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  Example of chart showing time series data for median listing price in zip code 07860

We all believe in harnessing the power of data to help people make informed decisions. One of the biggest decisions that any American makes is where to live or which home to buy. We sought to use real estate data made available through Nasdaq datasets to make this decision easier and better informed.

What it does

Given any zip code, our web application shows the desired data and has interactive charts to display housing data for a given zip code and time frame. It also provides links to information on local schools in the community.

How we built it

We used Python/Flask for the backend, and wrote simple HTML frontend to display the form and graphs. We used web scraping to build the link that directs users to the top schools in the area.

Challenges we ran into

There were a few challenges. The first was getting the graph to display -- there was an issue with the mpld3 python module, and we had to manually change the library to render the graph correctly. We also ran into challenges in how to collect information about local schools.

What we learned

• Real estate data is a valuable resource for making crucial decisions about where to live.
• Having the right data at everyone's disposal can help people find affordable areas to live.
• How to display time series graphs in an interactive way.
• How to use large datasets like the Nasdaq datasets and visualize them in an easily understandable way.

Built With

• flask
• python
• quandl