In this episode of Financial Modeler's Corner, host Paul Barnhurst talks with Narek Grigorian, a seasoned financial modeler and investment professional with deep experience across real estate, energy, and biotech sectors. Narek shares candid stories about the worst financial model he ever worked on, one that looked polished but was fundamentally flawed, missing key components like taxes, depreciation, and tenant turnover. He explains how these mistakes can mislead decision-makers and why it’s critical to understand not just the numbers but also the assumptions behind them.

Narek Grigorian currently works at US Capital Global, a global investment banking firm focused on real estate and energy projects, particularly in the lower to middle market space. He holds degrees from Georgetown University and the University of Maryland and brings a passion for financial modeling, innovation, and teaching. Narek believes that while all models are inherently imperfect, they still provide valuable guidance, if built with thoughtful assumptions and continual refinement.

Expect to Learn:

Why can you never fully trust a model just because it looks polished.
The critical real estate assumptions that are often overlooked, like tenant turnover and escalating costs.
How to approach energy modeling with real-time data inputs and complex variables.
Why biotech models are uniquely challenging due to regulatory and clinical uncertainties.
Tips and shortcuts Narek uses in Excel to make modeling faster and more reliable.

Here are a few quotes from the episode:

““The toughest part is you have the wardrobe of clothes but have to create the person.” - Narek Grigorian
“Power Query and Power BI are essential tools for modern financial modelers.” - Narek Grigorian
“Vacancy rates and market growth assumptions are the most common pitfalls in real estate models.” - Narek Grigorian

Narek’s practical and approachable style makes this episode valuable for anyone involved in financial modeling whether you’re just starting out or have years of experience. His clear advice on key assumptions, Excel techniques, and industry-specific challenges offers useful takeaways to help you create more accurate and effective models.

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