October 26, 2012
prepared for the
Realtors Association of Maui
King Kamehameha Golf Club
by Paul H. Brewbaker, Ph.D.
TZ Economics, Kailua, Hawaii
July 13, 2012
Copyright 2012
Paul H. Brewbaker, Ph.D.
Be careful what you wish for
ƒ In August 2005 I spoke to the Kauai Board of Realtors
ƒ I had all new data courtesy of Hawaii Information Service, their MLS provider
ƒ I get just as good Maui data from Terry Tolman at RAM—good enough to dig deep
ƒ My message on Kauai in 2005: it’s over, you just had your best year, prices are
going to peak in the next 12 months and then you will become the living dead
ƒ Oops!
ƒ Angry Realtor® ladies harangued me from the front row, bracelets jangling:
“but when, when will prices start rising again?!?”
ƒ To quiet them I said: October 26, 2012 ...and they all put it in their Palm Treos
ƒ Oops.
Slide copyright 2012, TZ Economics
1
Oahu, Maui single-family median sales prices: at
quarterly frequency, recovery appears in the last year
Quarterly, 000$, s.a. (log scale)
800
Maui
Oahu
400
Recessions shaded
200
2000
2002
2004
2006
2008
2010
2012
Slide copyright 2012, TZ Economics
Sources: Realtors Association of Maui, Honolulu Board of Realtors; seasonal adjustment and trend extraction by TZE
2
Oahu, Maui single-family median sales prices: at
monthly frequency, recovery appears in the last year
Monthly, 000$, s.a. (log scale)
800
Maui
Oahu
400
Recessions shaded
200
2000
2002
2004
2006
2008
2010
2012
Slide copyright 2012, TZ Economics
Sources: Realtors Association of Maui, Honolulu Board of Realtors; seasonal adjustment and trend extraction by TZE
3
Maui economic data
Slide copyright 2012, TZ Economics
4
Maui non-agricultural payroll employment (jobs)
Monthly, 000, s.a. (log scale)
75
70
65
60
55
50
45
90
95
00
05
10
Slide copyright 2012, TZ Economics
Sources: Hawaii DLIR, Hawaii DBEDT; seasonal adjustment and calculation of generalized autoregressive conditional
heteroskedasticity annualized standard deviations of the log change in payroll employment by TZ Economics
5
Great Recession = beat-down; 9/11 = Black Swan:
Maui nonagricultural job count conditional volatility
.07
9/11
.06
91:09
09:11
.05
The Great
Recession
(cluster)
.04
.03
.02
.01
.00
90
95
00
05
10
Slide copyright 2012, TZ Economics
Sources: Hawaii DLIR, Hawaii DBEDT; seasonal adjustment and calculation of generalized autoregressive conditional
heteroskedasticity annualized standard deviations of the log change in payroll employment by TZ Economics
6
Maui visitor arrivals since the 1980s:
rebuilding from the loss of Aloha Airlines, ATA
Thousands, s.a. (log scale)
80
200
Recessions shaded
180
40
160
140
Aloha
Aloha
20
120
9/11
90
95
00
Domestic
05
10
90
95
00
05
10
International
Slide copyright 2012, TZ Economics
Sources: Hawaii Tourism Authority, Hawaii DBEDT; seasonal adjustment by TZ Economics
7
Maui monthly visitor arrivals, seasonally-adjusted
Recessions shaded
Thousands (log scale)
220
200
180
Aloha
Aloha
160
9/11
140
00
02
04
06
08
10
12
Slide copyright 2012, TZ Economics
Sources: Hawaii Tourism Authority, Hawaii DBEDT; seasonal adjustment by TZ Economics
8
Maui monthly visitor arrivals and payroll
employment exhibit strong co-movement
Visitor arrivals (right scale)
Non-ag jobs (left)
240
Thousands (log scale)
Arrivals
Aloha
Aloha
76
200
180
72
160
68
Thousands (log scale)
220
Recessions shaded
140
9/11
64
Jobs
60
56
00
02
04
06
08
10
12
Slide copyright 2012, TZ Economics
Sources: Hawaii DLIR, Tourism Authority, Hawaii DBEDT; seasonal adjustment by TZ Economics
9
Maui nonagricultural payroll employment
64
Thousands (log scale)
60
10.5
56
10.0
9.5
52
Thousands (log scale)
Private (right scale)
Public (left)
9.0
Recession shaded
8.5
8.0
02
03
04
05
06
07
08
09
10
11
12
Slide copyright 2012, TZ Economics
Sources: Hawaii DLIR, Hawaii DBEDT; seasonal adjustment by TZ Economics
10
Maui unemployment rate
10
Recessions shaded
Aloha
Aloha
Percent, s.a.
8
6
9/11
4
2
0
00
02
04
06
08
10
12
Slide copyright 2012, TZ Economics
Source: Bureau of Labor Statistics, U.S. Department of Labor, Hawaii DLIR, Hawaii DBEDT; data through May 2012,
seasonal adjustment of Hawaii data by TZE
11
U.S. and Hawaii unemployment rates
12
Recessions shaded
U.S. average
Neighbor Islands
Oahu
Percent, s.a.
