Listing Manager: National MLS Platform · Cassandra St. Fort
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Listing Manager: A National Real Estate Listings Platform

Built from scratch. Scaled MLS by MLS. Live nationwide.

A first-of-its-kind self-serve listings platform that let real estate agents submit their own MLS listings for review and publish, starting with a NorthStar MLS pilot and scaling to 7+ MLSs and 10K+ agents.

Scope10K+ Agents · 7+ MLS Integrations
ApproachPhased, MLS by MLS
Outcome6 Days per MLS, Was 6 Weeks
01Situation

Nothing like this existed before. Agents had no self-serve way to submit listings, and every MLS had its own rules for how a listing needed to be entered.

This was new ground for the organization and the industry, so it had to be built from scratch: a self-serve platform where agents entered their own listings, we reviewed each one, and published it to the MLS on their behalf.

Every MLS added its own layer of complexity. Each one required a different set of fields, its own rules for what was required versus optional, and its own credentialing process just to gain access to its staging site.

Why it mattered

Manual, MLS-by-MLS onboarding doesn't scale past a handful of markets. Without a self-serve platform, growth into new metros meant growth in manual work, not just growth in agents.

02Action

Led a cross-functional team of 26 across 6 disciplines through a phased rollout, starting with a NorthStar MLS pilot and scaling MLS by MLS.

1
Built the platform from the ground up: a self-serve site where agents entered their own listings, which were then reviewed and published to the MLS on their behalf.
2
Ran joint sprint planning across Engineering, RPA, Data Science, MLS Credentialing, and the LBC team to align five groups on one roadmap and manage the dependencies between them.
3
Replaced manual, business-analyst-driven MLS scraping, which was error prone and labor intensive, with data science and AI-driven scraping.
4
Launched a NorthStar MLS pilot, then scaled to 7+ MLSs, reaching 10K+ agents across Florida metro markets and Minnesota.
5
Managed a 26-person team across Engineering, Data Science, RPA, Architecture, UX, and the LBC team.
03Challenge

This wasn't a rollout of an existing tool. It was building the tool, and the credentialing process behind it, from nothing.

  • Aligning Engineering, RPA, Data Science, MLS Credentialing, and the LBC team on one roadmap, with dependencies running in every direction
  • Replacing manual, error-prone MLS scraping with data science and AI, after years of business analysts doing it by hand
  • Every MLS required its own set of required and optional fields, so no single entry form worked across markets
  • MLS credentialing could only be done by one trained person with access to that MLS's staging site, a single point of failure
  • Building operations support into the go-to-market plan for the moments RPA failed and an agent needed a person in their metro or region office
04Results
10K+real estate agents reached
7+MLSs integrated and live
6 Daysto onboard a new MLS, down from 6 weeks
~86%reduction in MLS onboarding time

Listing Manager didn't exist before this team built it. Going from 3 sprints to 6 days per MLS wasn't a tweak, it was proof that manual, one-person-at-a-time credentialing could be replaced with a system that scaled.