The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Mark Zandi, Ph.D.
Chief Economist
Celia Chen, Ph.D.
Director of Housing Economics
Brian Carey
Economist
8
Moody's IEconomy-com
Housing at the npplng Point
The Outlook for the U.S. Residential Rut Estate Market
Table of Contents
Executive Summary ........................................................................... 5
Historical Assessment .................................................................. 11
The boom .............................................................................. 11
The bust ................................................................................ 11
Economic contribution .......................................................... 13
Explaining History ....................................................................... 14
Behind the boom ................................................................... 14
Behind the bust ..................................................................... 16
House-price primer ................................................................ 18
Measuring House-Price Risk ........................................................ 19
Income-to-price ..................................................................... 19
Price-to-rent ........................................................................... 20
User cost-to-rent .................................................................... 20
Leading House-Price Indicator ..................................................... 21
Specification .......................................................................... 21
Estimation ............................................................................. 22
~dation ............................................................................... 23
UIPI's oudook....................................................................... 24
Structural Economic Model ......................................................... 25
Theory ................................................................................... 25
Historical data ....................................................................... 26
Equilibrium equation ............................................................. 26
Adjustment equation ............................................................. 28
~tion............................................................................... 30
Alternative specifications........................................................ 30
\aluation ...............................................................................30
Price oudook ......................................................................... 31
Most At-Risk Metros .................................................................... 33
Housing Crash? ........................................................................... 40
Crashes in history .................................................................. 41
•
Inflation and rates .................................................................. 41
Housing-related employment ................................................. 42
Mongage equity withdrawal ................................................... 42
Financial markets ................................................................... 44
Conclusions................................................................................. 46
Appendices ...................................................................................... 47
About Moody's Economy.com ....................................................... 190
3
Housing at the npplng Point
The Outlook for the U.S. Residential Real Eatate Market
Executive Summary
The U.S. housing market downturn is in full swing. New and existing home sales and singlefamily housing consauction are sliding, inventories of unsold homes are surging to new record
highs, and house prices are £alling in an increasing number of areas.
Housing's problems began just over a year ago when activity peaked, but have increased substantially in recent months. The bright optimism of homebuyers, builders and lenders has abrupdy
devolved into increasingly dark pessimism.
Housing's previous boom and current downturn are not evident &om coast to coast, but largely
along the coasts. Housing activity in the Northeast from southern Maine to just south of Wlshington, D.C., and in Florida and California, has £allen off dramatically in recent months. There
are sundry problems inland, including in Arizona and Nevada, in and around Detroit, and in
Chicago and Minneapolis.
The housing boom was based on strong fundamental underpinnings. Very low mortgage rates,
more ample mortgage credit, portfolio shifting by households spooked by the collapse in the
equity market, nesting in the wake of 9/11, surging construction costs, a better job market, and
tougher restrictions on new housing development all fueled the record housing activity.
The boom was ultimately also infected by speculation, however: Short-term investors or flippers
with the objective of purchasing and then quickly selling homes for a profit became increasingly
prevalent in many of the most active markets. Speculators were particularly artracted to the condominium market and other second and vacation homes areas.
The catalyst for the unwinding of the housing boom was the steady tightening in monetary policy between the summer of 200+ and earlier this year: While long-term interest rates and thus
frxed mortgage rates have risen only modestly, short-term rates and thus adjustable mortgage
rates have risen substantially more. This has been particularly hard on the housing market as
most first-time homebuyers could only afford to purchase a home in these previously very active
markets with an aggressive ARM loan. As the rederal Reserve continued to tighten rates, even
these loans have become unafford.able for most first-timers.
Housing's downturn has turned even more dramatic with the rapid flight of the flipper from the
market. As the prospects of making a profit have devolved into a scramble to limit their losses,
these investors have gone &om sending home sales and prices shooting higher to driving sales
and prices lower: Adding to flippers' financial woes are their rising mortgage payments and difficulty in being a landlord and renting their now longer-term investment. All of this has seemingly
occurred overnight.
To date, the housing downturn has been generally orderly And is characterized best as a correction and not a crash. Sales and consauction are now well below their peaks and still falling, but
the level of activity remains very high by broader historical standards. House prices have turned
soft in many markets, but at least so far have yet to show any appreciable decline.
Whether the housing correction unravels into a crash will largely depend on the secondary or
indirect effects &om the housing downturn. These include the impact on the job market, on
consumer spending via the housing wealth effect, which has seemingly been supercharged by
unprecedented mortgage equity withdrawal. and on 6.nancial intermediaries and the global financial system as mortgage credit quality weakens. The larger these effects, the more serious the
blow to the broader economy, which in tum will reverberate back onto the housing market.
5
Housing at the Tipping Point
The Outlook for the U.S. RHidentlal Reel Estate Mmtlet
So ~ the indirect effects
from the housing downtum have been very
modest. The job market
outside·of housing-related indusuies has held
finn as Rush businesses
with ample financial
resources continue to
expand their operations.
Consumer spending has
remained sturdy. as heretofore healthy compensation gains have offset any
negative fallout from the
Nota: Among 100 181g88t metro 8 1'8811
fading equity withdrawal
and the increasingly
negative wealth eft'ect.
Mortgage delinquencies and foreclosures are rising, but from record lows, and credit problems
appear a long way from threatening well-capitalized commercial banks and thrifts or the confidence of global investors who have been avaricious buyers of mongage securities.
......
....
Chart 1: Markets at SigDIRcaat Risk of Hoasc-PI'iu Dediaes
According to the LHPI
The objective of this study is to assess the severity of the unfolding housing downturn. It considers how much longer housing activity will weaken, the degree of the downturn, and which regions
of the country will experience the most pronounced reversal. This assessment is done through the
prism of house prices. Home sales and consauction activity will closely follow house-price trends.
To these ends, Moody's Economy.com has developed two different approaches to projecting
house prices for each of the nation's 379 meaopolitan areas and divisions. The first is a leading
indicator approach, in which several measures of housing market imbalances that have historically led changes in house prices are combined economeuically to determine the probability that
house prices will fall measurably in the coming year. The imbalances accounted for in the Leading House Price lndicatoJ; or UIPI., include housing affordability, non-housing related employment growth, the physical supply and demand balance in the market, and a measure of houseprice· overvaluation/undervaluation.
According to the l.HPI, over 100 of the nation's 379 meao areas have a significant probability of
experiencing price declines by this time next year {see Chan 1). These areas account for nearly
one-half of the value of the nation's single-family housing stock. The highest probability of price
declines is in metro areas throughout California, and in and around New York City. Probabilities
are nearly as high in the rest of the Northeast Corrido.; many florida meao areas, and in sundry
areas in the Midwest and Mountain West. It is important to note that the probability of houseprice declines remains measurably lower in Texas and most of the Southeast and Farm Belt and,
to a lesser degree, in the Paci6c Northwest.
The second approach is based on a structural economettit model of housing supply and demand. The model is based on statistically estimating the historical relationships among economic, demographic, financial, and housing-related variables. House-price forecasts are produced by
extrapolating these relationships into the future. A wide range of variables is accounted for in
this approach, including, but not limited to, everything from low mongage rates and more aggressive mongage lending, to solid demographic trends and a better job market, to constraints on
the supply of new housing.
According to the structural econometric approach, nearly 20 of the nation's meao areas will experience a crash in house prices; a double-digit peak-to-aough decline in house prices (see Table
1). These sharp declines in house prices are expected along the Southwest coast of Florida, in
the meao areas of Arizona and Nevada, in a number of California areas, throughout the broad
\Y.ishington, D.C. area, and in and around Detroit. Many more meao areas are expected to ex8
•,
·j
•'
rot
't'i
I·
.._.... .. IM ripping Point
•• .,.,...... for tfle U.S. Residential Real Estate Market
perience only house-price corrections in which peak-to-n-ough price declines remain in the single digits. In addition to the some 30 meao areas that are ahudy experiencing price declines,
the saucrural econometric approach identifies 70 other meao areas that will soon experience a
measurable decline in prices. It is im.ponant to note that price declines in various markets are
expected to extend into 2008 and even 2009.
With over 100 metro areas representing nearly one-half of the nation's housing stock experiencing or about to experience price declines, national house prices are also set to decline. Indeed,
odds are high that national house prices will decline in 2007; the fust decline in nominal national house prices since the Great Depression.
:I•
y
While the broader economy is expected to bend under the weight of the listing housing market, it
is not expected to break. Economic growth has weak£ned and will remain below the economy's
potential as long as the housing corm:tion is unfolding; unemployment will edge highet; but even
during the worst of the housing downrum, expected early next yea~; the expansion should remain
intacL 1his optimism is predicated on the view that the secondary effects &om housing's downrum will remain largely contained and that policymalcus will not misstep. A much darlcer scenario
is not difficult to construct, but the more sanguine scenario remains the most likely one. Moody's
Economy.com will continue to update the toOls and analysis described in detail in the srudy that
follows to assess the health of the housing market and the broader economy.
7
Houalng at the npplng Point
TM Outlook for the U.S. R•ldentlal Real Estate Marbt
October 2001
...
'"'
,..
Table1: Metropolitan Areas That Will Suffer Hous~rtce Declines
According to the Structural Econometric Model
Peak-to-Trough
Peak
% House Price Decline
Quarter
Trough
Quarter
Cape Coral, FL
·18.6
05:4
07:2
WI
Reno, NV
-17.2
05:4
08:4
~
llan:ed,CA
-18.1
05:4
09:2
Stockton, CA
-15.7
05:4
08:4
Sarasot8, FL
-14.0
05:4
07:3
Naples, FL
-13.8
05:4
07:3
Tucaon,AZ
·13.4
06:1
08:2
·12.8
05:4
09:2
08:2
Frasno,CA
•12.1
·12.5
05:4
06:1
09:2
Atlantic Clly. NJ
·12.2
05:4
08:2
Vallejo,CA
·12. 1
05:4
09:2
WahlngtDn, DC
•1J.O
05:4
08:2
,..
Reddlng,CA
·11.1
06:1
08:2
Pn
Detroit. Ml
·11.7
05:3
06:4
...
Rlverslde,CA
·11.4
06:1
08:4
Cll
Bloomington, L
·t1.1
05:3
06:4
Es
Bakerafleld, CA
·11.1
06:1
09:2
Ill
Grweley,CO
·10.7
06:1
08:2
Be
Sallnas,CA
·10.3
05:4
08:2
Vu
Santa AM. CA
·10.0
06:1
08:4
s.
08:2
Be
06:1
09:2
Pu
06:1
08:2
Prl
06:1
07:2
Ill
05:4
08:2
Cc
w.rr.n, Ill
....
....
....
05:4
05:3
06:4
Gr
Allentown, PA
·•2
05:4
08:2
Pc
08:2
Le
05:2
06:3
St
05:4
08:2
He
07:1
10:2
MJ
Vlulla,CA
.,.
.,.•
"
.,,,
06:1
05:4
08:4
Rockford,IL
., l
06:1
09:1
,..
Santa e.t.r., CA
.ra
05:4
08:2
Sa
~~com,lnc.•--..-•halpOICIII~
Mt
Las Vega, NV
Chlco,CA
Sacramento, CA
Carson City, NV
Phoenix, AZ.
Punt8 Gorda, FL
San Diego, CA
N....... NY
Fort Walton Beach, FL
Santa Rosa, CA
Ocean City, NJ
8
,r
t
•
.
·1.8
·1.3
·8.5
...,
All
.
~
••
ti
,.
ifl
.•
'
Lo
Dl
HI
''"""'"" .1tthe Tipping Point
•••• •lulluok for the U.S. Residential Real Estate Market
t •• ttln t ·
Metropolitan Areas That Will Suffer House.Prlce Declines (cont.)
"''meting to the Structural Econometric Model
% House Price Decline
Peak-to-Trough
Peak
Quarter
Trough
Quarter
Wuu.ostor, MA
-7.0
05:4
07:2
New Orleans, LA
-6.7
05:4
07:3
··"umaw, Ml
-6.5
06:1
09:2
Oellland, CA
-6.4
05:4
08:2
f ort Collins, CO
-6.1
05:3
07:2
Portland, ME
-5.9
06:1
07:1
t ort Lauderdale, FL
-5.9
05:4
07:3
Wett Palm Beach, FL
-5.7
05:4
06:3
Mlami,FL
-5.5
06:1
08:2
Edlson,NJ
-5.2
06:1
08:2
t.oa Angeles, CA
-4.8
06:2
08:4
Denver, CO
-4.6
06:2
08:2
Napa,CA
-3.8
06:1
06:3
Providence, Rl
-3.6
05:3
07:2
Now York, NY
-3.5
06:2
08:4
Champaign, IL
-3.5
05:4
09:1
l.ssex County, MA
-3.1
05:3
06:3
Bethesda, MD
-3.0
05:4
08:2
noulder,CO
-2.8
05:4
06:3
Yuba City, CA
-2.6
05:4
06:3
<;alt Lake City, UT
-2.3
06:1
06:3
Boston, MA
-2.2
06:2
06:3
t•ueblo, CO
-2.1
06:1
06:3
Prescott, AZ
-2.0
06:1
08:2
Madera,CA
-1.8
07:1
09:2
Colorado Springs, CO
-1 .6
06:2
06:3
Grand Junction, CO
-1 .3
06:2
06:3
Portland, OR
.0.8
07:3
09:2
lewiston, ID
.0.8
07:1
08:2
St. George, UT
.0.5
07:3
08:2
tionolulu, HI
.0.3
07:2
08:4
Milwaukee, WI
.0.3
07:2
08:3
ttagerstown, MD
.0.2
07:3
08:2
Medford, OR
.0.2
07:3
08:2
SanJose,CA
.0.2
07:1
07:2
~A<oody's ~Inc. •
www.economy.com • hefpOeconomr-com
9
.....
·--·- -------....------"'!"'-~Hauling at the Tipping Point
The Outlook for the U.S. RHidenUal Real Estal8 Market
Housing at the Tipping Point
The Outlook for the U.S. Residential Real Estate Market
HistoricalAssessmeut. The U.S housing market cycle of the past decade has
been unprecedented. The market, which
boomed during the decade extending
rrom the mid-19905 through much of last
year, is now In full retreat. At the pinnacle
o£ housing activity In 2005, home sales,
housing construction, and house-price
gains were shattering previous records.
Activity has since fallen sharply, with no
sign that the downdraft will soon end.
The impact on the broader economy
has been substantial. During the boom,
housing contributed significantly to
overall growth. The 2001 recession was
as modest as it was in large pan due to
housing's strength. Housing's recent decline is becoming an lncreamgly heavy
weight on growth, and poses a growing threat to the cunmt c:xpansion.
1lae boom. The housing market has
enjoyed an unprecedented run in the
decade between the mid-19905 and last
yen Home sales, housing consttucrion, and house-price gains soared,
shattering all previous records.
Booming tranSaCtion volumes were particularly notable. New and existing home
sales surged from close to -+ million units
annually In 1995 to almost double that
at last summer's all-time peak (see Chan
1). The turnover rate, or the proportion
of the owner-occupied housing srock that
turned over in a home sale, also rose to a
new record high. Some 8% of the housing srock transacted in 2005 alone.
Chart 3). The increase in homeownership was broad-based across income,
age and ethnic groups. Homeownership had been largely unchanged in the
quarter century prior to this period.
While sales for existing and new
single-family units were robust, activity in the condominium market
in~ the most earlier in this decade. Condo sales just about doubled
between 2000 and last summer's apex
of near 1 million units annualized.
The most impressive aspect of the housing
boom, h~ was the surge in house
prices. According to the National Association of Realtors, the median single-£amUy existing house price has risen some
$75,000 during the past five years to
$225,000, a whopping gain of over 50%.
Other house-price measures, including
the Office of Federal Housing Enterprise
Oversight purchase only repeat-sales
house price index, tell the same story.
After inflation, house prices rose by an
astonishing 10% last year (see Chart -1-).
Housing construction has also soared.
Single-family housing starts, which were
near 1.25 million units at the start of the
decade, registered an astonishing 1.75
million units last year (see Chart 2). At
their very peak at the start of this ~
some 1.8 million annualiz£d units were
starred. Construction last year even
dwarfed the activity in the late 1970s
, when the outsized baby boom generation
began forming households. just over one
million households were fonned last yeat;
compared to almost two million in 1979.
The steady rise in the homeownership
rate also reflects the previous strength of
the housing market. The proportion of
households that owned their own home
rose to a record 69%, up a full five percentage points from a decade earlier (see
While the housing boom was evident in
many parts of the country, conditions
were particularly strong along the coasts
and sundry inland areas. Of the nation's
379 metro areas, +t have experienced a
doubling in housing values during the past
five years (see Chart 5 and Appendix 1).
11lc bust. Housing rnaiket activity has
unraveled quickly this yem: New and
existing home sales have slid nearly 15%
since peaking last summer, with similar
declines for single-family homes, condominiums, and new homes. Total home
Chart l: ...JiousiDa Coastnu:don.••
Chart 1: Reaml Home Sales••.
Sbtglc-fadly lwubtJ stm1s, lllfJ
9
1.75 ,-------.;..._----------~
8
7
6
5
4
3
2
70
75
80
85
90
95
00
05
70
75
80
85
90
95
00
05
...
,....
Hou•Jng at the Tipping Point
The Outlook for the U.S. Ruldantlal Real Estate Market
c ...
Chart 3: ...Homeowucrship•••
ROIIIeCJWIIDShfp relk, If,
Chart 4: ...And Hoase-Price Growth
Real pri« growda, sfnp-JamiJy, If, duiJige yau 11p
70~--------------------------------~
12~----------------------------------~
t ••
te
"'
M+-----------------------------~~~
Jl
l4
~+---------------------------~HHHH~
~
M~HHHHHHHHHHHHHh~~~HhnrlHHHHHHHH~
tl
12
70
75
80
85
90
sales are now running more than one
million units below last summer's apex
(see Chan 6). the wealcening in sales is
evident across the country, but to date has
been most pronounced in the western U.S.
With. sales sliding, unsold inventories of
homes for sale are piling up. New and
existing units for sale are fast approaching
a reami 4 million units, double the invattory available at the start of the decade. lhe
months supply of unsold inventory at the
current sales me is thus surpjng biglw:
There are over eight months of unsold
condo inventory. sevm months of existing single-family homes for sale, and six
months of new homes. When the rnarl4et
~ at its best, months supply was running no higher than four months. Given
prospects for funher sales declines and
greater unsold inventory, at least through
the remainder of the yez; months supply
is likely to spun over the record 10 months
95
70
05
00
75
that prevailed at the depths of the recessions in the early 1990s and early 1980s.
Six months of inventory is often thought to
be consistent with a sturdy housing market
that can support real ho~ gains.
lhe inventory situation may be evm worse
than these numbers suggest, at least in the
new home rnarla:t The Bureau of Census,
the provider of the new home sales and
inventory data, does not account for cancelled sales contracts. A growing roster of
homebuilders such as the Ryland Group,
Ton Brothers and KB Homes is reporting
rising cancellations in addition to sharp
drops in orders and mounting inventories.
Indeed, the cancellation rate for some of the
nation's largest public buildeJ:s is now well
over one-tbinl, well above the one-&fth that
has prevaiJed in recent years (see Chart
n.
With sales falling and unsold inventory
soaring, national bouse prices are now
80
85
90
95
05
00
falling (see Chan 8). Actual transaction
prices, which include various types of
price discounting that are not accounted
for by measured prices, are likely Caning substantially given the apparent
sharp increase in their use, particularly
by increasingly paniclced homebuUders.
House prices have turned particularly
soft at the high end of the single-family market and in the condo market.
rna
the
aly
rec
un
pll
(se
ha:
inf
House prices are falling in an increasing
number of metro areas. YeaJ~oover-year
price declines are evident in the area
&om Portland, ME through Boston to
Providence, Rl, in Michigan and Ohio,
and Minneapolis. They are falling on a
sequential quarterly basis and will soon
be declining versus a year ago in a large
number of areas. The most nolable include Baltimore, MD, Wclshington, D.C.,
Miami, Fl., Las Vegas, NY, Phoenix, AZ,
San Diego, CA and Sacramento, CA.
Chart 6: Sales Are Now Slhtlna, and laftncorles Souing. ••
Oaart 5: Where lloase-Pric:es More 11aaa Doubled
Ho
tO I
rh
ffil
be
Sti
tho
ye
n
in·
lh
AI
ac
(
2000-.2005
8.8
4.0
3.8
8.4
3.6
3.4
8.0
3.2
DC
3.0
7.6
2.8
2.6
7.2
&uc.: NAR, BOC
2.2
6.8
03
12
2.4
04
05
06
1
lfou•l"' al tha Tipping Polnl
fhe Oulloolc for lhe U.S. Residential Real Estate Martcet
( hert 7: .. As Cancellatioas M011Dt
C41Wt'ILI1foll r11~ %
II
f
...
!lource: Cradlt Suisse, based on public: builders
.12 .
-
ll . ..
~
,,
~
~
~
~
05
04
HomcbuUders have finally responded
ro lhe clear weakening in housing demand and softer house prices by slashing
1hclr new consttuction. Single-famdy housing starts, which surged to a
m:old high of 1.8 million annualized
unlrs at the start of the yea~; have since
plunged to less than 1.1- million units
(Oft Chan 9). The decline in pennits
has been even more substantial, suggest"'" funher declines in startS this fall.
conditions in the months ahead. The
Mortgage Bankers Association's purchase
applications index, which measures the
volume of applications for mongage loans
to purchase a home and rypically leads
honie sales by a couple of months, continues to decline. The Realtors' pending
home sales index, which measures existing homes that are under a sales contract
and thus leads existing sales, which measures closings, also continues to slide.
rhe impact of this on residential invest•ncnt spmding and thus GDP has only just
htJCUn. Housing completions, which lag
~•ans. are only starting to decline. Indeed,
•he number of units complet£d so far this
~Tar is still up over last ~·s record pace.
I he double-digit decline in real residential
mvrstment in this ~·s second quarter is
•hu.1lik£Jy to repeat in corning quarws.
Perhaps, most ominously. the National
Association of Home Builders' diffusion
index, which measures builders' perceptions of current and expected buyer activity in the new home market, continues
to plunge. A reading below 50 indicates
that more builders view conditions as poor
than good. 1be index currently stands at
30, just above the record low set in the
depths of the 1990-1991 recession when
single-family housing starts were half of
what they are currently (see Chan 10).
.\II ur the leading indicators of housing
·" nYiry definitively point to even weaker
( :...rt 9:
Builders Slash Consttactioa
Economic contribudon. The housing
market boom and subsequent bust have
been insttumental in shaping the broader
economy's perfonnance over the past decade. indeed, no sector of the economy
has made a more signi&cant contribution.
Of the real GOP growth that has occurred since the stan of the decade, fully
one-founh is due to housing. Real GDP
growth since Y2K has been 2.6% per annum. If the housing market had simply
been neutral with respect to the economy
during this period. then per annum real
GDP growth would have been 2%.1
Housing played an unprecedented role
during the 2001 recession. Unlike in past
recessions when housing activity declined
sharply. weighing heavily on the economy,
it contributed substantially to growth
1
This n:suh is l.al on • simulodcJn a( Moody's Fmnamy.can's
UOKmtaillliXilic ra:xidsys~a~L
Chart 10: OmiDoas l.eadiDg ladicator
UIOO
80 . . . . . . - - - - - - - - - - - - - - --
1.1100
--.. 1,900
1.700
70
1,700
1,500
60
UJOO
1,300
50
1,500
1,100
1,400
40
900
I..JOO
.....-'
30
!,.ZOO
Homeullclanl'
Index (l)
500
I , 100 t-+-+-+-+-+-H-+-H--H!-+-H!-+-H!-+-HH--HI-+~
00
01
02
03
04
05
06
700
85
90
95
00
05
13
Houalnget the Tipping Point
The Outlook for the U.S. Residential Real Estate Mlftlet
Cban 11: Houma's Oucslzcd Coatribadoa to Growth
Pm:aiAip pollll amlrlhdoa .Ill raJ GDP srowda
or---------·---
0.0
00
01
02
03
04
05
slowed &om above its potential to below
since the beginning of the year. due entirely
to housing's shift &om boom to bust.
baveho~
1.2
while less than
one-half of families have some
stockholdings,
only one-fourth
of families have
holdings worth
more than
$30,000. Well
over two-thirds
own their own
home, and more
than one-half
llefdnd
INJmn. Driving the housing boom were a number of fundamental forces. A combination of low
and falling interest rates, favorable
demographics, increased resaictions
on homebuilding, household portfolio
shifting, and a substantial expansion
in the availability of mongage credit
fueled the record housing activity.
owners' equity
that is greater
06
than $30,000.
throughout the downturn. Residential
invesunent fell by an average of one-third
during previous recessions since \\brld
Wlr U, but rose during the 2001 recession. Housing's contribution increased
substantially during the expansion, adding
a full percentage point to real GOP growth
in both 2004 and 2005 (see Chan 11}.
The most direct link between housing and
the broader economy is through residential invesunent, which is comprised of
homebuilding. remodeling and renovation.
With a record number of new and increasingly spacious homes built last year. residenrial invesunent soattd to well over 6%
of GOE 1bis compares to 4.5% of GOP in
2000, and is the highest GDP share since
a very brief period during the housing
boom i.mmediardy following \\brld War IL
There is also a substantial wealth effect
resulting &om soaring house prices and
homeowners' equity that has indirectly
boosted the economy by powering robust consumer spending. For every $1
increase in housing wealth, an estimated
seven cents in extra spending occurs over
the subsequent nearly two-year period.
Households own nearly $20 trillion worth
of housing and have more than $11 ttillion
in homeowners' equity. The median
amount of equity owned by homeowners
is an estimated close to $70,000 according
to the Federal Reserve's Survey of Consumer Finance. With the stock market yet
to fully recover &om its post-Y2K bust,
housing is far and away the largest asset in
the household balance sheet. Indeed,
14
For wealthie~; higher-income households,
the wealth effect largely works through its
influence on their views regarding their
long-term financial well-being. With rising
housing values and thus net worth, these
households do not feel the wgency to save
for their children's college education or
their own retirement. Their saving rate
declines, and their spending increases.