10
Neighbor Isles
like mainland
8
6
4
2
Neighbor Isles
like Oahu
0
00
02
04
06
08
10
12
Slide copyright 2012, TZ Economics
Source: Bureau of Labor Statistics, U.S. Department of Labor, Hawaii DLIR, Hawaii DBEDT; data through May 2012,
seasonal adjustment of Hawaii data by TZE
12
Kauai monthly real nonresidential building permits
Recessions shaded
Million 2011 dollars, log scale
100
BOH data
10
DBEDT data
1
75
80
85
90
95
00
05
10
Slide copyright 2012, TZ Economics
Sources: Bank of Hawaii, Hawaii DBEDT, U.S. Bureau of the Census; deflation and Hodrick-Prescott filter by TZE
13
Maui monthly real residential building permit values
Million 2011 dollars, log scale
Recessions shaded
100
10
BOH data
DBEDT data
1
75
80
85
90
95
00
05
10
Slide copyright 2012, TZ Economics
Sources: Bank of Hawaii, Hawaii DBEDT, U.S. Bureau of the Census; deflation and Hodrick-Prescott filter by TZE
14
Quarterly new housing units authorized
by building permit: no glut any time soon
4
3
000 units, s.a., log scale
2
1.0
1
0
000 units, s.a., log scale
Oahu, Hawaii, Kauai (right scale)
Maui units (left scale)
Recessions shaded
0.1
Recessions shaded
80
85
90
95
00
05
10
15
Slide copyright 2012, TZ Economics
Sources: Bank of Hawaii, Hawaii DBEDT; deflation and Hodrick-Prescott filter by TZE
15
Maui existing home sales volumes
Monthly units, s.a. (log scale)
Recessions shaded
160
80
Condominium
Single-family
40
00
02
04
06
08
10
12
Slide copyright 2012, TZ Economics
Sources: Realtors Association of Maui, Hawaii DBEDT; seasonal adjustment by TZE
16
Maui existing home sales volumes
1000
Quarterly units, s.a. (log scale)
Recessions shaded
100
Condominium
Single-family
10
80
85
90
95
00
05
10
Slide copyright 2012, TZ Economics
Sources: Realtors Association of Maui, Hawaii DBEDT, UHERO, Prudential Locations; seasonal adjustment by TZE
17
Maui existing home sales median prices
800
Quarterly units, s.a. (log scale)
Recessions shaded
400
200
Single-family
Condominium
100
95
00
05
10
Slide copyright 2012, TZ Economics
Sources: Realtors Association of Maui, Hawaii DBEDT, UHERO, Prudential Locations; seasonal adjustment by TZE
18
Mythbusters: the jobless [sic] recovery
Slide copyright 2012, TZ Economics
19
Honolulu Star-Advertiser (July 14, 2011) http://www.staradvertiser.com/business/20110714__Jobless_recovery_persists_in_Hawaii.html
Nonagricultural payroll recovery:
not fast enough is not the same as not at all
P
P
T
140
136
620
132
600
128
580
U.S. (right scale)
Hawaii (left)
560
Million jobs, s.a. (log scale)
Up: the new down
2010-2012
Jobless
recovery
2001-2003
640
Thousand jobs, s.a. (log scale)
T
124
540
U.S. recessions shaded;
Vertical line is 9/11
520
00
02
04
06
08
10
12
Slide copyright 2012, TZ Economics
Sources: Hawaii DLIR and DBEDT, U.S. Bureau of Labor Statistics; national data through May 2012, seasonal adjustment of
Hawaii data through April 2012 by TZE
21
Nonagricultural payrolls: Neighbor Islands took a
bigger hit than Oahu, now are slowly re-accelerating
P
T
U.S. recession shaded
460
170
450
Oahu −5.5%
165
440
Neighbor
Isles
−10%
160
430
155
420
150
410
Oahu (right scale)
Neighbor Isles (left)
145
05
06
07
08
Monthly jobs in 000 (s.a.) log scale
Monthly jobs in 000 (s.a.) log scale
175
400
09
10
11
12
Slide copyright 2012, TZ Economics
Sources: Hawaii DLIR, Hawaii DBEDT; seasonal adjustment by TZ Economics
22
U.S. real GDP indexed to cyclical peaks:
recession ended 3 years ago
Index of real GDP: peak quarter = 1.000
1.100Previous 10 recessions*
Great Recession 2008-09
2012Q1P
1.050
2007Q4
1.000
0.950
Recession
Recovery
0.900
2006 2007 2008 2009 2010 2011 2012 2013
*Recessions since World War II—averages aligned to peak quarter of each business cycle and to 2007Q4
Slide copyright 2012, TZ Economics
Source: Professor Robert Hall, Stanford University and Chair, NBER Dating Committee; Bureau of Economic Analysis,
U.S. Department of Commerce; includes 2012Q1P data and NABE forecasts though 2013Q4
23
Distribution of annual U.S. real GDP growth
forecasts in the May 2012 NABE survey
Percent change and
geometric mean
5
5
4
3
3
2
2
1
1
0
0
0
5
10
2012
15
2013 forecast: 2.68%*
4
2012 forecast: 2.37%
20
0
5
10
15
20
2013
Slide copyright 2012, TZ Economics
Source: National Association for Business Economics (public summary at http://www.nabe.com/publib/macsum.html);
histograms and mean calculations by TZE
24
U.S. real GDP growth NABE forecasts May 2012
Percent change, quarterly, annualized
P
T
Top five forecasts
4
Bottom five forecasts
0
Actual
NABE May 2012
-4
Recession shaded
