While shopping for books, boots, and broccoli can be done with a tap of your smart phone, shopping for a home has always been a bit more complicated. That’s changing.
Artificial intelligence, already in use to help buyers find homes more quickly on sites such as Homes.com and HomLuv (a sister site to NewHomeSource.com), is anticipated to grow in accuracy and improve home shoppers experience, says Mark Law, vice president of product at Builder’s Digital Experience (BDX).
“We’re still in the infancy stage of AI, but the ability to gather information from buyers can make it easier to design homes that match what shoppers are looking for and to streamline our marketing,” says Diahann Young, director of innovation and new product development for Pulte.
By aggregating information from thousands of home shoppers, builders will be able to identify a house type, a price range, a location, and specific features that appeal to large groups of buyers and replicate those attributes. Right now, AI is simplifying the homebuying process, making it easier to find homes online more quickly and to gather information.
“As consumers, we’ve become accustomed to seeing ads pop up almost instantly as soon as you search for anything,” says Dr. Mark Lee Levine, a professor at the Burns School of Real Estate and Construction at the University of Denver. “So the idea that if you search for a 3,000-square-foot home and then you’re sent other options for homes of that size isn’t surprising.”
Algorithms and VR for Smarter Shopping
“Data is the secret sauce to AI,” says Chung Cheong. “The ambition is to help consumers quickly narrow their choice from the thousands of homes on the market in their preferred city to a few relevant homes they can see on their mobile phone.”
Machine learning takes what we know about you from your search and what we know about other shoppers in that area and sorts listings to make the process faster, says Cheong.
“Price Perfect lets shoppers play ‘What if’ – for instance, ‘What if I subtract a bedroom from my wish list, how much will that save?’” says Cheong.
Are You Smarter Than a Chatbot?
Tech innovations often solve consumer problems, one of which, says Cheong, is that 50 percent of consumers’ questions online go unanswered. An improved version of a chatbot can help.
“Consumers today are most likely to look for information online from their phone,” says Bassam Salem, CEO and founder of Atlas RTX, a provider of chatbots for homebuilders and for New Home Source. “But after 8 to 12 seconds, we’re likely to click away if we don’t get an answer to a question that we’ve typed into a box.”
Today’s chatbots have sophisticated capabilities to understand the meaning of sentences and speak more than 100 languages, says Salem.
“They’re not as smart as a human, but they can cover common topics within seconds,” Salem says. “We’ve developed conversational AI for a real-time experience and the ability to bring in a human if necessary. What’s best is a combination of a human and a chatbot.”
Searching for a community or information through a chatbot interaction can be more appealing that filling out an online form, says Salem.
“Consumers want their information gathering to be impersonal at first,” says Levine. “They like the idea of being able to ask questions and get information without revealing too much about themselves.”
A chatbot is most valuable if it can provide a concrete answer, says Levine; if the answers are nebulous, people get frustrated and won’t return to the site.
AtlasRTX’s chatbots know what they don’t know, says Salem, and flag a human for help when needed. At the same time, the chatbot continues the “conversation” online to keep the buyer engaged.
For buyers, a well-designed chatbot can provide quick information at any time of day, says Young, who says Pulte is exploring this technology.
Law says New Home Source’s chatbot is most active between 6 p.m. and 6 a.m. In addition, it remembers where someone has been on the site so when they return, they don’t have to start over with irrelevant information. “A chatbot offers a one-to-one real time conversation, unlike a website, which is a one-to-many relationship that’s not interactive,” says Law.
Searching With Images
Many buyers start their home search by looking at photos. Approximately one-third of all Google searches are for images, says Young.
“AI can help optimize image recognition by scanning images and finding examples that match what buyers are searching for, such as houses with a red roof,” says Young. “AI can help us deliver the images that buyers want. So, if they’re looking at kitchens, we can show them a big view but we also need to make sure they see images of the cooktop, too, since that’s what they also want to see.”
InScene, a BDX product, uses AI to identify millions of images, including general information, such as labeling a room, and specific information, such as the name of the cooktop manufacturer and the granite center island that appear in the image.
“With InScene, builders can create an interactive hotspot so that buyers can focus on individual items and see what their options are for upgrades,” says Law.
Future Innovations With AI
As more buyers interact with images and chatbots, builders will be able to provide more relevant information based on the patterns that are revealed, says Law.
“For example, if buyers moving from Seattle to Austin are looking for information about HOA fees, that information can be provided anytime someone signs on and the site detects they’re from Seattle,” says Law. “If buyers moving from Arkansas to Austin are more interested in lot sizes, that information can be pushed to the top for those buyers.”
Consumers have an optimized experience when they get better information and better recommendations based on their interactions and the patterns of other buyers. In the future, Law expects an even more tailored experience for buyers.
“We’re using the wisdom of the masses to help with individual needs,” says Law.
Eventually, machine learning will help builders make decisions tied to identifiable trends in consumer preferences.
“Builders can use machine learning to gather information on early market trends so they can be ready for future needs,” says Young. “They can predict where to build and what people want to buy by gathering consumer shopping data. Perhaps we’ll even see dynamic pricing with automatic adjustments based on shopping patterns.”
Michele Lerner is an award-winning freelance writer, editor and author who has been writing about real estate, personal finance and business topics for more than two decades.