Inspiration

When you're looking for a new place to live, what are the criteria that influence your choice? Is it the price, the size of the apartment or the location? Often it is a combination of these.

A student who is newly moving to St.Gallen is probably looking for an inexpensive apartment close to his university. A family with small children, on the other hand, is more interested in having their new home as close as possible to a kindergarten. Commuters may want to live within walking distance of a train station. So everyone has their own individual requirements for their future home base.

No matter how your everyday life looks like, everyone has to go shopping at some point. And preferably just around the corner. But how do I find the apartment that meets all my requirements?

Portals for apartment advertisements show me where to find cheap apartments in the city. But I have to find out for myself how far it is from my university, the train station or the nearest supermarket. That's tedious, time-consuming and simply sucks.

What it does

Find your Flat combines the apartment offer with your individual needs. Just tell us how close the new apartment should be to your university or where your pain threshold is to the next shopping opportunity and we will find the right apartment for you.

How we built it

At the beginning, we identified possible criteria that could influence the search for housing. Based on our own experience, we compiled a catalog of must-have and can-have criteria and thought about how to obtain the necessary location data, what the input and output data are, and in what form we want to receive and present them. We considered both qualitative (e.g. distance calculation as radius or in walking minutes) and technical aspects (tools and methods for calculations, visualizations and UI).

We source the apartment listings via urban indicators from cividi, and get the location data from the POI dataset of the Open Data St. Gallen platform and the OpenStreetMap API. The data preparation is done using Python, where we use OSRM for the distance calculations and thus determine the distances for each apartment and individual criterion. Based on the user's input parameters (proximity to the university or supermarket), we filter our dataset and display all eligible apartments on the UI realized with Streamlit.

Challenges we ran into

The biggest challenge was to design a functioning and performant data structure and infrastructure within a short period of time in order to build up an environment that is as useful as possible with little technical effort and that can ultimately be made publicly accessible.

The organization of the supermarket data turned out to be a major challenge, as it was not available on the Open Data Platform. In the end, however, we found what we were looking for on OpenStreetMap.

Accomplishments that we're proud of

  • What we set out to create, we have fully achieved according to our expectations (both technically and in terms of content)
  • It was our first hack and we didn't know exactly what to expect. We are even more pleased with what we have achieved in such a short time
  • We worked very well as a team and complemented each other perfectly

What we learned

  • Public data offers an incredibly high diversity and can be used to address an extremely wide range of use cases
  • Dealing with Streamlit, Openstreetmaps, OSRM and the Open Data Platform of St. Gallen

What's next for Find Your Flat

  • Integration of further criteria (e.g. freeway connections, relevant public transport hubs, playgrounds, etc.)
  • Consideration of the catchment areas of schools
  • Integration of further real estate platforms
  • Display of objects that are slightly outside the thresholds
  • Determination of threshold values based on user input
  • Comparisons and rankings with other results
  • Classifying criteria in positive and negative ones and weighting them

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