Python package for scraping and analysis of flat market.

It helps users collect and view information about city districts (neighbourhoods) as well as specific flats. Its aim is to help users in their search for flats. It can also be used as a source of information about the flat market.

District maps provide summary data about various city districts. The maps show average flat area, average flat price and average price per square meter for the different city districts. The districts can also be colored according to the values of average flat area, price and price per square meter to enrich the visualization. The maps should help with identifying which districts are more favorable for one's needs and desires. The information may also be used to compare the price (per square meter) one pays for one's flat with the average prices in the given district.

Flat maps provide data about the specific flats. This should help users to search and choose flats. The maps show the location and other characteristics of flats, such as the price, type of flat, price per square meter, area and age of listing. It is also possible to filter through the flats according to their type, price, area and age, so that users can display only the flats that they are specifically interested in. The urls of the specific flats are also provided, so that when a flat is deemed interesting one can easily go to its website.

Numerous graphs detailing several flat market characteristics in time are provided. The graphs should provide an overview of the housing situation over time. This may help users to look at trends or any changes with respect to the prices, area, prices per meter square and the number of available flats. One can also see the proportions of different types of flats that are available. Moreover, there is a possibility to choose to view the graphs only for specified flat types, so that if one is specifically interested only in certain types of flats, one can easily obtain the relevant information. Such information may then be beneficial in the search for flats as well as in the decision-making process to choose a specific flat.

Install by running

pip install .