October 26, 2012 - REALTORS® Association of Maui, Inc.

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 

• In August 2005 I spoke to the Kauai Board of Realtors 

• I had all new data courtesy of Hawaii Information Service, their MLS provider 

• I get just as good Maui data from Terry Tolman at RAM—good enough to dig deep 

• 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 

• Oops! 

• Angry Realtor® ladies harangued me from the front row, bracelets jangling: 

“but when, when will prices start rising again?!?” 

• To quiet them I said: October 26, 2012 ...and they all put it in their Palm Treos 

• Oops. 

Slide copyright 2012, TZ Economics 

1

Oahu, Maui single-family median sales prices: at 

quarterly frequency, recovery appears in the last year 

800 

Quarterly, 000$, s.a. (log scale) 

400 

Maui 

Oahu 

Recessions shaded 

200 

2000 2002 2004 2006 2008 2010 2012 

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 

800 

Monthly, 000$, s.a. (log scale) 

400 


Oahu 


200 

2000 2002 2004 2006 2008 2010 2012 

Sources: 


Realtors Association of Maui, Honolulu Board of Realtors; seasonal adjustment and trend extraction by TZE 

3

Maui economic data 


4

Maui non-agricultural payroll employment (jobs) 

75 

Monthly, 000, s.a. (log scale) 

70 

65 

60 

55 

50 

45 

90 95 00 05 10 


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 

.04 

The Great 

Recession 

(cluster) 

.03 

.02 

.01 

.00 

90 95 00 05 10 


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) 

200 

180 


80 

160 

40 

140 

120 

9/11 

Aloha 

Aloha 

20 

90 95 00 05 10 

Domestic 

90 95 00 05 10 

International 


Sources: Hawaii Tourism Authority, Hawaii DBEDT; seasonal adjustment by TZ Economics 

7

Maui monthly visitor arrivals, seasonally-adjusted 

adjusted 

220 


Thousands (log scale) 

200 

180 

160 

Aloha 

Aloha 

140 

9/11 

00 02 04 06 08 10 12 


Sources: Hawaii Tourism Authority, Hawaii DBEDT; seasonal adjustment by TZ Economics 

8

Maui monthly visitor arrivals and payroll 

employment exhibit strong co-movement 


76 

72 

68 

64 

60 

Visitor arrivals (right scale) 

Non-ag jobs (left) 


9/11 

Jobs 

Aloha 

Aloha 

Arrivals 

240 

220 

200 

180 

160 

140 


56 

00 02 04 06 08 10 12 


Sources: Hawaii DLIR, Tourism Authority, Hawaii DBEDT; seasonal adjustment by TZ Economics 

9

Maui nonagricultural payroll employment 


Private (right scale) 

Public (left) 

10.5 

10.0 

9.5 

9.0 

8.5 

Recession shaded 

64 

60 

56 

52 


8.0 

02 03 04 05 06 07 08 09 10 11 12 


Sources: Hawaii DLIR, Hawaii DBEDT; seasonal adjustment by TZ Economics 

10

Maui unemployment rate 

10 

8 


Aloha 

Aloha 

Percent, s.a. 

6 

4 

9/11 

2 

0 

00 02 04 06 08 10 12 


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 


Percent, s.a. 

10 

8 

6 

4 

U.S. average 

Neighbor Islands 

Oahu 

Neighbor Isles 

like mainland 

2 

0 

Neighbor Isles 

like Oahu 

00 02 04 06 08 10 12 


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 

100 


Million 2011 dollars, log scale 

10 

BOH data 

DBEDT data 

1 

75 80 85 90 95 00 05 10 

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 

100 

10 

BOH data 

DBEDT data 

1 

75 80 85 90 95 00 05 10 

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 

000 units, s.a., log scale 

1.0 

0.1 

Oahu, Hawaii, Kauai (right scale) 

Maui units (left scale) 



4 

3 

2 

1 

0 

000 units, s.a., log scale 

80 85 90 95 00 05 10 15 


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) 

160 

80 

40 

Condominium 

Single-family 

00 02 04 06 08 10 12 


Sources: 

