Designing the Trust & Coordination Engine of a Fixed-Fee Real Estate Marketplace
Role
Product Design
CLIENT/ORG
Bricklist Ltd.
Timeline
Apr — Sep 2025
Team
Me, 2 Full stack devs & Marketing
Credit
0 → 1 product, UX, IA & Product Strategy
Tools
Figma, Monday, Sheets
Overview
Bricklist is a fixed-fee real estate platform built as a broker-free model for the Mauritian market. By removing brokers, the product had to provide what people previously relied on them for: trust, coordination, and clarity.
This case study shows how I designed Bricklist as a system, not just an interface.
Bricklist
Want to skip to
01 / Context: Market & Problem
02 / Mapping the Transaction Journey
04 / Execution, Speed & Outcomes
05 / Reflection & Growth
Want to skip to:
Want to skip to
01 / Context: Market & Problem
02 / Mapping the Transaction Journey
03 / Designing the System
04 / Execution, Speed & Outcomes
05 / Reflection & Growth
01
Context: Market & Problem
The Market
In Mauritius, property transactions historically moved through small broker networks, not open listings.
As tourism and foreign capital increased, the change wasn't just rising prices. It was uneven visibility. Well-connected buyers saw opportunities early. Others encountered them late — or after they had progressed elsewhere.
Access didn't disappear.
It became inconsistent.
Structural Pressures Observed
1.
Price Acceleration
Without public reference points, pricing was shaped through conversations instead of comparable listings.
→ Expectations drifted upward and varied widely
2.
Coordination Bottlenecks
Progress depended on agent availability across regions.
→ Delays occurred between steps, not at decisions
3.
Service Fragmentation
Each broker operated differently.
→ Listing quality and clarity varied significantly
The Structural Gap
Agents weren't just intermediaries — they held the process together.
Verified listings
Arranged visits
Relayed offers
Maintained follow-ups
They acted as the transaction's memory.
But the system itself had no shared state
Information
lived in conversations
Progress
lived in follow-ups
Trust
lived in relationships
The market worked — yet participation depended on connections and patience.
The Product Challenge
Bricklist introduced a fixed-fee model, removing continuous mediation. Now the platform had to provide what people previously relied on agents for: clarity + continuity.
How do you turn a relationship-driven market into an information-driven one without losing trust?
02
Understanding Where Clarity Breaks
Methodology
Before designing screens, I tried to understand how a property deal actually moves in practice.
Not how platforms describe it — but how it unfolds across days, actors, and people. I mapped the journey across four actors: Buyer, Seller, Agent, and Time.
Journey Map — 4 Actors
Discovery
Enquiry
Visit
Negotiation
Close
Buyer
Searches listings
Contacts agent
Visits property
Signs contract
Seller
Lists property
Hosts visit
Receives offer
Accepts terms
Agent
Qualifies listing
Brokers contact
Arranges viewing
Relays terms
Maintains memory
Time
Day 1
Day 3–7
Week 2
Week 3–4
Month 2+
Roles Played by the Agents
While agents were seen as facilitators, they were actually performing structural tasks.
1
Maintaining whether a listing was still relevant
2
Synchronising availability between two unrelated schedules
3
Translating intent ("I'm interested") into a next step ("book it")
4
Keeping both sides aligned on what stage they were in
Where Deals Slowed Down
Early Stage — Trust Ambiguity
Buyers had to verify the listing being built.
Is the information complete?
Is the broker credible?
Is the price fair?
What effect happened before engagement
Mid Stage — Progress Ambiguity
After a visit, momentum weakened.
Negotiations moved to informal channels. The deal didn't fail — it just lost pace.
→ The delay lived in the communication
What I Learnt from This...
The problem wasn't property access. It was about understanding across time.
What happened
Was understood across time
What's agreed
Was unclear outside of direct contact
What's next
No structured prompt to move forward
Understanding Where Clarity Breaks
While agents were seen as facilitators, they were actually performing structural tasks
1 / Validating whether a listing was still relevant
2 / Synchronizing availability between two unrelated schedules
3 / Translating intention (“interested”) into a next step (“visit”)
4 / Re-initiating stalled conversations
5 / Keeping both sides aligned on what stage they were in
1 / Early Stage — Trust Ambiguity
Buyers first tried to verify the listing itself:
Is this still available?
