LOS ANGELES—The largest initial public offering of any California company in 2014, Lending Club's $865-million stock market debut earlier this month gave it a valuation of $8.5 billion, putting it ahead of all but 14 US banks. At the moment, such a bullish entrance for an online lender could occur only in the consumer segment, but Realty Mogul's Jillene Helman thinks commercial real estate is up next.

The key ingredient in online lending's secret sauce is big data, and the Realty Mogul CEO tells GlobeSt.com the speed and flexibility that automated technology provide make it especially apt for this sector. Helman, whose Los Angeles-based company recently signed a $73-million deal with Direct Lending Investments in which the hedge fund will exclusively provide loans to fix-and-flippers through the platform, recently sat down with GlobeSt.com to chart the advantages online lenders can offer compared to traditional banks.

GlobeSt.com: What makes commercial real estate a prime area for a more automated approach as far as lending is concerned?

Helman: You're seeing this trend with a lot of different lending capacities: Lending Club and Prosper are doing it with consumer credit, and you've got OnDeck doing it with small business loans. And I think real estate is a natural vertical for online lending for a couple of reasons. One, it's a really big market. About $300 billion in commercial real estate trades in the US on an annual basis; there are massive amounts of demand.

But another one, equally important, is the amount of time it takes to do those loans. Historically, it has taken 45 to 60 days to close transactions, because you're pulling documentation and you're doing underwriting—there's a lot of manual work that goes into it. Our vision, and the way we've started to build up the company, is an automated version of that, taking closing time from 45 or 60 days to 30 days or less. Rather than ask a borrower for tons and tons of data, we've the ability to go get that data ourselves through APIs [application program interfaces] and other computer methodologies to pull in information.

I'll give you a small example. Rather than asking our borrowers for tax returns, we ask for an authorization to get their tax returns directly from the government. All they have to do is check a box saying that we have authorization, and then we can go pull those automatically through our technology. So we're streamlining the process, with the effect that we're able to close a lot faster, and we're also automating a great deal of the underwriting. Rather than have human beings underwrite every single piece of the file, we're building technology to do that.

GlobeSt.com: It stands to reason that another factor is that a lot of buyers, and therefore borrowers, really need to make things happen more quickly these days, especially given the competition for the assets they would be bidding on.

Helman: Yes, speed is a competitive advantage.

GlobeSt.com: Lending Club recently had a successful IPO, and OnDeck will be following shortly. Has the success of these companies encouraged the real estate industry to take this format more seriously?

Helman: It provides legitimacy to the idea of online lending. Right now you've got two companies, one of which has IPO-ed and another which is IPO-ing, and it can't be a public company if it's not real. It says something about the future of the business, which is that right now we're on a massive growth trajectory.

GlobeSt.com: Where do you expect Realty Mogul to be going in 2015?

Helman: We're planning on going from 30 employees today to over 100, if employee growth is any indication. We're planning on much higher volume, much higher scale and the ability to help a lot more people fund a lot more real estate loans.

GlobeSt.com: In terms of big data's ability to expedite transactions, where will we see advances over the next couple of years?

Helman: A big part of it is the collection of the data. One of the reasons that Lending Club got the valuation that it did in its IPO was that it had all these years of data and payoff history. We're still at the early stages of that for our company. We have about two years of data, but we need more volume and more data in order to make our decision algorithms smarter.