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11.433J / 15.021J Real Estate Economics
Fall 2008
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MIT Center for Real Estate
Week 10: Commercial Markets
• Tracking markets with data: absorption, vacancy,
rent, completions and construction.
• Office space: economic sectors, rental elasticity,
technology and the workplace.
• Industrial space: inventories, manufacturing,
R&D.
• Retail space: centers versus stand-alones, sales,
income, obsolescence.
• Hotels: Is there more than GDP?
MIT Center for Real Estate
Some Market Accounting Fundamentals
vt: Vacancy Rate (vs “availability rate”)
St: Stock of Space
Ct: Construction starts of new space
Abt: net absorption of space
Lt: Average lease term
Nt: Average Renewal rate
Abt= (1-vt)St - (1-vt-1)St-1
St = St-1 + Ct-n
Gross Abs = St (1-Nt)/Lt
Average Lease up time = vt /[(1-Nt)/Lt]
MIT Center for Real Estate
A lease Rent index: Average, Repeat, Hedonic Rent
(CB Vouchers) (average annual$/sqft over lease term)
log(R) = α0 + α1SQFT + α2GROSS1 + α3GROSS2 + α4TERM + α5HIGH
1991
n
+ α6NEW1 + α7NEW2 + Σ β iDi + Σ δjS j
i=1979
(1)
j=1
V ariab le
D en ver
C incinnati
H ou ston
S an
F rancisco
W ashington
C onstant
1. 8153
2. 0887
2. 0700
2. 4211
2. 2169
1
-8. 42e-07
-4. 57e-06
0. 0993
0. 0574
Squar e F eet
1. 08e-06
1
G1
0. 0952
G2
0. 0728
T er m
0. 0290
H igh
3. 35e-07
1
1
0. 1420
1
0. 0633
0. 0196
0. 0203
0. 0260
0. 0120
0. 1048
0. 1293
0. 0586
0. 1119
0. 0361
1
na
1
na
na
D um m y 1980
0. 2860
na
0. 1290
1
na
D um m y 1981
0. 4775
na
0. 3480
0. 3664
D um m y 1982
0. 5992
1
0. 3925
0. 4847
0. 1872
D um m y 1983
0. 5468
0. 1305
0. 3300
0. 4193
0. 2176
D um m y 1984
0. 5394
0. 1385
0. 1995
0. 4879
0. 3996
D um m y 1985
0. 5402
0. 1128
0. 1646
0. 4525
0. 4113
D um m y 1986
0. 3556
0. 1378
0. 1314
0. 3408
0. 4422
D um m y 1979
-0. 0681
0. 0315
0. 0468
1
0. 0172
-1. 03e-07
0. 0316
0. 0082
0. 0790
0. 1177
0. 0684
1
1
MIT Center for Real Estate
Lease (Rent) Fundamentals:
• An Efficient forward market implies:
R t,n = R t,n-m + R t+n-m,m
[The first superscript designates the date for which
occupancy begins, the second the lease term]
or: the difference between a three year lease and a
5 year lease signed today equals a forward
commitment (three years hence) for a 2 year lease.
• Hence if the market is expected to improve, longer
lease terms command a higher average rent and
vice-versa.
• How to test the efficiency theory?
MIT Center for Real Estate
For the last 25 years, on average lease rent is 2%+ higher for
each year longer in Term. But yearly, this varies inversely
with market vacancy. Why? (Minneapolis Data)
0
Term
Vacancy
2008.4
2009.3
2010.2
-0.1000
2005.4
2006.3
2007.2
2008.1
5
2002.4
2003.3
2004.2
2005.1
-0.0500
2000.3
2001.2
2002.1
10
1998.2
1999.1
1999.4
0.0000
1997.3
15
1995.2
1996.1
1996.4
0.0500
1992.2
1993.1
1993.4
1994.3
20
1991.3
0.1000
1990.1
1990.4
25
1987.1
1987.4
1988.3
1989.2
0.1500
MIT Center for Real Estate
In Most Markets large blocks of space rent for less
than small! Why isn’t the whole worth more than the
sum of the parts?
