters of Art Science and Bachelors in Sonology from the Royal Conservatory of The Netherlands in The Hague, University of Leiden.

 

Peer-to-Peer Loans: Can Economically Intuitive Factors Improve Ability of Proprietary Algorithms to Predict Defaults?

LendingClub unemployment default rates

As compared to traditional lenders, peer-to-peer platforms (P2P) are supposed to use sophisticated data-intensive proprietary algorithms to assess a borrower’s credit risk. Since an increasing number of loans are now being underwritten by these algorithms, either directly through the platform or via partnerships with traditional banks, investors and regulators now need to understand how the […]

LendingClub unemployment default rates

As compared to traditional lenders, peer-to-peer platforms (P2P) are supposed to use sophisticated data-intensive proprietary algorithms to assess a borrower’s credit risk. Since an increasing number of loans are now being underwritten by these algorithms, either directly through the platform or via partnerships with traditional banks, investors and regulators now need to understand how the algorithms work in practice. In this post, we evaluate one aspect of the performance of these algorithms, namely, whether they make adequate use of predictors that economic theory suggests are drivers of credit risk. Specifically, we focus on unemployment rates in the area where the borrower resides. Using peer-to-peer consumer loans offered by LendingClub, we find that local unemployment rates can significantly improve the predictive power of their algorithm.

Our analysis is based on 600,000+ 36-month maturity loans originated between 2011 and 2015. The number and dollar amounts of loans issued over this period is shown in Figures 1 and 2.

Total number of loans issued between 2011 and 2015.
Total dollar value of loans originated between 2011 and 2015.

To examine whether unemployment can boost LendingClub’s algorithm, we compare the default rates of loans that have similar default risk according to LendingClub but were issued in areas with different unemployment rates. For each year of loan origination, we first categorize a loan according to LendingClub’s assessment of its credit risk. Specifically, we categorize a loan as “low risk”, “moderate risk,” or “high risk” based on its interest rate — which is set by LendingClub’s algorithm. Then, we categorize a loan as being in a low, moderate, or high unemployment area based on the unemployment rate in the zip-code where the borrower resides. Finally, we measure the unemployment rate effect as the difference in default rates for loans in the same risk category but different unemployment rate category.

The default rates for loans in different risk-unemployment categories is shown in Figure 3.

The average percentage of loan defaults in low and high-unemployment areas by three risk categories. Shadowed bars show default rate in low-unemployment areas and solid bars present default rates in high-unemployment area.

This figure plots the average default rates for each risk-unemployment category, averaged across the years of origination. Loans issued to borrowers who live in areas with high unemployment rates default at markedly higher rates. For example, for high-risk loans, the default rate in high unemployment areas is about two percentage points higher in that of low unemployment areas. Furthermore, loans in high unemployment areas default at a higher rate across all years. In Figure 4, we plot the differential in default rates for the high-risk loans for each year of origination.

The difference in loan default rate between high and low-unemployment areas for high risk loans.

As seen, the differential is positive across all years in the sample and is about 1.5% even for the most recent years.
Returns on the loans in the high-unemployment category have also been noticeably lower. In Figure 5, we compare annualized rates of return across loans from the different risk-unemployment categories. For example, for the high-risk loans, the returns differential is 60 basis points.

Annualized returns for loans. Shadowed bars show returns in loans originated in low-unemployment areas and solid bars show returns in loans originated in high-unemployment areas

Using a Cox proportional hazard model, an industry-standard framework for analyzing default risk, we confirmed that the unemployment effect described here is statistically significant.

Our analysis on drivers of credit risk for P2P loans is ongoing, and we will update you with our new findings in future posts.

Authors:

Written by Atay Kizilaslan and Aziz Lookman.