Real estate disruptor Opendoor uses deep learning to figure the right price for a house, but its head of data science says deep learning is a tool to uncover something deeper, something about the structure underlying all phenomena.
Opendoor, the seven-year-old startup that went public in February via merger with a special-purpose acquisition company, or SPAC, believes it has the artificial intelligence capability to optimize what is currently a very messy task of selling your home. The company will give you cash money, after answering a few questions in the app, so you can be on your way to unloading your home without the tedium of weeks or months of dealing with brokers and buyers. It’s all about increasing the liquidity of homes, and bringing the residential real estate market online. The market is only 1% «penetrated» as far as online sales, according to the company. Achieving all that involves a healthy dose of machine learning forms of AI, according to the company. But just what kind of ML? The details are somewhat obscure. In the company’s prospectus for IPO, Opendoor describes using ML on a unique dataset of home sales to derive the pricing predictions that will help it decide what price to pay a homeowner: To try to uncover the nature of those algorithms — the secret sauce, as it were — ZDNet spoke to the company’s head of data science, Kushal Chakrabarti, via Zoom. Chakrabarti signed on to Opendoor just a year ago, after serving previously in an advisory capacity. Chakrabarti came to machine learning via a circuitous path. A decade ago, as a research scientist at the University of California at Berkeley, where he got his degree, Chakrabarti worked on the Human Genome Project, the quintessential Big Data application of our time. «I’ve been working on data science since before it had a name,» he remarked. After Berkeley, Chakrabarti had numerous posts, both in established companies, such as Amazon, where he served as engineering lead, and startups he helped create, such as micro-loan company Vittana. In many of these instances, he worked with the technologies of machine learning, such as recommendation systems at Amazon back in 2005. Also: Amazon AWS’s AI team seeks the profound in the industrial Despite that fact, «I was a skeptic of a lot of the deep learning work that was coming out in those early days,» recalled Chakrabarti. Eventually, he came around. «It’s unquestionable at this point that it’s clearly better at a number of things,» he said. Deep learning is a «really, really good tool to understand how lots of different bits of information interact with each other, and dramatically better at teasing out those interactions than humans have ever been.» The scale of the problem at Opendoor, namely right-sizing the entire housing market, is one of the big things that attracted Chakrabarti «There aren’t that many trillion-dollar problems out there,» observed Chakrabarti. «This is one of the most challenging problems I’ve ever come across.» Deep learning, Chakrabarti said, is «one tool we’re using» to plumb the depths of that trillion-dollar problem. What’s hard about solving the home sales question is the nature of the problem. It consists of numerous variables whose mutual dependencies have to be inferred from data. «There are tens of millions of homes we have to consider,» explained Chakrabarti. The common mis-conception, he said, is that it’s all «location, location, location,» in real estate. In fact, there isn’t just one point at any moment in time, there are a plethora. «There’s location, but then there’s three and four and five bedrooms, and there’s whether the view has a northern exposure, etc.
