Scraping Real Estate Data With Python: Step-by-Step

Real estate brokerage platforms offer a huge variety of listings that are updated constantly, making them a good target for web scraping. That said, keeping up with the real estate market in real-time and at a large scale is challenging.

In this guide, you’ll learn how to collect public property data from Redfin with the help of Oxylabs Real Estate Scraper API and Python. You can scrape real estate data like prices, sizes, number of beds and baths available, and addresses, increasing the likelihood of finding a good deal or understanding the market better.

You can find the following code on our GitHub.

1. Prepare environment

You can download the latest version of Python from the official website.

To store your Python code, run the following command to create a new Python file in your current directory.

touch main.py

Install dependencies

Next, run the command below to install the dependencies required for web scraping and data processing. Let’s use Requests, Beautiful Soup, and Pandas.

pip install bs4 requests pandas

Import libraries

Now, open the previously created Python file and import the installed libraries.

import requests
import pandas as pd
from bs4 import BeautifulSoup

2. Prepare the API request

After importing the libraries, the following step is to prepare the payload for the API request. Using Real Estate Scraper API, you’ll need to retrieve credentials for API authentication from the Oxylabs dashboard. Replace USERNAME and PASSWORD with your retrieved credentials.

USERNAME = "USERNAME"
PASSWORD = "PASSWORD"

payload = {
    "source": "universal",
    "url": "https://www.redfin.com/city/29470/IL/Chicago",
}

For this example, let’s scrape real estate listings in Chicago. You can replace the url value with a Redfin home listings page of your choosing. Make sure the source parameter is set to universal.

NOTE: You can find all the parameters and more samples in our documentation.

3. Send request

Use the declared credentials and payload to send a POST request to the API. Pass the credentials and payload to the auth and JSON parameters, respectively.

response = requests.post(
       url="https://realtime.oxylabs.io/v1/queries",
       auth=(USERNAME, PASSWORD),
       json=payload,
)
print(response.status_code)

If everything works as expected, you should see a 200 status code printed out in your terminal.

As a best practice, consider adding this line after your POST request. It guarantees that the data you receive from the API is what you expect instead of an error code.

response.raise_for_status()

Here’s the full code for sending the request:

response = requests.post(
    url="https://realtime.oxylabs.io/v1/queries",
    auth=(USERNAME, PASSWORD),
    json=payload,
)
response.raise_for_status()
print(response.status_code)

If you get an HTTPException, check if your payload and credentials are correct.

4. Extract HTML

The API response comes back in JSON format, equivalent to a dictionary in Python. You can use the response object and the Beautiful Soup library to extract the HTML content as follows.

html = response.json()["results"][0]["content"]
soup = BeautifulSoup(html, "html.parser")

The Soup object will be used to extract the necessary data from the HTML content. Use CSS selectors to select specific data from the HTML content.

5. Parse data from HTML

Start with collecting every home listing found on the page. Navigate to the web page, right-click on the part of the listing that includes the data you need, and click Inspect.

You should see that the parent element has a class called bottomV2. Use it to select each listing from the HTML content.

data = []
for listing in soup.find_all("div", {"class": "bottomV2"}):
	...

For cleaner code, create a function called extract_data_from_listing and write your data extraction code there. The function should accept the HTML content of the listing as an argument and return a dictionary containing the extracted data.

def extract_data_from_listing(listing):
	...

Next, implement the created function.

By inspecting the price and address fields, you can see that they’re both span elements with homecardV2 and collapsedAddress classes, respectively. Let’s use them to retrieve the values.

price = listing.find("span", {"class": "homecardV2Price"}).get_text(strip=True)
address = listing.find("span", {"class": "collapsedAddress"}).get_text(strip=True)

For the rest of the fields, you can see that they all contain the same stats class.