For less woeahhy ~Ids. the wahh do
feet bas been empowaul by inaeased mongage hollOWing. Until wry m:m~ home
equity bonowing was surging. and cash-out
re6nancing remains strong. All togetha;
gross mortgage equity withdrawal (MEW)
totaled an astonishing over $1 trillion annualized in the 6JSt quarter of Ibis yaa; equal
to nearly 10% of disposable Income (see
OJart 12}. EYm afla- IJlOI1Ba< originalion
fees and dosing COSIS, MEW toiBial S900
billion earlier this yea!; c:ompuul to closu
Explaluing History. The housing
market cycle has been driven by a wide
array of forces. Some of them more
fundamental and thus longer-lasting, while others more temporary. An
understanding of what is behind the
housing boom and bust is necessary to
gauge the housing market's prospects.
*
<:Ia
10
5
0
·5
thi:
me
bet
pri-
Dri
~
The most significant force behind the
housing boom bas been the low, and until recently, falling, user cost of housing.
The user cost measures the net mortgage
interest cost of borrowing, and is equal
to the difference between the after-we
effective mortgage rate and borrowers' expectations of future house-price growth.
we:
The user cost has more or less fallen
since peaking in the early 1980s, but
it turned sharply negative early in this
decade (see Chan 13). Not since the
late 1970s had the user cost been consistently negative. Rdlecting the lower
user costs was very high housing affordability. Throughout the 6rst half of
,01
Fre
we
anc
~
era
(01
l.>e
ag£
ori.
ace
Bo
in
dec
inc
ma
to $200 billion as
recently as 2000. Clwt 11: The Hoaac Has Been a Cash~
Gross .......,., e.,...ty ab1ldioa, $ WJ
lUI
(0
Housing's eco-
nomic conaibudramatically
with the recent
slide in activity.
With consauction falling and
the wealth effect
fading, housing
is expected to
add nothing to
the economy's
growth this yeK
GOP growth has
Fu
• Home equity boiTowlng
• Cuhoout 1'111
•
tion bas shifted
800
inc
Cut
inE
get
Cepllelg81na
Sowol: a....p.n & l<eMedy
600
(0\.
de
ra\
400
' tb
200
'In
p
tlue
hou
ond
0
00
01
02
03
04
OS
06H1
~Mo
8
•tnue~nu "' the Tipping Point
1he 'Mlook for the U.S. Residential
t
Real Estate Martcet
h•rt I J: llriving the Boom WerE Negative User Costs..•
'II .
User cost of housing, "'
Soun:e: Moody's Economy.com
•,
behind the more recent rise in user costs
and the fast-weakening housing market. 3
.
The rapid expansion in
the av.Wability of mortgage m:dit also fueled
the housing boom.
,, !
Subprime, Alt-A. affordable and high loan-to·
value mortgage lend;
ing has surged during
&
-+--+--+-+-+-+-+-+-+-+-+--f--f--f--f--f~f-1 the past decade. SubTO
75
80
85
90
95
00
05
prime loans, or loans
to mortgage borrow•ht!i decade, the household earning the
ers with blemished or no credit histories,
Further fomenting the expansion of mortand Alt-A loans, loans to investors or to
mrtlian income could afford to purchase
gage credit is the adoption of scoring
llt'twcen 125% and 135% of the median
borrowers with incomplete documentation technology, risk-based pricing, and direct
pnrcd home, according to the Realtors.
of their financial or employment histories,
marla:t techniques.5 Mortgage lenders
have ballooned &om essentially nothing
have been emboldened to extend more
a decade ago to an estimated $1 nillion
llnvmg user costs lower were falling mortcredit by their ability to assess risk. target
last year. accounting for one-seventh of
•:agc rates. Fixed mortgage rates, which
borrowers within certain risk profiles, and
w•·n: hovering near 8% (as measured by
all mortgage debt outstanding. Many
price that risk. The popularity of interlu·Lidie Mac) at the start of the decade,
households are being approved for mortest-only and option payment mortgages is
wt·n: consistendy below 6% between 2003
gage loans that would not have been able
a good example of this. Some one-fourth
to obtain any credit just a few years ago.
.m~llate 2005. Rates on adjustable mortofnonconformingmortgageo~tions
,:.•gcs fell even more stwply when the Fedare currendy of these exotic mortgages
··r.d Reseive slashed the federal funds rate
Driving the expansion of credit is the burin which borrowers pay only the interest
htllnly 1 percent through mid-2004.
geoning mortgage backed securities mardue or just a minimum amount that does
ket, where bonds backed by the interest
not fully cover the interest, with the balll~·dining mortgage transaction costs also
and principal payments made by mortgage ance added back into the loan's principal
, ••ntributed to the falling user cost. Averborrowers are issued and traded. Histori(see Chan 14). 10 and option loans were
.•~c fees and points on purchase mortgage
cally, the primary source of funding for
virtually nonexistent just a few years ago.
•11iwnations are under 50 basis points,
residential mortgages was depository institutions, including commercial banks, thrift Housing activity has also been supported
.•n:ording to the Federal Housing Finance
lluard. This compares to 100 basis points
institutions and credit unions. As recendy by household portfolio shifting. Housing
ntrhe mid-1990s and 200 basis points two as the mid-1980s, depositories held nearly
,Jt·rades ago.1 The mortgage origination
two-thirds of residential mortgages. • To'See "Moundng MottJ!iiF t.e.aa&c. • Rqiona1 RMJ!dod
mdustry has been effective in using inforday, almost two-thirds of mortgages have
R.Mow,Nay 2004.
mation technology to lower its cost strucbeen securitized.
lltre, with many of the benefits accruing
Owners of
Chart H: ...IDc:ftasiDgly Agtessiw: lenders...
11, borrowers.
these mortgage
SJidn of rwn-amJonrdngiiiDI1gfl,ge originations
backed securih1rther pushing user costs lower were the ties include a
mt:reasingly heady expectations regarding wide array of
~+---------------------------investors from
huure house-price growth. Strongly risSource: l.olri'erforman
mg prices begat expectations of even bigmutual funds to
ger future price gains, pushing user costs
global financial
15+---------------------------lo•wer, and fueling even stronger housing
institutions.
olcmand and higher prices. It is the un10
•aveling of these lofty expectations that is The RMBS mat·
ket facilitates
I~ on dlaliom lhe falml Hol&sinJ filllna: a-d.
the provision
5
I •·
I
i
t
1
of mortgage credit as it is particularly efficient at allocating the risks involved
in extending such credit. Investors can
more precisely take on the amount of
prepayment and credit risk they are able
to tolerate. Given that the MBS market
is more than $1- trillion deep, investors
also face substantially less liquidity risk
than when investing in other assets. The
large market also reduces the costs of
purchasing insurance or hedging the
risks involved in an MBS investment. All
of this is recognized by bank regulators,
who require depositories to hold more
capital against a mortgage than an MBS.
In die: calculation of die: user cost, c:xpeaed bouse-pril%
,:,.IWih is assumed equal ro house-price pins Cilia' die: pasr
•hrtt ya!S. ~ lnfomwlon suppons lhls W:w ol ' - '
In~ cxp«Qdons ue focmal. See "Is~ a Bubble
"' che Housing Muloa. Case and Shlllcr. BI"DGitblp Arpm on
1, ...omic AcfM!y, Scpremher 2003.
• This is based on ella
liom the Ftdetal Racrve
Boonf's Flow ol Funds.
0
98
99
00
01
02
03
04
05
06H1
15
Houalngat the npplng Point
HoL
The OUtlook for the U.S. Residential Rul Estate Martcet
The
Chart 15: ...And Nesting Post-9/11
c:b
shon-renn investors 2b). For contrast, the lowest investor shan:
in the nation was in North Dakota, where
or flippers, those
1.30 , . - - - - - - - - - - - - - - - - - . 11.2 looking to mala: a
only 8% of originations were to investors.
Share of llOI1SUIIlel' spending
quick profit. Flip11.1 pers speculating
1.25 Sources: BEA. Moody's
Econamy.com
.Bddnd dac bust. The housing boom has
in housing eventu11.0
1.20
rapidly devoM:d into a,biast as many of the
ally infected a large
forces supponing the boom have faded.
10.9 number of markets
1.15
Mongage transaction costs can scarcely go
in communities
1.10
10.8
throughout the
lo~ lenders and their regulators are reNonheast, Florida,
thinking their most aggressive underwrit10·7
1.05
ing standards, households are becoming
and California, and
accustomed to the threats of terrorism,
increasingly even in
1.00
10.6
li
meaopo 'tan areas
and cash and stocks are once again attractive investment alternatives to housing.
0.95 +--+-+---+-1---+---f--+--+-+---+-+ 10.5 in the Mountain
03
os
West and Midwest.
95
97
01
99
Even homebuyas
The catalyst for the rapid shift &om boom
to bust in the housing market, howeves;
has easily provided households the best inplanning to live in their homes may have
has been the tightening in monetary policy.
vestment returns of any asset since the start been dabbling in a fonn of speculation by
of the decade, especially considering that
expecting the extraordinary price gains of
Between mid-2001- and earlier this ~ the
for the majority of homeowners, a home is
recent years to extend long into the future, Federal Reserve steadily tightened policy,
and thus buying bigger homes or addpushing the fedeml funds rate wget up
a highly lcmaged i.nvestment.6 Cash re&om 1% to its current 5.25%. Long-renn
tums have, until very recently, been paltry.
ing to and improving their existing one.
rates ultimately rose in response. but
Despite a half year of monetary tightenmuch more modestly, with the yield on
ing, yields on money lOlllket accounts are
The jump in investor demand is evident
10-year 1ieasury bonds rising &om a low
still low by historic standan:ls. Long-renn
in the HMDA mongage originations data. 7
bonds have performed well, but yields are
of 3.5% to closer to 5%. Rates on adjustThese very comprehensi\'e data show that
now very low and corporate bond spreads
the investor share of national purchase
able mongages and fixed mortgage rates
originations for single-family housing dou- l1UM:d higher in sympathy, with fixed
exttaordinarily narrow. Investors must
mongage rates rising about 100 basis points '
also be anxious over the prospects that
bled between 2001-2005 to over 16Cf.. In
foreign imestors will tum more cautious
some of the previously more active housand ARM rates rising 250 basis points.
in their bond pun:hases given ~ weakening markets, the shan: surged even more
ing dollar. Stoek prices have revived, but
(see Chan 16). In Florida, for example,
Higher mongage rates when mixEd with
rhey are still below their post-Y2K peak.
the investor share soared to 30% last yea~;
very lofty house prices have undennined
with investor shares of over 50% in meao
housing affordability. The Realtors afNesting also boosted housing demand early areas along the stare's west coast. The
fordability index has plunged, and is
on in the boom. Heightened fears of ternow closing in on 100, meaning that
highest investor share in the country last
rorism and ttaYd convinced households to
year was along the New jersey beach, with the household earning the median inaavd less and stay closer to home, arlezt
three-quaners of originations in Ocean
come can afford to purchase just 100%
for awhile. Spending by consumers on
of the median priced home at prevailing
Oty, NJ by investors (Appendices 2a &
foreign ttaYd plunged in the wakE of 9/ll,
while at the same time, the share of conChart 16: laftstor DemaluiAiso 5aJged
sumer spending devoted to owner-occupied
Non-owur-o«upW origfMtioa sluiR of J..f.fla-ily origiutimu
housing rose sharply (see Owt 15). Nest'~orHomc
30-r---...... ~Aa.
ing has induced households to purchase
clara 1ft '-*1 on repons
bigger homes and to spend more on home
by .-ly alllllllftllll'
25
impromnent and home entertainment.
laxlas - - roquiml
cr.
10 submit dlls
infamwloD for purposes
While there have been solid fundamental
reasons for the housing boom, activity
surged due to soaring investor demand.
Investor demand increased for second
and vacation homebuyers, those with a
generally long investment horizon, and for
oliiiCIDillarinc 1IIIIIIPII'
lmd!D& dlsairniJwlan.
1bc KMDA clara 1118)'
u.a- illlnvalor
~-·
18
14(
13!
t3(
12!
12(
11!
11(
10l
1CX
91
me
Th
by
fh
m1
are
me
fur
Fal
fio
~
1'0!
lOr
.~
.u
a~
hu
.Lt;
'"
li.l
,·u
pc
.lb
..h
15
,,..
Ill.
10
IC
5
lalaS OR easier on an
lbc HMDA Is conslsmlr
wllh clara &um
l..oW'afonnance
14!
'"'
20
IUidmlale lhc lewd and
demand, • hocncbu)'as
Ill..: a linllldallncan!Ye
10 dllm lhcy wiD 1M in
lhe reslllcn« aslcndin&
flo
0
U.S.
Florida
South Arizona Nevada
Carolna
Idaho
Vermont
~~lnc.·~•lllllpOeconomy.com
001
..
Hou•lng at the npplng Point
u.s. Re•ldentlal Real e.t.te Mllrket
The Outlook for the
Chart 17: HoasiDJ Afl'oJ:dabOity Is SlidinJ. ..
Housing alfonLdriJity index
Chart 18: ...Pardcalady for &ode Monpaes
1~~--------------------------------,
~5~------------------------------~
Housing Affordablllty Index
140+-----------~~--------~--------;
s
1~+-----------~~~~-;~-----A--~
he
130+-----------~~~~~-+~~~.---;
200
-1·yr ARM. 100'11. LTV, loO
-5-yr ARM. &n. LTV. loO
---------+-+---1
175+----NAR_=m~~-·---~-------~~~---1
125+---------~r---~------r+--~~~
:0
120
115
110
105
100
Sources: Moody's Ecanomy.com, NAR
Soun:e:NAR
95
75+-~~4-~~~~~-+~+-~-+~+-~
85
90
95
mortgage rates and terms (see Chan 1n.
This is the lowest level of aflordability
by this measure since the mid-19805.
The collapse in afl'ordability has been
much more pronounced in those metro
areas where house prices have risen the
most. Miami is illustrative, with the affordability index plunging from near 120
earlier rhis decade to near 60 today. Af.
fordablllty in Us \tgas 1m CIMd from a
high of over 130 to less than 70 amendy.
'Mishingron. D.C. afl'ordabill1y has dropped
from a very afl'ordable 160 to below 90.
Falling afbdability has bem particularly dif.
6cuh fOr thst-dme homebuyers, gi\'al rheir
generally lower Incomes and~· kcording to rhe Raltors, the afl'ordability index
for homebuyels. which was as high as 90
earlier in the decade, has laDen to only 70;
a 2~year low. Housing demand has been
hit han:l, as thst-t:ime buym account£d for
as much as one-half of home sales last year
in many large marlc£ts across the counuy.
fur a time, IDOl1gliF lenders were able to
cushion tbe blow ol tighrening rnonelal')'
policy on alfordabiJity by heavily~
ing 10 and option mortgages. The affordability of even these emtic loans bas fil1lm
sharply. ~as the Federal RtseM
pushed shon-wm raRS highet ~on
the Realtoas afordability measure, a 1-year
10 ARM loan widt nodting down is now
only marginally more afti:ndab]e than a more
uadidoual monpge Joan (see Clwt 18).
00
05
90
85
ing slalldards evm furthet MortgaF cndit
<P.JS~ity concans are rising and regu1atms are
growing irlclemlgty nervous and have become inaeamlgl.y vigilant in their ovmight1
R.einfmdng the shift from housing boom to
bust is the rapidly-exiting ilMstot Higher
borrowing costs, more cautious lenders,
and, most importandy, the realization that
house prices were no longer headed higher
have induced flippers to stop buying, and
if possible, to seD. l..onger-tenn investors are also re-evaluating their strategies.
Even if they were wiDing to look through
the likely near-tenn weakening in housing
values, it is difficult to justify such an investment as the cash or income rerum on
housing has £aDen sharply in recent years.
As measured by the ratio of e&ctive apart-
95
00
05
the decade, in contraSt. housing cash yields
wa-e in the double dips. while stock, bond
and cash yields were in the low single digits.
With investors accounting for as much
as one-fourth of home sales in the most
active marlcets last ~ housing demand has collapsed as they have made
a run for the proverbial dooc Many of
the Rippers likdy have yet to sen, sug·
gesting they will continue to weigh on
the market for sometime to come.
Not only is the downdraft in housing
demand conttibuting to the housing
bust, so too is a surfeit of new housing
supply. New housing consauction,
including single and multi-family
consauction and manufactured housing
placements, has been extraordinary in
recent years. Total new supply was well
over 2 million units annualized between
late 2003 and early this yeaz: This is
ment rents to house prices, housing's cash
yield has been cur nearly in half since the
start of the d£cade (.see Olart 19). At currendy under 7%, it is
lower than that on oflice Chart 19: BoasiDJ Is No l.oaJU a Bay
space. and is fast-apCasla yleW
~themer5%
yield on stocks, longa:nn bonds, and cash
itsel£9 Ar. rhe stan of
15 ~----------~~~~-------------,
·~..,.:~alssued
sewal~pldnesan
home equity ll1d latiiiCIItpF
~durlnal005.
*-
-.p~Ua-
---~
70&63:Z9.pcl[
·~...---lian
GlabiiiiiiiAnllydcs- ....s b
dllllllllysB. The CllftSIIIIallll
r.--ls die "-rllhe pa.
CI!Dqpllllo. lbellalasqi'Et.
lllallian 8.!111.-ly 15cuntlllly.
98
97
98
99
00
01
02
03
04
05Currant
17
.....
Houalng at the npptng Paint
The Outlook far the U.S. Residential Real Estate Mitbt
Cbart 10: MOft Supply ThaD Demand
house price index
from Case Shiller, a
division of mongage
2,400 'T"""---------------~
Sources: Moady's Ecanomy.com, c-us
services company
2,300 + - - - - - - - - - - - - - - - - . f ' . r - i
Fiserv. Each of these
house-price measurtS
2,200 -+---------------1-\:-J--~
has its advantages
and limitations.
2,100 +----------+-+-ft-++.~---+-1
The most favor2,000 -ht----------+---------"'d
able attribute of the
OFHEO series is its
1,900
ability to measure
house-price changes
1,800 +--+--r-++------J.---------i
based on repeat sales
1,700 +-4-++-+-+-f-f--+-1-++-if-1-+-4-++-+-+-f-f--+-1-+-+-+'
of the same homes
01
03
00
02
04
05
06
over time. Thus
OFHEO controls,
wen above trend housing demand for
at least in pan, for the quality of homes
new housing, which is composed of the
sold since it is based on matched pails of
home. During any quarter, the house-price
sum of household fonnations, what is
needed to replace the stock of homes that
index includes in its sample a home that
become obsolete each yea~; and second
is sold in the current quaner if there are
data available on at least one other sale
and vacation homes. Indeed, trend
demand, while rising steadily, is stiR below of this bouse in previous quaners. This
2 million units annually (see Chart 20).
is not exactly a constant quality index,
The gap between new housing supply
since improvements or additions made to
and demand has thus been steadily
a home between sales are not controlled
widening and now stands at near
foJ; but it is much closer to a constant
500,000 units, equal to one-fourth of
quality index than the Realtors' measure.
cwrent annual supply. The overbuilding
is evident in record high homeowner
A weakness of the OFHEO data is that
vacancy rates and stubbornly high nearits coverage is limitt.d to houses that were
record vacancy rates for rental units.
purchased by Freddie Mac or Fannie Mae,
Overbuilding appears most pronounced
mostly leaving out the lower house-price
in the Northeast and Midwest, and,
tias dw are transacted with govmunent
somewhat surprisingly given robust
loans such as FHA and Community Reinhousehold growth, in Florida. California,
vestment Act loans and upper house-price
and to a lesser degree, the Pacific
tiels that use jumbo loans or even cash.
Northwest and the Mountain West also
The current limit on a Freddie Mac or Fanappear overbuilt, albeit a bit less so.
nie Mae loan is $..17.000, wdl below me
median price in many of the lJlaliG:Is dw
Housc·Price PriJUr. To assess the
enjoyed the strongest appreciation n:cendy.
severity of the unfolding housing
OFHEO also excludes condominiums fOr
downturn, the remainder of this study
itS measure, a particularly signi&cant omiswin focus primarily on the prospects for
sion currently given that the condo market
house prices. Prices re8ect changing
has been particularly active in recent yem.
housing demand and supply and also
impact a wide range of other economic
Another weakness of the OFHEO data for
activity. &om consumer spending to
metro areas is that it includes home valmortgage delinquency and defaulL
ues based on refinance transactions that
There are three sources of house price data often bias the indexes. There are sevmd
available for the nation and a laige number soun:es of refinance bias. Fust, valuations
of metropolitan areas. These include
&om refinance transactions are based on
the National Association of Realtors'
house-price appraisals, rather than actual
median existing house price, the measure
home purchase prices. v.duations based
used most prevalently in this study, the
on appraisals are constructed under differrepeat-sales house price index available
ent circumstances than those SU110unding
from OFHEO, and the reDE:at-saleS
purchase prices, as appraisers operate
Sinsfe·, muld· muJ numtif~ Junasbag, dts, J mo• .MA
k\:==--'"IT'A:~F=====~=I
18
under specific types of prtSSUreS and may
employ different comparable properties in
estimating value than were used, at least
implicitly, in the formation of a purchase
price. Second, refinance appraisals may
during periods of rapidly changing prices
to the extent that they utilize historical
price data that may quickly become out
date. Finally, houses that are refinanced
may be houses that have appreciated the
most. Indeed, houses with weak or neg-.
tive bouse-price appreciation may have
insuflident equity that precludes their
owners &om refinancing at the most favorable interest rates. While OFHEO bas
constructed a purchase only index for the
national house price, the metro area price
indexes still incorporate refi. transactions.
The OFHEO data are also lagged a bit
given the 30 to 1-5 day lag time &om
origination to Fannie and Freddie
OFHEO receives data on new fundinp
for one additional month following the
last month of the quartet: These fundings contain many loans originating in
that most recent quarter, and especially
the last month of the quarteJ: While
this is not a particularly significant poboo;
lem in a more stable housing JDarl<d, it
is a problem in a fast.changing one.
give an even more accurate repre54ma~ · ~
tion of price movements. Calculated
a similar manner to the OFHEO dala,
the CSI is a repeat-purchase house
index. Since the price data upon
the index is based are home sales, the
improves upon OFHEO in that the
does not have a reli bias, nor is it
to prices based on home sales iJlvoht. .,
ing a conforming mortgage. The main
disadvantage to using the CSI is that il
9 considerably in reporting; as Ions
,
as four months after the quarter ends.
The Realtots' data are based on 5lU\'eY
from Rgional realror &Ociations. 1be ....
dian price captures acrual home~ .
across the house-price specaum. but 11111
swayed by dilrerences in the mix c£00..
aansacted from period ro period. MOlleMi ~
NAR data are only available for appada. . .
150 metro areas.- Moody's Econorny.c:om
does construct estimates ofhouse pried
the nation's remaining metro areas bised
other housing indicators (:lee
Hou1lng at the npplng Point
706
.y
n
ag
The Outlook for the U.S. Rnldentlal Real Estate Market
Mct!u++IIJ 1
£stlnwdns Mldlm f:ldsdD&SiJiale-Fadly ..,_ Pdas
value of occupied homes from the decennial census with growth
rata from the tq)Cilt purchase bouse price Index from OFHEO.
Moody's Economy.mm estimates historical dara for medim
existing single-fmnily home prices for all counties, meaopolilan
areas and swes. The clara has a quarwly perlodidty as
far back as the early 1970s depending on the aJU.
The principal data soun:es used to esdmate this clara 11e the National
Association of Realtors (NAil), lhe California Association of R.ealt01S
(CAR), the Florida Association of Ralrms (FAR), die Ollice of
Federal Housing Enteaprise Ovmight (OFHEO) and lhe U.S. Census
Bureau. 1be NAR provide median cxisliDg bouse price dabl for
cmr 150 meuopolitan areas. CAR provides data for 12 Califomia
meaopolitan mas dirmly and 11 iDdJm:dy. FAR provides dara for
11 F1otida mettopolllan mas. OFHEO provides repeat sales house
price indices for Oft!' 300 meao BJeaS. The Census provides clara on
the median value of oa:upied homes from the decennial census.
Step 3) This estimate is then adjusted to account for the differences
between the decennial census &gun: and data from the NAR by
applyiDg the appropriate rqional adjustment series creat.ed In
the 6m step to die prdiminary meaopolitan area estimate.
Step 4) Where available these estimates~ replaced by
published house prices from die NAR. CAR and FAR.
Step 5} Preliminary esdmates by county 11e then made using the
median value of oa:upied homes from the U.S. Census Bureau's
decennial CCDSUS and Infilling In the lntercen511 years with growth rates
from Moody's Economy.com's estimate of median household income.
Step 6) Coumks located in a meao ~are !hen adjusted to mau:h the
newly published mettopoliran home prices. This Is done by raldDg the
ratio of the prelimiJwy C10UD1:f house price to a wr:f&hred avaaae of the
coundes in the meao, using home sales as a Might, and applying it to
Step 1) The lbst step Is the c:mation of a regional series that relates NAR the 6nal meaopolilan estimate. If die county is not In a meao area, !hen
house prices to the decennial census median value of occupied homes division dablls used Again. a ndo or prdimjnuy county to a weighted
by population a. This adjustment series is used In a later SfeP·
avaaae of CXJWllies in the division is applied to the fiDal division.
Step 2) A preUmlnary estimate of median existing single-family home
prices by meaopolitan area is then calculated by infilling the median
The wotk presented in the remainder of
!his srudy is based on the Realtms' malian
existing house price data. Most importantly.
the Rtahms' daal are the dmdiest, wilh the
metro area clara released within six weeks
a&a- the end or me quarra: Mormva; at rhis
jUIICDU'e in the housing c:ycle. the Reallor
dara are seanlngty more acaua~e at picking up turning poinrs in house prices across
the country. The rdinana: and conforming
loan biases in the OFHEO dala are likdy
causing that measure ro miss the cunau
Step 7) Srate estimates ~ created from a ~ted average
of the couudes in a slate using home sales as the weight.
rapid slowing or outright declines in house
prlcts now ocaming in many pllces.
