Executive Summary • Commercial rents in the Houston office market have reached an all-time high, with asking prices on combined Class A and B space up 12% since the end of 2010. • Not surprisingly, the growth in the oil industry has spurred area employment to record levels, particularly in light of the recent boom in shale oil and gas, which has driven expansion in transportation and logistics areas as well. • An analysis of the relationship among employment, crude oil prices and office rents finds that lagged changes in oil prices exert a more direct impact on short-term changes in Houston rent growth than do changes in office employment growth. • A model of future rents based on lagged changes in rent growth, lagged changes in occupancy growth, lagged changes in oil prices and lagged changes in the degree of new space under construction suggests that rents should peak over the next one to two quarters before falling back to current levels. Oil’s Impact on Houston Office Rents Houston Oil’s Impact on Houston Office Rents 333 Clay Street, Suite 3700 • Houston, Texas 77022 • P:713.522.5300 Houston has experienced one of the strongest post-recession rebounds in private-sector employment, as shown in Figure 1. While strength in the oil industry has been responsible for a large part of the area’s growth, we note that the local economy is quite diverse. Houston has a particularly large medical establishment presence that includes the Texas Medical Center—the world’s largest medical complex. In addition, Houston’s port is quite active, with the Port of Houston handling about 70% of all the containerized cargo in the U.S. Gulf of Mexico; in 2012, it was the top-ranked U.S. port in terms of foreign tonnage. 1 1 Sources: http://www.texasmedicalcenter.org/about-tmc/ and http://www.portofhouston.com/business-development/trade-development-and-marketing/trade-statistics/ Diversified Local Economy…but Oil Still Plays a Large Role Figure 1 Office-Using Payrolls: Current vs. Pre-Recession Peak and Recession Trough Percentage Change from Pre-Recession Peak to Current (December 2013) Office-Using Payroll Counts 100,000 1,300,000 300,000 Houston Boston Chicago Dallas Los Angeles* Miami* New York City Philadelphia San Francisco Washington, D.C. 500,000 700,000 900,000 1,100,000 December 2013 Pre-Recession Peak (Jan-06 Through Dec-09) Recession Through (Jan-08 Through Jun-12) *Indicates Metropolitan Division, rather than Metropolitan Statistical Area. Source: Moody’s. 6.1% 1.2% (1.6%) 8.2% (6.8%) (4.7%) 0.6% (4.8%) 9.5% 0.7%
Transcript
Executive Summary
• Commercial rents in the Houston office market have reached an all-time high, with asking prices on combined Class A and B space up 12% since the end of 2010.
• Not surprisingly, the growth in the oil industry has spurred area employment to record levels, particularly in light of the recent boom in shale oil and gas, which has driven expansion in transportation and logistics areas as well.
• An analysis of the relationship among employment, crude oil prices and office rents finds that lagged changes in oil prices exert a more direct impact on short-term changes in Houston rent growth than do changes in office employment growth.
• A model of future rents based on lagged changes in rent growth, lagged changes in occupancy growth, lagged changes in oil prices and lagged changes in the degree of new space under construction suggests that rents should peak over the next one to two quarters before falling back to current levels.
Houston has experienced one of the strongest post-recession rebounds in private-sector employment, as shown in Figure 1. While strength in the oil industry has been responsible for a large part of the area’s growth, we note that the local economy is quite diverse. Houston has a particularly large medical establishment presence that includes the Texas Medical Center—the world’s largest medical complex. In addition, Houston’s port is quite active, with the Port of Houston handling about 70% of all the containerized cargo in the U.S. Gulf of Mexico; in 2012, it was the top-ranked U.S. port in terms of foreign tonnage.1
1 Sources: http://www.texasmedicalcenter.org/about-tmc/ and http://www.portofhouston.com/business-development/trade-development-and-marketing/trade-statistics/
Diversified Local Economy…but Oil Still Plays a Large Role
Figure 1
Office-Using Payrolls: Current vs. Pre-Recession Peak and Recession Trough Percentage Change from Pre-Recession Peak to Current (December 2013) Office-Using Payroll Counts
100,000
1,300,000
300,000
Houston Boston Chicago Dallas Los Angeles* Miami* New York City Philadelphia San Francisco Washington, D.C.