-8
06
07
08
09
10
11
12
13
14
Slide copyright 2012, TZ Economics
Source: BEA (http://www.bea.gov/national/index.htm#gdp); NABE Outlook “Subpar Recovery Exists; Forecasters Expect
Improvement in 2012,” (May 21, 2012) (http://www.nabe.com/publib/macsum.html)
25
Federal Reserve core PCE inflation and real GDP
growth rate projections released in June 2012
Central tendencies and ranges FOMC
meeting participants’ projections for PCE
inflation 2012-14 and over the longer run
Percent
Percent
Central tendencies and ranges FOMC meeting
participants’ projections for real GDP growth
rate 2012-14 and over the longer run
Slide copyright 2012, TZ Economics
Source: Federal Reserve Board minutes of the Federal Open Market Committee meeting June 20, 2012
(http://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20120620.pdf)
26
Federal Reserve economic outlook June 2012:
2% longer run forecast consistent with policy goal
Slide copyright 2012, TZ Economics
Source: Federal Reserve Board Members and Federal Reserve Bank Presidents (advance release with FOMC minutes)
(June 20, 2012) (http://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20120620.pdf)
27
What the Fed wants to avoid with an explicit inflation
target: misinterpretation of inflation risk (like before)
January 1994: FOMC begins raising the fed funds target;
no announcement, no forecast, no statement of strategy,
markets over-reacted to inflation risk (bond sell-off)
10
8
Percent
6
4
2
Recession shaded
0
89
90
91
92
93
94
95
96
After the 1990-91 (Gulf War) recession
Slide copyright 2012, TZ Economics
Source: Federal Reserve Board (http://www.federalreserve.gov/releases/h15/current/)
28
Treasury yields in early-2000s had series of “false
starts” before Fed removed monetary policy
accommodation at a “measured pace”
7
Even before the Fed
finished easing in
2003, and despite
explicit fed funds
announcements, longterm bond yields
experienced a bumpy
transition and return to
LR equilibrium as the
Fed removed policy
accommodation,
2004-2006
6
Percent
5
4
3
2
1
Recession shaded
0
98
99
00
01
02
03
04
05
After the 2001 (dot.com) recession
Slide copyright 2012, TZ Economics
Source: Federal Reserve Board (http://www.federalreserve.gov/releases/h15/current/)
29
Treasury yields now are guided by explicit statements
of Fed policy goals and long-run equilibria
6
Now the Federal Open
Market Committee
(FOMC) tells you their
short-term forecast,
longer-term steadystate equilibrium
expectations and
policy goals
Recession shaded
QE1
5
QE21
Operation Twist
Percent
4
3
2
1
0
06
07
08
09
10
11
12
13
14
15
16
Fed funds target rate
trajectory implied by
FOMC participants’
own forecasts
After the 2007-09 Great Recession
Slide copyright 2012, TZ Economics
Source: Federal Reserve Board (http://www.federalreserve.gov/releases/h15/current/;
http://www.federalreserve.gov/monetarypolicy/files/fomcminutes20120125.pdf)
30
Year-end fed funds rate forecast distributions
(FOMC, June 20, 2012) and weighted averages
Percent (in 25 b.p. increments)
5.0
5.0
5.0
5.0
4.0
4.0
4.0
4.0
3.0
3.0
3.0
3.0
2.0
2.0
2.0
2.0
1.0
1.0
1.0
LR: 4.105%
2014: 1.105%
2013: 0.513%
2012: 0.303%
0.0
0.0
0
5
10
15
1.0
0.0
0
5
10
15
0.0
0
5
10
15
0
5
10
15
Number of forecasts for each rate level in 25 basis point increments (bar length),
and means
Slide copyright 2012, TZ Economics
Source: Federal Reserve Board (http://www.federalreserve.gov/monetarypolicy/files/fomcminutes20120125.pdf)
31
Nominal and real U.S. Treasury yields
Jul 06
5
3
Jul 07
Jul 2007
Jul 2006
4
2
Jul 08
Jul 2009
Jul 09
3
1
Jul 10
Jul 2008
Jul 2010
Jul 11
0
2
Jul 2011
June 2012
June 2012
-1
1
U.S. Treasury yield curve
30
-y
r
20
-y
r
10
-y
r
r
7y
r
5y
30
-y
r
20
-y
r
10
-y
r
7yr
5yr
1y
2- r
y
3- r
yr
FF
0
1y
2- r
y
3- r
yr
FF
-2
Treasury inflation-protected securities
(TIPS) yield curve
Slide copyright 2012, TZ Economics
32
Long-term inflation expectations: difference
between nominal and real U.S. Treasury yields
Percentage points, difference
between nominal U.S. Treasury
yields and real (TIPS) yields
3
Jul 06/07/08/11
2
1
2.0% PCE
deflator goal
June 2012
Jul 09/10
Feb 2009
0
1.2-1.7% PCE
deflator 2012
FOMC forecast
central tendency
-1
Nov 2008
r
-y
30
20
-y
r
r
-y
10
yr
7-
yr
5-
FF
1y
2- r
y
3- r
yr
-2
Slide copyright 2012, TZ Economics
Source: Federal Reserve Board (H.15 constant maturity yields); calculations by TZE through June 2012, inflation risk
premium is 15-20 b.p. (See http://www.federalreserve.gov/pubs/feds/2012/201206/201206pap.pdf)
33
Still waiting for the double-dip?
