Zillow | What It Is & How I Use It

Zillow is the most visited real estate website in the United States, providing home valuations, listings, rental information, and mortgage tools. For custom web application development, Zillow's data and API integrations are valuable when building real estate platforms, property management tools, investment analysis dashboards, or CRM systems for real estate agents. The Zillow API provides access to property data including Zestimate valuations, property details, comparable sales, and neighborhood information. Real estate businesses frequently need custom applications that pull Zillow data into their own workflows for lead management, market analysis, and client-facing property search experiences.

How It Started

Zillow was founded in 2006 by Rich Barton and Lloyd Frink, both former Microsoft executives. Barton had previously founded Expedia while at Microsoft, building one of the first websites that gave consumers direct access to airline pricing information that had previously been controlled by travel agents. With Zillow, Barton applied the same philosophy to real estate: he wanted to give homeowners and buyers access to home valuation data that had traditionally been controlled by real estate agents and appraisers. The Zestimate, Zillow's automated home valuation algorithm, was the killer feature that drove initial adoption and made "Zillowing" your home value a common American pastime.

Unknown Fact

Zillow's most expensive mistake was Zillow Offers, its iBuying program where the company used its Zestimate algorithm to buy homes directly from sellers, renovate them, and resell them for a profit. The program launched in 2018 and initially seemed like the future of real estate. But in November 2021, Zillow abruptly shut down Zillow Offers after losing over $500 million on the homes it had purchased. The algorithm was consistently overpaying for properties, and the company found itself sitting on thousands of homes it could not sell at a profit. Zillow had to lay off 25 percent of its workforce. The failure became one of the most cited examples of the limits of algorithmic decision-making when applied to complex, local markets.