Zillow offers $1m prize for 'Zestimate' fix
Seattle-based online real estate company Zillow has devised a novel solution to its 5% nationwide median error rate. The firm has launched a competition encouraging participants to improve its "Zestimate" home valuation algorithm and win $1m. The move comes after an Illinois homeowner filed a lawsuit against Zillow that claimed the firm's technology had repeatedly undervalued her house.
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And the Zillow Prize Goes to… Team ChaNJestimate!

Congratulations to Team ChaNJestimate, the $1 million grand-prize winner of Zillow Prize, a global competition that brought together the brightest minds in data science to improve the Zestimate home valuation.

Watch ChaNJestimate team members as they learn about their million-dollar win.

Meet the Zillow Prize Champions

Teammates Chahhou Mohamed, Nima Shahbazi and Jordan Meyer – whose names and initials inspired their team name’s clever play on the Zestimate – are spread across Morocco, Canada and the United States. But they didn’t let thousands of miles or multiple time zones foil their efforts. The team shared ideas and code through Slack and GitHub – and they have yet to meet in person.

When Zillow Prize launched in May 2017, Jordan had never participated in a Kaggle competition before, though he was a long-time member of the platform and enjoyed learning from other expert data scientists and seeing the diverse set of problems Kaggle competitions aim to solve. The potential of winning $1 million convinced him to compete in Zillow Prize’s initial qualifying round, which included nearly 4,000 teams and individual competitors.

Around the same time, Nima and Chahhou – experienced veterans in Kaggle’s data science competition circuit who had partnered on previous competitions – entered as a two-person team.

After advancing to the final round as separate teams, Nima and Chahhou convinced Jordan to join their group – creating a powerhouse team with a range of data science expertise and an even more sophisticated algorithm that blended their separate models from Round One. Jordan’s specialty in developing deep neural network systems – which grew from an initial interest in biologically-inspired algorithms, or computer systems that mimic the neural circuitry of the brain – was a powerful addition to Chahhou’s neural network expertise and Nima’s strong track record of building accurate predictive models.

Over the six-month course of Zillow Prize’s final round, all realized that competing for the grand prize was not for the faint of heart. In addition to their regular day jobs, Jordan estimated he spent 400 hours on his contributions to the team’s final product, while Nima recalls spending 7-8 hours a day, seven days a week, fine-tuning the algorithm that would eventually clinch the million-dollar prize. Now, as Zillow’s data scientists begin to incorporate Team ChaNJestimate’s work into the Zestimate model, the weight of knowing their efforts will help millions of homeowners in America better understand one of their biggest life’s investments is settling in.

While they don’t have any immediate plans for their prize money, Nima jokes that it only seems appropriate to use part of it for investing in real estate.

The Final Zillow Prize Leaderboard

When Zillow first launched the Zestimate, it was revolutionary. So many people visited Zillow on our first day to check out their home values that the website crashed. Up until that point, computer-generated estimates of home values were like the Wizard of Oz – hidden behind a curtain where only mortgage lenders or appraisers could access them. Thirteen years later, the Zestimate home valuation algorithm remains an important data point for homeowners, sellers and buyers. We’ve come a long way in that time, narrowing the median error rate of the Zestimate to a record-low of 4.5 percent nationwide. That means half of all Zestimates were within 4.5 percent of the eventual sale price of the home. Now, thanks to our Zillow Prize winners, we’ll push the Zestimate’s error rate even lower. Homeowners are so passionate about their homes and tracking their homes’ value. The Zestimate matters, and that’s why we never rest in striving to make it more precise.

The road to the $1 million winner

Beginning in May 2017, teams of data scientists competed in two rounds. In the initial qualifying round, teams were tasked with decreasing the Zestimate’s error rate of 4.2 percent in three counties. The top 100 teams then advanced to the final challenge: build a new home-valuation algorithm that predicts the actual sale price of homes.

To snag the $1 million grand prize, the winning team had to beat the accuracy of a Zillow benchmark model when compared against actual home sales between August and October 2018. Their algorithm also needed to score the largest improvement to Zestimate accuracy among all the final competitors.

What Zillow Prize’s winner means for the Zestimate, and you

The introduction of the Zestimate in 2006 marked the first time homeowners had instant access to information about their homes’ estimated values, for free. It changed the world of real estate.

Zillow constantly strives to make the Zestimate more precise, reducing the nationwide error rate from 14 percent in the early days to 4.5 percent today. Zillow is committed to implementing innovative ideas – from within the company and through outside efforts like Zillow Prize – to push the Zestimate’s boundaries even further, all to help you make the best decisions about real estate.