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11.433J / 15.021J Real Estate Economics
Fall 2008
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MIT Center for Real Estate
Week 9: Housing Markets
• Sales, mobility and turnover: the market for for
housing services. Gross demand.
• Vacancy, sales time and prices: the “large” impact
of small net changes.
• The net demand for housing.
• The full annual cost of housing ownership:
consumption and investment motives.
• Housing demand “bubbles”.
• New Development and the behavior of housing
supply.
MIT Center for Real Estate
Gross annual flows in the US housing market (2000)
Annual Growth:
.81%
Population: 275m
Household size:
2.62
Annual Growth:
.83%=.832M
Households: 105.48m
2.5m HH
7.4m HH
Renters:
35.6m HH
NET: +.21M
Owners:
69.8m HH
2.5m HH
NET: +.62m
2.6m HH
MIT Center for Real Estate
As income increases so does housing expenditure: what is
the income elasticity (∂E/E)/(∂y/y)?
Average Value of Home Owned by Married Couples As a Function of Income, 1989 AHS
Age of Head of Household
With Children
Without Children
Household Income
25-34
35-44
45-54
55-64
65+
Less than $20,000
43,822
70,817
65,407
72,928
81,514
$20,000 - $29,999
51,145
73,206
77,353
76,427
100,750
$30,000 - $39,999
61,964
75,588
77,720
87,030*
101,464*
$40,000 - $49,999
93,814
98,544
111,975
102,495*
113,643*
$50,000 - $74,999
109,679
122,282
114,804
117,287
152,532*
$75,000+
182,377
190,244
196,848
171,571
160,292*
adapted from DiPasquale and Wheaton (1996)
Values reported by home owners
*small sample size
MIT Center for Real Estate
Is there a housing consumption elasticity with respect to
household size?
Average House Value for Homeowners by Income and Household Size for Households with Head Aged
35-44, 1989 AHS
Household Size
Income
1 Person
2 People
3-4 People
5+ People
All
Less than $25,000
52,506
51,438
69,840
57,516
60,648
$25,000 - $39,999
79,327
80,365
75,599
81,564
77,868
$40,000 - $59,999
113,421
106,365
104,897
107,873
106,247
$60,000 +
150,791
161,205
162,889
165,728
163,023
All
83,840
104,787
109,993
111,307
107,519
adapted from DiPasquale and Wheaton (1996)
Values reported by home owners
MIT Center for Real Estate
Housing Tenure: Younger households and poorer
households are most likely to rent. Is renting a “lifestyle
choice” or are some “constrained” to rent?
Homeownership Rates by Age and Income,
1990 CPS
Income (thousands)
Age of Head of Household
<20
20-29
30-39
40-49
50+
All Incomes
25-34
21.7%
37.3%
53.4%
58.9%
68.5%
44.3%
35-44
36.6
55.2
68.3
77.6
85.4
66.5
45-64
59.4
73.1
81.5
85.6
90.5
78.1
65+
67.5
84.9
87.6
89.6
91.7
75.5
All Ages
48.3
58.3
68.0
74.9
84.3
64.1
adapted from DiPasquale and Wheaton (1996)
MIT Center for Real Estate
Most households move because the current home or
location they live in has become “inadequate”.
Reasons for Moving, 1999*, AHS
% of Total Responses**
Total
Owner
Renter
Housing related reasons
56.4
56.4
42.3
Job related reasons
23.6
15.1
24.3
Family changes (marriage, divorce, etc.)
22.6
14.1
16.2
Miscellaneous other
15.3
11.4
11.3
Displacement by government or private sector
5.2
2.7
5.4
Disaster loss (fire, flood, etc.)
0.6
0.3
0.5
* Reasons for moving cited by households who had moved within the last 12 months.
** Respondents could cite reasons in more than one category.
adapted from DiPasquale and Wheaton (1996)
MIT Center for Real Estate
Renters Move More: Lower Transaction costs, less
maintenance… Older people move less: because they
own, or do they own because they move less?