500,000
700,000
900,000
1,100,000
December 2013 Pre-Recession Peak (Jan-06 Through Dec-09) Recession Through (Jan-08 Through Jun-12)
*Indicates Metropolitan Division, rather than Metropolitan Statistical Area.Source: Moody’s.
6.1%
1.2%
(1.6%)
8.2%
(6.8%)
(4.7%)
0.6%
(4.8%)
9.5%
0.7%
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Even though Houston has a relatively low number of office employees as a percentage of private sector workers (Figure 2) it still has a sizable office market, particularly when compared to the Dallas metro area, which has 40% more office workers occupying only 10% more space (Figure 3).
Figure 2
Office Employment as a Percentage of Total Private Sector Employment, December 2013
Figure 5
Oil & Gas Publicly-Held Companies Headquartered in or around HoustonFortune 500 Ranking Where Appropriate (#)
Figure 4
Houston Major Private-Sector Employers
Figure 3
Medium-Sized Metro Areas: Office and Population Employment
By Metropolitan Statistical Area
Houston, TX 24.7% Dallas, TX 30.6%
United States 25.5% Boston, MA 31.5%
Miami, FL 27.8% Atlanta, GA 32.1%
Philadelphia, PA 28.0% San Jose, CA 32.8%
Seattle, WA 28.6% San Francisco, CA 35.0%
Chicago, IL 29.4% New York City* 36.3%
San Diego, CA 29.5% Washington, D.C. 39.3%
Los Angeles 29.8%
*New York City only (not full MSA).Source: Bureau of Labor Statistics.
Companies
Adams Resources & Energy Energy XXI NRG Energy
Allis-Chalmers Energy Enterprise Products Partners (64) Oasis Petroleum
Anadarko Petroleum (207) EOG Resources (233) Oceaneering International
American Electric Technologies Evolution Petroleum Occidental Petroleum**
(125)
Apache (167) ExxonMobil* (2) Oil States International
Enbridge Energy Partners (381) Noble Energy Western Gas Partners
* While ExxonMobil is technically headquartered in Irving, Texas, they have a sizable and growing presence in Houston: the company signed a 478,000 square foot lease in The Woodlands in December 2013, and is building a 3 million square foot campus comprising 20 buildings just south of the Woodlands, with more than 10,000 employees scheduled to be working onsite by 2015.
** Occidental Petroleum announced on February 14, 2014 that it will be moving its headquarters from Los Angeles to Houston, where the firm will oversee its operations in the United States, the Middle East, northern Africa and South America.
Source: Houston Chronicle, CNN Money, Wikipedia.
Houston Major Employers
Employee Count*
Houston Major Employers
Employee Count*
1. Memorial Hermann Healthcare 19,500 6. Kroger Company 12,000
2. United Continental Holding 17,000 7. Schlumberger
Limited 10,000
3. ExxonMobil 15,000 8. National Oilwell Varco 10,000
4. Shell Oil Company 13,000 9. B. P. America, Inc. 9,537
5. The Methodist Hospital System 13,000 10. Baylor College of
Medicine 9,232
* Employee numbers are for the ten-county region, and not the city only.Source: City of Houston Comprehensive Annual Financial Report, FY 2012
Source: Bureau of Labor Statistics, U.S. Census Bureau and CoStar.Occupied space includes all properties > 10,000 square feet.
Growth in the energy sector has made Houston a very active (and expanding) office market, with five of the top ten private sector employers in the oil and gas business (Figure 4).
