$2B Multifamily Business Data and AI Enablement

With 5 PMSs, 10+ listing platforms, and separate marketing and finance data, no property had one agreed record. Even a simple question, like which listing platform brings the cheapest leases, meant matching data by hand across several systems.

What it meant for the business:

• Portfolio insights required combining data from multiple systems.
• Teams worked from inconsistent numbers, which limited leadership visibility.
• Analysts spent their time reconciling data instead of analyzing it.
• Budget and resource decisions lacked a shared data foundation.

The same scattered data blocked other questions too:

• Which properties are increasing marketing spend while signing fewer leases?
• How do occupancy and leasing trends compare across the portfolio?
• Where should next quarter’s marketing budget go?

Before: the cost-per-lease question meant exports from marketing, several PMSs, and finance, then manual matching in spreadsheets.

After: the same question goes into the AI assistant. The answer can include:

• Cost per lease for each listing platform, ranked
• A comparison with the portfolio average
• Follow-up questions in plain English, such as “Show me properties where spend rose but leases fell”
• Dashboards that track leasing, occupancy, and spend over time

The analyst reviews the answer instead of building it.

Beyond the numbers:

• One source of truth: every team works from the same property record and the same metrics.
• Smarter marketing spend: budget decisions are based on measured listing platform performance.
• Faster answers: portfolio questions are answered on demand, not after a round of exports.
• Less manual work: automated refresh, alerts, and notifications replace routine follow-up.
• A foundation for future AI: structured data supports new assistants, analytics, and automation.