S iz e D is c o u n t in O f f ic e M a r k e t
30
28
26
24
22
Space Discount
20
18
16
14
12
10
1985
R e a l T W R R e n ts
R e a lG a p 2 0 K R e n t
1987
1989
1991
1993
F o re c a s t5 y2 0 k
R e a lG a p 1 0 0 K R e n t
1995
1997
1999
2001
2003
F o re c a s t5 y1 0 0 k
2005
2007
2009
2011
2013
2015
MIT Center for Real Estate
Lease - versus – Own?
• Tax implication? Leases are deductions, as are debt
payments.
• Accounting implications? Only ownership shows on the
balance sheet (loophole).
• Corporate Prestige. But you can easily purchase the
naming rights to a building.
• Firm Specific Capital. Facility has little other use, and so
developer would charge higher lease payments since
residual value is zero. Holdup issue.
• Expansion and other options.[see: Benjamin, et.al.]
• Correlation between firm’s business and local real estate
market.
• If your corporate cost of capital is Ic, how is IcP >< R?
MIT Center for Real Estate
Office (3,110 million sq. ft)
Multiple
Owners
Office and
Industrial Space
Usage in square
feet by Tenure,
1991
(50 metro areas
CBRE)
Single Owner
Multiple
Renters
Single Renter
Industrial (9,055 million sq. ft)
Multiple
Owners
Multiple
Renters
Single Owner
Single Renter
MIT Center for Real Estate
The North American Industry Classification System
(NAICS) & Office Employment
11 Agriculture, Forestry, Fishing, and Hunting
21 Mining
22 Utilities
23 Construction
31-33 Manufacturing
42 Wholesale Trade
44-45 Retail Trade
48-49 Transportation and Warehousing
51 Information
52 Finance and Insurance
53 Real Estate and Rental and Leasing
54 Professional, Scientific and Technical Services
55 Management of Companies and Enterprises
56 Administrative and Support and Waste Management and Remediation Services
61 Educational Services
62 Health Care and Social Assistance
71 Arts, Entertainment and Recreation
72 Accommodation and Food Services
81 Other Services (except Public Administration)
92 Public Administration
MIT Center for Real Estate
Office Space usage by SIC
Office Employment* in Dallas and Chicago, 1989
Dallas
Standard Industrial Classification
(SIC)
Chicago
Total (thousands)
Office (thousands)
Total (thousands)
Office (thousands)
Manufacturing
184.7
16.2
499.1
49.4
Mining
17.4
10.3
1.3
0.6
Construction
47.5
0.6
93.8
0.4
Transportation, Communication, and
Utilities (TCU)
92.4
7.1
148.5
6.2
Trade
287.9
28.1
613.6
51.1
Finance, Insruance, and Real Estate
(FIRE)
122.9
122.9
246.0
246.0
Services
314.8
105.8**
730.2
227.0
Total Private
1067.6
291.0
2332.5
580.7
adapted from DiPasquale and Wheaton (1996)
* Those employees occupying separate office space from on-site manufacturing
** includes advertising, computer and data processing, credit reporting, mailing and reproduction, legal and social services,
membership organizations, engineering and management services.
MIT Center for Real Estate
Rental Elasticity of Office Space Demand
[see also: Hakfoort and Lie]
Office space per square foot and rent in US$
All sectors/All cities
600
Avg sq. ft per worker
500
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
Rent in US$ per sq. ft
Figure by MIT OpenCourseWare.
MIT Center for Real Estate
Square feet/worker.
Changes in professional
Occupation ratio: Rental cost of occupancy, technology?