Realtors Association of Maui, Hawaii DBEDT; seasonal adjustment by TZE 

16

Maui existing home sales volumes 

1000 


Quarterly units, s.a. (log scale) 

100 

Condominium 

Single-family 

10 

80 85 90 95 00 05 10 

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) 

400 

200 

100 

Single-family 

Condominium 

95 00 05 10 

Sources: 


Realtors Association of Maui, Hawaii DBEDT, UHERO, Prudential Locations; seasonal adjustment by TZE 

18

Mythbusters: : the jobless [sic] recovery 


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 

Thousand jobs, s.a. (log scale) 

640 

620 

600 

580 

560 

540 

520 

P T 

Jobless 

recovery 

2001-2003 

U.S. recessions shaded; 

Vertical line is 9/11 

P T 

Up: the new down 

2010-2012 

U.S. (right scale) 

Hawaii (left) 

00 02 04 06 08 10 12 

140 

136 

132 

128 

124 

Million jobs, s.a. (log scale) 


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 

Monthly jobs in 000 (s.a.) log scale 

175 

170 

165 

160 

155 

150 

145 

U.S. recession shaded 

Neighbor 

Isles 

−10% 

P T 

Oahu (right scale) 

Neighbor Isles (left) 

Oahu −5.5% 

460 

450 

440 

430 

420 

410 

400 

Monthly jobs in 000 (s.a.) log scale 

05 06 07 08 09 10 11 12 


Sources: Hawaii DLIR, Hawaii DBEDT; seasonal adjustment by TZ Economics 

22

Index of real GDP: peak quarter = 1.000 

U.S. real GDP indexed to cyclical peaks: 

recession ended 3 years ago 

1.100Previous 10 recessions* 

1.050 

1.000 

0.950 

0.900 

Great Recession 2008-09 

2007Q4 

Recession 

Recovery 

2012Q1P 

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 


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 

5 

Percent change and 

geometric mean 

5 

4 

2012 forecast: 2.37% 

4 

2013 forecast: 2.68%* 

3 

3 

2 

2 

1 

1 

0 

0 5 10 15 20 

0 

0 5 10 15 20 

2012 2013 


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 

P T 

Percent change, quarterly, annualized 

4 

0 

-4 

-8 


Actual 

NABE May 2012 

Top five forecasts 

Bottom five forecasts 

06 07 08 09 10 11 12 13 14 


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 

Central tendencies and ranges FOMC meeting 

participants’ projections for real GDP growth 

rate 2012-14 and over the longer run 

Percent Percent 


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 


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) 

10 

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) 

8 

6 

Percent 

4 

2 


0 

89 90 91 92 93 94 95 96 

After the 1990-91 (Gulf War) recession 


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 

Percent 

6 

5 

4 

3 

2 

1 

0 


98 99 00 01 02 03 04 05 

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 

After the 2001 (dot.com) recession 


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 

Percent 

6 

5 

4 

3 


QE1 

QE21 

Operation Twist 

Now the Federal Open 

Market Committee 

(FOMC) tells you their 

short-term forecast, 

longer-term steadystate 

equilibrium 

expectations and 

policy goals 

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 


Source: Federal Reserve Board (http://www.federalreserve.gov/releases/h15/current/; 

http://www.federalreserve.gov/monetarypolicy/files/fomcminutes20120125.pdf) 

30

Year-end 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 

LR: 4.105% 

3.0 

3.0 

3.0 

3.0 

2.0 

2.0 

2.0 

2.0 

1.0 

2012: 0.303% 

1.0 

2013: 0.513% 

1.0 

2014: 1.105% 

1.0 

0.0 

0 5 10 15 

0.0 

0 5 10 15 

0.0 

0 5 10 15 

0.0 

0 5 10 15 

Number of forecasts for each rate level in 25 basis point increments (bar length), 

and means 


Source: Federal Reserve Board (http://www.federalreserve.gov/monetarypolicy/files/fomcminutes20120125.pdf) 

