Landing Page/Option Selection
Initial Map Results
Detailed Map Results
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
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.