Buying and Selling Homes Algorithmically with Opendoor’s VP of Research and Data Science, Kushal Chakrabarti

Jun 24, 2021

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For many people, the process of buying and selling a home will undoubtedly be the most difficult decisions they will make in their lifetime. Is the price you’re paying for your home fair? Is the price you’re selling your home for an adequate sale price? For a long time, realtors have been oracles with the answers to those questions, but times are changing. Today, Opendoor, a data-driven real estate start-up, is putting data to work to answer those questions for you.

“Opendoor from a technical perspective, is the hands down single most fascinating technical challenge I've ever come across. If you look at the questions that we have to answer, they're just fascinating and you have to answer them in the right ways. Otherwise they just don't really work. You think about the prediction problem, you think about the optimization, probably you think about the portfolio optimization problem. There's so many different pieces to the problem and you just have to do it.”

Kushal Chakrabarti is the VP of Research and Data Science at Opendoor, a company that is reimagining the way homes are bought and sold by moving the process online and empowering buyers and sellers to make informed decisions by taking an algorithmic approach and removing the ambiguous nature of the home buying process. On this episode of IT Visionaries, Kushal explains some of the processes Opendoor uses to help make home offers, including the importance of clean and trustworthy data. Plus, Kushal opens up on his personal journey, including how he got into data science and some of the trends he sees in the A.I. and machine learning space.

Main Takeaways

This is Trendy: There are two popular trends happening in A.I. and machine learning. The first is the democratization of tools, which in return is making it easier for data scientists to quickly test and measure their models. The second is the abundance of third party data that is readily available, which is making it more difficult for data scientists to know which data they are getting is trustworthy data, and which is not.
Trust in Numbers: All great algorithms start with great data, but having a high fidelity of data is one of the key differentiators for any high-performing model. When you’re mixing first-party data with third-party data, be intentional about how you create strategic data models that fit your business.
Asking the Right Questions: As a data scientist you must be asking the right questions otherwise, your models will have a tendency bias introduced to them. When models have bias, you might get an answer that is close to your hypothesis, but it won’t be the correct answer.

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