Why Real Estate Financial Models are more complex than models for other assets?

By Dr Nikodem Szumilo and Natalie Bayfield

Real Estate is the dark art of the ‘Ten Mil’ asset deal done in the champagne bar, using nothing more than a multiple of income to justify the sale. Real Estate is an agents game. After all Trump hasn’t written any books on financial modelling. No wonder it is considered the third asset by the rest of the financial community. But in fact, Real Estate Asset Modelling can be complicated and there is a very good argument to be made that it is more complex than for any other asset class.

The first point to be noted is the duality of Real Estate income streams. Real Estate income has characteristics similar to those generated by bonds as well as equities.

A building occupied by a government agency on a perpetual lease can be used as a simple example of a cash flow stream that resembles a risk-free security with a coupon. Both generate income which is easy to predict and will be received by the owner of the property/security indefinitely. In this case, the financial model for the building is relatively simple as the only additional variables that need to be considered are operating expenses and depreciation. As their current and expected values are easily estimated, the risk-free rate of return could be easily applied to estimate the value of the property.

However, most leases have rents that are more complex than a coupon payment and tenants which are not free from default risk. This introduces uncertainty and complexity into the income stream making both the cash flow and the discount rate more difficult to model. In the UK a standard institutional lease might be reviewed on an upwards-only basis every 5 years. This means that the revenue is guaranteed to be no less than a certain amount, but can still rise. The possible increase in rent needs to be reflected in financial models but requires a different discount rate than the risk-free portion of the revenue. Consequently, having an increasing rent shifts the expected value of future income upwards but corresponds to a higher overall discount rate.

The discount rate also needs to reflect the credit risk of the tenant. While the minimal contracted rental can be discounted using the same techniques as a perpetual bond the rate will be calculated differently. In the bond market credit spread is calculated based on the risk of default and the percentage of the face value of the bond that can be recovered. For Real Estate the recoverable amount is the value the building when tenant defaults (which depends on the value of a lease that can be agreed with someone else at that time). Consequently, the coupon-like cash flow should be discounted based strictly using the probability of tenant default but the recoverable value is linked to market conditions at the time of default (and not dependent on the tenant).

The discount rate is therefore determined by the risk free rate, the default probability of the tenant and asset-specific risk defined by market conditions. While the first two factors resemble discounts used for fixed-income securities, exposure to marked conditions suggests that there is a component that should be priced similarly to equities. As market conditions define the expected value of future income and asset value in case of tenant default, financial models need to include their predictions. However, the expected level of income has to be discounted using a rate that reflects its sensitivity to the market. This means that, calculating the appropriate rate requires measuring the exposure of the asset value to changes in the market.

Market-risk exposure for stocks thus can be estimated using various asset pricing models. However, these are very difficult to apply in Real Estate due to some market inefficiencies such as low liquidity or asymmetric information. Consequently, Real Estate Financial Models are complex not just because the peculiar characteristics of Real Estate income streams require us to borrow and amend techniques from other asset classes. The complexity arising from inherent Real Estate market limitations also make the selection of an appropriate discount rate a rather granular and detailed exercise. Perhaps the finger in the air approach doesn’t look so bad after all?

Natalie Bayfield is Chairwoman of Bayfield Training and a pioneer in Real Estate Financial Modelling.

Niko will be speaking about the complexity of Real Estate Financial Modelling at the Financial Modelling World Championships and Global Training Camp (GTC) in London, Tuesday 6th December 2016. If you would like to attend the GTC, meet Niko, Natalie and other world class financial analysts please visit the ModelOff website at and enter the discount code BayfieldGTC16. And don’t worry if you missed it. There are camps coming up in Sydney, Toronto, New York and Hong Kong. See the website for details.