It is important ro nore tbat none of the bcJlR..
price data~ able ro measure c:banges in
the use of various incendves and disoounrs
that ~ not rdlectM in aaual aansaction
prices. In roday's walcoing housing ~Dada.
for example. sellers are reponmly o&ring a.
myriad o£ incaldves, from 6xing rhe deck ro
help with financing. in order ro complete a
sale. Ifso, then actual~ bouse prices
would be evm weakEr than rneaued prices.
Mawul. . Jloase.
Pdoe Rislt. 1bere
are sevaal tndirbwl
Chart 11: Boase·Prka aDd BoasehoLllnCOIH
identifying whether housing is appropriately valued is to compare bouse prices with
household incomes. 10 Over long periods,
house-price gains have closely mirrored
household income gains nationally and
across mettopolitan amiS (see Chan 21).
That housing values and household incomes
should be cbdy relmd Is basal on the
special importance mast ~seem
ingly place on owning lhdr own home. Ibis
impouauce is seemingly rooted in bolh
household psydlology and the significam tax
aclvanllvs ofbomeownaship.
Holasehokls
and Wier CX1SIS.
have historically pun:hased as much housing
as mar inmmes will aDow. lhe saong relationship betwem bola prices and incomes
caA a1so be tstablished through me cost of
land and construction OOSIS. The value of
land is tiltimarely decennine.d by irs opportunity cast, which in tum equals lhe value of
goods and services produced in rhe geogmphy. GM:n a amsmnt labor share of output.
me growth in land values and inoomes will
dtus be cquivalmr. lhe growth in c:onsnuction cosrs also dosdy aacks incomes since
these cosrs are pl!dominandy labor oosrs.
I--to-Price. A
popular approach for
tfGusiD& Mmal An Analysis."Ia llrualriap Alpas,lilll200l.
approaches ro asS!! ssing
the pmspeas fir house
prices by gauging
whecber prlas are
measumbly ova"· or
wxlawlued. 1hr3e
approachcs lypicllly
~compuing
prices with household
incomes, rcms,
75
80
85
90
95
00
OS
.. Sec c-. Kml ad~ Rdlcn, "Is
n.-. Bubble In lbc
1t
.......
Housing at the Tipping Point
The Outlook for the U.S. Residential Real Estate Maftlet
""'
Chart ll: Bouse-Prims and Apartment Rents
3.2
3.0
2.8
pdcelndU \.
/
L_
OFHEO
2.6
2.4
2.2
2.0
1.8
1.8
1.4
1.2
1.0
0.8
/
OFHEO house-
Index: 1985=o1.0
Sources: Global Real Analytlcs,
I
/
..,
L
/
pl'DYided via awning
is silbstamlally dif
ferent from the cnst
of those services via
renting. Households
will C\'mlUaJly adjust, ~will house
prices and rents.
__./
ies the operating cost of owning the home,
and is subtracted from the annual apartment
rmt per square foot to obtain the annual
net income per square foot from housing. 12
The annual net income per square foot from
housing is muhiplied by the median size
of the house to obtain the gross annual net
income. finally. the median existing house
price is divided by the gross annual net income to derive the price-UH:amings rado. 13
The strong relationship between
hcu;e prices and
ranis
The national house PE has soared from
~ Elecllva
rents is also due to
less than 10 at the start of the decade,
to near 15 currendy (see Chan 23). PEs
the laUtology that
~
have expanded substantially more in
house prices equal
metro areas like ~t Palm Beach, Fon
the present value of
95
00
05
85
90
Lauderdale, Miami, las Vegas, Phoenix
the furore services
and San Diego (see Chart 24). In conprovided by housing.
traSt, more modest housing PE expanWhen bouse prices and household ina>mes
Those services are equal to wbat it would
sion is evident in middle-America mardiverge suh5tantially, this is only suggestive
cnst the homeowner to rent her home
kets, such as Kansas Oty, Indianapolis
that a housing madC£t is awmlued ex- specuJ. back to herself, wbk:h in tum is equal to
and Pittsburgh (see Appendix 4).
the rent on a compamble apartment.
tiYe. House prices and incomes can diw:lge
ow:rexlmded periock wbm IWiqgagt: mtts or
other aansaaion costs are st£adily rising or 6dl- House price and rent growth can diverge Ova' User mst-I(J.renL A third common approach is a type of accounting exercise in
ing as !hey have done aver the past quam:r c:m- examded periods, howewl; due to forces
a.uy,IDr example. or wbm non-labor amstrucunrelated to speculadon. Steadily rising or
which the user cost of housing is CODlpallal
to renrs or the net present value of owntion cm15, such~ the cnst o£ materials, are
6dling J:llCJt1gaF ratr.s or other tmnsaaion
ing a home is calculated and compared to ·
growing at a pmislmdysaong or~ tare.
costs,~ in the availability of mortgage
auiit. tax law changes that impact the cnst of prevailing house prices. 1+ If the user cost
By this l11aiSI.lre, national bouse prices
homeownasbip via renting are an examples
are appmximately 50% above their longof fiaams thatcan cause house price and
12 1'ropcny- and lllllmalallcc aJI1S 1ft ___.Ill be
nm histmic:allevel ~to bousebold
rent growth to divage for em:nded periods
olr.t by die 1D0rf111e I n - deduaioa.
incomes, and more than double in some
of time. Eventually. these bas abare, and
"Due 111 limladons In cia ...u.bllily for- ua
high-pric!d metro areas CWJe Appendix 3).
bouse price and rent gains COIM:JF. If the
and house sila, those calculalions an: llndted 1D 59
-mel die ll&liotL
gap betMm price and rent gains is lalgie and
See HiDuDc1besJ. C.; Maya; C.; IIIII Sinal. 'I, 2005,
Prf«...o.rmt. Another aadidonal approach
conDnues to persist. howr:va; then specula"l\saassna Hiab House ~'rita! Bubbles, Fu~
Mlspacepdoas,• &dcralllacnc Bcudr of Hew liJrlr Sllljf
to gauging whether housing is OYer- or
tion is Wa:Jy afreaing housing madcl:ls.
Rtporu, ao. 218,111111 Smith. G., IIIII Smilh, M., 2006.
undavalued is to COIIlpiR bouse prices
"Bubble. Bubble. Where's the llubble1, ronJJcomiD& ID
11 That is, to value
wirh apartl11CIU rems.
The gap can be lllf3SUl'ed by an equivalent
Alferon ~AaMty.
houses by the amount o£ net income («
housing PE ratio dJat
net rent) !hey generate. This is similar to
values bouse prices
re1a1ive to the net in0uut 13: H0115ba& PE Batio Soars Naliollally•••
the stock marker's eaming5 yield or taking
come or rent !hey can
Media aisdng ,.,._ pt"f«-to-lld ~ rmt
the imme, the price-to-aminp ratio CWJe
Melhodology 2). Ower lang periods. housegeoenue. lbisisdone
15 -r-----------------;
~: NAR. Global Reai.An8lytlca. Moody's Economy.cam
price gains and the growth in apartment
by detennining an
annual JI1011811Fpa)'14 + - - - - - - - - - - - - - - - - - t t t
rents have tradcr.d closely across the nation
and mettopotiaan areas CWJe Chan 22).
mmt m;iog data on
1
......-::: -_...-
14
......,.p
mumm~bouse
That fundamental housing values and renrs
are closely relar£d is simply due to tbe fact
dw multiFamily housing and single-liunily
housing are c1aie substituteS. If bouse prices
deYiale substamially &om rents, then this
suggests dw the cnst of housing seMces
II
Sec ~J~ 2004, "'be~ l!daliansblp aa-
tlouse Plic:es IIIII Rae.- l'ilooMa...., &anon*:~ Dltaa.1ian Scria,
llocd ol GcMmaD ollhe Fedmllesaw St-. No.l00+-50
20
13+--------------------------------------------------------,+
prices. loan-to-value
nu:ios, and conaaa
mon:gage rms.lbe
annual mongage paymentis then divided
by the median-sizl:d
home to derennine the
amwall1lOl'tgiiF paymenton a per square
foot basis, which
11
+--------------------tttH
9 +-ll~~~~~~fKI~~tllltHIIHliHltHIIH
8
94
.
•
.....
I
!lousing at the npplng Point
1he Outlook for tha U.S. Residential Real Estate Market
October 2006
\tethodolpsy l
l"stimating Housing's PE
~hxxiy's Economy.com estimates hisrorical housing price-aming;
In some cases, the median house size is proxied from similar or
nearby metro areas (e.g. Las Vegas by Phoenix) .
•.• aitJS for metropolitan areas and the nation. lhe principal dara
"maus used to estimate dais data are the National Association o£
Step 3) The annual mongage payment per sq. ft. proxies rental operating cost· and is subttaeted from the annual apamnent rent per
sq. ft. to obtain the annual net rental income per sq. ft. from hous'<lep 1) An annual mortgage payment is calculated using data on median es. It is imponant to note that property taxes and maintenance
costs are assumed to be offset by the mortgage interest deduction.
•'Xb'ting house prices, loan-to-value ratios, and effective ~ rates.
lkJitors, the Federal Housing Fmance Board, the Census Bureau's
.\merican Housing Survey, and Global Real Analytics.
Step 2) The annual~ payment 6 divided by the median sizEd
home to detennine annual mo~ payment on a per square foot basis.
tne median size of owner-ocx:upied homes is available for the US. every
IWO yean; and for metropolitan areas on a multiple year cycle. Data were
inrerpolab:d fix inteMning yems. In cases where only one data point was
awilable, this size is used throughout the analysis period.
•, measurably higher than rents or the net
prl'Sellt value of owning a home is lower
1han house prices, then the housing market
.., deemed to be overvalued or speculative.
l'his approach io; very sensitive to the
measurement of housing costs, however,
mcluding things such as property tiXCS and
maintenance costs. These costs are very
•lifficult to measure act:urately, particularly
.11 a metro area level. Risk premiums and
.lio;count rates, things that can not be direcdy
'lhserved, must also be assumed to perform
1he calculations of the user cost and net
pn:sent value. It is also wonh noting that
1he results in some cases are hard to explain.
In one of the studies, for example, it is found
•hat Los Angeles house prices have always
l11.-en undervalued to varying degrees.
the appropriate level of house prices thus has
consequential limitations. Simply comparing
household incomes and apamnent rents
to house prices ignores the possibility that
they may diverge for extended periods of
rime. Accounting CXEClises are useful, but the
results are severely impaired by the quality
of the data used and the assumptions made.
lhe methods developed and employed in
this study provide an alternative approach
to identifying housing markets at risk of
experiencing price declines that address, at
least in pan, these limitations.
Leading House Pritt lndk:ato~ The leading house price indicatoa; or LHPI. measures
the probability that a metro area will experience a measurable house-price decline during
the coming yem:
Housing P/E rdtio
West Palm Beach
SanlaAna
&mF~~ t
~~~~~~~:::t::::::·
SanOiego
Las Vegas
Mlani
f:iijillt--1'Ci..ljlll_ljlll
m2005
Chicago
Kansas City
• 2000
u.s.
e 1995
lndanapols
~~--+---+---~--~--~--~~
0
20
Moody's Economy.com, Inc.
40
60
80
Step 5) The median existing house price is divided by the gross annual net rental income to derive the price-to-net rent or earnings ratio.
Each of the previous effons at determining
Chart 14: ...And by Much Moft iD Some Metro Arus
Phoerix
Step 4) The annual net rental income per sq. ft. from houses is
multiplied by the median size of the house to obtain the gross annual net rental income.
100
120
• ~ • halpOeconom)•aJm
140
The LHPI econometrically identifies and
combines variables
that have historically led changes in
housing values. This
LHPI detennines
the probability of a
significant decline in
future house prices;
it does not provide an
estimate of the magnitude of that change.
Since the LHPI does
not impose a fuced
formal relationship
among the included
variables, it can reflect changes due to a
wide variety of causes.
S~cijication.
Many variables were tested
in the construction of the LHPl, but five
variables were ultimately found to lead
house prices by approximately one yeac
These variables include non-housing related employment growth, housing affordability, a measure of house price over- or
undervaluation, the physical balance between new housing supply and demand,
and a variable that captures the volatility
and persistence in house prices.
The current value of these variables,
properly combined, thus provides a oneyear-ahead forecast of house prices. More
precisely. the lHPl is an econometrically
estimated relationship between the oneyear lagged value of these variables and a
binary dependent variable, equal to one
when house prices have declined on a
year-ago basis, and zero otherwise.
Non-housing related employment is equal
to total employment less employment in
housing-related industries, which includes
a wide range of industries from construction to mongage finance. 15 Historically,
house-price declines have occurred during
periods of declining employment. Excluding housing-related employment is necessary since these jobs are direcdy tied to the
housing market and therefore not accurate
"See Appendix 16 of this stUdy for a cumplete ck&nldon nl
houslng-rdaud indUSinCS.
21
Hou•lng at the Tipping Point
The Outlook for the U.S. Residential RNI Estate Market
Chart 15: Probability of a Bouse-Price Duli:De
UIPI PraJit:kd (x-axfs) v.s. AcDud {y-G.Xis), ~
100
..------------..r--"T"'nr-/-'1"2n..,
90+------------&-~~~H\H
80
1
~--------------~,A~j~f\~~~
months of excess
supply in the marlc£t, similar to an
invmtory-to-sales
ratio. A thRe-year
period is suftidmt
to abstract from the
60~----------~~M~~~~~~~ temporary vagaries
of housing sup~+-------------~~~--~~~-L~-L-1
ply and demand
~'
~+--------~~~--------------~
as the national
30 +-----~-"2__-"7'1f.r.,."-1P---------------i average length of
20+-----~~~----------------------__,
time it takes for
homebuilders to go
10
~
from planning to
10 20
30 40 ~
60 70 80 90 100 completion is appraximarely a year
and a hal£ lhe
greater the months of housing supply. the
indicators of underlyingjob growth when
greater the slack, and the hlgber the risk of a
housing marlc£rs are in ftux. Not coinhouse-price decline.
cidentally, some of the metro areas with
the quickest non-housing related employlhe balance between new housing supply
ment growth last yea~; such as Las Vegas
and demand varies considerably across
NY, Phoenix AZ, Cape Coral fL and Fori
the country. Markets are well-balanced
lauderdale fL and Rivelside CA. are also
those that enjoyed the most robust house- in areas such as Fon lauderdale FL. and
~bington D.C., but appear oversupplied
price gains (see Appendix 5).
around Boston and New York City and
Housing affordability, a key factor inftuenc- parts of the Midw1:st (see Appendix n.
ing housing demand, is also an important
leading indicator of bouse prices. lhe Re- 1he degree of bouse price over- or undervaluation is derived &om the sauctural
altors affordability index adapted for metropolitan areas is used in the UiPl. or the econometric model described later in the
study. The model produces an equilibrium
nation's 379 metro areas, affordability has
wealcened over the past year in all but 50,
or long-run house price that is deramined
and in 74 metro areas, the index cunendy by a range of factors, including personal
stands below 100. In other words, house- income, household wealth, the vacation
home share of housing stock, a 9/11
holds earning the median income can
dummy variable, the risk-adjusted return
not afford to purchase the median priced
on altemati:R ~tments, and a proxy for
home at prevailing mongage rates and
terms. While some of these aRaS, such as structural changes in the mortgage market.
The difference between actual and equilibSan Francisco CA. San Diego CA and New
York City NY, are perpetually unaffordable, rium house prices measures the degree of
over- or tmdervaluation.
others are new to the ranks of the unaffordable (see Appendix 6).
Not surprisingly, the most ovuvalued metro
areas are concentrated in the previously
lhe physical balance between new housing
supply and demand also alfecrs house prias. heated housing nwkets along the coasts
and in the Mounrain 'M:st (see Appendix
Pricing is~ in metro areas where the
8). Miami fL tops the list, while the smallsupply of new housing outpaces underlying
er inland CaJifomia metro areas are also
demand Supply is measured by housing
notably ovavalued. A few housing markets
completions over a tbrte-year period. while
are deemed to be undervalued, but lhe
demand is measured by the sum of household formations, vacation home demand and number of such areas has dwindled.
replacemeru denwtd over the same~
Measured house prices are volatile and exyear period. To account for the different size
of each madcet, the leYel of exr.ess supply is
hibit persistence. Smaller metro areas with
divided by avmtge annual demand to oblain thinne~; less-active, housing markets experiro+--------------Ar-n+ffiH~~~r-~
ov.
22
ence large swin~ in prices. 1his volatility
is particularly pronounced in the Realtors
median house price data as it can be signifl
candy affected by the mix of homes that are
aansacting. Price movements are also persistenL If bouse prices are rising strOngly
in a metro area, then bomebuyers, sellers,
lenders and builders anticipate further future price gains, which in tum affect their
behavior and thus become self-ful6lling.
rab
0
0
Dc1
Me'
'\an
Inc
Nu
rut
R-s
Ad
1be vol-......., and 1V'I"Cic:TPn,... in bouse prices
...nJ;"'
c---'i.E
are captumi in the lHPl through lWO dummy
llt
variables. 1he fbst Is set equal to one ifthe
·
metro area experienced a sequential price
decline in the most recent quarter and zero
othelwisc, and the second is set to one if the
area experienced a year-over~ price decline Cc
in the most recent quarter and zero odlelwise. Nc
Nc
.EsdlnGdon. The LHPI is estimated over a N•
more than 20-year period extending back
No
to the mid-1980s using ordinary-leastHo
....
squares (OLS).
While there are inherent econometric
problems with using OLS to estimate a
probability model, the most significant
being that the results may not be bounded
between 0 and 1, OLS estimation is appropriate for the LHPI. lhe objecti:R
of the lHPJ is to identify the metro area
housing marlc£ts at risk of experiencing a
future price decline. Probability estimates
that may fall below 0 are therefore of liale
concern. Moreover; the number of estimates above 1 is so rare that it is virtually
a nonexistent problem. There is also a
clear linear relationship between predicted
model estimates and the actual historical
probability of decline. That is, a probability estimate of 50% has an actual historical
probability of occurring very close to 50%
of the time (see Chan 25).
According to the regression results, the degree of house price over- or undervaluation
is the most important determinant of the
probability of future house-price declines,
accounting for 30% of the variability in the
LHPl (see Table 1). Non-housing employment growth and housing affordability
each account for approximately 20%.
Non-housing employment impacts the
LHPI over an extended period. While job
gains are a source of additional housing
demand, new job holders usually do not
become immediate homeowners. Indeed,
H
H
D
D
fi
c
it
s
Housing at the npplng Point
The Outlook for the U.S. Residential Real Eatal8 Market
.ty
fable 1: ProbahOityofHouse-Price Decline Equadoa
IS
ni6.are
er-
y
5,
I·
r
!S
my
Dependent \Uiable: Probability of House-Price Decline
Method: Pooled Least Squares
"ample: 1985:1 2005:4
Included obsetvations: 8..
~umber of cross-sections used: 379
rotal panel (balanced) observations: 31836
R-squared
Adjusted R-squared
S.E. of regression
Dwbin-'Wu.son stat
O.H9
0.138
0.345
1.101
ladepmdmt \lilriables
ne
;e.
Coellideat Std. Error t-Statistic
Constant
Non-housing employment, 4 quaner lag, % change year ago
Non-housing employment, 8 quaner lag, % change year ago
Non-housing employment, 12 quaner lag, % change year ago
Non-housing employment, 16 quarter lag, % change year ago
House Price (Over/Under) v.duation, +Quarter Lag, %
Housing Affordability, +Quarter Lag, Index
Housing Supply. +-Quarter Lag, Months
Dummy 1 if% change in house price < 0, quaner-to-quarter. +Quarter Lag
Dummy= 1 if% change in house price< 0, year-over-yea~; 4-Quaner Lag
=
0.3608
-0.2906
-0.5..37
-0.5671
-0.4328
0.0068
-0.0012
0.0018
0.0259
0.1167
0.0149
o.o8..-.
0.0811
0.0752
0.0730
0.0002
0.0001
0.0002
0.00..7
0.0059
24.19
-3.....
-6.71
-7.54
-5.93
31.63
-13.15
7.68
5.49
19.93
Beta~ght
3%
6%
7%
5%
29%
20%
8%
5%
17%
Fixtd Effects Not Shown
employment growth has a four-year lagged
impact on house-price declines-with the
smallest impact in the first year and largest
impact in the thild year. Contributions
[or the other variables are more modest,
including 10% for the new housing supply
and demand balance measure, 5% for the
sequential quarterly price decline dummy,
and the remaining 15% for the year-overyear price decline dummy.
Vlllfdado& Historically. the l.HPI has accurately iclenti6ed those meao area housing
ltl1llic£ts mast at risk of experiencing future
price declines, and has also acc:urate1y identified those marlcets at least risk of experiencing future price declines.
l1tis is evident by classifying rruu:icets as
heing either High Risk. those with a prob.1bility of a year-over-year house-price dedine of over 50% at some time during the
.-oming yeu; or Elevated Risk. those with a
probability of between 33% and 50%. The
.average risk of such a house-price decline,
.IS measured by the percent of times there
were price declines across all metto area
marlcets OYtt the entire more than 20 )UIS
considered. is 16.5%. Marlcets with a probability of price decline less than 33% are cJas.
si6ed as Normal Risk.
Metto area markets classified as High Risk
markets experienced lower bouse prices
one year later 62% of the time. Elevated
Risk markets suffered lower prices one year
later 39% of the time. All other markets
had lower prices just 12% of the time.
These probabilities increased to 84% and
66% in the High Risk and Elevated Risk
groups, respectively. when considering
price declines over a subsequent twa-year
period (see Chart 26).
The lliPI is particularly accurate in ideatifying High Risk markets that experienced
subsequent price declines during the late
1980s and early 1990s, the last time there
were broad-based declines across the country. Over the 20-year period used in the
construction of the LHPI, the peak number
of High Risk markets was the 65 identified
in the tbiid quarter of 1987. The share of
identified High Risk marlcets that actually
ultimately experienced price declines during
this period ranged from 75% to 100% (see
Appendix 9a>.
During these yaus. most of the High Risk
marlcets ~in the Northeast and oil-parch
stares. The fonnerwas entering recession,
wbiJe the 1ancr was 5liJl reaJYering from the
mid 1980's oil-price ooiJapse. Among the 55
metro areas identified as High Risk in the lbst
quarter of 1988, house prices were lower one
year larer in #of these rnadcm, with au addilional10 rnadG:ts experiendng a price decline
wiibin twO yr.&IS (see Appendix 9b). The only
High Risk rnarlcEt that did nor experientt a
decline within this period was Beaumont-ft>n
Arthw; 1X; where house prices managed 10
eke out a very smaD gain one year lata; beCore rising the year aCta: ln addition. of the
47 Elevated Risk marlcets identified in the
lbst quanerof 1988,35 actually experienced
price declines in the following year and an
but one area experienced prices declines
within twO yws.
1he period since the end of die 2001 recession, a period of saong broad-based bouse
23
Housing at the npplng Point
The Outlook for the U.S. Resld*1ttlll Real Estate Market
Chart 16: lBPI Probability of Dedille Across Risk Groups
90
Source: Moody's Ec:onomy.com
Chartl7: 1he IBPI Has Peged Housiag's Becalt
Perfoi'IIUUlCe
111
25~--------------------------------r
Ill
Number of high rtak metro . . . .
4-quarter lead (l)
80
20
70
ul
Jill
'"'
...u
1n
60
Shant of metro.,_ wilh
pllce declnea, % (R)
15
50
10
40
10
"""
30
20
u
Ri
5
Ra
5
10
ae
(I
{t-
Yf
0
High risk
Normal risk
Elevated risk
price gains, is useful to demonstmte the abilil¥ of rhe lHPI to accurately identify madc£rs
with a low probability of experiencing furore
price dedines. Indeed, only a handful of
mean areas vme idetuifled by rhe lHPI as
being High~ between 2002 through 2004,
and few marlcels actually did experience price
dttlines during this period
Owt
esee
2n.
1he lHPI was smningly least~
aroundY2Kandrhe200l downtum. 1be
lHPI did not identify a hugle increase in the
number of High Risk marlc£cs; yet, rhe actual
number of metro areas experiendng bouse
price dedines did in fact increase sharply.
Most vme small midwestern meao areas,
hawe\a; that experienced. only 'YaY brief and
modest house price dedinrs.
UfPI's Oudoolr. The message &om the
current reading of the LHPl is disconcening. Over 100 metro areas, together ac-
02
03
counting for nearly one-half of the nation's
housing stock, are at a High or Elevated
Risk of experiencing house-price declines
during the coming year (see Chart 28).
Seventeen of the .36 High Risk meao ma
marlcets are in Califomia (see Ouut 29 b
largest 100 meao areas, and Appendices 10
and 11 for an metro areas).. The areas range
from Los Angeles, Riverside, San Diego and
Santa Ana in southern CA, to OUco, Salinas,
Santa Rosa, \Wlqo, and Redding in JJOJtbfm
Oilibnia, and Balcmfidd, FRsno, Merad,
Modesto, Saaamemo, StoclaDo, Madaa and
VJSalia in the Cenaal \tiDey. These marlcets
are generally cbaracterimJ by both seyere
OYerYaluaDon and low housing atbdability.
In~ meao mas in the Cenaal \filley
are among the most ovavalued in the nation,
despite relaliwdy lower nominal house prices
than odJer at-risk 1llliiRrs in the Slate and
nation. In contrast, nonhan and southern
04
05
06
CalifDmia meao areas are more burdened by
low and FaDing housing affordability.
The diference between overvaluation and
low housing aft'ordability is subtle. The
northern and southern coastal California
markets hiM: historically been burdened
by high house prices and low affordability.
reflecting their tight~ constrainiS. Consequendy. current high house prices are
less out of line compared to their historical
nonns. Rapidly rising house prices are only
a n:cent phenomenon in the Cenaal \91ley, howevel; where house prices rum: been
propeDed well above what history 5UggleSIS
is consistent with in-migration from other
higher cost markets. If these migration
inJJows slowed or cvm halted. men the existing population would be unwilling or unable
to suppott the current higher pricing. In fact,
house prices in the Cenaal v..lley are now
moving Iowa:
ar
al:
pl
he
Cl
H
El
It
m
st
5\.
ar
~
st
p;
in
cr
bl
at
at
p;
af
Sl
\i
Clwt 19: Madds at SipiBcallt Risk of su&rma &om
Chart l8: Marbts at Risk of a Price DuliDe Aa:ordbaa to
the lBPl
Falllaa Houc-Prica
IT
1~~-------------------------------------------------------------------------T~
• • Elevated ltak lllll'kaCa (L)
120
• • High rtak martats (l)
c
40
100
lo
45
- " Hauling stDdc at ltak (R)
Source: Moody's Economy.com
80
\.'
c
35
51
P.