Two of the four largest refineries in the U.S. are in Houston, and Houston is also home to 40 of the nation’s 145 publicly-traded oil and gas exploration and production firms.2 Of the 24 Fortune 500 companies located in Houston, 20 of them are in the oil and gas business, and as shown in Figure 5, Houston is home to numerous others. In addition, while Texas produced almost 30% of the US’s natural gas output and more than 25% of the US total of crude oil in 2011,3 Houston-area oil and gas companies are responsible for an even greater share of output once international production is included; many international operations are still run out of a Houston headquarters.2 Source: Greater Houston Partnership, energy data sheet as of April 2013 from
www.houston.org.
3 Source: EIA State Energy Data System (SEDS) http://www.eia.gov/state/seds/
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Engineering services firms, particularly those that support the construction and maintenance of oil and gas pipelines, dominate the Harris County landscape. As shown in Figure 6, these firms comprise 40% of all oil and gas-related establishments, and roughly 20% of oil and gas-related employment.
Oil is Correlated with Office Employment…While Office Employment Is Correlated with Asking Rents
It should not be surprising, then, to see that private-sector employment has been tied closely to oil prices, as shown in Figure 7, suggesting a high degree of correlation between the two variables.
Similarly, office-using employment and asking prices on office rents appear linked (Figure 8), although perhaps not surprisingly, employment and rents show significantly less volatility than oil prices.
Figure 6
Harris County Private Sector Oil and Gas-Related Establishments and Employment Count, Q2 2013
Figure 7
Houston Metropolitan Area Office-Using Employment and West Texas Intermediate Spot Oil Prices
Figure 8
Houston Metropolitan Area Office-Using Employment and Houston Class A & B Office Asking Rents
Establishment Type Number of Establishments
Number of Employees
Engineering services 1,538 43,695
Crude petroleum and natural gas extraction 861 53,238
Support activities for oil and gas operations 532 22,876
Oil and gas field machinery and equipment 266 35,277
* Combined CBD and Suburban regions.Source: Bureau of Labor Statistics, CoStar.
Employment: Office-Using Employment (LHS) Houston Area Class A & B Asking Rents* (RHS)
Off
ice
Em
plo
ymen
t in
000
s, S
A
Askin
g R
ent, $ / S
F
$17
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More generally, nominal oil prices and Houston-area rents have tended to move together (Figure 9), though the relationship has diverged on several occasions.
The following variables were tested to model Houston-area Class A and B office rents:
1. Changes in lagged office rent growth 2. Houston Metropolitan Statistical Area office-using
employment 3. West Texas Intermediate (WTI) spot oil prices 4. Available square feet 5. Net absorption 6. Occupancy rate 7. Total availability rate 8. Total rentable building area (RBA) 9. Amount of space under construction 10. Total number of active rotary rigs in Texas and in the
U.S. (quarterly average)
Both office rents and oil prices were deflated by the Consumer Price Index (CPI) in order to remove the general level of consumer price inflation from both series.4 For the sake of simplicity, the change in the change of office
employment, real rents and occupancy rates5 are referred to as the “change in employment growth,” “change in rent growth,” and “change in occupancy growth,” respectively. The change in CPI-deflated oil prices is referred to as “change in oil” throughout the remainder of this paper.
Oil Prices Matter More Than Employment
Demand for office space is generally driven by employment. Given the long-term nature of leases and the costs associated with lease terminations, the quantity of space demanded is generally sticky. As a result, in an environment of unchanged employment there should be little net absorption of space (aside from moves driven to increase space efficiencies.) When changes in employment growth are regressed on changes in lagged oil prices, we find that oil price changes explain 20% of the variability in changes in Houston metro-area employment growth. Knowing that lagged oil price changes help explain changes in employment growth, we test whether lagged oil price changes matter more than lagged changes in employment growth in determining changes in the future growth of rents.
Figure 9
West Texas Intermediate Spot Oil Prices and Houston Class A & B Office Asking Rents
$0 $17
$140 $27
$19
Q1 ‘99
Q1 ‘01
Q1 ‘02
Q1 ‘03
Q1 ‘04
Q1 ‘05
Q1 ‘06
Q1 ‘07
Q1 ‘08
Q1 ‘09
Q1 ‘10
Q1 ‘11
Q1 ‘12
Q1 ‘13
Q3 ‘99
Q3 ‘01
Q3 ‘02
Q3 ‘03
Q3 ‘04
Q3 ‘05
Q3 ‘06
Q3 ‘07
Q3 ‘08
Q3 ‘09
Q3 ‘01
Q3 ‘11
Q3 ‘12
Q3 ‘13
$28
$21$56
$23$84
$25$112
* Combined CBD and Suburban regions.Source: Bureau of Labor Statistics, CoStar.
