Inspiration
It's generally known that rental in Munich is a huge problem for everyone. As a fresh international student, I can not afford such high rents over than 600 Euro if I don't obtain a student dormitory.
The Studentenwerk Muenchen provides Housing Referral Service, which has loads of housing informations. However, unfortunately, their information lists in a table and quite difficult to use for us.
We care so such about the transport traffic, thus, a convenient renting tool becomes a necessity.
What it does
This rental tool is not as same as other normal apartment rental tools.
First of all, we developed an location visualization function which can visualize all house location, it add marks to web map that helps us access rental information directly through these marks.
The most significant function is about recommendation and automatic ranking. This tool even analysis all possible transport traffic condition such as U-banh, S-banh even the MVG More (bike) and other variety conditions (Rental, Environment, ...). As results, it gives the best choice of room position or areas.
How I built it
- Python implement server side crawler, it achieves all rental info from Studentenwerk
- Server side geocoding and multi-bike info analytics
- Transport traffic based position ranking algorithm
Challenges I ran into
- Information filtering
- Multidimensional decision making
- Ranking Model
Accomplishments that I'm proud of
- Intuitionistic and highly efficiency Interaction powered by visualization
What's next for Room Rental Tool for Munich Students
- More rental data;
- More reference data from different dimensions;
- More accurate model;
- Full user study testing.
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