30
25
60
~
20
0
24
tv
20
h
le
15
0
31
10
VI
5
•
0
•
Hlgtllllk merUtl
EleYslsd lflk markelll
Nelle: Among 100 largest metro . . . .
~
l!ooncln¥com. Inc. • www.-..ny.com •llllfp. .conom,am
a
k
a
ttoualng at the Tipping Point
the Oudook for the U.S. Residential Rul Estate Market
llther factors conoibuting to the high risk
·•l price declines in California is modest
uun-housing related employment growth,
l'•trricularly in northern California, and
tr.·ent indications that new housing con''ruction is outstripping underlying demand for new homes.
10
5
-0
by
y
it-
'e
The second largest concentration of High
Risk markets is in the Northeast Corridor:
Rarnstable Town (Cape Cod) and Worcester (MA); Atlantic City, Edison, and Ocean
City (NJ); Nassau-Suffolk and Kingston
(NY); Portland (ME), Providence (RI); and
Woishington, D.C. Most of these metro
areas are highly overpriced and many are
also experiencing weak non-housing employment growth. In some cases, excess
homebuilding is also a problem. New York
City and Baltimore, are not classified as
High Risk, but are at the high end of the
Elevated Risk group.
It is notable that the Philadelphia, PA
metro area is not considered to be at substantial risk of price declines, yet, some
surrounding smaller Pennsylvania metro
areas are at substantial risk. House-price
gains in the Philadelphia area have been
strong in recent years, but nothing compared to the growth experienced elsewhere
in the corridor: Investor demand has increased in the downtown condo market,
but is not evident elsewhere in the metro
area. Metro areas such as York, Reading,
and Allentown, PA are at higher risk in
pan because prices have risen sharply and
affordabillty has fallen due to strong migration from less affordable housing markets
such as Baltimore, Philadelphia, and New
York, respectively.
Miami and Naples are the only High Risk
rnarlcet in Florida, but an addiDonal15 JIW'o
k£ts in the smte are at EJevared Risk, including
Cape Coral. Deltona. ron Lauderdale, Fort
Wcllton, GainesWie,JacksonviDe, l.alcdand,
Ocala, Orlando, Palm Bay, Panama Oty. 1brt
St. Lucie, Punta Gorda, Salzota and \\bt
Palm Beach. Miami is lhe most owervalued.
housing marlcet in the nation and among the
~ affordable. The comparaiM:ly lower risk
of the Florida madcets vis+vis the California
and EEl: Coast madcets resuhs from Florida's
vibrant llOil-botmng employment growth. An
addibonal downside dsk in the Florida markets, howeve~; is not captured by the UfPI;
and that is the sharp downturn in the con-
do market, which is a large pan of many of
the state's housing markets. 16
~tern
metro areas at High Risk are
Honolulu (HD; Carson Oty (NV); St. ltGeorge (U'Ij; and Greeely (CO). House
prices have already turned down on a year
ago basis in the latter metro area. Other
markets at Elevated Risk include Phoenix, Prescott, and Tucson (AZ); Las Vegas
and Reno (NV); and Coeur d'Alene OD).
Despite rapid bouse-price appreciation
that have led to significant ovenraluation
in these markets, house prices are being
sustained by well above average non-housing employment growth and well-balanced
new housing supply that is being supponed by surging population growth.
Rockbd (lL) and Saginaw CMO are the only
meao 3l12l in the Midwr.st at High Risk.
Rockbd's housing rnadcet 1m been pumped
up by rnigtants 6orn Chicago's more~
maria and
6orn strength at DaimleiOuys&:r facility. one o£ the few expanding
domestic aulD plants. lhe influx of Chicago
residents may be slewing the mix of homes
towatd more apensiwe homes, rhus ekwting
the median pdtt measure and rendering the
rnadcet bigbly OYeMlued. The ownoaluation
and a net decline in non-housing emplaymem
am
me
are contribwing to Rockford's High Risk designation. Other meao 3l12l in the region at Elevm:d Risk of house-pice declines are Davenport and Warloo (JA); OJampaign. ~.
and I<anlcalcEe (IL); l...ansing CMO; Minneapol6
(MN); and Madison and Milwaulc£e (WO.
In the aliamarh of Hurricane Kauina, New
Odeans QA) is identified as High Risk. This is
primarily due to the fact that house prices hlM=
risen at an annualimi late of 20% in the posthurricane period. Not since hyper-inflation
period of the early 1980s hlM= bouse prices
increased so much in this metro area. Consequently. the area's housing is deemed ro
be overvalued Although recent house-price
appreciation is a reflection of the massive
desrructi.on of the housing stock, rebuilding is gaining momentum. As new supply
comes on line, house prices will come under
significant pressure.
to identifying metro areas at risk of experiencing house price declines is a structmal
econometric model A structural model of
the housing market is based on estimating
statistical relationships atnong the various
wide range of variables that affect housing
demand, supply and price.
The structwal model used in this study
can determine whether metro area housing
markets are ovetWlued, the degree to which
overvaluation exists, and bow these markets will ultimately adjust. The model, in
conjunction with forecasts of the economic,
demographic, and financial drivm of the
housing market, is also used to produce
explicit metro area house-price forecasts.
The information provided by a structural
model is richer than that provided by a leading indicat01; including the magnitude and
timing of a change in house price in addition
to the direction of that chanse, but it also
has its clear disadvantages. Most impor-_
tantly, a structural model cannot anticipate
events that ~ rieYer occurred historically.
and may not fully reflect the myriad factors
that affect housing demand, supply and
prices. Moreove~; the forecasts produced by
such a model are only as accurate as the forecasts of the drivers. Fundamentally. howeve~;
the leading indicator and suuctural model
approaches are complements rather than
substimres, as they provide diffamt types of
information about the future ofhouse prices.
The theoretical basis for the structural model, its estimation and validation. and the outlook for house prices derived from the model
are presented in the discussion that follows.
11aeory. The structural econometric
model of housing demand, supply and
price allows for serial correlation and mean
reversion in the housing markeL Mean
liversion implies that in the long run,
housing markets move toward equilibrium.
1n each metro area k and each period
t, it is assumed that there is a long-run
equilibrium value for the unit price of
housing space that is determined by:
p·dt
= f(x.J
(1)
Strac:tural Ecoaomelric ModeL An al-
ternative to the leading indicator approach
"Condominium dala IIRiilnlred
rar mcao uasllld ue rhus
RDl cllm:dy incorporaiB!Into the LHPI.
Where p· is the real equilibrium house
value per quality adjusted square foot in the
metro area, and x... is a vector of explanatory
variables. Equation (1) can be thought of
25
....
,..
Housing at the Tipping Point
The Outlook for the U.S. Rnldentlal Real Estate Market
as the reduced form of a long-run housing
supply and demand relationship. 17
The explanatory variables in the equilibrium
equation can include real household
income, real household non-housing
wealth, the age and ethnic composition of
the population. regulatory conditions and
permitting requirements, structural changes
in lenders' underwriting standards, and the
long-run risk-adjusted return to housing
and other household assets.
Tbe change in real house prices is
determined by:
M'tk
= a.._flP.x.t + bk(P" a~<. pa~<) +
c..I1P"...
+ Da~<
(2)
The 6rst term in equation (2) is a serial
correlation tenn where a... is the serial
correlation coefficient, the second term
is an enor correction term where bk is
the rate of mean revemon. and the third
term captureS the immediate adjustment
to changing fundamentals where G. is
the degree of adjustment. The vector Dtk
includes various business cycle factors,
such as unemployment and user costs, that
impact changes in house prices around its
long-run equilibrium. Tbese factors are also
interacted with the adjustment termS a, b,
and c. The degree of serial correlation and
the rate of mean revmion are affected by
where the economy is in its business cycle.
It is impouaut to nol£ that equation (2) can
be wriDm in difli:rent equadm fOrm and its
dynamic paupeaties ccamined. The puamen:ss
a... and~ detmnine wbelher bouse prices
exhibit oscillaiDry or damped behavior; and
<XJIIYelgeDt or diYergmt behaviot as
The user cost of housing, which measures
the after-tax cost of homeownership, is a
key explanatory variable in the model, and
is equal to:
Where Utk is the user COSt, Taxtk is the
effective marginal tax rate, r... is the
effective mongage rate, Ptax"' is the
" II em llso be clalwd liam urban "-f. Sec Clpozza.
Damls; Hdslcy, R., 1989, "1be fundemanals ofl.md Plica
aad
Cirowlh.•}wmal cf IJrilrm Ea~nGm~a, 26, l!JS-306.
01
0oppoaaall. 2004, Clllculac lhcdynmllc p-opalilsclrquaa
(2)undrrlhc~~-P"dc .. P"k,a-
u.-
21
effective property taX rate, Mlk equals
maintenance costs and obsolescence, and
P"tk represents the homeowners' expected
house-price growth over the horizon of
their homeownership, and is estimated
using long-run household income growth.
Rist.orlad .DGt4. The structural model
estimated presented in this study is
based on the Realtors' median existing
house-price data. While not shown, the
estimation results based on the OFHEO
and CSW repeat-sales house price data are
not materially difrerent. 19
1he model also uses a plethora of other
historical housing market, economic,
and demographic data at the national,
state, and metro area levd that has been
constructed by Moody's Economy.com.
Historical data ranging from home sales
to household income to apartment rents,
etc. are derived from various government
sources and trade organizations. but are
cleaned and adjusted to be on a consistent
basis across metro areas and over time. A
comprehensive list of the variables tested
in the estimation is shown in Table 2.
Equililniuna etpuldon. Ibe model is
estimated in two stages. In stage 1, the
equilibrium house price in Equation (1)
is estimated. In stage 2, the adjustment
house-price equation in Equation Q) is
estimated using the 6tted values for the
equilibrium house price from smge 1. Both
equations are estimated using pooled crosssectional estimation with fbted dfects.20
Fwe pools have been constructed across
the 3 79 metro areas included in the
estimation (see Appendix 12). 1be
pools are based on geography. with pool
1 including East Coast metro areas,
"Thr tine ~clbouse-pta 8piiiUiMian- bro.lly
spealdJII, similar owr the kqlmiL NaNaDliiiiMIIIellll
em vuy CIIIISidmbly, baweva: Nrx surprislnaly. the two
lqN!III~ IDdica a siallllrin cams o l - 1 1 ovr~
dme. wldlc price pwtb 8CIXIIdlng 1D che NARis r.r more
volacile.. lhc mrrelalioD bawml pD'IIIh acconlin& 10 che
nadonal OFKa) llld C$/dla is about~ while
condadaas wllh NARFJWIII m much -scu, ac obauc ofO'Irt.
"'A crlddsm ol dlis lppiQIICb is !hat ir Is assumed that chete
is a coinlqnoiiDc tdadonstaip atiiOIIll chc ....ubla lndudcd
in che equilibrium cqwuion. whm In (.a •"- !MY 1101 he.
Samdud 111111 moe laiS for colncqpadon based upm Ulc:kyfulltr or~ Dicky-fullt:r 1ft 11111 apprupriarc 1n a
ptmdsea:iage used 111 tblsSNdy. archc um.n thtory,
which Is used • die bats for che dmvalion ul dw
cquillbrlum tqllllioo. Is comcc. Jcownu, then theft IsM
~rdldonshlpUIOIIgche~. Nnmhrii!!IS.
che crlddsm applies.
pool 2 including Mountain West metro
areas, poolJ including Florida metro
areas, pool .. including metro areas in
the interior of the country, and pool
S including metro areas on the West
Coast. The industrial and demographic
makeup of the metro areas in each pool
is similaJ; as is the supply side of their
housing markets, including the de~ of
building constraints and the prevalence of
restrictive regulatory requiRments.
1he pooling creates a large number of
observations, over 10,000, to allow for
greater experimentation in the variables
included in the estimation. A large number
of inreraction termS were thus tested.
The most important explanatory variable
in the equilibrium house-price equation,
Equation (1), is real per capita income
(see lllble Ja). The income elasticity of
equilibrium house prices is higher for the
interior metro areas and those in the East
Coast-both of which are slow growing
regions in terms of population growth. A
1% increase in real per capita income in
a metro area in these regions leads to an
approximately one-half of a percentage
point increase in real bouse prices. This
means that households are buying 5%
more housing when incomes rise 10%.
Income is not significant in the Florida
pool This is likely due to the large number
of migrants and wealthier second and
vacation bomebuyers from outside the state
wbo purchase homes in the scare. Florida
house prices are closely related to national
income trends, including the ongoing
skewing of the income distribution. To
capture this, the ratio of national average
household income to median income was
included in the equilibrium equation for the
Florida pool As this ratio rises, suggesting
that higher income households nationally
are doing relativdy wdl, so to does Florida
equilibrium house prices.
The income elasticity of equilibrium house
prices on the East and West Coasts was
affected by 9/11. After the terrorist attack.
households o:aveled much less and thus
stayed at home more. This prompted a
substantial increase in housing demand and
thus equilibrium prices in these regions.
This nesting effect was not evident in the
rest of the counay, at least not statistically.
,.
.
II
II
I
C2
I
tl
H
ft
n
u
So
]
E
E
p
ro
c
E
f
I
t
l
2
Housing at the npplng Point
The Outlook for the U.S. Residential Real &tat. Marlt"at
lilble l: \iuiable Definitions and Soarces
\\lriable
caSe Shiller~ House Price Index
csw
Median Existing House Price
National Assodarinn ofRalrors
OFHEO Repeat SaJes Price Index
OFHEO
Consumer Price Index
Avuage Household Income
BEA, BOC, Bl.S, MEDC
BOC,MEDC
Household Non-Housing Wealth
FRB, BOC, BIS, Equifax. MEDC
Home Equity Unes Outstanding at Co~ Banlcs FRB
er
West meao areas. Ihe impact is particularly saong in Florida, where investors
have been availing themselves with these
new mortgage products: a 100 basis point
increase in the HELOC share of bank assets generates a 900 basis point increase in
equilibrium house prices.
BIS,MEDC
Median Household Income
f
of
Soarces
Toral Commerdal Bank Assets
fRB
Coasaw:don Coscs
Bl.S, R.S. Means
E&cth'e .Apamnmt IWlt
Global Real Analytics
Housing Stock
BOC,MEDC
Households
BCX:,MEDC
Population by Ace Cohort
BOC.MEDC
Foreign Immigration
BOC.MEDC
Unemployment Rate .
Bl.S
S&P 500 Stock Index
S&P
1ieasury lntelal Rates·
FRB
Effective MortgaF Rate
FHFB,MEDC
Effective Fersonallncome 1ilx Rate
BEA
Property 18x Rate
BEA, BOC, MEDC
Note: These variables are available at a mettopolitan area levd from the source or are
consoucted by Moody's Economy.com
1515 - Bureau of Labor Statistics
BOC-BureauofC~
FRB - Federal Reserve Board
~EDC - Moody's Economy.com
FHFB - Federal Housing finance 8oanl
l >FHEO - Office of Federal Housing Enterprise Oversight
Equilibrium house prices have also been
.tffected by a substantial shift in mongage
lending underwriting standards in recent
rears. Subprime and alternative-A mort~ges. 10 and option ARMs have become
,ubstantially more prevalent, expandmg the availability of mortgage credit to
households that did not previously have
.u:cess to any type of credit. This is mea,ured in the equilibrium equation by the
'Jtio of total commercial bank assets in
home equity lines of crediL Ihe explosive
~rowth of HB..OCs is symptomatic of this
,Jemocratization of mortgage crediL One
example of this is the popularity of piggyback loans, which have been used aggressively by lenders and borrowers to avoid
the cost of homeowners insurance. In a
piggyback loan, the borrower takes out a
first mortgage with a 20% downpayment
that is paid for by a HELOC. Ihe impact
of the change in underwriting standards
is most important in the heated and expensive markets in Florida and the West
CoasL Ihe impact is also important on
the East CoasL Underwriting standards
have an insignificant impact on prices in
the interior and fast growing Mountain
~~lnc.·~•hlfpOacouorr¥CQm
The collapse in stock prices and the
plunge in shon-term interest rates earlier
in this decade also elevated housing as
an attractive alternative investment for
households. Households were incited
to engage in seemingly rational portfolio
shifting by the high risk-adjusted returns
to housing compared to the risk-adjusted
returns on stocks and cash. This is
measured in the equilibrium house-price
equation by the difference between the
risk-adjusted returns on stocks and cash,
weighted according to their share of
assets in the average household balance
sheet, and the risk-adjusted return on
housing. The risk-adjusted return is in
tum measured by a Sharpe ratio, proxied
by the ratio of a five-year moving average
of returns to the standard deviation
of those returns. 21 A 100 basis point
increase in the risk-adjusted returns to
stock and cash results in a 22 basis point
decline in equilibrium house prices. This
impact is uniformly evident across all
metro areas.
Ihe age composition of the population also
affects equilibrium house prices. Those
between the ages of 50 and 64 tend to have
saong demand for second and vacation
homes. As the large baby boom generation
has IIlOVed inro this cohort, second and
vacation home demand has significantly
increased, lifting housing demand and
prices. This is most prevalent in pans of
the countty where the housing stoCk is
dominated by such homes. Ibis effect is
captured in the equilibrium house-price
equation by the share of stock in second
and vacation homes interacted with the
share of the population between the ag.:s
of 50 and 64. As would be expected, the
elasticity of equilibrium house prices to
this variable is much higher in the Aorida
and Mountain West pools, to which retiree
migration is sttongest, and lower in the
inland and East Coast markets. In Aorida,
27
...
..
Housing at the Tipping Point
The OUtlook for the U.S. Residential Real Estat8 Market
.
,.
Table la: Equilibrium Bouse-Price Equation (Equation I)
Dependent Variable: log of Real House Price
Method: Gl5 (Cross Section Weights)
Sample: 1980:12006:1
Included observations: 105
Number of cross-sections used: 389
Total panel (balanced) observations: 40845
\t
"-
"'
,,
~
\1
II
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
"'
0.997
0.997
0.123
35,874
Independent \iarlabla
AI
!\,
~
CoeJBdent
Real Per Capita Income, Region 1
Real Per Capita Income, Region 2 and Region 5
Real Per Capita Income, Region 4
Ratio of Average to Median Household Income, Region 3
9/11 Dummy Interacted with Real Per Capita Income, Region 1
9/11 Dummy Interacted with Real Per Capita Income, Region 5
9/11 Dummy Interacted with HELOC Share of Bank Assets, Region 1
9/11 Dummy Interacted with HELOC Share of Bank Assets, Region 3
9/11 Dummy Interacted with HELOC Share of Bank Assets, Region 5
Relative Risk-Adjusted Return
Y.acation Home Share of Stock Interacted with Population Share 50-64, Region 1
v..cation Home Share of Stock InteraCted with Population Share 50-64, Region 2
V..cation Home Share of Stock lnteracte~ with Population Share 50-64, Region 3
Y.lcation Home Share of Stock Interacted with Population Share 50-64, Region 4
V..cation Home Share of Stock Interacted with Population Share 50-64, Region 5
9/11 Dummy Interacted with 5-year Population Growth, Region 3
Std. Error
t-Statistk
0.487
0.320
0.528
0.301
0.293
0.451
0.055
0.0290
0.0256
0.0038
0.1049
0.0359
0.0271
0.0038
16.8
12.5
137.4
2.9
8.2
16.6
14.5
6
5
5
0.093
0.072
-0.002
0.001
0.006
0.008
0.005
0.0043
0.0020
0.0001
0.0001
0.0004
0.0002
0.0003
21.5
36.5
-34.2 :
7.0
16.2 '
42.6
16.3
It
ll
for caunple, a 100 basis point incRase in
the share of the population between 50 and
64 lifts equilibrium bouse prices by nearly
79 basis points.
S)'SIEIIWic difFmnces in the IMl3F quality
of bcusing across areas. The fix£d dfec1s
also capture the impact of diose land supply
consaaints that do not vary ova- time.22
The 6nal wriable indudal in the equilibrium
equation is included only for the Florida
pool. and is designed to capaue the uniquely
suong migration Bows, both domestic and
inr.emalional. into the stare. Builders in the
Stat£ have been unable to meet the significant
acceleraJ:ion in population growth with
enough new consuuaion in n:cent years.
resulting in tiglua- housing marlcEts and
high£r prices. Migration and population are
lildy to accderace further in coming yem
wiih continued strong mlgn immigtation.
and JIJQl'e impcmand.y increased retiree
migmtion by the aging baby boom generar.ioo.
The equilibrium equation is estirnaiEd with
meao area fix£d dfeas in order to capture any
v.mab1es that change substantially over
the COUJSe of the business cycle wa'e DOC
included in the equilibrium equation. Most
notable would include consauction COSts
and the user cost of housing. These variables
were tested in the adjustment equation,
which is described in the discussion rhat
follows. The residuals &om the equilibrium
equation thus provide an estimate of
the overvaluation or undel\laluation of
metro area house prices rda~M to their
long-run equilibrium. Overvaluatiam and
Jl
f·laiS ol dJC mara IUD elfccls I'Cjeeilhali~ rfttt IS oft
-at dJC .001 canlldence levU. 'ilmdar lr.e.lflr IIIIW rlirt·l.'l
-
not liland 111 be slgniftclnr.
a:.
H
H
H
H
H
H
H
H
~
~
0.004
0.344
0.0004
0.1409
10.3
2.4
FiRd Ef&c:t9 Not Shcnm
28
0
b
I
undervaluation can be due 10 temporary
business cycle forces and/or speculation.
~apllllbL The~ house-
price equaDon ~IH!!!jlw;s 00w bouse prices
that deviate &om their long-run equiJDium
ultimately rerum to that equilibrium.
The fitted values &om the long-run
equilibrium house-price equation in
Equation (1) are thus an important
cxplana10ry variable in the adjusanent
house-price equation in Equation (2) (see
Table Jb). The contemporaneous change
in house prices 10 changes in the longrun equilibrium price ranges &om 10%
to 15%. This response is measurably
smaller than that found in other studies
and may reflect the unique housing market
conditions of tttent years. The response is
strongest for the Florida, Mountain West.
~
tv
r.
l
L
L
l
L
L
l
l
l
1\
1\
~
1\
~
f
a
[.
Housing at the Tipping Point
The Outlook for the U.S. RHfdentlal Real Estate Malket
rable 3b: Adjustment HoDH-Price Equation (EquatioD 1)
llependent Variable: Log of the Change in Real House Price
~ethod: GLS (Cross Section Weights)
<;ample: 1978:1 2006:1
Included observations: 113
Number of cross-sections used: 389
fotal panel (unbalanced) observations: +3,781
AU independent variable are differences in logs or logs
'lbe mean reversion variable represents the difference between equilibrium and acrual house prices.
R-squared
Adjusted R-squared
S.E. of regression
f.-statistic
tic
.8
.5
.4
.9
2
6
5
5
5
2
)
Durbin-Warson stat
Independent v.rlables
Equilibrium House Price, Region 1
Equilibrium House Price, Region 2
Equilibrium House Price, Region 3
Equilibrium House Price, Region 4
Equilibrium House Price, Region 5
House Price lagged 2 Quarters, Region 1
House Price lagged 2 Quarters, Region 2
House Price Lagged 2 Quarters, Region 3
House Price Lagged 2 Quarters, Region 4
House Price Lagged 2 Quarters, Region 5
House Price Lagged 3 Quarters, Region 1
House Price Lagged 3 Quaners, Region 2
House Price Lagged 3 Quaners, Region 3
House Price Lagged 3 Quarters, Region 4
House Price Lagged 3 Quarters, Region 5
Mean Rm:rsion, Region 1
Mean Rm:rsion, Region 2
Mean Rm:rsion, Region 3
Mean Rm:rsion, Region 4
Mean Rm:rsion, Region 5
Unemployment Rate, Region 1
Unemployment Rate, Region 2
Unemployment Rate, Region 3
Unemployment Rate, Region 4
Unemployment Rate, Region 5
User Cost, Regions 1 and 5
User Cost, Region 2
User Cost, Region 3
User Cost, Region 4
Mean Rm:rsion Interaction with User Cost, Region 1
Mean Reversion Interaction with User Cost, Region 2
Mean Rm:rsion Interaction with User Cost, Region 3
Mean Rm:rsion Interaction with User Cost, Region 4
Mean Rm:rsion Interaction with User Cost, Region 5
0.13
0.13
0.03
15.8
2.32
C.IBdmt Std. Error
0.09
0.02
0.13
0.02
0.12
0.04
0.08
0.01
0.12
0.01
0.19
0.01
0.17
0.02
0.14
0.02
O.CH
0.09
t-Statistic
4.53
7.57
3.29
11.72
8.09
13.64
9.89
7.04
14.13
o.15
o.cn
11.14
0.22
0.23
0.14
0.10
0.15
0.07
0.08
0.12
0.04
0.13
-1.03E-03
-9.03E-04
-2.89E-03
-1.06E-03
-2.05E-03
-l.OSE-03
. -2.98E-03
-1.65E-03
-6.28E-04
-6.04E-03
-l.lOE-02
-2.17E-02
-3.03E-03
-1.79E-02
0.01
0.02
0.02
0.01
0.01
0.01
0.02
0.03
0.01
0.01
2.17E-04
2.21E-04
4.28E-04
6.30E-05
1. 71E-04
2.16E-04
4.42E-04
5.36E-04
1.02E-04
2. 72E-03
3.95E-03
5.90E-03
1.34E-03
3.01E-03
15.82
13.17
6.82
16.65
11.76
5.52
4.44
4.25
6.45
9.20
-4.73
-4.08
-6.75
-16.8"fo
-12.02
-4.85
-6.74
-3.08
-6.17
-2.22
-2.78
-3.68
-2.26
-5.96
Fixed E&cts Not Shown
and West Coast metto areas and weakest
for the East and inland metro areas.
