From one source to a multi-source real-estate workflow.
An engagement that began with Flexmls expanded to Zillow and WIREdata, adding cross-source analysis and automated delivery to Google Sheets.
Discuss a similar data workflowFlexmls · Zillow · WIREdata
A repeatable real-estate research workflow delivered to Google Sheets.
Start with the data the client needs now.
The initial requirement focused on Flexmls. The first source established a working path from acquisition to the client’s existing tools.
As the engagement developed, the client needed a broader view. The scope expanded to Zillow and WIREdata, with analysis across the source datasets and automated Google Sheets delivery. Each new source had a reason to enter the workflow: it contributed information the initial source alone could not supply.
More sources become more useful together.
Establish the first source
Build the access and delivery path around the initial Flexmls requirement.
Add complementary sources
Extend acquisition to Zillow and WIREdata, accounting for each platform’s structure and access requirements.
Connect the workflow
Bring source data into cross-source analysis and deliver the results automatically to Google Sheets.
Three source connections by themselves are not the result. The useful result is a research process that can compare or combine their data and place the output where the client already makes decisions.
The destination shaped the system.
Google Sheets was part of the delivered workflow, not an afterthought or a generic CSV export. Automated delivery put the cross-source analysis in a form the client could use without switching research tools.
Expanding a source mix and operating it over time are related but different scopes. This case demonstrates the expansion from Flexmls to two complementary sources and the resulting analysis workflow; it does not claim a particular refresh cadence or service level that has not been specified for the engagement.
Expansion is part of the architecture.
A single-source project became a multi-source data workflow. The value grew through coverage, integration and delivery, rather than ending with the first scraper.
This is the engagement pattern ScrapingDome is built for: establish a useful data source, then extend the data layer as the business needs more from it.