How do you model multiple properties in a portfolio and aggregate into portfolio metrics?

• A. Build a single model for all properties using weighted averages; compute portfolio metrics.
• B. Model properties separately and only sum cash flows; ignore diversification.
• C. Build individual property models; sum NOI, cash flows, capex; compute portfolio-level IRR, cash-on-cash, DSCR; apply diversification and correlation.
• D. Use random numbers to generate portfolio metrics.

Answer: (C)

Modeling multiple properties in a portfolio relies on preserving each property's unique cash-flow drivers and then aggregating them in a way that reflects diversification and how properties interact through timing and leverage. The best approach is to build individual property models, sum NOI, operating cash flows, and capex across all properties, and then compute portfolio-level metrics such as IRR, cash-on-cash return, and DSCR. By doing this, you keep the timing of cash flows and the financing structure intact for each property, while also capturing how they combine at the portfolio level. Importantly, you incorporate diversification and correlation across properties, recognizing that cash flows may move together in some scenarios but differently in others, which affects overall risk and return.

This approach beats the idea of a single model built from weighted averages, which can smooth out timing differences and obscure how each property contributes to risk and return. It also outperforms only summing cash flows while ignoring diversification, which misses the benefit (and risk) reduction that comes from owning multiple, differently behaved assets. Relying on random numbers provides no disciplined foundation or replicable drivers, making portfolio metrics unreliable for decision-making.