Inspiration
There are two things that are of utmost importance for buying a house. The first is the intent of purchasing a house, and the second is the content to support this decision.
My Neighborhood tries to bridge this gap between the intent of the user and the content available to the user.
If our user has the intention of buying a house, My Neighborhood will take care of the stressful research that goes behind the scenes, to check for eligibility for getting a loan.
But we do not stop at this, we also give useful resources that can help with eligibility in case they are not able to meet the minimum requirements.
What it does
My NeighborHood is a web application that takes user information and checks if they are eligible to begin the home-buying process. The app calculates an approval score based on the user's input. This approval score then indicates to them where exactly in their budgeting plan they can improve to help their case of eligibility. The formula considers information such as DTI, FEDTI, LTV, and credit score. All of these categories of information have a maximum number of points that can be accrued and applied to the total score, which results in 100 points for a perfect approval score. The top score for DTI is 30 points towards the total. If the DTI is less than 36% and the mortgage makes up less than 28% of the individual's monthly expenses, then they receive 30 points out of the 100 point total score calculation. If they have 36% DTI or lower but they do not have the under 28% mortgage ratio, then they receive 20 points. If they are between 36% and 43%, then they only get 10 points. If they're between 36% and 43%, but their mortgage ratio is less than 28% of your debt then they receive 20 points. The maximum number of points for and individual’s credit score is 40 points. If the user’s credit score is 640 or higher, then they will receive all 40 points and if it is lower, then they receive no points. The maximum number of points that can be earned for LTV is 20 points. If the user’s LTV is 80% or below, then they receive 20 points, otherwise they receive only 10 points. The maximum number of points an individual can receive for FEDTI is 10 points which will be applied to the total score if the user’s FEDTI is less than or equal to 28%.
How we built it
Our team used figma in order to create a prototype of the website. To handle the data, our team used Google Colab and the python programming language and various python libraries such as pandas, matplotlib, numpy, smtplib, and ssl. We used the dataset that was provided by Fannie Mae and created graphs that can be used to draw insights about where individuals tend to need to improve in order to become approved.
Challenges we ran into
Some of the challenges we ran into included calculating the values used for determining the DTI, FEDTI, and the LTV. What was also challenging was figuring out a proper user journey for a potential home buyer using this application.
Accomplishments that we're proud of
We’re proud of the fact that four strangers at the Technica Hackathon were able to come together for a common purpose and create a solution in the span of 24 hours and overcome the challenges we faced with coding and designing the website.
What we learned
From the experience, we all learned more about the housing market and how it is normally navigated, and how too make that process more understandable.
What's next for My NeighborHood
For the future scope of this project, we would like to ensure that the static website can put in the data of the user and generate logins for them. Then they can put in their scores and that informs them of their eligibility to get the loan.
We want to also store their history so that they can be aware of the last time they made this check.
Finally, we want to be able to build a system where once the user qualifies for a loan, we can show them various options for homes that are within their budget so that this will act as a one stop shop for their home buying requirements.
Built with
Figma for the UI/UX: For creating the static website where the user comes in with their data and that generates the email for them to get their report Google Collab + Python: Numpy + Pandas - to process the batch file Matplotlib - to generate the pie chart and bar graphs Smtplib + ssl - to send the email with the generated information
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