Flatisfy

Flatisfy is your new companion to ease your search of a new housing :)

It uses Weboob to get all the housing posts on most of the websites offering housings posts, and then offers a bunch of pipelines to filter and deduplicate the fetched housings.

It can be used as a command-line utility, but also exposes a web API and visualisation, to browse through the results.

Note: It is targeted at French users (due to the currently supported websites), and in particular at people living close to Paris, as I developped it for my personal use, and am currently living in Paris :) Any feedback and merge requests to better support other countries / cities are more than welcome!

Note: In this repository and across the code, I am using the name "flat". I use it as a placeholder for "housing" and consider both are interchangeable. This code is not restricted to handling flats only!

Getting started

1. Clone the repository.
2. Install required Python modules: pip install -r requirements.txt.
3. Init a configuration file: python -m flatisfy init-config > config.json. Edit it according to your needs (see below).
4. Build the required data files: python -m flatisfy build-data --config config.json.
5. Use it to fetch (and output a filtered JSON list of flats) or import (into an SQLite database, for the web visualization) a list of flats matching your criteria.
6. Use python -m flatisfy serve --config config.json to serve the web app.

Configuration

List of configuration options:

• data_directory is the directory in which you want data files to be stored. null is the default value and means default XDG location (typically ~/.local/share/flatisfy/)
• max_entries is the maximum number of entries to fetch per Weboob backend (that is per housing website).
• passes is the number of passes to run on the data. First pass is a basic filtering and using only the informations from the housings list page. Second pass loads any possible information about the filtered flats and does better filtering.
• queries is a list of queries defined in flatboob that should be fetched.
• database is an SQLAlchemy URI to a database file. Defaults to null which means that it will store the database in the default location, in data_directory.
• navitia_api_key is an API token for Navitia which is required to compute travel times.

Constraints

You can specify constraints, under the constraints key. The available constraints are:

• area (in m²), bedrooms, cost (in currency unit), rooms: this is a tuple of (min, max) values, defining an interval in which the value should lie. A null value means that any value is within this bound.
• postal_codes is a list of allowed postal codes. You should include any postal code you want, and especially the postal codes close to the precise location you want. You MUST provide some postal codes.
• time_to is a dictionary of places to compute travel time to them. Typically,

  "time_to": {
    "foobar": {
        "gps": [LAT, LNG],
        "time": [min, max]
    }
  }
  

  means that the housings must be between the min and max bounds (possibly null) from the place identified by the GPS coordinates LAT and LNG (latitude and longitude), and we call this place foobar in human-readable form. Beware that time constraints are in seconds.

OpenData

I am using the following datasets, available under flatisfy/data_files, which covers Paris. If you want to run the script using some other location, you might have to change these files by matching datasets.

• LaPoste Hexasmal for the list of cities and postal codes in France.
• RATP stations for the list of subway stations with their positions in Paris and nearby areas.

Both datasets are licensed under the Open Data Commons Open Database License (ODbL): https://opendatacommons.org/licenses/odbl/.

License

The content of this repository is licensed under an MIT license, unless explicitly mentionned otherwise.

Contributing

See the CONTRIBUTING.md file for more infos.

Thanks

• Weboob
• The OpenData providers listed above!
• Navitia for their really cool public transportation API.
• A lots of Python modules, required for this script (see requirements.txt).
• Kresus which gave me part of the original idea (at least proved me such software based on scraping can achieve a high quality level :)