Occupied Square Feet Per Worker
TW Rent Index, 2004$ psqft
180
22
175
20
Occupied sqft Per Worker
TW Rent Index, 2004$
2009
24
2008
185
2006
26
2004
190
2002
28
2001
195
1999
30
1997
200
1995
32
1994
205
1992
34
1990
210
1988
36
1987
215
1985
38
1983
220
1981
40
1980
225
MIT Center for Real Estate
Impact of Technology: Breakdown of Workers at
Home (x1000)
1991
1997
Growth (%)
19,967
21,478
7.57
Paid
7,432
10,116
36.11
35 Hours or
More
1,070
1,791
67.38
94
583
520.21
Total at Home
Full-time, not
self-employed
Source: Bureau of Labor Statistics, Torto Wheaton Research
MIT Center for Real Estate
Investment, Office Employment and Office
Net Absorption (1981-2009): bricks vs clicks
25%
20%
15%
10%
5%
0%
-5%
-10%
Office Employment
Real Private Non-res Investment
Net Absorption
2009.3
2008.1
2006.3
2005.1
2003.3
2002.1
2000.3
1999.1
1997.3
1996.1
1994.3
1993.1
1991.3
1990.1
1988.3
1987.1
1985.3
1984.1
1982.3
1981.1
-15%
Las Vegas
Los Angeles
Long Island
Oakland
Orange County
St. Louis
Washington DC
Boston
Cleveland
Houston
Ventura County
Detroit
Jacksonville
Wilmington
Austin
Orlando
Fort Worth
Tampa
MIT Center for Real Estate
How to Explain the recent Absorption Deficit
Across Markets
(1992 q1 to 1999 q4)
1.8%
1.6%
1.4%
1.2%
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
-0.2%
-0.4%
MIT Center for Real Estate
Across Markets, Deficit Explained by Numerous Factors
(dependent variable: office job growth – absorption)
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.73304
0.53735
0.48814
0.00268
53
Coefficients Standard Error
Intercept
0.00532573
0.00255
% of 1999 Single-Tenant Stock less % 1992 Single-Tenant Stock 0.05573823
0.02557
% of New Office Using Service jobs from 92to99 that Were B&P
0.01157904
0.00286
1999.4 Multi-Tenant Office Stock
-0.00000001
0.00000
FIRE Employment as % of all Office Employment 1999.4
-0.01309827
0.00583
Average quarterly TW Rent growth (1999.4$) 1992.1 to 1999.4
0.27158579
0.06570
Variable
% of 1999 Single-Tenant Stock less % 1992 Single-Tenant Stock
% of New Office Using Service jobs from 92to99 that Were B&P
1999.4 Multi-Tenant Office Stock
FIRE Employment as % of all Office Employment 1999.4
Average quarterly TW Rent growth (1999.4$) 1992.1 to 1999.4
t Stat
2.09023
2.17969
4.04977
-1.38736
-2.24851
4.13370
Observations
Essentially Part of the Intercept
More B&P Employment, Bigger Deficit
Weak Evidence that Deficit is Smaller in Larger Markets
Smaller Deficit in Markets With FIRE Concentration
The Demand for Space is Sensitive to Rental Growth
MIT Center for Real Estate
Office Tenant Base: Increasingly Smaller
Service Companies, Less Large Financial
Companies
Change in Jobs x 1,000
225
150
75
0
-75
-150
Financial Activities
TWR Office Outlook XL
Office-Services
2006.3
2006.1
2005.3
2005.1
2004.3
2004.1
2003.3
2003.1
2002.3
2002.1
2001.3
2001.1
2000.3
2000.1
1999.3
1999.1
-225
MIT Center for Real Estate
Industrial Space Occupancy by SIC and Building Use
(CBRE, 1991)
Industrial Tenants, 1991
Building Use (millions of sq. ft)
Industry of Occupant (SIC)
Manufacturing
Distribution
R&D
Other
2422.8
807.1
140.4
2.7
Transportation / Communication / Utilities (TCU)
50.8
474.3
12.4
