Zillow, a large U.S. online real estate and home information marketplace[i], is a real estate platform that, at the simplest level, facilitates connections between home buyers (landlords) and sellers (renters). Data and algorithms have played a critical role in attracting more users to the Zillow platform; the company has been praised for collecting home transaction data since early in its history, and using this data to create products of significant value to users.
One example of a value-additive, data analytics-driven product is the Zillow “Zestimate,” which is the sale (or rental) price that Zillow would estimate for the property given all public and provided information that Zillow has on the property. This information is publicly available and intended to be used as one point in decision making – whether that be a seller trying to decide whether it is worth it to list their home for sale, or a buyer trying to decide on an appropriate offer price.[ii]
The Zestimate algorithm is, at its core, informed by the sale of comparable properties. Zillow looks at the sale prices of multiple comparable properties, and tries to tease out the home value given property features ranging from the basic (e.g., neighborhood, square footage, number of bedrooms and bathrooms) to the nuanced (e.g., fireplace, stainless steel appliances, original wood floors). The resulting Zestimate is, in theory, a highly accurate estimate of the home’s true market value – and freely available to all.[iii]
The “Zestimate” is a key component of Zillow’s virtuous cycle and therefore to increasing usage
For active buyers and sellers, the resulting Zestimate is valuable because it helps inform their asking price and offers; in a sense, it forces more pricing transparency during the listing and search process, which was previously rather opaque. For homeowners contemplating a sale, the Zestimate for their own property can help them understand whether it is the right time to list a property, and the Zestimates for other properties can help them identify which pre-sale home improvements might have positive ROI (e.g., painting, refinishing floors, etc.).
Since the algorithm is fueled by comparable home transactions, it needs growing volumes of listing data to improve accuracy. In its early days, Zillow rolled out numerous incentives to encourage agents and homeowners to list on Zillow, and then used the data from those listings to improve the algorithm and produce more accurate Zestimates. As Zillow listings grew, the Zestimate median error rate fell from 13.6% in 2006 to 8.0% in 2015[iv], and eventually to 1.9% by 2020[v]. As the error rates fell, the Zestimates became more trusted and ubiquitous, thus creating standalone value that encouraged users to engage with Zillow
This is an example of the “AI factory’s virtuous cycle” (see below) described in Competing in the Age of AI:
More transaction data leads to improved algorithms, which generate more accurate Zestimates (a better service), which attract more users seeking pricing information.
The Zestimate creates value for all parties, but this value – and Zillow’s capture – is bound to plateau as the Zestimate becomes more accurate
With enough usage and data, Zestimates, in theory, will reach a point where they are so good – perhaps even perfect – and any further improvement would be impossible or unprofitable. At that point, the value created by Zestimates will plateau.
One of the core observations from Competing in the Age of AI is referred to as “the collision between traditional and digital operating models” (see below), which states that a digital operating model allows a firm to avoid this value plateau:
If Zillow were to continue focusing on the Zestimate product, they would effectively be behaving like a firm with a “traditional operating model” in the sense that they are foregoing the unique scalability of a digital operating model. In order to unlock the potential for exponential value creation, it is essential that they capitalized upon their data and algorithms to find new ways to create and capture novel, incremental value that further fuels the virtuous cycle.
Zillow’s best opportunity to jump the curve and create exponential value for buyers, sellers, and themselves may lie in iBuying
iBuying, or making an instant cash offer to homeowners in exchange for receipt of a home as-is, is a growing trend in the U.S. Homeowners who accept an iBuy offer generally report that they feel that they accepted a lower price than they would have through a traditional sale process, but were willing to do so for the convenience and certainty that an iBuyer can offer.[vi]
Zillow launched Instant Offers as an experiment in iBuying; it is a relatively early experiment in a fiercely competitive space, but the Zestimate algorithm creates unique opportunities for Zillow to create value through sales and capture it through commissions and arbitrage.
Zillow has through the Zestimate algorithms , in theory, a relatively accurate picture of the home’s current (and potential) market value, which gives the homeowner peace of mind that the offer is reasonable and gives the future buyer peace of mind that the sale price is reasonable – a peace of mind is of great value in the home-buying process! Zillow is able to capture value through profit on the sale, and by using the transaction and home improvement data to improve the core Zestimate product.
[i] Zillow Group, Inc., 10-K Filing, 2019.
[iv] Bundrick, Hal M., “Putting Zillow’s Zestimates’ Accuracy to the Test?”. NerdWallet, 12 November 2015, https://www.nerdwallet.com/blog/mortgages/putting-zillow-zestimates-accuracy-test/.
[vi] Million Acres, “What is an iBuyer? iBuying explained”. https://www.fool.com/millionacres/real-estate-market/real-estate-innovation/what-ibuyer-ibuying-explained/.