Cushing, OK WTI Spot Price FOB (LHS) Houston Area Class A & B Asking Rents* (RHS)
$ p
er B
arre
l
Askin
g R
ent, $ / S
F
4 We adjust for the non-stationarity of the data series by differencing each series once (and twice for real rents, employment levels and occupancy rates), while total net absorption—representing quarterly changes in activity—is already stationary. Tests for stationarity are shown in Appendix I.
5 If employment moves from 491k in Q1 2000 to 500k in Q2 2000 and to 504k in Q3 2000, the “change in the change” is (504k-500k)-(500k-491k) or-5k.
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Below, we show correlations between changes in rent growth and changes in employment growth (Figure 10) and correlations between changes in rent growth and changes in oil prices (Figure 11).
What Happens When We Try to Predict Changes in Rent Growth?
We find that changes in employment growth lagged from one to 12 quarters have no significant correlation with changes in rent growth across the sample time period (Figure 10) whereas changes in oil lead changes in rent growth by one quarter with a correlation of 0.27 (Figure 11). This finding is not altogether surprising; asking rents today should be correlated with anticipated changes in employment. Put another way, asking office prices are a function of future changes in demand, and future changes in employment are most correlated with recent changes in oil prices. From a modeling perspective, using changes in lagged oil prices versus changes in employment is advantageous given reporting delays; data on quarterly employment from the Bureau of Labor Statistics are not available until at least three weeks after the end of the quarter, whereas oil prices are published on a daily basis.
Even though there is a significant correlation between the current change in the growth of rents and the current change in the growth of employment, contemporaneous changes are useless in making future forecasts. As such, there is no lagged value of employment growth that is significant in forecasting future changes in rent growth.
Given oil’s importance as a predictive factor in Houston rents, a model to determine whether the active total rig count (in both Texas and the U.S.) provides any additional information over and above that provided by spot oil prices is tested. However, as Figure 12 suggests, the trajectory of rigs follows (and slightly lags) the path of oil prices, making oil prices a better explanatory variable for rent forecasting.
Figure 10
Cross-Correlogram: Changes in Rent Growth and Changes in Employment Growth
Figure 11
Cross-Correlogram: Changes in Rent Growth and Changes in Oil Prices
Changes in Growth of Rents with Current and
Lagged Changes in Growth of Employment
# of Quarters Lag (Changes in
Employment Growth)Correlation
0 0.28
1 0.09
2 (0.06)
3 0.07
4 0.01
5 (0.02)
6 0.19
7 0.00
8 0.02
9 (0.04)
10 0.01
11 (0.11)
12 0.02
13 0.00
14 (0.02)
15 0.09
Changes in Growth of Rents with Current and Lagged
Changes in Oil Prices
# of Quarters Lag(Changes in Oil Prices) Correlation
0 (0.17)
1 0.27
2 0.12
3 (0.11)
4 (0.07)
5 0.04
6 0.01
7 (0.01)
8 0.02
9 0.08
10 (0.02)
11 (0.13)
12 (0.15)
13 0.04
14 0.07
15 0.08
Source: Studley, Inc.
Figure 12
West Texas Intermediate Spot Oil Prices and Count of Active Rotary Rigs in Texas
$0 0
$140 1,200
240
Q1 ‘99
Q1 ‘00
Q1 ‘01
Q1 ‘02
Q1 ‘03
Q1 ‘04
Q1 ‘05
Q1 ‘06
Q1 ‘07
Q1 ‘08
Q1 ‘09
Q1 ‘10
Q1 ‘11
Q1 ‘12
Q1 ‘13
$28
480$56
720$84
960$112
*Combined CBD and Suburban regionsSource: CoStar and Department of Energy, EIA.
