Introduction
Client Overview:
The client, an Ontario-based open-ended mutual fund, focuses on real estate investments, acquiring high-quality rental housing through impact-driven partnerships. Their $301M NAV and $554M AUM portfolio includes PBSA, multi-family housing, and micro-units, delivering a 10.51% net return since inception.
Need:
The client struggled with managing their real estate portfolio due to manual processes in Debt Summary, Rent Roll, and NOI variance analysis. Errors arose from manual updates and inconsistent reporting between Entrata and Yardi systems. They needed an automated solution for accurate, real-time insights and streamlined data consolidation.
Solution:
OHI deployed a team to automate the client’s financial processes, integrating debt summaries, rent roll comparisons, and occupancy analysis into a unified system. Automation eliminated manual updates, ensuring accuracy and efficiency. A real-time dashboard and standardized data inputs enhanced decision-making and data integrity.
Challenges:
Data Integration:
Combining data from different systems (Entrata and Yardi) to provide a unified view of the property and financial metrics was complex and there was a requirement of mapping of accounts to make them in line and uniform.
Data Handling:
To make historical comparisons, such as comparing previous rent with new rent and debt schedules, it was a necessity of a new layer of complexity as the original models were not tracking the past records effectively. Creating a framework to store and analyze historical data alongside current metrics posed a challenge.
Consistency in all Models:
We had to ensure that all models (debt summary, rent roll comparison, KPI dashboard, MTM Model, etc.) follow consistent structures and can be easily updated without breaking the flow of data, especially when dealing with changes in rent rolls, occupancy summaries, and loan details.
Automation in Processes:
It was challenging to automate the highly manual system but required building robust models in Excel and Power Query to handle large datasets and multiple properties. We had to ensure that automation could handle dynamic data inputs while updating the models.
Reducing Manual Errors:
Moving from manual entry to automation, was a challenge but ensuring that data was consistently accurate, required extensive testing and refinement to avoid discrepancies and errors to get accurate results.
Results
Debt Summary Model:
Automated loan detail and debt rate updates, centralized input summaries for seamless monthly updates, integrated historical mortgage data for trend analysis, and implemented instant property value updates, minimizing manual work and enhancing financial accuracy.
Meeting Template for Monthly Meeting (MTM) Model:
Integrated data from Entrata and Yardi into a unified template, removing inconsistencies and standardizing variance comment submissions.
KPI Dashboard:
Consolidated data from multiple properties into an easy-to-read format, provided visual insights for key performance indicators over 12 months, enabled comprehensive portfolio-level analysis, and facilitated historical comparisons for informed decision-making.
AMR Report (Annual Rent Roll Comparison):
Automated rent roll data aggregation using Power Query, introduced dynamic data slicing by month, provided immediate visibility into rent gains/losses, and reduced error risk by automating data consolidation.
Overall Impact:
Achieved significant time savings, improved data accuracy through automation, enabled better decision-making with real-time insights, and ensured scalability for adding new properties or data updates seamlessly.
If you are interested in knowing more about how OHI can help your organization, reach out for a customized cost-benefit analysis tailored to your needs.
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