Doorsteps, an online tool owned by operator Move that guides consumers through the homebuying process, has added rental search capability to its listing search app Doorsteps Swipe, along with improved functionality for gauging user preferences and recommending listings based on them.


Screen shot showing a Doorsteps Swipe user rejecting a listing.

Doorsteps CEO Michele Serro said that users have requested rental search capability more than any other feature, and sees it as a way to cater to the “person thinking about buying 18 to 20 months out” who may move to another rental before trying to buy.

If Doorsteps Swipe is like Tinder for real estate, then rentals are the eye candy that can attract renters.

Doorsteps walks first-time buyers step by step through the homebuying process, so adding functionality that attracts future homeowners jibes with its overall mission. Serro calls the app a “gateway drug into Doorsteps.” The app encourages users to “sync with Doorsteps” to save listings and also promotes apps.

Since operator Move owns Doorsteps, Swipe is able to stock its database with listings through direct feeds from more than 800 multiple listing services that are updated as frequently as every 15 minutes.

The app obtains its rental inventory through those feeds along with partnerships with property management firms and third-party data providers including rental site Zumper, Serro said. Zumper recently began feeding listings to

Doorsteps, which rolled out the first version of Swipe this spring, has also introduced other features to the new version that add a little more meat to the app’s intentionally bare bones.

Doorsteps Swipe's summaries show pet-policy preferences based on a user's activity.

Doorsteps Swipe’s summaries show pet policy preferences based on a user’s activity.

Users may view pet policies on rental listing pages and ratings of nearby schools on both rental and for-sale listings. They can also search by drawing an area on a map, and view more detailed summaries of their preferences.

The whole premise of Swipe is that online real estate search sites — with their sophisticated search tools and detailed property and community information — are too complicated for some consumers, particularly those who aren’t serious buyers and may just be testing the waters.

“You kind of have to know what you’re looking for,” Serro said of the typical listing site search experience. “We’re trying to get people to discover places that they normally wouldn’t.”

So Swipe boils the search process down to its basics. Surfacing listings based either on location or at random, the app asks users to approve or dismiss a listing based on a single photo (though users may tap a photo to pull up some additional information before deciding).

Users swipe right or click a thumbs-up icon to “like” a listing, and swipe left or click a thumbs-down icon to reject it. That’s earned Swipe comparisons to Tinder, a popular dating app that matches users with each other by using the same functionality (swipe to approve or disapprove) to determine mutual interest.

If users view listing pages, they may tap an inquire option that pulls up a listing’s MLS ID number, brokerage name, listing agent and the contact information of the listing agent. From that page, users can immediately contact the agent by tapping an email or call option.

By analyzing a user’s activity, Swipe is able to hone in on a user’s preferences and serve up properties that match them more closely. Fypio and Relocality are among other apps that are using “machine learning” to pioneer “predictive search” in the real estate world.

The newest version of Swipe leverages its machine-learning to greater effect by firing out push notifications to users whenever a listing matching a user’s preferences hits the market.

The app determines a user’s preferences based on criteria they specify in its search tool (users may specify only price, bedrooms and square footage), but also based on the user’s behavior — by processing the characteristics of the properties users have liked or disliked, as addressed earlier.

The new version of the app can also gain a more sophisticated understanding of a user’s preferences because it factors in the school and pet policy information now available.

Many websites specialize in rentals or for-sale listings, but some listing portals that carry them appear to have placed more of a focus on improving their quality and better monetizing them.

Serro said Swipe has only scratched the surface of predictive search. Doorsteps is already testing ways to gauge neighborhood preferences by weaving cards into Swipe’s like-or-dislike shell that represent things like restaurants, parks and house styles.

“What Swipe has taught us so far is that there are lots of things that you can put into this shell for an early-stage buyer/renter,” she said. “With a little machine learning involved, we can deduce not only the house they want to live in, but also maybe more important the type of life they are looking for.”

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