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11.433J / 15.021J Real Estate EconomicsFall 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?


Some Market Accounting Fundamentals

vt: Vacancy Rate (vs “availability rate”)St: Stock of SpaceCt: Construction starts of new spaceAbt: net absorption of spaceLt: Average lease termNt: Average Renewal rateAbt= (1-vt)St - (1-vt-1)St-1 St = St-1 + Ct-n

Gross Abs = St (1-Nt)/LtAverage Lease up time = vt /[(1-Nt)/Lt]


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 βiDi + Σ δjSj (1) i=1979 j=1

V ariable

Denver

C incinnati

H ouston

San Francisco

W ashington

C onstant 1. 8153 2. 0887 2. 0700 2. 4211 2. 2169

Square F eet 1. 08e-06 1 3. 35e-07 1 -8. 42e-07 -4. 57e-06 -1. 03e-07 1

G 1 0. 0952 0. 0993 0. 0574 0. 0172 1 0. 1420

G 2 0. 0728 0. 0315 1 0. 0316 1 0. 0633 0. 1177 1

T erm 0. 0290 0. 0196 0. 0203 0. 0260 0. 0120

H igh 0. 1048 0. 1293 0. 0586 0. 1119 0. 0361

D um m y 1979 -0. 0681 1 na 0. 0082 1 na na

D um m y 1980 0. 2860 na 0. 1290 0. 0790 1 na

D um m y 1981 0. 4775 na 0. 3480 0. 3664 0. 0684 1

D um m y 1982 0. 5992 0. 0468 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


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?


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)

-0.1000

-0.0500

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1987

.119

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1988

.319

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1991

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1993

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1994

.319

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1996

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1997

.319

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1999

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2000

.320

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2002

.120

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2003

.320

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2005

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2006

.320

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2008

.120

08.4

2009

.320

10.2

0

5

10

15

20

25

Term Vacancy

MIT Center for Real EstateIn 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

1 0

1 2

1 4

1 6

1 8

2 0

2 2

2 4

2 6

2 8

3 0

1 9 8 5 1 9 8 7 1 9 8 9 1 9 9 1 1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5

R e a l T W R R e nts F o re c a s t5 y2 0 k F o re c a s t5 y1 0 0 kR e a lG a p 2 0 K R e nt R e a lG a p 1 0 0 K R e nt

Space Discount


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?


Office and Industrial Space Usage in square feet by Tenure,

1991 (50 metro areas

CBRE)

Office (3,110 million sq. ft)

Multiple RentersSingle Renter

Single Owner

Multiple Owners

Industrial (9,055 million sq. ft)

Multiple Renters

Single Renter

Single Owner

Multiple Owners

MIT Center for Real EstateThe North American Industry Classification System

(NAICS) & Office Employment11 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 Services61 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


Office Space usage by SICOffice Employment* in Dallas and Chicago, 1989

Dallas Chicago

Standard Industrial Classification (SIC) 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.


Rental Elasticity of Office Space Demand[see also: Hakfoort and Lie]

600

500

40

40 50 60 70 80 90 100

30

30

20

20

10

100

0

Avg

sq. f

t per

wor

ker

Rent in US$ per sq. ft

Office space per square foot and rent in US$All sectors/All cities

Figure by MIT OpenCourseWare.


Square feet/worker. Changes in professional Occupation ratio: Rental cost of occupancy, technology?

175

180

185

190

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1980

1981

1983

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2008

2009

20

22

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26

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38

40

Occupied sqft Per Worker TW Rent Index, 2004$

Occupied Square Feet Per Worker TW Rent Index, 2004$ psqft


Impact of Technology: Breakdown of Workers at Home (x1000)

1991 1997 Growth (%)

Total at Home 19,967 21,478 7.57

Paid 7,432 10,116 36.11

35 Hours orMore 1,070 1,791 67.38

Full-time, notself-employed 94 583 520.21

Source: Bureau of Labor Statistics, Torto Wheaton Research

MIT Center for Real EstateInvestment, Office Employment and Office

Net Absorption (1981-2009): bricks vs clicks

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

1981

.1

1982

.3

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.1

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.3

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.3

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.1

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2009

.3

Office Employment Real Private Non-res Investment Net Absorption


How to Explain the recent Absorption Deficit Across Markets

(1992 q1 to 1999 q4)

