Let’s say you have a client looking for a modern home. Searching by styles for homes listed in the MLS is notoriously inconsistent. Computer vision can fix that. Imagine being able to tell your client that, on average, only three modern homes are sold in the area they want to live each quarter and that only one is currently on the market. Imagine the time you could save by having this information at the beginning of their search. Knowing this data empowers agents instantly, allowing them to communicate these facts with certainty and helping clients adapt their search process accordingly.
Imagine you just read a story featuring five interior design experts who all believe dark countertops are going out of style. However, your background in real estate has taught you real estate is hyperlocal, and that may or may not be true for your market. But do you know for sure what the trend is in your market? Computer vision can unlock this information for you.
Data integrity and reliability
Many professionals in the real estate industry and other business sectors today ask the same question, “What if AI is wrong?”.
That’s a great question, but it needs to be put in context. It’s important to frame this question by considering how we use data to make real estate decisions.
Agents know from real-world experience that Public Records are inconsistent, unstandardized, and may contain outdated information. Aggregated across 3,000+ counties in the US, certain data points may be partially complete in some markets and completely blank in others.
Multiple Listing Services or MLS data is much cleaner. But even then, MLS data isn’t perfect and has its limitations. As an agent is marketing the listing, undesirable characteristics may be left out or features embellished. In particularly hot markets, many agents may only populate the MLS’s required fields. Even when populated perfectly, data across MLSs isn’t perfectly standardized.
These gaps, or inconsistencies in traditional datasets, make it incredibly hard to build models or provide insights at scale. There is just too much noise.
Solving multiple problems
On the other hand, computer vision can scan every image of every property. It can consistently define how variables are treated. Is a stoop a porch? Is a 6-foot by 6-foot cement landing a small patio?