Team A

Inspiration

The inspiration for our project stemmed from the need to assist potential homebuyers in evaluating their readiness to purchase a home. Understanding the complexities of real estate and the various factors affecting homebuying readiness, we aimed to develop a solution that not only evaluates their current status but also offers actionable suggestions for improvement.

What it does

Our solution is a comprehensive evaluation system that takes in the relevant data of potential homebuyers and analyzes it against key criteria that determine homebuying readiness. It then provides a clear assessment of their readiness status and, if they are not prepared, offers tailored recommendations to improve their position. These recommendations are personalized and aimed at guiding individuals towards a stronger position for homeownership.

How we built it

We created a back-end solution using Java that includes 2 classes: Main and Person. The Main class scans through the input file, creating Person objects. These objects are stored in a Hash Map, improving time complexity and allowing the objects to be easily accessible. The results of whether a person can buy a home or not is written to an output file, which is displayed to users in the console. The Person class contains a constructor that holds the properties of the Person object, such as their credit card payment, monthly income, car payment, etc. When a Person object is created in Main, the constructor sets the properties. The Person class also calculates the conditions for approval of a home purchase, including the credit score being above 640, the loan-to-value percentage, the debt-to-income ratio, and the front-end debt-to-income ratio, and contains a boolean variable to store whether the person can purchase a home or not.

Challenges we ran into

While coding the program, we realized that using an ArrayList for storing the Person objects would result in higher overall time complexity. We weighed our options, and after discussing, we decided upon using a HashMap, which had the ability to store the ID of the Person alongside the Person object. For the front-end, we attempted to use React, however, we found it challenging, since we were not experienced with the language. Additionally, the workshop covering React had issues connecting to the Internet, so we were not able to fully understand and implement the code due to those interruptions. As a result, we had to learn how to use Jupyter to display our front-end data.

Accomplishments that we're proud of

For most of us, HackUTD was our first hackathon, so we are proud of creating a functional product. We learned how to use new platforms, such as Git, Jupyter, and DevPost. Even though we didn’t connect the front and back-end of the product, we still learned how to implement both of them, and possible ways to connect them.

What we learned

Throughout this project, we gained substantial insights into the multifaceted aspects influencing a person's readiness to buy a home. We learned the importance of personalization in recommendations and how various factors interplay in influencing an individual's homebuying capability. Additionally, we enhanced our skills in data analysis, machine learning, and user interface design.

What's next for Team A

Moving forward, Team A aims to refine the model further by incorporating more real-time data and utilizing more advanced machine learning techniques to improve accuracy. We also plan to expand the platform to offer additional resources such as financial planning tools, access to educational materials, and potentially collaborate with financial institutions or real estate agents to provide more comprehensive support for aspiring homebuyers. The goal is to continually enhance our solution, making it an all-encompassing tool for anyone aspiring to own a home.