-0.4%-0.2%0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%

Tam

pa

Fort

Wor

th

Orla

ndo

Aus

tin

Wilm

ingt

on

Jack

sonv

ille

Det

roit

Vent

ura

Cou

nty

Hou

ston

Cle

vela

nd

Bos

ton

Was

hing

ton

DC

St. L

ouis

Ora

nge

Cou

nty

Oak

land

Long

Isla

nd

Los

Ang

eles

Las

Vega

s

MIT Center for Real EstateAcross Markets, Deficit Explained by Numerous Factors

(dependent variable: office job growth – absorption)

Multiple R 0.73304R Square 0.53735Adjusted R Square 0.48814Standard Error 0.00268Observations 53

Coefficients Standard Error t StatIntercept 0.00532573 0.00255 2.09023% of 1999 Single-Tenant Stock less % 1992 Single-Tenant Stock 0.05573823 0.02557 2.17969% of New Office Using Service jobs from 92to99 that Were B&P 0.01157904 0.00286 4.049771999.4 Multi-Tenant Office Stock -0.00000001 0.00000 -1.38736FIRE Employment as % of all Office Employment 1999.4 -0.01309827 0.00583 -2.24851Average quarterly TW Rent growth (1999.4$) 1992.1 to 1999.4 0.27158579 0.06570 4.13370

Variable Observations% of 1999 Single-Tenant Stock less % 1992 Single-Tenant Stock Essentially Part of the Intercept% of New Office Using Service jobs from 92to99 that Were B&P More B&P Employment, Bigger Deficit1999.4 Multi-Tenant Office Stock Weak Evidence that Deficit is Smaller in Larger MarketsFIRE Employment as % of all Office Employment 1999.4 Smaller Deficit in Markets With FIRE ConcentrationAverage quarterly TW Rent growth (1999.4$) 1992.1 to 1999.4 The Demand for Space is Sensitive to Rental Growth

MIT Center for Real EstateOffice Tenant Base: Increasingly Smaller Service Companies, Less Large Financial

Companies

-225

-150

-75

0

75

150

225

1999

.1

1999

.3

2000

.1

2000

.3

2001

.1

2001

.3

2002

.1

2002

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2003

.1

2003

.3

2004

.1

2004

.3

2005

.1

2005

.3

2006

.1

2006

.3

Financial Activities Office-Services

Change in Jobs x 1,000

TWR Office Outlook XL


Industrial Tenants, 1991

Building Use (millions of sq. ft)

Industry of Occupant (SIC) Manufacturing Distribution R & D Other Total

Manufacturing 2422.8 807.1 140.4 2.7 3,373.00

Transportation / Communication / Utilities (TCU) 50.8 474.3 12.4 0.7 538.3

Wholesale Trade 260.1 1047.0 43.8 2.5 1,353.40

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

adapted from DiPasquale and Wheaton (1996)

Industrial Space Occupancy by SIC and Building Use(CBRE, 1991)


Velocity (J.I.T. technology) = Shipments (sales) / Inventories

70

80

90

100

110

120

130

140

1980

1982

1984

1986

1988

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1992

1994

1996

1998

2000

2002

Sales/GDP Inventories/GDP Inventories/Sales

Index 1980 = 100(Billions $, seasonally adjusted)


Warehouse Demand: Δ Space/worker (+10%)= Δ space/$inventory (-60%) + Δ $ inventory/worker (+70%)

1250.0

1300.0

1350.0

1400.0

1450.0

1500.0

1989

.119

89.3

1990

.119

90.3

1991

.119

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1992

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1994

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1995

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1996

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1997

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1998

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2005

.1

1000.0

2000.0

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4000.0

5000.0

6000.0

7000.0

Whs Occupied Sqft Per Worker Whs Occupied Sqft Per Inventories

MIT Center for Real EstateIndustrial demand: Δspace/worker (+40%) =

Δproduction/worker (+70%) + Δspace/production (-30%)

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100.0

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300.0

400.0

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1990

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1991

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1992

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1994

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1995

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0.0

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Mfg Occupied Sqft Per Worker Mfg Occupied Sqft Per Ind Prod

MIT Center for Real EstateLogistics (S.C.M.): what enters the country

at one place does not stay there!


