Lease Comps
Designing a Data-Driven Platform for High-Volume Real Estate Decisions
30% faster navigation
25% reduction in task completion time
Real estate professionals needed to process large volumes of property data and make time-sensitive pricing decisions, but workflows were fragmented, slow, and cognitively demanding.
I led the effort to define the problem and redesign core workflows—unifying data, reducing friction, and enabling faster, more confident decisions.
Context
Lease Comps is a core platform where real estate professionals evaluate comparable properties, analyze pricing, and make decisions in fast-moving markets.
They navigate multiple datasets, filters, and views—often stitching together fragmented workflows to reach a single decision.
Speed and clarity are critical. Inefficiencies directly impact their ability to act quickly and price with confidence.
Impact
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30% faster navigation across core workflows
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25% reduction in task completion time
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Improved efficiency in high-frequency, data-heavy tasks
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Reduced cognitive load in decision-making workflows
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More consistent and scalable interaction patterns across the platform
The Problem
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High-volume data inputs
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Fragmented workflows across multiple views
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Cognitive overload during comparison
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Slow, multi-step decision-making
System Complexity
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Multiple interdependent datasets
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High data density requiring filtering + comparison
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Tradeoffs between speed and accuracy
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No clear default workflow structure
My Role
I owned the end-to-end design of core workflows within the Lease Comps platform.
This included:
• Defining interaction models and information architecture
• Designing workflows for data exploration, comparison, and decision-making
• Partnering closely with Product, Engineering, and Data teams to align on feasibility and tradeoffs
• Delivering development-ready UI, prototypes, and UX QA
I was responsible for translating ambiguous requirements into clear, scalable system behavior.
Key Design Decisions
Reducing Cognitive Load in Property Comparison
PROBLEM
Users had to interpret dense, multi-column data across multiple views.
DECISION
Prioritized key signals and restructured information hierarchy to reduce visual noise.
TRADEOFF
Reduced immediate visibility of all raw data in favor of faster, clearer comparisons.
Streamlining
Multi-Step Workflows
PROBLEM
Users needed to move between multiple views to complete a single task, increasing friction and time-to-completion.
DECISION
Consolidated key steps into cohesive workflows, reducing unnecessary transitions and preserving context.
TRADEOFF
Increased complexity within single views in favor of reducing overall workflow fragmentation.
Balancing Data Density with Usabilty
PROBLEM
The platform needed to support high data density without overwhelming users.
DECISION
Introduced clearer hierarchy, progressive disclosure, and structured layouts to manage complexity.
TRADEOFF
Required prioritization of what information is immediately visible versus accessible on demand.
Reflection
This project reinforced the importance of designing for decision-making under complexity, not just usability.
By focusing on how users interpret data, move through workflows, and make tradeoffs, I created a system that supports both speed and accuracy at scale.