Immo Eliza Scraping

📖 Overview

Immo Eliza Scraping is a web scraping project designed to extract real estate listings from the Immoweb website. The project collects property details, including information on houses and apartments for sale, and stores them in a CSV file for easy access and analysis.

Features

• Scrapes property URLs from multiple XML sitemaps provided by Immoweb.
• Filters the extracted URLs to focus on houses and apartments for sale.
• Collects detailed property information, including:
  • Property ID
  • Locality name
  • Postal Code
  • Price
  • Type of proterty (house or apartment)
  • Subtype of property (bungalow, chalet, mansion,...)
  • Type of sale (note: exclude life sales)
  • Number of rooms
  • Living area (area in m²)
  • Equipped kitchen (0/1)
  • Furnished (0/1)
  • Open fire (0/1)
  • Terrace (area in m² or null if no terrace)
  • Garden (area in m² or null if no garden)
  • Number of facades
  • Swimming pool (0/1)
  • State of building (new, to be renovated, ...)
• Saves the extracted data into a CSV file for further analysis or reporting.

📦 Repo structure

.
├── data/
│ └── extracted_details.csv
│ └── filters.csv
│ └── property_details.csv
├── scraper/
│ └── accept_cookies.ipynb
  └── filtered_urls.ipynb
│ └── one_house_immoweb.ipynb
│ └── properties_details.ipynb
│ └── scraper.py
├── .gitignore
├── chromedriver.exe
├── main.py
├── README.md
└── requirements.txt

🛠 Installation

Prerequisites

• Python 3.6 or higher
• pandas library
• requests library
• BeautifulSoup library from bs4

Steps

1. Clone the repository:

   git clone <repository-url>
   cd immo-eliza-scraping

2. Create a virtual environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

3. Install the required libraries:

   pip install pandas requests beautifulsoup4 lxml

🚀 Usage

1. Download XML files:

   • Run the script filtred_urls.ipynb to download the XML sitemap files and extract property URLs:

2. Filter URLs:

   • The filtered URLs are saved in filters.csv.

3. Scrape Property Details:

   • Run the script main.py to scrape detailed information from the filtered property URLs:

4. Output:

   • The scraped property details will be saved in property_details.csv.

🔍 Contributing

Contributions are welcome! If you have suggestions for improvements or new features, feel free to create a pull request or open an issue.

📜 Timeline

This project was completed as part of the AI Boocamp at BeCode.org in 5 days