Slide copyright 2012, TZ Economics
34
Spring (May) 2011: double-dip headlines keep
double-dip recession fears alive and investors scared
Slide copyright 2012, TZ Economics
Sources: web sites as illustrated (all from the end of May 2011)
35
“Overshooting” models of asset price dynamics
Simulated asset price dynamics in
a model of “slow-moving capital”
Pt
Sound effect:
“boy-yoy-yoy-yoy-yoing”
P0
PT
t0
“The key implication is that supply or demand shocks must be
absorbed on short notice by a limited set of investors. The risk
aversion or limited capital of the currently available investors,
including intermediaries, leads them to require a price concession in
order to absorb the supply or demand shock. They plan to ‘lay off’
the associated risk over time as other investors become available.
As a result, the initial price impact is followed by a price reversal that
may occur over an extended period of time.”
Time
Simulated asset price dynamics in
a logistic growth model
Slide copyright 2012, TZ Economics
Sources: Darrell Duffie, “Asset Price Dynamics with Slow-Moving Capital” Journal of Finance vol. 65 no. 4 (August 2010) pp 12391267 (http://www.darrellduffie.com/uploads/pubs/DuffieAFAPresidentialAddress2010.pdf); TZ Economics calculations
36
S&P 500 Index: overshooting + oscillation after
Congressional FAIL (Aug. 3, 2011; Nov. 23, 2011)
1500
1941-43 = 10 (log scale)
1400
Trading range prior to
the August 3, 2011
federal debt ceiling
“deadline”
1300
1200
Supercommittee FAIL
1100
Overshooting + oscillation
after Aug. 3, 2011* FAIL of
Congress to commit to
long-term deficit reduction
1000
11:1
11:2
11:3
11:4
12:1
12:2
*Federal debt ceiling “deadline”
Slide copyright 2012, TZ Economics
Sources: Standard & Poor’s, Federal Reserve Bank of St. Louis; daily closing data through June 21, 2012
37
S&P Case-Shiller price indexes (“relative to Jan 2000”):
“double-dip” is overshooting + oscillation
January 2000 = 100, s.a., log scale
P
T
200
Los Angeles
San Diego
San Francisco
Denver (relatively stable)
Denver
Phoenix
100
Las Vegas
U.S. recession shaded
2006
2007
2008
2009
2010
2011
2012
Slide copyright 2012, TZ Economics
Sources: Standard & Poor’s; seasonal adjustment using Census X-12 filter by TZ Economics
38
Median CA existing single-family home prices:
“double-dip” or “overshooting + oscillation?”
P
Quarterly, thousand $, s.a. (log scale)
900
T
Oahu (Honolulu) its own private Idaho
800
700
600
San Jose, Sunnyvale, Santa Clara
Anaheim, Santa Ana, Irvine
San Francisco, Oakland, Fremont
500
400
San Diego, Carlsbad, San Marcos
Los Angeles, Long Beach, Santa Ana
300
U.S. recession shaded
05
06
07
08
09
10
11
12
13
Slide copyright 2012, TZ Economics
Sources: Honolulu Board of Realtors, National Association of Realtors; seasonal adjustment by TZE using Census X-12
ARIMA filter, data through first quarter 2012
39
Oahu housing market most stable—relatively—of
Hawaii Islands and many mainland urban markets
800
800
Oahu
700
700
Orange Co., CA
600
600
500
500
400
800
400
05
06
07
08
09
10
11
12
800
700
700
600
600
05
06
07
08
09
10
11
12
Kauai
Maui
500
500
400
400
05
06
07
08
09
10
11
12
05
06
07
08
09
10
11
12
Slide copyright 2012, TZ Economics
Sources: Honolulu Board of Realtors, Realtors Association of Maui, National Association of Realtors, Kauai Board of
Realtors / Hawaii Information Service; seasonal adjustment by TZE using Census X-12 ARIMA filter
40
Oahu median single-family home prices returning to
the upper end of “the zone,” 2005-2012
700
Thousand dollars, log scale
600
500
400
300
92
94
96
98
00
02
04
06
08
10
12
Slide copyright 2012, TZ Economics
Sources: Honolulu Board of Realtors; TZ Economics
41
Unraveling of median single-family home prices on
two Neighbor Islands: that was the bottom
Quarterly, thousand $, s.a. (log scale)
800
Maui
Kauai
700
600
Kauai*
500
Maui
400
U.S. recession shaded
05
06
07
08
09
10
11
12
13
*April-May 2012
Slide copyright 2012, TZ Economics