0.7
Wholesale Trade
260.1
1047.0
43.8
2.5
Retail Trade
19.4
175.1
5.8
0.2
200.5
Services
90.6
202.2
129.8
1.8
424.4
Other
73.0
190.4
21.6
31.1
316.1
Total
2916.7
2896.1
353.8
39.0
6,205.60
Manufacturing
Total
3,373.00
538.3
1,353.40
adapted from DiPasquale and Wheaton (1996)
MIT Center for Real Estate
Velocity (J.I.T. technology) =
Shipments (sales) / Inventories
Index 1980 = 100
(Billions $, seasonally adjusted)
140
130
120
110
100
90
80
Sales/G DP
Inventories/G DP
Inventories/Sales
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
70
MIT Center for Real Estate
Warehouse Demand: Δ Space/worker (+10%)=
Δ space/$inventory (-60%) +
Δ $ inventory/worker (+70%)
7000.0
1500.0
6000.0
1450.0
5000.0
1400.0
4000.0
1350.0
3000.0
1300.0
2000.0
1000.0
1989.1
1989.3
1990.1
1990.3
1991.1
1991.3
1992.1
1992.3
1993.1
1993.3
1994.1
1994.3
1995.1
1995.3
1996.1
1996.3
1997.1
1997.3
1998.1
1998.3
1999.1
1999.3
2000.1
2000.3
2001.1
2001.3
2002.1
2002.3
2003.1
2003.3
2004.1
2004.3
2005.1
1250.0
Whs Occupied Sqft Per Worker
Whs Occupied Sqft Per Inventories
MIT Center for Real Estate
Industrial demand: Δspace/worker (+40%) =
Δproduction/worker (+70%) +
Δspace/production (-30%)
600.0
45000.0
40000.0
500.0
35000.0
400.0
30000.0
25000.0
300.0
20000.0
200.0
15000.0
10000.0
100.0
5000.0
0.0
1989.1
1989.3
1990.1
1990.3
1991.1
1991.3
1992.1
1992.3
1993.1
1993.3
1994.1
1994.3
1995.1
1995.3
1996.1
1996.3
1997.1
1997.3
1998.1
1998.3
1999.1
1999.3
2000.1
2000.3
2001.1
2001.3
2002.1
2002.3
2003.1
2003.3
2004.1
2004.3
2005.1
0.0
Mfg Occupied Sqft Per Worker
Mfg Occupied Sqft Per Ind Prod
MIT Center for Real Estate
Logistics (S.C.M.): what enters the country
at one place does not stay there!
MIT Center for Real Estate
Logistics (S.C.M.): what determines which port is
used by whom, for what, from where?
NA
Import
Traffic
[U.S./Canada/Mexico Container Traffic (TEUs)]
1.8
2.0
14.4
19.4
.8
1.8
.8
MIT Center for Real Estate
Trade Flows and Warehouse Demand. Why do:
Imports need more space than exports?
Ports often need none?
W hs. Net Absorption (sf x 1000)
YoY % Growth in X,M (BoP basis,
5%
0
0%
-50,000
-5%
-100,000
-10%
W hs Net Absorption (sf x 1000) (L)
Export Growth (R)
Import Growth (R)
2006
50,000
2004
10%
2002
100,000
2000
15%
1998
150,000
1996
20%
1994
200,000
1992
25%
1990
250,000
MIT Center for Real Estate
Retail sales closely follow personal income, but
grow at only 80% of the rate! (times series studies have
difficulty identifying additional demographic effects)
8
6
4
2
0
-2
-4
Real Income Grow th
Real Retail Sales Grow th
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
-6
MIT Center for Real Estate
Retail Sales across 52 cities: more than just
personal income: labor force participation
and climate matter as well.
Clothing (logs):
sales/pop = .41 inc/pop + .37 emp/pop
+ .45 Jan Temp -.03 pop [R2 : .53]
Food/Beverage eaten in (logs):
sales/pop = .89 inc/pop - .26 emp/pop
+.09 Jan Temp -.06 pop [R2 : .58]
MIT CRE Thesis: 2008
MIT Center for Real Estate
Some contend that housing wealth impacts retail demand, but Housing
Wealth has had only Small Impact on Consumption! Much of recent
housing Wealth Gains Went Back into Housing!