31

Nominal and real U.S. Treasury yields 

5 

Jul 06 

4 

Jul 07 

Jul 08 

3 

2 

Jul 2007 

Jul 2006 

3 

Jul 09 

Jul 10 

1 

Jul 2009 

Jul 2008 

2 

1 

Jul 11 

June 2012 

0 

-1 

Jul 2010 

Jul 2011 


0 

FF 

1-yr 

2-yr 

3-yr 

5-yr 

7-yr 

10-yr 

20-yr 

30-yr 

-2 

FF 

1-yr 

2-yr 

3-yr 

5-yr 

7-yr 

10-yr 

20-yr 

30-yr 

U.S. Treasury yield curve 

Treasury inflation-protected securities 

(TIPS) yield curve 


32

Long-term inflation expectations: difference 

between nominal and real U.S. Treasury yields 

3 

Percentage points, difference 

between nominal U.S. Treasury 

yields and real (TIPS) yields 

2 

1 

0 

-1 

Jul 06/07/08/11 


Jul 09/10 

Feb 2009 

Nov 2008 

2.0% PCE 

deflator goal 

1.2-1.7% PCE 

deflator 2012 

FOMC forecast 

central tendency 

-2 

FF 

1-yr 

2-yr 

3-yr 

5-yr 

7-yr 

10-yr 

20-yr 

30-yr 


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? 

dip? 


34

Spring (May) 2011: double-dip dip headlines keep 

double-dip dip recession fears alive and investors scared 


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” 

P t 

Sound effect: 

“boy-yoy-yoy-yoy-yoing” 

P 0 

P T 

t 0 

Time 

“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.” 

Simulated asset price dynamics in 

a logistic growth model 


Sources: Darrell Duffie, “Asset Price Dynamics with Slow-Moving Capital” Journal of Finance vol. 65 no. 4 (August 2010) pp 1239- 

1267 (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 

1400 

1941-43 = 10 (log scale) 

1300 

1200 

1100 

Supercommittee FAIL 

Trading range prior to 

the August 3, 2011 

federal debt ceiling 

“deadline” 

1000 

Overshooting + oscillation 

after Aug. 3, 2011* FAIL of 

Congress to commit to 

long-term deficit reduction 

11:1 11:2 11:3 11:4 12:1 12:2 

*Federal debt ceiling “deadline” 


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 

P T 

January 2000 = 100, s.a., log scale 

200 

100 

Denver 

Los Angeles 

San Diego 

San Francisco 

Denver (relatively stable) 

Phoenix 

Las Vegas 


2006 2007 2008 2009 2010 2011 2012 


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?” 

Quarterly, thousand $, s.a. (log scale) 

900 

800 

700 

600 

500 

400 

300 


P T 

Oahu (Honolulu) its own private Idaho 

San Jose, Sunnyvale, Santa Clara 

Anaheim, Santa Ana, Irvine 

San Francisco, Oakland, Fremont 

San Diego, Carlsbad, San Marcos 

Los Angeles, Long Beach, Santa Ana 

05 06 07 08 09 10 11 12 13 


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 

relatively—of 

Hawaii Islands and many mainland urban markets 

800 

700 

600 

Oahu 

800 

700 

600 

Orange Co., CA 

500 

500 

400 

800 

05 06 07 08 09 10 11 12 

400 

800 

05 06 07 08 09 10 11 12 

700 

700 

600 

500 


600 

500 

Kauai 

400 

05 06 07 08 09 10 11 12 


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 

400 

05 06 07 08 09 10 11 12 

40

Oahu median single-family home prices returning to 

the upper end of “the zone,” 2005-2012 

2012 

700 

600 

Thousand dollars, log scale 

500 

400 

300 

92 94 96 98 00 02 04 06 08 10 12 


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 

700 

600 

500 

400 



Kauai 

Kauai* 


05 06 07 08 09 10 11 12 13 

*April-May 2012 


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 

• “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) 

• 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 

• 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 


43

Trade-offs, 1976-2011, between Hawaii inflation and 

unemployment (and Fed’s s target inflation zone) 

Hawaii’s 

Phillips Curve 

12 

8 

With oil and other supply shocks (1974, 1979), 

and high “unanchored” inflation expectations 

Inflation (%) 

4 

FOMC goal: 2% 

PCE inflation 

0 

Trade-off 1983-1998 

-4 

Forecast 

Trade-off since 1998 

0 2 4 6 8 10 

Unemployment (%) 


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 

Inflation (%) 

pˆ 

t 

8 

6 

2006 

Assumed long-term nonaccelerating 

inflation 

equilibrium: 

* 

( pˆ 

) 

* t , ut 

4 

2007 

2008 

2011 

* 

ˆ t p 

2 

2010 

Post-recession petroleum 

inflation, financial frictions 

(Hawaii Clean Energy Policy?) 