NA Import Traffic

19.4

14.4

1.8

2.0

1.8

Logistics (S.C.M.): what determines which port is used by whom, for what, from where?

[U.S./Canada/Mexico Container Traffic (TEUs)]

.8

.8

MIT Center for Real EstateTrade Flows and Warehouse Demand. Why do:

Imports need more space than exports? Ports often need none?

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Whs Net Absorption (sf x 1000) (L)Export Growth (R) Import Growth (R)

Whs. Net Absorption (sf x 1000) YoY % Growth in X,M (BoP basis,

MIT Center for Real EstateRetail sales closely follow personal income, but

grow at only 80% of the rate! (times series studies have

difficulty identifying additional demographic effects)

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2000

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Real Income Grow th Real Retail Sales Grow th


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


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15

Consumption Rate (L) House Prices (R)

Consumption rate, % share of disposable income Existing single family house price, % change year ago

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!

MIT Center for Real EstateHence going forward Housing Related

Sales are going to Suffer the most

Source: BOC.

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Q1

Housing Related Non-Housing Related

Year/Year Change (%)

Core Sales Almost at 2003 Minimum


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)


0

5,00010,000

15,000

20,000

25,00030,000

35,000

40,00045,000

50,000

1970

1972

1974

1976

1978

1980

1982

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1992

1994

1996

1998

2000

0

5,00010,000

15,000

20,000

25,00030,000

35,000

40,00045,000

50,000

Value put in Place (millions of $2001) Shopping Center Sqft (x 1,000)

But is Construction Moving Beyond the Shopping Center Format ? Walmart?

Millions of $2001 Sqft (x 1,000)


The small E-Commerce Share doubles every 3-4 years: Will clicks cannibalize Bricks next decade?

0

10

20

30

40

50

60

70

80

99Q4 00Q2 00Q4 01Q2 01Q4 02Q2 02Q4 03Q2 03Q4 04Q2 04q4 05q2 05q40.6

1.1

1.6

2.1

2.6

3.1

Retail sales, % change year ago (L) E-commerce, % change year ago (L) E-commerce, % of total (R)


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


National Hotel MarketRooms Sold vs. Real GDP: GDP and room rates are

all that matter!

0

500000

1000000

1500000

2000000

2500000

3000000

69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99010002000300040005000600070008000900010000

Rooms Sold Real GDP


Can you detect the “rental elasticity of hotel demand?

3000000

4000000

5000000

6000000

7000000

8000000

9000000

1987

1989

1991

1993

1995

1997

1999

2001

2003

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2009

2011

2013

2015

100

105

110

115

120

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130

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140

145

150

real income rooms sold Real Room Rate


Full Service Hotels at 9/11: Learning from the first Iraq war!

National Model Forecasts from 2002 (1st Quarter)

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

80,000

Com

plet

ions

/Abs

orpt

ion

(Num

ber o

f Roo

ms)

60

62

64

66

68

70

72

74

Occ

upan

cy R

ate

(%)

Completions A bsorption Occupancy

Forecas t


90

100

110

120

130

140

150

1987.4 1990.4 1993.4 1996.4 1999.4 2002.4 2005.4 2008.458

61

64

67

70

73

76

TWR Real ADR Occupancy

Real ADR Index ($ per room night) Occupancy Rate, %

Just as Forecast: A Remarkable Post 9/11Turnaround: Occupancy first then ADR =

Mean Reversion Forecast

11.433j / 15.021j real estate economics · mit center for real estate for the last 25 years, on...

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