Zillow launched major updates to its Zestimate home valuation model, including an updated algorithm. The changes allow the algorithm to react more quickly to current market trends and improve the national median error rate to 6.9% -- an improvement of nearly a full percentage point for more than 104 million off-market homes, according to the company.
The new Zestimate algorithm uses neural networks, the latest machine learning approach, and incorporates a deeper history of property data such as sales transactions, tax assessments and public records, in addition to home details such as square footage and location.
Neural networks are artificial intelligence systems that imitate how the human brain works. They are able to map hundreds of millions of data points efficiently, drawing connections among inputs and using the relationships formed to produce or predict an output. In the case of the Zestimate algorithm, the neural network model correlates home facts, location, housing market trends and home values.
“Since we introduced the Zestimate in 2006, we have never stopped innovating in order to provide consumers with the most accurate home valuations,” said Dr. Stan Humphries, Zillow chief analytics officer and creator of the Zestimate. “The new architecture we're debuting today represents another significant step forward in our efforts to harness big data to create more certainty for consumers, which leads to better decisions.”
Fifteen years ago, the Zestimate gave people instant access for the first time to an estimated value for millions of homes across America for free. Over the past decade and a half, Zillow released multiple major Zestimate algorithm updates as well as incremental improvements between major upgrades, and now calculates valuations for more than 104 million homes across the country.
