Case Study: My NeighborHood
In 2022, I had the chance to compete in Technica, the world’s largest hackathon for women and underrepresented genders. This is a solution for previously marginalized individuals and how they can approach the housing market with less hassle.
My Role
For this hackathon, I worked in a team of four. Along with two programmers and another UI designer, I led and developed the user experience journey of this application and the programmers coded the back end. Over a period of 24 hours, we collaborated to work with the data provided by the sponsor of this challenge, Fannie Mae.
The Challenge
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.
The Approach
Our team started by reading the data files provided to us by the sponsor and determining the calculations of each approval factor, such as loan-to-value and debt-to-income. From then, we were able to know what starting point the users were going to have to come from to operate the application.
The Discovery
During the discovery phase, we read and analyzed the dataset to examine samples of homebuyer and property information. We then had to figure out how we can present the qualifications for approval to the user using this particular data.
The research revealed that in order to quickly see where their stats are falling short in the housing market, as well as how to rectify those circumstances, homebuyers would need to have house approval factors calculated from their monthly debts and habits. The resulting information would have to be presented to them in a way that communicates their current standing and the steps they can take next to improve. They often did not know of any resources to help their situation, which proved to be a huge barrier to getting approved for a house loan.
Core Problem
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.
The Solution
With the data calculations/factors and task flow done, I had a pretty good idea of what the product needed to be. My next step was to create quick sketches and wireframes. I sent the prototype for quick user testing with a few friends so I could validate the ideas and find any major usability issues. Luckily, none of them had any issues with getting their score or getting to the email and results prompt. The solution I developed was to establish a straightforward experience of understanding the conditions that apply to being able to get approved for a home through this mobile application. We prioritized implementing features to help potential buyers accomplish their goals of determining if they have the means to buy a home, as well as receiving meaningful suggestions about steps they can take to improve their position in the housing market.
Slider Bar Form
By having slider bars for each of the relevant approval attributes, the potential homebuyer gets to comfortably enter their data rounded to the nearest whole number for general legibility. They can then submit the data and receive the resulting approval/non-approval decision based on the score calculated by their input from the form.
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.
Colorblind Accessible Palette
The team and I decided to implement a completely colorblind accessible color palette to the interface to behave as an inclusive application.
The 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.