Is the information complete?
Is the seller serious?
Most effort happened before engagement.
2 / Mid Stage — Progress Ambiguity
After a visit, momentum weakened:
no shared next step
negotiation moved to scattered chats
follow-ups depended on initiative
The deal didn’t fail.
It drifted.
What I learnt from this...
The problem wasn’t property access.
It was shared understanding across time.
Every participant repeatedly reconstructed context:
What happened
What’s agreed
What comes next
So the product didn’t just need listings.
It needed to carry 'state' between humans.
Not digitizing real estate
but stabilizing a multi-step social process.
03
Mapping the Transaction Journey
01 / System Design
Designing the System
From journey analysis to a structured two-layer product framework that absorbs responsibilities previously handled by brokers.
The journey analysis revealed a simple pattern that shaped everything that followed: two distinct failure modes were occurring at two distinct moments in the transaction lifecycle — and both needed to be absorbed into the product itself.
The Pattern
Overview
Traditional brokers compensated for trust and coordination failures through manual intervention — a model incompatible with Bricklist's fixed-fee structure.
Finding
The journey analysis revealed a simple pattern. Two distinct failure modes, at two distinct moments in the transaction lifecycle — both needing to be absorbed by the product itself.
"Trust broke before engagement.
Coordination broke after."
Core finding · Journey analysis
The Two Failure Modes
These were the two moments where deals fell apart. Both were previously patched by brokers operating manually across both sides.
1 / EARLY STAGE — TRUST AMBIGUITY
At the listing stage, buyers couldn't confidently evaluate whether a listing was worth engaging with. Incomplete information, unverified claims, and absent signals of credibility caused drop-off before any contact was made.
Most effort happened before engagement.
2 / MID STAGE — PROGRESS AMBIGUITY
Once contact was made, neither party had a shared understanding of next steps. Deals dissolved, and both sides blamed ambiguity — not bad intent.
No shared state meant no shared progress.
Design Principles
These two failure modes led to two design principles. Everything that follows was designed to support one of these two goals — nothing exists outside of them.
1 / LAYER 1 · TRUST FILTER
Improve listing quality before buyers engage.
Surface structured, verified information at the listing level so buyers can make confident decisions without needing to contact the seller for basic facts.
Pre-engagement
Quality gate
Listing quality
2 / LAYER 2 · SELF-SERVICE COORDINATOR
Maintain clarity and progress after engagement.
Give both parties a shared view of outstanding items, next steps, and current status — eliminating ambiguity that stalls deals post-contact.
Post-engagement
Transaction clarity
Pipeline
What this means for the design
The product didn't just need listings.
It needed to carry shared state between buyer and seller — an always-current understanding of:
1 /
What happened
2 /
What's next
3 /
What each party needs
So the product didn't just need listings. It needed to carry shared state between buyer and seller — an always-current understanding of what has occurred and what's coming next.
03
Designing the System
02 / Layer 1
Designing Trust Infrastructure
The first breakdown occurred before buyers ever contacted a seller. Most effort was spent verifying whether a listing was worth engaging with in the first place.
Context
Buyers weren't asking the obvious question.
Expected question
"Can I buy this property?"
Actual question
"Can I trust this information?"
In the traditional model, brokers performed this validation manually. They filtered listings, verified details, and acted as an initial trust layer between both parties.
Without continuous broker involvement, the product had to assume that responsibility.
Design Goal
Create enough confidence at the listing level for buyers to make informed decisions before initiating contact.
This meant reducing uncertainty around four things:
1 /
Property quality
2 /
Listing completeness
3 /
Seller credibility
4 /
Information consistency
The objective wasn't simply to collect information.
It was to establish trust before engagement.
Decision 1 / SELLER ONBOARDING AS A QUALITY GATE
Treat onboarding as qualification, not data collection.
Rather than optimizing onboarding for speed, I designed it as a qualification process. Every property entering the marketplace needed to meet a minimum quality standard before becoming visible to buyers.
This shifted onboarding from a form-filling exercise into the first layer of trust infrastructure.
Structured onboarding ensured every listing entered the platform with a consistent baseline of information quality.
Decision 2 / STANDARDIZE PROPERTY INFORMATION
Reduce variability — not by limiting, but by structuring.