Cushing, OK WTI Spot Price FOB (LHS)
Active Rotary Rigs in Texas (RHS)
$ p
er B
arre
l
Rig
s
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Explaining Rents via Lags of Oil, Net Absorption and Prior Rents
Using the complete data set from 1999, changes in rent growth are modeled using the variables previously mentioned on page 4; the most robust equation is presented in Figure 13. (Appendix III shows cross-correlograms of rents against
each of the variables tested.) The in-sample results show that 49% of the variation in changes in rent growth is explained by the four variables shown and lagged prior-period errors (the AR term).
In order to test the above model’s ability to accurately forecast future rent trends, a re-estimation of the above
equation is made using data from Q3 2001 through Q4 2011. The corresponding out-of-sample forecasts (Figure 14) show a root mean square error (RMSE)6 of $0.22, just 1.2% of the actual average inflation-adjusted rents of $17.95 over the Q1 2012 - Q4 2013 period.
The model above highlights the sensitivity of future price changes to not only changes in the price of oil, but also changes in occupancy and construction trends. Note that changes in oil prices affect changes in rent growth only one quarter ahead; in contrast, the changes in the other variables affect rent growth changes 6-7 quarters ahead. Oil price changes affect rents only in the short term.
6 The standard deviation of the residuals (actual less forecast values).
Change in Rent Growth, lagged 6 quarters 0.29 2.68 0.01
Change in Occupancy Growth, lagged 7 quarters -12.18 -2.15 0.04
Change in Oil Prices, lagged 1 quarter 0.01 1.89 0.07
Change in Space Under Construction, lagged 7 quarters -8.11E-08 -2.01 0.05
AR(1) term* -0.36 -2.97 0.00
R-Squared: 53.0%
Adjusted R-Squared: 48.8%
* AR(1) term indicates that the current period error, et is correlated with the prior period’s forecast error, et-1.** The adjusted R2 is adjusted for the degrees of freedom (which corresponds to the number of variables in the equation.)Source: Studley, Inc.
As of late, office space under construction in Houston has been climbing even as occupancy rates have risen from 85.3% seven quarters ago to 86.3% as of Q4 2013. A tremendous office construction boom has been underway: Houston currently has 35% more new office
space under construction than Manhattan (Figure 15).
While a significant percentage of Houston’s new office
construction is pre-leased, the roughly 25% of space
that hasn’t been spoken for has the potential to slow
or reverse the increase in occupancy rates in the near
term, particularly if tenants trade a larger existing lease
for a smaller, but more efficient, space in a new building.
Similarly, given the negative coefficient on the “space
under construction” variable in the regression equation,
we can also surmise that the increase in construction
activity will be a source of downward pressure on rents.
In the short term, we believe that local area rents are at or near a peak. The outlook for the next six months7 is for a modest increase in real rents on the order of $0.20/sf before we see rents returning to current price
levels by the end of the year. Given the relatively low
level of inflation at present, the forecasted rent change on
a nominal basis is similar to a few pennies higher.
One caveat: new space tends to have higher prices
than existing space; moreover, new space is more apt to
have a listed asking price (versus existing space, where a
significant percentage of asking prices are withheld from
databases such as CoStar.) With “under construction”
space in Houston at a record high as a fraction of total RBA,
the higher prices associated with new space could offset
some of the downward price pressure exerted by a rising
occupancy rate and an increase in space under construction,
potentially mitigating some of the price decline we forecast
* Future RBA = RBA Under Construction + Existing Class A and B RBA.Source: CoStar as of Q4 2013.
7 Given the 1-quarter lag in the oil variable for the regression equation, we assume that the average price for a barrel of oil in Q1 2014 is equal to the average from January 2, 2014 - January 27, 2014 ($93.90) and that oil in the 2nd and 3rd quarter rises modestly to $95/barrel. We further assume that inflation rises by a compounded 0.3% per quarter from Q1 2014 – Q3 2014, the average rate of quarterly inflation during Q2 2013 – Q4 2013. All other explanatory variables have sufficient lags that we are able to use current data as of Q4 2013 for our future forecasts.
