Advanced Discounted Cash Flow (DCF) Model Concepts
If there is a more advanced DCF model out there, we haven’t seen it. Nor heard of it.
Our proprietary DCF analyses are more advanced than most if not all appraisal companies. They are also more advanced and report more pertinent results than most if not all DCF software analyses including Argus.
Our models are so advanced that an analyst can easily change cap rates and discount rates at the tenant level, or through time, if desired.
Our analyses are founded on the notion that tenant income and expenses can differ dramatically. As does tenant-level space timing, retention, renewal probability and value.
Another key ingredient to our proprietary models is actual lease integration including expense and expense reimbursement allocation.
Actual Lease Integration
In a perfect world, all tenants would have identical expense reimbursements and pro-rata shares.
In reality, this is seldom, if ever the case.
With most standard DCF software providers or models, it can be impossible to model actual lease documents with varying expense reimbursements. Software developers and DCF model developers often times glorify or simplify expense reporting for DCF analyses.
We have solved this problem by allowing for the integration of an unlimited array of income expense reimbursement scenarios. Our tenant-level expense inputs allow each line item to be sorted out if needed, for the tenant in-place and then changing to ‘market’ when vacant.
Our expense reimbursement sections even allow for upcoming lease renewal probabilities- if a renewal probability of say 70% is modeled for the tenant, it flows through to the expense reimbursements so that the total income stream related to the tenant and probability is in-line with what the analyst desires to be modeled.
Our tenant-level inputs allow for all clauses of the lease to be integrated into the valuation, including income and expenses and varying renewal probabilities for option periods.
Tenant-Level Value & Reporting
One of the huge benefits of our proprietary DCF model is that it breaks out value between each tenant or income/cash flow component.
By quickly and easily viewing discounted cash flow analyses for each tenant or cash flow, both on an aggregate dollar and dollar per square foot of rentable area basis, the analyst can more accurately and easily spot errors, understand specific value drivers, and understand specifically how much each tenant contributes to the total value.
This is a giant leap into understanding the how and why of the model.
The final valuation is also broken out per tenant:
The weighted average value conclusion per space can be concluded on an individual basis, or, simply all equal amongst the property.
If modeled outputs or values vary significantly between tenants, the analyst can either understand more quickly why values are different, or spot errors that need to be addressed.
This is far superior than seeing one final value conclusion with all tenants combined into one magic number. Most importantly, it limits errors.
For users of the appraisal like banks and owners, it helps understand risk and retention factors related to large and small users. If one tenant provides most of the value, it would make sense to try retain them with more aggressive renewal packages.
It also helps underwrite a deal- if most of the value comes from a credit tenant credit or a non-credit tenant, it is easier to justify higher or lower risk. Again, different discount rates and cap rates can be used per tenant, if desired.
Tenant-Level Value Conclusions
Our models automatically report a value for each tenant assuming a 3-year hold, 5-year hold, 10-year hold and the 1st year direct capitalization (direct cap) value. The direct cap value can be either stabilized (not including leaseup costs) or non-stabilized (deducting leaseup costs).
Weight can then be applied to each scenario depending on what is most applicable, or left at the default equal weight:
The reported values can also be overridden with any of the 30+ value scenarios, if desired.
The result is that the final value can be fine tuned down to the tenant-level.
Say, for instance, one space is fully stabilized and another space is fully vacant. In this case, it may make more sense to have the fully stabilized space value based on equal weight of all four value scenarios. But the vacant space may have a more applicable value if only looked at through the lense of the longer-term hold scenarios.
Or, perhaps the longer terms scenarios are determined to not be reliable and are too speculative. The value conclusion could be based more on short-term expectations like the 1st year direct cap value and 3 to 5 year holding periods.
Rather than having the final value a complex property come down to one scenario being applied to all tenants, our model allows for tenant-level value conclusions, fine tuned to their unique characteristics.
Our advanced DCF model reports over 30 values automatically, through time, for each tenant as well as the total property:
By showing value based on 1-10 year hold periods, the analyst can better understand how the discounting process and income assumptions change value through time. In some cases, it becomes easier to address speculative vs. known data and attribute more weight to any scenario that is more appropriate.
It is also important to properly apply cap rates to NOI streams, taking into account stabilization costs separately. Many leading software providers improperly capitalize before tax cash flow, not NOI. They can also capitalize NOI taking into account leaseup costs in NOI. Seldom if ever do they break out the direct cap calculation to understand what is actually going on.
All individual tenants, as well as the total DCF of all tenants combined, report advanced output pages:
This provides sophisticated insight into value drivers. It also helps spot errors in fields that may be higher or lower than expectations.
Take Year 1 Modeled Vacancy for instance. If the output for this factor shows much higher or lower than what the analyst originally expected, it can either provide a sound basis for understanding why the tenant value is higher or lower, or, be a red flag that perhaps their finger slipped or they entered a figure into the wrong input. If a stabilized space displayed over 10% vacancy, there could be an input error.
Total Modeled Vacancy is another crucial output. It can be very easy to double count vacancy factors or not take into account enough vacancy for non-stabilized properties. This output line displays how much vacancy was actually taken into account within the model, at a tenant-level and total property level. It is an output only. Not only is it a math check, but it provides a basis to understanding how much income will be lost per tenant and per the total property. If it is unusually high or low, inputs may be better if tweaked.
Summary & Conclusion
This post was written to provide a very brief overview of our advanced valuation techniques, but also, to show analysts and software companies what is truly possible with real estate valuation software.
We are actively seeking software partners to bring next-level real estate solutions to market.
Our DCF model is currently not available to post to the internet but we hope one day to offer it out.
This post was originally published and written on 4/25/2017.