Zillow awards $1 million to team that reduced home valuation algorithm error to below 4%

Two years ago, Zillow, an end-to-end real estate and rental marketplace that facilitates property buying, selling, financing, remodeling, and more, launched the Zillow Prize. An open contest on Google’s Kaggle, it challenged teams to improve on the accuracy of Zillow’s home valuation algorithm. The endeavor took months of solid effort, but a winner has finally emerged.

The Seattle company today announced that team ChaNJestimate — whose members include Chahhou Mohamed, Jordan Meyer, and Nima Shahbazi, hailing from Morocco, the U.S., and Canada, respectively — will take home the $1 million prize for a model that bested the Zillow “Zestimate” benchmark by approximately 13 percent. (The Zestimate’s nationwide error rate is 4.5 percent; the team’s work pushes it to below 4 percent.) The second- and third-place teams, Silogram-2 and Team Zensemble, will be awarded $100,000 and $50,000, respectively.

To achieve this new level of accuracy, team ChaNJestimate leveraged deep neural networks — layers of mathematical models modeled after neurons in the brain — and other machine learning techniques to “directly” estimate home values and trained their AI systems using publicly available data, including rental rates, home prices, commute times, and other contextual information, such as road noise.

“It’s amazing to know that millions of people will benefit from our ideas,” said Shahbazi, who competed as a team with Mohamed against Meyer in the contest’s initial qualifier, but who decided to join forces with Meyer for the final round. “We brought every novel idea we could to our code and kept experimenting. For every idea that worked, there were a hundred that didn’t work. But we kept going.”

Zillow says that parts of the winning algorithm will be incorporated into Zillow’s Zestimate model, along with ideas inspired by other top competitors. Currently, the Zestimate is within $10,000 of a given home’s sale price, and Zillow expects the improvements could bring it $1,300 closer to the actual price.

“People are incredibly passionate about their home and understanding its value, and we are amazed by the winning team’s hard work the past two years to make the Zestimate even more precise,” said chief analytics officer Stan Humphries.”We’ve been on a 13-year journey making the Zestimate more accurate, and hosting Zillow Prize allowed us to invite thousands of brilliant data scientists from around the world to join us on this journey. We’re so proud that the winning team’s huge achievement, and the work of all the teams in the competition, will provide millions of homeowners with a better understanding of one of their biggest life investments.”

Zillow introduced the Zestimate in 2006. Its 7.5 million statistical and machine learning models analyze hundreds of data points to automate the valuations of 110 million properties across the U.S.