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How a Property Management Firm Generated New Leads with Real Estate Data Extraction

Overview

Real estate data extraction is one of the most popular use cases we handle at Grepsr. Property intelligence companies and businesses alike rely on this data to access everything from pricing and property details to tenant history, financial records, and market trends. These insights are high stakes for having a competitive edge in the real estate industry.

 

In this case, a leading property management company approached us to collect property ownership transfer records (deeds) from five government websites. 

They requested structured property deeds data daily (covering the current day and the two previous days) from those sites, along with the supporting files that were attached in each record. 

 

Their goal was to identify new landlords and non-occupant owners so they could reach out and offer their property management services — turning public data into qualified sales leads.

Generating New Leads with Real Estate Data Extraction
Key points
  • A property management company needed fresh, organized property deeds data daily (covering the current day and the two previous days) from five county websites to find new leads and track ownership changes.
  • Since each run included records from three different days, the supporting deed documents with a unique identification number needed to be clearly grouped by date to avoid confusion.
  • Also, the sites frequently uploaded duplicate entries or blank documents, making clean data extraction challenging on our end.
  • Grepsr offered smart quality checks, sorted supporting PDFs into date-specific folders, and built filters to catch duplicates and blank files before delivery.
  • With clean, ready-to-use data delivered daily by Grepsr, the partner could spot new opportunities faster, reach out to new property owners, and grow their business.

Challenges

As soon as we heard that the targeted sites were government portals, we knew that there would be problems. So, the first thing that hit us when extracting the deeds data was navigating the unstructured website content and file availability. 

The five county systems published new deed records almost daily, and each supporting file came with a unique identifier. However, since each extraction run pulled data from the current day and the two previous days, the PDFs needed to be grouped by date to maintain clarity.

Next, there was a natural data redundancy pattern in the records because property deeds can involve multiple filings (e.g., corrections, amendments) under similar owner or property names, causing naturally overlapping entries. 

Not just that, some sites would temporarily upload incomplete or blank documents during the addition of new deed records. So when our crawlers were extracting the data, they captured those blank and incomplete files, which resulted in the final dataset being incomplete. 

But just like how every cloud has a silver lining, the challenges were quickly resolved by coming up with workarounds for each.

Great customer support when it’s needed. They are fast to reply, and fast to fix any problem we have had with design changes on a website we are scraping from. Their personal approach is what made me choose their service.

Bergur E. Business Owner, Furniture

150 K+

property deed records processed monthly

98.57 %

delivery accuracy

400 +

duplicate records filtered automatically

Solutions

To streamline review and delivery, we introduced a downstream process that automatically organized supporting files into date-specific folders, making it easier for them to trace each document back to its corresponding record. 

Now, instead of all PDFs landing in a single dump, each day’s files are neatly sorted into separate folders to find what they need, when they need it.

Next, we added a custom alert system to monitor supporting file counts. If any PDF goes missing or doesn’t match the expected number, the system flags it instantly, preventing silent errors from slipping through.

Then, we dealt with the issue of blank and incomplete files by implementing a file size threshold alert. In this, any PDF smaller than 300 bytes is marked as potentially invalid and is reviewed before delivery.

And for natural redundancy in property deed records, we built in smart deduplication logic to catch overlapping entries and filter them out. That way, our partner only receives clean and accurate data that’s ready for action.

Eventually, Grepsr ensured a streamlined, reliable, and fully automated data extraction process, giving the property management solution high-quality data it could use every day without any hiccups.

Solutions

Similar challenges faced across the industry:

Lack of technical know-how to automate routine data extractions

Businesses need fresh data to gather the best insights. To that end, one or two data extractions a day does not suffice. They need a system that can easily schedule crawl runs at specific intervals, as well as on demand.

Lack of resources - time, money and manpower - for data sourcing at scale

Data extraction is extremely tedious and highly error-prone. Most businesses lack the infrastructure to perform high volumes of data sourcing, and at a quality that yields the best results.

Overcoming data source restrictions

Most websites place limits on how many requests can be made in a set time period, and regularly block bots from accessing their content.

PROCESS

Getting started with Grepsr

Start with Grepsr in a few easy steps. Leave the data sourcing heavy lifting to us, so you can focus on innovation and growth.

1

Initial project consultation

First, we'll discuss the specifics of your web data needs and the KPIs you would like to have in order to ensure successful project execution.

2

Instrument web crawlers

We'll then set up automated extractions specific to your use-case, and send you a sample dataset before moving on to a full-scale crawl.

3

Begin data collection

Once you've approved the sample data, we will start scaling and performing the full run, and deliver the data in the agreed timeframe.

4

Hassle-free maintenance

Our team will ensure that all subsequent runs are running well, and that your data is delivered as scheduled with the least disruption.

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