HomeBuy

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  Future Innovation

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  Slides Title

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  Detailed Overview

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  Our Mission

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  App Sample Screen

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  Database Sample

As a team of prospective and current college students looking to soon move into new apartments or homes, we have become all too aware of a lack of directly homebuyer-focused analysis of homebuyer eligibility and connection to necessary resources.

We decided to take a step towards a more equitable and accessible data processing system by developing HomeBuy, a streamlined and user-friendly tool for any homebuyer to easily and intuitively verify their homebuyer eligibility, accompanied with analysis of a database comprising of 10,000 homebuyers.

We built HomeBuy in two different stages. The first was a database stage, which processes and analyzes a database of 10,000 homebuyers (provided by Fannie Mae), with an option to display a summary of the data or check an individual homebuyer’s eligibility by ID, offering suggestions for ineligible homebuyers. The second was an application stage, where we created a user-focused and intuitive app to enter homebuyer data and check the eligibility to buy a home based on financial analysis, also offering suggestions for ineligible homebuyers.

One of the biggest challenges we ran into was reading and processing such a vast data file. However, after resarching Java methods and complex data structures, such as ArrayLists, we learned how to use the .split() and parseInt() methods in Java to read data imported into a text file line by line and storing each variable separately for later analysis. This was crucial to our eventual success because it would have been impossible to process data of 10,000 homebuyers without figuring out an effective way to loop through all the rows and columns while efficiently managing the data given. We also had to essentially learn JavaScript as an obstacle in implementing our user interface.

Accomplishments we're proud of, besides successfully being able to read a 10,000-line database, include learning and exploring how to work with various user interfaces platforms, such as repl.it, Thunkable, and CodePen. We're also proud of coding both backend and frontend in less than 24 hours, utilizing knowledge of frontend languages like JavaScript in tandem with backend languages like Java. We realized that our proudest accomplishments were more about the learning experience than what we were able to code: experiences like learning about the existence of new platforms despite not using them, or trying and failing to convert data to a JSON file, taught us the most.

Although there were many ideas we had for HomeBuy, some features were simply infeasible to implement in the timespan of 24 hours. As such, some future plans for HomeBuy include developing a full mobile app to expand accessibility to users nationwide, connecting users to consultation based on eligibility using communication API such as Twilio, creating a bar chart to visually display our data summary, fully integrating frontend and backend with JSON and/or Swing to connect efficient database processing with smooth front-facing interaction, and incorporating additional features, such as ability to add additional types of debt, or slider bars which automatically calculate eligibility of homebuyers.

Built With

• css
• eclipse
• html
• java
• javascript
• repl.it
• thunkable