An AI-assisted CMA tool for pricing homes with confidence
- CompanyCompass
- RoleLead designer
- Date2020
In 2020, I led design at Compass to bring a new Comparative Market Analysis (CMA) tool to their real estate platform, helping realtors guide their clients toward prices that balance client expectations with market realities. I collaborated closely with a cross-functional team of front-end engineers, AI/ML engineers, product managers, and user researchers from initial kickoff through final launch.
The problem
Homes are unique. Each carries a range of plausible market values, shaped by qualitative differences that are hard to measure, buyer demand, recent sales, timing, and location.
Home buyers and sellers tend to lack the experience and market knowledge needed to accurately assess a property’s likely sale value. Realtors, with their access to data, tools, and experience, are relied upon to guide clients through this complex and consequential process.
CMA documents
Realtors use CMAs to structure pricing conversations with their clients, showing how market conditions and subtle property differences influence value. A strong comparable might match the subject in size and layout, but photos reveal a dated kitchen. Another similar home may look like a perfect match—until you see it sits inches from a noisy highway. These details help align the realtor’s perspective with the client’s and reinforce the realtor’s expertise and value.
CMA builder
Building CMAs requires searching for, analyzing, and selecting comparable properties, assessing location and neighborhood quality, and adding notes and adjustments to highlight nuances and clarify the appropriate pricing strategy. The final output is a pricing range used as a starting point for client pricing conversations.
Comparables
Comparables are used as pricing analogies for the subject property. The challenge for realtors is that no two properties are exactly alike. For example, a comp may be a close match but still differ in important ways. It may have a better view, be located in a less desirable part of town, or have sold during stronger market conditions. These differences can significantly affect the final sale price.
Adjusting comp sale prices
Realtors often adjust comparable prices up or down to more closely resemble the subject property, correcting for differences that would otherwise make it a less accurate benchmark. Within CMAs, these adjustments highlight where a comparable diverges from the subject and how those differences affect price. Historically, legacy tools have made applying these slow and difficult.
Suggested adjustments
Highlight areas where the ML thinks the comparables differ from the subject property.
Details
Tool tips explain the suggestion and how we calculated it using our model.
AI / ML suggested adjustments
I worked closely with ML engineers to identify opportunities where models could surface useful signals without overriding agent judgment. We leveraged Compass’s wealth of listing data—including property details, historical sales, market trends, and amenities—the machine learning team and I designed an AI-assisted approach to accelerate CMA creation while generating more meaningful pricing insights.
The goal of this work was not to replace agents with automation, but to combine human expertise with machine intelligence to support better and more accurate pricing decisions. The suggested price adjustments was one such example.
Agent feedback
Provided a way for agents to leave feedback about the suggestions. The feedback gathered from this menu helped us gauge the quality of the suggestions and helped us improve the model in the future.
Learnings & explorations
We explored ways to keep the adjustment builder as simple as possible. An earlier version displayed one adjustment at a time. It looked good in certain situations but became complicated when the ML suggested 2+ adjustments. Research revealed that the interface put too much visual weight on the suggested adjustments, overshadowing the primary input field. The visual and conceptual imbalance triggered a deep negative reaction in the realtors we spoke with.
Realtors firmly told us during usability studies that they were the experts, and the design oversold the technology’s abilities to do the work for them. They saw the primary value of technology as something that augments their knowledge and assists them, not replaces them. We took this to heart and used it to guide future iterations and the final build.
The results
We launched the CMA and the adjustment builder experience to the Compass Real Estate platform in 2020. Following the launch, the number of documents created using the CMA platform increased dramatically and was welcomed positively amongst Compass realtors. It played a crucial role during the build-up to our public offering.
▲ 3x increase
in CMAs created at Compass
▲ 2X faster CMA creation time
due to streamlined workflows and suggested adjustments