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We perform an Augmented Dickey-Fuller (ADF) test on quarterly data from Q1 1999 through Q4 2013 as follows:
Houston Metropolitan Statistical Area Office-Using Employment (Moody’s) NR NR R
Houston-Area CPI-Deflated WTI Spot Oil Prices (EIA) NR R n/a
Available Square Feet* (CoStar) Data Begin in Q3 2005 NR R n/a
Net Absorption* (CoStar) R n/a n/a
Occupancy Rate* (CoStar) NR R** R
Total Availability Rate* (CoStar) NR R n/a
Size of Existing Office Market–Total RBA* (CoStar) NR R n/a
Amount of Office Space Under Construction* (CoStar) NR R n/a
Texas and U.S. Active Rotary Rig Count (Baker Hughes) NR R n/a
NR = HO Not Rejected at 5% SignificanceR = Reject HO at 5% Significance* Houston area suburban and CBD office market, Class A and Class B only. ** Rejected at 4.6%; given the proximity to the 5% threshold, 2nd differences are used for the “occupancy” variable (changes in occupancy growth).Source: Studley, Inc.
APPENDIX I: Tests for Stationarity
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Many economic time series and price series are “random walk” processes, where the current value of a variable is equal to the variable’s past value plus an independent, normally distributed error term. The best estimate for the value of a random walk process at time t+18 is equal to the value at time t. With a random walk process, the change in any period is random, but the variance of the series grows with time.
Random walks are examples of non-stationary series—series where the means, variances and covariances change over time. Non-stationary series also include series that “trend” or “cycle” (as with seasonal variations), or have a random walk with a trend or cycle component added to it (Chart 1).
APPENDIX II: Why a Regular Linear Regression “Won’t Do”
Chart 1
Non-Stationary Series Examples
Table 1
Statistics from Regression of Houston Class A & B Office on Office-Using Employment
10
0
(10)
(20)
60
20
30
40
50
Source: Investopedia.com.
Trend Trend + Cycle Cycle Random Walk Random Walk + Trend
When a series is defined as a random walk process, it is easy (but statistically incorrect) to perform a linear regression without recognizing the impact of correlated error terms. For example, a regression of rents on office employment produce a high R2 or goodness-of-fit and high t-statistics, but grossly misstates the significance of the slope coefficient (Table 1); additionally, the residuals from this regression show a very high degree of correlation from one period to the next (Chart 2 below), which violates the assumption of independence.
In order to correctly capture the statistical relationship among rents, oil prices and office employment, we must properly account for the fact that in their original form, the series are not stationary and must be adjusted accordingly.8 More generally, because of the recursive relationship, the best estimate
of yt+s for any time t+s with s>=1 is yt.
Chart 2
Residuals from Linear Regression of Houston Office Rents on Houston Office-Using Employment
(4)
4
(1)
(2)
(3)
Q1 ‘99
Q1 ‘00
Q1 ‘01
Q1 ‘02
Q1 ‘03
Q1 ‘04
Q1 ‘05
Q1 ‘06
Q1 ‘07
Q1 ‘08
Q1 ‘09
Q1 ‘10
Q1 ‘11
Q1 ‘12
Q1 ‘13
Q3 ‘99
Q3 ‘00
Q3 ‘01
Q3 ‘02
Q3 ‘03
Q3 ‘04
Q3 ‘05
Q3 ‘06
Q3 ‘07
Q3 ‘08
Q3 ‘09
Q3 ‘10
Q3 ‘11
Q3 ‘12
Q3 ‘13
0
1
2
3
Source: Studley, Inc.
