OnDeck Capital IPO: A Treadmill Lender

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Summary:

OnDeck Capital (ONDK) has engineered a treadmill lending platform for small businesses resulting in 49.2% of 2014 originations from repeat customers.

This is a risky way to grow the business as ending the refinance of any repeat customer risks default. OnDeck is on the treadmill too and cannot get off.

OnDeck also has substantial balance sheet risk and should not be seen nor valued in the same terms as LendingClub (LC). We recommend not investing in OnDeck.

OnDeck Capital (Pending:ONDK) is an online originator of short-term loans for small businesses.

It has priced its IPO between $16 and $18 a share which places its valuation north of $1B despite being unprofitable.

It will get this valuation when its stock opens for trade on the NYSE on Wednesday, December 17, 2014 because investors view the company as “fintech” and a “marketplace lender” like LendingClub (NYSE:LC) which had a very successful IPO a week earlier.

But, we would avoid investing in OnDeck’s stock as the company’s business is dependent on “treadmill” leading practices that binds borrowers to ongoing refinance.

OnDeck is not LendingClub. It has substantial balance sheet risk as it books most of its originations with offsetting liability.

Furthermore, as a non-bank, it does not have the backstops of a bank in the event of a liquidity crisis caused by requirements to repurchase excessive loan defaults that back its securitization facility.

Unlike LendingClub or alternative student-loan originator SoFi (which uses alma mater as a key risk variable), OnDeck does not tout its ability to offer substantially lower interest rates than banks through curation (a/k/a risk-based pricing).

OnDeck suggests that it creates value for small businesses and distinguishes itself from traditional financial institutions by offering quick-approval, 3 to 24 month term loans. The company suggests that these types of loans are sought by small businesses to fund money-making opportunities with quick paybacks.

For example, a Christmas ornament store with seasonal inventory buildup or a custom yacht dealer that needs some upfront payment from the manufacturer would be ideal candidates for an OnDeck term loan.

But, this is not the case. A near majority of OnDeck’s small business customers have chronic cash flow problems and seem to get stuck on a treadmill of term loan refinance. Furthermore, the churn is growing.

From its S-1,

“We believe the behavior of our repeat customers will be important to our future growth. For the year ended December 31, 2013 and the nine months ended September 30, 2014, total originations from our repeat customers was 43.5% and 49.2%.”

When OnDeck says that its nine month YoY growth in loan originations is 171%, a fair portion of this is repetitive refinance of term loans from existing customers.

OnDeck has “engineered” a treadmill lending platform through the following practices:

(1) originating brief term loans of 3 to 24 months quoted in “cent-on-the dollar” averaging around $1.17, but amortized and paid DAILY via automatic ACH such that the ARP is 60%;

(2) requiring the same origination fee of around 2.5% of principal for refinance from existing customers even though the incremental cost of qualifying repeat customers is substantially less than new customers;

(3) half-heartedly offering a revolving line of credit alternative, which would have spared repeat customers repetitive origination fees, by limiting the revolving line to “a maximum line size of $25,000, repayable within SIX months of the date of the latest funds draw.

The problem is that OnDeck has become as dependent on repeat customers as they have become dependent on it. The consequences of OnDeck turning off treadmill borrowing of a repeat customer could be loan default.

We recommend against investing in OnDeck.

Disclosure: The author is long LC. The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it. The author has no business relationship with any company whose stock is mentioned in this article.

LendingClub: The First Big FinTech IPO

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LendingClub (Pending:LC) is a San Francisco-based online peer-to-peer lender. On Wednesday, December 10th, 2014, it is scheduled to price 57.7 million shares for between $12 and $14 a share and will begin trading on the NYSE the next day.

This IPO will raise $900 Million for the company, be the second largest IPO of the year behind Alibaba’s (NYSE:BABA) whopping $21 Billion offering and be one of the top 10 technology IPOs of all time.

Based on the upper range price of $14/share and about 370 million shares outstanding after the IPO, which includes a 8.7 million share option likely to be picked up by underwriters, LC will have a market value of $5.18 Billion. Yet, the company currently is GAAP and EBITDA unprofitable and will remain so for the next year or two.

Despite its short-term profitability prospects, what makes LC so valuable today is its proprietary, innovative and, last but not least, value-creating “FinTech” – applications of computer and software technology to financial services that, for whatever reason, traditional financial institutions have failed to adopt.

