Absorption Forecasts for Houston’s Office Market with Changes in Local Job Growth and National Rig Counts
May 2016 I Vol. 2, ISSUE 6 I Download PDF
The deeper, longer lasting downturn in the oil industry is now anticipated to have a more substantial impact on Houston’s overall economy, including net losses in employment. While 2015 weathered the oil downturn and Q1 2016 was likely the bottom, the depth of the oil downturn will likely lead to net job losses in Houston of -10,800 in 2016 and -30,000 jobs in 2017, then rebounding to 62,900 jobs in 2018 and 97,100 jobs in 2019, according to the Institute of Regional Forecasting of the University of Houston.
NAI Partners has developed a statistical model for forecasting how Houston’s economy — as measured by job growth — influences demand (net absorption) for office space. In 2015, we forecasted an annual net absorption of 2.252 million sq. ft. based on a forecasted job growth of 14,500, which was only 683,000 sq. ft. less than the realized net absorption of 2.935 million sq. ft. This is remarkable considering that annual net absorption has ranged from -418,000 to 9,537,000 sq. ft. over past 16 years. Here, we forecast how net absorption in Houston’s office market is likely to respond to changes in Houston’s job growth in coming years.
Based on projections for job growth, we forecast decreasing net absorption in 2016 and 2017, which then rebounds in 2018 and 2019 (Figure 1). Specifically, in 2016 and 2017, net absorption is forecasted to dip to 1.508 and 0.849 million sq. ft., respectively, but with substantial exposure to potential negative net absorption of -1.823 and -2.553 million sq. ft., as shown by the 80% prediction interval (black lines above and below dashed red line for absorption forecast). Net absorption will rebound in 2018 and 2019 to 4.040 and 5.216 million sq. ft., respectively, as strong job growth returns to Houston. It is important to note that, while job growth (Q4/Q4) explains 43% of variation in annual net absorption of Houston’s office space, our forecasts of net absorption are only as good as the job projections on which they are based. To this end, the job forecasts hinge on the level at which active rig counts return following their bottom, given the ties between Houston’s economy and the oil industry.
Since 2014 when the oil downturn began, the oil pullback has been deeper than initially anticipated. As the new swing producer, the U.S. has shown a much slower swing compared to the collective decision making of OPEC as an organization. Consequently, the oil pullback is lasting longer than even the Saudi’s likely expected. While this is not a repeat of the 1980s, in which Houston was simultaneously experiencing a banking crises, an overbuilt commercial real estate industry, and an overall slowdown in national economy, the current pullback in the oil industry is as deep as the 1980s and the 2008-2009 Great recession, as measured by the declines in rig counts and WTI prices.
Despite a strong national economy, the effects of the oil downturn are now likely to spread throughout Houston’s overall economy, including impacts on job growth. How will changes in employment alter demand for office space in Houston? Demand is measured by net absorption, the change in occupied space in units of square feet of rentable building area from one time period to another. Positive net absorption occurs when there is an increase in occupied space, while negative net absorption occurs when there is a decrease in occupied space.
At NAI Partners, we have developed a statistical model for forecasting how annual net absorption in Houston’s office market varies with changes in job growth. For example, last year we forecasted annual net absorption of office space to be about 2.252 million sq. ft., with an 80% prediction interval of -0.996 to 5.499 million sq. ft., based on projections for job growth of about 14,500 in Houston. As it turns out, Houston’s job growth in 2015 was about 15,800 and its net absorption of office space was about 2.935 million sq. ft. Not only did our forecast fall within the 80% prediction interval, actual net absorption in 2015 was only 683,000 sq. ft. (26.4%) greater than the forecast. This is pretty good considering that net absorption from 1999 - 2015 ranged from a low of -418,000 sq. ft. to over 9,537,000 sq. ft. Here, we use the most recent employment forecast for Houston from the Institute for Regional Forecasting at the University of Houston to make quantitative predictions for how annual net absorption of office space will change in coming years.
