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A featured contribution from Leadership Perspectives: a curated forum reserved for leaders nominated by our subscribers and vetted by the Real Estate Business Review Advisory Board.

AI-Bridging the Human Element


I joined Redfin six years ago and founded our analytics engineering team. Redfin’s objective is to redefine real estate in the consumer’s favor. We’re using data and machine learning to deepen the relationship between our real estate agents and our customers. The very first thing we were looking at was how we could predict what homes our customers would be interested in, so we built an algorithm called, the “Redfin Matchmaker” and started making recommendations to our customers. We found that our algorithm was much better at predicting what homes the customer would like. It turned out that when Redfin recommends a home, customers are four times as likely to click on that house as they are on a home that fits the criteria of their own saved search. Also, pricing for a house is an emotional topic; someone lives in that home and has a connection to that home. We aim for our algorithm to be more than just a standalone number, to show users what homes were used to create that number, which is very important, as most providers give you only the figure. That is how we have tried to make it a part of the conversation and not as something that puts distance between the agent and the customer.
2. What sort of infrastructure do you feel should be used to power AI?
When we first wrote our algorithm years ago, we had tested it and were ready to turn it on full blast. So it was late at night, which was when cloud computing costs were cheaper and so we started to roll out that algorithm to run all of our data and a couple of minutes later, we got a phone call from AWS saying, “hey,do you mind turning off that algorithm for a few minutes, you are taking up too much of our computing resources right now!” This is funny now because AWS has added so much compute power since then, we would have a hard time gobbling it all up.
when Redfin recommends a home, customers are four times as likely to click on that house as they are on a home that fits the criteria of their own saved search