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RPA Web Scraping for Data-driven Success in Real Estate


Did you know that Zillow, the leading online real estate and rental marketplace has a database of over 100 million homes in the US? 

This number continues to grow as the pioneers have been leveraging Big Data and data science since its inception in 2006. 

Zillow has always been at the forefront of using large datasets and advanced analytics to provide actionable insights to home buyers, sellers, real estate professionals, as well as their competing agencies (although not presented on a silver platter but indirectly).

Their current market capitalization is estimated at around $10.23 billion as of April 2024.

Just from this information enough, you can probably guess that their profits are going through the roof. That’s the power of harnessing data – leading to data monetization/data-driven success. 

The rudimentary step to achieve this kind of success is access to quality data. Now, you cannot possibly collect data from a real estate listing site manually, so, you might need to automate data extraction for real-time insights. 

Therefore, this blog right here is here to teach you how RPA web scraping is pivotal for real estate professionals who require quick, accurate, and actionable data to draw insights from historical patterns and predict future trends.  

RPA in Real Estate 

We already know from this blog that RPA is a replicator.

They are bots that are programmed to automate repetitive tasks and mimic human interaction to boost the efficiency of the task at hand. RPA bots work together to automate entire workflows that are based on rule-based actions and have defined triggers. 

Thus, simple tasks that capped human creativity due to meaningless labor like voluminous data entry, tracking inventory, and file management can now be error-free and faster than ever with RPA. 

When combined with Artificial Intelligence and Machine Learning, RPA can pave the way for intelligent automation, like handling the entire customer onboarding for your business. 

It can do more than just generate reports, enter data, and file documents. RPA can even handle complex customer queries and make informed decisions for some business processes. 

Let’s talk about RPA in the context of the real estate industry. 

RPA in real estate also involves creating software robots that imitate typical human interactions with a system helping automate multiple repetitive high-volume tasks. 

It aids real estate agencies in streamlining their property management operations, elevating their client service, and improving the quality of their listings.

RPA also handles the automation of the agency’s financial operations like tax billing, portfolio management, tenant onboarding, payment reminders, and more. 

RPA Use-Case in Real Estate Examples

Tenant Onboarding

Let’s assume, you assign a person to manually collect tenant information by making them fill out different forms, and documents and validate them. This is probably going to take hours if not days to complete this clerical task only for compliance purposes. 

But with RPA, you can automate the collection and the validation process by measuring it against predefined criteria to seamlessly integrate the tenant’s information into the company database. 

Tax Billing

Do you know what people dread doing the most in the US? You’re right, it’s doing their taxes. That’s understandable. It is quite complicated and making any mistakes could cause you to end up in the clink. 

But, don’t worry if RPA is integrated into your business system, it automatically updates tax rates & rules to generate accurate tax invoices and submit tax filings within the given deadline. 

Payment Reminders

RPA can even send personalized reminders regarding payments to tenants or other clients based on the payment due dates and outstanding balances on its own. Then, it also tracks payment statuses and updates the accounting records accordingly.  

Portfolio Management 

RPA aggregates property data from multiple sources, like real estate listings, market reports, and financial statements into a database. Afterward, as a cherry on top, it consolidates this data into a centralized repository for portfolio analysis. 

These are a few ways professionals in real estate have opted to RPA for fast, efficient, and streamlined operations.

Data Extraction in Real Estate 

Data for stakeholders from the real estate industry is like a compass to an explorer in the wilderness. Guiding investors, directing agents, and providing valuable insights to developers for unlimited opportunities and competitive edge over rivals. 

With the 4th industrial revolution, the advancement in digital technology took new heights with the invention of the internet, thus igniting the idea of extracting and leveraging internet data.

The creation of the World Wide Web back in 1989 involved 3 major elements that are indispensable when it comes to web scraping today, i.e. URLs, embedded hyperlinks, and web pages containing different forms of data like text, images, & videos. 

Traditionally, data extraction used to be manual, the individual supposed to be analyzing the data, also, had to be the one extracting the data from external sources. 

This was undoubtedly the most time-consuming process which also caused many errors and inconsistencies. Real-time data couldn’t even be imagined at this point. 

