How do You Scrape Zillow for Real Estate Listings Data using Python and LXML?

Zillow is regarded as one of the leading real estate companies in the United States, for it has a variety of housing listings, including homes for sale, rent, and those not on the market. Zillow was established in 2006 as a popular online real estate platform for home buyers, renters, and real estate professionals. It has a search interface where users can search for properties by location, price range, property type, and trends. Zillow is an excellent tool for anyone in the real estate market, given the company’s large audience and the range of information it has at its disposal.

Zillow has several unique selling points, including the Zestimate tool, which provides information on the market value of homes and lots using data collected from the public domain and information supplied by users. Although Zestimate is not an actual property appraisal, it helps set an essential value for the user. Zillow also provides several features for estimating monthly house payments with home loans, monitoring rental prices, and more, making the website a comprehensive guide to properties and real estate.

What is Zillow Data Scraping?

Zillow data scraping is the collection of data on various properties across the United States from Zillow, a real estate website. This comprises scraping data from the Zillow property listing interface, including price, address, and property descriptions, among others, by capturing the HTML content of Zillow. Web scraping allows you to obtain vast amounts of information on real estate without copying the data manually, which takes a lot of time.

Real estate agents, investors, and analysts conduct Zillow web scraping to analyze the housing market, evaluate promising deals, and understand apartment rates. However, it should also be noted that HOs can fall into legal and ethical grey areas of web scraping, and as mentioned, Zillow has terms of service banning such data harvesting. Scraping Zillow data requires considering these guidelines carefully and, when possible, opting to use the Zillow API or licensed data services.

Advantages of Real Estate Data Scraping

The Zillow data scraper assists in obtaining large datasets from websites and enables the gathering of significant amounts of information from various sources.

Getting Data from Multiple Sources

Gathering data from various sources requires processing and refining the datasets to ensure smooth analysis processes. That is why copying from such sources as listing sites, property portals, and individual agent websites is reasonable. Doing this will provide you with a better and more detailed perspective on what is really happening in the real estate market.

Spotting New Opportunities

Real estate data scraping can also increase opportunities and help make the right choices. For instance, investors can use scraped data to research properties that are either undervalued or overpriced. Along the same line, scraped details can be used to set appropriate prices and create marketing strategies based on genuine listings.

Custom Data Solutions

People can scrape the data to create reports and graphs that serve their purposes, whether for individuals, businesses, or clients. The scraped data can be used for big data and data mining techniques to predict future property values or market trends.

Consumer Behavior Insights

By web scraping the number of visits, favorites, and shares of listings, one can identify consumer opinions and trends. Based on such results, users can observe fluctuations over time in buying, selling, and renting behaviors.

Strategic Planning for Developers

Developers can use scraped data to determine the market feasibility of a new project by analyzing trends and competition in certain areas. Identifying price ranges of similar properties assists in setting appropriate prices for new developments.

Market Analysis and Trends

Using Zillow data scraping, people can track property price fluctuations over time based on location, enhancing real estate investment decisions. Website statistics on clicks and saves can indicate the popularity or demand for homes in specific locations.

Property Comparisons

The Zillow website offers publication history, building characteristics, price history, neighborhood details, and more. Users can search for properties in preferred areas and compare them side by side.

Geospatial Analysis

Zillow provides data on facilities, schools, security, and other factors that influence housing choices. This data can be scraped for deeper geospatial analysis. Heat maps and graphs may be created using aggregated data to analyze market trends and identify high-interest property areas.

Real Estate data Scraping challenges

Web scraping real estate websites is not free from issues and hurdles. Here are some common obstacles:

Complex Web Layouts

Property websites often have complex and evolving design patterns, making them challenging to scrape. This might lead to unstructured data, which must be rectified manually.

Anti-Scraping Technologies

Restrictions on scraping implemented in property websites include the use of scripting languages like JavaScript, Asynchronous JavaScript, and XML (AJAX), and CAPTCHA tests. These can prevent you from obtaining the data you need or, worse, get your IP address blocked. Workarounds are required to bypass these obstacles.

Data Quality Issues

Real estate prices may fluctuate, and collecting large amounts of information may result in outdated data. It is essential to process and filter the data collected for better analysis.

Legal Concerns

The legality of web scraping is a disputed issue, even on real estate sites. Scraping freely available data is usually safe; however, if it is copyrighted, caution is necessary. Consulting a legal professional is the best approach to ensure compliance with relevant laws.