Use this class to select all elements at once and parse them separately.

stats = listing.find_all("div", {"class":"stats"})
try:
    bed_count_elem, bath_count_elem, size_elem = stats[0], stats[1], stats[2]
except IndexError:
    raise Exception("Got less stats than expected")

bed_count = bed_count_elem.get_text(strip=True)
bath_count = bath_count_elem.get_text(strip=True)
size = size_elem.get_text(strip=True)

If Redfin’s page structure changes, raise an exception to know what went wrong.

After parsing each data entry, construct dictionaries and append them to the previously declared list.

entry = {
    "price": price,
    "address": address,
    "bed_count": bed_count,
    "bath_count": bath_count,
    "size": size,
}
data.append(entry)

Here’s the full code for extracting data from the HTML content:

def extract_data_from_listing(listing):
    price = listing.find("span", {"class": "homecardV2Price"}).get_text(strip=True)
    address = listing.find("span", {"class": "collapsedAddress"}).get_text(strip=True)
    stats = listing.find_all("div", {"class": "stats"})
    try:
        bed_count_elem, bath_count_elem, size_elem = stats[0], stats[1], stats[2]
    except IndexError:
        raise Exception("Got less stats than expected")

    bed_count = bed_count_elem.get_text(strip=True)
    bath_count = bath_count_elem.get_text(strip=True)
    size = size_elem.get_text(strip=True)

    return {
        "price": price,
        "address": address,
        "bed_count": bed_count,
        "bath_count": bath_count,
        "size": size,
    }


data = []

for listing in soup.find_all("div", {"class": "bottomV2"}):
    entry = extract_data_from_listing(listing)
    data.append(entry)

6. Save to CSV

Lastly, dump your collected data into a CSV file using pandas.

df = pd.DataFrame(data)
df.to_csv("real_estate_data.csv")

The complete code

Here’s the full code for scraping real estate data from Redfin with Oxylabs Real Estate Scraper API:

import requests
import pandas as pd
from bs4 import BeautifulSoup


def extract_data_from_listing(listing):
    price = listing.find("span", {"class": "homecardV2Price"}).get_text(strip=True)
    address = listing.find("span", {"class": "collapsedAddress"}).get_text(strip=True)
    stats = listing.find_all("div", {"class": "stats"})
    try:
        bed_count_elem, bath_count_elem, size_elem = stats[0], stats[1], stats[2]
    except IndexError:
        raise Exception("Got less stats than expected")

    bed_count = bed_count_elem.get_text(strip=True)
    bath_count = bath_count_elem.get_text(strip=True)
    size = size_elem.get_text(strip=True)

    return {
        "price": price,
        "address": address,
        "bed_count": bed_count,
        "bath_count": bath_count,
        "size": size,
    }


USERNAME = "USERNAME"
PASSWORD = "PASSWORD"

payload = {
    "source": "universal",
    "url": "https://www.redfin.com/city/29470/IL/Chicago",
}

response = requests.post(
    url="https://realtime.oxylabs.io/v1/queries",
    auth=(USERNAME, PASSWORD),
    json=payload,
)
response.raise_for_status()

html = response.json()["results"][0]["content"]
soup = BeautifulSoup(html, "html.parser")

data = []

for listing in soup.find_all("div", {"class": "bottomV2"}):
    entry = extract_data_from_listing(listing)
    data.append(entry)


df = pd.DataFrame(data)
df.to_csv("real_estate_data.csv")

Final word

As results prove, using Python along with Redfin Scraper API is a seamless way to automate real estate data collection processes required for insights into the real estate market. 

Oxylabs Scraper API enables you to extract data from Redfin and bypass typical challenges associated with web scraping. Please refer to our technical documentation for more on the API parameters and variables discussed in this tutorial.

Under Oxylabs’ real estate umbrella, you can find more target-tailored scrapers: Zillow Data API, Zoopla Scraper, and MLS Scraper API.

For more tutorials on popular targets like Amazon, Zillow, Craigslist, IMDb, and many others, check our blog.

If you have any questions, feel free to reach out by sending a message to support@oxylabs.io or live chat.