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This review was last updated on July 31, 2023.
ListAssist is software for automating property marketing descriptions and image-based home search.
Platforms: Browser
Ideal for: Teams, agents and marketing managers
Top selling points:
- Integrated image scanning/computer vision
- Blend of AI and user insight
- Room-by-room feature tagging
- Image-based home search
- Simplified editing and versioning
- Very quick to learn
Top concern:
Eventually, ListAssist users will need to contend with the dizzying array of MLS regulations, word and character counts, etc. This may require additional manual editing or dissuade some from being willing to adopt writing-specific software.
What you should know
ListAssist offers prompt and image-based, AI listing description generation for agents, as well as a larger-scale home search experience using image search, based on a form of AI called computer vision. Restb.ai may be a familiar name in that space, and for now, the comparison is hard to not use, at least for the sake of context.
ListAssist’s latest version now further leverages its ability to read pictures, using it to create an image-based property search experience for brokerages and teams, meant to be deployed on websites. The company is now offering two forms of AI-supported marketing for the industry, one to help launch a property into the market, and a second to help aspiring owners find it.
ListAssist ventured into the U.S. proptech market by way of Las Vegas in 2022. Unsure of how its initial AI-supported property descriptions would fare here at home, the company’s founders toed around in the water at Inman Connect Las Vegas, then spent some post-show time visiting playmakers in the industry. At this point, the company is now hip-deep in US real estate, claiming a big win by securing the partnership of TheMLS, an 18,000-member Los Angeles-based multiple listing service.
That MLS deal is based on ListAssist’s computer vision tools, which also powers its home search app, called ListAssist HomeSearch, driven by a chat interface on a client’s website. In regard to the former, the software can help MLS members auto-populate listing sheets, suggest points to highlight in a description, create the description and ensure the MLS that agents are using compliant photos, minus watermarks, branding elements or inappropriate imagery.
In terms of search, a chatbot works to engage a website user with a search prompt, and like many other large language model-based experiences, shoppers can enter long-form descriptions, specific amenities and location must-haves. While this is not a new concept (Doss, Zillow, Redfin, etc. use it), ListAssist’s ability to populate the chat with a concise visual search result of matching properties does help it stand out in terms of keeping a user clicking.
The search pulls directly from the agent’s MLS feed, and also reads the text for nuance that can drive suggestions outside their initial wants and needs. Mosaik offers this as well, and it’s damn useful. After all, would you replace driving around for three months for knowing on day one that your buyer would pay above their budget to be located near a body of water? Well, that’s what data from image-based home search can do for you. It detects the gaps in the user’s fences. Clever girl.
The property description component of ListAssist uses computer vision to scan uploaded photos, reading images from each room to identify prominent features and display them as a series of tagged writing prompts. Saving, editing and regenerating content is very intuitive. This is an ideal solution for marketing teams tasked with cranking out original copy day after day, the creatively drained agent and the tech-savvy team that wants to further automate business tasks they find tedious.
There’s little to weigh down ListAssist; it’s lightweight and the user experience never distracts from the product’s primary directive. Using photos to extract physical information about the home is a crazy-sharp use of computer vision tech, as it never leaves the user without a writing prompt. Users merely select, or deselect, the items it identifies for each room, add a few custom terms and generate a draft description. Again, you can add your own catchy terms and phrases for each room, too.
If you like what it puts out, simply mark as done, copy it and go. There’s also an internal rating system for each initial result so the ListAssist team can know what’s working.
Perhaps my favorite component is its editing interface, which divides the property description into multiple sections (intro, bedroom section, kitchen section) so each part can be addressed and tightened up individually. This is because trying to edit a big block of text can be distracting. Think of it in the same way you might stage each room of property with its own purpose.
Along the user’s left is a numbered sequence identifying each step of the workflow, hovering over the list reveals its hidden function, another subtle but witty bit of UX engineering to stay focused on the overall minimization of user interaction.
The initial login screen keeps a horizontal scroll of properties within the system for easy access and new edits.
The real power here stems from the marriage of AI and user insight. The software can’t add the subjective context of what makes a property unique, such as views it offers, undocumented historical facts or how the neighborhood is performing. Not yet, anyway.
The point is, ListAssist is well-crafted software, clearly representative of the problem it wants to solve, and now has verifiable case studies to prove itself. Its industry colleagues working in the same space may function as competitors but at this point in the AI game, I’m all for the-rising-tide-lifting-all-ships approach.
And as someone who has written and read a lot of listing descriptions over the years, I can say with granite-firm certainty that there’s no shortage of agents who could use the help.
Have a technology product you would like to discuss? Email Craig Rowe
Craig C. Rowe started in commercial real estate at the dawn of the dot-com boom, helping an array of commercial real estate companies fortify their online presence and analyze internal software decisions. He now helps agents with technology decisions and marketing through reviewing software and tech for Inman.
