This past week at the AltFi Global Summit in New York, marketplace lending leaders gathered to provide an update on the state of the industry and give their thoughts on the keys to future growth.
PeerIQ CEO, Ram Ahluwalia, emphasized the need for standardization in data, reps and warranties, and a robust 3rd party ecosystem to improve market integrity, transparency, and liquidity.
In this week’s newsletter, we highlight the recent “True Lender” ruling against CashCall, Inc. and summarize the new SoFi transaction, SoFi Professional Loan Program 2016-D.
CFPB vs. CashCall
On August 31st, courts sided with the Consumer Financial Protection Bureau (CFPB) against CashCall, a payday lender started in the early 2000s, asserting that CashCall was the “True Lender” on loans that were originated through Western Sky Financial (WSF). CashCall partnered with the Cheyenne River Sioux Tribe to expand its lending footprint outside of California in 2009 in a relationship similar to the “rent-a-bank” strategy that several online lenders currently use.
The CFPB alleged that CashCall “engaged in unfair, deceptive, and abusive acts and practices” in servicing loans outside of its licensed state at usurious rates. According to the court decision (linked in the articles below):
- CashCall was “true lender” on the loans
- The court ruled that CashCall bore “the entire monetary burden and risk of the loan…and had the predominant economic interest”, in addition to several other conditions protecting WSF from financial loss, and was therefore ruled “true lender”.
- Since CashCall is the “true lender”, the court concluded that the choice of law provision in the loan agreements (a tribal lender on a Native American Reservation) should be disregarded in favor of the borrowers’ home states.
- The terms of these loans were in contradiction with the usury laws of the states in which the borrowers resided, and were therefore ruled void.
The result of this case has similarities to the Madden v. Midland decision, which also asserted the precedence of state usury laws.
This decision increases regulatory uncertainty and consequently may motivate funding banks to align their economics with the loan performance.
SoFi Student ABS Priced Tighter
SoFi is flexing its distribution capabilities by bringing a personal loan and mortgage deal to market in quick succession.
SoFi’s Professional Loan Program 2016-D (SPLP 2016-D) closed in August, shortly following the SPLP 2016-C deal in July. The capital structure of SPLP 2016-D is consistent with the last 2 deals, as expected of a repeat shelf like SoFi (Exhibit 1). The deal has four tranches with approximately $437 million of bonds, backed by $483 million of student loan collateral.
Exhibit 1 Summary of SPLP 2016-D Capital Structure
Source: PeerIQ, Bloomberg, DBRS
Exhibit 2 shows that SoFi has continued to grow their origination pipeline, allowing them to consistently securitize loan collateral. Additionally, the credit quality continues to remain high. Weighted-average borrower income and free cash flow statistics continue to improve.
Exhibit 2 Characteristics of SoFi Student Professional Loan Programs
Source: PeerIQ, DBRS
Correspondingly, and due to other factors including its expanding distribution capabilities, SoFi 2016-D priced tighter across the capital structure than SoFi 2016-B and SoFi 2016-C (Exhibit 3).
Exhibit 3 Spread Comparison of SoFi Student Securitization Tranches
Source: PeerIQ, Bloomberg
At current pace and growth volumes, we re-affirm our prediction that SoFi will be one of the US’s largest non-bank issuers globally, and that securitization will become an important financing solution for non-bank lenders more generally.
PeerIQ, and other ecosystem providers with a focus on improving transparency, standardization, comparability, and market integrity will be instrumental in allowing whole loan and ABS investors to participate with confidence.
Conferences PeerIQ will speak at:
- We are excited to see many of our friends at IMN’s ABS East Conference that kicked off today. Be sure to stop by CEO, Ram Ahluwalia’s ABS East panel, “Establishing Long Term Funding for Marketplace Lending: Re-Booting a Primary ABS and Secondary Loan Market”, at 2:00PM on Monday, September 19.
- American Banker Marketplace Lending and Investing Conference on September 27-28 in New York.
- Context Summits 2016 Alternative Lending Summit on September 28 in Dana Point, CA.
- “Ecosystem” is the New Watchword in Marketplace Lending (AltFi, 9/15/16)
- Marketplace Lending News Roundup – September 3, 2016 (LendAcademy, 9/3/16)
- Wall Street’s Insatiable Lust: Data, Data, Data (WSJ, 9/12/16) Hedge funds and other sophisticated investors are increasingly relying on predictive insights gathered from data and analytics intermediary companies as they seek insights into a company’s sales and health that aren’t readily available from conventional sources.
- Ruling Boosts CFPB, Raises Marketplace Lending Questions (BNA, 9/6/16) The decision in the recent case against CashCall may have implications on loan origination in the MPL space.
- At BlackRock, a Wall Street Rock Star’s $5 Trillion Comeback (NYT, 9/15/16) BlackRock founder, Larry Fink, believes the future of finance lies with rules-based, data-driven investment styles.
- Europe Said to Threaten Revolt Over Bank Capital-Rule Revamp (Bloomberg, 9/16/16) European national regulators argue that proposed revisions to risk-assessment rules would cause debilitating capital requirement increases.
- The First Marketplace Lending Policy Summit Takes Place in Washington (LendAcademy, 9/14/16) Industry leaders and regulators gathered in Washington DC for a policy summit.
- OCC Framework for Marketplace Lending, Fintech to Come in Fall (BNA, 9/15/16) The OCC intends to finish a blueprint by December outlining how it will regulate FinTech.
- U.S. Households’ Income Shows Biggest Jump Since Recession (Bloomberg, 9/13/16) Job gains boosted the median inflation-adjusted household income to 5.2% in 2015.
- An Algorithm to Predict a Bestseller (WSJ, 8/31/16) The authors of “The Bestseller Code” claim their algorithm created to analyze text data can pick out a future New York Times-list best seller with 80% accuracy.