My real estate development experience has primarily involved medium to large-sized residential projects. I’ve learned that regardless of the real estate sector or whether the goal is acquisition, resale, or long-term ownership, the same best practices apply to ensure the model’s accuracy and efficiency.
Best practices in real estate financial modeling maximize a project's potential success. The key to all best practices is their simplicity, clarity, accuracy, and efficiency. By focusing on these qualities, the model will provide investors with a reliable framework ensuring sound decision-making.
Are you planning to get into real estate as an investment banker, lender, developer, or a potential owner? Don’t underestimate the importance of a financial model that can provide vital information regarding the risks and potential rewards. What are the best practices that make that model accurate and efficient?
Best Practices in Creating an Accurate and Efficient Model
The thread that runs through the best practices is their focus on consistency, clarity, reliability, and flexibility. These are some of the most relevant ones in more detail:
Establish the Reason for the Model
All real estate financial models are mathematical representations (usually in Excel spreadsheet format) of a real estate project. There are several types of models, each with its own objectives:
- Development models where the project involves the construction of residential, commercial, or industrial properties. These models include variables such as land and construction costs, financing costs, marketing expenses, and operating costs over the period needed to complete the project.
- Acquisition models examine the potential returns of purchasing an existing property to either hold, renovate, or resell.
- Cash flow models analyze income and expenses over a specific timeframe to determine an investment's viability in terms of its funding capability.
- Sensitivity Analysis Models evaluate how changes in key variables such as interest rates, market conditions, and construction costs can affect the potential profitability of a real estate investment.
You need to clearly define the purpose of your model and structure it to provide the best information for your specific project.
Keep Your Model Simple
In my experience, most real estate investors, developers, and lenders are more comfortable with the marketing, design, and construction aspects of real estate than with mathematically oriented financial models.
That’s why simplicity and clarity in your model design are so important. While models deal with many interactive variables, the most efficient ones avoid unnecessary complexity. A clear, simple model is easier to understand and less likely to have errors.
Although Excel has limitations regarding formatting, it offers convenient ways of labeling, color-coding, and organizing the sections of your model so that they are easy to understand and navigate. Remember that the model is a communication tool to help non-mathematical decision-makers justify their investment in the project.
Ensure that Data Used in the Model is Valid
Your model is only as reliable as the data used in its application. The following variables are just some of the data used in creating a real estate financial model:
- Rental rates
- Occupancy levels
- Building costs
- Taxation
- Market-related data
- Financing terms
You will need to research the many data sources to find the most reliable. These may include local and national associations such as the National Association of Realtors (NAR) and National Association of Home Builders (NAHB) and companies such as Bloomberg and Fannie Mae, to name a few.
Keep a Close Track of Assumptions
The assumptions are all the key variables and parameters that form the foundation of your financial model. This includes purchase price, financing terms, rental, occupancy, expenses, and exit cap rates.
To track changes, you should document all assumptions fully on a separate sheet. Take a few seconds to record the changes made to the model from time to time. This will be invaluable if you need to retrace, reverse, or modify changes.
You should also introduce version control as you create different scenarios using the updated values. Retain and record each version—comparing them will give you a clear picture of how changing conditions affect the project's viability.
Use Sensitivity Analysis to Test the Flexibility of Your Model
Because a project may extend over a period of years, there is a high probability that assumed values will change considerably during that time. Sensitivity analysis allows you to measure how much the rate of return or profitability is affected by the changes in each value.
In the simplest terms, sensitivity analysis can help you assess your model's reliability and identify the key factors that drive its outcomes.
Scenario analysis can enhance accuracy when coupled with sensitivity analysis. In this method, you adjust inputs to establish a “best case/ worst case” comparison.
Validate Your Model
The real estate financial model is a tool for making critical financial decisions. It’s essential that you first test the formulae, calculations, and logic used in the model by running a simulation and checking the results against historical industry data and established market values. This validation process, when done correctly, can instill confidence in the reliability of your model and the decisions it supports.
Review and Update Your Model
The accuracy and efficiency of your model can be checked and reviewed by:
- Getting colleagues in the real estate industry to run your model using their own data and to report back to you with any inaccuracies or errors they pick up, or
- Reviewing the model's performance against actual data, analyzing the discrepancies, and adjusting the assumptions accordingly.
You can implement updates regularly by:
- Updating variables from new local market data and information to make your model more accurate.
- Modifying assumptions to allow for macroeconomic changes in the economy.
Research Tools other than Excel for Complex Modelling
Excel is widely used for real estate financial modeling, but if the data and calculations are complex and require a more advanced tool, I recommend researching specialized software and programming languages such as Python to maximize the accuracy and efficiency of your model.
Conclusion
Best practices for creating an efficient and accurate real estate financial model center on ensuring the integrity of the input data. Calculations should be kept as simple as possible, assumptions should be verified and made reliable, and data should be constantly reviewed and updated.