Sources: Realtors Association of Maui, Hawaii Information Service; seasonal adjustment by TZE using Census X-12
ARIMA filter
42
Three relevant trade-offs: inverse relationships
between three pairs of variables provide guidance
Honolulu inflation vs. unemployment
ƒ “Supply shocks” from recent energy impulses (Arab Spring, Japan seismic shock),
unemployment falling from over 6.5% to 5.5%, while core inflation rebounds from
near zero to 2% (plus energy boost), consistent with historical recovery transitions
Real hotel room rate appreciation vs. hotel vacancy rates (1 minus occupancy)
ƒ Consistent with historical pattern, breakout above 73% occupancy from below
implies return to real rates of appreciation, rising real yields
Rates of home price appreciation vs. months of MLS inventory remaining
ƒ Transition from 12 months (2008) to 4 months (2012) remaining implies an
acceleration of home price increase this year to range of 5-10%, rising
Slide copyright 2012, TZ Economics
43
Trade-offs, 1976-2011, between Hawaii inflation and
unemployment (and Fed’s target inflation zone)
12
Hawaii’s
Phillips Curve
With oil and other supply shocks (1974, 1979),
and high “unanchored” inflation expectations
Inflation (%)
8
4
FOMC goal: 2% PCE inflation
0
Trade-off 1983-1998
Forecast
Trade-off since 1998
-4
0
2
4
6
8
10
Unemployment (%)
Slide copyright 2012, TZ Economics
Source: Bureau of Labor Statistics, U.S. Department of Labor, Hawaii DBEDT and DLIR; all calculations by TZE
44
Recent events: supply shocks since the recession
Oahu inflation and unemployment data, 1998-2011
p̂t
8
Assumed long-term nonaccelerating inflation
equilibrium:
*
*
Inflation (%)
(pˆ , u )
2006
6
t
2007
t
2008
4
2011
pˆ t*
2
Post-recession petroleum
inflation, financial frictions
(Hawaii Clean Energy Policy?)
2010
2009
0
1998
-2
1
2
3
4
5
6
ut*
7
Unemployment (%)
ut
Slide copyright 2012, TZ Economics
Source: Bureau of Labor Statistics, US Department of Labor; calculations by TZE
45
Low-sulfur petroleum price benchmark:
North Sea (Europe) Brent crude oil
P
Monthly, dollars/barrel (log scale)
160
T
80
40
U.S. recession shaded
05
06
07
08
09
10
11
12
Slide copyright 2012, TZ Economics
Sources: Federal Reserve Bank of St. Louis
(http://research.stlouisfed.org/fred2/series/MCOILWTICO/downloaddata?cid=32217)
46
Historical quarterly trade-offs, 1978-2011,
between Hawaii hotel room real rate
movements and hotel vacancy rates
Real room rate changes (% y-o-y)
15
Think of it as a trade-off between
appreciation (in real room rates)
and unemployment (of hotel rooms)
10
5
0
-5
-10
-15
.10
.15
.20
.25
.30
.35
.40
.45
Hotel vacancy rate (%)
Slide copyright 2012, TZ Economics
Sources: Hospitality Advisors LLC, Hawaii DBEDT, U.S. Bureau of Labor Statistics; regression by TZE
47
Hawaii hotel room real rates and vacancy:
more aggressive yield recapture in last few years
Real room rate changes (% y-o-y)
15
10
end-2011
5
0
-5
-10
-15
.10
2009Q3
.15
.20
.25
.30
.35
.40
.45
Hotel vacancy rate (%)
Slide copyright 2012, TZ Economics
Sources: Hospitality Advisors LLC, Hawaii DBEDT, U.S. Bureau of Labor Statistics; regression by TZE
48
Trade-offs, 1994-2011, between Oahu SF home price
movements and months of inventory remaining
40
% change P(single-family)
The Sklarz Curve
Again, think of it as a trade-off
between inflation (in home prices)
and unemployment (of houses)
30
20
10
Shaded intersection: 2012 forecast
0
-10
-20
0
4
8
12
16
20
24
Months of inventory (-5)
Slide copyright 2012, TZ Economics
Sources: Honolulu Board of Realtors; regression by TZE as anticipated in as anticipated in Norm Miller and Mike Sklarz,
“A Note on Leading Indicators of House Price Trends,” Journal of Real Estate Research 1:1 (Fall 1986) pp. 99-109
49
Trade-offs between Oahu SF prices and inventory
remaining: you just missed the first year
40
% change P(single-family)
Spring 2012
30
Summer 2009
Brewbaker says the
recession is over?
