Zillow Scraping with Python

ATTENTION:

As of 2019, this code no longer works for most users. Zillow is now able to
detect the use of all/most automated webdrivers, and will display an
unlimited number of CAPTCHA’s when the site is launched in a webdriver.
I have no interest in putting more work into this project, but am leaving
it up to serve as an example of how to webscrape using Selenium with Python.

WARNING: Use this code at your own risk, scraping is against Zillow’s TOC

Basic tool for scraping current home listings from Zillow, written in Python
using Selenium. The code takes as input search terms that would normally be
entered on the Zillow home page. It creates 11 variables on each home listing
from the data, saves them to a dataframe, and then writes the df to a CSV file
that gets saved to your working directory. Using zip codes as search terms
seems to yield the best results, the scraper works at a rate of about 75
zip codes per hour (compared to the Zillow API limit of 1000 homes per 24h).

There are two files, zillow_runfile.py and zillow_functions.py. Clone this
repo to your working directory, open the runfile and step through the code
line-by-line. The zillow functions are sourced at the top of the runfile.

This tool uses a for loop to iterate over a list of input search terms, scrape
the listings of each, and append the results to a dataframe. Function zipcodes_list()
allows the user to compile a large list of zip codes to use as search terms,
using the package zipcode. For example,
st = zipcodes_list(['10', '11', '770'])
will yield every US zip code that begins with ‘10’, ‘11’, or ‘770’ as a single
list. Object st could then be passed to the scraper.

Some things to keep in mind

• You will need to edit the input parameter of function init_driver within zillow_runfile.py
  to point to the local path of your web driver program (required by Selenium).
• The max return for each search term (i.e. each zip code) is 520 home listings.
• Zillow will periodically throw up a CAPTCHA page. The script is designed to
  pause scraping indefinitely until the user has manually completed the CAPTCHA
  requirements (at which point it should resume scraping).
• There tends to be a small amount of NA’s on every search, however foreclosure
  properties seem to be more likely to return NA’s. So the more foreclosures
  there are in a search, the more NA’s there will be.

Software Requirements/Info

• This code was written using Python 3.5.
• Selenium (this can be PIP installed, written using v3.0.2).
• The Selenium package requires a webdriver program. This code was written
  using Chromedriver v2.25.

Example of the output dataframe

df.head(n = 6)

                 address     city state    zip    price  sqft bedrooms  \
0      3011 Bissonnet St  Houston    TX  77005   575000  1820        3   
1          4229 Drake St  Houston    TX  77005   615000  2611        3   
2        2237 Wroxton Rd  HOUSTON    TX  77005  2095000  5492        4   
3      4318 Childress St  Houston    TX  77005   540000  2438        4   
4       2708 Werlein Ave  Houston    TX  77005  1449000  3905        4   
5  5402 Buffalo Speedway  Houston    TX  77005  1995000  4658        3   
  bathrooms days_on_zillow           sale_type  \
0         2             NA      House For Sale   
1         3             NA   For Sale by Owner   
2         5             NA      House For Sale   
3         4              2  Townhouse For Sale   
4         5              1      House For Sale   
5         4              5      House For Sale   
                                                 url  
0  http://www.zillow.com/homes/for_sale//homedeta...  
1  http://www.zillow.com/homes/for_sale//homedeta...  
2  http://www.zillow.com/homes/for_sale//homedeta...  
3  http://www.zillow.com/homes/for_sale//homedeta...  
4  http://www.zillow.com/homes/for_sale//homedeta...  
5  http://www.zillow.com/homes/for_sale//homedeta...