Mobility Rates* by Age and Tenure, 1989, AHS
Tenure %
Age
Owner
Renter
All
Under 25
24.6
56.8
49.9
24-34
18.7
44.6
33.4
34-44
8.5
32.8
16.7
45-54
5.7
28.4
11.3
55-64
4.0
19.6
7.2
65+
2.2
12.2
4.6
All
7.6
35.7
17.8
•Heads of household in each category who had moved withing the last 12 months,
as a percent of total households per category.
•adapted from DiPasquale and Wheaton (1996)
MIT Center for Real Estate
Vacancy = a spell (length of time)
Rental:
Vacancy rate = Incidence rate x Duration
Incidence rate = % of units loosing tenant
per month.
Duration = # months necessary to lease up
Owner:
Average Sales time = Vacant Inventory/
Sales (units/month)
[or # movers/month]
[Empirics: see Gabriel-Nothaft]
MIT Center for Real Estate
The relative role of Incidence and Duration: which
changes most across time, across markets?
Metropolitan Area
New York City
Los Angeles-Long
Beach
Chicago, IL-IN-WI
Washington, DCMD-VA
Houston, TX
Duration
Proportion
continuously
occupied
Time period
Vacancy rate
Incidence
Percent of
period
Months
Sample size
1/87-6/88
2.3
34.1
6.6
0.9
72.3
1070
1/90-6/91
2.7
41.4
6.4
0.8
68.4
1245
1/93-6/94
3.6
30.9
11.7
1.5
74.5
1231
1/87-6/88
4.6
44.9
10.1
1.3
64.1
1146
1/90-6/91
5.7
48.8
11.7
1.5
59.5
1336
1/93-6/94
9.9
57.1
17.4
2.3
53.0
1490
1/87-6/88
5.9
41.4
14.1
1.8
64.6
1284
1/90-6/91
7.1
43.1
16.6
2.2
61.4
1191
1/93-6/94
7.1
40.0
17.7
2.3
64.4
1208
1/87-6/88
3.2
42.3
7.5
1.0
64.6
531
1/90-6/91
7.8
51.1
15.2
2.0
59.0
542
1/93-6/94
6.6
49.7
13.2
1.7
59.9
528
1/87-6/88
20.4
74.8
27.3
3.5
41.2
567
1/90-6/91
16.8
64.1
26.2
3.4
44.9
629
1/93-6/94
12.0
61.9
19.4
2.5
49.0
599
Decomposition of Vacancy Rate into Incidence and Duration
Figure by MIT OpenCourseWare.
MIT Center for Real Estate
Are there “structural” differences in vacancy,
mobility and sales time between markets?
Homeowner Vacancy and Mobility Rates by Metropolitan Area,
1989 AHS
Vacancy Rate
Annual Mobility
Rate
Incidence
Ratio (years to sale)
Duration
Minneapolis/St.Paul
0.6
8.9
0.067
Los Angeles
0.9
9.1
0.099
San Francisco
0.9
8.6
0.104
Detroit
1.0
6.9
0.145
Boston
1.0
5.5
0.181
Washington, D.C.
1.1
10.6
0.104
Philadelphia
1.2
5.8
0.208
Phoenix
2.8
12.0
0.234
Dallas
3.9
10.1
0.386
adapted from DiPasquale and Wheaton (1996)
MIT Center for Real Estate
US Single Family:
Sales, Inventory, Sales time (Duration)
Exis ting and new as % of s tock
10
9
8
7
6
5
4
3
SF Sales Rate
Source: NAR
For Sale Inventory
duration
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
2
MIT Center for Real Estate
How owners transition from one house to another:
lateral moves or “churn”
The risk of owning two homes (bridge financing).
What happens when there is no such mechanism?
Matched Household
One house
Sale of
previous home
Household
“change”
Mismatched Household
Searching for home
Matched Household
Owning two homes
Successful
search
MIT Center for Real Estate
Buyer strategy: once new house found, will have to own a
second home. Maximum Buyer Offer would be such that
this cost negated the advantage of move to new house
L: expected sales time
i : interest rate, opportunity cost of time
iLP: holding cost of owning 2nd home
during sale process at price P.
Buyer Max Offer (BMO):
BMO x iL = Net gain from moving.
