About Client:
The client is a billion-dollar REIT, managing over 80,000 properties across 16 metropolitan markets nationwide.
Background:
Client’s Investment Management team relies on Property Scorecards to make high-stakes investment decisions. These scorecards bring together key data—financial performance (rent, occupancy, NOI, cash flow), operational insights (turnover, management efficiency), and market context (demographics, income levels, population trends). With everything in one place, the team gets a clear 360° view of each opportunity.
Previously, pulling these datasets was slow and inconsistent. Streamlining the process not only saved valuable time but also ensured every multi-million-dollar decision is backed by accurate, reliable intelligence, directly driving client’s portfolio growth, profitability, and market positioning.
Challenge:
For years, the Investment Management team built property scorecards manually to support Build-to-Rent (BTR) investment analysis. Each time a new community was under review, they had to gather lists of nearby properties (owned, JV-owned, or third-party managed), then calculate financial and operational metrics within 3, 5, and 10-mile radii. This process was:
- Manual & error-prone – A locally saved Python script had to be run by team members, leaving room for inconsistencies.
- Resource-heavy – Calculating property KPIs required cube processing and significant analyst time.
- Slow to update – Property lists in a separate schema had to be refreshed and joined with demographic data, while census data was updated only annually, delaying insights.
- Fragmented – Financial, operational, and demographic metrics were stored in different locations, across schemas, and at varied granularities, making it difficult to get a holistic view.
