In today's world, most real estate purchases get done through online data made available by real estate websites. As such, access to such information becomes crucial for both agents and companies dealing with the sector.
Here, web scraping makes its presence known. But how do web bots aid real estate companies and make their task easier?
Well, let us have a look:-
Real Estate Scraper- An Introduction
In the business of real estate, web extraction is the process of automatically extracting property data from lists. These listings get available through various real estate websites.
Through realtor data scraping, details of several properties within a locality become handy. These get saved in a structured table format as a spreadsheet or a database. The data gathered gets used to make informed investment decisions.
The information gathered for estate purposes can involve property, pricing, home buyer or customers, competitors, and public records.
Top Methods To Extract Real Estate Information
Scraping data for real estate is a task. As such, some of the best ways to extract such data include the following:-
In-house Web Bots
Extraction can get done in any language, ranging from Ruby, JavaScript, and Python. It involves building web crawlers with the use of web extraction libraries. Open-source bots can also serve the purpose. It helps users perform modifications and customization of pre-built code based on chosen extraction needs.
Off-the-Shelf Web Bots
Technical and general users can use pre-built web scrapers for extracting data from any source. These tools get classified into low code and no code bots, cloud scrapers, and browser extension web scrapers.
Web Scraping Application Programming Interface
A different way to scrape data from external and internal sources is Web Scraping APIs. It is a good option for real estate businesses if the intended website for extraction supports API technologies.
Web Scraping Advantages For Real Estate Business
Real estate businesses gain a lot from web scraping. As such, mentioned below are some key benefits of web data extraction for the industry:-
Comparing and Analyzing Property Values
Most real estate agents rely on single listings to estimate the property's value. In such situations, if any company wishes to stand out, it can do it through various sources of data.
Here, web scraping helps by fetching structured data from available public lists on websites. When any property becomes available online, real estate agents can use data scraping to gather relevant information. The information collected can get used for supporting one's price. Since online information provides the entire picture, the realtor can accurately assess the value of any property.
Geotargeting
Collecting location intelligence is a crucial thing for any real estate agency. The insights are present in the raw data of the real estate market. However, gathering all this information gets difficult manually.
As such, some web scrapers provide beneficial features like Geotargeting. It thus ensures that the real estate web scraping requests come from a particular geographical region per the agency's need.
Creating Property Listings for Clients
Web scraping helps real estate enterprises to prepare lists of potential sellers and buyers. The scrapers even help automate the generation of leads and ensure a regular inflow of property sales around the year.
Web scraping help create property lists with accurate information to aid clients, adding to the credibility of real estate agencies.
Gaining Insights of Real Estate Industries
The estate industry has a dynamic character, and thus it changes constantly. The task here is to know where it is presently and where it stands shortly. To know about the property and perform investment decisions, it becomes crucial to understand the market direction.
As such, the initial data of the estate market contain such information. However, it becomes difficult to gather such data manually. Here, web scraping acts as a boon by providing all the relevant information timely.
Maintaining an Aggregator
To know about real-time information from an array of data sources, real estate aggregators make use of web scraping. In this, therefore, huge volumes of scraped data get investigated to gain meaningful insight.
From finding ongoing trends and patterns to determining the ideal moment of buying and selling or changing price tactics, web scraping aids in every aspect. Web data extraction even helps real estate companies know the future market direction.
Ways to Create Real Estate Scrapers
For building a real estate scraper, there exist several tools and techniques. Well, mentioned below is one of the easiest ways to create a web scraper.
So, let us get started:-
Installing Python, Beautiful Soup, and Requests
Go to the official website of Python and download the given version. The site recognizes the operating system automatically. Here, download and follow the installation instructions. After a while, all the dependencies will get downloaded.
Sending HTTP requests using Requests
After reaching the URL, the server receives a request from the browser. From there, the server answers by forwarding the HTML and other required files to the browser.
To imitate this with the scraper, a folder with a file name has to get created. Open it with an editor. Inside the newly created folder, make a new file. Now, import the Requests library.
However, even after defining the URL, there is a possibility of being blocked by CAPTCHA when the request gets sent. For that, the correct header and params need to get created.
Testing the Request
The next step is to test the request to ensure it works properly. For that, write print (response), and run the script. After the script gets run, make sure that a success status code showing 200 from the server is received.
Structuring the Bot Into a Class and Defining the Integral Methods
Begin by giving an enhanced structure to the scraper. At this point, create a class named the EstateScraper and add the headers. Define a fetch() technique, paste the intended request, and return the response.
Here, it is crucial to specify the variable params and URL to the method. In the end, define run () and add the params and URL while adding another variable.
To finish the setup, add the last code to make the scraper run properly. If everything gets done correctly, a status code of 200 will show again.
Defining the Parsing Method
In the parsing() method, Beautiful Soup will get used for parsing the HTML and identifying the relevant elements. Begin by opening the target URL. Inspect the given page and look for the main parent element. Also, add a parser in the run() method by adding the self. parse, right after the variable res.
Accessing the Scripting Tag
When the inspection of the <il> elements get done, it becomes evident that most information is present inside a <script> tag as JSON data. Here, begin by extracting all the relevant child elements in the deck. Select the <script> tag.lso, and delete the prior print() statements.
Also, add import JSON to the dependencies in the file.
Extracting the Required Data
Make new results = [] variable for storing the data that gets extracted. Pertaining to the price, use a CSS selector and update the parsar with the given information.
Next, specify the range. To set it, find the last navigation page. With that, the scraper can skim through each page and find the relevant data. For performing debugging, the status code of each request needs to get printed.
Finally, add the extracted information into a CSV file. To do this, import csv at the Python file's top and define the to_csv() method." Then, in the run() method, mention self.to_csv().
After running the code, the scraper will create a new CSV file inside the root directory and append the stored data.
Final Thoughts
Today, various factors determine investment prospects and returns. However, with real estate web scraping, it is now feasible to know and monitor crucial impacts in the real business and make data-driven decisions. As such, data extraction through web bots has revolutionized the world of real estate businesses and is likely to influence more in the times to come.