BMO = Net gain/ iL
MIT Center for Real Estate
Seller strategy: What minimum (certain) price (SMA)
would be as profitable as putting the house back on the
market and eventually getting a price of P – but
discounting that price by the expected sales time.
Seller Min. Accept (SMA) = P/ (1+ iL)
MIT Center for Real Estate
Bargaining theory: Negotiated price lies between
BMO and SMA, assuming that BMO>SMA
P = BMO - ½[BMO – SMA]
(Solving for P – which is also part of the
formula for SMA)
= Net Gain x 1 + iL
iL
1 + 2iL
Outcome: price moves almost inversely
to sales time: hence proportionately to sales
and inverse to vacancy = a High elasticity
MIT Center for Real Estate
US Single Family Market: Prices move
closely with Sales, Inverse to Duration
Single-fam ily s ales as % of s tock / Duration
10
Change in real sf price,
12
9
7
8
7
2
6
-3
5
4
-8
3
SF Sales Rate
Source: NAR
Duration
2006
2008
2000
2002
2004
1996
1998
1990
1992
1994
1986
1988
1980
1982
1984
1974
1976
1978
1970
1972
-13
1968
2
Real Hous e Price change
MIT Center for Real Estate
Predicting Prices involves predicting
Sales, Vacancy and Duration.
1). Sales are complicated: new households, marriages and
divorces, lateral mobility, tenure changes..
2). Some evidence that sales are pro-cyclic: mobility is helped
by income security, but much is un-researched.
3). Vacancy is much easier = housing stock – occupied units
(also called households)
4). Construction adds to the housing stock.
5). Changes in “ex ante demand” (growth in population,
household split ups….) impact household formation and
hence occupied units.
6). Households (“ex post demand”) is different from “exp
ante demand” which is the number of potential households.
MIT Center for Real Estate
Residential vacancy rates move remarkably little (in
comparison to commercial. Supply seems quite disciplined
relative to demand.
U S R e n ta l M u lti-F a m ily v s . H o m e o w n e r V a c a n c y R a te s
11
10
9
8
7
6
5
4
3
2
1
R e n ta l Mu lti-F a m ily (2 o r M o re U n its in S tru c tu re )
H o m e o w n e r S in g le -F a m ily (1 U n it in S tru c tu re )
H o m e o w n e r S in g le -F a m ily (1 o r Mo re U n its in S tru c tu re )
2002.1
2001.1
2000.1
1999.1
1998.1
1997.1
1996.1
1995.1
1994.1
1993.1
1992.1
1991.1
1990.1
1989.1
1988.1
1987.1
1986.1
1985.1
1984.1
1983.1
1982.1
1981.1
1980.1
1979.1
1978.1
1977.1
1976.1
1975.1
1974.1
1973.1
1972.1
1971.1
1970.1
1969.1
1968.1
0
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3.2
4
2.8
3
2.4
2
2.0
1
1.6
0
1.2
-1
0.8
-2
0.4
-3
0.0
New Jobs (L)
Sources: BLS, BOC, TWR.
Total Housing Starts (R)
Total housing starts,
millions of units
5
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Year-over-year change in total
employment, millions
Vacancy moves little because of near Prefect Historic
correlation between job growth (demand) and Housing
Production – except for 2000-2006
MIT Center for Real Estate
Theories of Vacancy and Prices (or rents).
1). If vacancy is always “constant”, at some “structural” rate
V*, prices must be adjusting quickly so that ex ante = ex
post = stock(1-V*). Implication: ex ante and ex post
“demand” are difficult to distinguish. This is the Theory of
“structural” Vacancy
2). With large systematic vacancy movements, prices or rents
must be “sticky” and not adjusting quickly. Implication: ex
ante can be measured and distinguished from ex post. This
characterizes commercial real estate (next).
3). What determines ex ante housing demand?
Demographics? Income?