0 

2009 

-2 

1998 

1 2 3 4 5 6 7 

Unemployment (%) 

* 

u t 

u t 


Source: Bureau of Labor Statistics, US Department of Labor; calculations by TZE 

45

Low-sulfur petroleum price benchmark: 

North Sea (Europe) Brent crude oil 

160 

P T 

Monthly, dollars/barrel (log scale) 

80 

40 


05 06 07 08 09 10 11 12 


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 

15 

Real room rate changes (% y-o-y) 

10 

5 

0 

-5 

-10 

-15 

.10 .15 .20 .25 .30 .35 .40 .45 

Think of it as a trade-off between 

appreciation (in real room rates) 

and unemployment (of hotel rooms) 

Hotel vacancy rate (%) 


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 

15 

Realroom ratechanges(%y-o-y) 

10 

end-2011 

5 

0 

-5 

-10 

2009Q3 

-15 

.10 .15 .20 .25 .30 .35 .40 .45 

Hotel vacancy rate (%) 


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 

The Sklarz Curve 

% change P(single-family) 

40 

30 

20 

10 

0 

-10 

Again, think of it as a trade-off 

between inflation (in home prices) 

and unemployment (of houses) 

Shaded intersection: 2012 forecast 

-20 

0 4 8 12 16 20 24 

Months of inventory (-5) 


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 

%change P(single-family) 

40 

30 

20 

10 

0 

-10 

Spring 2012 

Summer 2009 

Brewbaker says the 

recession is over? 

-20 

0 4 8 12 16 20 24 

Summer 2011 

“Double dip” fetishism 

Months of inventory (-5) 


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 

40 

%chng P(single-family) y-o-y 

30 

20 

10 

0 

-10 

Single-family 

%chng P(condominium) y-o-y 

30 

20 

10 

0 

-10 

Condominium 

-20 

0 4 8 12 16 

-20 

0 4 8 12 16 

Months of inventory remaining (-5) 

Months of inventory remaining (-5) 


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 


180 

160 

Days 

140 

120 

100 

80 

2000 2002 2004 2006 2008 2010 2012 


Source: Realtors Association of Maui 

52

Maui SF days on market seasonality (right) 

and underlying trend (left); data through June 2012 

200 

180 

1.2 

160 

1.1 

140 

120 

1.0 

100 

0.9 

80 

2000 2002 2004 2006 2008 2010 2012 

Maui single-family days on market (s.a.) 

and underlying trend through April 

00 01 02 03 04 05 06 07 08 09 10 11 12 

Maui single-family days on market 

seasonal adjustment factors (avg. = 1) 


Source: Realtors Association of Maui; seasonal adjustment by TZE 

53

Sklarz Curve, RAM version: use “days on market” 

60 

Single-family pricechange (%) 

40 

20 

0 

-20 

Prices rising 

Prices falling 

-40 

80 100 120 140 160 180 200 

Days on market (trend) (-5 months) 


Source: Realtors Association of Maui; regression by TZE 

54

Toxicity: learning from mortgage delinquency 

• The Great Recession, concluding the sub-prime mortgage lending-driven housing 

“bubble” that preceded it, had legacy of wide geographic delinquency variation 

• Great Recession notable for anomalous spike in serious delinquency 

• Setting up a comparison: house price movements, delinquency changes 


55

U.S. delinquency rate on single-family residential 

mortgages, domestic offices of commercial banks 

12 


10 

Percent, s.a. 