One of the largest sources of ambiguity came from inconsistent listings. Important details were often missing, difficult to compare, or communicated differently across sellers.
Not this
More information.
This
More reliable information.
Property creation was structured around standardized inputs, required fields, and guided content entry — to reduce the variability that made listings hard to evaluate.
Screen
Insert screens here
Standardized listing structures reduced information asymmetry and improved comparability across properties.
Decision 3 / INCREASE CONFIDENCE THROUGH TRANSPARENCY
Surface what buyers need to know, before they have to ask.
Trust is difficult to establish when users are forced to infer important details. Instead of relying on conversations to fill information gaps, key property attributes, media, and ownership-related details were surfaced directly within the listing experience.
This allowed buyers to evaluate opportunities independently before reaching out.
Screen
Insert screens here
Critical information was surfaced upfront to reduce verification effort before engagement.
Layer Outcome
What changed
Listings transformed from simple advertisements into trusted transaction starting points.
The system now absorbed part of the validation work previously performed by brokers.
Before
Buyers spent most effort determining whether a property was credible.
After
Buyers spent effort deciding whether a property was relevant.
While Layer 1 improved confidence before contact, a second problem emerged once buyers and sellers began interacting.
Resolved
Trust was the bottleneck.
New problem
Maintaining momentum was.
Continue to Layer 2
Before designing screens, I mapped the real transaction journey.
I looked at the flow across:
Buyers
Sellers
Brokers (traditional model)
The Bricklist system (proposed)
The goal was to identify where responsibility shifts when brokers are removed.
Layer 1 — Designing the Trust Filter
1 / Decision 1: Seller Onboarding as a Quality Gate
Ensure consistent, high-quality listings so buyers can trust the marketplace without human mediation.
In traditional real estate, brokers filter sellers before listings ever reach buyers.
In a fixed-fee platform, that responsibility disappears unless the system takes it on.
Rather than optimizing onboarding for speed, I designed it as a deliberate quality gate.
Takeaway
These insights directly shaped the system architecture:
Layer 1: A Trust Filter to professionalize listings upfront
Layer 2: A Self-Service Coordinator to guide transactions forward
Everything that follows in this case study exists to absorb the work that brokers previously did manually.
Two layers.
One goal: close the deal.
Journey analysis revealed a repeating failure. The same two friction points caused deals to fall apart — and traditional brokers patched both manually. Bricklist's fixed-fee model couldn't afford that.
Layer 1 — Designing the Trust Filter
1 / Decision 1: Seller Onboarding as a Quality Gate
Ensure consistent, high-quality listings so buyers can trust the marketplace without human mediation.
In traditional real estate, brokers filter sellers before listings ever reach buyers.
In a fixed-fee platform, that responsibility disappears unless the system takes it on.
Rather than optimizing onboarding for speed, I designed it as a deliberate quality gate.
Takeaway
These insights directly shaped the system architecture:
Layer 1: A Trust Filter to professionalize listings upfront
Layer 2: A Self-Service Coordinator to guide transactions forward
Everything that follows in this case study exists to absorb the work that brokers previously did manually.
Personas
Personas based on....and for future purposes
User Journey
Across our web-app & different competitors, so far seen
Information Architecture
Mapping all the key features & modules, we wanted to add in the first launch version.
User Flow-Therapy(New)
Created a Therapy user flow, including different features in between.
Domain of Creation
Layer 1 — Designing the Trust Filter
Replacing broker-led vetting and professionalism
Decision 1: Seller Onboarding as a Quality Gate
Replacing broker-led vetting and professionalism
This is the canvas where creativity meets functionality. It's where our vision blossoms into an interactive reality. Let's dive into how our user interface design brings forth an engaging and intuitive journey for every user, one tap at a time.
04
IMPACT
Impact-outcome of this project
I concentrated on the before-and-after metrics for this project, which were measured over a span of 2-4 months*
65%
reduction in drop off rates, in the sign up journey(A/B Test)
1.5x
increase in active user base measured before/after 4 months—compared with Webapp
35%
engagement growth(MAU) measured over 45 days
12%
increase in Lead Conversion rate measured over 45 days
80+ avg
NPS Score(from 65+), within 3 months
Deadlines & reliability