How to Scrape Zillow Website Data?

This step-by-step guide helps you scrape address, prices, real estate providers, and URLs.

Install packages to scrape Zillow data.

Requests and lxml are external Python libraries that must be installed with pip.

pip install lxml requests

Installing the Unicodecsv library is also necessary if you want to write CSV files.

pip install unicodecsv
                        

Scrape Zillow Website

Below is the Python code for scraping Zillow data. First, import the required libraries and modules.

from lxml import html
import requests
import unicodecsv as csv
import argparse
import json

The information extracted can include redundant white space. S,o create a method clean() to eliminate any such gaps.

def clean(text):
    if text
        return ' '.join(' '.join(text).split())
    return None

To keep things organized, keep headers distinct from other functions. You may use this function to retrieve the headers. This method also makes it easier to update headers because you don't have to alter the core code.

def get_headers():
    # Creating headers.
    headers = {'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,'
                '*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',
      'accept-language': 'en-GB;q=0.9,en-US;q=0.8,en;q=0.7',
      'dpr': '1',
      'sec-fetch-dest': 'document',
      'sec-fetch-mode': 'navigate',
      'sec-fetch-site': 'none',
      'sec-fetch-user': '?1',
      'upgrade-insecure-requests': '1',
      'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
                    'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36'}
    return headers

The code can organize the results by either the cheapest or newest. If no sort option is used, it may return unsorted listings.

These cases necessitate a different request URL when scraping Zillow data using Python lxml. Define a create_url function that returns the correct URL depending on the criteria.

def create_url(zipcode, filter):
    # Creating Zillow URL based on the filter.


    if filter == "newest":
        url = "https://www.zillow.com/homes/for_sale/{0}/0_singlestory/days_sort".format(zipcode)
    elif filter == "cheapest":
        url = "https://www.zillow.com/homes/for_sale/{0}/0_singlestory/pricea_sort/".format(zipcode)
    else:
        url = "https://www.zillow.com/homes/for_sale/{0}_rb/?fromHomePage=true&shouldFireSellPageImplicitClaimGA=false&fromHomePageTab=buy".format(zipcode)
    print(url)
    return url

To troubleshoot a problem, you may need to review the answer text. Create a method save_to_file() that saves the response to a file.

def save_to_file(response):
    # saving response to `response.html`


    with open("response.html", 'w', encoding="utf-8") as fp:
        fp.write(response.text)

Clarify a function. Use the write_data_to_csv function to save the extracted data to a CSV file.

def write_data_to_csv(data):
    # saving scraped data to csv.


    with open("properties-%s.csv" % (zipcode), 'wb') as csvfile:
        fieldnames = ['address', 'price','real estate provider', 'url']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        for row in data:
            writer.writerow(row)

To retrieve the result, create a get_response() method that sends an HTTP call to Zillow.com. The method also calls save_to_file(), which saves the response to a file.

def get_response(url):
    # Getting response from zillow.com.


    for i in range(5):
        response = requests.get(url, headers=get_headers())
        print("status code received:", response.status_code)
        if response.status_code != 200:
            # saving response to file for debugging purpose.
            save_to_file(response)
            continue
        else:
            save_to_file(response)
            return response
    return None

Create a parse() function that integrates.

create_url(),
get_response(),
clean(),

and return a list of Zillow properties.

The parse() method parses the response text using lxml. It then used XPath to find HTML elements in the response and extract the relevant data.

def parse(zipcode, filter=None):
    url = create_url(zipcode, filter)
    response = get_response(url)


    if not response:
        print("Failed to fetch the page, please check `response.html` to see the response received from zillow.com.")
        return None


    parser = html.fromstring(response.text)
    search_results = parser.xpath("//div[@id='grid-search-results']//article")
    print(search_results)
   
    print("parsing from html page")
    properties_list = []
    for properties in search_results:
        raw_address = properties.xpath(".//address//text()")
        raw_price = properties.xpath(".//span[@class='PropertyCardWrapper__StyledPriceLine-srp__sc-16e8gqd-1 iMKTKr']//text()")
        raw_broker_name = properties.xpath(".//div[@class='StyledPropertyCardDataArea-c11n-8-84-3__sc-yipmu-0 jretvB']//text()")
        url = properties.xpath(".//a[@class='StyledPropertyCardDataArea-c11n-8-84-3__sc-yipmu-0 jnnxAW property-card-link']/@href")
        raw_title = properties.xpath(".//h4//text()")
        address = clean(raw_address)
        price = clean(raw_price)
        #info = clean(raw_info).replace(u"\xb7", ',')
        broker = clean(raw_broker_name)
        title = clean(raw_title)
        property_url = "https://www.zillow.com" + url[0] if url else None
        properties = {'address': address,
                      'price': price,
                      'real estate provider': broker,
                      'url': property_url,
                    }
        print(properties)
        properties_list.append(properties)
    return properties_list

