An AI-assisted CMA tool for pricing homes with confidence
In 2020, I led design at Compass to bring a new Comparative Market Analysis (CMA) tool to market. The goal was to help realtors guide their clients toward pricing decisions that balance customer 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.
Company
Compass
Role
Lead designer
Year
2020
An AI-assisted CMA tool for pricing homes with confidence
In 2020, I led design at Compass to bring a new Comparative Market Analysis (CMA) tool to market. The goal was to help realtors guide their clients toward pricing decisions that balance customer 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.
Company
Compass
Role
Lead designer
Year
2020
An AI-assisted CMA tool for pricing homes with confidence
In 2020, I led design at Compass to bring a new Comparative Market Analysis (CMA) tool to market. The goal was to help realtors guide their clients toward pricing decisions that balance customer 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.
Company
Compass
Role
Lead designer
Year
2020
The problem
Homes are unique. Each has a range of plausible market values, shaped by hard-to-measure factors like condition, location, recent sales, demand, and timing. Most people lack the experience and market knowledge to accurately assess a property's likely sale value. That's why clients rely on realtors to guide them through this complex, high-stakes process.
The problem
Homes are unique. Each has a range of plausible market values, shaped by hard-to-measure factors like condition, location, recent sales, demand, and timing. Most people lack the experience and market knowledge to accurately assess a property's likely sale value. That's why clients rely on realtors to guide them through this complex, high-stakes process.
The problem
Homes are unique. Each has a range of plausible market values, shaped by hard-to-measure factors like condition, location, recent sales, demand, and timing. Most people lack the experience and market knowledge to accurately assess a property's likely sale value. That's why clients rely on realtors to guide them through this complex, high-stakes process.
Competitive market analysis (CMAs)
Realtors depend on Comparative Market Analysis (CMA) documents to help discuss pricing with their clients. They show how market conditions and property differences affect a home's value. A strong comparable might match the subject property's size and layout, but photos reveal a dated kitchen. Another might look like a perfect match, until you see it sits inches from a noisy highway.
Competitive market analysis (CMAs)
Realtors depend on Comparative Market Analysis (CMA) documents to help discuss pricing with their clients. They show how market conditions and property differences affect a home's value. A strong comparable might match the subject property's size and layout, but photos reveal a dated kitchen. Another might look like a perfect match, until you see it sits inches from a noisy highway.
Competitive market analysis (CMAs)
Realtors depend on Comparative Market Analysis (CMA) documents to help discuss pricing with their clients. They show how market conditions and property differences affect a home's value. A strong comparable might match the subject property's size and layout, but photos reveal a dated kitchen. Another might look like a perfect match, until you see it sits inches from a noisy highway.
CMA builder and editor
Building CMAs requires searching for, analyzing, and selecting comparable properties. Realtors then assess location and neighborhood quality, and add notes and adjustments to highlight nuances and clarify their pricing strategy.
CMA builder and editor
Building CMAs requires searching for, analyzing, and selecting comparable properties. Realtors then assess location and neighborhood quality, and add notes and adjustments to highlight nuances and clarify their pricing strategy.
CMA builder and editor
Building CMAs requires searching for, analyzing, and selecting comparable properties. Realtors then assess location and neighborhood quality, and add notes and adjustments to highlight nuances and clarify their pricing strategy.
Comparable properties
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.
Comparable properties
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.
Comparable properties
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.
Suggested price adjustments
Highlight areas where the ML thinks the comparables differ too much from the subject property.
Suggested price adjustments
Highlight areas where the ML thinks the comparables differ from the subject property.
Adjustment tooltip
Tool tips explain the suggestion and how we calculated it using our model.
AI / ML suggested adjustments
I worked closely with ML engineers to find places where AI could surface useful signals to users without getting in the way. We leveraged Compass's wealth of listing data, including property details, historical sales, market trends, and amenities, to design an AI-assisted solution that sped up CMA creation.
The goal wasn't to replace agents with automation, but instead to pair human expertise with AI to support more accurate pricing decisions. One such example: suggested price adjustments.
AI / ML suggested adjustments
I worked closely with ML engineers to find places where AI could surface useful signals to users without getting in the way. We leveraged Compass's wealth of listing data, including property details, historical sales, market trends, and amenities, to design an AI-assisted solution that sped up CMA creation.
The goal wasn't to replace agents with automation, but instead to pair human expertise with AI to support more accurate pricing decisions. One such example: suggested price adjustments.
AI / ML suggested adjustments
I worked closely with ML engineers to find places where AI could surface useful signals to users without getting in the way. We leveraged Compass's wealth of listing data, including property details, historical sales, market trends, and amenities, to design an AI-assisted solution that sped up CMA creation.
The goal wasn't to replace agents with automation, but instead to pair human expertise with AI to support more accurate pricing decisions. One such example: suggested price adjustments.
Feedback loop to help improve the model
We provided a way for agents to leave feedback about the suggested adjustments. The feedback helped us gauge their quality and usefulness, and improve the model over time.
Learnings and 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 visceral negative reaction in the realtors we spoke with. We wanted to instill the appropriate amount of trust in our users.
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.
Learnings and 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 visceral negative reaction in the realtors we spoke with. We wanted to instill the appropriate amount of trust in our users.
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.
Learnings and 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 visceral negative reaction in the realtors we spoke with. We wanted to instill the appropriate amount of trust in our users.
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, CMA creation tripled and realtors responded positively. The tool played a key role in the build-up to Compass's public offering
The results
We launched the CMA and the adjustment builder experience to the Compass Real Estate platform in 2020. Following the launch, CMA creation tripled and realtors responded positively. The tool played a key role in the build-up to Compass's public offering
The results
We launched the CMA and the adjustment builder experience to the Compass Real Estate platform in 2020. Following the launch, CMA creation tripled and realtors responded positively. The tool played a key role in the build-up to Compass's public offering
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3x increase in new CMAs
In CMA docs created by Compass' Real estate agents
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2X faster CMA creation time
Due to streamlined workflows and suggested adjustments