20
10
0
-10
-20
0
Summer 2011
“Double dip” fetishism
4
8
12
16
20
24
Months of inventory (-5)
Slide copyright 2012, TZ Economics
Sources: Honolulu Board of Realtors; regression by TZE as anticipated in as anticipated in Norm Miller and Mike Sklarz,
“A Note on Leading Indicators of House Price Trends,” Journal of Real Estate Research 1:1 (Fall 1986) pp. 99-109
50
The Sklarz Curve during the last housing
cycle—trough to trough (1999-2011)
40
%chng P(condominium) y-o-y
%chng P(single-family) y-o-y
40
30
Single-family
20
10
0
-10
-20
30
Condominium
20
10
0
-10
-20
0
4
8
12
Months of inventory remaining (-5)
16
0
4
8
12
16
Months of inventory remaining (-5)
Slide copyright 2012, TZ Economics
Source: Honolulu Board of Realtors (raw data); seasonal adjustment using Census X-12 ARIMA filter, regressions on
natural log of months of inventory remaining (lagged 5 months) by TZE through April 2012
51
Maui single-family home days on market
200
Recessions shaded
180
Days
160
140
120
100
80
2000
2002
2004
2006
2008
2010
2012
Slide copyright 2012, TZ Economics
Source: Realtors Association of Maui
52
Maui SF days on market seasonality (right)
and underlying trend (left); data through June 2012
200
1.2
180
160
1.1
140
1.0
120
100
0.9
80
2000
2002
2004
2006
2008
2010
2012
00 01 02 03 04 05 06 07 08 09 10 11 12
Maui single-family days on market (s.a.)
and underlying trend through April
Maui single-family days on market
seasonal adjustment factors (avg. = 1)
Slide copyright 2012, TZ Economics
Source: Realtors Association of Maui; seasonal adjustment by TZE
53
Sklarz Curve, RAM version: use “days on market”
Single-family price change (%)
60
40
20
Prices rising
0
Prices falling
-20
-40
80
100
120
140
160
180
200
Days on market (trend) (-5 months)
Slide copyright 2012, TZ Economics
Source: Realtors Association of Maui; regression by TZE
54
Toxicity: learning from mortgage delinquency
ƒ The Great Recession, concluding the sub-prime mortgage lending-driven housing
“bubble” that preceded it, had legacy of wide geographic delinquency variation
ƒ Great Recession notable for anomalous spike in serious delinquency
ƒ Setting up a comparison: house price movements, delinquency changes
Slide copyright 2012, TZ Economics
55
U.S. delinquency rate on single-family residential
mortgages, domestic offices of commercial banks
12
Recessions shaded
Percent, s.a.
10
8
6
4
2
0
1995
2000
2005
2010
Slide copyright 2012, TZ Economics
Source: Federal Reserve Bank of St. Louis (http://research.stlouisfed.org/fred2/series/DRSFRMACBS); graphic by TZE
56
Mortgage delinquency rate by county in Hawaii:
first mortgages, home equity loans and lines
Percent of loans, at year end
14
Big Island
Kauai
Maui
Oahu
12
10
8
Recessions shaded
6
4
2
0
2000
2002
2004
2006
2008
2010
2012
Slide copyright 2012, TZ Economics
Source: Federal Reserve Bank of New York (http://data.newyorkfed.org/creditconditions/); graphic by TZE
57
Hawaii mortgage delinquency rates
Percent of mortgages outstanding
4
30-59 days delinquent
90+ days delinquent
3
90+
2
“Sub-prime”
financial crisis
notable for sharp
increase in
longer-term
mortgage
delinquency
rates
30+
U.S. recessions shaded
1
0
1980
1985
1990
1995
2000
2005
2010
Slide copyright 2012, TZ Economics
Source: Regressions and seasonal adjustment by TZE; data from Mortgage Bankers Association, TZE
58
Mortgage delinquency near cyclical peak (2010Q3)
≥90 days past due by county (darker is higher)
22.7
18.3
15.9
15.6
13.6
13.2
12.8
11.4
10.1
9.4
9.1
8.8
8.2
7.6
7.6
6.9
5.9
5.3
4.5
3.7
3.4
1.8
1.0
0.0
Dade, FL
Broward, FL
Palm Beach, FL
Clark, NV
Riverside, CA
San Joaquin
San Bernadino, CA
Bronx, NY
Maricopa, AZ
Sacramento, CA
Contra Costa, CA
Los Angeles, CA
Hawaii, HI
Maui, HI
San Diego, HI
Orange, CA
Santa Clara, CA
U.S. average
Kauai, HI
San Francisco, CA
Honolulu, HI
Dane, WI
Cherry, NE
Todd, SD
Slide copyright 2012, TZ Economics
Sources: Federal Reserve Bank of New York based on credit-reporting agency TransUnion LLC’s Trend Data database
(http://data.newyorkfed.org/creditconditionsmap/)
59
Foreclosures as a percent of housing units by state
0.333
0.328
0.319
0.309
0.308
0.294
0.202
0.193
0.189
0.169
0.149
0.148
0.134
0.133
0.117
0.115
0.115
0.112
0.112
0.110
0.096
0.093
0.091
0.087
0.085
0.00
0.10
0.20
Georgia
Arizona
Nevada
California
Illinois
Florida
Ohio
Michigan
South Carolina
Utah
Wisconsin
Colorado
Indiana
Delaware
Idaho
Texas
New
Oregon
Kansas
Minnesota
Iowa
Missouri
Tennessee
Louisiana
Massachusetts
0.30
Washington
Kentucky
Virginia
New Mexico
Oklahoma
Pennsylvania
Rhode Island
North
Maryland
Connecticut
Alaska
Alabama
Nebraska
New Jersey
Hawaii
New York
Wyoming
Mississippi
South Dakota
Arkansas
Montana
Maine
West Virginia
North Dakota
Vermont
0.085
0.084
0.084
0.083
0.077
0.077
0.076
0.074
0.073
0.072
0.067
0.066
0.054
0.053
0.051
0.043
0.041
0.037
0.035
0.031
0.029
0.015
0.011
0.007
0.007
0.00
0.10
0.20
0.30
Slide copyright 2012, TZ Economics
Sources: Realty Trac (http://www.realtytrac.com/trendcenter/), data for May 2012; graphic by TZE
60
Percent change in single-family prices
Using trend data: the inverse relationship between
changes in home prices and delinquency rates
appears cyclically in diagonal, flattened orbits
25
2004
1987
20
15
1980s
10
5
2012Q1
Prices rising
0
Prices falling
1992
1997
-5
2010
-10
1
2
3
4
5
6
7
Delinquent as percent of all loans
Slide copyright 2012, TZ Economics
Data sources: Prudential Locations, UHERO, Mortgage Bankers Association, Guy Sakamoto (Bank of Hawaii); all
calculations by TZE
61
Digging deeper into linkages:
house price movements and delinquency changes
ƒ Rising prices are associated with falling delinquencies (up to lags, etc.)
