Everyone in society is likely going to buy a home at some point in their life. They will most likely meet realtors, see a million listings, gather all the information they can about the area, and then make a choice. But why make the process so complicated?

MeSee lets users pick and recommend regions of potential housing interest based on their input settings, and returns details such as: crime rate, public transportation accessibility, number of schools, ratings of local nearby business, etc.

How we built it

Data was sampled by an online survey on what kind of things people looked for when house hunting. The most repeated variables were then taken and data on them was collected. Ratings were pulled from Yelp, crime data was provided by CBC, public transportation data by TTC, etc. The result is a very friendly web-app.

Challenges we ran into

Collecting data in general was difficult because it was hard to match different datasets with each other and consistently present them since they were all from from different sources. It's still a little patchy now, but the data is now there!

Accomplishments that we're proud of

Finally choosing an idea 6 hours into the hackathon, get the data, get at least four hours of sleep, and establish open communication with each other as we didn't really know each other until today!

What we learned

Our backend learned to use different callbacks, front end learned that googlemaps API is definitely out to get him, and our designer learned Adobe Xd to better illustrate what the design looked like and how it functioned.

What's next for MeSee

There's still a long ways before Mesee can cover more regions, but if it continues, it'd definitely be something our team would look into. Furthermore, collecting more sampling data would definitely be beneficial in improving the variables available to users by Mesee. Finally, making Mesee mobile would also be a huge plus.

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