Our challenge was to think beyond just enhancing a standard real estate website and create a vision for what finding a home online could look like in the near future.
All major real estate companies use the same configuration for their search page: a search field and filters above a split screen of listings in a grid and a map.
Several of my colleagues at Fictive Kin had gone through the home buying process recently, so we got started by sharing our experiences using real estate websites and quickly uncovered some universal pain points.
It was clear to us that the traditional real estate website search method is broken in two specific ways:
The urge to make sure you've seen every listing stems from a lack of confidence in the search results.
Using too many filters returned very few listings, but not using enough meant hours of sifting through the pile.
The anchoring feature of real estate websites is the ability to save a search. Saved searches are meant to reduce labor of the user by allowing them to quickly repeat a set of search parameters.
However, we knew from our group discussion that saving a search didn't do much in terms of reducing labor and that it possibly created more labor.
We mapped out a search-centered experience to get a better look:
The part of the experience where the user spends the most time is browsing listings—a tedious experience with or without the back-and-forth action of adjusting your filter settings.
Saving a search merely allows you to exactly repeat the tedious experience again at another time.
Our ultimate goal was to mitigate the lack of control and fluidity, which meant we needed to accommodate users who have very specific needs as well as those who are all-around flexible or are still figuring out their needs.
We listed example search parameters of the two extremes on a spectrum of flexible to constrained:
With these constraints and flexibilities in mind, I took a shot at creating a new search/browse interface:
We wanted the experience to center around browsing, not searching. This meant that in a personalized home search experience the user would only need to configure their search once.
After that, Keller Williams must get to know each individual user, learn what they are looking for beyond just their constraints and flexibilities, and surface listings that matched their needs and taste.
We mapped out what a person-centered experience might look like:
With a personalized dashboard at the center of the experience, relevant listings are brought to the user, lightening the effort and creating a more enjoyable and efficient experience.
Now that we knew we wanted a personalized dashboard, we had to figure out how it would be populated. We started by defining how the user would train the algorithm, because that would inform what content we had to work with and how it could be organized.
Post-search, the user trains the AI by telling it exactly what it wants to see, through:
The culmination of AI training and collected data results in hyper-personalized modules that predict what a user might want to see or add to their search.
Smart suggestions come in the form of:
These modules can be mixed in with the training modules to make up the content of the personalized dashboard.
We all knew from experience that searching for a home is more than just browsing listings. It’s also learning about neighborhoods, keeping a close eye on the market, picking up on real estate terms and learning the process, and going to open houses and first-time buyer workshops.
We brainstormed other modules that we could intersperse into a user’s feed that would round out the experience and further build the user’s relationship with the Keller Williams brand.
Each time a user logs in, they'll see a stack of new listings, market updates, and various interactive modules that make smart suggestions and collect insights based on what the user likes, follows, or hides.
As someone who is excited by AI and all of its potential, I loved working on this blue-sky project for Keller Williams. I'm delighted by the recommendations I see every time I open my Spotify app and or log onto Netflix, and it was so fun see how that kind of algorithm could be applied to a real estate website.
Something I was thinking about while working on this, and something I'm still unsure of, is the societal effect an algorithm can have when it plays a key role in helping someone find a home.
Every major city in the U.S. faces the issue of gentrification and rising home prices, and without careful testing and implementation, an "innocent" personalization algorithm could possibly have unintended effects.