$ /
SF
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APPENDIX III: Cross-Correlograms of Changes in Rent Growth Against Proposed Regression Variables
Correlogram of Changes in Rent Growth
Autocorrelation Partial Correlation AC PAC Q-Stat Prob
1 (0.51) (0.51) 15.93 0.00
2 0.01 (0.35) 15.93 0.00
3 0.22 0.07 19.02 0.00
4 (0.18) 0.01 21.13 0.00
5 (0.11) (0.24) 21.86 0.00
6 0.38 0.22 31.35 0.00
7 (0.40) (0.11) 42.37 0.00
8 0.20 0.02 45.13 0.00
9 0.04 0.00 45.25 0.00
10 (0.15) (0.01) 46.92 0.00
11 (0.03) (0.19) 46.98 0.00
12 0.12 (0.17) 46.08 0.00
13 (0.21) (0.12) 51.50 0.00
14 0.22 0.02 55.16 0.00
15 (0.13) (0.10) 56.53 0.00
Cross-Correlogram: Changes in Rent Growth and Changes in Employment Growth
Cross-Correlogram: Changes in Rent Growth and Changes in Oil Prices
Change in Rent Growth, Change in Employment
Growth(-i)
Change in Rent Growth, Change in Employment
Growth(+i)
i lag lead
0 0.28 0.28
1 0.09 (0.26)
2 (0.06) 0.01
3 0.07 (0.01)
4 0.01 (0.20)
5 (0.02) 0.13
6 0.19 0.07
7 0.00 (0.25)
8 0.02 0.07
9 (0.04) (0.02)
10 0.01 (0.17)
11 (0.11) 0.09
12 0.02 (0.12)
13 0.00 0.14
14 (0.02) 0.09
15 0.09 (0.07)
Change in Rent Growth, Change in Oil Prices(-i)
Change in rent growth, Change in Oil Prices(+i)
i lag lead
0 (0.17) (0.17)
1 0.27 (0.03)
2 0.12 0.06
3 (0.11) (0.12)
4 (0.07) 0.16
5 0.04 0.02
6 0.01 (0.05)
7 (0.01) 0.02
8 0.02 (0.00)
9 0.08 (0.11)
10 (0.02) 0.01
11 (0.13) (0.05)
12 (0.15) (0.07)
13 0.04 0.15
14 0.07 (0.11)
15 0.08 0.08
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APPENDIX III: Cross-Correlograms of Changes in Rent Growth Against Proposed Regression Variables
Cross-Correlogram: Changes in Rent Growth and Changes in Available Square Feet
Cross-Correlogram: Changes in Rent Growth and Changes in Occupancy Rate Growth
Cross-Correlogram: Changes in Rent Growth and Net Absorption
Cross-Correlogram: Changes in Rent Growth and Changes in Total Availability Rate
Change in Rent Growth, Change
in Available Square Feet(-i)
Change in Rent Growth, Change
in Available Square Feet(+i)
i lag lead
0 (0.05) (0.05)
1 (0.11) (0.27)
2 (0.08) (0.19)
3 (0.08) (0.11)
4 (0.03) (0.10)
5 (0.11) (0.23)
6 0.11 0.22
7 0.07 0.01
8 0.07 0.05
9 0.12 0.06
10 0.10 0.16
11 0.11 0.03
12 0.00 0.29
13 0.08 (0.10)
14 (0.05) 0.22
15 0.10 0.18
Change in Rent Growth, Change
in Occupancy Rate Growth(-i)
Change in Rent Growth, Change
in Occupancy Rate Growth (+i)
i lag lead
0 0.39 0.39
1 (0.19) (0.30)
2 (0.01) 0.23
3 0.04 (0.15)
4 0.05 0.06
5 (0.11) (0.12)
6 0.30 0.07
7 (0.37) (0.13)
8 0.26 0.20
9 (0.02) (0.19)
10 (0.03) 0.16
11 (0.06) (0.19)
12 0.14 0.13
13 (0.25) (0.11)
14 0.30 0.09
15 (0.36) 0.02
