The Most Important Things to Keep In Mind When Real Estate Financial Modeling

The Most Important Things to Keep In Mind When Real Estate Financial Modeling

Real estate financial models are often the basis for decisions made by investors during all phases, pre- and post-acquisition, ranging from purchase and sale pricing, levels of renovation, capital expenditure decisions, loan considerations, and much more. 

Since a lot is riding on the accuracy of these models, it’s important that they not only reflect the assumptions that the investors intended (data-entry accuracy), but also include prompts for information that result in a comprehensive set of results from which an informed decision can be made (model selection). Aside from these two fundamental requirements, there are many factors within an investor or broker’s control that can lead to better models. 

Here are some of the best practices for real estate financial modeling: 

  1. Use the highest quality data you can for inputs. 

    By highest quality I mean the most accurate and/or justifiably reasonable. This is critical when inputting both factual data, like square footage and lot size, and assumptions, like projected project costs, future interest rates, pricing, and more. 

    Remember, a top rule of thumb for financial modeling is “garbage in, garbage out.” The lower the quality of the inputs, the lower the quality of the outputs (model results). With that being said, it’s best to make sure you’re using the most recent data available. If you are revisiting a model you did over one month ago, it’s worth going through all of your inputs and assumptions and making sure everything is still as accurate as it can be. Assume that every input will be questioned by an outsider and you will need to justify. And whenever possible, use source documents for the property being modeled. 
  2. Focus on the end-goal

    Determine which investment metrics and/or other data sets you will focus on and make sure you are working with a model that can accurately produce what's needed. 

    It is easy to get distracted by the plethora of figures that are easily generated by models. Before running a model, be sure to identify what question you are asking and what result(s) you are seeking. You should also identify any potential shortcomings of the model and/or questions you are asking—the information they don’t provide is as important to be aware of as what they do provide.
  3. Trust, but verify

    This applies both to information provided by someone else that you use in a model, and to models created by someone else. If a model was created by someone else, go through it with a fine-toothed comb.

    Remember, one faulty, overly-aggressive or overly-pessimistic assumption can have a significant impact on model results. Never assume that someone's market assumptions are correct—including ones related to leasing, rent growth, expense growth, future values, resale costs/timing, etc. 

    If a deal seems too good to be true, you may want to act quickly and decisively before someone else does. In this case, it’s always best to get second and third opinions fast from people who know the niche you are focusing on well. Get them the facts about the property, your model assumptions, and the backup data used to produce your assumptions, so they can comb through it and see if they agree.

    “Many a fortune was made by acting quickly.” —Someone smart
  4. Beware of hidden and less-common costs or expense line-items

    Items like downtime between tenants, turnover costs, leasing commissions, etc. are often unintentionally omitted from analyses—even by the savviest investors. If appropriate given the situation in which a model is needed, be aware of tax implications and the impacts of inflation on cash flow and future liabilities. 
  5. Hope for the best, and plan for less-than-ideal outcomes

    Account for the possibility of being incorrect and/or being correct but the market changing dramatically. Use sensitivity analyses or multiple versions to establish a range and, if appropriate, assign probabilities and get a weighted best guess. 
  6. Don’t overthink it, don’t underthink it

    Apply the 80/20 principle, but only when appropriate. When it comes to applying the 80/20 principle in modeling, one can get 80% of the output (desired result) with only 20% of the effort/inputs. There are times where a simple model will suffice to provide an investor with what is needed to make a decision. Strike a balance between enough detail to get an accurate picture and not more than those affected request and/or need. Remember, since assumptions are often incorrect, tweaking inputs in painstaking detail is often a fruitless effort.
  7. “Measure twice, cut once”

    Double (and triple) check your inputs—both facts and assumptions. Even the best, smartest, savviest real estate analysts enter information incorrectly. Double checking entries is annoying, but it’s worth doing every time.
  8. Stay organized

    Make sure that each version of a model is named appropriately and, if possible, accompanied by a description of what is being modeled. This is important not only for easy retrieval and editing but also to ensure that the correct version is shared with others. Sending an incorrect model to partners, lenders, clients or others can cause big problems!
  9. Revisit previously run models

    Going back through previous models and checking to see how assumptions lined up with reality is a great way to monitor oneself. By looking at trends (e.g. consistently underestimating a renovation budget by 10% or being too aggressive on market rent growth assumptions) you will be able to see potential flaws and adjust future assumptions accordingly.
  10. Remember: the future will almost always be different than what the model suggests

    This isn’t necessarily because you aren’t good at modeling, but because everyone—even the best of the best—have to make guesses. The key is to a find a balance of conservative, realistic, and probable. Too conservatively and it’ll be hard to buy deals, to aggressive and things get ugly quickly if the market turns…especially if you’re highly leveraged.
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