Transcript 580_l20

Housing Demand and Supply
Today
• Return Exam
• Second Paper – Discussion
• Housing Services and Supply
Demand
• Related to:
– Price
– Income
– Demographics
Hhsize = 2
• HH Size
• “Life Cycle”
Hhsize = 6
High income
Low income
Housing, h
Income Elasticities
• If our income increases by $500, do we
move? Why?
• Economists feel that the appropriate
measure to use is “permanent income,”
related to wealth.
• Permanent income elasticities are probably
somewhere between 0.5 and 1.0. Best
guess may be 0.5 to 0.7. Discuss.
Price Elasticities
• Think back. How did we get price of
housing?
• Is a $100,000 house half as expensive (per
unit housing) as a $200,000 house?
• Presumably price of housing decreases as
you move further out. Why?
• Most estimates of price elasticity of demand
are less elastic than -1.0 (between 0 and -1.0)
Price Elasticity and Expenditures
• Price elasticity is probably around -0.7 in absolute
value.
• Suppose you own a house worth $100,000, and
value is a straight multiple of rents (housing
prices).
• As you move further out, price of housing falls by
20%, and that price elasticity is -0.7. What
happens to expenditures?
• E = (% D Q)/ (% D P).
Price Elasticity and Expenditures
• E = (% D Q)/ (% D P).
• -0.7 = (% D Q) / (-0.2) -- Why?
• 0.14 = (% D Q)
So, we’re buying 14% more housing, at 80% of the
previous price.
New house will cost:
V* = 100 * (1.14) * (0.8)
V* = 91.2.  Our expenditures .
Moving Costs
• Changing housing consumption is costly.
Why?
• You have to MOVE.
– Search costs
– “Adventure in moving”
• O’Sullivan gives one graph. I’m going to
give you a different one.
Suppose
• You just got
married.
• You want 2 kids.
• You expect your
income to go up a
little bit each
year.
• Moving is a pain.
• What do you do?
Adjustment w/o
Moving costs
Late y
Early y
Housing, h
BUT
• If you move it
costs you … and
you KNOW it
Adjustment w/
Moving costs
Late y
Early y
• INSTEAD, you
buy a little more,
early …
• And a little less,
late.
Housing, h
What happens?
• You avoid the moving costs.
• Point here, is that households don’t move
every time their incomes change …
• Or every time the housing price changes.
• We want a story that is realistic.
Supply
• Think, for now, of housing as entirely rental stock.
• What do we know?
– It is durable. Dwellings can last for 100 years
or more.
– Most of our housing supply comes from “used”
stock, rather than new stock.
– Supply of services is pretty inelastic. Only 2 to
3% of the housing on the market in any year is
new.
Housing Services and Housing Supply
$ of rent
• Assume owner has
bought the house
new.
• Rents it at $1
per/unit of
services.
• Provides Q*.
Why? Excel - OS_CH14
Revenue
Maximum Profits
Cost
Q*
Quantity of services, Q
Housing Services and Housing Supply
$ of rent
• What happens as
house gets older?
• A> more expensive
to maintain
Revenue
Maximum Profits
Cost
• We provide less of it.
Q**
Q*
Quantity of services, Q
Marginal Analysis
• House ages,
provide less
MC
$
MR
• Price (MR) rises,
provide more
Quantity
Q*
Table 2 – Percentage Population and Housing Unit Changes – 1970 – 2000
1970
Population
% Pop.
% Oc Unit.
% Pop.
% Oc Unit.
% Pop.
% Oc Unit
D 1970-80 D 1970-80 D 1980-90 D 1980-90 D 1990-2000 D 1990-2000
New York
NY
7,894,851
-11.0
-1.7
3.5
1.1
8.9
6.9
Chicago
IL
3,362,825
-11.2
-3.9
-7.6
-6.4
4.0
3.5
Los Angeles
CA
2,816,111
5.2
10.0
16.1
7.0
5.8
4.7
Philadelphia
PA
1,948,609
-14.3
-3.5
-6.3
-2.7
-4.4
-2.2
Detroit
MI
1,511,336
-22.7
-13.8
-15.7
-14.7
-7.8
-10.6
Houston
TX
1,232,407
25.7
41.9
2.2
2.3
18.0
15.2
Baltimore
MD
905,759
-14.1
-2.7
-6.7
-1.8
-12.2
-6.9
Dallas
TX
844,189
6.9
23.3
10.8
12.4
16.6
11.7
Washington
DC
756,510
-16.9
-3.6
-5.0
-1.4
-5.9
-0.5
Cleveland
OH
751,046
-26.8
-12.9
-12.6
-8.9
-5.5
-4.7
Indianapolis
IN
744,570
-6.1
9.8
4.3
11.5
6.7
9.2
Milwaukee
WI
717,124
-12.0
2.0
-1.3
-0.5
-5.1
-3.5
San Francisco
CA
715,674
-5.3
1.3
6.4
2.2
7.0
7.6
San Diego
CA
696,566
22.8
34.3
23.7
23.4
9.7
10.4
San Antonio
TX
654,289
18.3
30.3
17.4
23.1
20.1
21.5
Supply Curve May Be Kinked
Price
New housing is built
with about CRTS.
Older housing – once
it’s built, it’s built.
Glaeser and Gyourko
(2005) tell this type of
story.
Yet, we do see
substantial decrease
in inner city supply.
Supply
new
old
Housing
Table 5 – Asymmetric Supply Estimates – Instrumental Variables
Table 5 –
Asymmetric
Supply
Estimates –
Instrumental
Variables
Supply Increases
(1)
3SLS
(2)
Constrained
3SLS
Supply Decreases
(3)
(4)
Constrained
3SLS
3SLS
1970-1980
N
Supply Elasticity
Standard Error
300
1.3244
0.1470
288
1.2902
0.1460
50
0.1004
0.0693
62
0.2569
0.0486
265
0.9332
0.3703
288
0.9140
0.2386
86
0.0849
0.0346
62
0.2296
0.0320
N
Supply Elasticity
Standard Error
269
0.9972
0.2244
288
0.9361
0.1730
82
-0.1025
0.0341
62
-0.0899
0.0458
Three Decade Means
1.0849
1.0467
0.0276
0.1322
1980-1990
N
Supply Elasticity
Standard Error
1990-2000
Pooled Estimates – Three Decades
N
Supply Elasticity
SEE
836
1.2373
0.1408
218
0.0847
0.0292
Sources
Goodman, Allen C., “The Other Side of Eight
Mile,” Real Estate Economics 33 (2005): 539569
Goodman, Allen C., “Central Cities and Housing
Supply: Growth and Decline in U.S. Cities,”
Journal of Housing Economics 14 (December
2005): 315-335