Differences between Direct and Sublease Asking Rents Yield Insights into the Strength (or Weakness) of Houston’s Office Market
October 2016 I Vol. 2, ISSUE 10 I Download PDF
Houston’s office market undergoes cycles, whereby the supply and demand of office space rises, peaks, falls, and bottoms over a repeating seven-to-eight year period of time. While key CRE metrics (e.g., vacancy) show such market cycles, asking rents do not tend to fluctuate accordingly. This is largely because asking rents do not reflect free-market principles that manifest in realized leases, effective rents, and concessions. In light of this, an instructive approach is to examine how meaningful information can be extracted from asking rent data to guide professionals in assessing changes in market rents.
Landlords representing direct space rarely advertise asking rents that are at the bottom of the range they are willing to accept. For this reason, gross and base (NNN) asking rents for direct space have an upward trend, with little change during down markets. For example, direct asking rents of Class A space in Figure 1A over the past two-year downturn in Houston show a change of only $1.21, whereas actual effective rents of closed deals are down $6 to $8 per sq. ft. on average. In contrast with landlords, tenants subleasing their office space have a different mindset guiding their economic decisions, leading them to be more forthright with their asking rents. As a result, sublease asking rents for both Class A and Class B space tend to fluctuate more with true market conditions, as seen with the current and past downturns in Houston (Figure 1A).
Despite limitations of asking rent data, we can derive more information in market conditions and rent trends through additional analyses of the data. Specifically, if we take the difference between direct and sublease base rent and express it as a percent of direct base rent, then much stronger shifts in asking rents can be seen, reflecting truer market conditions (Figure 1B). The greater the percent difference between direct and sublease asking rents, the more likely landlords will discount their asking direct rents, and hence the softer are market conditions. Currently, there is a 35% gap between sublease and direct base asking rents for Class A buildings, the largest percent difference between sublease and direct based asking rents that has been seen over the past 17 years. Likewise, the Class B market has softened but with only a 22.8% difference between direct and sublease base asking rents. In sum, Houston’s Class B market is helping to stabilize the more dramatic downturn in Class A buildings.
Houston’s office market undergoes cycles whereby it rises, peaks, falls, and bottoms, a cycle that repeats itself through time. Vacancy, as a percentage of stock inventory, is a key indicator of market dynamics, as it measures changes in supply through new construction and demand through net absorption. Class A and B buildings show market cycles in their vacancies of about seven to eight years, as seen from 2000 to 2007 and from 2007 to 2014 (Figure 2). Because percent vacancy is inversely related to the phase of the market cycle, increases in vacancy rates equate with a falling or bottoming market, whereas decreases in vacancy rates equate with a rising or peaking market.
Houston is well into the falling phase of its office market cycle, a market environment which favors tenants over landlords. In a free market, rent prices should reflect the cyclical dynamics of supply and demand of office space. This is difficult to assess, however, as most available data are for asking rents, rather than realized effective rents of closed deals. Landlords that represent direct space rarely advertise asking rents that are at the bottom end of the range that they are willing to accept. For this reason, gross and NNN asking rents by landlords for direct space have a steady upward trend, showing little change during down markets (Figure 3).
In contrast, tenants subleasing their office space tend to have a different economic mindset that guides their asking rents, leading them to be more forthright with their quoted asking rents. As a result, differences between direct and sublease asking rents can provide real insights into office market conditions that more closely align with actual market cycles. Here, we examine how meaningful information on market conditions and market rents can be extracted from asking rent data by conducting an additional set of analyses on available asking rent data.
The primary difference between gross and base (NNN) asking rents are the various expenses associated with office occupancy, including property taxes, utilities, insurance, and maintenance. As shown in Figure 3A, direct gross asking rents show a steady upward climb, with little to no declines for Class A or Class B space over the past 16 years. To this end, gross asking rents for direct office space do not reflect market conditions or rent trends as evidenced by real supply and demand that manifest in vacancy measures (Figure 2). For these reasons, direct gross asking rents give little insight into actual market conditions.