Serial conelaDon tenDS, house prices lagged
twO and three quarters, are also included
in the adjusanent equation, refl£alng the
persislmce of house-price cbanges. ~
price persistence is strongest in the East Coast
and Mounlain Vlkst meuo areas, wl!h a serial
correlation coefficient of Oftl' 0. 4, and wealcest
in the inland marlcets, wl!h a cod6cient of
less than 0.2. 1bis suggests that specu1atM:
pressures are least likely to deYeJop in the
inland ll1lllkets. These results are consistent
with those bmd in other srudies, where serial
contlaDon at the national level ranges from
0.25 to0.5.
Reversion of house prices back to their
equilibrium price is most pronounced in
the West Coast markets and weakest in
Hou.lng at the Tipping Point
The Outlook for the U.S. Raldentlal Real Estate Market
the inland markets. The mean reversion
is calculated as the equilibrium price less
the market price. Thus, for example, if
this term is positive, that is, prices are
below equilibrium, then price growth
will be Casta ~ Coast meuo areas
have historically experienced the most
volatile house prices, with large price gains
eventually followed by shalp price declines.
House prices in the inland markets, in
conuast, tend not to deviate far from their
equilibrium, which in tum dampens any
reversion back to equilibrium.
There are two business cycle variables
in the adjustment equation, including
the unemployment rate, and the user
cosL These variables come in with the
comet signs and are slgni&canL That
is the higher the unemployment rate
and user cost, the slower real price
growth, The direct impact of these
factors on the adjustment to equilibrium,
howeve~; is small relative to the impact
of serial correlation and mean reversion,
contributing less than one basis point for a
100 basis point increase.
A wide range of interaction terms was also
tested in the adjustment equation in an
effon to capture the impact of infonnation
costs and business cycle effects on serial
correlation and mean reversion. The
interaction of mean reversion and user cost
was found to be significant and with the
comet sign. For example, the adjustment
back down to equilibrium in an overpriced
market will be quicker the higher the user
cosL H~ similar to the business
cycle effects, the impact of this interaction
term is small
VIIIULUion. The modd was validated
by determining the degree to which
metro area house prices were overvalued
or undervalued in the late 1980s, and
comparing this to actual house-price
performance through the early 1990s.
This historical period was chosen to
validate the modd as it is the last time
house prices rose sharply in large parts
of the country and were subsequently
followed by sharp price declines.
<Mrvaluation or undemlwuioo is
dea:rmined by the difference between acmal
metro ami bouse pliers and the prices
expectEd based on long-run fundamem.al
30
economic and demographic facrms as
determin£d by the equilibtiwn house-price
equation, Eqwuion (1). This calculation was
done for both the fourth quaner of 1987 and
the fourth quaner of 1989 (:;ee Appendix 13).
As of the fourth quarter of 1987, 44 metro
areas extending from Boston, MA to
lienton, NJ were deemed to be overvalued
by more than 20%, meaning that actual
prices were over 20% greater than prices
determined by the equilibrium equation.
While house prices in most of these areas
continued to rise in 1988, all of them were
experiencing price declines by the early
1990s. Most of these markets experienced
double-digit peak-to-trough price declines.
Half a dozen metro areas were determined
to be undervalued by more than 10% as of
the fourth quarter of 1987, such as 1\>rtland.
OR, Denvet; CO and Dettoit, MI. Each of
these metro areas experienced snndy and
consistent price growth throughout the early
1990s. lhe com:lation coefficient between
the degree of over/undervaluation as of the
fourth quarter of 1987 and subsequent
house-price growth was -0.69
A similar ex£Icise was perfonned for the
fourth quarter of 1989. By this time, a
large number of California metro areas
from San Francisco to San Diego were
determined to be overvalued. The Santa
Cruz metro area just south of the Bay Area.
for example, was nearly 35% overvalued.
House prices in all o£ these markets were
peaking by late 1989, and all experienced
peak-to-troUgh price declines ranging
from 10% to 25%. lhe price declines
continued for some of the markets into
1995. 1he correlation coeflicient between
the degree of over/undervaluation as of the
fourth quarter of 1989 and subsequent
house-price growth was -0.75.
NAR price series presented in this study.
The better 6t using the OFHEO and CSW
indices likely results from the fact that the
NAR price data are more volatile than the ·
repeat purchase house price indexes. An ·
imponant similarity between the NAR an ·
OFHEO series is that the metro area pools
found to provide the best model were
the same using either series. A notable
difference between the model results using
the CSW repeat-purchase price indexes
and Realtors data is that serial correlation
is lower and mean reversion sligbdy higher
using the NAR data.
A number of different variables were tested
in the equilibrium house-price equation,
but ultimately not used. Most notable
is a variable measuring the percentage of
land within a metro area that is available for
development Growing land constraints in
a growing list of metro areas are an oft-cited
reason for rapidly rising house prices. The
inability to 6nd a relationship is likely due
to the quality of the data. Another notable
variable ultimately not included in the
.
equilibrium equation is foreign immigration ·
and foreign direct investment Increasing ·
globalization has likely also played a role
in lifting house prices in recent years. That
it wz not found to be significant likely
reflects measurement problems, particularly ~
at a metro area level
Consauaion costs were also tested in the
model. but found not to be statistically
significant 5eYeral measures of construction
costs were tested. based on national data and
RS. Means annual~ indices. 1beir
insignificance likely reflects the inadequacies
of the dala rather than the unimportance of
constnJCiion costs' influence on prices.
In borh the fourth quaner of 1987 and the
founh quarter of 1989 validations, there
were no major enors. That is, no large meao
area that was decamined to be overvalued
(undervalued) subsequently experienced
substantial house-price gains Oosses>.
ValudoL The degree to which meao
area housing marlcets are over- or
underwlued is determined by calculating
the difference between current actual
house prices and the prices expected based
on long-run fundamental economic and
demographic factors as determined by the
equilibrium equation, Equation (1).
Alternative spuijfaltfons. A large
number of alternative specifications were
tested. The model was estimated using
the OFHEO and CSW repeat-sales house
price indices. The results were sumewhat
stronger than the model based on the
Currently. the most overvalued meao area
is Miami (see Appendix 14). Actual prices
in the meao area are estimala:l to be 60%
greater than their long-run equilibrium price.
Other metro areas that are ovawlued by OYer
30% by this measure are locar.ed in Sowb
I
i
I
(
"""'""9 at the Tipping Point
"'• Oudook for the U.S. Residential Real Estate Market
t
hart 30:
~malaed
Housiag Markets
=
""Jhly ov11rp• it.f!d 2 SO above hisloric average
Ovarprlcwd "' t SO above the hlsiOric average
Based on the NAR median house-price, 200602
Sources: Moodv's Ec:onomv.com. NAR
Jlurida. throuW"tout much of California. along
1hc New jersey beach. the New York area,
.md L1s ~gas. Metro areas in which the
•hlference between actual and expected house
prices is more than one standart.l deviation
.•w·.ry from that cxpcricnL'Cd hi.o;tori<.-ally arc
must prevalent in the Northeast, Florida and
1 alifomia Co;cc Chan 30).
I
I he model identifies a numhl-r of menu an...
.IS
the Midwest that are I'M!IV.dued, but are
l unlikely to be spet:ulativc. Income gn!Wth
111
j
I
.1nd demogr.1phic trends in an:as such il'i St.
luuis MO and Columbus 011 have been
middling. at be.-.1. Since hou.o;e prk:es in thl'SC
.ueas have hc:en steadily rising, however; they
.1re identified as overvalued.
>nly £hiny-two of tht• nt-arly 379
metro amL'i included in the analysi-; are
nmsiden:d underv.dued. That is, the
.·urrent a<.·tual holl'ie prire i'i significantly
1,'55 than ito; long-run equilibrium. Texas
.md upstate New \brk metro area-; populate
1he r:mks nf the undervalued markets.
l
rhe national hou.'iing market, as measured
hy a weighted average of the metro areas
where the weights are equal to the value
, •f their single-family housing stock. is
.werv.dued by approximately 21%. This is
'he largest degree of overvaluation over the
period fur whkh NAR data are available
hack 111 the late 1970s.
Pria CJUdoolr. 'lhe house-price outlook
tlerivcd from the structural econometric
modd is equally as wonisorne as that impUed
hy the UtPI. National house prices are
projected to fall on a year-over-year basis
between the child
quarter of this year
and mid-2007
(see Chart 31). 23
House prices
are expected
to post even a
small decline in
calendar year
2007; the first
annual decline in
nominal house
prices since the
Great Depression
(see Table 4).
Peak to trough.
the decline will
amount to not
quite 5%. Prices are projected to stabilize
in 2008 and post a mid-single digit gain in
2009, but will not re-achieve its previous
high until early in the next decade.
The house-price oudook varies considerably
acn~s the nation. Of the nation's 379 metro
an.-ao;, ll are cxpectt'll to sulfer a house-price
crash, which is defined w be a more than
I 0% pt.-ak-to-trough dt'Ciine in prices. An
additional2 4 areas wiU experience prke
declines of be£Ween 5% and I0%, and 25
more wiU see prices fall by as much as 5%.
speculation. Short-rerm investors were
aggressively purchasing properties and
bidding up prices in the quest for a quick
profit. These Dippers are now being
wrung out of the market as the rents
they are collecting are not keeping up
with their rising mortgage payments, and
expectations of selling quickly at a higher
price have been dashed.
Crumbling housing aiTordabilicy has also
locked out first-time homebuyers in these
marke£S. While lenders remain anxious
to extend credit, even their most atttactive
loans are unable to overcome the impact
of higher interest rates on aiTordabilicy.
AITordabilicy is a particqlarly nettlesome
problem for the Centtal V..lley, where
household incomes are generally lowe.:
Those who have moved to the regions
from the wealthier parts of California, in
search for more aiTordably housing, have
bid up house prices in the region to the
point that many nf the long-time residents
are no longer able to move.
Even this dim outlook assumes tha£ the
job market, outside of housing-related
industries. remains sturdy. This is not
the case for Detroit and surrounding
areas, whose economies are reeling from
layoffs at the domestic auto makers. As
displaced, previously high-paying workers
leave for jobs elsewhere, housing demand
and prices are fading. The indus£ry's
rationalization and its fallout on the
housing market are expected to continue
throughout the remainder of this decade.
The large southern California and broad
The most serious price declines are
l'Xpected along the west coa.o;t or Florida,
including the Cape Coral, Naples and
Sarasota metro areas, the Central V.11ley
of California, including Bakersfield,
Chico, Fresno and Merced, the metro
areas of Arizona and Nevada, the New
Jersey Reach,
W.&shingron D.C.,
Chart 31: Nadonal House-Price FaD in 2007
and Detroit (see
M~dian singl.e-farniq existing ltouK-pria
J\ppcndkes 15a
& 15b).
16.----------------------------------------,
ago
% change,_.
All or these areas,
save Detroit
and California's
Central V.illey,
were severely
infected by
Source: NAR
..,;;; pnccs
···N~,;.;..;.n
arc rqual1n a wciglurd
avrlllj(C ul house: prices
lor 1hc narion's 379 mcno
.uas. the wt'll(h15 ""'
ha.o;nl un lhc value ul1hr
huustllJI stud< in lOOO.
4+---------~-----------r--~~----~~
00
03
06
09
31
Housing at the Tipping Point
The Outlook for the U.S. Residential Real Estate Market
October-•
·
.
Table 4: U.S. Housing and Mortgage Market Outlook
Change
Single-Family
Change
Multifamily
Change
Existing Single-Family Home Sales, U.S.
Change
Northeast
Change
Midwest
Change
South
Change
West
Change
Existing Condo and Co-Op Sales
Change
--------
Existing Homes, Median, U.S.
Change
New Homes, Median, U.S.
Change
Freddie Mac Repeat PurcHase, U.S.
- Change
~trordablllly
2004
2008
2005
2007
2008
2009
.
201.
Index
9hange
--·.~
1.
I
~ental Vacancy Rate
1.60 1.71
1.8
6.8
1.27 1.36
3.2
7.2
0.33 0.35
-3.5
5.3
1.85
8.4
1.51
10.4
0.35
0.5
1.95
5.2
1.60
6.6
0.35
-1.0
2.07
6.3
1.72
7.2
0.35
2.6
1.91
-8.1
1.55
-9.8
0.36
0.3
1.65
-13.2
1.34
-13.8
0.32
-10.4
1.60
-3.5
1.27
-5.1
0.33
3.1
--
1.61
-1.1
1.28
0.9
0.33
-0.1
m
%VA
m
%VA
m
%VA
m
%VA
m
%VA
4.73
2.4
0.70
1.0
1.16
3.8
1.75
3.7
1.08
2.5
5.00
5.7
0.71
0.2
1.23
6.1
1.88
7.0
1.13
4.6
5.44
8.9
0.71
0.3
1.26
2.8
2.00
6.8
1.21
7.0
5.91
8.6
5.35
-7.0
0.66
-8.3
1.24
-8.6
2.23
-8.1
1.14
-6.4
5.25
-1.9
0.64
-1.8
1.22
-2.0
2.20
-1.3
1.14
0.3
5.27 5.33
- - -·1.1 pn
0.4
0.64
-0.1
1.21
-0.4
2.21
0.6
1.16
1.9
0.65
-0.7
1.21
-1.0
2.24
-0.1
1.20
1.3
1\1
9.2
1.37
8.1
2.32
15.6
1.34
11.4
6.17
5.76
4.4
-6.7
0.78
0.72
·0.7
-8.0
1.40
1.36
2.5
-2.8
2.51
2.43
8.3
-3.3
1.40
1.22
4.1
-13.1
ths
%VA
0.60 0.66
4.5 10.2
0.73
11.0
0.81
10.8
0.89
10.1
0.75
-6.3
0.76
0.5
0.78
2.3
0.80
3.4
he
ths
%VA
0.91
3.1
1.09
11.7
1.20
10.1
·-----
0.98
7.6
o.n
%
130.0 127.7 132.2 126.7 115.8
5.6 -1.8
3.5
-4.1
-8.7
8.4
8.9
9.8
10.2
Total Originations
2.11
98.3
0.79
1.31
38.8
lie
------P~~ase~~ - - - - - - - -
2.84 4.06 2.n 3.12
34.7 43.3 -31.8 12.3
0.93
1.11
1.27
---------·-·--·· 1.57
--1.90 2.95 1.50 1.54
- ----···50.7
·34.8 . 29.1
- -. ····- 46.0 .... .....·........... -·
12.3 17.2 18.8 34.3 30.6
. .......
-
---
- -~----,
~-
9h~e
.~:.;......;.;_,;;..;~;_;_;;_----- - -
~1!~----- ·-- ·- ----- --· - ··
~~~y__ -·· ---------- - - ---·.g!_l_~!!i!.... ---- -· - ------ - - -- ··--
~~~~~ --··--··· .... . . ···-· ---· ·_C?!!!!!!I/!!. - - · -·--- . . .. -· .·:. : ___ . -__
Other
.
%YA
~2000$
3.3
_
8.5 _ 12.8_____ ~~~-1~&
. . .. - ···- . - .
... .. _.,.
-
-·
--
448.5 469.9 509.4 559.9 608.0
·- - -- -·· . ·- . ··-·
%YA
0.4
4.8
-··--·--- --- .. 8.4. - 9.9 8.6
---···
-~- 237.1 246.3 272.6 304.9 336.3
%YA
0.2
3.9
10.7
11.9
10.3
29.5 31.1
31.9 34.4 ·- 39.2
· - --~ - -- -·- ·-·
.%YA
4.4
----- --5.3.... .. 2.7 7.8 14.1
.
_ !>!_ 181.9 192.6 204.8 220.3 232.0
__
. . 6.4 7.6 5.3
%YA
0.0
5.9
--·- --- -----0------------ - - -·- --- ----- - .
- --
...
.,
Fa
c:x
pr
(I
co
L'll
Itt
al!
Bt
pi
lil
Ul
te
~~
-----------·----- '
Ji
-'
<;!
-
106.9 107.6 108.2
-7.7
0.7
0.5
9.9
.,.
Residential Investment
nv
---" '
222.4 214.3 216.3 222.3 230.3:
3.6 .
-3.6
0.9
2.8
2.3
239.4 230.0 231.4 238.6 247.7'
2.2
-3.9
0.6
3.1
3.8;
289.5 286.1 285.8 293.5 304.2.
2.7
8.2
-1.2
-0.1
3.7 :
8.4
7.9
7.8
111.4 112.5.
3.2
1.1 I
7.6
- ·-
-
7.6 \
&
If
((
2.80
-10.2
1.51
1.28
54.1
2.49
-10.8
1.39
1.10
55.9
2.17 2.21
2.31
--13.0
2.0
4.5
1.36 1.40 1.47
. -- --0.81 . -. · ----0.81
- 0.84
62.6 63.3 63.5
-- .
·- -·-··-22.7 --·-. - ·- - -- . ---23.5
··---·
20.3 22.0
25.6
. --·-- . -·-·-------- -- -
--------- .
----- - -·---------~~Originations
- - - - - - --~
Refi Share
%
------·
--- .. _. - ----· ·- -- ---%
ARM Sts.e~~~
---·-----·
______--- - -------- --- - ·--··- ·----_____
Residential Investment
·-- ----- ---·--·-·-··- -----··--·-·- ·---------- ---------b$
~esid~al Construction Put-in-Place
387.8 420.6 _ 474.6__
634.6 576.9 575.5 597.3
563.~~-1-.:r
_Change
_
fCI
Sl\
--------
..
trl$
%VA
trl$
. 2.J
1.30
1.8
0.36
9.0
-:.
ths$
154.4 166.1 178.2 192.7 217.4
%VA
5.8
7.6
7.3
8.1
12.8
. 172.6 185.1 191.5 217.9 234.2
thsS
%VA
3.6
7.2
3.5
7.5
13.8
1987=100 187.0 199.6 213.0 236.4 267.6
%VA
13.2
7.9
6.7
6.7
11.0
Index
%VA
0.81
-9.5
I
...
1.28
1.10 1.01
0.94 0.92 0.93
----·--·-··-- ·----· -- -·6.6
-14.0
-7.8
-7.4
-1.7
0.5
llot1pp Orlgll1811ou. SAAR
32
I
m
%VA
m
%VA
m
%VA
Housing Starts
New Home Sales
Change
2003
2001 2002
'
Foracut
History
Units
I
~:!.
-9.1
5.4
-1.1
-0.2
3.8
....
..
590.8 560.5 558.1 576.0 601.8
··· - ---------- -·-··· --- ...
~8
~1
44
a2
~5
. ····--·- -- - -·- - ··· · -··-·· -- - . 319.8 ....296.8- ··- .. 299.2 ...... 313.4
290.2 ..
. . ... 4.7
-4.9 ..
-7.2 -2.2 --· 3.1
..
43.2 42.5-·- 44.3 -. 46.0 . 49.0
. --- -·. . ...
10.1
-1.6
3.9
·- -- .. . ---·· 4.2 - . -···-·· 6.6
- .
227.3-- 220.7 - ·---· - .. .. 230.8 239.4
223.8 . ..... ..
..
-.. -2.0
-·
-- .-2.9
----·-- 1.3 ·· ·-3.2 . . 3.7
· -·- -·
-
·~·····
~
--
-
·- ~
. ~
--
lnc.•~•~Oi;p:l:IOift
...
"
"
l
I
J
j
i
.
•
•tomsing at the Tipping Point
11111 Outlook for the U.S. Residential Real Estate MaBet
.• w York City region are also expected to
,,IJ,·r measurable price decllnes. Riverside
ond ~anta Ana (Orange County) suffer the
''""' in California, while Nas.'illU (Long
1.l.111d) NY and Edi..;on NJ are hit hardest
n :-.Jew York. First-time homcbuyers and
pn·ulators, pn."Viously very important
.nurces of housing demand in these arcao;,
ut· fast leaving the market. Overbuilding is
•t.,.l a mounting problem on Long island.
ll11using marketo; and house prices are
1•rnjected to hold up well throughout the
national housing downturn. The laJKest
kxas metm areas wiU enjoy continued
'turdy price J.,rruwth, a.o; will most of the large
metro arcao; in the nation's Southeao;t and
I arm Belt. Adanta (iA and Charlone NC, for
··xample, will enjoy low single.Jigit houseprice gaino;, a.o; will St. l..oui.o; MO and Kansas
t 'ity MO. ThL'SC markciS experienced staid
ounditiono; when the rest of the national
ln1using market was booming and are now
··njoying wry high housing affonlahility.
llomehuildL·rs thmughnut thl'SC area.o; haw
.tlso been largely SULU'S.'iful in matching nl-w
.. upply wirh underlying demand.
Behind thio; hmL'iC·pricc outlook are
projec:tions nf a wide mnge of variables
mnging fmm per t-apita income and
unemploymL'Ilt to mun~ rates and lending
1crms. Bnliltlly, th~ forccasiS are based on
•he expct·ration rhat the national and nearly
.all metnl :tn.-'.1 l't:onnmil's remain rec:cssionlree. Giwn J.'Cill'r.dly UtLo;h btLo;ine...-o;cs with
''rong halant't' shccto;, employment and
mcome g;.lino; will skJW funher, but continue
111 expand.H Unemployment edges higher
' inm ;on- .okw ••u;d11c ,.,,n'fll"'ns. oncludo~~~t ahc dumcsloc
ouao mak.'l'o. " ""'' .~ rh• .urllnoos, rho· ~per inoluslly. ;onol
undry noooKinr.ohL• m•nul:oo run•rs rhu""' lo..UOR in rmok
•• .mpc:ntllHl
wuh ( hma
October 2006
into next year in response, but very modestly
so. Nationally. the unemployment rar.e is
expected to rise from its current under 5% to
just over 5% at its peak.
is dropping. CoriSttuction payrolls have
thus contracted during the first half of this
year, weighing on the metro area's broader
economic growth.
Interest rates are also expected to remain
stable, which assumes that the Federal
Reserve has completed its tightening cycle
and that long-term rates as measured by
the 10-year li'easury yield will remain ncar
'5%. This implies rtxed mortgage rates of
near 6.5% and ARM rates of no more than
5.5% through next year.
The metro area's housing marlo!t has been
upended by a collapse in affordability and
wringing out of speculation that was rampant
just a few months ago. The metro area's a(:.
fonlability index currendy sumds a1: only 70%.
With this generally positive backdrop of
continued sturdy job and income growth and
smble interest rates, the hotl'iing downturn
has more to run, hut it should remain
orderly. That is, while home sales, holLo;ing
l'Onsauction and house prices wiU decline
further through mid-2007, the declines will
not he p~'CipitotLo;, and at bottom, activity will
still be about as stmng as during some of the
he;t housing )'e'.li'S in the IQQOs.
Most At-Risk Metros. Those largest metro area housing markets expected to experience a l'~olSh in hotLo;e prices, a more than
I0% peak-to-trough decline, are considered more carefully in the disctLo;sion that
follows. These metro arca.o; include, Las
Vegas, Miami, Na.o;,o;au-Suffolk, Riverside,
Sacramento, Salinao;, Santa Ana, Stockton,
Tucson and WolShingron, D.C.
Aftl'r sevcr.d yc-.1rs uf hooming conditions,
the I.Jis Vqas hotLo;ing market is rapidly
weakening. Home sales are off substantially,
unsold inventories arc up by more !han
nne-third, and dcvdupers are canceling
residential prujects-panicularly condo
projects. The median existing single-family hotLo;e prke ha.o; faUen from its peak of
late la.o;t year, and residential COriSttuction
Prospects are for substantially more price
declines. According to the Las Vegas
lHPI, there is a 43% probability that
hotLo;e prices will be lower one year from
now. Moody's Economy.com expects the
decline in house prices to continue though
the mid-2009, with a total price correction, peak to trough, of 13%.
The risks are also to the downside, particularly due to an expected substantial
erosion in mortgage credit quality in the
metro area. 10 and option-ARMs, mort·
gages nt substantial risk, account for a very
high share of mongage debt outstanding;
among the highest in the nation. Mortgage credit quality is already weakening.
The las Vegas housing downturn will
be mitigated, however, by sturdy net inmigr.ltion and continued employment
gains in the leisure and retail industries.
Gaming activity remained strong in rhe
second quarter and l.as Vcgao; is on track
to record another firm, if not stellar, year.
longer term, Las Vegas will hcnefit from
its low living and business costs relative
to neighboring economies, particularly
in California.
Miallli's booming housing market is unraveling. Home sales are currently half
their 2004 peaks according to the Florida
Las Vegas.Pal'lldlse, NV Metropollt8n Statistical Am
1
999
47.6
6.2
661.8
7.7
4.2
8.7
1,321 .3
19,919
6,937
130.6
8,822
59.1
10,290
2000
2001
7802
2003
51.6
53.3
57.5
49.8
4.5
3.7
3.2
7.9
730.9
780.1
697.7
726.7
5.4
4.2
0.6
4.0
4.6
5.5
5.8
5.2
4.7
4.7
8.2
9.3
1,393.2 1,456.0 1,515.5 1,575.2
21.282 21,871 22,148 27,354
7,008
9,378
7,836
4.942
137.4
148.6
160.1
181 .1
7,847 15,332 18,703 31,614
60.1
51.3
47.5
48.2
9,787 13,161 14,614 15,711
200.!