Then came the time to code, gathering data from real estate websites would require using Beautiful Soup – an HTML parser, a library of commonly used algorithms written in Python. 

This made it easier for coding professionals to extract data from multiple sources by running the program.

However, what about the individuals that were not proficient in coding? Access to public web data in structured form was still a ripe fruit hanging tantalizingly out of reach for them. 

To solve this problem, a plethora of tools, software, applications, and services boomed to take care of business’s data needs.

So, let’s talk about automated data extraction, specifically, RPA Web Scraping. 

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RPA Web Scraping For Real Estate 

Web scraping is the process of extracting data from multiple sources on the web. However, many websites implement anti-bot securities to protect the privacy of their data. 

They require genuine users to solve CAPTCHA and interact with GUI elements. These are hurdles in the quick-paced and efficient data extraction process. 

To bypass these hurdles, bots programmed using robotic process automation play a crucial role. They identify the dynamic elements and simulate human interaction with them both from the website’s front and back end. 

Then, they let the crawler seamlessly extract data from the targeted data fields.  

Especially in real estate sites, extracting specific details about certain properties from a state, city, property images, or street address is challenging even using crawlers. 

Luckily, with RPA bots, we can navigate the complex website structure without any disruptions in the workflow and extract the required inventory information. 

Let’s understand this better with a case study from our experience. 

Real-Life Case Study 

We have a recurring client operating in the real estate industry. Let’s call them “A Home”. They have daily data requirements with an odd use case. 

Let’s go through their process step by step. 


We get a folder with input to add, daily. The input contains data extracted from MLS Listing. It contains property ID, address city, address state, address zip, and address state in a CSV file. 

(Keep in mind that the dataset that we get contains thousands of properties and each of their details and the picture above is only a sample representation.) 


What they want us to do next is enter these data points collected from MLS listings into a real estate listing site’s search bar. 

So we enter the city, street, state, and zip code of each property on the real estate listing site. 



Once we do that we directly reach the page with property details. From there, what they want is the property’s first image. 

Yes, that is all they want. They want the URL of only the first image of the property. 

Now, what makes us put our thinking cap on is what they would use the data for. 

Visual content like image data is typically utilized for marketing and advertising purposes. 

So, companies can use these images in their marketing strategies as promotional materials and advertisements to attract potential customers. Such high-quality images make the inventory look more appealing and increase the chances of conversions. 

Image URLs also allow users to filter and search for properties based on visual criteria. Particularly, when they already have preferences regarding the appearance of their future homes. 

Additionally, by analyzing the images that appear prominently for the specific keyword, the company can strategize to ensure that their own images are the ones that show up when the keyword is searched and increase their Share of Voice (SOV). 

Hence, businesses can refine their image optimization strategies, improve image visibility, and attract more traffic by analyzing the best-performing images. 

Now, they are more likely to gain customers since people are instinctively drawn towards the offer that has visually appealing content compared to its competitors who only provide limited information. 


Let’s get back to the topic. After we reach the property detail page, the next thing we do is click on the first image of the property listing, open it in a new tab, and copy the URL. 


Finally, we include the image URL of each property in the respective rows sent by the client in their first dataset, exactly in its place. 

Imagine doing this manually for thousands of properties. Impossible!

This is when RPA web scraping comes in handy. 

Robotic Process Automation

By incorporating RPA you can streamline the entire data extraction workflow. 

Each data from the provided data fields that had to be entered manually is now automatically integrated into the crawler written by Grepsr’s web scraping experts. 

This bot itself emulates human actions with the website, everything from entering the points, going to the next page, opening the image in the new tab, and collecting the URL address. 

This way we eliminate manual data entry errors, and extract the images with the help of the RPA bot’s smooth navigation of this dynamic website. 

Therefore, this case study sets an example of the power of utilizing RPA for a competitive edge. 

Grepsr‘s RPA web scraping saves time, effort, and resources by lowering the operational expenses of human resources. Whilst guaranteeing the reliability of data accuracy, quality, and unparalleled efficiency. 

Hit us up if you want to take your business to the next level of success with actionable data and data-driven growth!

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