MIT Center for Real Estate
Sticky versus Adjusting Prices
Households ex post
Ex Ante
Demand
D
(in reaction to shift in ex ante demand)
D’
V* (structural vacancy)
Reduction
in Vacancy
S(1-V*)
Increase in Price
Price
MIT Center for Real Estate
Vacancy may be constant but House Prices move perfectly in
response to ex ante demand changes (job growth)
– except for 2000-2006
6%
12%
5%
10%
4%
8%
6%
job growth
3%
4%
2%
2%
1%
0%
0%
-2%
-1%
based on 4-qtr
moving averages
-2%
-6%
-8%
19
69
19 Q3
71
19 Q1
72
19 Q3
74
19 Q1
75
19 Q3
77
19 Q1
78
19 Q3
80
19 Q1
81
19 Q3
83
19 Q1
84
19 Q3
86
19 Q1
87
19 Q3
89
19 Q1
90
19 Q3
92
19 Q1
93
19 Q3
95
19 Q1
96
19 Q3
98
19 Q1
99
20 Q3
01
20 Q1
02
20 Q3
04
20 Q1
05
20 Q3
07
Q
1
-3%
-4%
Job Grow th
Real Median Home Price Grow th (lagged)
real median home prices
Housing and U.S. Job Growth
MIT Center for Real Estate
Ex Ante Demand: The Baby Boom makes its way
through the age distribution: [see Eppli-Childs]
Distribution of households by age of household head, 1960-2000
Millions of Households
30.0
25.0
1961 - 1970
20.0
1971 - 1980
15.0
1981 - 1990
1991 - 2000
10.0
2001 - 2010
5.0
0.0
Under 25
25-34
35-44
45-54
55-64
Age of Household Head
65-74
75+
MIT Center for Real Estate
Household Structure matters as well.
Single rental propensity = 56%. Married rental propensity = 22%
80.0
Households by Type
%
60.0
40.0
Married
20.0
Single
0.0
1960
1990
2000
Figure by MIT OpenCourseWare.
MIT Center for Real Estate
The importance of correctly measuring “Price”
1). Prices versus Quantity versus Expenditure
P: price of the same thing over time
(OHHEO index)
Q: Physical quantity and quality of space (how to
measure).
E = PQ: how much you spend
(NAR average price of house that sells)
2). For new houses (1.2m SFU annually)
ΔP/P = 4%, ΔQ/Q = 4%, ΔE/E = 8% (1965-1990).
3). For all houses (6m Sales annually)
ΔP/P = 7%, ΔQ/Q = .5%, ΔE/E = 7.5%
ΔQ/Q >0: new homes better than old + remodeling
MIT Center for Real Estate
4). Measuring ΔE/E is easy, how to measure ΔP/P?
5). Hedonic equation (again) with time variables for the
period each home sells in [D1=1 if sold in period 1, =0
otherwise]. The coefficients on these variables, βi measure
the price level in that period relative to the first period in
the sample
P = [X1α1X2α2...] eβ1D1+β2D2+... βTDT
Estimation technique: convert to linear regression .
log(P) = α1log(X1)+ α2log(X2)+... + β1D1+ β2D2+... βTDT
MIT Center for Real Estate
6). Repeat sale price index. Look only at homes that sell
more than once over the time period. Dependent variable is
price change between sale dates. Independent variable is
again a set of dummy [0,1] variables for each period.
Suppose an observation has the first sale in period i, the
next was n periods earlier. T
log(Pi)-log(Pi-n) =
Σ βtIt
t=1
For this observation, It is zero for all years except for those
in the i to i-n interval. The coefficients βt are then the
inflation rate in prices in that year. Sources: OFHEO, CSW
MIT Center for Real Estate
US Average Housing prices (OFHEO): Price
levels in line with income growth until 2001+
1 9 7 5 =1 0 0 ( Co n s ta n t
180
170
160
150
140
130
120
110
100
90
80
1975
1980
1985
In c o me p e r W o r ke r
1990
1995
In c o me p e r Pe r s o n
2000
2005
Ho me Pr ic e
MIT Center for Real Estate
But the growth varies enormously by market.
Demand depends not just on price levels but the
“annual cost of owning”
1 9 8 0 = 1 0 0 (C o n s ta n t $ 2 0 0 5 )
350
300
Bos ton
250
Los
A ngele
s
Chic ag
o
200
150
Nation
100
Dallas
50
0
1980
1985
1990
1995
2000
2005
MIT Center for Real Estate
7). What does it cost to own one unit measure of Q? This
obviously influences how many measures you want.