2COS
'mJ1C.llors
62.6
68.5
9.0
9.4
"ChiJIIge
Total Employment (000)
871.3
812.5
6.9
7.2
"Chsnge
Unlllnployment Rate
4.4
3.9
11 .1
10.3
Personal Income Growth
1,648.5 1,710.6
Population,_,
31,741 30,479
Singl~amlly Permits
4,654
8,758
Multlr.mlly Permits
305.1 Existing Horne Price ($11111)
264.9
37,990 48,626 Mortgage Originations ($1111)
Net Migration (000)
61.0
49.2
18,311
12,711
Personal Bankruptcies
Moody's Eiconomy.com, Inc. • -8QCIIICIII'¥COm • hetp0ecor101n~
Gross Metro Product, C$8
2C06
2007
2003
2009
2010
87.2
76.9
80.3
83.7
4.4
4.4
4.2
4.2
947.3
977.3 1,009.8 1,044.6
3.2
5.3
3.2
3.3
3.4
4.0
4.3
4.2
4.1
3.9
6.5
7.9
7.7
8.5
8.2
1,n8.9 1,841.9 1,906.3 1,973.3 2.026.3
29,372 29,108 27,575 27,719 27,010
6,470
10,417
3,723
4,440
4,580
312.3
296.4
284.8
284.5
281.6
38.901 34,126 29,056 28.275 28,538
37.9
54.7
48.9
52.1
50.0
11,582 13,679 14,561 15,140 16.267
73.7
7.5
917.8
33
Housing at the Tipping Point
The Outlook for the U.S. Rasldentlal Reel Estate Market
69.0
2.7
983.5
1.2
71.3
76.7
79.9
83.4
3.3
3.7
4.2
4.3
74.0
73.6
3.2
0.6
1,009.3 1,021.6 1,004.3
2.6
1.2
-1.7
997.3
-0.7
1,018.6 1,043.0
2.1
2.4
4.3
5.4
6.6
5.9
5.1
6.1
5.9
3.1
6.1
6.6
3.7
4.3
8.4
5.5
2,221.0 2,260.3 2,288.7 2,314.5 2,335.7 2,356.7 2,376.0
6,740
9,603
9,922
6,374
5,998
6,626
6,711
6,793 13,253 16,198
6,477
7,168
8,232
7,356
271.8
349.9
221.2
138.2
159.6
184.3
134.7
8,116 13,814 18,282 28,675 27,814 38,534
9,050
7.2
1.0
11.1
12.4
5.8
27.6
24.3
12,690 12,446 14,447 14,607 14,467 12.604 16,579
Association of Realtors, construction is
down by one-third, and bouse prices are
now falling. The previously heated condo
market is unwinding most quickly as
investors nee the market, placing funher
downward pressure on the single-family
market. Anecdotal repons of a sutging
number of vacant units and conversions
back to rental apamnents abound.
The tlliiil<et is deemed to be among the most
overvalued in the nation as investor demand
was particulady rampant not long ago.
Housing afordability has also collapsed with
the previous nmup in prices. Alfordabilily is
so low it bas been driving residents to move
to higher afbdability areas in places ranging
from Fon l.auderdaJe nett doot; to Deltona
up the scare's east roast.
Additionally. a significant pan of the recent
real estate frenzy in Miami has been fueled
by foreign blvestment infiows, notably from
Latin American countries. These infiows
could easily dry up or even gp into reverse
should economic conditions change. A general drop in commodity prices could cause
profits to fall in latin American economies
and thereby reduce the amount of capital
that makes its way to the U.S. and Miami.
101 .0
97.8
7.5
3.2
1,190.1 1,217.8
3.6
2.3
102.9
105.3
107.9
1.9
2.3
2.4
1,218.8 1,215.3 1,222.7
0.1
-0.3
0.6
"Change ·
Total Employment(-)
"Change
Unemployment Rate
Personal Income Growth
Popul.aon (000)
Single-Family Pennlta
MultlfemHy Pennlta
Exldng Home Prtce ($The)
Mortpge Ortgl..-tloM (SliD)
Net Mlgrdon (DOG)
Personal IIMikruptcla
Miami's job market is also less buoyant,
particularly compared to other metro areas
in the dynamic state. Tourism has improved, but job gains are lagging in retailing
and educational and health services.
The anticipated housing lll8lic£t com:aian
will weigh on Miami's economic outlook
through 2008 when the housing rnarlcet is
expected to hit bottom. The tislcs to this OUt·
look are on the downside, as a latge number
of investoiS exit the m.arlca
Nassau-Suffolk's housing rnarlcEt is fast
weakening. Median prices of existing
single-family homes declined in the second
quarter-the 6m quarterly decline in the
metro division since late 1997. Prices are
barely rising on a year-ego basis, and the
risks for further price decUnes are growing.
House-price growth in Nassau-Sufl'olk began slowing in early 2005-well before the
national slowdown got under way. R£cent
deceleration has been swi&a; howeva; the
peak of price growth on a year-<m:r-year
basis was in the 6ISt quarter of 2005 when
prices were growing 16.5%. Prices were
up 0\'tt the year by only 2% in the second
quarter of 2006. Rapid price appnriation
112.2
116.4
3.7
1,233.8 1,240.6
4.1
0.9
0.8
4.7
4.8
3.3
3.4
3.8
4.8
4.1
7.8
3.4
-0.2
1.5
6.2
4.7
4.6
2,737.0 2,760.7 2,778.3 2,794.3 2,607.8 2,812.2 2,808.1
4,883
4,176
4,221
3,284
3,675
5,438
5,058
1,262
1,775
1,493
1,146
911
1,180
899
413.2
190.7
213.8
249.3
313.5
382.8
484.5
15,446 12,688 23,696 34,251 52,795 35,232 41,927
5.3
8.3
4.8
4.1
1.4
.a.o -17.2
9,908
8,339
9,241
8,417
9,338
8,120 10,581
34
Graee Metro Prvcluct C$8
Gross Metro Product. CSB
"Change
Total Employment (000)
"ChBIIgfl
Unemplorm-ll AMI
Plti'SOnllllncome Growth
Population (000)
Slngle-F.mly Pennlta
~Permits
Existing Home Prtce (SThs)
Mortgage Orlglnllllone (SMH)
Net Migration (000)
Person.~ Bankruptcies
93.4
90.8
96.0
2.8
2.7
2.8
1,059.3 1,072.2 1,085.4 1,103.8 1.123.3
86.4
88.5
3.6
2.4
1.8
1.2
1.2
1.7
1.8
3.7
3.8
3.7
3.8
3.8
6.4
5.5
4.9
5.2
5.3
2,405.7 2,435.4 2,462.9 2,493.2 2,527.2
8,565
8,649
8,836
9,110
8,513
9,074
8,603
8,803
8,838
13,211
386.9 .
374.9
367.2
375.6
366.6
34,749 31,242 27,060 26,167 26,202
13.7
14.1
14.1
17.6
11.6
11,190 12,956 13,875 14,846 16.224
and higher interest rates over the past year
have led to plummeting atfordability in the
metro division.
Putting downward pressure on the metro
division's housing IDIU'lcet is the fact that
afordability has eroded substantially. According to Moody's Economy.com estimates,
a median-income earning family in NassauSutrolk can afford only 84% of a medianpriced single-family home. Declining housing
affordability and lackluster job growth in
Nassau-Sufl'olk are keeping the demopphic
oudook WCik: last year Nassau-Sufl'olk lost
population for the first time since 1990. The :
greatly overvalued real estate, combined with
a weak economic and demographic outlook,
puts Nassau-Sufl'olk on the list of metro aJ'o
eas of most concern. According to the Leading House Price Indicatol; there is a gn:arer
than 50% chance of a price decline 0\'tt me
next year continuing rhrough mid-2008,
which could be as large as 8%.
The combination of a scan:ity of buildable
land, affordabilily relative to New 1bik 01y
and high incomes has dmm house pric:es on
Long Island up over the past yeat The metro
division Mi the sevwth-highest per capim
income in the nalion. Money from New 1bik
120.7
123.1
127.8
130.1
125.5
1.9
1.8
2.0
1.9
1,251.7 1,260.8 1,267.2 1,278.6 1,291.0
0.9
0.7
0.9
1.0
4.0
4.0
3.8
3.9
3.8
7.5
4.7
3.3
3.8
3.6
2,815.1 2,822.5 2,829.4 2,836.9 2.844.5
3,325
4,204
3,462
3,330
3.332
1,478
1,103
1,174
1,312
1,201
447.3
453.5
470.4
453.5
444.8
37,956 31,805 26.211 24,834 24,564
-7.2
-6.0
-6.0
-6.8
-6.7
8,598
9,406
6,407
7,562
8,161
3.7
o.s
~~Jnc.·~·helpOacol~
linusing at the Tipping Point
1ho Outlook for the U.S. Residential Real Estate Market
October 2006
Mlverslde-San Bernardino-Ontario, CA Metropolitan Statlatlcal Area
1999
2000
!001
2002
2003
200~
200:
74.6
96.4
103.7
69.7
n .9
82.5
88.2
9.3
9.9
7.0
4.4
5.9
6.9
7.6
938.9
988.4 1,029.8 1,064.6 1,099.2 1,159.9 1,217.1
3.2
4.9
5.3
6.5
4.2
3.4
5.5
5.1
5.2
5.3
6.2
6.3
5.7
5.0
7.8
7.5
4.7
9.2
6.1
5.8
7.5
3,189.5 3,279.1 3,382.3 3,503.3 3,645.3 3,785.9 3,910.0
19,018 19.090 23,596 30,038 35,965 43,142 45,485
8,321
1,903
2,406
3,821
2,436
6,287
5,523
128.3
138.6
155.7
175.9
218.9
372.2
296.9
16,723 15,014 32,248 46,082 79,439 84,293 121,442
52.9
61.4
74.0
91.4
109.7
108.4
91 .4
21,443 18,513 21,267 20,853 18,398 13,841 16,502
t :ity has
helped to suppon the housing mar·
ket in Nao;sau-SuiTolk over the past yl-ar. The
largest bonus payout in w.dl Street's hLo;tory
in lhe first 4uarter of this yt-ar provided a
1emporary suppon to the local housing markel As the good fonunes of Wall Street begin to fade in 1he set:ond half of thi.o; year and
lhe economy L:uulo;, a significant soun:e of
suppon for the housing market will vanish.
Wolgcs and salaries on l.ong Lo;land have been
growing at a slower dip than the state and
national awmgcs over 1he JYdSI st:ver.d tJUarters as job gruwrh ruL'i been tepid at hcs1.
The impact of the housing slowdown will
be substamial un Na.'i.'iUu-Sulfolk's bnrc~der
econnrny. Cunslrut·titm and other housingrelatt'tl induo;trit'S have hl'lpcd 10 suppon
the mt·lm divi.o;inn's cmnomy nver the pa.o;t
year; induo;lrics nurside of housing have
han:ly ht:cn adding In paymlls. Job gn1wth
ha.o; slowed to a rrc~wl n"t·endy, with hoth the
gt1t1ds- ilnd private o;crvice-pnxlucing sectors
nl 1he economy clcpcricncing a slowdown.
The labor fnn·c ha.o; mntrdl.'ll'tl n."Cl"Jltly and
the unemployment r.lle, while stillluw, has
ri.o;cn fmm l.fl% in .January 10 4.1% in July.
lbc N:L'i.'iilu-Sulfnlk l"Conomic gn1wth outJunk i.o. 1hc wt•ai«."St among the metro arca.o;
pmlik'tl in 1his study. The metro area will
he hindcn.'tl hy high costs, out-migration,
and land shonages over the forecast horizon.
The L'tlurutionlhcalthcare industry will be
1he main suun:e nf job growth going forwanl,
where l"unher gains wiU be tepid at best. The
largl"St ncar-term risk is to rhe area's housing
markets and housing-related jobs. Overall,
Na'i.o;au-SuiTulk will underpedorm the U.S.
uvcr the fon't-ast horizon but will grow on
JYM with the New '\brk Gty economy.
The Riverside-San Bernardino housing market and economy have slowed
lndJC~tor·,
2006
Gross Metro Product, CSB
107.8
%Change
Total Employment (000)
%Change
Unemployment Rate
Personal Income Growth
Population (000)
Slngle..family Permits
Multifamily Permits
Exlatlng Home Price (STha)
Mortgage Originations (SMH)
Net Migration (000)
Personal Bankruptcies
4.0
1,239.0
1.8
4.7
5.9
4,014.1
38,029
5,582
390.3
102,012
64.6
9,726
. ·-4---- .. - · ·
measurably since the beginning of this
year. Construction permit issuance is off
by about 10% from 1005, and the median
sales price is down by 4% from the March
peak as of midyear. Construction employment has leveled off since the beginning of
the year, hut it too is expected to be weak,
with funher declines into 2007. The one
factor favoring an orderly adjustment in
Riverside's houo;ing market is that it is one
of the most balanced markets in the state
m 1cnns of supply and demand. Thus,
balance should return if new supply mtxlcrdles lunher and 1he economy continues
1 c.xpand.
n
Mnre broadly, the mte of total job growth
ha.o; hl"Cn cut in half and industrial pnxluclinn gmwrh lags the natitmal m1e. Fun her,
wn.o;umer loan dclint~uency rates in Riverside shot up in 1his ye-c~r's first half.
The most disturbing sign for the economy in
1he near 1enn is a worsening of household
L
·n'tlit 4uality in thi.o; year's lirs1 hal{ The
hn1adest such measure, lhe delinquency
r.ue un all mnngage and consumer credit,
jumpctl from below average to above average
in jll'it six months: this breaks a two-year
111:nd of solidly low r.ucs. Rising interest
r.ues, panK:ularly shon-term rates that impact Riverside's substantial adjuslllble rate
mongagc debt, combined with high energy
hills and slower .iob growth, generate considl"t"dble downside risk for the near term.
There are some indications, howcve~ that
the economy remains in good heallh. First,
more complete employment data from
unemployment insurance records through
the end of last year indicate that growth
may not be slowing quite so precipitously.
Second, the unemployment rate is holding
steady at just over 4.5%. lbiid, while the
2007
2008
2009
2010
110.7
115.0
119.2
123.4
2.7
3.9
3.7
3.5
1,255.6 1,287.3 1.322.1 1,357.3
1.3
2.7
2.5
2.7
4.9
4.8
4.5
4.4
6.2
6.5
6.7
6.4
4,096.9 4,194.6 4,303.4 4,421 .8
36,078 33,234 32.194 31,956
3,566
4,505
4,505
5,032
371 .8
358.2
357.5
384.6
88,734 73,615 72.297 73,846
41.6
54.6
63.5
70.7
11,199 11,809 12,173 13,055
-- - - - --------- -- ·-- -
·-
------
housing market is adjusting to higher interest rates, its adjustment to date has been far
from debilitating to the economy.
A primary driver of lhe economy remains
trade and trdn5portation: Riverside is becoming the crossrnads for sou1hem Glli[omia
mmmen:e, a.o; reflected in rising employment
in n-.msportation and warehouo;ing. Indeed,
its cofll"Cntration in these two industries is
50% higher than lhe l>"t.atewide average: its
lot:ation tJUotient i.o; I.5, uo;ing the state as the
hast:, and it is ri.'iing as trucking, rail, and air
rrmspon expand. Future growth will be driven in part by r.lil: BNSF currendy is sean:hing
for a site for a sectmd intennodal rail yard,
with Victorville as the fmntrunnet With shipments through LA. pons ril>ing at a doubledigit pace, additional rail capacity i.o; required.
Industrial production growth may be below
the U.S. average, hut manufacturing p-c~y
rolls are holding stL-ady. As the low-cost area
for manufacturing in southern California,
Riverside's industries expand with the broader Southwest economy. The outlook, howeve~ is not as bright as rr.u.le and 1ranspon
because much of the manufacturing 3(."tivity
is related to comptments for homchuildlnfrCabricated metal products and dcctrical
equipmenl With housing expected to be
soft through next year, industrial production
will not likely rebound in the very near term.
The long-tenn outlook remains solid for
Riverside-San Bernardino a'> its economy
becomes increasingly globally linked and internally diversified. Low costs of living and
strong in-migration trends, both domestic
and international, bode well for the metro
area's economy. The near term is subject to
considerable volatility, however, depending
upon lhe palh of adjustment of housing
markets and the ability of households to
35
Housing at the Tipping Paint
The OUtlaak for the U.S. R•*ntlal Real E8tllte Market
61.6
8.5
65.3
8.0
797.1
770.5
3.5
5.4
4.1
4.3
7.1
9.1
1,767.2 1,608.5
10,964 13,488
3,511
3,325
132.7
143.9
11,474 10,423
24.8
30.6
9,832
8,310
n.7
76.9
81.4
69.0
5.8
5.8
3.8
5.4
2.0
846.1
859.1
880.4
819.0
832.3
1.7
1.5
2.5
1.6
2.7
4.7
5.5
5.7
5.4
4.5
6.9
6.9
3.8
5.3
6.1
1,867.1 1,925.3 1,974.8 2,014.6 2,042.3
14,719 17,614 18,165 18,523 16,380
3,476
3,802
4,485
4,667
3,715
374.9
207.9
246.9
314.8
172.3
26,212 36,410 54,627 45,430 53,347
37.0
14.0
47.2
48.4
26.8
8,167
8,716
8,380
7,497 11,001
66.6
continue to spend freely, with risks clearly
on the downside. The long-tenn outlook,
nevertheless, remains above average.
SaCI'aiiWIIO's housing marla:t is slowing rapidly, and is casting a shadow over
the metro area's broader economy. Prices
are falling, and demand for new housing is quickly drying up. ConstrUction,
which was a leading source of employment
growth in recent years, has contracted
nearly 3.5% this year &om its peak. Sales
of existing homes have fallen at a similar
pace. Additionally, some 3,000 construction jobs have been lost in the metro area
since the beginning of this )'eaJ:
Median house prices are currently Falling in
most of California's mmo areas, but Sacramento and the RSt of lhe Cenaal v.illey are
experiencing the steepest decline. According
to the NAR, lhe median home price in Sacramento has FaDen from a peakof$3M,OOO
in the fourth quarter of 2005 to $3 76,000 in
the second quaner of this yem; which is only
0.9% greater dum the same time last y.:ac
like other inland California madcets, aJ.
fordability rela.tM: to the coastal CalifonUa
l1laMts pumped up Sacramento's housing markers during the boom. Sacramento
seemed lilcE the perfect untapped IIJII1'lcet. Its
~ population, proximity to the red-hot
Bay Area, and low prices made Sacramento
very aamctiw: to speculators and relocatots.
Indeed, Oakland, San jose and San Francisco
contribured the most in-migrants to Sacramento in 2004, according the IRS data.
Builders aggressively dcvdoped the area,
wilh ~tial pennits reaching an all-time
high in 2005. Although household formation was Strong throughout the period,
Sacramento is left with a near record-high
36
Groa Metra Product, C$8
"Change
Total Employment (000)
"Change
Unemployment Rat.
Persanellncclme Growth
Papulation (000)
Single-Family Pwmlts
Multlf8mlly Pennlts
Existing Horne Price ($Thll)
Mortgage OrtgJnatlana (SMII)
Net Migration (000)
Person.. BMkruptcles
months of inventory of unsold homes, according the California Realtors Association.
As the housing marlcet slows, and price
growth wealcens in the Bay Area, Sacramento's
housing rnark£t will not aaraa the same level
of speculative buying or vacation home imestment that supports high pria:s in southcn
CaiOOmia or the Bay Area. 1berefOre. Moody's
Economy.com estimates d1at it is more lilcdy
than not that house priers in Sacramento will
decline even further over the next 12 months,
lasing roughly 10% from their peak value.
Further casting a cloud on the meoo area's
economic oudook is the sta~r:'s 6scal outlook.
Currently, Calibnia's stall: 6scal conditions
have improva:l. allowing JIKJre spending to be
directed IO'Mil'd local poanmmt. Scale~
erations spending rose by 8%, and drus SIBle
and local gmoanment employment are each
on the rise, which is giving a near-lEnD boost
to Sacramento's labor IJiali<Et. For the 2~
2007 6scal ycu; ~ cbe swe may not be
able to mau:h iJ:s current 7% l'e\'alUe wowth
rare as the economy slows and energy costs
begin to 13ke a bile out of OOI}Xll'm pro&ts
The c:omcdon in Sacramento's housing
marl«t will persist for sometime. Although
the comx:tion will not be enough 1D send the
meao area into an economic recession, it will
be saoere enough to stall growth early next
yem: The meuo area's longer-term prospects
remain favorable. Sac:rameniD remains a magnt.t for relocation thanks to its proximity to
the Bay Area and its relativdy low cost of living. The entire Centtal \9lley is experiencing
strong in-migration, and Sacramento enjoys
the greateSt benefus of this trend. A high proportion of the meao area's in-miglants tend
to be young. well-educated 6unilies with high
median incomes, which will support solid
housing marla:t conditions in the long run.
85.2
86.9
89.7
92.4
4.7
1.9
3.2
3.1
95.1
2.9
899.3
905.6
921.7
941.1
960.9
2.2
0.7
1.8
2.1
2.1
4.6
4.4
4.6
4.9
4.8
6.7
6.4
6.8
7.0
6.6
2,076.2 2,102.0 2,132.3 2,165.4 2,198.5
10,441 10,856 11,938 13,521 13,333
3,501
2,158
2,854
3,291
2,597
373.2
355.8
347.5
353.0
366.3
42,530 37,598 32,598 32,914 34,341
19.7
11.2
14.9
17.0
16.3
6,975
8,139
8,639
8,952
9,648
The housing JIUilicet in SaiiDas is wealc£n..
ing marlcEdly. House-price appreciation has
been down on a quaner-ro-quarr.er basis for
the last two quarters, and c:wrendy srands
about 4% below the peak hit at the end of
last yem: Permitting activity has been trending sharply downward since the end of 2005,
indicating that homebuilders are taking a
proactive approach to softening demand
Conll'ibuting to the paring in home demand
is extraOrdinarily low housing aftOrdabiliJ¥Salinas is one of the ten most expensive met-
ropolitan areas to live in nationally. House
prices have soared while the median tiunily
income in Salinas is barely in the top third of
the nalion's metropolitan llmiS. Net migmtion ttends reflect the metro area's OYapriced
housing l1lllrl4m. According to the Ccmus
Bureau. over 7,000 residents on net migrared
from Salinas last yem; a 50% increase compared to 2004 and a scvmfold increase cornpared to 2002. The der.erioraling migration
trends indicate d1at while inYetors may have
been piling into the rnadcet. residems were
being priced out of the metro area.
Despite the efforts of builders, plummeting
home sales are exacerbating the large
discrr:pancy between the increase in new
supply and new demand. Moody's
EConomy.com estimates that Salinas has
one of the highest excess supply indicators
in the nation. This indicates that the pace
of new construction over the past several
years has vastly outsaipped new demand.
This, combined with a highly overvalued
housing market, results in a high IllPI for
Salinas, which is among the most at-risk
markets in the nation for a house-price decline over the next yeaJ:
With only middling economic growth, significant weak£ning in the housing ma:rlcet
~
llllliSing at the npplng Point
t hn Outlook for the U.S. Residential Real Estate Market
October 2006
·;,,tin as, CA Metropolitan Statistical Area
1999
2000
2001
2002
2003
2004
~nos
lnd:c<Jtors
200G
2007
2008
2009
2010
10.9
11.7
7.5
127.3
11.9
12.9
2.7
127.8
13.2
13.8
14.8
15.3
15.7
162
2.4
4.9
4.2
2.4
3.3
3.2
3.0
126.9
-0.7
8.2
4.6
414.6
1,064
134
563.7
8,081
-4.8
1,613
127.3
Gross Metro Product. C$8
%Change
Total Employment (000)
% Changs
Unemployment Rate
Peraonallncome Growth
Population (000)
Single-Family Pennlts
Multifamily Pennlts
Existing Home Prlc:e (SThs)
Mortgage Originations (SMD)
Net Migration (000)
Personal Bankruptcies
14.4
130.0
12.5
5.5
129.6
128.5
129.1
0.5
8.0
4.9
418.3
1,519
162
648.0
6,949
-1.1
1,509
130.9
133.0
135.0
9.0
123.6
1.2
3.4
3.0
2.1
-0.4
-1.4
9.7
6.7
396.3
1,484
574
258.4
2,706
4.2
2,056
7.3
8.3
403.2
1,505
209
307.5
2,556
2.6
1,739
7.7
3.7
408.2
890
166
311.4
5,328
0.7
1,673
8.9
1.1
411.6
1,054
168
336.6
6,870
-1.4
1,647
9.0
6.2
414.4
1,047
308
389.1
10,214
-1.9
1,719
will have a palpable impact nn thio; metro
.trea's housing marker. .Job growth is peakmg. and the nut look for two of the metro
.trea's three la~cst indu.o;trics is lad<lu.o;tet
J'he mt:tm an•a's Ja~e govcmmt•nt Sl.'l'tOr
knds some stability tu the area's economy,
hut is nut a gmwth driver: guvemment jobs
mmprise 14% ur Salina.o;'s job ha.o;c, well
above tht• 16% national average. Salina.o; 's
dominant a~rit:ultur..tl industry has been
expanding samngly. An·urding to our estimates ul litml t.•mpluyment, howt.over, nmdirions arc likely tu weaken in the ncar term.
The tuurism induo;try is a bright spot, addingjohs m a stt.-ady dip of about 2% year
over yt·ar, with t.'Xpt."t.'tations that job gains
willt.·untinut.· :11 thlo; pace in the outlook.
The ldsun: and huspiraliry' industry contributes I tl% tu the nll'tm aR:a's job base, compan:d 111 rhe I0% national average. On the
pJu.o; sidt', SaJina.o;'s job rnL'it! ha.o; a slightly
lower than ;tvt'r.t~t· e.xposun: to housing-rclatedt•rnploymenl.
Whilt.• tht.· S:tlina.o; housing lllllli<et is expet:ted
to signilk:mtly t·nm't·t over rhc next yt"Jr. rhe
me1n1 :tl\':1 will avoid sinking back into recession. ·nw hmLo;ing mm"t.'tion wiU be enough
to put a big dent in t."t.'onomie growth next
yeat lluwt'VCr. hy .WOB, it should be back
0.3
7.2
3.2
412.1
1,296
134
675.4
9,652
-7.1
1,926
on track a.o; an about aver.age pcrformct low
indu.o;trial diver.;ity and low t.-ducational attainment willl<t:ep Salina.-; from out'itripping
the national averJge over the long term .
Houc;ing market activity is slowing in the
Santa Ana-Anaheim-Irvine metro division. llou.o;c prict.'S have fallen by roughly
'l. '>%since 1-Chruary ao; mC'J.o;urcd hy the
<:alili.1mia A'i.'ioci:ttion of Realtors' median
.;ales prit·c for single-family homes. Cono;trut·tiun of sin~le-family homt.-s aln·ady had
slowed in n:sponsc tu a similar price •t4ju.o;tmem in ZOO+, and lo; now holding stt•:tdy.
The adju.c;tmcnt in the hou.o;ing market io;
modest so far, hut Santa Ana's hou.o;ing
market io; unlikely to n:bound anytime
suun. Sentiment is souring. and the currcctiun io; litr from over, with prices expected
to f:tll funher. The metro divlo;ion's housing
m:trket ha.o; developed C.'(t'l'SSt.'S over the pa.o;t
st.ovt·ral yt•ars that It.~..tvt• it highly overpriced
and among the metm area's most at risk of
n:gistcring a huu.o;c-price decline one year
fmm now.
