belowmrkt

belowmrkt

belowmrkt

Hand holding smartphone showing property listing interface with home details, price, and map view.
Hand holding smartphone showing property listing interface with home details, price, and map view.
Hand holding smartphone showing property listing interface with home details, price, and map view.

Role:

Lead Product Designer

Role:

Lead Product Designer

Role:

Lead Product Designer

Role:

Lead Product Designer

Role:

Lead Product Designer

Date:

2019

Date:

2019

Date:

2019

Date:

2019

Date:

2019

Tags:

Machine Learning, Real Estate

Tags:

Machine Learning, Real Estate

Tags:

Machine Learning, Real Estate

Tags:

Machine Learning, Real Estate

Tags:

Machine Learning, Real Estate

Summary

Summary

Summary

belowmrkt applies machine learning to property search, adapting filters to each user from the very first interaction. It learns fast, feels personal and makes discovery effortless.

belowmrkt applies machine learning to property search, adapting filters to each user from the very first interaction. It learns fast, feels personal and makes discovery effortless.

belowmrkt applies machine learning to property search, adapting filters to each user from the very first interaction. It learns fast, feels personal and makes discovery effortless.

Real estate mobile app filter feature on multiple phones showing buy, rent, and sorting options with red accent theme.
Real estate mobile app filter feature on multiple phones showing buy, rent, and sorting options with red accent theme.
Real estate mobile app filter feature on multiple phones showing buy, rent, and sorting options with red accent theme.
Real estate mobile app filter feature on multiple phones showing buy, rent, and sorting options with red accent theme.
Real estate mobile app filter feature on multiple phones showing buy, rent, and sorting options with red accent theme.

the challenge

the
challenge

Teaching AI to learn from little

belowmrkt set out to make property search smarter through adaptive filters that learned from behaviour. The challenge wasn’t scale but scarcity. Building a machine learning system that could understand user intent from just a few interactions. It needed to feel responsive and personal without relying on big data or long histories.

the challenge

Teaching AI to learn from little

belowmrkt set out to make property search smarter through adaptive filters that learned from behaviour. The challenge wasn’t scale but scarcity. Building a machine learning system that could understand user intent from just a few interactions. It needed to feel responsive and personal without relying on big data or long histories.

Close-up of real estate mobile app showing Ocean Club Residences and Skyline Drive property listings.
Close-up of real estate mobile app showing Ocean Club Residences and Skyline Drive property listings.
Close-up of real estate mobile app showing Ocean Club Residences and Skyline Drive property listings.
Close-up of real estate mobile app showing Ocean Club Residences and Skyline Drive property listings.
Close-up of real estate mobile app showing Ocean Club Residences and Skyline Drive property listings.

the constraints

the
constraints

Small data, smaller window

The team had weeks, not months. Data volume was limited and the model had to start learning from the first session. There was no room for complex onboarding or training loops. The product needed to show intelligence quickly while keeping the interface simple enough to launch fast.

the constraints

Small data, smaller window

The team had weeks, not months. Data volume was limited and the model had to start learning from the first session. There was no room for complex onboarding or training loops. The product needed to show intelligence quickly while keeping the interface simple enough to launch fast.

Three floating mobile screens showcasing property search, create alert, and listing results in a 3D layout.
Three floating mobile screens showcasing property search, create alert, and listing results in a 3D layout.
Three floating mobile screens showcasing property search, create alert, and listing results in a 3D layout.
Three floating mobile screens showcasing property search, create alert, and listing results in a 3D layout.
Three floating mobile screens showcasing property search, create alert, and listing results in a 3D layout.

the process

the
process

Designing learning into interaction

We focused on how people naturally explore properties. Quick scans, shortlists and revisits. Each of those actions became input for the learning model. I led the design direction and shaped the product behaviour around these micro-patterns, working with engineers to define how filters adapt in real time. The interface stayed minimal by design: every gesture carried meaning and every scroll improved the next result.

the process

Designing learning into interaction

We focused on how people naturally explore properties. Quick scans, shortlists and revisits. Each of those actions became input for the learning model. I led the design direction and shaped the product behaviour around these micro-patterns, working with engineers to define how filters adapt in real time. The interface stayed minimal by design: every gesture carried meaning and every scroll improved the next result.

Collection of real estate app screens displaying property listings, search results, and filter options on multiple phones.
Collection of real estate app screens displaying property listings, search results, and filter options on multiple phones.
Collection of real estate app screens displaying property listings, search results, and filter options on multiple phones.
Collection of real estate app screens displaying property listings, search results, and filter options on multiple phones.
Collection of real estate app screens displaying property listings, search results, and filter options on multiple phones.

the principles

the
principles

Machine learning made human

The intelligence stayed invisible. Instead of sliders or manual weighting, filters refined themselves as users browsed. The design avoided technical language and emphasised feedback. Subtle animations and layout shifts that showed the system was listening. The goal was to make the app feel intuitive, not algorithmic.

the principles

Machine learning made human

The intelligence stayed invisible. Instead of sliders or manual weighting, filters refined themselves as users browsed. The design avoided technical language and emphasised feedback. Subtle animations and layout shifts that showed the system was listening. The goal was to make the app feel intuitive, not algorithmic.

Real-estate app sort and filter options next to scrolling results list.
Real-estate app sort and filter options next to scrolling results list.
Real-estate app sort and filter options next to scrolling results list.
Real-estate app sort and filter options next to scrolling results list.
Real-estate app sort and filter options next to scrolling results list.

the reflection

the
reflection

Invisible intelligence

belowmrkt kept its intelligence out of sight. The model adapted quietly while users focused on discovery, not data.

the reflection

Invisible intelligence

belowmrkt kept its intelligence out of sight. The model adapted quietly while users focused on discovery, not data.

Impact

Impact

Impact

belowmrkt launched quickly and gained early traction. Users spent less time adjusting filters and more time exploring listings that matched their intent. The adaptive system improved engagement metrics week by week without extra data collection. A lightweight build proved powerful enough to shift expectations for property discovery.

belowmrkt launched quickly and gained early traction. Users spent less time adjusting filters and more time exploring listings that matched their intent. The adaptive system improved engagement metrics week by week without extra data collection. A lightweight build proved powerful enough to shift expectations for property discovery.

belowmrkt launched quickly and gained early traction. Users spent less time adjusting filters and more time exploring listings that matched their intent. The adaptive system improved engagement metrics week by week without extra data collection. A lightweight build proved powerful enough to shift expectations for property discovery.

Braive

Workhiro

Workhiro

Workhiro