8). Sometimes it can cost you nothing to own a home.
[example: 100k property, 100% LTV, 8% interest, 6%
appreciation, 25% marginal tax rate:
After Tax
Loan Interest appreciation net
Year 1
100
6
6
0
Year 2
106
6.36
6.36
0
Year 3
112.36
6.72
6.72
0
9). Assumes that you use the additional borrowing each year
to offset the interest you just paid. Also assumes no
transaction costs [Fleet’s instant Home Equity program].
MIT Center for Real Estate
10). At the end, you have no equity, but were able to enjoy
extra consumption of 6% each year. [this is the choice of
someone with a high “rate of time preference”]
11). Alternatively, you could not borrow, have 6% less
consumption each period and at the end have housing
equity to finance your retirement = saving through
housing. [choice of someone with a low “rate of time
preference”]. How do you finance retirement with housing
equity? Reverse mortgage? Downsize? Sell and Rent?
12). The discounted value of these two strategies is identical,
so the annual total cost (in either case) for 100k is :
u = 100k x [ i (1- t) - ΔP/P] t = income tax rate
MIT Center for Real Estate
14). IF the annual cost of owning Q “units” of housing
quality (PQ=100k in previous example) is:
u = PQ [ i (1- t) - ΔP/P]
What is Impact of: P (level) – versus - ΔP/P (price
appreciation)
15). And households are freely able to move and buy at
different locations (different values of Q) within the
market then should not the annual cost of owning one unit
of Q (U=u/Q) be constant across locations? Then:
P = [ΔP + U]/i(1-t)
16). Or Price levels for (comparable) housing should be
positively correlation with price appreciation.
The cost of owning for 1st time
MIT Center for Real Estate homebuyers in the lowest marginal tax
bracket. [deducting inflation is key]
Cost Components of Home Ownership
1978
1980
1982
1984
1986
1988
1990
House Price (1990 dollars)
79666
$79,983
$75,602
$75,076
$76,069
$77,357
$73,706
Mortgage Rate
9.40%
12.53%
14.78%
12.00%
9.80%
9.01%
9.74%
Marginal Tax Rate
22%
21%
19%
18%
18%
15%
15%
Mortgage Amount
$63,733
$63,986
$60,481
$60,061
$60,855
$61,885
$58,965
Down mayment (20%)
$15,933
$15,997
$15,120
$15,015
$15,214
$15,471
$14,741
Closing Costs
+ $1358
$1,363
$1,288
$1,279
$1,296
$1,318
$1,256
Total:
$17,291
$17,359
$16,409
$16,295
$16,510
$16,790
$15,997
$6,375
$8,213
$9,049
$7,414
$6,301
$5,981
$6,076
+ $3,214
$3,223
$3,268
$3,298
$3,212
$3,107
$2,988
Before-Tax Cash Costs
$9,589
$11,435
$12,318
$10,711
$9,513
$9,088
$9,064
Less Tax Savings
- $435
$979
$1,201
$899
$655
$266
$308
After-Tax Cash Costs
$9,155
$10,456
$11,117
$9,813
$8,848
$8,822
$8,756
- $8,393
$8,206
$3,810
$2,430
$2,705
$3,274
$1,815
$761
$2,250
$7,307
$7,383
$6,143
$55,48
$6,940
+ $1,233
$1,742
$1,674
$1,490
$923
$1,103
$1,083
$1,995
$3,992
$8,981
$8,873
$7,067
$6,651
$8,024
Upfront Cash Required:
Annual Cash Costs:
Mortgage Payment*
Plus Other Costs**
Less Nominal Equity Buildup
Subtotal:
Plus Opportunity Cost
Total Annual Costs:
*30-yr, fixed rate mortgage. ** Include insurance, maintenance, taxes, fuel, and utilities. adapted from DiPasquale and Wheaton (1996)
MIT Center for Real Estate
Are there Housing “Bubbles”?
•
Bubble: Housing demand is rising-because prices are rising-because
housing demand is rising! No reason to buy other than the fact that
others are buying.
u
⇒
Demand
⇒ Vacancy
⇑
Expectations
•
•
•
•
⇓
⇐
Prices
⇐
Sales time
Watch out if everything has “positive feedback” and is reinforcing
everything else. What stops a bubble?
