Zillow scraper in Python

Overview

This project is an open-source tool developed in Python for extracting product information from Zillow. It's designed to be easy to use, making it an ideal solution for developers looking for Zillow product data.

Features

• Full search support
• Extracts detailed product information from Zillow
• Implemented in Python just because it's popular
• Easy to integrate with existing Python projects

Important

• Use rotating residential proxies, zillow will block if you make multiple requests with the same IP,

Install

$ pip install pyzill

Examples

import pyzill
import json
ne_lat = 38.602951833355434
ne_long = -87.22283859375
sw_lat = 23.42674607019482
sw_long = -112.93084640625
pagination = 1
#pagination is for the list that you see at the right when searching
#you don't need to iterate over all the pages because zillow sends the whole data on mapresults at once on the first page
#however the maximum result zillow returns is 500, so if mapResults is 500
#try playing with the zoom or moving the coordinates, pagination won't help because you will always get at maximum 500 results
pagination = 1
proxy_url = pyzill.parse_proxy("[proxy_ip or proxy_domain]","[proxy_port]","[proxy_username]","[proxy_password]")
results_sold = pyzill.sold(pagination, 
              search_value="miami",
              min_beds=1,max_beds=1,
              min_bathrooms=None,max_bathrooms=None,
              min_price=10000,max_price=None,
              ne_lat=ne_lat,ne_long=ne_long,sw_lat=sw_lat,sw_long=sw_long,
              zoom_value=5,
              proxy_url=proxy_url)
              
results_sale = pyzill.for_sale(pagination, 
              search_value="",
              min_beds=None,max_beds=None,
              min_bathrooms=3,max_bathrooms=None,
              min_price=None,max_price=None,
              ne_lat=ne_lat,ne_long=ne_long,sw_lat=sw_lat,sw_long=sw_long,
              zoom_value=10,
              proxy_url=proxy_url)

results_rent = pyzill.for_rent(pagination, 
              search_value="",
              min_beds=1,max_beds=None,
              min_bathrooms=None,max_bathrooms=None,
              min_price=10000,max_price=None,
              ne_lat=ne_lat,ne_long=ne_long,sw_lat=sw_lat,sw_long=sw_long,
              zoom_value=15,
              proxy_url=proxy_url)
jsondata_sold = json.dumps(results_sold)
jsondata_sale = json.dumps(results_sale)
jsondata_rent = json.dumps(results_rent)
f = open("./jsondata_sold.json", "w")
f.write(jsondata_sold)
f.close()
f = open("./jsondata_sale.json", "w")
f.write(jsondata_sale)
f.close()
f = open("./jsondata_rent.json", "w")
f.write(jsondata_rent)
f.close()

For homes

import pyzill
import json
property_url="https://www.zillow.com/homedetails/858-Shady-Grove-Ln-Harrah-OK-73045/339897685_zpid/"
proxy_url = pyzill.parse_proxy("[proxy_ip or proxy_domain]","[proxy_port]","[proxy_username]","[proxy_password]")
data = pyzill.get_from_home_url(property_url,proxy_url)
jsondata = json.dumps(data)
f = open("details.json", "w")
f.write(jsondata)
f.close()

import pyzill
import json
property_id=2056016566
proxy_url = pyzill.parse_proxy("[proxy_ip or proxy_domain]","[proxy_port]","[proxy_username]","[proxy_password]")
data = pyzill.get_from_home_id(property_id,proxy_url)
jsondata = json.dumps(data)
f = open("details.json", "w")
f.write(jsondata)
f.close()

For departments

import pyzill
import json
property_url="https://www.zillow.com/apartments/kissimmee-fl/the-nexus-at-overbrook/9DSWrh/"
proxy_url = pyzill.parse_proxy("[proxy_ip or proxy_domain]","[proxy_port]","[proxy_username]","[proxy_password]")
data = pyzill.get_from_deparment_url(property_url,proxy_url)
jsondata = json.dumps(data)
f = open("details.json", "w")
f.write(jsondata)
f.close()

import pyzill
import json
property_id="CgKZT4"
proxy_url = pyzill.parse_proxy("[proxy_ip or proxy_domain]","[proxy_port]","[proxy_username]","[proxy_password]")
data = pyzill.get_from_deparment_id(property_id,proxy_url)
jsondata = json.dumps(data)
f = open("details.json", "w")
f.write(jsondata)
f.close()