8 

6 

4 

2 

0 

1995 2000 2005 2010 


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 

14 

Percent of loans, at year end 

12 

10 

8 

6 

4 

2 


Big Island 

Kauai 


Oahu 

0 

2000 2002 2004 2006 2008 2010 2012 


Source: Federal Reserve Bank of New York (http://data.newyorkfed.org/creditconditions/); graphic by TZE 

57

Hawaii mortgage delinquency rates 

4 

Percent of mortgages outstanding 

3 

2 

1 

U.S. recessions shaded 

30-59 days delinquent 

90+ days delinquent 

90+ 

30+ 

“Sub-prime” 

financial crisis 

notable for sharp 

increase in 

longer-term 

mortgage 

delinquency 

rates 

0 

1980 1985 1990 1995 2000 2005 2010 


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 Dade, FL 

18.3 Broward, FL 

15.9 Palm Beach, FL 

15.6 Clark, NV 

13.6 Riverside, CA 

13.2 San Joaquin 

12.8 San Bernadino, CA 

11.4 Bronx, NY 

10.1 Maricopa, AZ 

9.4 Sacramento, CA 

9.1 Contra Costa, CA 

8.8 Los Angeles, CA 

8.2 Hawaii, HI 

7.6 Maui, HI 

7.6 San Diego, HI 

6.9 Orange, CA 

5.9 Santa Clara, CA 

5.3 U.S. average 

4.5 Kauai, HI 

3.7 San Francisco, CA 

3.4 Honolulu, HI 

1.8 Dane, WI 

1.0 Cherry, NE 

0.0 Todd, SD 


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 

0.202 0.294 

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 0.30 

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 

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 

Sources: Realty Trac (http://www.realtytrac.com/trendcenter/), data for May 2012; graphic by TZE 

0.00 0.10 0.20 0.30 


60

Using trend data: the inverse relationship between 

changes in home prices and delinquency rates 

appears cyclically in diagonal, flattened orbits 

Percent change in single-family prices 

25 

20 

15 

10 

5 

0 

-5 

-10 

2004 

1987 

1980s 

2012Q1 

1992 

1997 

2010 

1 2 3 4 5 6 7 

Delinquent as percent of all loans 

Prices rising 

Prices falling 


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 

• Rising prices are associated with falling delinquencies (up to lags, etc.) 

• In cyclical markets, rising phase of house price cycle has similar inverse 

relationship with falling phase of mortgage delinquency cycle (up to lags) 

• 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 

• Complex nonlinearity actually masks underlying orbit—embedding inverse 

relationship—in delinquency-price change space 


62

Trends, cycles and bubbles 

• We’ve got all three—simultaneously 

• Arbitrage and factor mobility (capital, labor, technology) implies convergence to 

common trend in long-run home price appreciation 

• Cycles are not bubbles, and presence of the former is neither necessary nor 

sufficient for the latter 

• Hawaii has had more cyclical housing valuations since early-statehood (1960s) 

• Many microeconomic factors distinguish regional performance differences even in 

shared macroeconomic environment 

• Looking for signs of cyclical upswing, indications of its imminence 

• Remember: Hawaii peak for sales 2005, peak for values 2006—six years ago 


63

Average Oahu single-family and condominium 

existing home sales prices 


1000 

100 

Single-family 

Condominium 

10 


1960 1970 1980 1990 2000 2010 2020 


Sources: Honolulu Board of Realtors; TZ Economics 

64

Remarkably, until the sub-primehousing 

bubble, 

longer-term trends were roughly equivalent 

Indexes, s.a. (1987 = 100), logs 

400 

200 

100 

Oahu (median prices) 

Las Vegas (Case-Shiller) 

Regressions of the 

natural logarithm of the 

two price indexes on a 

constant and a time 

trend, Oahu (1980- 

2011) and Las Vegas 

(1987-2004) 


50 

80 85 90 95 00 05 10 


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 establish long-term trend 

No longer log-linear 

S&P Case-Shiller home price index 

January 2000 = 100.0 (log scale) 

200 

100 

Bubblicious 

Overshoot 

Oscillation 

? 