Marginal buyers who are very sensitive to the price level and not just
price inflation and the reduction in u.
New supply, new supply, new supply!
Were we in a price bubble from 2000-2006? Demographics, low
interest rates and greater credit say make fundamental sense, but….
MIT Center for Real Estate
“Uncharted waters”: restoring historic “P/R Balance”
requires 20% price decline and 20% rent increase!
1975=100
Const $ 2004
170
160
150
140
130
?
120
110
100
90
1975
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
80
Home Price
Rent
MIT Center for Real Estate
A totally unprecedented rise in Home ownership. Rising
ownership share fueled house prices. Why did ownership soar?
Homeow nership Rate, %
Renter Households, Mil.
70
40
38
68
36
34
66
32
30
64
28
26
62
24
22
60
1965
1970
1975
1980
1985
Hom eowners hip Rate, %
1990
1995
2000
2005
20
2010
Renter Hous eholds, Mil.
MIT Center for Real Estate
Credit “availability” matters as much as interest
rates. Recent Subprime market offers credit to all.
$ Billions
Percent
700
28
600
24
500
20
400
16
300
12
200
8
100
4
0
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Subprime Loan Origiations
Subprime as % of Total Originations
Subprime Originations as % Total Mortgage Debt Outstanding
MIT Center for Real Estate
Subprime Market will implode!
[Wheaton, 2005]
Cumulativ e Share of Loans With Rate Res ets , %
80
60
40
20
0
2002
2004
Subprime
2006
2008
Jumbo
2010
2012
A lt-A
2014
PCC
MIT Center for Real Estate
Mortgage Delinquencies and rising foreclosures mean
a return to renting. How long will it continue?
Mortgage delinquency rate, percent past due
2.0
Sub-prime (L)
Source: MBA.
Prime (R)
2007Q3
10
2007Q2
2.2
2007Q1
11
2006Q4
2.4
2006Q3
12
2006Q2
2.6
2006Q1
13
2005
2.8
2004
14
2003
3.0
2002
15
2001
3.2
2000
16
1999
3.4
1998
17
MIT Center for Real Estate
In addition, housing production has outstripped
household formation by more than at any time previously
Housing Starts Less New Households
Millions
1.0
0.8
0.6
0.4
0.2
1997-2007
6.0 Mil
1983-1989
3.5 Mil
1964-1973
4.4 Mil
0.0
-0.2
-0.4
-0.6
Sources: Bureau of the Census, Moody’s Economy.com, Torto Wheaton Research.
2004
2000
1996
1992
1988
1984
1980
1976
1972
1968
1964
1960
-0.8
MIT Center for Real Estate
Individuals “Discover” Real Estate and Gobble up the Excess
Supply as Investment and 2nd Homes
Investment and 2nd Home Loans as Share of New Loans, %
0
10
20
30
40
50
Nation
San Diego
Sacramento
Riverside
Miami
1999
2005
Tampa
Phoenix
Orlando
Las Vegas
Fort Myers
Atlantic City
Source: Loan Performance, Torto Wheaton Research
MIT Center for Real Estate
Phoenix Prices 1998-2006 cannot be explained by Phoenix
area economic fundamentals
PHOENI (#43)
5.5
5.4
RHPI
FO RC1
FO RC3
FO RC4
FO RC6
5.3
5.2
5.1
5.0
4.9
4.8
4.7
4.6
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
1997
1999
2001
2003
2005
Econom ic data
1.044
1.032
DEMP
DPO P
DRINCE
1.020
1.008
0.996
0.984
0.972
0.960
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
MIT Center for Real Estate
The simple statistics are suggestive: prices appreciate
more where second home buying is on the rise.