One of the most prominent economists that forecasts Houston’s job growth is Dr. Robert Gilmer, formerly of the Dallas Federal Reserve Bank and current Director of the Institute of Regional Forecasting at the University of Houston. Figure 2 shows Dr. Gilmer’s forecasts for job growth under three scenarios of recovery from the oil downturn, each of which is based on a different level of active U.S. rig counts following the likely bottom of Q1 2016. Dr. Gilmer’s forecasts are based on three different scenarios for the return of rig counts, while assuming a strong, stable U.S. economy.
Total U.S. rig counts have dropped from 1,930 in August 2014 to 404 in May 2016, a 79% decrease. The three scenarios concern the level at which active rig counts return, including a high return of 1,650 active rigs, a medium level of 1,500 rigs, and a lower level of 1,300 active rigs. The number of active rigs being strongly tied to the performance of the oil industry. Dr. Gilmer places a 30%, 50%, and 20% chance on each of these three scenarios, respectively.
Figure 2 shows historic job growth through 2015, and job forecasts for each of these three scenarios in the level of return in active rig counts, along with a weighted average of the three scenarios of rig counts. In all three scenarios, jobs decline in 2016 between -7,400 and -13,500. In 2017, positive job growth of 10,300 occurs under the scenario of high rig count return, but the medium and low rig count scenarios show job losses of -34,200 to -57,600. In 2018, job growth returns, ranging from 20,100 to 97,200 new jobs and in 2019 job growth ranges from 69,400 to 107,300 new jobs. Recall, all these scenarios are based on the assumption of a strong, stable U.S. economy, which itself may well falter in 2018 or 2019.
Job growth is a strong economic predictor of net absorption in Houston’s office market (Figure 3). Demand for office space as measured by net absorption does increase with job growth (Figure 3). The explanatory variable of job growth (Q4/Q4, year over year change) on the x-axis is scaled in thousands of jobs per year. The response variable of total annual net absorption on the y-axis is scaled in millions of square feet per year for Class A and B space combined. The solid red circles are the empirical data points for 1999 - 2015, for which the 2009 point corresponds with the the Great Recession.
The solid red line in Figure 1 is the linear regression model describing the statistical relationship between job growth and net absorption, of the form y=mx+b. Specifically, y=0.0344x+1.88, where y is net absorption, x is job growth, m is the slope of the line, and b is the y-intercept. The coefficient of determination (r2) indicates how well the data fit this linear statistical model. In this case, r2 = 0.43, that is 43% of variation in net absorption is explained by job growth. This is a fairly large percentage given the many factors simultaneously occurring in economics and commercial real estate which could obscure any such relationship. Yet, at the same time, that leaves 47% of variation in net absorption explained by other factors.
The slope of the line, m = 0.0344, describes how y changes as x increases, that is an increase by 1 unit of the x variable increases the y variable by how much. Because the y-axis is scaled in millions and the x-axis in thousands, the slope of 0.0344 means that on average 34.4 sq. ft. of net absorption occur for every one job. The 95% confidence interval for this slope is 12.7 to 56.0 sq. ft. of net absorption per job. The dashed blue lines are the 80% prediction intervals (upper and lower bounds) for net absorption. That is, there is an 80% probability that absorption will be in this range for a given level of job growth.
The y-intercept, b = 1.88, describes how much absorption occurs when job growth is zero. Even with low to near zero job growth, Houston still tends to experience net absorption of about 1.88 million sq. ft. The 95% confidence interval for y-intercept is 0.313 to 3.445 million sq. ft. This aspect of net absorption becomes more important for estimates of job growth that are in the vicinity of zero.
We can evaluate the statistical model of Figure 3 using 2016 numbers to date for job growth and net absorption of office space. Note, the statistical model is only for data from 1999 - 2015. Houston’s job growth through April 2016 is a loss of about -6,800 jobs, consistent with a pending loss of -7,400 to -13,500 jobs for 2016 as a whole. With a weighted average forecast of -10,800 jobs lost, the prediction is for 1.508 million sq. ft. of positive net absorption in 2016, with an 80% prediction interval of -1.823 to 4.840 million sq. ft. That is, there is an 80% probability that net absorption will be between -1.8 to 4.8 million sq. ft. for -10,800 lost jobs. As of early June 2016, there has been 2.1 million sq. ft. of net absorption. Not only does this value of net absorption fall within the 80% prediction interval, but it is clearly strong enough to put to rest concerns for the lower end of the prediction interval of negative net absorption.