Change in Rent Growth, Net
Absorption(-i)
Change in Rent Growth, Net
Absorption(+i)i lag lead
0 0.26 0.26
1 (0.14) 0.04
2 (0.06) 0.10
3 0.17 0.13
4 (0.03) (0.03)
5 (0.05) 0.04
6 0.03 0.06
7 (0.09) (0.11)
8 0.04 0.18
9 (0.09) 0.04
10 (0.10) (0.06)
11 (0.08) (0.01)
12 (0.02) (0.04)
13 (0.14) (0.14)
14 0.06 (0.05)
15 (0.08) 0.04
Change in Rent Growth, Change in Total Availability
Rate (-i)
Change in Rent Growth, Change in Total Availability
Rate (+i)
i lag lead
0 (0.04) (0.04)
1 (0.14) (0.32)
2 (0.06) (0.14)
3 (0.11) (0.15)
4 (0.01) (0.07)
5 (0.12) (0.26)
6 0.14 0.24
7 0.03 (0.02)
8 0.09 0.08
9 0.13 0.01
10 0.12 0.20
11 0.10 (0.02)
12 0.02 0.31
13 0.09 (0.12)
14 (0.04) 0.25
15 0.08 0.13
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APPENDIX III: Cross-Correlograms of Changes in Rent Growth Against Proposed Regression Variables
Cross-Correlogram: Changes in Rent Growth and Changes in Existing RBA
Cross-Correlogram: Changes in Rent Growth and Changes in Number of Active Rotary Rigs in Texas
Cross-Correlogram: Changes in Rent Growth and Changes in Amount of Space Under Construction
Cross-Correlogram: Changes in Rent Growth and Changes in Number of Active Rotary Rigs in the U.S.
Change in Rent Growth, Change
in Existing RBA(-i)
Change in Rent Growth, Change
in Existing RBA(+i)
i lag lead
0 (0.10) (0.10)
1 0.04 0.11
2 (0.21) (0.24)
3 0.18 0.20
4 (0.11) (0.26)
5 (0.04) 0.12
6 (0.17) 0.05
7 0.24 (0.01)
8 (0.23) 0.03
9 0.08 0.29
10 (0.02) (0.25)
11 0.04 0.31
12 (0.03) (0.02)
13 0.02 0.05
14 (0.10) (0.07)
15 0.26 0.14
Change in Rent Growth, Change in Texas Rigs(-i)
Change in Rent Growth, Change in Texas Rigs(+i)
i lag lead
0 0.26 0.26
1 0.12 0.04
2 (0.04) 0.04
3 (0.12) 0.03
4 0.10 (0.08)
5 0.05 (0.02)
6 0.06 0.03
7 0.01 (0.11)
8 0.04 0.03
9 (0.15) (0.10)
10 (0.14) (0.10)
11 (0.10) (0.08)
12 (0.01) (0.04)
13 0.10 (0.02)
14 0.07 0.05
15 (0.00) 0.00
Change in Rent Growth, Change in Space Under Construction(-i)
Change in Rent Growth, Change in Space Under Construction(+i)
i lag lead
0 0.20 0.20
1 (0.14) (0.07)
2 0.13 0.21
3 (0.13) 0.02
4 (0.11) 0.21
5 0.03 0.03
6 0.15 (0.03)
7 (0.31) 0.14
8 0.17 (0.00)
9 (0.07) (0.09)
10 (0.08) 0.08
11 (0.03) (0.14)
12 0.04 (0.06)
13 (0.07) (0.06)
14 0.11 (0.02)
15 (0.20) (0.14)
Change in Rent Growth, Change in U.S. Rigs(-i)
Change in Rent Growth, Change in U.S. Rigs(+i)
i lag lead
0 0.24 0.24
1 0.16 0.04
2 (0.07) 0.03
3 (0.10) 0.04
4 0.09 (0.13)
5 0.08 (0.01)
6 0.02 (0.00)
7 0.02 (0.09)
8 0.04 0.04
9 (0.13) (0.10)
10 (0.14) (0.12)
11 (0.07) (0.05)
12 (0.02) (0.01)
13 0.12 (0.01)
14 0.06 0.04
15 (0.02) 0.03
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