Sprouting Homes

Sprouting Homes will guide you to make an intelligent growth investment in gentrifying communities. Simply enter a city, and Sprouting Homes will provide the data needed to secure a good investment.

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Inspiration

Our group members were interested in real estate, so we decided to make a program that aids in the identification of promising real estate markets. From this, our idea for identifying gentrification in its early stages formed. We believed investors were not considering the benefits of using a gentrification model and we wanted to demonstrate its potential.

What it does

This project will assist risk-taking real estate investors by identifying possible gentrifying cities, which are areas that are increasing in economic value. Once a user inputs their desired city and state(abbreviated - All Caps), a graph will appear that illustrates the housing value of both the low tier and high tier housing of that city for the past 10 years. This program also implements a modification of the McKinnish, et al model of gentrification and displays any other necessary information in order to determine if a city is experiencing gentrification. With this knowledge, real estate investors can intelligently invest in communities that are experiencing economic growth.

How we built it

We connected to an updated Zillow data file which provided house incomes from 2011 to 2021. Then, we implemented this data in our gentrification calculation using the Mckinnish et al model. We also used a comprehensive IRS datafile which provided data about housing income, salary and property taxes which provides more important information to the user.

Challenges we ran into

Our initial plan of connecting to a Zillow API was not possible due to the exclusivity of the company. We resorted to using a slightly primitive datafile of the housing values. We initially planned to create a heat map of the gentrification from the desired zip code; however, this proved to be a very problematic and complex endeavor that our group was neither experienced with nor able to overcome. Unsurprisingly, we ran into a lot of bugs which took up a lot of time.

Accomplishments that we're proud of

We are proud of our use of python modules such as matplotlib to create graphs/tables and the Tkinter GUI to create this program. We are also pleased with our use of the large CSV data files to analyze data and determine if a city is experiencing gentrification.

What we learned

This is our first hackathon, and we learned how to graph, plot, and create tables using features such as matplotlib and how to create a GUI. We learned the basics about APIs and how to create our own through the Postman workshop. Our group learned a lot about CSV files and how to turn them into live databases through the DropBase ama. This session was especially useful since our program used multiple CSV files for data.

What’s next

We want to continue refining our program until it is free from bugs. With more time, capital, and networking, the Sprouting Homes tool would be able to utilize complex data from premium real estate APIs (such as Zillow). This would allow for the Sprouting Homes tool to consistently pull and access more recent data, allowing for automated data updates, improving the user experience.

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