While Santa Ana's t.'t·onomic growth has
been quite mbu.o;t, its strength has been
predicated upon the booming housing
1.0
7.4
3.1
414.6
1,306
99
671.3
7,936
-2.1
1,268
1.4
1.6
1.5
7.8
5.1
422.1
1,402
201
638.3
5,918
-1.1
1,608
7.5
5.3
426.5
1,354
200
651.7
5,873
-0.7
1,675
7.3
5.1
430.8
1,332
216
676.5
6,029
-0.9
1,816
market, darkening its outlook as the housing qde turns down. The slowdown in
the national housing industry is magnified
in the Santa Ana division MSA because of
ito; concentration of the mongage finance
industry, and the fallout is already t.ovident.
tiundn:do; have been laid off from Santa
Ana-ha.'it.-d mongnge originators, putting
hundrcdo; of thou.o;ands of square feet of ofIke space h-..tck on the market. f·ununately,
thlo; came when the mt.·tm ollice V'dcant.-y
r.ue W'..t.'i a n:corc.l low nC'..trly b%. The r.ue
jumped up above 7% in the second quarter---!>till a Vl'ry klW r.ue. But then: l\lukl be
mnsic.lcr..tble downo;ic.le pressure on oftk:e lease
r..ttco; as new spucc begins to be t·ompletcd.
Other fanurs still suppon the economy.
howewt Manuracturing, panit·ularly related to tel·hnology and aerospace, is holding
ito; employment steady <l'i industrial production outpaces the narional average. Tr..tvcl
and tourlo;m alo;o n:main strong, supponing a broad arr..ty of services. International
rrdde and corpor..tte hC'..tc.ltjuaner funt.'tions
funher drive the ct.'l.momy fmwarc.l.
The second quaner impnwement in the delinquent.)' rctte for mongagc and home equity loans provides t.'Vidence of an economy
Santa Ana-Anahelm-b'vine, CA Metropolttan Division
1999
2000
2001
121 .0
130.7
1322
8.5
8.1
1.1
1,345.2 1,388.8 1.413.6
3.6
3.2
1.8
2.7
3.5
4.0
10.1
6.3
2.8
2,815.9 2.857.0 2,895.3
7,679
6.814
6.010
4,560
5,706
2,601
280.7
316.6
354.0
27.924 21,453 49,982
13.0
12.1
10.8
12,167
9,164 10,193
2002
2003
2004
2005
lndiC3tors
143.1
134.6
153.4
162.8
Gross Metro Product, C$8
7.1
1.8
6.3
6.1
%Change
1,403.5 1,428.9 1,456.6 1,490.8
Total Employment (000)
1.9
-0.7
1.8
2.3
%Change
4.8
4.3
3.7
Unemployment Rate
5.0
6.7
Peraonallnc:ome Growth
4.7
5.8
2.5
2,927.8 2,959.3 2,982.1 2.988.1
Population (000)
6,108
4,828
4,103
6,794
Single-Family Permits
3,140
4,428
3,040
MuftlfamllyPennlts
5.002
414.3
489.7
624.9
691.2 Existing Horne Price (SThs)
72,353 108,983 73,189 80,788 Mortgage Originations ($Mil)
5.8
5.0
-3.9
-21.2
Net lllgntJon (000)
9,606
9,167
7,641 11,653
Personal Bankruptcies
Moodv'SEconamy.cam,lnc.•~·~
2005
2007
169.3
173.0
4.0
2.2
1,506.5 1,513.8
1.1
0.5
2008
2009
2010
178.6
184.0
189.2
3.2
3.0
2.8
1,535.1 1,561.5 1.587.6
1.4
1.1
1.7
3.5
3.7
3.6
3.5
3.4
4.8
4.9
5.2
5.5
5.2
3,000.0 3,026.7 3,058.1 3,093.0 3,128.5
7,667
5,331
6,131
6,746
7,600
6,052
3,520
4,234
4,862
5,010
652.7
706.9
675.7
653.5
664.1
86,998 58,718 49,437 48,754 49,724
-16.4
12
3.6
3.1
-2.6
9,710
6,942
8,079
8,614
8,965
37
Housing at the Tipping Point
The Outlook for the U.S. Residential Real Estate Market
October 200fl :
Stockton, CA Metropolitan Stdstlcal Are•
14.3
9.2
178.7
4.2
8.8
6.5
552.4
4,189
14
149.9
2,578
7.9
2,887
15.0
5.2
185.9
4.0
6.9
8.9
588.3
5.350
42
168.7
2,763
11.3
2,397
15.4
2.6
191.2
2.9
7.4
3.8
592.9
4,005
334
208.2
6,991
19.3
2,450
18.2
5.2
194.1
1.5
8.8
3.6
612.4
5,654
489
247.4
8,355
14.0
2,484
18.9
4.3
197.3
1.6
9.0
5.1
631.3
8,935
108
285.0
13,365
13.7
2,813
that has faltered but not fallen. This is in
direct conaast to rising rateS seen statewide
and nationwide. Ihe house-price correction seems so far to be concentrated at the
high end of the rnarla:t, causing little disruption so far to household balance sheets.
There is a redevelopment upside for Santa
Ana over the next several yems. Orange
County will see a shift in the manufacturing
and engineering operations of Boeing as it
plans to vacate its Anaheim facility and relocate all of its 3, 700 employees to another of
its plants at Huntington Beach, also within
Orange County. Employees will move between 2007 and 2010. As this is simply a
transfer within the meao area, the direct
economic impacts from a maao sense are
minimal But, the facility in Anaheim is
physically huge-1.5 million~ feet of
indusaial and office space. Ihe availability
of such space generates good potential for
redevelopment that will contribute to the
county's long-tenn growth.
Ihe neaMenn outlook for Santa Ana-Anaheim-bvine is quite weak until the pam of
both local and national housing marla:ts
clems towan:llhe end of next yem: Santa Ana
should rebound quiddy from this selback.
howeveJ; supportEd by a healthy tourism
industry with a m:ord-bigh horel occupancy
rate, rising defense spending. stable manufacturing. and expanding business and professional service employment. Santa Ana's
considetable number ofhe.adquanas of
intemational corporate operations will also
support the economy. panicu1arly as local
direct foreign iiM:stment may accelerate if the
dollar fil11s in value vmus Asian cwtenCies as
expeaed. longer tenD, the economy will be
held back by high business and housing C0S1S
and increased congestion, but a higbly skilled
labor bt:e. close links to the global economy
38
17.7
4.7
200.7
1.7
8.5
8.8
649.2
6,229
495
344.5
13,350
12.3
2,762
18.8
5.0
205.5
2.4
7.5
5.4
664.1
5,684
185
430.7
18,221
8.9
3,224
Grva Metro Product, C$8
%Change
Totsl Employment ( • )
%Change
Unsmployment Rad8
Personsllncome Growth
Popullltlon ( • )
Slngle-Fsmlly Permlta
Multlfsmlly Permlta
Existing Home Price (STha)
Mortpge Originations ($Mil)
Net Migration (000)
Personal Bsnkruplcles
and good quality oflife factms will maintain a
growth tate just below the national average.
Ihe Stockton housing market is already
showing signs of weakness. Permits for new
construction of residential housing have
started to drop oft: and the median house
price has declined in each of the past twO
quarteJS. Ihe median house price peaked
at $445,000 at the end of 2005. Since then,
prices have declined by 4% to $427,000 in
the second quarter of 2006.
Ihe meao area's housing rnarla:t benefited
from its location near the booming San Francisco and Oakland meao divisions. While
Stockton's median house price is nearly
twice as high as the U.S. average. it remains
well below that of neighboring San Francisco
and Oakland, and provided an aODrdable
alternative for investors and shelter seekers
alilce. Consequently, Stockton's housing market is highly ovupriced; median house prices
nearly doubled from the beginning of 2002
to the end of2005. wilh year-over-year price
appreciation reaching a height of 29% in the
second quarterof2005.
Ihe rapid house-price appreciation. combined with very low income levels, has
caused a steep decline in metro area housing afiordability. which is placing greater
downward pressure on housing demand
in Stockton as fewer buyers from outside
of the meao area are buying. A Stockton
f"amily earning the median income can
afford a house that is priced at just 50%
of the median house price. Nationwide,
a family can afford a bouse that is valued
at 20% above the median price. As house
prices continue to fall. the downward pressure on affordability will subside; however,
it is expected to remain wen below the
national average over t&e forecast horizon.
19.5
5.2
209.8
2.1
7.4
5.9
675.9
5,233
168
423.1
14,194
5.7
2,005
20.0
2.4
211.5
0.8
7.9
5.1
685.4
6,650
74
393.9
12.249
3.3
2,354
20.8
2.9
214.7
1.5
7.7
5.3
696.7
6,174
284
3n.O
10,169
4.8
2,499
21.1
2.6
218.3
1.6
7.4
5.4
708.5
5,998
302
377.6
9,956
5.0
2,595
21.6
2.4
221.6
1.5
72.
I
~·
.•
1'
:·
.;.
5.2
720.2
5,919
395
367.3
10,128
4.8
2,806
Stockton's high dependence on agriculture
will keep per capita income well below
both the state and national averages.
Stoclaon's economy will have a harder time
than others digesting the weakening in the
housing market. Ihe meao area's main
drivers, the 6mn economy and service-providing industries, will provide some suppon
for Stockton. HoweYel; the meao area will
fed the pinch through rapidly weakening
employment in residential real esta~related
indusaies. Over the past ten years. the
booming housing rnarla:t has helped Stockton construction payrolls make a signi&cant
contribution to employment growth. Payrolls 'i
have expanded at an avemge aimual rate of
.;
;
nearly 10% during that time, wilh the saon- 1·
gest growth coming in the late 1990s and the .~
beginning of this decade. Over the past few
years. the pace of payroll growth has decelerated but has remained well above both the
national avemge and me pace of total meao
area payroll growth. Now, as the housing
rnarlet slows. construction payrolls are backing olf as well Indusoy payrolls have already
declined from their peak earlier this yem:
As a consequence, expect Stockton's eco-
nomic expansion to weaken substantially
through the 6rst half of 2007. Moody's
Eoonomy.com expects the decline in house
prices to continue though tbe end of 2008,
wilh a total price correction of more lhan
15%. in addition. a steeper-than-ccpeaed.
downtum in non:hem California's housing
market constituteS a sizable downside ri9k for
tbe highly exposed meao area. Once tbe metro area digests the housing correction, saong
demographics and the meao area's serviceproviding indusuies will help generate snmiy.
sligbdy above~ economic expansion.
Stockton will benefit from its low living costs
relative to neighboring meao areas, though
Housing at the Tipping Point
The OUtlook for the U.S. Residential Real Estate Market
Tucson, AZ Metropolitan Statistical Area
1999
2000
:>001
2002
2003
;•oo.J
2005
22.6
8.2
336.4
3.8
3.2
5.9
828.9
7,234
1,500
117.1
3,892
11.3
3,666
23.7
4.6
350.0
4.0
3.7
7.6
848.6
6,816
963
120.9
3,285
15.0
3,255
24.3
2.9
347.4
-0.7
4.3
4.3
861.2
6,298
1,174
127.3
6,590
8.1
3,914
23.7
-2.8
345.8
-0.5
5.6
2.5
877.2
6,114
1,033
146.0
7,875
11.8
4,311
24.7
4.4
348.1
0.7
5.2
4.7
890.5
7,598
312
156.4
11,968
8.6
4,574
25.5
3.3
360.0
3.4
4.6
7.6
906.5
9,604
917
176.9
8,158
11.2
4,303
26.2
2.7
365.9
1.6
4.4
7.2
924.8
11,166
478
229.1
9,076
12.8
5,771
low educational attainment levels will continue to constrain income growth.
Tucson's heretofore booming housing market is reversing rapidly. In the second quarter of this yem; single-Camily permit issuance
is retrenching. off by just under 26% on a
year-ago basis. The median existing price in
Tucson is also reversing sharply and unexpectedly, dropping by an annualized 21%.
While the median price data can be quite
volatile, the sharp drop, combined with
weakening in permitting, suggests that the
Tucson housing market is well past peak.
Overvaluation and erosion in housing affordability are contributing to the large
downside risks for this housjng'market ln
the last five years, Tucson has gone from being a highly affordable market to being decidedly unaffordable. Although the meao
area maintains an affordability advantage
vis-a-vis southern California and Las Vegas.
the relative affordability will be a less compelling draw as these housing markets also
cool. As a consequence, we expect house
prices in Tucson to decline by almost 13.5%
over the next two years, one of the largest
declines in the nation.
The meao area's robust economy will keep
the housing correction from taking back
an even larger share of the near 80% price
gains over the past 6ve years. Economic
growth in the Tucson economy continues
to accelerate, despite signs of a slowdown
at both the state and national levels. Moreove~; although housing-related industries
have been important drivers in Thcson,
payroll growth is generally spread out
among its major industries. Indeed, employment excluding housing-related industries has been growing at a well above
average pace. Professional and business
lnd<cato<s
2006
2007
2003
Gross llll8tro Product, CSB
%Change
Total Employment (000)
%Change
Unemployment Rata
Personal Income Growth
Population (000)
Single-Family Permits
Multifamily Permits
Existing Home Price (STha)
Mortgage Originations ($1111)
Nat Mlgrdon (000)
Personal Bankruptclu
27.8
6.1
381.1
4.2
4.3
8.7
943.2
8,724
569
240.9
8,078
13.1
3,451
28.7
3.3
390.0
2.3
4.5
7.9
962.5
7,600
595
226.8
7,553
13.8
4,016
29.6
3.0
397.5
1.9
4.4
6.7
977.1
7,104
803
220.3
6,768
9.1
4,348
services and leisure and hospitality have
been the main drivers behind the growth
and these industries will help insulate the
metro area from the housing correction.
Moreove.; growth in expon and business invesanent-related industries should continue
as long as the U.S. dollar remains weak.
Additionally. the recent reaffirmation by
lnco limited's Board of Directors of Phelps
Dodge's merger bid augurs well for Tucson
given that Phelps Dodge's headquaners
are located in the meao area. Indeed, if
approved by shareholders and regulators,
the bid should bring additional high-paying
administrative and management jobs to the
metro area as the new company consolidates operations, providing a boost to consumer industries. These positive fort:es that
will create additional high paying jobs in
Tucson will help provide a floor for housing
prices over the next several quarters.
As the air is let out of the bubble, the metro
area's housing marlcet will continue to
receive suppon from fundamental drivers,
such as expon and business investment
fmns, that will prevent more drastic declines from occurring. Tucson's economy
will remain a saong performer:
Housing markets have clearly turned in the
W.Shington meao division. Sales have
dropped considerably, and inventory-tosales ratios have doubled or tripled in most
pans of the division. House prices peakEd
at the end of last year:
Housing market conditions vary considerably across the area. In geneml. me areas that
had the biggest boom in housing marlcets are
now suffering the most A growing number
of propased condo developments are being
converted to apartments or canceled entirely.
2009
2010
31.4
30.5
3.1
3.1
417.3
·407.0
2.4
2.5
4.3
4.2
7.1
7.2
994.3 1,013.1
7,067
7,144
828
960
221.9
228.6
6,744
6,652
11.8
13.0
4,604
5,063
This trend began in Northern Vuginia. but has
recently spread to the District of Columbia
and Suburban Maryland
Prince George's County. which was a laggard in the housing boom, is not suffering as
badly. It is one of the few areas that are still
showing house-price gains. While unsold
inventories are up, they remain lower than
average for the area at just over one month.
Behind the downturn is a sharp decline in
housing albdabilitydue to the previous
nmup in prices and higher borrowing~A family making the median income can only
afford 86% of the median priced home. Not
roo long ago, alfordability was among the
highest in the nation among large metto areas.
The weakening housing market casts a
cloud upon the oudook of an otherwise
suong economy. Thanks to governmentrelated activity, professional and business
services are leading growth. Unemployment is low, boosting incomes. Household
finances are suong, although mongage
credit quality has begun to deteriorate.
11le snength of !he economy is continuing
to stimulan: commertial development One
common location b development is near
metro srops. A number of prqeas are being
approved or proposed in the division. Prince
George's County recently approved the 6rst
pieces of a planned S1 billion prqea near the
Greenbelt Meao station, for example. 11le
6rst phase including apartments is scheduled
for completion in 2008. When the ten-year
project is complete, it will include large quantities of of6ce and retaillenrertainmeru space
as well as a hotel and OYtt 2,000 residences.
Alexandria of6cials are trying to facilital£ 2
million square feet of new development near
the Braddock Road Meao station on land cur39
Houalng at the 'npplng Point
The Outlook for the U.S. Reeldentlal Real Estate Market
~:
I JQg
2000
200 I
2007
(003
,'QQ.\
:OGc
198.4
220.9
175.5
188.1
211 .7
168.7
184.3
6.7
4.3
2.0
4.0
5.1
5.5
5.3
2,035.4 2,132.4 2,169.7 2,175.3 2,230.5 2,296.0 2,348.7
2.5
2.9
2.3
4.8
1.8
0.3
3.8
3.9
3.5
4.0
2.8
3.4
4.1
2.8
7.1
2.5
4.2
8.3
7.7
9.3
7.0
3,669.1 3,748.2 3.828.0 3,894.3 3,955.1 4,018.5 4,066.4
21,740 22,920 22,234 23,686 24,042 22,848 22,804
7,896
8,269
5,540
9,584
8,509
7,332
7.232
321 .6
412.2
191.4
227.9
262.1
157.8
162.6
15,443 12,875 27,357 38,139 s1.n8 49,234 70,104
21.3
9.2
40.8
41.5
27.0
20.1
38.4
20,488 18,450 20,221 19,300 17,804 14,996 16,803
rendy occupied by industrial and warehouse
properties. In addition, Metro is looking fDr
pannas ro develop land near stations in fair.
fax and Prince George's cmmties.
The pRSence of the federal government, a
highly educated workforce, solid popuJa..
tion trends and the development of the
local technology hub will enable the w..shington metro area to maintain sturdy job
growth, which in tum will mitigate the
worst of the housing downturn. Growth
in federal activity and spending will begin
to slow, while consumer and business demand for tourism, services and retail remains
soong. longer term, growing high-tech induscries will mluce the metro area's reliance
on the federal govanmem. although that will
always remain an important component of
the \\2shington economy.
l..onga'-a:rm prospects fDr the housing marlcet
will also benefit from increasingly tight reslric·
lions on development. Rlr example, Loudoun County supervisors in early Sqxember
imposed restrictions on growth in the 'M5tml
pans of the county that will reduce the IUllllber of houses that can be built in affeard poFtions of the county by about half oompared ro
rules in effect today. The Center for Rtgional
Analysis at George Maion University has te·
cendy concluded that the area will evmrually
be significantly undersupplied if these types of
restrictions don't ease.
Hoasing Crash? The house-price outlook derived from the LHPI and sttuctural
econometric model is consistent with a
national housing tll8l'lu:t correction, not
a crash. Indeed, the house-price declines
antidpated in coming quarters are in a
broader historical context quite modest. If
this outlook comes to pass, then national
house prices will have risen at nearly a
40
lr.CIIC lt C' '
Gross Metro Product, C$8
"Change
Total Employment (000)
"Change
Unemployment Rate
Peraonallncome Growth
Population (000)
Slng!H=amlly Permllll
Multifamily Perrnllll
Existing Home Prfca (Slha)
lllortgage Originations ($Mil)
Nat Mlgrallon (000)
Pereonal Bankruptcies
5% per annum pace this decade. This is
greater than growth during the 1990s, and
compares very favorably to the 2.5% per
annum growth in consumer price inflation.
The logic behind a housing correction and
not a crash seem 'Mill-rooted in hlstori·
cal experience. As previously mentioned,
nominal national house prices have not
declined during a calendar Y'=31' since the
depths of the Great Depression.
The very n:cent experience in Australia and
the U.K. adds to this confidence. Housing
activity and prices soared in both nations
earlier in the decade, with gains comparable to those experienced in the most active
u.s. ~ts. U1ce here. mortgage equity
withdrawal was substantial and powered
consumer spending and broader economic
growth. These economies reached their
capacity and inflationary pressures dcvdoped sooner than in the U.S., prompting
both the Bank of England and Reserve
Bank of Australia to tighten poUcy weU
before the Federal Raerve. Rates are now
comparable, with the U.K. target rate currently set at -4.75%, the Australian rate at
6%, and the funds rate at 5.25%.
:006
"00-
.i003
~000
.'Ill (l
229.5
235.4
242.7
249.7
256.4
3.9
3.1
2.7
2.6
2.9
2,403.4 2,430.5 2,465.0 2,503.4 2,541 .4
2.3
1.1
1.4
1.8
1.5
3.0
3.2
3.1
3.0
2.9
6.3
5.4
5.1
5.2
5.1
4,115.7 4,165.1 4,214.2 4,283.0 4,311.5
21,334 22,994 23,296 22,752 22,828
9,184
6,255
5,962
5,827
6,306
414.4
394.5
365.0
390.8
384.1
59,273 53,328 48,340 45,649 48,041
11.7
11.2
10.2
9.2
8.2
9,453 1o,n5 11,512 12,057 13.118
licult, but these differences seem small
compared to the similarities. 2'
Optimism also seems warranted due to
the nation's well-capitalized and highly
profitable financial intermediaries. In past
house-price collapses, financially &agile
lenders who were taking properties back in
repossession had no choice but to dump
those properties back on a reeling market
at a significant discount. A self-reinforcing
plunge in pricing ensued. Such a possibility seems remote today as lenders are
awash in capital.
If as anticipated the housing market cor-
rects and does not crash. then the broader
economy will slow gracefully. There may
be a period in the next few months when
the weaker housing market feels like it is
undermining the economic expansion, but
this period should prove brief.
WhUe a housing market correction and not
a crash is the most likely outlook, the risks
ate decidedly skewed to the downside.
The probability that a darker scenario will
play out is low, but high enough to warrant careful consideration.
Housing markets in Australia and the
Cnullcs in history. There has never been
U.K. have corrected in a very orderly way.
a'crash in national house prices, but there
House-price growth stalled, but did not
have been plenty of sizable regional housing
fall in either country (see Chan 32). MEW market crashes. Most notable are the colhas declined and consumer spending and
lapse in California house prices in the early
broader economic growth have moderated
1990s, New England prices beginning in
in response. but the economies of both
the late 1980s, and in Texas and other pans
nations continue to expand. If anything.
of the Southwest in the mid-1980s. Peak-whousing and economic acciviry have seem"The pctplllllaaa ol J11011P11eS in rhc US. Is lbai 11111ellrhcr
ingly revived in recent month.'i. rhere are
111111 rhc .Jjusable IR1111J11111P typical in rhc U.K. IIIII
differences between the U.S., U.K. and
AlumdiL 1bc blawm rhc~ ecoaomyofa~
housin& mad4llf his bua aBhlaDcd by risin& &lobal clannl
Ausaalian experiences, which may make
and pria:s far lhe nation's --a--. The U.K. CQ1110111Y
the impending adjustment in the lJ .S.
has ra:dved a welklmcd boost from Sll'IJl1F' gloW aade IIIII
housing ~t and economy more difcapllal flows from OPEC IIIII orhcrCIIIIIJIIOCiilr lllllioRs.
Housing at the npplng Point
The Outlook for the U.S. Residential Real Estata Market
Chan 31: Aassie and British Prices Adjust Gracefully
Moreover, the
Productivity growth remains strong, but is
likely peaking. The pace of technological
supports to the
low inflation that
change, so key to underlying productivity
~~---------------------------------,
% change year ago
have more or less
gains, could hardly be as rapid as in the
Sources: ASS, NBS
prevailed during
past decade when it was fueled by the inthe past decade
corporation of the internet into nearly all
are weakening.
business practices. Rising factory utilization rates and falling unemployment also
For much of the
decade, energy
suggest that less productive capital and
talented labor will be increasingly used.
and other commodity prices
10
were low, the dolThe slowing in productivity growth is oclar was strong and cuning at the same time that labor comrising, and propensation and thus unit labor cost growth
ductivity growth
are accelerating (see Chan 34). Despite
was accelerating.
their wide profit margins, businesses will
05
06
03
04
Commodity prices try to pass this along to their customers
are now high, the
through higher prices for their wares. labor
dollar has been falling and is likely to fall
costs are far and away their most significant
trough price declines during these episodes
more, and productivity growth will at hest
were a stunning 20% to 30%.
cost, and unlike c:ommodity prices, they are
much less likely to recede quickly.
hold its own.
fhere arc numerous other examples of
more modest, albeit substantial price
Polkymakcrs appear willing to tolerate inllaThe higher energy and other commodity
prices of the past scver.d years have yet
tion above their truget and a less pmpitio11o;
declines. Most recently wa.o; a sharp adto affect inflation more broadly, but they
jusnnent in San Francisco Ray Area house
inllation backdrop as long as inllarion expectations remain anchored and prospect<; are that
remain a serious inflationary threat. Busiprices in the wake of the YlK tech hust
nesses have heen willing to shoulder the
.md in v-.uious Midwestern metro areas
inllation will soon m:ede. Indeed, implied
wracked by the manufacturing downturn
linancial burden of their higher material
10-ycar inOation expectations in lfeasury incosts, at least so far: This may be due to
,·arUer this decade.
Dation-protected securities remain near 2.5%,
their record-wide profit rnar&'ns. the small
about where they were a year ago and the
share such costo; account of their total costs, year before that. These cxpct:tations feel very
·\ccording to OfHEO, there have been
f,935 instanCL'S during the past thiny yc-.us
and the likely belief that material prices will tenuous, howevc.; and there is a palpable risk
when house prices have fallen on a year-ago modcr.ue. This thinking becomes increasthey become untcthcred. The li:deral Reserve
basis in one of the nation's 379 melro areas. ingly less compelling, however; the longer
would quickly respond by tightening policy
rhis amount-; to I 0% of the time or once
further; sacrificing the ho11'iing market and
material prices remain high. and panicularly if they were to move highe[
,·very ten quancrs. According to the Realnear-term economic growth to ensure stable
lOtS, there have hcen tl,480 four-quaner
inflation and the l't:onomy's longer-tenn
periods of metro area house-price declines;
The dollar has slid lower in recent years, which growth prospects. Given the already very
fragile housing market, L'Ven a small fur.•mounting to 14% of the periods or once
has put upw.ud pressure on import prices.