2002-2005 C u m u lative ch an g e in HPI, %
100
y = 0.0273x + 14.536
R2 = 0.4201
80
60
40
20
0
-1,000
0
1,000
2,000
2002-2005 cu m u lative ch an g e in s h ar e s
o f in ve s tm e n t an d 2n d h o m e lo an s , BPS
3,000
MIT Center for Real Estate
MIT Study: Investors/2nd Homes also are
Prevalent in Center City Condo Markets
• Study areas: Boston, Atlanta, Chicago, San Diego
• Survey of 47 new condo projects covering 11,000 units
found 32-38% of new sales to “non-occupiers”
• Analysis of tax records showed 23-30% of all city condo
tax bills sent to different address
•
Largest non-occupier share in San Diego, lowest in
Atlanta and Boston
MIT Center for Real Estate
2nd homes contribute to the greater volatility of condos
relative to Single Family Homes: NYC
Real Condo
Real Single Family
Multi-Housing Permits (5+ Units)
30
300
250
25
200
20
150
15
100
10
50
5
0
0
1980
1985
1990
1995
2000
2005
MIT Center for Real Estate
Price stability requires a drop in duration, which requires a
big reduction in the For Sale Inventory. Net flows into (+) and
out (-) of the Inventory : history and a recovery scenario
Average Annual Change, Ths.
2001-2005
2006-2007
2008-2010
Total households
Owner Households (-)
due to overall growth
due to changes in homeownership rate
1,100
1,100
700
400
1,200
450
800
-350
1,200
600
800
-200
Total completions
Completions for Sale (+)
1,700
1,450
1,750
1,500
1,000
700
Demolitions (-)
200
200
200
Net Conversions from Rent to Own (+)
200
100
-200
Non-Occupier Demand* (-)
200
200
200
Change in For Sale Inventory
150
750
-500
* Demand for 2nd homes and "investments" from domestic and foreign buyers.
MIT Center for Real Estate
What we do and don’t know about
Housing Supply!
• Do construction costs move with the “cycle” (i.e.
does land really get all excess profits)?
• Why are construction costs so variable across the
country (when many inputs are tradable)?
• How important is “time” or “delay” in adding to
cost? More than just interest expense?
• How is the industry organized differently in fast as
opposed to slow growing areas?
• Maintenance and Investment in existing structures.
MIT Center for Real Estate
Construction Costs: Declining gradually in constant $,
and immune to the level of building activity
F ig u re 3 4 :
W a s h in g to n , D C Ap a rtm e n t C o n s tru c tio n R e a l C o s t In d e x vs N e w Ap a rtm e n t S u p p ly
1 8,0 0 0
1 15 .0 0
1 6,0 0 0
1 10 .0 0
1 4,0 0 0
1 00 .0 0
1 0,0 0 0
8 ,00 0
95 .0 0
6 ,00 0
90 .0 0
4 ,00 0
85 .0 0
2 ,00 0
01
99
03
20
20
19
97
19
95
19
91
89
93
19
19
87
Y e ar
19
19
85
19
83
19
79
77
75
81
19
19
19
19
73
19
71
19
69
0
19
67
80 .0 0
Building Permits Issued
1 2,0 0 0
19
Cost Index 1970 = 100
1 05 .0 0
C o n stru ctio n C os t Ind e x
A p a rtm en t B u ild ing P e rm its
MIT Center for Real Estate
Housing construction during the cycle:
Starts → Inventory → Completions
[Inventory of Units under construction, 1000s]
900
800
700
600
500
400
300
68
70
72
74
76
78
80
82
84
86
88
90
92
94
Figure by MIT OpenCourseWare.
MIT Center for Real Estate
What impacts the concentration of the
Home Building Industry (T. Somerville)?
• Builders are “bigger” in high volume MSA
markets (i.e. each builds more).
• Concentration (e.g. top 10 share) does not change
as market volume and market size vary.
• Thus high volume markets do not have more,
same size builders, but rather the same number of
builders – each building more units.
• Equals = Monopolistic competition.
• Larger # of regulatory agencies (towns) leads to a
greater number of smaller builders. Why?
MIT Center for Real Estate
Maintenance, Improvements, and
expansions as “Supply”
• It is rational to let buildings eventually deteriorate.
With discounting, the net benefits of maintenance
decline over time.
• Major improvements, expansions constitute a huge
annual market (30% as large as new
development).
• Improvements are “rational” and are more likely
to occur when housing is a “good investment” (i.e.
low P and high expected ΔP/P ).
• The Elderly improve less = another way of
consuming your housing equity! (instead of a
reverse mortgage)