How to Extract Price and Other Details from Zillow

What is Zillow?

Zillow is an online platform that accumulates and organizes publicly available property data. It is an easy-to-use tool that enables users to access actual rent, mortgage, buying, selling, renting, and financing with high transparency. Depending on this, the buyers can make property-buying decisions with confidence.

The prime aim of Zillow is to please its customers with a unique and transparent experience in buying and selling homes. It works with real estate agents, builders, brokers, landlords, and property managers to combine technology with high-quality service.

Benefits of Scraping Data from Zillow

In every country, several real estate firms compete with each other for a piece of the real estate market pie. Only excellent quality and quantity of data promise a competitive advantage over the rest of the competitors.

Zillow data extraction offers valuable insights into existing market trends and lets you predict where the market will go shortly. Using the extracted information, you can easily create an efficient and strategic marketing campaign and gain a more stable role in the real estate industry. Listed below are some of the benefits of scraping Zillow real estate data.

Price Monitoring: Prices are important for consumers when buying a property. No matter if they can save a buck or two, they can go to any extent to do so. In today’s era, more than 80% of customers match rates on different online sites before they make any final decision. Hence, it becomes crucial for a company to track competitors’ pricing. However, it is essential to extract price details from Zillow for price comparison and adjust pricing on your website, making it appealing to new buyers.

Better Analysis: Pricing is the primary factor in the real estate industry. Some real estate brokers tend to charge a hefty commission to the buyers. In such an instance, Zillow data scraping gives a detailed insight into different property details, consumer needs, and competitor analysis. You can easily collect important information about the in-demand properties, reasonable pricing, features customers are looking for in a home, and more. It will help you to properly categorize information, segregate it, and satisfy the needs of buyers or sellers.

Competitors’ Tracking: In real estate, eyeing your rivals is mandatory. Only when you know what your competitors are doing can you sell more successfully? Understand the specific strategy of your rivals to win in the market. It will help you to make more defined plans than theirs. Having competitor insights will keep you on top of the competition and allow you to develop new ideas.

Reviews & Ratings: With several rivals looking to hurt other companies, it is normal for brands to get negative feedback unknowingly. Sometimes, customers also post their dissatisfaction in the form of their reviews. In such instances, iWeb Data Scraping provides Zillow data extraction services to assist all negative reviews and maintain a positive brand reputation.

List of Data Fields

• Area
• Number of Rooms
• Number of Floors
• Property Types
• Location
• Property Size
• Property Type – Rental, Sale, Mortgage
• Reviews
• Prices
• Insurance
• Credit and Mortgage Records

How To Scrape Zillow?

Zillow is a data-rich website. Scraping can provide you with complete datasets.

Here, we will scrape Zillow data using Python. Our target page will be https://www.zillow.com/homes/forsale/Brooklyn, -NY_rb/. From here, we will extract the size, price, and address.

First, we created a folder and then installed all the necessary libraries.

On this page, we have nearly 40 listed properties from Zillow. We will use BS4 to scrape our target data. All these properties have a class . You can get this by inspecting the element.

The price tag is in the class StyledPropertyCardDataArea-c11n-8-69-2_sc-yipmu-o kJFQQX. Similarly, the size is in while the address in

while the address in

Now, we have everything to make our Zillow data scraper ready.

Next, we run a ‘for’ loop to reach every property in the Zillow property list. Then, we will use the find function of BS4 to find our target elements.

After finding it, we will store it in a list. After printing, we will get the result like this.

We found only nine results out of 40. It is because Zillow can only scrape using JS rendering. Before that, we will scrape Zillow by altering page numbers. We only need to run another ‘for’ loop to find all properties on different pages. But now, we will run the loop only for ten pages. Each page has 5610 listings. As we have 40 properties, there are 140 pages in total.

JS Rendering

Here, we will scrape Zillow data using JS rendering. We will load the website in a browser and extract the required data.

We will use Selenium to implement the task. First, we will install it.

>> pip install selenium

Import all the libraries.

Now, we will consider the path where our Chrome driver is available.

The target_url variable stores the web link used for data scraping.

driver=webdriver.Chrome(PATH) 
driver.get(target_url)

We will find HTML elements on the page when we have to scroll down to load a website completely.

html = driver.find_element_by_tag_name('html') 
 html.send_keys(Keys.END)

Here we are using .send_key() to speed a PAGE_DOWN key press.

Extract the page source code using the .page_source method of the selenium driver.

Then, we used the same BS4 code from our last section to extract the needed data.

Complete Code

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