How Market Leasing Assumptions Work in Commercial Real Estate
Market leasing assumptions define what happens after a tenant lease expires in a commercial property. Since it’s unknown whether the tenant will renew their lease, two sets of assumptions are typically used. One set applies if a new tenant needs to be found, while the second set is used if an existing tenant renews their lease. Between these two scenarios lies a renewal probability that creates a weighted average to model potential outcomes.
At a high level, the concept of market leasing assumptions is straightforward. However, the nuances involved with multiple leases, complex assumptions, and varying levels of uncertainty can lead even seasoned commercial real estate professionals to become confused. In this article, we’ll take a deep dive into market leasing assumptions, dispel common misconceptions, and illustrate the concepts with relevant examples.
Market Leasing Assumptions: Office Building Example Part 1
To set the stage for our discussion, let’s begin with a simplified office building case study. Consider a 15,000 square foot building analyzed starting January 1, 2017, with the following rent roll:
- A large tenant occupying 7,500 square feet
- A medium-sized tenant occupying 5,000 square feet
- A small tenant occupying 2,500 square feet
The large tenant’s lease is set to expire in three years on December 31, 2019, meanwhile the medium-sized tenant’s lease expires the following year on December 31, 2020. Lastly, the small tenant’s lease will end a year later, on December 31, 2021.
Both the large and medium-sized tenants will experience an annual rent escalation of 3%. The small tenant too will see the same, but only during the second and third years of their lease. To streamline our analysis, we’ll disregard any reimbursements, leasing commissions, or tenant improvements.
Expense Assumptions
Now, let’s consider the following projected expenses:
- Property Taxes: $55,000 per year, escalating at 3% annually
- Insurance: $15,000 per year, escalating at 3% annually
- Maintenance: $25,000 per year, escalating at 3% annually
- Miscellaneous Expenses: $12,000 per year, escalating at 3% annually
Given the assumptions above, we can draft a preliminary ten-year pro forma starting January 1, 2017:
Starting in year four, we begin to encounter a significant lease rollover risk within this property. With one lease expiring in year four, another in year five, and a third in year six, the question arises: what will occur once these leases expire and how do we account for this in our ongoing analysis?
Understanding Market Leasing Assumptions
Before we finalize our analysis, it’s crucial to dive deeper into how market leasing assumptions influence this scenario. When considering scenarios in which leases may or may not renew, we utilize market leasing assumptions to evaluate worst-case and best-case projections.
Let’s explore some key components of these assumptions:
- Renewal Probabilities: It is essential to quantify the likelihood of a tenant renewing their lease. This takes into account the health of the business, market conditions, and the terms of their current lease.
- Market Rates: Determining the current rental rates in the market is vital, as this will influence the attractiveness of your property to potential new tenants.
- Vacancy Periods: Estimating how long the property might be vacant between tenants is a critical component that will directly affect cash flow.
Through these factors, we can arrive at projections that reflect potential rental income under current market conditions, as well as costs associated with obtaining new tenants or accommodating renewing tenants. This balanced perspective is crucial for making informed investment decisions.
Conclusion
In summary, market leasing assumptions present a vital framework within commercial real estate. The management of lease expirations, tenant renewals, and associated risks are essential for maximizing returns and managing cash flow effectively. As we proceed in this series, we’ll delve further into more complex scenarios and provide comprehensive case studies that illustrate the impact of these assumptions in real-time decision-making.
