125 page Presentation - REALTORS® Association of Maui, Inc.

Prepared for the 

Realtors Association of Maui 

July 8, 2011 

by Paul H. Brewbaker, 

Principal, TZ Economics 

Maui Housing: Double-Dip? 

Dip? 

Single-dip, and this is IT! 

Copyright 2011 

Paul H. Brewbaker, Ph.D.

Where is Hawaii in the cycle? 

Slide copyright 2011, TZ Economics 

1

Extracting the underlying trend from seasonally- 

adjusted data clearly reveals recession/recovery cycle 

Monthly, in million $, s.a., (log scale) 

140 

120 

100 

80 

GE tax (right scale) 

WH on wages (left) 

U.S. recessions shaded 

200 

100 

Monthly, in million $, s.a., (log scale) 

00 02 04 06 08 10 


Sources: Hawaii DoTAX, DBEDT; smoothing using Hodrick-Prescott filter by TZE 

2

Extracting the signal from the noise (through May): 

Hawaii general fund revenue is in cyclical recovery* 

Monthly in million $, s.a., log scale 

400 

300 

200 

*So stop acting like it isn’t 

02 03 04 05 06 07 08 09 10 11 12 


Sources: Hawaii DBEDT, DLIR; seasonal adjustment using Census X-12 ARIMA filter and Hodrick-Prescott filter extraction 

of private employment trend by TZE 

3

Home price indexes: long-term housing returns are 

equated by arbitrage, but supply constraints 

amplify Hawaii’s s valuation cycle 

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

400 

200 

100 

50 

Hawaii 

Iowa 

Colorado 


80 85 90 95 00 05 10 15 

Restricting development only 

increases the amplitude of 

the home price valuation 

cycle and makes life better for 

high net-worth real estate 

investors who can carry 

wealth through the trough 


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 

4

The cycle in production (homebuilding)—new new units—Hawaii 

and U.S.; is there such a glut of housing in Hawaii? 

4.0 

4.0 

Quarterly, thousand units, s.a., log scale 

2.0 

1.0 

0.5 

Hawaii 

U.S. 

2.0 

1.0 

0.5 

80 85 90 95 00 05 10 15 

Quarterly, million units, s.a.a.r., log scale 


Sources: Federal Reserve Bank of St. Louis (http://research.stlouisfed.org/fred2/series/HOUST/downloaddata?cid=32302), 

Hawaii DBEDT, County Building Departments; seasonal adjustment and trend extract of Hawaii data by TZE 

5

Make the appropriate macroeconomic comparisons 

across space and time: what stands out? 

• This is not the mainland (duh) so don’t act as if everything that happens 

there matters here 

• Which is more relevant to Hawaii: the NBC Nightly News (KHNL TV) or 

the news from NHK Tokyo (Hawaii Public Television)? Which gets more 

viewers? 

• Hawaii’s economic experience is dichotomous: Oahu and the Neighbor 

Islands have had different Great Recession outcomes 


6

Real Hawaii personal income indexed to last three 

cyclical peaks: this recession was not Hawaii’s s worst 

110 

P T 

Index, cyclical peak = 100 (log scale) 

105 

100 

95 

90 

Peak 1980 

Peak 1993 

Peak 2007 

US peak(07) 

Great Recession 

(t − 2) (t − 1) t 0 

(t + 1) (t + 2) 

Years 

Current 

recovery 

so-so 


Sources: 

BEA, BLS ; deflation and indexation by TZE 

7

Real personal income by county (indexed to 2007 

level—no 2010 data yet) Neighbor Islands hurting 

102 

Index, 2007 = 100, log scale 

100 

98 

96 

94 

Maui 

OAHU 

U.S. 

HAWAII 

KAUAI 

MAUI 

92 

05 06 07 08 09 10 

Sources: 

BEA, BLS ; deflation and indexation by TZE 


8

Hawaii private sector jobs: recovery is proceeding… 

520 

P T 

Thousands, s.a., log scale 

510 

500 

490 

480 

470 

460 

2.7% Quarterly 

annualized growth 

of trend component 

(actual data: 2.6%) 

2.4% Semiannual 

annualized growth 

of trend component 

(actual data: 2.7%) 

450 

U.S. recession shaded 

04 05 06 07 08 09 10 11 




9

…but only on Oahu: total private + public jobs 

⇒ Neighbor Islands are not participating 

Oahu (right scale) 

180 

Neighbor Isle (left) 

P T 

460 


175 

170 

165 

160 

155 

450 

440 

430 

420 


150 


03 04 05 06 07 08 09 10 11 12 

410 




10

Monthly unemployment rates: 

Oahu its own private Idaho 


12 

10 

8 

6 

4 

2 

U.S. 

