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Real estate websites helped turn automated real-estate assessment into a classic AI problem. The latest approach by a leader in the field gets a boost from deep learning.

What’s new: Zillow developed a neural network that predicts the value of homes across the United States. The system narrowed the error between earlier estimates and actual selling prices by 1 percent, achieving a median error rate of 6.9 percent. In addition to making it available online, Zillow plans to use it to improve its own real estate business.

How it works: Zillow’s Zestimate system previously employed roughly 1,000 separate non-machine-learning algorithms, each tailored to a different local market. The new network estimates the value of 104 million dwellings nationwide, updated as frequently as daily.

  • A global model can outperform an ensemble of local models because home sales are sparse in any given area, a Zillow representative told The Batch.
  • The architecture incorporates convolutional and fully connected layers that enable it to learn local patterns while scaling to a national level. Inputs include square footage, lot size, number of rooms, vintage, location, tax assessments, prior prices, days on the market, sizes of nearby homes, and proximity to a waterfront.
  • Zestimate also incorporates earlier models, such as a vision system that analyzes photos for value-enhancing upgrades like marble countertops and stainless steel appliances.
  • Since February, the company has used its estimates as the basis for cash offers on 900,000 homes. It believes the system’s improved accuracy will enable it to boost that number.

Behind the news: Zillow has been tweaking Zestimate since 2006. The new neural network grew from a hackathon in which 3,800 teams from 91 countries competed for a $1 million prize. The winning team used a combination of deep learning and other machine learning techniques. The company incorporates machine learning into other aspects of its business as well, Zillow vice president of AI Jasjeet Thind said in an interview for DeepLearning.AI’s Working AI series. For instance, the company is developing a natural language search system for parsing legal documents.

Why it matters: Between inspections, negotiating a price, and filling out reams of paperwork, buying a home is a complex ordeal. A tool that helps buyers and sellers alike get a fair price could be a big help.

We’re thinking: How much does a GPU rack add to the value of a home?

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