Finally, use the parse() method and write_data_to_csv() to save the retrieved property listings as a CSV file.

if __name__ == "__main__":
    # Reading arguments


    argparser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
    argparser.add_argument('zipcode', help='')
    sortorder_help = """
    available sort orders are :
    newest : Latest property details,
    cheapest : Properties with cheapest price
    """


    argparser.add_argument('sort', nargs='?', help=sortorder_help, default='Homes For You')
    args = argparser.parse_args()
    zipcode = args.zipcode
    sort = args.sort
    print ("Fetching data for %s" % (zipcode))
    scraped_data = parse(zipcode, sort)
    if scraped_data:
        print ("Writing data to output file")
        write_data_to_csv(scraped_data)

The code also uses the argparse module to offer the script command-line capability. That is how you specify the ZIP code and sorting choice to the script.

Here is a flowchart illustrating the execution order of the functions provided for web scraping Zillow using Python.

How Do You Scrape Zillow Data Without Getting Blocked?

In general, web scraping Zillow can effectively collect real estate data, but it must be done as carefully as possible to avoid blocking.

Respect Robots.txt

The rules governing web scraping limit which portions of the Zillow website can be accessed with a bot and which sections cannot. These rules are in a file named robots.txt. Before scraping, check Zillow's robots.txt file to determine which pages are allowed or restricted. Ensure that you do not scrape these restricted areas to avoid violating their policies.

Use Different IP Addresses

Websites like Zillow monitor IP addresses to detect scraping activity. Using different IP addresses or rotating proxies can help prevent detection and potential IP bans. This technique makes your scraper appear as if requests are coming from multiple users rather than a single automated script.

Handle CAPTCHAs

Zillow and similar platforms may require CAPTCHA tests to verify if a user is human. CAPTCHAs present challenges that bots struggle to solve. If you encounter CAPTCHAs, consider using CAPTCHA-solving services. To minimize CAPTCHA triggers, scrape at a slow rate and mimic human browsing behavior.

Headless Browser

A headless browser is a browser that can navigate the web and interact with pages without displaying a user interface. Tools like Puppeteer and Selenium can automate browsing and help scrape Zillow data more effectively by simulating human behavior.

Respect Rate Limits

Some websites restrict the number of requests a user can make within a certain timeframe. If you exceed Zillow's rate limits, your scraper may be blocked. Monitor your request rate and stay within safe limits to avoid detection. If you get blocked, slow down your scraping and reduce the number of requests.

Use Anti-Detection Tools

Certain tools and libraries help disguise scraping activities by making requests appear more like those of real users. Anti-detection techniques include rotating user agents, disabling browser features that detect automation, and mimicking user interactions to reduce the likelihood of being flagged as a bot.

Conclusion

In addition to listings, Zillow has ventured into Zillow Offers, where homeowners are offered cash for their homes. Zillow Home Loans with mortgage services are provided through the platform to ensure easy accessibility. Websites like Zillow might give you warnings before entirely blocking you, like showing you an error message. Look for error messages like “403 Forbidden” or “429 Too Many Requests.” Some tools and libraries help hide the fact that you’re scraping from Zillow’s detection systems. Use anti-detection plugins or libraries to make your requests look more like a real user’s activity. For example, these tools might disable certain browser features that Zillow could use to detect bots. If you always scrape the same data in the same order, it’s easier for Zillow to recognize and block your pattern. For example, don’t always scrape the same data type or visit pages in the same order. Randomly choose what data to collect and the paths you take through the website.

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About the author

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Zoltan Bettenbuk

Zoltan Bettenbuk is the CTO of ScraperAPI - helping thousands of companies get access to the data they need. He’s a well-known expert in data processing and web scraping. With more than 15 years of experience in software development, product management, and leadership, Zoltan frequently publishes his insights on our blog as well as on Twitter and LinkedIn.