Real State: Where to stay in Madrid based on your interest and open data
We saw, with surprise, that the real estate market was very witness-oriented (houses and homes for sale or rent), and yet there was no guidance on the environment, the neighborhood. Fotocasa is now launching an initiative, but it seems that it does not have much data as it relies heavily on what users say.
Madriwa collects data from many sources in real time to provide useful information to home seekers for rent or ownership.
The main features that this version incorporates are:
- Automated data loading.
- More than 100 different sources.
- User profiles related to their interests.
- Neighborhood classification tailored to each user.
- My sites: The user can position up to 3 sites (work, boyfriend, gym).
- Nearby: Nearby report and isochrones.
- Witnesses: We incorporate all AirBNB from Inside AirBNB.
Technologies in this project:
- Scrappers and ETL mainly in Python with our own framework (to be open sourced soon).
- Database is Postgres (postGIS).
- REST API on Nestjs
- FrontEnd: React and Leaflet OSM
- DevOps on GitLab
- Running on our own container running HA platform.
A course with all the Product Management and Development using Madriwa as the use-case is available. Write to firstname.lastname@example.org if you are interested.
We have also prepared an API so that individuals and real estate agents can upload and query data to the platform.