Direct base (NNN) asking rents do show some modest changes in asking rents, but such variation is small compared with market conditions that manifest in realized leases. For example, direct base asking rents of Class A space in Figure 3B over the past two-year downturn in Houston show a change of only about $1.21, whereas effective rents of realized deals are down closer to $6 to $8 on average. While direct base asking rents do fluctuate with market conditions, such shifts are not on scale with actual market conditions.
IIn contrast with direct asking rents, sublease asking rents show important temporal changes with shifts in market conditions, for both gross and base sublease asking rates and both Class A and Class B buildings (Figure 3A and 3B). This is not surprising given that tenants putting their office space up for sublease are likely more forthcoming in their asking rates given their motivation to diminish their financial obligations with the lease. Nevertheless, gross sublease asking rents for Class A and Class B office products tend to be slightly less revealing of actual market conditions than base (NNN) asking rents, as indicated by the greater fluctuations in sublease base asking rents. Sublease base asking rents for Class A and Class B space tend to fluctuate more with market conditions, as seen with the downturn of past two years in Figure 3.
Given that base sublease asking rents are most responsive to market conditions, we have developed an index that is indicative of market trends and fluctuations in rents. As both direct and sublease base asking rents show temporal variation, we can take the difference between the sublease base asking rent and the direct base asking rent (Figure 4A) to yield a spread between direct and sublease base asking rents. We then divide that dollar value by direct base asking rent and multiply by 100 to get a percentage of sublease base asking rent relative to direct base asking rents (Figure 4B). The higher this percent difference of sublease from direct base asking rent, the more likely landlords will discount their asking direct rents, and hence the softer the market conditions and rents. Values that near zero indicate scenarios in which the sublet market is as strong as the direct market.
Since Houston’s falling office market began in late 2014, the difference between direct and sublease base asking rents for Class A buildings has grown from less than $3.77 to $8.52. Historically, Class A buildings have shown an average of $3.52 difference between direct and sublease base asking rents, with a 95% confidence interval of $3.15 to $3.89. At the current $8.52, Houston’s Class A rents are way outside this expected range (Figure 4A). In Q1 2015, sublease base asking rents were only 15% different from direct base asking rents. Currently, there is a 35% gap between sublease and direct base asking rents for Class A buildings, the largest percent difference between sublease and direct based asking rents that has been seen over the past 17 years. (Figure 4B).
Similarly for Class B buildings, the difference between direct and sublease base asking rents has grown from $0.82 to $4.29 since 2014. Historically, Class B buildings have shown an average of $1.97 difference between direct and sublease base asking rents, with a 95% confidence interval of $1.62 to $2.21. At the current $4.29 difference, Houston’s Class B rents are outside of this range (Figure 4A). Sublease base asking rents in Q1 2015 for Class B buildings were only 4% different from direct base asking rents. Currently, sublease base asking rents are 22% of direct base asking rents (Figure 4B), on par with that seen from 2004-2005 bottom of market cycle (Figure 2, Figure 4B).
In sum, we see that the Class B market rents have not softened nearly as much as Class A market rents. Overall, Houston’s Class B market is helping to stabilize the more dramatic downturn in Class A buildings.
Commercial real estate data on office space were obtained from CoStar in early October 2016. The statistical analyses and data visualization were performed using the R software and programming language:
R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Dr. J. Nathaniel Holland is a research scientist with 20 years of experience in using the scientific method to extract information from complex multi-dimensional data. He joined NAI Partners in 2014 as Chief Research and Data Scientist. At NAI Partners, Nat leverages his sharp intellectual curiosity with his skills in statistical modeling to guide data-driven business decisions in commercial real estate. Like many data scientists in the private sector, Nat joined NAI Partners following a career in academia. Prior to taking up data analytics at NAI Partners, he held professorial and research positions at Rice University, University of Houston, and the University of Arizona between the years of 2001 and 2014. Nat is the author of more than 50 scientific publications, and he has been an invited expert speaker for more than 60 presentations. Trained as a quantitative ecologist, he holds a Ph.D. from the University of Miami, a M.S. from the University of Georgia, and a B.S. from Ferrum College.