I originally built this real estate financial model to be an equity ramping non-debt option feasibility study tool. However, I've been trying to find the time over the last year to upgrade the template so it works exactly the same except there would be an option to finance some percentage of the total cost of each deal. Up to 6 deals can be modeled over a continuous 15-year timeframe, including options for joint venture waterfalls with IRR hurdles.
Updated Template: Self-storage Model
Every single other real estate model I've done has an option for debt financing, so now I can include this self-storage model in that group. In this update I make an input where the user defines the percentage of the total costs that are financed. This rate applies to all deals.
I did a few other updates that were complementary to this. They include adding a sensitivity table that shows 36 different IRRs at the project level (before any joint venture distributions) based on 6 varying debt financing percentages and 6 different exit cap rates. You will also see a debt service coverage ratio on each of the monthly details for each deal.
When you start to use leverage, occupancy rates, rent rates, and expenses become much more important when figuring out what is feasible.
Note, if you still don't want to model debt financing, you can simply make the input for that assumption 0% and the model will run exactly as it did before.
This update took about 2 hours to complete.
Check out all real estate models here.
Why is it important to include debt financing in real estate models?
Modeling debt financing in a real estate model is critical for various reasons, reflecting both the unique characteristics of real estate investments and the broader financial context. Here's why it's significant:
Leverage: Real estate investments often involve significant amounts of debt, also known as leverage. By modeling debt financing, investors and developers can better understand the impact of borrowing on the potential returns and risks of an investment
Cash Flow Analysis: Debt financing has direct implications for an investment's cash flows, affecting interest payments, amortization schedules, and covenants. Understanding these dynamics is essential for evaluating the sustainability and profitability of a real estate investment.
Risk Assessment: Borrowing increases the financial risk of an investment. By incorporating debt into a model, investors can analyze different scenarios (like changes in interest rates or property values) to understand how they might impact the ability to meet debt obligations and the overall risk profile of the investment.
Tax Implications: Interest payments on debt may have tax implications that need to be considered within a real estate model. In many jurisdictions, interest expenses are tax-deductible, which can affect the project's after-tax returns.
Investor Considerations: Different investors have different risk tolerances and return expectations. By modeling debt, a developer or investor can tailor the capital structure to meet specific investor needs and preferences, potentially making the investment more appealing to a broader investor base.
Regulatory Compliance: Depending on the jurisdiction and type of real estate (e.g., commercial vs. residential), there may be legal and regulatory considerations related to the amount and type of debt that can be used. Modeling this accurately is necessary for compliance.
Loan-to-Value Ratios (LTV): Lenders often use metrics like LTV to assess the risk of a loan. Modeling debt helps in understanding these ratios and ensures that the project stays within the limits set by lenders.
Sensitivity Analysis: A well-constructed real estate model with debt financing allows for sensitivity analysis, letting investors and developers see how changes in key variables like interest rates, occupancy rates, or other economic factors might affect the investment.
Alignment with Market Practices: Since debt financing is common in real estate, modeling it provides a more realistic representation of how deals are structured in practice. It aligns the model with market norms and standards, making it more useful and credible.