Based on recently released job forecasts for Houston, we make quantitative predictions of how net absorption in the office market will change with job growth in Houston. We forecast net absorption in 2016, 2017, 2018, and 2019 based on job growth under the three scenarios of return in active rig counts. Figure 4 shows historic net absorption of office space from 1999 - 2015 in solid black line with open circles, which has an annual mean of 3.449 million sq. ft. The forecasted values for net absorption from 2016 - 2019 are plotted in various colors with dashed lines. The different scenarios of oil recovery suggest similar levels of net absorption in 2016, ranging from 2.4 to 2.7 million sq. ft., but then diverging in 2017.
Under a recovery with a return to a higher number of active rigs (red line, Figure 4), net absorption is predicted to be 1.625 million sq. ft. in 2016 (80% prediction interval -1.696 to 4.946 million sq. ft.), followed by 2.233 million sq. ft. in 2017 (80% prediction interval -1.043 to 5.510 million sq. ft.), 5.219 million sq. ft. in 2018 (80% prediction interval 1.902 to 8.535 million sq. ft.), and 5.109 million sq. ft. in 2019 (80% prediction interval 1.802 to 8.417 million sq. ft.). This is the most optimistic scenario for demand for office space given Houston’s economy and the oil downturn.
Under a medium rig count recovery (blue line, Figure 4), net absorption is predicted to be 1.498 million sq. ft. in 2016 (80% prediction interval -1.834 to 4.830 million sq. ft.), followed by 0.704 million sq. ft. in 2017 (80% prediction interval -2.715 to 4.123 million sq. ft.), 4.140 million sq. ft. in 2018 (80% prediction interval 0.887 to 7.393 million sq. ft.), and 5.566 million sq. ft. in 2019 (80% prediction interval 2.217 to 8.914 million sq. ft.). This is the most optimistic scenario for demand for office space in coming years given the different likelihoods of oil recovery in Houston.
The third scenario is a low rig count recovery (green line, Figure 4). Net absorption is predicted to be 1.415 million sq. ft. in 2016 (80% prediction interval -1.925 to 4.756 million sq. ft.), followed by -0.010 million sq. ft. in 2017 (80% prediction interval -3.633 to 3.433 million sq. ft.), 2.570 million sq. ft. in 2018 (80% prediction interval -0.690 to 5.830 million sq. ft.), and 4.263 million sq. ft. in 2019 (80% prediction interval 1.006 to 7.521 million sq. ft.).
We have assumed 80% prediction intervals. This is a probability of 0.80, which means that, while we are 80% certain, 2 out of 10 cases may fall outside this prediction interval given the noise associated with the data. If this were NBA free throws, we would likely bet on the shooter at 80% to win the game, but in two instances we would lose our bet. In predictive analytics, it is important to note whether the new values of the predictor variable (job growth) is within the range of the original data on which the projections are based. Extrapolation far outside the original data range can lead to unreliable predictions. In our case, job growth of original data ranges from -110,000 to +115,000. Forecasted job numbers are well within this data range, which increases the likelihood of a reliable prediction.
Commercial real estate data on office space were obtained from CoStar in June 2016. Data for Class A and B buildings were combined for office space. Job and employment data were obtained from the Federal Reserve Bank of Dallas, and calculated based on Q4/Q4 year-over-year changes in job growth. 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/.
We used linear regression to examine the predictive effects of annual changes in employment (Q4/Q4 year-over-year) on annual total net absorption (direct plus sublease) from 1999 - 2015. Assumptions of linear regression that could render a biased statistical model were tested. None of the assumptions were violated, including statistical outliers in absorption, overly influential points in job growth, statistical outliers in employment, normality in absorption, unequal variance, heteroscadascity, and serially correlated residuals (nonwhite noise error). There was a statistically significant, positive relationship between job growth and total net absorption for office space (F2,15=11.47, p=0.0041, r2=0.47).
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.