1. Improve the Home Search for Clients
Ever since home 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.
Now let me say this. Because all of us receive data from the same source “MLS” or Multiple Listing Service. Zillow, Realtor.com and Trulia receive their information from aggregator. So their information is second or third hand. Also these big three providers are going after the highest ranking on Google so you, the home buyer, will go to their site, and your information is then sold to Realtors as leads at around $70-$100 per lead. So you will finds about 20% of the homes you find on one of these sites has already been sold but still shows as available.
The MLS information is input by the listing agent and they are human. So it is possible that some information could be incorrect in the beginning or could be forgotten altogether. This is why it’s very important to have a good Realtor representing you whether you are buying or selling.
2. Refine the Transaction Process
While dotloop currently uses a sophisticated algorithm to run its all-encompassing end-to-end platform, Zillow data scientists are employing machine learning to refine the transaction process of the future. I have been using Dotloop now for 4 years and it has made our transactions run extremely smooth.
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.
3. Predict Appraisals and Market Values
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. As an example; Here locally homes in the Aledo ISD will sell for about $20,000 more than the same home in the Azle ISD
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.
One startup, I know of, is working on AI that can precisely predict future rent, identify future market trends and anomalies, and capture arbitrage between asking price and market price by comparing as many as 10,000 property attributes and researching as far back as 50 years on every multi-family property in the U.S.