,·very sewn quancrs over the same period
The decline has heen COill'enmued against the
t.;ee Chan 3.3).
curo, pound and Canadian dolhu; howem:
Chart 33: A History of Prke Dttlines
ln}llation 11nd r11ta. Higher inflation ;md
The impoct on inOarion Number of nuuirets suffering yetJT-aver-ye~~r priu declines
•merest r.ucs than anticipated remain a sub- is·sun: to be more pro-.rantial threat to the housing market. UnnounL-cd if the Chinese 200
180
derlying inllation has pushed higher since
allow the yuan to 31>the beginning of the year and now stands
preciate substantially
160
well above polit:ymakers' impUcit target.
funha; as is anticipated. 140
Other Asian producers,
120
Core consumer price inflation, excluding
including the Japanese.
100
volatile food and energy prices, is currently are expet.ted to foUuw
.:xpanding at just under 3%. This comthe Chinese lead. With
80
pares to ncar 1% at its nadir in late 2003
such a large share of
60
and its target of between 1.5% and 2.5%.
US. consumer goods
40
The core consumer expenditure deflator is
produced in Asia, the
20
~trowing at over 2%, compared to a low of
impact on consumer
0
just over l% and a target of between 1%
pril:e inflation will
and 2%.
be measurable.
81
86
01
06
91
96
House-prius
41
Housing at the npplng Point
The Outlook for lhe U.S. Residential Real Estate Market
Chart 14: Aueleradnalabor Costs Threaten to Iplte bdladon
'I cuRgc yetJr ago
8~------------------------------------,
96
04
02
00
the largest
increases in
housing jobs
over the past
three years
accounted for
fully one-third
of the national
job gains in
these industries. These
areas include
PhoenixAZ,
Las Vegas NY,
Riverside CA.
Santa Ana CA.
Los Angeles
CA. ~h
ther rise in rates would push a correcting
market into a aash.
Housing-nLuecl anpfoylnent. The housing correction also threatens to come unraveled if the job market does not hold up
as well as expected. Given that housingrelated industries now account for such a
large share of jobs and an even larger share
of job growth, this is a measurable risk.
Nationwide, a rttOnf almost one-in-b:n jobs
are now in housing-related indusaies.26 Employment in these industries 'lJf:W by an average o£30,000 per month ova- the past three
yem, adding some 1.2 million jobs in total
and acmundng for almast one-fourth of an
the payroll jobs CRated during the period. No
other industry. save healthcare, has contributed E much to the samgth of the job rnadca
The link between housing and jobs is
even stronger in the previously most active housing markets across the counay.
Housing is particularly important to the
job market in Florida. where housing-related industries account for an astounding nearly one-sixth of all jobs (see Chart
35).27 Other areas with notably out-sized
employment shares in housing include Arizona and Nevada, the New jersey beach,
and Myrtle Beach, SC (see Appendix 1n.
job gains in housing-related industries
have also been highly concentrated reyJonally. The ten metro areas experiencing
"SetAppaldix 16ba ~ I!Kollhe iDdumlos included as
~lnllusais.
"Mcuo aras ill !he chlllt ae class;W baed on :!: one-Mif
• 5Wida1d dr:vlllion amund dlt nadonaJ _ , . m-.
ington DC, Orlando, Fl, Atlanta GA, San
Diego CA. and Tampa FL
With the n:cent sharp tum in housing
activity. housing-related indusnies have
begun shedding workers. Since March, the
losses have averaged 10,000 per month,
equal to 50,000 in total This has already
left a measurable imprint on overall employment trends. Avmge monthly job gains of
165,000 lzt year and early this year have
recently slowed to monthly gains of 125,000.
This slowing in trend employment growth
has thus been entirely due to housing.
n:lated industries range as much as 50%
above the economy-wide average, and
those working in the heretofore booming
industry have enjoyed record sales commissions and bonuses. 28 The loss of this
income could weigh heavily on consumer ·f
spending and thus broader activity, spooking otherwise financially healthy businesses to tum much more cautious. or
cowse, this in tum could reverberate back
onto housing demand. This negative selfreinforcing dynamic will be particularly
potent in areas where housing activity was
previously most active and its role in the
economy larget:
Monpgc apdty wiaWnnwd. A similar
vicious cycle could be ignited by a more
potent than anticipated negative housing
wealth effect. As house prices and housing
wealth surged in recent years, homeowners
were able and willing to spend much more
aggressively. With the recent weakEning
in housing, the wealth effect threatens to
tum ovetwhelmingly negative, pressuring
consumer spending and the expansion,
and ultimately turning the housing correction into a aash.
Housing wealth has soared in recent years
with the surge in house prices. Homeowners now own nearly $22 ttillion worth of
housing, almost double what they owned
at me end of the 1990s. After netting out
what they owe in mortgage debt, their homeowners' equity has nearly doubled during
the same period to a whopping more than
Employment in industries outside of housing has so far been unaffected by housing's
layolls, and that is expected to largely
continue (see Chan 36). Flush businesses
with pristine balance sheets should be
able and willing to look through housing's '"Tbls is bucd Oft the Fcdcnl I!GeM"s flow o( funds and
problems and any broader economic fall2004 Sliney ol Cansumcr FIIIGKZ.
out and retnain
sturdy in their
Clwt 33: Wheft lluasing-Rdatedjobs Aft Most Important
investment and
Share of tDitJI ~ .2006Q2. 'I
hiring.
The risk is
that they will
not, particularly given that
those working
in housing are
generally more
highly compensated than those
in other industries. Average
hourly earnings
in housing-
.Over13'llt
·9.5-13.0'llo
[]Less IIW'I9.5'llt
Moody'a EcarlcJn¥com.lnc. • -~ • helpOeconorn~cam
Housing at the npplng Point
The Outlook for the U.S. Residential Real Estate Market
Chart 36: Housing lbrukus to lnfcc:llh~
Mondaly job grawda, das, 3 mo. MA
250
October 2006
Broad~r job
Markel
Sources: BLS,
Moody's Economy.com
200
150
Potential
100
50
0
-50
·100
+-
-150
02
03
04
$11 trillion. With the stock market still
struggling to make its way back to its Y2K
rcnm.J high, housing is fur and away the
largest a:>sct in households' collective balance sheet (see Chan 37).
Iiomcownership is also substantially
bma1.kr-b:to;ed than st01..·kownership. Well
over two-thirds of households own at least
,me home, while less than one-half of
households own any stocks. The median
;tmount of el.juity owned by homeowners
is ;m l'Stim:m:d dose to $70,000, while
stuc:kholders nwn only $40,000 in stocks.
Mureowr, mure than thrce-lounhs of
l:tmilics have homeowners' equity that
is ~reatl'r than $10,000, while less than
unc-founh uf families have stockholdings
wnnh more than $30,000.
It is also wnnh noting that housing wealth
v:tries substantially across the country.
.-\ver.1ge homeowners' equity ranges from
uver $100.000 in California and Hawaii to
less than $50.000 in Indiana and South
Dakota.!" Across metro areas, homeown·
ers in the Bay Area of California are the
most house-rich, with average equity of
over $500,000. Homeowners in South
Rend. IN and Buffalo, NY in contrast have
ei.Juity of less than SiO,OOO.30
The wealth elfect postulates that changes
in household wealth measurably impact
.. llli., b Jn..;.d un dara <krMd £rom crcdic bumw lila
fn1111 Cn.-clirl'urecast.com, 1 joint>entute o£ Moody's
hooumy.n~~nand 1-:!luil'ax.
'" ,,,,,.,;s r~ n:uiun'• ewer 3.000 coundes, N1ntudca Counry,
~ ha.. t~ hidlc.r ~homeowners' equlry ol' 1M!' S2.5
mdftun. llltlllWl'SI 1:1 1(1~ CDuJiry, 50 wirh equity o£
I<'SS rhan 57500.
~\'llllablr
household
spending. If
household
wealth is rising
(falling), then
households will
spend more
(less) out of their
current income,
and thus save
less (more).
The idea behind
the wealth effect,
simply put, is
that if households
05
06
bt:come wealthie.;
it is not necessary for them to save as much today as they
are better pn:pan:d for their future financial
needs. There is no longer the same nt:ed to
save for such thin~ as their children's college
education or their own retirement
There has been much research into the
magnitude of the wealth clfect, with most
studies finding that 3% to 7% of increased
wealth is spent within the following year or
two. In other words, for every $1 increase
in wealth, there is an estimated 3 to 7 centc;
in additional subsequent spending. There
i.e; a consensus that the housing wealth cfrcct is mcasuro~bly greater than the stock
wealth effect.u Driving housing's more
powerful wealth effect is the much broader
and deeper ownership or homes than
stocks. House prices have also proven to be
less volatile than stock prit:es, so any houseprice gain is thought to be more durable
and thus safer to respond to. u
There io; also substantial t:vidence
rhat the housing
wealth effect hao;
become even more
potent during the
recent housing boom. This has occurred
through the heightened ability and willing~ess of homeowners to tap the equity in
their homes through increased mongage
borrowing, or what has been labeled mangage equity withdrawal or equity extraction.
MEW has soared during this decade, from
some $350 billion in 2000, according to work
done by ll!SC'.u-chers at the Federal ReseNt:, to
$950 billion in 2005 (see Chart 12, page 14).
Even after mongage origination fees and closing costs, MEW~ more than $700 billion
last year; equal to ahnost 8% of disposable
income. MEW occurs through home equity
borrowing, cash-out refinancing and capital
gains realizations, all of which have been used
aggressively by homeowners in recent years.
MEW is most pronounced in those areas
where there is substantial homeowners' equity. Some 20 metro areas were the beneficiaries of MEW that was near a whopping 20%
of disposable income in the SCI..-onc.l quarter
of 2006 (see Chart 38 and Appendix 18). H
In areas arounc.l the San Francisco Bay Area
and ne-.u- Los Angeles, MEW is closer to 30%
of db-posable income. MEW is also notably
substantial in the rest of California, Florida,
and throughout much of the Nonheast.
There is much debate among economists
regarding the degree to which MEW has
added to the wealth effect and thus housing's contribution to consumer spending
and broader economic growth.
..p..,;..
"The
Mt:W r..11111111cs""' a.,, 1-.-d on data lnnn
Cn:ditfum-·"""· llk'>l' e.timA>l"< an- dni..-.J ·~ thr
IIICthodolc'K)' SUJW'Sitd hy mJ ~ (;..,_""'1'1111 &f
Kmncdy. hlli.Jilrtrduttu 1~ dihnrlJIIdcoli)ing '"""'T chna.
Chart 37: Honsingls Households' K£y Asset
$ tril
25~------------------------------------~
Soun:e: Federal Rtiii8MI
20 ;---------------------+--;
......
.;-;..; ~~~
'ihilkr, l005. "li""f"frinR
Wealth t:lfms: Thr ~.:lc
M.ulo.~ wt-r.;u.o,thr H~
M:rtk'l; """""•s in
,\bmtffWirllftiCJ,
v,lfumto , .
l!.'illt'l.
" 1M .randanl dcviatiun ul
rhr ycar-oYCr·yo:ar pcn:mr
ciuanJic in n-clian cxi.'llinJI
house price! is las than 3'1.
ovrr the pa51 qwum
anrury. <>liiiJIIiml 11> more
dllll'l 15'1. In rhc S6d' 'iOO.
60
65
70
75
80
85
90
95
00
05
43
Housing at the npplng Point
The Outlook for the U.S. Residential Real Estat. M'"arket
Chart 38: MEW Has Been Sabstandal AloDg the Coasts
Sfulrc of dispos4We income, 2006Q2, It;
.Over10%
·5-10%
...... lhan5%
One side of the debate holds that MEW
has been a minor factor in stimulating consumer spending; that the cash raised from
equity withdrawal has simply been a substitute for other soun:es of cash that would
have been used instead. This view holds
that the equity withdrawal has allowed for
households to diversify their balance sheet.
out of housing into other financial assets.:M
The other side of this debate holds that MEW
is a source of cash that is new to many homeown£15 and has pawm!d much greater conSWJJ£r spending than otherwise wotdd have
been the case.3' ~view holds that many
homeownas have historically been liquidity<onsaained and thus could not lift their
spending even if they wanted to when house
prias and rheir net worth irKmlsed. The
unprm:dented demoaatization of IOOiqrpgt;
credit has aDowed the housing wt:ahh eft'r.ct to
6nally be fully realimL
Those on this side of the debate also argue that many homeowners rum: a very
shon-rerm focus; that is they value current
spending much more than spending in the
futun=. 36 The benefits of saving are clear,
but these households have trouble maintaining the self-control needed to do so.
Indeed, past research has shown that owning a home has historically been a way for
,. this IIJIIIIICDlls weiHnk:ulmd 111 Faoll, 2006. ·u.s.
MEW Raulus allalance-Sbeer Sideshow," }P MDop OaK
faJolool* RaaDdl Note.
"this side of dJc upmea1ls wdHnlcularai Ill Haiaus,
2006, "Houslaa Holds !he Key 10 Fed Folley,. Cialolmalt Sclclu
Glallal Etlllllllllla Rlpa; #137.
•
10
this wauld -maJy be mcm: applialble 10 )'OIIJIICl' 01
ICJ'IIICI'Income hot•scholds
myopic households
to fon:e themselves
to save. When making their monthly
mongage payments,
these households
wen= building equity
that could not be
easily tapped, or
not without great
expense. This of
course is no longer
the case.
The use of MEW to
finance increased
spending may
have also been
supeJ'Chuxed in recent years because of
an optimistic shift in the expectations of
homeowners n=garding futuR bouse price
growth. If homeowners auly believe that
their house price will continue to appreciate at the double-digit per annum rate of
recent years, then it would seem perfecdy
reasonable to bonow and spend more aggressively today. judging by the surge in
housing investor demand in recent yeaiS.
this may in fact describe the behavior of a
fair number of homeowners.
It is equally hard to argue, however; that
many lower and even middle-income
homeowners have not tapped their homeowners' equity through MEW to finance
increased spending; spending they could
not have financed in the past. For these
less wealthy households, the wealth effect
has been empowered by increased mon-
gage borrowing.
The risk is that those advocating a greater
role for MEW in driving consumer spending are more right than wrong. If so, then
fading MEW could very well undermine
spending and the expansion. The implica.
tions for the housing market would be clem:
HIUIJidld llllll'lrds. Another serious
threat to the housing market Ues in the
heretofon= burgeoning mongage backed
securities markets.
The nadon's soaring housing activity has
increasingly not been financed by traditional
financial inlmncdiaries, such as banks and
thrifts, but by global iiM:st.oJs via their booming demand for rnongage ~bonds.
furejgn holding!; o£U5. mongage-baclcEd
debt~ SUJF.l to OYer $3.5 trillion, equal
to 30% of tbe U5. 6nancial assetS held by
f0reignas. 38 just a decade ago, beign holdings of these secmities amounted to a bit
more than $500 blllion equal to near 15% of
their us. financial holdings ($ee Owt 39).
The reality of MEW's impact on consumer
spending lies between these two polar
views. 37 It is hard to argue that higher-income homeowners are spending measur•This Is based aa falcnl 11acn>e flow of Funds da1a and
ably mon= in response to the increase in
lllcludes GSE.mucd deb! and laidmdaii!UIIIpp b8c:kal
their housing wealth than in the past simsa:urilles. This SOIII£Wha- fmdp holdlnp of U5.
~debe as n::sklaulll MBS Is combined wllb
ply because it is easier to pull equity OUt
~bonds Ill me Flow of funds daJa. To pullhis iniO
of their homes. These households have
~ diCft Is some SlO ailliaa Ill U.S. monpaoc ddK 8lld
substantial financial n=soun:es and access
just over 52.8 ailllon in GSE«<K ouiSIIIIIdlnsto all types of credit.
Chart 39: Big Playas iD the Mortgage-Backed Mullet
and are thus able to
FomplloWfngs of v.s. 111011pge-badrai ddJf
quickly change their
spending in response 3,600 , - - - - - - - - - - - - - - - - - - . . - 32
to any change in their
net worth. Thus,
30
for wealthie~; highel'28
income households,
2,600
the wealth effect
laigely works through
24
its inftuence on their
views regarding their
22
1 600
long-tenn financial
•
20
well-being.
18
"See "MEW Maaas,"llqiortal
finllnciallnlcw, Apdl20061ior
• deailal dlscuaiDo of !he
cvidcnce SUJIIICIIIIDI dlls view.
16
Mclodfl~lnc.·~·t..-IOeconan~
Housing at the npping Point
The Outlook for the U.S. Residential Real Estate Market
Chart .fO: Many lkcent Borrowers Have little Equity•..
SUR of mortg11ge origiudcms with eqqif:y of las dum Iocr.
85
90
95
00
01
02
Origination year
Global investors, Rush with U.S. dollars
earned in uade, have been atuacted to US.
mortgage-backed bonds given their extra
yield over low-yielding li"easuries and their
heretofore solid credit per£onnance. Investment banks have also adeptly engineered
these securities to make them seemingly
better lit the risk tolerance and other idiosyncratic investment criteria of global investors, and the burgeoning number of hedge
funds has provided a ready vehicle through
which to make these investments.
It is unclear. however. how these new secu-
rities will per£otm as mortgage credit quality erodes, and it is also unclear whether
global investors fully appreciate this. It
is not dilficult to imagine that global investors' heretofore insatiable appetite for
U.S. mortgage-backed debt would quickly
sour as their per£onnance weakened.
There are reasons to be concerned that
mortgage credit will soon measurably erode
given the heretofore surge in adjUS13hle rate
mortgage bonowing by lower-income new
homeowners who have put little down on
their homes. The homeowne!S' equity behind
almost one-half of the loans originated last
~and over one-fourth of those~
in 1004 is less than 10% of the homes' value
(see Chan 40).39 After accounting for realtor
and other fees, these homeowne:JS would have
very little if any equity left if forced to sell their
homes quickly. For context, less than one-
,. This 15 cstillllled by AJ5t American IS o( Sqxembcr 2005.
.-\mxdillJitO the RcaltOIS, median exi5IID& house prices ' - 111X
clllllj\Cd apprm.bly llin<"c then.
03
04
Chart +1: ..• Particalarly11tose with ARMs
Cum..Luive s~ of mortg11ges outstllndfng
05
<-5%
<0%
tenth of the loans originated over a decade
ago have such_ razor-thin equity cushion.
a
A much higher proportion of adjustable rate mortgage loans is secured with
homes in which there is very little equity. Some one-third of ARMs outstanding have equity that is less than 10% of
the home's value, and almost one-sixth
have no equity at all (see Chan 41). For
those ARMs originated in 200+ and 2005,
well over one-third have less than 10%
equity, and an astounding more than
one-fourth are financially upside down.
<5%
<10% <15%
% homeowners' equity
<20%
<25%
the housing market weakens. These borrowers have had a difficult time staying
current on their debt obligations when
rates are low and the housing market
strong. They are sure to have even more
trouble in the environment now unfolding. There are an estimated $1.1 trillion
in outstandingsubprime 2004 and 2005
mongages, and of these, at least 40%, equal
to S-440 billion, have less than 10% equity. -10
Given the continued strong ARM origination volume during the first half of 2006,
an estimated $750 billion in mortgages
outstanding are at measurable risk of suffering some kind of credit problem in the
next several years. This is equal to almost
8% of all mortgage debt outstanding.
The most at-risk borrowers are those
who took on ARMs in 200+ and 2005
with little down and at a low initial teaser
rate. With interest rates on the rise, those
• Subprime adjwnable rate monr,;rge lOIIlS ""' <kr111rd to he
with the low initial rates are particularly
those: loan.• urt,:inatcd with a r-.arr nf '"'" II.... Prime:
exposed to an outsized increase in their
adjusable rare monr,;rge loons qrwcd duri11J1this period
mortgage payments in coming quarters
had lmen:st rarrs ol near ....._
and years. First
American estimates
Chart +l: Most at Risk Mortgage Borrowas
that $400 billion in
Sluue of 04-05 origilllltimls witfa ecpaity of las thlln Iocr.
ARMs were origi60
nated in 2004 and
Soun:e: Firat American
1005 at inirial rates
50
of less than 3%,
,-1-and or these, some
40
40%, equal to $160
30
billion, have less
than 10% equity
20
(see Chart 42).
New subprime
ARM borrowers that
put litde down are
also at greater risk
as rates rise and
10
0
1.01.5
2.02.4
3.03.4
4.04.4
5.05.4
6.06.4
7.07.4
8.08.4
9.o9.4
lnlllalrale
45
Housing at the npplng Point
The OUtJook for the U.S. Residential Real Estate Marbt
It is also conceivable that an oft-cited benefit or the mongage backed securities market, namely its ability to diffuse mongage
credit risk more widely, is also a drawback. Given that the risk is so diffuse, it
is unclear to investoiS who is bearing the
risk and to what degree. If even a single
investor visibly stumbles when credit quality erodes, liquidity in the market could
quickly evaporate. Other investors not
lmowing who is next to suffer may decide
not to engage in any further transactions
until the proverbial dust clears.
Under some scenarios, the problems in the
rnottgage-backed marlcet would spill over
into the rest of the U5. fixed income and
stock marlu:ts. Skittish global investors
would propel bond yields higher and stock
prices lowu The turmoil in US. financial
marlu:ts would immediately remberate
around the world, engendering a global financial evmL
There is historical precedent £or this. The
asset backed securities market froze in the
wake of the Asian crisis and the collapse of
I..ong-Tenn Capital Management in 1998.
liquidity was restored quickly, but only
due to aggressive monetary easing and aggressive buying by Fannie Mae and Freddie
Mac. The new Federal Reserve chainnan
is of coUISe untested, and the GSEs are no
longer in a position to come to the rescue
in the next securities market crisis.
The eamomk: fallout of this dadcer sa:narlo
could be
debilitating if the &tt 11aw
aedit, so vital to a well-functioning housing
marlcet, is shon<ircuited. Mongage rates
would rise further; and evm the availability
mongage cmlit could be impaired. It would
very
or
or
at me very least fmce u5. mongage lmdels
to rein in dleir most aggressive underwriting.
further exacerbating conditions in the deb:riorating housing marlu:t and potmtially igniting
a negative self.n:inforcing cycle. What is cx:peaed ID be a small disruption 1D the economy could quickly nun iniD a major problem,
and for r:be housing rnarl«:t, a cash.
Conclusions. The nation's housing llUU'kets are at a tipping point, as the decadelong boom is fast unwinding. Home sales,
construction, and house prices, which
surged to record highs late last year and
early this yea.; are quickly fading.
me heretofore surging
numbeiS or first-time 1mym and investms
rhat powered the previously exuaordinary
housing activity.
tM: to investors. It was
Optimism that the unfolding adjustment
in c:he housing and mongage markets will
simply be a correction and not a collapse
is based on the strength of the broader job
market and the balance sheets or finaricial
intermediaries. This optimism is also supported by the heretofore orderly adjustments by the U.K. and Aussie housing
markets and economies.
While housing's unprecedented strength
was based on sturdy fundamentals, the
through-the-roof conditions evident at the
peak were fueled by the increasing speculation of buyers and sdlers, buildas and
lmdcs, and securities issuas and inYestms.
While the national housing marlu:t is expected to correct and not crash, a number
of significant meao area housing marlu:ts
will. Moreovc; the risks of a c:larker scenario
unfolding in tnany more pans of the country
are skewed decidedly to the downside. It
is difficult to gauge just how sharply an asset market infected by speculation.lilc£ the
housing marfcEt, will adjust as sentiment
shifts. The broader economic fiillout of this
could be debilitating. What is expected to
be a small disruption to the economy could
quickly nun into a major problem.
The camlyst for housing's Reent downturn
tightening. Even
modesdy higher immst rates have undermined housing afbdability and me ability or
first-time homebuym ro remain in the market. and made housing inatasingly less amac-
This srudy is an effort to compn:hensively
gauge the mounting risks in the housing
marlcet in order to help those who depend
on, and wbo are a&:cted by. the rnarlc£t to be
bena- prepared. It will be updated as conditions in the marlu:t unfold.
~the n:deral ~·s
Housing at the Tipping Point
The Outlook for the U.S. Residential Real Estata Martcet
Table of Contents -Appendices
Appendix 1: Median House·Price .................................................... 49
Appendix 2a: Non·Occupied Owner Share of Single·Family
Purchase Originations, by State, 2005 ....................... .58
Appendix 2b: Non·Occupied Owner Share of Single·Family
Purchase Originations, by Metro, 2005 ...................... 59
Appendix 3: Median Hom;e Price-to-Household Income Ratio.............. 68
Appendix 4: Price-to-Net Rent Raio ................................................. 76
Appendix 5: Non-Housing Employment Growth ............................. 78
Appendix 6: Moody's Economy.com Housing Affordability Index ...... 86
Appendix 7: Supply Balance Indicator ............................................. 95
Appendix 8: House-Price Indicators, NAR Median House Price ..... 104
Appendix 9a: Leading House-Price Indicator High Risk v.ilidation ..... 112
Appendix 9b: 1988Q1 Leading House-Price Indicator
Performance Validation ............................................ 114
Appendix 10: Probability of House-Price Decline ........................... 122
Appendix 11: Leading House-Price Indicator Economic Drivers ...... 131
Appendix 12: Metropolitan Area Classifications ............................ 138
Appendix 13: House-Price Over/Undervaluation Validation ........... 143
Appendix 14: House-Price Over/Undervaluation, Current ............. 153
Appendix 15a: Metropolitan Area Housing Risk, Largest 100
Metro Areas Ranked by Near-Term Oudook............. 162
Appendix 15b: Metropolitan Area Housing Risk, by MSA .............. 165
Appendix 16: Defining the Housing-Reblted Industry .................... 174
Appendix 17: Real Estate Employment as a Share of Total
Employment ............................................................ 176
Appendix 18: Mongage Equity Extraction as a Personal
Disposable Income .................................................. 185
47
@FedFRASER