Download Real Estate and Land Use

Nonresidential Real Estate
© Allen C. Goodman, 2002
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Review 4-Quadrant Model
• Real estate is a useful linkage to land use theory
and actual land usage.
• Demand for real estate (use of space) comes from
occupiers of space. They are willing to rent the
use of space.
• For firms, space is a factor of production.
• For households space is a commodity
• Supply of real estate comes from the construction
sector and depends on the price of those assets
relative to the cost of replacing or constructing
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them.
Two explicit linkages
• 2 explicit linkages between the asset market
and the property market
– Rent levels in the property market are central in
determining the demand for real estate assets.
– In the construction or development sector – if
construction increases and the supply of assets
grows, not only are prices driven down in the
asset market, but rents decline in the property
market as well.
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Four Quadrant Model
Rent R ($)
P = R/ 0.05
(1)
(2)
(3)
(4)
R = 40 – (S/10E)
C = (P – 200)/5
S = 100 C, and
P = R/i
320
(5) S = [E/(iE +2)][800 –4000i]
Price P ($)
If E = 10 m; i = 0.5,
S = 240 sq.ft./worker
 24 m sq.ft. of C/yr.
C = (P – 200)/5
 Rents = $16/sq.ft.
 P = $320/sq.ft.
R = 40 – (S/10E)
16
240 Stock S (sq. ft.)
24
S = 100 C
4
Construction C (sq. ft.)
Four Quadrant Model
Suppose  falls from 0.05
P = R/ 0.05
to 0.04.
P = R/ 0.04
Q2 line rotates.
New equilibrium has to
satisfy other
conditions.
Price rises.
Stock increases.
Price P ($)
Rent falls.
Trace it through.
C = (P – 200)/5
Rent R ($)
R = 40 – (S/10E)
Stock S (sq. ft.)
S = 100 C
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Construction C (sq. ft.)
R = 40 + z – (S/10E)
Demand Shift
Rent R ($)
P = R/ 0.05
(1)
(2)
(3)
(4)
R = 40 + z – (S/10E)
C = (P – 200)/5
S = 100 C, and
P = R/i



S >240 sq.ft./worker
>24 m sq.ft. of C/yr.
Rents > $16/sq.ft.
P > $320/sq.ft.
R = 40 – (S/10E)
16
320
Price P ($)
240 Stock S (sq. ft.)
24
C = (P – 200)/5
S = 100 C
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Construction C (sq. ft.)
Natural Vacancy Rates
• Comes from inventory theory
• What are inventories?
• Why do firms keep them?
– Orders and production are not evenly matched.
• Why don’t they like to keep them?
– Costs $ to hold them, guard them, maintain
them. $ could be used elsewhere.
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How does this fit in for RE
• Landlords hold vacant space to satisfy needs of
demanders.
• It’s optimal to hold a certain amount of vacant
space for the same reason it’s optimal for
merchants to hold inventory.
• What are landlords’ costs?
–
–
–
–
Property taxes
Interest on the mortgage
Insurance
Security
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Suggested relationships
 R = f (VA – VN); VA = actual; VN = natural
As (VA – VN),  R .
Key part of the argument is that rents are slow
to change. Why?
Long term leases (typically 3 – 15 years on the
office market)
High transactions and search costs, particularly for
buyers.
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Supply is slow to adjust
Additions to office space stock are no more
than 1 – 2% per year. If vacancy rate V =
0.05S, and C = 0.01S, then:
C/V = 0.20  sizable, but not instant.
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Possible relationship
VN = f (D, Tp, TI, I, r, i)
VN = Natural vacancy rate
D = Change in demand (+, because landlords can
reasonably expect higher returns from keeping the property
off the market.
TI = Income tax rate (+. Because tax shelter aspect of real
estate  more office investment)
Tp, I, r = Property tax, insurance, interest rate (-, because
cost of holding idle resources ).
i = inflation (+, generally, because of assumption that real
estate is a good hedge against inflation).
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Possible relationship
Vacancy gap G
G = VN – VA.
Changes in rental rates, R, are functions of inflation i and
the vacancy gap G.
