Artificial Intelligence in Real Estate | Dotloop

The technology might seem daunting to many real estate agents and brokers, but a deeper understanding of the potential applications for AI and machine learning may lead to a new appreciation of the opportunities that lie ahead.

While the Zestimate has always incorporated key factors like square footage and number of bedrooms to determine a home’s value, Zillow’s algorithm has learned to analyze unstructured data, such as granite countertops versus Formica, by analyzing imagery pixels and thus provide a more detailed, precise home value.

1. Improve the Home Search for Clients

 
Ever since listings became available online, home buyers have been able to search for homes by selecting attributes like location, price, square footage and number of bedrooms. But even narrowing the property search to these parameters can still leave house hunters with hundreds of homes to consider, or worse, filter out otherwise suitable properties that don’t meet the search criteria.

Machine learning has made this process much less frustrating by analyzing a person’s search patterns and creating a more accurate picture of what they really want. Zillow, for example, can combine search data from a potential home buyer with that of similar buyers to produce a list of properties prospects actively searched while connecting them with other properties that align closely to their needs — much like Amazon recommends books a customer may like to read.

Several firms have developed AI applications that will serve as conversational interfaces with customers to answer simple and complex questions, such as “does the house have a pool?” and “how many cars fit in the garage?” If a customer wants to know if the property has a backyard, such platforms can add that extra layer of detail like the fact that the backyard features four oak trees.

As James Paine, founder of West Realty Advisors, San Diego, CA, notes, agents benefit when consumers are able to more accurately search for homes.

2. Identify Strong Client Leads for Agents

 
AI technology also offers a powerful tool for helping agents reveal their ideal clients. Zillow’s site, for instance, can instantly identify hundreds of data points that distinguish the serious buyer or seller from those who are “daydreaming” or “window shopping” houses.

Some systems utilize Natural Language Processing (NLP) to isolate high value, or human to human, touchpoints from low value touchpoints as a means of identifying contacts who are more engaged with the agent.

This means of precision identification helps specialty agents, such as a hyper-local expert, narrow the field of potential clients who match their niche or focus of business.

Machine learning has enabled programs like Zillow’s Premium Broker Flex to determine a high percentage of clients who are immediately looking for an agent and produce leads that are so accurately prequalified, agents don’t pay for them until they result in a closed deal, says Chen.

In the future, an agent might call upon a robot to set client appointments over the phone, in any language, using the brokerage’s CRM or cross paths at an open house with a bilingual robot, which acts as a translator for Mandarin-speaking visitors.
 

 
AI and machine learning gives brokers an edge in the recruitment process by providing deep analysis of a market and showing where the current demand is strongest, underserved and expected to grow. As a result, brokers and team leaders can move confidently into those areas with new hires.

According to Rudina Seseri, founder and managing partner of next-gen AI venture capital firm Glasswing Ventures, Boston, MA, meta-analysis has illustrated how algorithms outperform humans when it comes to hiring.

Of course, personality and cultural fit are variables that require human judgment, but an impartial thorough analysis can remove the guesswork when considering an agent’s performance history.

The goal — to help agents and teams provide the most seamless and surprise-free experience for their clients — will only be enhanced by machine learning that delivers faster closing times, smarter mobile apps, solid compliance checks, detailed reporting and autofillable data that reduces manual data entry and errors. At the end of the day, it will also help brokers and teams accurately assess how they’re performing by providing smart, robust reports.

 
By combining CRM and marketplace data, AI technology may also help agents and brokers better predict the future value of a home in a specific market. For instance, the system may synthesize information from a variety of sources, including transportation, area crime, schools and marketplace activity.

Because most buyers see a new home as an investment, having a more reliable forecast of its future value can make them much more confident about making such a major purchase.