Consumption rate, % share of disposable income
Existing single family house price, % change year ago
105
15
13
100
11
9
7
95
5
3
90
1
-1
-3
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
85
Consumption Rate (L)
House Prices (R)
1998Q1
1998Q2
1998Q3
1998Q4
1999Q1
1999Q2
1999Q3
1999Q4
2000Q1
2000Q2
2000Q3
2000Q4
2001Q1
2001Q2
2001Q3
2001Q4
2002Q1
2002Q2
2002Q3
2002Q4
2003Q1
2003Q2
2003Q3
2003Q4
2004Q1
2004Q2
2004Q3
2004Q4
2005Q1
2005Q2
2005Q3
2005Q4
2006Q1
2006Q2
2006Q3
2006Q4
2007Q1
2007Q2
2007Q3
2007Q4
2008Q1
MIT Center for Real Estate
Hence going forward Housing Related
Sales are going to Suffer the most
Year/Year Change (%)
15
10
5
0
-5
Core Sales Almost at 2003
Minimum
-10
Housing Related
Source: BOC.
Non-Housing Related
MIT Center for Real Estate
1967-1993 growth of: Retail store Sales (from establishments) ,
and alternative measures of retail square feet. Is the US over
supplied with retail space or is demolition widespread?
•
•
•
•
•
•
•
•
•
•
•
Restaurant and Entertainment: 102%
Furniture: 79%
Building Materials: 78%
Other Hard goods (Appliance…): 68%
GM: 46%
Clothing: 31%
Food at home 26%
Personal Income: 83%%
Neighborhood Centers (NRB): 143% (net)
Regional Malls(NRB): 238% (net)
All retail space (FW Dodge) 117% (gross)
MIT Center for Real Estate
But is Construction Moving Beyond the Shopping
Center Format ? Walmart?
Millions of $2001
Sqft (x 1,000)
50,000
50,000
45,000
45,000
40,000
40,000
35,000
35,000
30,000
30,000
25,000
25,000
20,000
20,000
15,000
15,000
10,000
10,000
5,000
5,000
Value put in Place (millions of $2001)
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
0
1970
0
Shopping Center Sqft (x 1,000)
MIT Center for Real Estate
The small E-Commerce Share doubles every 3-4
years: Will clicks cannibalize Bricks next decade?
80
3.1
70
2.6
60
50
2.1
40
1.6
30
20
1.1
10
0
0.6
99Q4
00Q2
00Q4
01Q2
01Q4
Retail sales, % change year ago (L)
02Q2
02Q4
03Q2
03Q4
04Q2
E-commerce, % change year ago (L)
04q4
05q2
05q4
E-commerce, % of total (R)
MIT Center for Real Estate
The Lodging Industry (Smith Travel Research)
[200 national hotel chains]
•
•
•
•
•
•
•
Rooms available (potential nights) = “supply”
Change in Rooms available = “net additions”
Rooms sold = “demand”
Change in Rooms Sold = “absorption”
Rooms Sold/Available = “occupancy”
ADR = Total room revenue/rooms sold
REVPAR = ADR x occupancy
MIT Center for Real Estate
National Hotel Market
Rooms Sold vs. Real GDP: GDP and room rates are
all that matter!
10000
3000000
9000
2500000
8000
2000000
7000
1500000
5000
6000
4000
1000000
3000
2000
500000
1000
0
0
69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
Rooms Sold
Real GDP
MIT Center for Real Estate
Can you detect the “rental elasticity of hotel
demand?
150
9000000
145
8000000
140
135
7000000
130
125
6000000
120
5000000
115
110
4000000
105
real income
rooms sold
Real Room Rate
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
100
1987
3000000
MIT Center for Real Estate
Full Service Hotels at 9/11: Learning
from the first Iraq war!
National Model Forecasts from 2002 (1st Quarter)
Forec as t
74
60,000
72
40,000
70
20,000
68
0
66
-20,000
64
-40,000
62
-60,000
60
Completions
A bs orption
Oc c upanc y
Occupancy Rate (%)
(Number of Rooms)
Completions/Absorption
80,000
MIT Center for Real Estate
Just as Forecast: A Remarkable Post
9/11Turnaround: Occupancy first then ADR =
Forecast
Mean Reversion
Real ADR Index ($ per room night)
Occupancy Rate, %
150
76
140
73
130
70
120
67
110
64
100
61
90
1987.4
58
1990.4
1993.4
1996.4
TWR Real ADR
1999.4
2002.4
2005.4
Occupancy
2008.4