R = f (i, G)
i  nominal terms
G  real terms
Let’s draw a picture
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Vacancy Dynamics
R = f (G)
Start at eq’m at which
vacancy rate = natural
rate.
This is consistent with a
constant rate of
construction, to
replace buildings that
wear out.
Vacancy rates, VN and VA
VA = VN
-
+ Change in real rent, R
S
S
C = h(R)
Construction C as a ftn of S
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Vacancy Dynamics
R = f (G)
Suppose we have a shift in QI
of the 4-Quad diagram.
 Decrease in V below the
natural vacancy rate. This
tends to push up real rent
so R.
Increase in R will eventually
lead to increases in
construction.
Since construction rises above
replacement level, stock
eventually  from S1 to S2.
Vacancy rates, VN and VA
G
VA = VN
VA
Change in real rent, R
S
S
C = h(R)
Construction C as a ftn of S
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Vacancy Dynamics
Since construction rises
above replacement level,
stock eventually  from
S1 to S2.
Vacancy rate  and G ,
reducing growth of real
rents.
Eventually VA  VN, R 
0.
New construction 
replacement percentage,
but replacement level is
associated with new,
higher stock of space S2.
R = f (G)
Vacancy rates, VN and VA
G
VA = VN
VA
Change in real rent, R
S
S
C = h(R)
Construction C as a ftn of S
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Vacancy Dynamics
What about negative demand
shock.
Key point is that minimum
construction is 0.
R = f (G)
Vacancy rates, VN and VA
VA
G
VA = VN
So, when demand , office mkt
may be left w/ high vacancy
and declining rents for a
while.
Must wait for depreciation, and
+ growth in demand
Change in real rent, R
S
S
C = h(R)
Construction C as a ftn of S
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We’ve concentrated on demand shocks
• Supply shocks
– In mid to late 1980s cost of financing new
office construction decreased.
– Interest rates fell.
– Lots of tax shelters led to a lot of building,
possibly overbuilding.
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Estimating VN
Does VN change or doesn’t it?
For a city,
R = b0 + b1E – b2V + 
R = change in base rent/sq.ft.
E = change in operating expenses
V = actual vacancy rate
If we estimate this city by city then we can get a
“natural” rate.
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Estimating
R = b0 + b1E – b2V + 
R = change in base rent/sq.ft.
E = change in operating expenses
V = actual vacancy rate
Schilling et al. found rates varying from
1% (New York City) to over 20%
(Kansas City)
Rate was low in Chicago, San Francisco,
Atlanta
Rate was high in Portland, Spokane,
Pittsburgh
WHY so different?
Central tendency through 70s and
90s was 8 – 9%.
R
VN
Set E = 0, or
E = constant
Solve for V
such that
R = 0!
V
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Bargaining Model
Wheaton/Torto use a “bargaining model.”
Argue that rents are set in bargaining between
landlords and tenants.
More tenants  higher optimal rental rate by
landlords.
Large amount of vacant space  more tenants
opportunities and lower optimal rental rate. Here
rental rates adjust rather than vacancy rates.
No role, really for new construction to influence rents.
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Other critiques
• Distinction between vacant and abandoned properties is
sometimes artificial. Vacant properties exert market
pressure – abandoned ones do not. If some cities are more
strict about tearing down abandoned buildings, their
vacancy rates will look low, when they’re really not.
• Need to model shifts in the structure of relationships that
produce VN. What if office tenants are moving to suburbs?
• Leases are becoming shorter and tenant turnover is
becoming higher. This would tend to lower VN.
• Many metro areas have initiated slow-growth policies to
limit office growth. Would tend to limit adjustment.
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Recent Research – San Francisco FED
Table 1
Natural Vacancy Rates, 2001.Q2
Estimated natural
vacancy rate
Actual
vacancy rate
Boston
7.2
8.7
Houston
17.0
13.6
Los Angeles
12.2
14.1
Phoenix
15.0
16.9
Portland
10.9
9.9
Salt Lake City
13.3
15.3
San Francisco
7.9
10.3
Seattle
10.9
9.4
http://www.frbsf.org/publications/economics/letter/2001/el2001-27.html
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John Krainer