EljakimHerrewijnen / funda-scraper

FundaScaper scrapes data from Funda, the Dutch housing website. You can find listings from house-buying or rental market, and historical data.

FundaScraper

FundaScaper provides you the easiest way to perform web scraping from Funda, the Dutch housing website. You can find houses either for sale or for rent, and the historical data from the past few year are also attainable.

Please note:

1. Scraping this website is only allowed for personal use (as per Funda's Terms and Conditions).
2. Any commercial use of this Python package is prohibited. The author holds no liability for any misuse of the package.

Install

1. The easiest way is to install with pip:

pip install funda-scraper

2. You can also clone the repository to your local machine with:

git clone https://github.com/whchien/funda-scraper.git
cd funda-scraper
export PYTHONPATH=${PWD}
python funda_scraper/scrape.py --area amsterdam --want_to rent --find_past False --page_start 1 --n_pages 3

Quickstart

from funda_scraper import FundaScraper

scraper = FundaScraper(area="amsterdam", want_to="rent", find_past=False, page_start=1, n_pages=3)
df = scraper.run(raw_data=False, save=True, filepath="test.csv", min_price=500, max_price=2000)
df.head()

You can pass several arguments to FundaScraper() for customized scraping:

• area: Specify the city or specific area you want to look for, e.g. Amsterdam, Utrecht, Rotterdam, etc
• want_to: You can choose either buy or rent, which finds houses either for sale or for rent.
• find_past: Specify whether you want to find the data in the past or the ones in the market. If True, only historical data will be scraped. The default is False.
• page_start: Indicate which page you want to start scraping. The default is 1.
• n_pages: Indicate how many page you want to scrape. The default is 1.
• min_price: Indicate the lowest amount for the budget
• max_price: Indicate the highest amount for the budget

The scraped raw result contains following information:

• url
• price
• address
• description
• listed_since
• zip_code
• size
• year_built
• living_area
• kind_of_house
• building_type
• num_of_rooms
• num_of_bathrooms
• layout
• energy_label
• insulation
• heating
• ownership
• exteriors
• parking
• neighborhood_name
• date_list
• date_sold
• term
• price_sold
• last_ask_price
• last_ask_price_m2
• city

You can use scraper.run(raw_data=True) to fetch the data without preprocessing.

More information

You can check the example notebook for further details. Please give me a star if you find this project helpful.