50 


1995 2000 2005 2010 


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) 

800 

Index, 1985Q1 = 100, s.a., log scale 

400 

200 

100 

Hawaii 

Iowa 

Hawaii 1983-2011* 

Iowa 1988-2006 

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 

50 

75 80 85 90 95 00 05 10 15 

*Regressions of the natural logarithm of individual state FHFA indexes on a time trend 

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 

800 


Thousand dollars, s.a., logs 

400 

200 

100 

Japan 

Bubble 


Oahu 

Orange County, CA 

50 

75 80 85 90 95 00 05 10 


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” 

• The distribution of home prices tells a lot more than one might think. 

• 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). 

• 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.” 

• The “width” or dispersion of the distribution is characterized by its second 

moment, the standard deviation (graphed relative to the mean). 

• The “tilt” of the distribution is characterized by its third moment, its skewness 

• The density in the “tail” of the distribution is characterized by the fourth moment, 

kurtosis. 

• Home prices are typically skewed, with a long tail (so-called “leptokurtosis”) 

• Some evidence (e.g. Kauai) that higher-order moments—skewness, kurtosis— 

rise in the transition from stable to robust home price upward movement. 


69

From the Kauai single-family data set: 

measures of central tendency, including the first moment of 

the distribution of existing home sales prices 



800 

400 

200 

Mean (first moment) 

Median 

90 95 00 05 10 15 


Source: Hawaii Information Service, West Hawaii and Hawaii Island Boards of Realtors; moment metrics calculated by TZE 

70

From the West Hawaii (Kona) single-family data set: 




800 

400 

200 


Median 


90 95 00 05 10 


Source: Hawaii Information Service, Kauai Board of Realtors; moment metrics calculated by TZE 

71

From the Maui single-family data set: 




800 

400 

200 


Median 


1990 1995 2000 2005 2010 2015 


Source: Realtors Association of Maui, moment metrics calculated by TZE 

72

From the West Hawaii (Kona) single-family data set: 

higher order moments of the distribution of prices—standard 

standard 

deviation (second), skewness (third) and kurtosis (fourth) 

16 

14 


S.D./mean (x100) (right) 

Skewness (left scale) 

Kurtosis (right scale) 

350 

300 

12 

250 

10 

200 

8 

150 

6 

100 

4 

50 

2 

90 95 00 05 10 

0 


Source: Hawaii Information Service, West Hawaii and Hawaii Island Boards of Realtors; moment metrics calculated by TZE 

73

From the Kauai single-family data set: 


standard 


16 

14 

12 

S.D./mean (x100) (right scale) 




350 

300 

250 

10 

8 

6 

4 

200 

150 

100 

50 

2 

0 

90 95 00 05 10 15 


Source: Hawaii Information Service, Kauai Board of Realtors; moment metrics calculated by TZE 

74

From the Maui single-family data set: 


standard 


14 

12 

S.D./mean (x100)(right) 



240 

200 

10 

160 

8 

120 

6 

80 

4 

40 

2 


0 

1995 2000 2005 2010 2015 


Source: Realtors Association of Maui, moment metrics calculated by TZE 

75

October 26, 2012 

• The same cyclical factors interacting with long-term trends, offshore arbitrage, 

and onshore macroeconomic fundamentals mattered in 2005 as in 2012 

• Different proportions, different mix, does not change the trend-cycle phenomenon 

• Hawaii’s home valuation cycle is rooted in microeconomic constraints on supply 

• Macroeconomics drives cyclical timing (frequency) and magnitude (amplitude) 

• In 2005 Kauai, Maui had reached points beyond which excessive bubbliciousness 

set the stage for more severe overshooting and oscillation, subsequently 

• Maui was not Phoenix, but it also was not Oahu—better than one, not the other 

• 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 

• 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 

• Don’t wait until 2018 to “have that conversation” 


76

Mahalo! 

Aloha! 

Paul H. Brewbaker, Ph.D. 

Principal, Economist 

TZ Economics 

606 Ululani St. 

Kailua, Hawaii 96734-4430 

4430 

paulbrewbaker@tzeconomics.com 


77

October 26, 2012 - REALTORS® Association of Maui, Inc.

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