How to Scrape Homes.com Property Listing [Full Guide and Code]

Step 4: Sending Requests Via ScraperAPI

The main challenge we’ll face while scraping Homes.com is avoid getting blocked by its anti-scraping mechanisms.

To ensure smooth and efficient scraping, ScraperAPI will:

• Rotate our IP and headers when needed
• Handle CAPTCHAs
• Mimic real traffic using statistical analysis and ML

And more.

That said, another feature we’ll be using is its geotargeting capability, which we can utilize by specifying the ‘country_code’ parameter in our payload. This ensures our data is region-specific and accurate.

Here’s how to set it up:

1. Construct your payload by including the API key, the constructed URL for the current page, and the country code. This tells ScraperAPI exactly what to fetch and from where.

	payload = {
		'api_key': API_KEY,
		'country_code': 'us',
		'url': url
	}

2. Send this payload to ScraperAPI. This step is like asking ScraperAPI to visit Homes.com on our behalf, but with the added benefit of geotargeting, ensuring we’re focusing on properties in Los Angeles as US visitors would.

	response = requests.get('https://api.scraperapi.com', params=payload)

By using ScraperAPI, we not only navigate around potential scraping issues but also ensure our data collection is relevant and focused.

Step 5: Parsing Homes.com HTML with BeautifulSoup

Once we receive a successful response from ScraperAPI, it’s time to parse the HTML.

Each time we go through the ten pages, properties_list grabs the listings from that page. We keep adding these to our main properties list. This way, by the end of our loop, properties has all the listings from each page, giving us a full picture.

	if response.status_code == 200:
    soup = BeautifulSoup(response.content, 'html.parser')
    properties_list = soup.find_all('div', attrs={'class': 'for-rent-content-container'})
    properties += propertites_list 

Step 6: Exporting Homes.com Properties into CSV File

Now that we’ve successfully collected the property listings we wanted, it’s time to organize them in a CSV file. This step ensures that the property information is easily accessible for analysis or application use.

To get started, we open a 'properties.csv' file for writing. We’ll do this using Python’s csv.writer, which helps us create a structured CSV file for storing our scraped data.

	with open('properties.csv', 'w', newline='') as f:
    writer = csv.writer(f)

Using the csv.writer, we set up the file and define column headers like 'title', 'address', and 'price'. This prepares our file to neatly store the property information.

	writer.writerow(['title', 'address', 'price', 'beds', 'baths', 'description', 'url'])

From here, we iterate over each property in the properties list. We’ll use BeautifulSoup to pull out information from each property listing within this loop and check if each element is present; if not, we use 'N/A' as a placeholder to avoid errors.

We search for the paragraph <p> element with the class 'property-name' to get the title.

	title_elem = property.find('p', attrs={'class': 'property-name'})
	title = title_elem.text.strip() if title_elem else 'N/A'

Similarly, we’ll obtain the address from a <p> tag with the class 'address'.

	address_elem = property.find('p', attrs={'class': 'address'})
	address = address_elem.text.strip() if address_elem else 'N/A'

The <ul> element with the 'detailed-info-container' class contains a list of details, as we saw in the HTML inspection. We’ll extract the list items for price, beds, and baths from it.

	info_container = property.find('ul', class_='detailed-info-container')
	extra_info = info_container.find_all('li') if info_container else []
	price = extra_info[0].text.strip() if len(extra_info) > 0 else 'N/A'
	beds = extra_info[1].text.strip() if len(extra_info) > 1 else 'N/A'
	baths = extra_info[2].text.strip() if len(extra_info) > 2 else 'N/A'

We’ll extract the description from the <p> tag with the class 'property-description'.

	description_elem = property.find('p', attrs={'class': 'property-description'})
	description = description_elem.text.strip() if description_elem else 'N/A'

We get the URL from the href attribute of the <a> tag and append it to the BASE_URL for a complete link.

	url_elem = property.find('a')
	url = BASE_URL + url_elem.get('href') if url_elem else 'N/A'

Note: It’s important to do this step because the URL showed in the HTML is fragmented. In other words, you wouldn’t get the complete URL without appeending BASE_URL to the extract link.

Once we’ve extracted all the details we needed from each property, we’ll write them as a new row in our CSV file. This way, each property’s information is stored in an organized and accessible format.

	writer.writerow([title, address, price, beds, baths, description, url])

Step 7: Error Handling and Final Touches

Within our loop, we use a try-except block to catch any exceptions during the get() request to ScraperAPI. This ensures our script doesn’t crash if a request fails.

	try:
    response = requests.get('https://api.scraperapi.com', params=payload)
    # Further processing
except requests.RequestException as e:
    print(f"Request failed on page {page}: {e}")

We also check the response status before parsing the HTML. If the status code isn’t 200 (success), we print an error message and skip the parsing for that page.

	if response.status_code == 200:
    # Parse HTML
else:
    print(f"Error on page {page}: Received status code {response.status_code}")

Finally, we conclude our script with a completion message.

	print(f"Scraping completed. Collected data for {len(properties)} properties.")

With this final step, we not only confirm we’ve successfully scraped Homes.com but also provide a quick insight into the volume of data we managed to scrape, making it easier to verify and proceed with further analysis or application of the scraped data.

Wrapping Up

Congratulations, you’ve successfully built your first Homes.com scraper!

To summarize, today you’ve learned how to:

• Use Python and BeautifulSoup to scrape real estate data
• Export property data into a CSV file for easy analysis
• Employ ScraperAPI to bypass anti-scraping measures effectively and focus our scraping on region-specific data

Scraping Homes.com is crucial for staying ahead in the dynamic real estate market, whether for investment analysis, market research, or tailoring marketing strategies.

Note: The selectors in this article are chosen for their current accuracy and should work well. But websites change, so it’s wise to check them now and then to avoid errors. If you notice changes, just tweak your script a bit.

If you have any questions, please contact our support team – we’re eager to help. You can also check our documentation to learn the ins and outs of ScraperAPI.

Until next time, happy scraping!