We rate LC a buy generally on the basis of its FinTech to drive future deal flow, rather than any current financial metrics. We also rate it a buy based on the size of its target market – $882 billion (with a ‘B’) in outstanding revolving consumer credit, which many consumers seek to refinance.

The purpose of this paper is to pinpoint where and how LC’s FinTech creates value.

If this IPO is successful, and all indications are that it will be, it will open up a floodgate of other IPOs from companies touting their FinTech and claiming enormous valuations despite no profits. For stock market investors, it is important early on to distinguish between proprietary, value-creating FinTech and easy-to-replicate and/or whiz-bang, fluff FinTech.

In its Amendment 3 to Form S-1, LendingClub lists six different areas that it applies FinTech: (numbers added are mine)

Our proprietary technology automates key aspects of our operations, including the (1) borrower application process, (2) data gathering, (3) credit decisioning and scoring, (4) loan funding, (5) investing and servicing, (6) regulatory compliance and fraud detection.

The purpose of this article is to present the case that LC’s most value-creating application of FinTech is at the intersection of scoring and loan funding.

The second is the speed at which the whole intermediation process takes place. Beginning-to-end speed enables LC to avoid balance sheet risk, so deadly in the past to traditional financial institutions, who were slow to tranch, securitize and sell bought mortgages in the prior decade. Indeed, isolating the value of beginning-to-end speed may be impossible as there would be no viable business here in first place without it.

Specifically, the core of LC’s value-creation is a more granular scoring than banks (“Base Risk Grade” A to G) of credit card and other debt refinancing applications based on a proprietary algorithm, and a more granular array of refinancing rates based on these scores.

LC bundles these loans into tranches based on scores and offer lenders, via securitized notes or directly, more granular investing options than previously.

Based on S-1 data, the following table represents the core of LC’s FinTech innovation:

LendingClub – Interest Rates on Standard Loans as a Function of Base Risk Grade, October 2014

Base Risk Grade Interest Rate (%)
A 6.03 – 8.19
B 6.67 – 11.99
C 12.39 – 14.99
D 15.99 – 17.86
E 18.54 – 21.99
F&G 22.99 – 26.06

Basically, LC is doing a better job than banks at matching credit card rates to consumer risk profiles and cherry-picking the A-to-D consumers by offering, according to its S-1, an average of 680 basis points (6.8 percentage points) below existing rates. E-to-G consumers, if they qualify at all, are offered refinancing rates above their current rates and are likely to decline LC’s loan offer.

At the same time, investors, lately financial institutions more than individuals, eagerly buy A-to-D tranches even though the loans are 680 basis lower than what consumers previously paid. This is because the risk profile has been granularized by LC to such an extent that the risk-adjusted rate of return for these A-to-D tranches has proven to be favorable relative to other offerings in the marketplace.

The following table is derived from a LendingClub information sheet for prospective investors found on their website:

LendingClub – Investor Nominal and Net Adjusted Rate of Return as a Function of Base Risk Grade, October 2014 for loans made in last 18 months

Base Risk Grade Nominal Return (%) Net Adjusted Return (%)
A 6.62 4.67
B 11.86 6.86
C 15 7.79
D 17.67 7.89
E 19.99 8.92
F&G 22.11 8.25

We complete the article with our case against other areas identified by LC as sources of FinTech innovation and value creation. These areas are loan origination, data gathering, scoring and loan servicing.

LC’s online loan application process significantly reduces origination costs and is quantifiable. According to its S-1, its “adjusted contribution margin” was a very healthy 44% of trailing 9 month revenue. Adjusted contribution margin is revenue less origination and sales and marketing costs. It excludes engineering and G&A and stock-based compensation, which is substantial especially in the quarter before this IPO.

Origination and sales and marketing costs, net of stock-based compensation, as a percent of total loan flow was a mere 2.14% for 9 months trailing. This compares with a reportedly 5%-7% for traditional brick-and-mortar loan origination operations.

While impressive, the real source of LC’s current valuation is the expectation for rapid scaling of deal flow, not unit margins. Deal flow is a function of LC’s ability to offer consumers significantly lower loan rates.

LC’s customers are, by and large, refinancing credit card debt. Origination fees are a one-time negative and a minor portion of the total financing costs. Cutting origination costs in half through FinTech is not the source of LC’s current and future deal flow.

Nominally, there is nothing very innovative or FinTech about LC’s data gathering. They start with FICO scores purchased from traditional agencies. According to their S-1, they supplement FICO scores with “behavioral data, transactional data and employment information.” But LC is vague (intentionally?) about what this data is, how it obtained it, and how it enhances credit decisions.

