Web Scraping in real estate is the process of automatically extracting property data from listings provided by various real estate websites. Details of thousands of properties within a locality can be saved in structed table format as a spreadsheet or to a database. Using this data informed investment decisions can be made.
Uses of Web Scraping in Real Estate
As in any other field of business, in real estate too, profits can be maximized by using data to your advantage. There is a wealth of property data in the public domain displayed by various real estate websites.
The primary use of web scraping in real estate is to build your own comprehensive database of property listings. Using web scraping, this data can also be updated on a daily basis since new properties are posted everyday by agents and property owners. It will contain all details related to the property along with their agent/owner contact details.
Help clients in making better investment decisions
The scraped data helps realtors to find properties with better match of client requirements and budget. Properties can be matched based on bed, bath, area requirements, amenities, locality, price range etc. The data will give realtors an edge in their business while selling or buying properties for their clients.
Monitor rental yields and vacancy rates
Using data obtained by web scraping you can find neighborhoods which offer higher rental yields for properties. This helps in investing in properties which provide better return of investment (ROI) in the long term.
Web scraped data will also help you avoid neighborhoods with high vacancy rates. Analyzing the data which is in spreadsheet format helps you make correct investment decisions based on such parameters.
Web scraping also helps you keep a watch on properties and their prices listed by your competitors in a locality. Pricing trends can be monitored for making better investment decisions. Most web scraping software allow you to performed automated scheduled web scraping, so that you always have the latest price data in your spreadsheet or database.
Property Data which can be collected using Web Scraping
All data publicly displayed by real estate websites can be scraped. This includes:
- - Property Address
- - Sale Price, Rental Price
- - Beds, Baths
- - Area, Parking Spaces
- - Amenities
- - Pricing and Tax History
- - Neighborhood details
- - Agent, Owner contact details
- - Days on Market, Year Built etc.
- - Images
Websites from which Real Estate Data can be Scraped
Web Scraping can be employed on any website. The following are some real estate websites which are popular internationally from which property listings data can be scraped.
- www.realtor.com / www.realtor.ca
Which software to use for real estate web scraping?
Please follow this link to see a list of the most popular web scraping software which you can use to scrape data from all types of websites including real estate.
In this article, we are going to use WebHarvy to scrape real estate data. The advantage of using WebHarvy for scraping real estate data is that it is very easy to use. The data which you need can be selected from websites using a very intuitive point and click user interface. We also have detailed demonstration videos related to all popular real estate websites which makes it even easier for you to start scraping property data.
Scraping property data from real estate websites
Zillow is the most popular real estate website which offers a robust suite of tools for buyers, sellers, landlords, renters and agents. Zillow has a database of over 100 million properties which can be sorted and viewed based on location and other criteria. Zillow lets you search for homes and apartments, list yours for sale or rent, connect with agents, owners and lenders etc.
WebHarvy can be used to scrape property listings data from Zillow. Using the point-and-click data selection interface of WebHarvy, you can select the data which you need to scrape from each property listing via mouse clicks. Details like property address, price, Zestimate, area, beds/baths, facts and figures, neighborhood details, pricing and tax history, property images etc. can be easily scraped.
Founded in 2005, Trulia was acquired by Zillow in 2015. Trulia’s mobile app and website are loved by many for their design and transparency. Trulia also helps users understand the potential of neighborhoods via neighborhood reviews.
The following video shows how WebHarvy can be used to scrape details like address, price, MLS, agent/owner phone number and name, etc. from Trulia property listings.
Realtor.com listings are the most accurate and closest to the MLS standard, which is updated regularly by realtors. 99% of Realtor.com listings are MLS listed properties.
Realtor.com and Realtor.ca websites can be scraped using WebHarvy for extracting property listings data.
RedFin is a large online real estate company which offers several unique features and benefits which makes it a viable alternative to Zillow and Trulia. RedFin, which started as a real estate broker, employs a large team of user rated agents whose compensation is proportional to their rating. The company also rebates part of commission back to purchasers.
RedFin property listings data can be scraped using WebHarvy as shown in the following video.
Advantages of using WebHarvy for Scraping Real Estate Websites
The biggest advantage of WebHarvy is that you can use it to scrape data from any website with an easy to use and intuitive data selection interface. WebHarvy works with all types of websites and not just the ones mentioned above. WebHarvy supports periodically scraping data from websites so that newly posted listings are saved daily to your database. The scraped data can be saved in a variety of formats (including Excel, XML, CSV etc.) or stored to a database.
To know more : Getting Started with WebHarvy