ƒ In cyclical markets, rising phase of house price cycle has similar inverse
relationship with falling phase of mortgage delinquency cycle (up to lags)
ƒ Some evidence that price deceleration raises the pace of increase in delinquency
(i.e. prices rising at a decreasing rate of increase can precede delinquency rise
ƒ Complex nonlinearity actually masks underlying orbit—embedding inverse
relationship—in delinquency-price change space
Slide copyright 2012, TZ Economics
62
Trends, cycles and bubbles
ƒ We’ve got all three—simultaneously
ƒ Arbitrage and factor mobility (capital, labor, technology) implies convergence to
common trend in long-run home price appreciation
ƒ Cycles are not bubbles, and presence of the former is neither necessary nor
sufficient for the latter
ƒ Hawaii has had more cyclical housing valuations since early-statehood (1960s)
ƒ Many microeconomic factors distinguish regional performance differences even in
shared macroeconomic environment
ƒ Looking for signs of cyclical upswing, indications of its imminence
ƒ Remember: Hawaii peak for sales 2005, peak for values 2006—six years ago
Slide copyright 2012, TZ Economics
63
Average Oahu single-family and condominium
existing home sales prices
Single-family
Condominium
Thousand dollars, log scale
1000
100
Recessions shaded
10
1960
1970
1980
1990
2000
2010
2020
Slide copyright 2012, TZ Economics
Sources: Honolulu Board of Realtors; TZ Economics
64
Indexes, s.a. (1987 = 100), logs
Remarkably, until the sub-primehousing bubble,
longer-term trends were roughly equivalent
Regressions of the
natural logarithm of the
two price indexes on a
constant and a time
trend, Oahu (19802011) and Las Vegas
(1987-2004)
Oahu (median prices)
Las Vegas (Case-Shiller)
400
200
100
U.S. recessions shaded
50
80
85
90
95
00
05
10
Slide copyright 2012, TZ Economics
Source: Standard & Poor’s, Honolulu Board of Realtors, TZE database; seasonal adjustment using Census X-12 ARIMA
filter and regressions by TZE
65
Bubblicious housing markets like Phoenix
will have to re-establish long-term trend
S&P Case-Shiller home price index
January 2000 = 100.0 (log scale)
No longer log-linear
200
Bubblicious
?
100
Overshoot
Oscillation
50
U.S. recessions shaded
1995
2000
2005
2010
Slide copyright 2012, TZ Economics
Source: Standard & Poor’s (http://www.standardandpoors.com/indices/sp-case-shiller-home-priceindices/en/us/?indexId=spusa-cashpidff--p-us----); seasonal adjustment by TZE
66
Some longer-term trajectories probably displaced,
but largely unbroken (Hawaii vs. Iowa)
Index, 1985Q1 = 100, s.a., log scale
800
Hawaii 1983-2011*
Hawaii
Iowa
400
Iowa 1988-2006
200
Geographic constraints and
regulatory constraints are why
Hawaii has cycles of large
amplitude and Iowa doesn’t:
restricting development only
increases the amplitude of the
home price valuation cycle
100
50
75
80
85
90
95
00
05
10
15
*Regressions of the natural logarithm of individual state FHFA indexes on a time trend
Slide copyright 2012, TZ Economics
Sources: Federal Housing Finance Agency (http://www.fhfa.gov/Default.aspx?Page=87) from sales prices and
appraisals; seasonal adjustment, index rebasing and regression by TZ Economics
67
Long-term median home price movements: despite
idiosyncrasies, arbitrage constrains co-movement
U.S. recessions shaded
Thousand dollars, s.a., logs
800
400
Japan
Bubble
200
Maui
Oahu
Orange County, CA
100
50
75
80
85
90
95
00
05
10
Slide copyright 2012, TZ Economics
Source: Realtors Association of Maui, Honolulu Board of Realtors, Prudential Locations, National Association of Realtors,
TZE database; seasonal adjustment using Census X-12 ARIMA filter by TZE
68
Finally: lessons from “higher-order moments”
ƒ The distribution of home prices tells a lot more than one might think.