Does LC data mine customer Facebook (NASDAQ:FB), Twitter (NYSE:TWTR), LinkedIn (NYSE:LNKD), eBay (NASDAQ:EBAY), Netflix (NASDAQ:NFLX), and Amazon.com (NASDAQ:AMZN) accounts? Can they get at customer cookies? Do they feed this data into a proprietary “spendthrift” algorithm? If so, we would be impressed. But, we just don’t know the extent of LC’s use of FinTech data gathering.

In addition, according to their S-1, LC does little to no independent verification of data supplied on applications.

LC definitely has a proprietary FinTech algorithm that spits out loan scores. But it is not the scoring algorithm per se that is innovative. It is the granularity of scores and related interest offerings that sets it apart from traditional banks issuing credit cards and making unsecured, small denomination consumer loans.

And it is the more granular tranching of consumer loans by score that is the innovative and value creator on the investor side.

Finally, there is nothing very innovative or FinTech about LC’s loan servicing, other than insisting that all loan repayments be remitted via ACH to avoid the more costly paper check in the mail approach that banks lazily accept.

LC has its own in-house collection teams that work delinquent loans for the first 30 days, but according to its S-1, it outsources subsequent servicing efforts to tradition collection agencies.

The is no mention of any FinTech way of dealing with delinquent accounts, such as automated text messages or use of social networks to shame. (How about @bobsmith is #LCdelinquent tweets?) Or offering 100 basis point credits on loans in return for being able to post on your Facebook page that you are delinquent?

Seriously though, LC has left many opportunities out there for start-ups to apply value-creating FinTech in the area of data mining and verification relevant to credit scoring and decisioning and use of texting and social media to improve collections.

Relating App Store Revenue Rank to Revenue

From two of my recent Quora posts:

Question: How much money does the average mobile game make?

There are tons of games on the App Store and Google Play. Everyone seems to know about the hit games. How much revenue does the run-of-the-mill game generate? Is the revenue curve steady and flat? Do some genres do much better than others?

My answer:

Question should be rephrased:

How much money does the MEDIAN revenue rank game make.. ie game with revenue rank 100,000 out of about 200,000+ on iOS Apple US?

Answer: near zero.

Relation between mobile game revenue and revenue rank is a severe power function, more severe than the bookstore relation estimated 10 years ago and used to justify “long tail” inclusiveness in online stores.

I have estimated that top 10 revenue rank games derive 50% of revenue whereas the “long tail” of mobile game revenue ranks — games ranked 10,001 – 200,000+ derive only 5% of revenue. Long tail here is far smaller than books where revenue rank books 10,001 – 200,000 derive 30% of revenue.

To go back to the first question, I have a more precise answer. I have estimated that the trailing 12 month global mobile game revenue, less 30% cut from Apple and Google is $11.2B. Mobile game long tail — games ranked 10,001 to say 200,000 get 5% or $560M. Divide that by # of games in long tail — 200,000 – 10,001 = 189,999 560,000,000/198,999 = $2,847 is the AVERAGE yearly revenue of a mobile game in the long tail– with revenue rank > 10,000.

Question: How can you estimate the revenue of a mobile app based on its revenue rankings in App Annie?

There should be an exponential drop off, so if someone has done a study with a few data

My answer:

It is a power function with an upward kink at game rank #3-4

mapping update
As far as games, these are my current estimated revenue run rate after 30% store cut worldwide on iOS and Google (note portion of revenue is 4:1 iOS to Google)

Big 3 — what I call the “and, of, the” of a Zipf power function.

Clash of Clans $1.8B (Supercell)
Puzzle and Dragons $1.6B (GungHo Online)
Candy Crush Saga $1.0B (King)

Next 7
Monster Strike $900M (Mixi)
Game of War: Fire Age $600M (Machine Age)
Brave Frontier $400M (Alim/gumi)
Hay Day $400M (Supercell)
Farm Heroes Saga $350M (King)
Battle in Warring Games $200M (Sumzap)
Pet Rescue Sage $175M (King)

Top 10 World Wide Mobile Games by Revenue Rank receive estimated 50% of mobile game app store revenue >>> $6.5B out of $11B

In contrast, I have estimated “long-tail” of mobile game app store revenue — games ranked 10,001 + to 240,000 receive around 5% of revenue.

This is a lot less that the original “long tail” estimates for book sales of book ranked 10,000+ of around 30%

Not much loss in cutting out the “long tail” in mobile games on the app store in return for great gain in app store discovery and quality of merchandise.

