The Highest Use for Residential Development
The Urban Housing
Market, Structures and Density.
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Week 3: The Urban Housing
Market, Structures and Density.
• Hedonic Regression Analysis.
• Shadow “prices” versus marginal costs.
• Land value maximizing FAR.
• FAR and Urban Redevelopment.
• Land Use competition: Highest Price for
Housing – versus – highest use for land
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Urban Housing
• Great diversity from historical evolution, changes
in technology and tastes.
• Multiple attributes to each house: size, baths,
exterior material, style….location
• Consumers value each of these attributes with the
normal law of micro-economics: diminishing
marginal utility.
• Huge industry has evolved to applying statistical
models to understand and predict diverse house
prices:
– Property Tax appraisals.
– Automatic Valuation Services for lenders, brokers…
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Hedonic Regression Analysis
1). Linear:
R = α + β1X1 + β 2X2 + β 3X3 + …
X’s are structural, location attributes
2). Log Linear:
R = e[α + β 1X1 + β 2X2 + β3X3 + …]
ln(R) = α + β1X1 + β2X2 + β3X3 + …
3). Log Log:
R = α X1 β 1 X2 β 2 X3 β 3
…
ln(R) = ln(α) + β1ln(X1) + β2ln(X2) +…
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Dallas apartment rent Hedonic
equation: 1998
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Optimizing House Configuration
• Builders and developers compare the incremental
value of additional house features against their
incremental cost.
• Profit maximizing house: where the cost of an
additional square foot, bath, fireplace falls to the
marginal cost of construction.
• But what about land, lot size, density or FAR?
– FAR: floor area ratio (ratio of floor to land area).
– Density: units per acre.
– Density x unit floor area = FAR
– % of lot “open” = 1-(FAR/stories) (stories>FAR)
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Optimizing House price (P) minus construction cost
(C) as a function of square feet
P (size)
$
C x Size
C
∆P/ ∆Size
S* Size (square feet)
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1). P = α - βF Optimizing FAR
α = all housing and location factors
besides FAR
F = FAR
β = marginal impact of FAR on price
per square foot.
2). C = µ + τ F
µ = “baseline” cost of “stick” SFU
construction
τ = marginal impact of FAR on cost per
square foot
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If each unit of floor are is unprofitable then so is land –
regardless of FAR. As FAR approaches zero, land profit is zero
no matter how profitable floor area.
:C
Co st
$/sq ft Floor
ion
ns truct
Co
Floor Profit Ho u
se P r
ice: P
FAR: F
$/sq ft Land
Land Profit
F* FAR: F
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3). p = F [ P – C] = F[α - µ] – F2[β + τ]
4). ∂p/∂F = [α - µ] – 2F[β + τ] = 0, or
F* = [α - µ] / 2[β + τ] , and
p* = [α - µ]2 / 4[β + τ]
5). How do prices and FAR vary by:
- Location
- Other factors that shift the parameters
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At “better” locations, the price of housing at any FAR is
higher. This yields a substitution of capital for land and
the optimal FAR rises.
st : C
n Co
$/sq ft Floor
ct io
Constru
Floor Profit Hou
se Pr
ice: P
FAR: F
$/sq ft Land
Land Profit
F* FAR: F
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Boston Back Bay Condominium Example
• From 1984 regression: R = 222 – 1.48F, for
new 2-bed, 2-bath with parking on Beacon
hill. (178-1.48F for end of Commonwealth
Ave.
• Construction costs: C = 100+2F
• F* = 17.5, p* = 46million (per acre)
• At F of 4.0, 2-bed, 2-bath existing land has
value of 10.6 million (1/4 as much!)
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How does land use “evolve”?
• City Development evolves from the center
outward – on vacant land at the edge.
• At each time period, there is a “shadow” value
for interior land that is already built upon.
• When does that “shadow” value exceed the
entire value of the existing structures?
• Fires, disasters create vacant land – shaping
development
• Gentrification? [Helms]
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The spatial Pattern of Economic Redevelopment
Re-
FAR
development
1850 1900 1950 2000
At each period
Redevelopment cost (value of existing structures)
Land Rent
1850 1900 1950 2000
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Economic Redevelopment
6). The sunk cost of existing structures generates a
barrier to the smooth adjustment of FAR.
7). Rarely do we see incremental FAR increases.
Rather old uses are destroyed and replace with
new.
8). Existing “older” structures:
P0 = α0 - βF0
δ = demolition cost per square foot
F0 = FAR of existing use
p0 = F0 [α 0 - β F0] :land acquisition cost
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9). p* - p0 > δF0 implies
F*(α-βF*) - F0 (α 0 - β F0) > δF0 +
F*(µ+τF*)
“increase in value of > “demolition plus
land and capital” development cost”
Most likely if α > α 0 (existing capital
deteriorated)
F*> F0 (new use much more dense)
See: [Rosenthal and Helsley].
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Boston Back Bay Condominium Example
(continued)
• Assume that historic properties have 75% of
the structure value versus new. Hence the
value of 1 acre of 4-story brownstones is:
4 x [166.5-1.48x4] x 43560 = 27m
• Thus even with significant demolition costs
the current historic stock might be ready for
“market demolition”.
• Ocean Front in LA? Mid Ring Tokyo?
• The lower existing FAR – the less the
opportunity cost of redevelopment.
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Land competition between groups
10). Pi = α - kid - βiF
d = distance from desirable location
F = FAR
i = 1,2 (different household types)
k1 > k2 , β 1 > β 2
i.e. 1’s value location more and mind
FAR more (value lot size more).
11). ∂Pi/ ∂d = - ki hence P1 steeper than P2
(previous lecture on location of groups)
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11). pi = maxF: F[α - kid - βiF – (µ + τF)]
Fi* = [α - kid - µ] / 2[βi + τ] ,
pi* = [α - kid - µ] Fi* / 2
since β 1 > β 2 , F1* < F2
*
12). ∂p*i/ ∂d = - kiFi*
Even though P1 is steeper than P2 it
could be the case that p*1 is less steep than
p*
2
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Group 1 is willing to pay the most for houses (P) near the
center, but group 2 is willing to pay the most for central land
(p) - it is the most profitable group to develop central land for.
Price: Housing (P), Land (p)
P1
p2 P2
p1
Distance (d)
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Examples of location and land bidding between
groups
• Miami Waterfront has high rise condos
populated by elderly who are never on the
beach. Those on the beach (younger
families) live inland!
• Why would wealthy families live in the
center of Paris or Rome, but at the edge of
Boston or Atlanta (with a few exceptions)?