Zillow is known to be one of the biggest real estate platforms in the United States. It has almost more than 200+ million visits per month. This huge number assures you that the website contains a vast amount of data. You can get valuable information on real estate and use this to your advantage. However, you need to run reliable and accurate web scraping methods to make this data useful.
You need services that can dive deep into the Zillow Data and help you get real estate insights. In this article, we will talk about the possible methods to scrape real estate data from Zillow. There is a way through the Zillow API; you can use Python and Zillow scraper, or you can also hire scraping companies to help you with it, as they have a trained team.
Can You Scrape Zillow Data?
As real estate business owners try to be a little more updated in the market, they have also adapted to the trend of Zillow scaping. Whether you need home buyers or are looking for competition, you can find it all there and use it wisely.
However, to scrape Zillow, you need specific guidelines to avoid the legal ban from the website. Remember that scraping the data that is already available through the search isn’t illegal. As you are looking for information, it is permissible. It is viewable by anyone who has access to the internet and website.
Zillow has made its data available through its API. This enables commercial integration with the Zillow group in the real estate industry market. The Zillow API is the greatest approach to commercially access information while adhering to Zillow's ethics and regulations.
The data you can scrape from Zillow includes information on the list of properties for sale in any city in their database, such as addresses, number of bedrooms, number of bathrooms, price, and other details. The retrieved data can be exported in many formats, which include .csv, .txt, .xlsx, and your own database.
Ways of Scraping Zillow Data
There are many different ways for Zillow to scrape services. Some go for the Zillow scrapers, some use Zillow API, and others may use Python to get useful information. We will talk about a few to help you get into the depths of Zillow data scraping.
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Using Zillow API
Zillow is now offering 22 separate APIs (application programming interfaces). These APIs are specifically designed to collect a variety of data. You can get access to the listings, reviews, and property information too.
Some API services require payment based on usage. In addition, Zillow transferred its data operations to Bridge Interactive, a company specializing in MLS information and brokerages. Bridge requires users to obtain the system's permission before using its endpoints, including those previously using Zillow's API. To understand it more, refer to Zillow's official website for the official API.
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Scraping Zillow with Python
If you are starting to look into more technical methods, then scraping Zillow using Python is one of the most used by developers and other data analysts. We will help you by walking you through the steps using the simple example; you can tweak them as per your need. Otherwise, the steps are only simple yet effective to have access to valuable information. Just follow the steps below:
Installing the libraries
To get started, you need to choose the library for it. There are 2 types of it.
- One is the Query Library. It gives you functions like Requests, UrlLib, or others, along with the parsing library with functions like BeautifulSoup and Lxml.
- Otherwise, you can use a complete library or scraping frameworks available online, such as Scrapy, Selenium, and Puppeteer.
If you are a beginner, the first option is slightly easy. However, for the more secure scraping, you need a second way. Let us provide sample scraper codes using Requests and BeautifulSoup libraries. These libraries of functions can get you the parse data.
You need a Python interpreter for it.
If an interpreter is already installed, the version will be displayed. To install the libraries, type the following command:
pip install requests
pip install beautifulsoup4
pip install selenium
Scraping data
Now, use the libraries for Zillow property data extraction, run IDE, and enter the sample code below. It might need some changes depending on the IDE you are using.
# coding: utf-8
# Web scraping Zillow
# import request module
import requests from bs4
import BeautifulSoup as soup
header = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64)
AppleWebKit/537.36 (KHTML, like Gecko)
Chrome/83.0.4103.97 Safari/537.36',
'referer':'https://www.zillow.com/homes/for_rent/Manhattan,-New-York,-NY_rb/?searchQueryState=%7B%22pagination'
}
# Enter Zillow URL for the city of your preference
url = 'https://www.zillow.com/homes/for_rent/Manhattan,-New-York,-NY_rb'
html = requests.get(url=url,headers=header)
Html.status_code
bsobj = soup(html.content,'lxml') #bsobj - Beautiful Soup Object
bsobj
# price list is a list variable that will contain
# the price information.
price_list = []
# loop through the price section of the data and extract
# the text and store it in the list.
for price in bsobj.findAll('div',{'class':'list-card-heading'}):
print('price is: ',price.text.replace( 'bd','b|' ).replace(
'|s','|' ).replace('io','io|').strip().split('|')[:-1])
price_list.append(price.text.replace('bd','b|').replace('|s','|').replace('o','o|').strip().split('|')[:-1])
print(price_list)
# address list is a list variable that will
# contain the address information.
address = []
# loop through the address section of the data
# and extract the text and store it in the list.
for adr in bsobj.findAll('div',{'class':'list-card-info'}):
address.append(adr.a.text.strip())
print(address)
import pandas as pd
# create a pandas data-frame to store the address
# and price information.
df = pd.DataFrame( price_list,columns = ['Price1','Price2','Price3','Price4'] )
df['Address'] = address
print(df)
The pandas' data frame 'df' contains information about the listings in Manhattan, New York. This software has loops for extracting the prices and addresses of the listed houses.
You can expand it by scraping additional data, such as the number of bedrooms and bathrooms. This can be saved as a CSV file on your PC using the following function: df.to_csv('file_path'). In place of file_path, enter the path to the CSV file as well as the file name.
Bottom Line
Finally, you know how to use the Zillow data. Right now, you can also use it to train AI. However, the best way is to use a USA Zillow scraper. At Scraping Home, we have all kinds of tools, and teams are ready to help you with accurate information anytime.