RIP Long Tail Justification for Online Store Inclusiveness: 2004:2014

A Startup Street Map of San Francisco 2000-2012

The streets of San Francisco south of Market Street (SoMa) have changed tremendously since the 70s TV cop drama “The Streets of San Francisco” was shot on location. The stars of the show, Karl Malden and a young Michael Douglas, surely would be amazed at the transformation.
image001
The transformation, of course, has been the tremendous growth in high tech startups locating in San Francisco since 2000, the year the new AT&T (then PacBell) ballpark opened in SoMa. Lately, there has arisen a backlash against this growth ranging from bus blocking to paint bombings to blogger bitching about the long lines at Tartine Bakery and Blue Bottle Coffee.

Below is a graphic record of this transformation via an interactive Google map of 1,971 venture-funded startups by street address by founding date between 2000 and 2012. The data comes from a join of two tables in a CrunchBase database made accessible by Enigma.io, a public database infrastructure company.
image002

By linking street addresses to founding dates, we show graphically the flow of startup locations over time, moving early on from South Park up 2nd toward Market and also fanning out over time from AT&T Park South and Southwest toward Market again.

For those who live in San Francisco or who visit regularly, a startup street map of the city is just a graphic record of something we have already sensed. For some, the spreading dots depicted above might look like lava oozing out an erupted volcano – unstoppable and suffocating.

New startups are not a threat to San Francisco’s greatness. There is plenty of cool, albeit gritty, in-fill space available in Mid-Market area near Twitter and Square or south of Mid-Market (SoMMa?) along 8th, 9th and 10th. Dare I mention Dogpatch for those founders who want a “Blade Runner” industrial decay vibe? Locating startups in both areas would enrich city life.

image004

The challenge to San Francisco’s greatness is the explosive growth of the startups already rooted who wish to remain in the city. Some of the notables are listed below. It seem reasonable to assume that about 10 startups among the 1,791 listed our TechCrunch database– less that 1%- will experience at 10-fold increase in the next 5 to 8 years from less than 100 to 1,000+ employees located in the city.

While urban areas like San Francisco make great homes for software startups, it is not clear that the city, or any built-up urban area, can scale well for software startups used to homey in-fill spaces in great neighborhoods like South Park or lower Potrero Hill. Two examples come to mind.

                           Founding Dates of Some Notable SF Start-Ups
StubHub 2000 CrunchBase 2007
Splunk 2003 Lyft 2007
Trulia 2004 Task Rabbit 2008
Digg 2004 Yammer 2008
TrueCar 2005 Rdio 2008
Reddit 2005 GitHub 2008
Twitter 2006 Bandcamp 2008
Justin.tv 2006 Square 2009
Zynga 2006 Pinterest 2009
Eventbrite 2006 Vungle 2011
Marin Software 2006 Circa 2011

Strictly speaking, Dropbox was founded in Massachusetts in early 2007, but relocated to San Francisco shortly thereafter. It has quickly scaled to 650 employees with a decent shot of tripling that in the next five years. To anticipate that growth, Dropbox has moved recently into a sleek office complex with room to spare in Mission Bay, a scorched earth redevelopment area south of AT&T Park.

Mission Bay is large enough to accommodate maybe a dozen Dropbox-like growth companies. But, the area is completely void of San Francisco’s funky charm. Its sterile environment is literally better for what it was originally intended – biotech companies. Working in Mission Bay seems no different than working in Sunnyvale or Pleasanton, appropriate sounding names of bland Bay Area suburbs.

Salesforce.com represents another case study of how a San Francisco startup intends to scale in the city. The company, the pioneer of software as a service (SaaS) business model, was founded in San Francisco in 1999. It has been very successful and by 2011 employs 3,000 in San Francisco, double that worldwide. It is now the largest employer in the city ahead of venerable Levi Strauss and Charles Schwab.

The company reportedly intends to add to its concentration in the Embarcadero area by leasing about 300,000 square feet in the Transbay Tower, a 61 story building now under construct. The Transbay Tower, pictured below, will be the tallest building in San Francisco, surpassing the iconic Transamerica Building.

Is salesforce.com’s SaaS – Software as a Skyscraper — the future of San Francisco? Will a software company replace an insurance company as the name mentioned in jokes about a new iconic symbol for San Francisco?
image005

A Drawing of the 61 Floor Transbay Tower
Source: San Francisco Business Times