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Forecasts for Net Absorption of Houston Office Space with Changes in Job Growth and Rig Count

 

December 2016 I Vol. 2, ISSUE 13 I Download PDF

 

Executive Summary

Houston’s economy is more diversified than it was in the 1980s, but a strong relationship still exists with the energy sector. Over the past two years, the oil industry has experienced a strong pullback, with WTI prices bottoming in January 2016 at about $35 per barrel but currently back up into the $50s. U.S. rig counts bottomed in May 2016 at 407, but that figure is currently back up to 597. Meanwhile, the Houston office market softened, with a dramatic increase in sublease space from 4.0 to 12.2 million sq. ft. over the 2-year period. According to the University of Houston’s Institute of Regional Forecasting, Houston had a modest pullback in jobs in 2016, but will avert a substantial recession in 2017, with strong job growth returning in 2018 and 2019. Given recent signs of a bottom in the oil industry, what do the coming years hold for Houston’s office market? Here, we forecast how Houston’s economy — as measured by job growth — is likely to shape demand (net absorption) for office space in coming years.

For 2016, we forecast an annual net absorption of 1.1 million sq. ft. based on a loss of -22,000 jobs, which is fairly close to realized net absorption of 750,000 sq. ft. as of the first week of December 2016. This is a reasonably close forecast considering that annual net absorption has ranged from less than -400,000 to more than 9,000,000 sq. ft. over the past 16 years. Based on projections for job growth, we forecast increasing net absorption in 2017, 2018, and 2019 (Figure 1). For 2017, net absorption is forecasted to increase to 2.0 million sq. ft. with only 4,500 new jobs anticipated. However, net absorption will rebound in 2018 and 2019 to 4.40 million sq. ft. and 4.75 million sq. ft., respectively, as strong job growth returns to Houston on the order of 75,000 and 85,000 new jobs.

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, but also on the continued strength of the overall U.S. economy.

Figure 1


Motivation

While this is not a repeat of the 1980s, in which Houston simultaneously experienced a banking crisis, an overbuilt commercial real estate industry, and an overall slowdown in national economy, the pullback in the oil industry has been as deep as that of the 1980s and the 2008-2009 Great Recession, as measured by the declines in U.S. rig counts and WTI prices. U.S. rig counts bottomed in May 2016 at 407, but are currently back up to 597. WTI prices bottomed in January 2016 at about $35 per barrel but are currently back up into the $50s. All indications are that 2016 has been the bottom for the oil industry.

With this oil downturn, the Houston office market did soften substantially, as indicated by the dramatic increase in sublease space from 4.0 to 12.2 million sq. ft. over the two-year period. 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. Given recent signs of a bottom in the oil industry, what do the coming years hold for Houston’s office market?

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. For 2015, our model forecasted annual net absorption of office space to be about 2.25 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 15,994 and its net absorption of office space was about 2.80 million sq. ft. Not only did our forecast fall within the 80% prediction interval, actual net absorption in 2015 was only 550,000 sq. ft. greater than the forecast. This is particularly strong prediction outcome considering that net absorption from 1999-2015 has ranged from less than -400,000 sq. ft. to more than 9,00,000 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.

Figure 2

Forecasts of Job Growth Based on Recovery of Rig Counts

One of Houston’s 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 based on a different level of return in active U.S. rig counts following the likely bottom of Q1 2016. The job forecasts assume a strong, stable U.S. economy.

Total U.S. rig counts have dropped from 1,930 in August 2014 to 407 in May 2016, a 79% decrease. The three scenarios of recovery concern the level at which active rig counts return, including a high return of 1,650 active rigs, a medium level of 1,500 active rigs, and a lower level of only 1,300 active rigs (the number of active rigs is strongly tied to the performance of the oil industry). Dr. Gilmer places a 30%, 60%, and 10% 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. In all three scenarios, jobs decline in 2016 between -18,200 and -27,100, for which the 30/60/10 weighted average is -22,000 jobs. In 2017, positive job growth of 27,000 occurs under the scenario of high rig count return, but the medium and low rig count scenarios show job losses of -2,800 and -19,200, for a 30/60/10 weighted average of 4,500 new jobs in 2017. In 2018, job growth returns to 33,800, 71,200, and 95,700 new jobs for returns to low, medium and high active rig counts. The 30/60/10 weighted average of these three scenarios is 74,800 new jobs in 2019. Forecasts for job growth in 2018 continue upward, with 53,400, 87,300, and 91,300 new jobs under returns of low, medium and high active rig counts. The 30/60/10 weighted average of these three scenarios in 2019 is 85,100 new jobs in 2019.

