What are the Advantages of Web Scraping for Property Sites, and what are the Steps Used

Web Scraping in Real Estate

It is the gathering of property and customer data from several online websites. However, this helps identify available properties and customer needs and optimizes prices. Data accumulated for real estate purposes are:

• Property: Space, number of floors, number of rooms, property types, etc.
• Pricing: Price range by property size, location, pricing, and home buyers
• Competitors: Property on competitor's website, price, marketing material.
• Public Records: Loans, insurance, average family income, surveys, etc

The Purpose for Scraping Real Estate Data

Real estate web scraping provides the extracted information on real estate that is authentic, precise, and updated. This helps predict whether the real estate market will reach high and to what price range properties will compete.

Especially for businesses, these data are valuable as it helps make better decisions, the correct pricing, and the best profit margin.

The most commonly extracted type of real estate data is:

• Sale price
• Property type
• Size
• Location
• Monthly rental price
• Amenities
• Property Agent
• Parking spaces

Best Ways to Scrape Real Estate Data

1. Create your web crawlers with the help of web scraping libraries. In-House Web Scrapers: Any language is used for scraping – Python and JavaScript. You can also use open-source web scrapers. These type of scrapers helps in modifying and customizing pre-built code based on scraping needs. However, no matter what scraper you are using, ensure to support with proxy server solutions to avoid getting blocked from websites you are scraping.

2. Off-the-Shelf Web Scrapers: Pre-built web scrapers enable large-scale data extraction compared to the in-house. Three types of pre-built scraping are:

• Low code and no code web scrapers
• Cloud web scrapers
• Browser extensions, web scrapers

3. Web scraping APIs: This is another helpful method for getting data both from internal and external sources. For example, if the website you wish to parse functions on API technology, APIs would be the best option to aggregate data.

Uses of Web Scraping in Real Estate

• Research on Property Market: Data scraped from real estate agencies' sites, property listings, government records, and public insurance are used for analyzing ongoing project developments, highly demanding properties and areas, local market expectations, loan ranges, mortgages, and insurance.
• Price Optimization: Real-time insights on competitors' website data on similar properties help business owners understand trending market values and customer demands. This help businesses optimize price strategies.
• Analyze Rental Yields and Vacancy Rates: The scarped data helps find neighborhoods with high property rental yields. This, in turn, helps in investment properties that offer better long-term ROI.
• Home Buyer Sentiment Analysis: Gathering information related to buyer reviews from property listing websites helps analyze customers' neighborhood requirements, exclusive features, realtors' and homeowners' relationships, reasons for moving, and lots more.
• Targeted advertisements: Users can target websites to specific geographic regions and collect region-specific data. Real estate agents can quickly generate customized offers and promotions targeting potential buyers in particular areas by evaluating price ranges and buyers' expectations.

Steps for Scraping Real Estate Data

Inspect the Website Code

The data which we need to extract is available in the nested tags. So, before scraping it, we first need to find them. To get this, right-click on the element and select Inspect. A browser inspector box will open. It will appear like this:

Look for the data You Want to Extract

Everything we need to extract is available in

tag with class component_property-card. The data referring to several beds and bathrooms are available under the data-label attribute having pc-meta-beds ad pc-meta-baths. Now, let's proceed with writing our code.

Workspace Preparation

Download and install Python programming language. After creating a new project, ease your task by using the below libraries:

• Selenium: For web testing and automating browser functioning.
• BeautifulSoup: For scraping HTML and XML documents
• Pandas: For data manipulation. Here, the extracted data is available in a structured format.

Write the Code

Now, import the previously installed libraries.

To extract website data, load it by configuring web driver for using Chrome browser.

Apart from beds and bathrooms, you can also scrape price, address, and property size. Let's declare the variables and set the scraped websites' URLs.

Let's find the tag with mentioned attributes. Here we are scraping data for at least two bedrooms and one bathroom:

Now, after getting the data, store them in Pandas library. It will structure the data into a CSV file.

After running the code, it creates a file listings.csv

Best Practices and Guidelines

• Remain Transparent: While web scraping for property sites, always use standard ways to get data. Never opt for deceptive means.
• Responsibly Scrape Data: Ensure that you follow the service terms page of the client's website.
• Scrape during Off Hours: Always try to scrape during off hours, as traffic is moderately low. This will speed up the activity of crawlers.
• Avoid the Same Crawling patterns: Several websites use the same anti-scraping method that might detect your spider if it is crawled in a similar pattern for a long duration. Always keep on altering the spiders' Crawling processes.

For more information, contact iWeb Data Scraping now! You can also reach us for all your web scraping service and mobile data scraping requirements.