Technica: MyNeighborhood
Context: In 2022, I had the chance to compete in Technica, the world’s largest hackathon for women and underrepresented genders. I was on a team of four. This particular challenge was for the title of “Best Hack for Adulting”, which was sponsored by Fannie Mae a the hackathon. MyNeighborhood is a solution for previously marginalized individuals and how they can approach the housing market with less hassle.
Problem: Understanding what it takes to purchase a home has always been challenging. The paperwork can be confusing and dense and it is hard to figure out if you have the necessary means to complete the purchase. Our challenge was to facilitate the eligibility period and make receiving that information more convenient and homebuyer-friendly.
There were gaps in knowledge for potential homebuyers when it came to knowing their approval status, and ways to help themselves in the housing market, in a timely and efficient fashion. The original process of sending documents back and forth was disjointed, lengthy, and confusing. Potential homebuyers feel frustrated and lost while they try to purchase a home during an already stressful period in their lives.
Timeline:
24 hours
Tools:
Figma
My Role:
UX/UI Designer
The Process
mapping —> sketch —> design —> prototype —> Test
Mapping
The team was given a brief of the problem that needed to be solved and the necessary qualifications for potential homebuyer eligibility. Here, I began mapping out the journey a user might make using the program, as well as prioritizing certain qualifying factors in relation to the user journey.
Sketch
I began exploring ideas as a way to generate possible solutions. By doing so, I generate sketches that involved existing features that display users’ credit-related information to them.
Design
We decided upon the best possible solution based on the sketches and expanded upon them. After the sketches came the wireframes for the design solution.
slider bar form
We decided upon the best possible solution based on the sketches and expanded upon them. After the sketches came the wireframes for the design solution.
Approval Score Meter
Our team developed a point system that takes the buyer data and compares it to the required qualification values for a house loan. Based on how much they qualify, a score out of 100 (100 being the most eligible) signifies the level of their likelihood to get approved. We decided the cutoff between non-approval and approval would come out to the 70-point mark based on the LTV, DTI, and FEDTI calculated from the form to the left.
Score breakdown
The score breakdown is to help the user clearly understand the data points that contributed to their approval score. It allows them to see their own data compared against the requirements, as well as a list of suggestions to help improve their opportunity to buy a house in the future. They get to see exactly where they are faltering financially in order to approve for the loan they need.
email & buyer results
Our application provides an output file of the results of the evaluation and then emails it to the user if they wish. It takes a summary of the previous data and results and combines it with a list of relevant articles on ways to improve buyers’ eligibility based on where they might currently fall short.
Prototype
We developed a high-fidelity prototype that users would use to test the application.
Test
I tested the design’s functionality with 3 participants by sharing the prototype with young adults who could be potential users of the functioning application. As a result, no participants reported any significant pain points.
Result
Our team's application and design were chosen to be the first-prize winners of the Best Hack for Adulting Challenge from Fannie Mae.