ƒ A normal, “bell-shaped” home price distribution would be symmetric,
characterized sufficiently by its first and second moments, the mean and the
standard deviation—the mean and median would be equal (symmetry).
ƒ Actual home prices have an asymmetric distribution—”tilted”—with most
transactions clustered around the median price (at which half the transactions are
above and below) and a small number of high-end transactions in a long “tail.”
ƒ The “width” or dispersion of the distribution is characterized by its second
moment, the standard deviation (graphed relative to the mean).
ƒ The “tilt” of the distribution is characterized by its third moment, its skewness
ƒ The density in the “tail” of the distribution is characterized by the fourth moment,
kurtosis.
ƒ Home prices are typically skewed, with a long tail (so-called “leptokurtosis”)
ƒ Some evidence (e.g. Kauai) that higher-order moments—skewness, kurtosis—
rise in the transition from stable to robust home price upward movement.
Slide copyright 2012, TZ Economics
69
From the Kauai single-family data set:
measures of central tendency, including the first moment of
the distribution of existing home sales prices
Thousand dollars, log scale
Recessions shaded
Mean (first moment)
Median
800
400
200
90
95
00
05
10
15
Slide copyright 2012, TZ Economics
70
Source: Hawaii Information Service, West Hawaii and Hawaii Island Boards of Realtors; moment metrics calculated by TZE
From the West Hawaii (Kona) single-family data set:
measures of central tendency, including the first moment of
the distribution of existing home sales prices
Thousand dollars, log scale
800
Mean (first moment)
Median
400
200
Recessions shaded
90
95
00
05
10
Slide copyright 2012, TZ Economics
71
Source: Hawaii Information Service, Kauai Board of Realtors; moment metrics calculated by TZE
From the Maui single-family data set:
measures of central tendency, including the first moment of
the distribution of existing home sales prices
Thousand dollars, log scale
800
Mean (first moment)
Median
400
200
Recessions shaded
1990
1995
2000
2005
2010
2015
Slide copyright 2012, TZ Economics
72
Source: Realtors Association of Maui, moment metrics calculated by TZE
From the West Hawaii (Kona) single-family data set:
higher order moments of the distribution of prices—standard
deviation (second), skewness (third) and kurtosis (fourth)
16
S.D./mean (x100) (right)
Skewness (left scale)
Kurtosis (right scale)
Recessions shaded
14
350
300
12
250
10
200
8
150
6
100
4
50
2
0
90
95
00
05
10
Slide copyright 2012, TZ Economics
73
Source: Hawaii Information Service, West Hawaii and Hawaii Island Boards of Realtors; moment metrics calculated by TZE
From the Kauai single-family data set:
higher order moments of the distribution of prices—standard
deviation (second), skewness (third) and kurtosis (fourth)
16
14
350
S.D./mean (x100) (right scale)
Skewness (left scale)
Kurtosis (right scale)
12
300
250
Recessions shaded
10
200
8
150
6
100
4
50
2
0
90
95
00
05
10
15
Slide copyright 2012, TZ Economics
74
Source: Hawaii Information Service, Kauai Board of Realtors; moment metrics calculated by TZE
From the Maui single-family data set:
higher order moments of the distribution of prices—standard
deviation (second), skewness (third) and kurtosis (fourth)
14
12
S.D./mean (x100)(right)
Skewness (left scale)
Kurtosis (right scale)
240
200
10
160
8
120
6
80
4
40
Recessions shaded
2
1995
2000
2005
2010
0
2015
Slide copyright 2012, TZ Economics
75
Source: Realtors Association of Maui, moment metrics calculated by TZE
October 26, 2012
ƒ The same cyclical factors interacting with long-term trends, offshore arbitrage,
and onshore macroeconomic fundamentals mattered in 2005 as in 2012
ƒ Different proportions, different mix, does not change the trend-cycle phenomenon
ƒ Hawaii’s home valuation cycle is rooted in microeconomic constraints on supply
ƒ Macroeconomics drives cyclical timing (frequency) and magnitude (amplitude)
ƒ In 2005 Kauai, Maui had reached points beyond which excessive bubbliciousness
set the stage for more severe overshooting and oscillation, subsequently
ƒ Maui was not Phoenix, but it also was not Oahu—better than one, not the other
ƒ End of last housing cycle is reasonably on track to fulfill the expectation that,
broadly-speaking, the 20-teens will exhibit Maui house price appreciation
ƒ If your 12-year old child thinks he or she is a teenager, then October 26, 2012
makes perfect sense as to when the next valuation cycle should emerge
ƒ Don’t wait until 2018 to “have that conversation”
Slide copyright 2012, TZ Economics
76
Mahalo!
Aloha!
Paul H. Brewbaker, Ph.D.
Principal, Economist
TZ Economics
606 Ululani St.
Kailua, Hawaii 9673496734-4430
[email protected]
Slide copyright 2012, TZ Economics
77