Web scraping is very useful for real estate professionals, as the housing market is one of the most dynamic ones. On our last real estate examples, we've scraped property listings from Funda, in Netherlands, and Rightmove in the UK.

• Web Scraping Zillow In Python
• Free Web Scraping Zillow

In this video I extract data from zillow.com for North Carolina in Microsoft Excel.If you want any data extracted from any site, you can contact me on my ema. Scraping Zillow Real Estate Data using WebHarvy. WebHarvy is a visual web scraper which can be configured to scrape data from any website. In this article we will see how WebHarvy can be used to scrape real estate data from Zillow. Scraping Property Data from Zillow listings. Our pre-built Zillow web scraper lets you extract data from thousands of home listings for sale and rent from leading real estate websites, quickly and easily without having to write any code.

This time, we're going to the US, both east side and west side. We're going to look for properties on Zillow, the number 1 real estate website in North America.

Once again, a different country means different rules on the format of the property ads. This time, the prices are in US dollars and the surface is in square feet.

Let's see how ScrapingBot performs in Zillow, on both to rent and to buy property listings.

1 • Rent

We're starting our scraping journey in Los Angeles, with this gorgeous flat:

And here are the data we retrieve:

The address has been well retrieved. The currencies have been collected, with the surface and the monthly rent.

You can also see which agency is managing this property.

2 • Buy

Now, we're going to scrape the property listing below:

You can see the collected data below:

As before, we retrieved the title, description, surface and price. The address is clear and includes the ZIP code.

The publishing date is also a great info to collect, as you want to see the newest ads, or older ones where the price could be negotiated.

You can test our API directly from your Dashboard, before integrating it
➡️Click here to test in live⬅️

Read more

Web scraping can often lead to you having scraped address data which are unstructured. If you have come across a large number of freeform address as a single string, for example – “9 Downing St Westminster London SW1A, UK”, you know how hard it would be to validate, compare and deduplicate these addresses. To start with you’ll have to split this address into a more structured form with house number, street name, city, state, country and zip code separately. It’s quite easy to parse address in Python and this tutorial will show you how.

Available Python Address Parser Packages

Python provides few packages to parse address in python –

• Address – This package is an address parsing library, it takes the guesswork out of using addresses in your applications.
• USAAddress – USAAddress is a python library for parsing unstructured address strings into address components, using advanced NLP methods. You can try their web interface at the link here.
• Street Address – Used as a street address formatter and parser. Based on the test cases from http://pyparsing.wikispaces.com/file/view/streetAddressParser.py
  

These packages get the job done for most of the addresses, using Natural Language Processing.

Address Parsing using the Google Maps Geocoding API

In this tutorial, we will show you how to convert a freeform single string address into a structured address with Latitude and Longitude using Google Maps Geocoding API. You can also use this API for Reverse Geocoding. i.e., you can convert geo-coordinates into addresses.

What is Geocoding?

Geocoding is the process of converting addresses such as – “71 Pilgrim Avenue Chevy Chase, Md 20815” into geographic coordinates like – latitude 38.9292172, longitude -77.07120479.

Google Maps Geocoding API

Google Maps Geocoding API is a service that provides geocoding and reverse geocoding for an address. So this Python script is a kind of wrapper for this API.

Each Google Maps Web Service request requires an API key that is freely available with a Google Account at Google Developers Console. The type of API key you need is a Server key.

How to get an API Key

1. Visit the Google Developers Console and log in with a Google Account.
2. Select one of your existing projects, or create a new project.
3. Enable the Geocoding API.
4. Create a new Server Key.
5. You can restrict requests to a particular IP address, but it is optional.

Important: Do not share your API Key, take care to keep them secure. You can delete an old one and generate a new one if needed.

API Usage Limits

Standard usage: 2500 free requests per day and 50 requests per second

Premium usage: 100,000 requests per day and 50* server-side requests per second

* The default limit can be changed

A Simple Demo – Parse Address using Python

The script below can accept address strings as a CSV, or you can just paste the addresses into a list. The script would output the results as a clean CSV file.

If the embed to parse address in python above does not work, you can get the code from GIST here.

Save the file and run the script in command prompt or terminal as:

Once it completes running, you will get an output in a CSV file data.csv. You can modify the file name from line no. 47. You can also modify the code to supply the file name as a positional argument too.

You can go ahead and modify the lines that read the addresses and writes it, to read from a data pipeline and write it to a database. It’s relatively easy, but beyond the scope of this simple demonstration.

Let us know in comments below how this script to parse address in python worked for you or if you have a better solution.

If you need professional help with scraping complex websites, contact us by filling up the form below.

Tell us about your complex web scraping projects

Turn the Internet into meaningful, structured and usable data

Web Scraping Zillow In Python

Free Web Scraping Zillow