Houston’s economy weathered the oil downturn in 2015, but only posted 6,000 new jobs. It now appears that Houston has averted a substantial recession in 2017, but with a modest pullback in 2016 expecting job losses of around -22,000. While 2017 will remain a slow year for recovery in job growth, 2018 and 2019 will see Houston’s economic engine kick into higher gears with strong job growth.

Figure 3

Job Growth Predicts Net Absorption in the Office Market

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 increases 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=0.034x+1.85, 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 57% of variation in net absorption explained by other factors.

The slope of the line, m=0.034, 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 0.034 means that on average 34 sq. ft. of net absorption occur for every one job. The 95% confidence interval for this slope is 12.5 to 55.6 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=1.85, 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 1.85 million sq. ft. The 95% confidence interval for the y-intercept is 0.293 to 3.411 million sq. ft. This aspect of net absorption becomes more important for estimates of job growth that are in the vicinity of zero.

Figure 4

2016 Forecast for Job Growth and Net Absorption

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 December 2016 is a loss of about -3,300 jobs, consistent with a pending loss of -22,000 jobs for 2016 as a whole (after Spring 2017 benchmarking). With a weighted average forecast of -22,000 jobs lost, the prediction is for 1.1 million sq. ft. of positive net absorption in 2016, with an 80% prediction interval of -2.25 to 4.46 million sq. ft. That is, there is an 80% probability that net absorption will be between -2.25 to 4.46 million sq. ft. for -22,000 lost jobs. As of the first week of December 2016, there has been 750,000 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.

Office Absorption for Different Scenarios of Houston’s Recovery from the Oil Downturn

Based on recent job forecasts for Houston, we make predictions for 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 a solid black line with open circles, which has an annual mean of 3.5 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 0.0 to 1.23 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 1.23 million sq. ft. in 2016 (80% prediction interval -2.11 to 4.46 million sq. ft.), followed by 2.77 million sq. ft. in 2017 (80% prediction interval -0.47 to 6.01 million sq. ft.), 5.11 million sq. ft. in 2018 (80% prediction interval 1.81 to 8.41 million sq. ft.), and 4.96 million sq. ft. in 2019 (80% prediction interval 1.67 to 8.25 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 1.07 million sq. ft. in 2016 (80% prediction interval -2.29 to 4.43 million sq. ft.), followed by 1.76 million sq. ft. in 2017 (80% prediction interval -1.54 to 5.05 million sq. ft.), 4.28 million sq. ft. in 2018 (80% prediction interval 0.887 to 7.393 million sq. ft.), and 5.57 million sq. ft. in 2019 (80% prediction interval 1.03 to 7.52 million sq. ft.). This is the most likely 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 0.93 million sq. ft. in 2016 (80% prediction interval -2.44 to 4.30 million sq. ft.), followed by 1.19 million sq. ft. in 2017 (80% prediction interval -2.15 to 4.54 million sq. ft.), 3.00 million sq. ft. in 2018 (80% prediction interval -0.23 to 6.23 million sq. ft.), and 3.66 million sq. ft. in 2019 (80% prediction interval 0.44 to 6.90 million sq. ft.). This is the least likely scenario for demand for office space in coming years given the different likelihoods of oil recovery in Houston.

Caveats and Uncertainty in Absorption Forecasts

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 sink their shots with the game on the line, 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.

Methodology

Commercial real estate data on office real estate were obtained from CoStar in early November 2016 for Class A and B buildings for the total MSA of each of Austin, Dallas/Fort Worth, Houston, and San Antonio. 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 (F1,15=11.34, p=0.0042, r2=0.43).


J. Nathaniel Holland, Ph.D., Chief Research and Data Scientist

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.


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