Find Your Perfect Roommate: Where Compatibility Begins at Home

Product Experience

Problem Space 

Problem Background  

Over the past two years, rent and living costs in North America have increased significantly due to inflation. Many people were also recently affected by layoffs while new graduates are starting new jobs in big cities post pandemic. As housing costs escalate and economic uncertainties prevail, many people increasingly seek alternatives to alleviate financial burdens which results in searching for roommates. People seek roommates to share resources, split bills, and reduce cost. There is a lack of a streamlined approach to roommate matching which often results in frustrating and time-consuming experiences for users. Existing platforms might overwhelm users with an extensive list of potential roommates which leaves them feeling unsure about how to identify the most compatible match. 

Personal habits, preferences and lifestyles make it difficult to accurately assess roommate compatibility solely based on surface-level information. The absence of a well-defined algorithmic approach to matching roommates can lead to potential mismatching and users feeling unsatisfied with their matched roommate. Additionally, users are often left to rely on trial and error, navigating through multiple interactions and interviews to gauge compatibility, consuming valuable time and energy. 

Because of the lack of a well-defined algorithmic approach to finding compatible roommates, the need to address the challenge of swiftly and efficiently assisting users in discovering highly compatible roommates is important. A product that offers an innovative approach to roommate matching, integrating algorithms, user-focused design and personalized insights is a potential solution to this problem. 

Research Insights

User Pain Points

Through conducting interviews with potential users, we identified recurring patterns:

  • Users seek roommates to lower rent and utility costs.
  • When searching for a roommate, users consider cleanliness, living habits, financial stability, cultural background, and social habits in their potential roommates.
  • Users use social media to verify legitimacy of potential roommates.
  • Currently, most users use Facebook to find a roommate.
  • A challenge users face is filtering through and finding someone who meets the criteria and would be compatible with existing roommates.


Three crucial findings emerged from the survey and user interviews, leading us to narrow the product’s focus significantly. This helped us pinpoint the essential factors for effective roommate searching: 

  • 96.7% responded to searching for roommates due to financial reasons
  • 80% of users prefer to search for potential roommates through compatibility-based matching.
  • 90% of users consider lifestyle compatibility as an important factor when choosing a roommate. 

Solution Explanation

Taking into account the pain points and stated needs of our target users, we recognized that the solution would involve developing a roommate matching platform centered around compatibility. For the purpose of creating an MVP, we selected the following features:

  • Ability for users to create their profile and specify their preferences. 
  • This feature would allow other users to get to know each other and find others who may be a good fit as a roommate. Users can find what they have in common with each other and what differences they may have to work through as roommates.
  • A display of the most compatible roommate matches. 
  • This feature involves a matching algorithm that assesses users’ preferences and profiles to suggest potential roommates who align closely with their criteria, making it easier for users to identify and consider the most suitable options. 
  • Ability for users to view profiles of potential roommates. 
  • This feature enables users to access detailed information about other individuals who are seeking roommates, helping them gather relevant details to evaluate compatibility and make informed decisions about potential living arrangements. 

Lofi Mockups

Hifi Mockups

Iterative Design Learnings

After showcasing our wireframes and prototype to users, we were able to make changes to minimize confusion and help people feel more confident when using the product. Additionally, we learned what users would like to see in future releases.

  1. There was some confusion on the phrasing for select questions and provided options.
  2. Users wish they had more information about their potential roommates, such as pets, smoking habits, personality, # of roommates, type of neighborhood.
  3. Users would also like to share more information about themselves to ensure high compatibility.

Implementation Details 

Technical implementation

Where is it hosted? Netlify
What is your tech stack? ReactJS / HTML / CSS / JavaScript / MongoDB
MERN stack

High level journey of a request:

  • Profile Creation Initiation
  • Preference Customization
  • Matching Algorithm Utilization
  • Dashboard Display and Ranking
  • Detailed Roommate Exploration:

Technical challenges

What was the hardest part of development?

Learning React js and developing full-front end alone (front-end development)

  • Developing and integrating a compatibility algorithm has proven to be considerably more intricate than initially perceived, particularly within the realm of backend development.

Does your app have any scaling issues?

It will be challenging be global, so we would pick one location first

What are some key takeaways?

For front-end development, it’s best to start building when lo-fidelity design is completed to divide up the work within time limit

Future Steps

  • We will continue the project and make it responsive first, then add a sorting filter to the results page. Hopefully we can pick one location to launch it.
  • Monetization plan
  • Launch plan

Here are some future steps to enhance MatchMyRoomie and potential features to consider adding:

Enhanced Compatibility Algorithm: refine and improve the compatibility algorithm based on user feedback and data analysis to increase accuracy in matching roommates.

User Profiles and Preferences: add more questions on the roommate quiz and allow users to create profiles specifying their preferences, habits and lifestyle, providing more data points for accurate matching. 

Location-Based Filters: integrate location-based filters to allow users to search for potential roommates in specific neighborhoods or areas. 

Real-Time Messaging: implement a real-time messaging feature to allow users to communicate with potential roommates directly within the platform.

Reference and Background Checks: integrate a reference and background check feature to enhance the trust and security in roommate matches.

Premium Subscriptions: offer premium subscription plans that provide enhanced matching algorithms, priority access to new matches and advanced communication features.

Identity Verification: provide an option for users to verify their identity to increase trust and safety within the platform.

Listing Section: implement a feature for apartment/house listings for landlords and owners to find renters. 

Monetization Strategies:

Freemium Model: offer basic matching and profile creation for free, while charging for premium features like advanced filters, compatibility reports and priority support.

Subscription Plans: provide different subscription tiers with varying features, such as personalized recommendations.

Advertisement Partnerships: collaborate with local businesses or services that align with roommate needs, such as furniture stores, cleaning services or moving companies and display targeted advertisements. 

Referral Programs: implement a referral program where users can earn credits or discounts by referring others to the platform.

Sponsored Listings: allow property owners or property management companies to promote their available rooms or apartments as premium listings.

Data Insights for Landlords: offer insights and analytics to landlords or property managers about popular roommate preferences, helping them tailor their offerings. 

Images - screenshots, marketing assets, etc.

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Product Manager Learnings:

Brigette Adlawan

Being a PM in Co.Lab taught me how to work with an agile and cross-functional team. I also learned how to prioritize product features based on factors such as user needs and technical feasibility. And most importantly I learned to better my ability to communicate effectively and gather feedback to ensure that everyone is on the same page.

Designer Learnings:

Emily Huang

Co.Lab helped me experience how an agile team might function and learn how to collaborate with developers. It prepared me with the understanding that in a real work environment, technical constraints will heavily impact how I design, which is completely different from the hypothetical environment of a bootcamp.

Developer Learnings:

Jie eun Lee

Co.Lab was a very interesting and challenging experience for me. I learned how to work in an agile team and learned ReactJS to develop this product. It taught me patience and resilience as I am responsible to do my part and work together with the team.

Developers Learnings:

Wasi Uddin


Being a backend developer at Co.Lab has taught me the power of patience and the art of teamwork. Navigating intricate systems requires patience, while collaborating with my team taught me the value of collective effort. Together, we build the Product.

Full Team Learning

As a team, we experienced a comprehensive learning journey that enriched our understanding of the product development process and reinforced the importance of collaboration. Throughout this experience, we learned to communicate effectively, emphasized on user needs, design-development synchronization and working together as an agile team.