ZipScout

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Inspiration

In our current day and age, freshly-grads and young adults are often troubled with finding affordable homes for purchase in attractive communities.

What it does

ZipScout provides an easy solution for glimpsing into community conversations and provides important real estate information about the neighborhood. Instead of searching multiple listings or websites for listing information, ZipScout streamlines this process into one search and creates a summary of the location. By typing in the name of the city you want to live in, ZipScout finds the median house price, population, average walk score, and picture of an example address for the area. To get a better sense of the community, we include three hot posts from the subreddit for the city so that users can discover popular conversation topics or activities in the neighborhood.

How we built it

The interface for the application is developed and hosted on Bubble.io. Bubble offers a unique set of features, such as easy API connection tools and an intuitive full-stack workflow system. For the backend, there were multiple API's used to gather and calculate data such as Zillow, Walk-Score, Natural Language Toolkit, Reddit, and GeoDB. The script was hosted on PythonAnywhere, which allowed for the calculations to be called directly to Bubble.

Challenges we ran into

Mixing and matching API's proved to be our biggest challenge. Many of the API's we looked at either did not have all of the values we were looking for or was not able to make connections on PythonAnywhere. There was also a lack of documentation for many of the API's we were looking at, which took time to break down and reconstruct for solutions. Working around the roadblocks took up most of our time, but the end product was a finely-tuned system that offered all of the information we were looking for.

Accomplishments that we're proud of

We are most proud of the number of integrations we were able to complete within the time we had. One of our biggest goals for this project was to make a product that was "in the moment" and was able to keep up with live feeds. By using API calls instead of downloading and storing data, we not only save time and space in our system but all of our data is constantly being updated and will be able to keep up with modern trends in a fast-moving space like real estate.

What we learned

Our team has gotten intimately familiar with using web-based software and learned how to deal with lacking documentation and roadblocks. By finding solutions to incoming problems, we DID NOT compromise the integrity of our goal to have a fully "live" application. This meant that when a connection did not work or integration was not meant to be, we learned to find other products or services that best fit our needs rather than giving in to "easy" fixes.