HouseSearch - A NWHacks 2019 Project

Contributors: Stephen Yang and Justin Aujla

Inspiration & Goals:

In the hectic environment of our modern day lives, buying a home has never been a more challenging and time-consuming task... At least, so I've been told. Hopefully, HouseSearch can help expedite the process by sorting houses by location, thus increasing affordability and convenience in the house buying process.

What It Does

Instead of directly sorting houses by price or number of bedrooms, we mainly sort houses by location. By inputting one's workplace, school, hobby locations, sports facilities, preferred parks and more, we can approximate the most economical home purchase with regards to travel distance and cost.

How We Built It

We mainly used Python, HTML, and JS, and also the Google Maps API and RetsRabbit API.

Challenges We Ran Into

Communicating between JavaScript and Python via POST and GET requests was a challenge for us, and understanding the Python requests library helped a lot with this. As well, the API's we used required lots of reading and exploring references, which was frustrating at times.

Accomplishments that we're proud of

For us, this was our first 24-hour hackathon. We are proud (and surprised) that we were able to stay focused throughout the night to work on the project.

What We Learned

Documentation and references are your best friends because they actually tell you how API's and Libraries work.

What's next for HouseSearch

We want to implement machine learning or more advanced mathematical concepts to more realistically choose a house based on user input locations. We would also like to increase the number of user inputs our program receives to better suit the user's needs.

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