Powered by an advanced natural language technology, homebuyers can enter longer, detail-rich requests, as they would on Google, to unearth more accurate inventory matches.
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Zillow continues its march toward a better overall homebuying experience with the advent of an improved way to find homes that match distinct buyers’ preferences.
Powered by advanced natural language technology, homebuyers can enter longer, detail-rich requests, as they would on Google, to unearth more accurate inventory matches. People can now find homes in the same way they would describe what they want to friends and family, the company shared in a Jan. 26 announcement to Inman.
This means longer phrasing can be interpreted by the app, such as “$700K homes in Charlotte with a backyard” or “open house near me with four bedrooms.”
“This new tool is a game changer for home shopping, because it helps shorten the sometimes long and stressful house-hunting process by creating an easy, more modern way to search,” said Jenny Arden, Zillow’s chief design officer, in the announcement. “And, it delivers relevant search results in a simple, uncluttered way.”
It can also benefit agents who more accurately list the desired features of their listings instead of merely reciting the number of bedrooms and bathrooms, for example. As is common in online home searching, Zillow will allow users to save their search terms to be alerted of new matches.
Zillow states it’s the first iteration of this kind of home search technology, however Localize, a New York City-based proptech, deploys a similarly organic search methodology. The company raised $25 million in 2021.
Localize gathers “billions of data points” to provide information on hundreds of building and neighborhood attributes including schools, future construction, weather conditions, proximity to popular stores, building violations, complaints, pests, natural light and high ceilings, among other aspects of properties and their communities.
The company also offers a hybrid, human-AI concierge service called Hunter that serves as a homebuying adviser and sends buyers personalized, curated listings daily that Localize then further adjusts based on user feedback to the results.
Regardless of whose natural language search was first or better, the advancements present the industry with powerful benefits. Buyer agents are incessantly burdened with the notion they need to get new listings in front of clients before competing apps.
In truth, it’s a battle that can’t be won and now, doesn’t even need to be fought. The more information buyers have about listings, the easier they are to serve and satisfy.
“Zillow’s natural language search feature takes users’ queries and scans millions of listing details to bring relevant results to the surface. At the same time, the feature is training machine learning models to better respond to search queries that use natural, human-like sentences,“ Zillow said.
Machine learning and business automation are components of artificial intelligence. The topic was brought up frequently this week at Inman Connect New York, in part due to the sudden spike in interest in ChatGPT, largely a natural language response mechanism, such as those used in lead capture and technical support. ChatGPT can also be queried to offer written descriptions, and thus, listing content.
To date, Zillow’s Arden seems correct in making comments that real estate’s future will be powered by proptech’s application of AI.
“We are proud to be the first in the industry to offer this smarter way to search and excited to see how it learns and evolves to help each Zillow shopper find their perfect home.”