Neighbor Isle 

Statewide 

Oahu 

P T 

9.1% U.S. 

8.4% Neighbor Isle 

5.9% Statewide (5.6% BLS) 

4.9% Oahu 

0 


04 05 06 07 08 09 10 11 


Sources: U.S. BLS, Hawaii DBEDT, DLIR; seasonal adjustment of Hawaii data using Census X-12 ARIMA filter by TZE 

11

The business cycle varies across time and space 

• The Great Recession was the worst U.S. recession since the Great 

Depression 

• The Great Recession was not the worst Hawaii recession 

• Hawaii’s early-1980s recession(s) was (were) deeper 

• Hawaii’s early-1990s recession was longer 

• Hawaii’s dot.com recession of 2001 wasn’t 

• Hawaii’s private sector jobs recovery is underway 

• Hawaii’s public sector job los was a deliberate “9/11” event 

• By a variety of measures Oahu is in far better shape than the mainland 

and the Neighbor Island essentially are the mainland 

Waiting for labor shortages and until after there is no inventory is too long 


12

A diversion: double-dips dips in the news 


13

Yikes! 

Scary headline! 


Source: Newsmax Wires (May 31, 2011), http://www.newsmax.com/Headline/housing-economy-foreclosuresrecession/2011/05/31/id/398297 

14

On the 

home 

front 

Honolulu Star-Advertiser (July 8, 2011) 

http://www.staradvertiser.com/news/20110708_ 

Oahu_home_sales_relapse.html 


15

Logistic growth function, k = 100, r = 0.10 and r = 1.9 

120 

100 

80 

Crunchy-granola bliss 

120 

100 

80 

x(t) 

60 

x(t) 

60 

40 

40 

20 

0 

r = 0.10 

20 

r = 1.9 

0 

1 21 41 61 81 101 121 

1 21 41 61 

t (time) 

t (time) 

A population with a carrying capacity of k = 100 growing at a logistic 

growth rate of 10%, converges smoothly (left panel); a higher intrinsic 

growth rate yields more rapid, oscillatory convergence (right panel). 


16

A made-up asset price trajectory displaying oscillatory 

convergence—higher 

“intensity” implies “wobbliness” 

p(t) (thousand $) 

650 

600 

550 

500 

450 

400 

350 

Period 1 equilibrium price: $350,000 

Period n equilibrium price: $600,000* 

(for example, assume that purchasing 

power limits the median household’s 

“carrying capacity” to the latter price) 

Oahu median 

SF home prices 

300 

*Uses Microsoft Excel’s built-in graphics 

smoothing algorithm applied to a logistic 

growth function with intrinsic growth rate 

of 1.5 and carrying capacity of 600 

0 5 10 

t (years) 


17

Look familiar? Oahu single-family median home 

prices from 2003 through May 2011 

700 

Thousand $, s.a., log scale 

650 

600 

550 

500 

450 

400 

“Double-dip?” or 

oscillatory 

convergence? 

350 

03 04 05 06 07 08 09 10 11 


Sources: Honolulu Board of Realtors; seasonal adjustment using Census X-12 filter and Hodrick-Prescott filter trend 

extraction by TZ Economics 

18

More complex asset price adjustment paths: an 

overshooting model of asset price decline 

Simulated asset price dynamics in a model 

of “slow-moving capital” by Darrel Duffie 

“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.” 


Source: 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) 

19

Meltdown after 2006: overshooting plus oscillation 

in S&P Case-Shiller 

home price indexes 

280 

January 2000 = 100 s.a., log scale 

240 

200 

160 

120 

Los Angeles 

San Diego 

San Francisco 

Las Vegas 

LAX 

SAN 

SFO 

LAS 

Is this a “double-dip?” 

or is this overshooting 

followed by oscillatory 

convergence? 

This is Vegas, baby 

80 

03 04 05 06 07 08 09 10 11 


Sources: Standard & Poor’s; seasonal adjustment using Census X-12 filter by TZ Economics 

20

S&P 500 daily closing values: 

post-Lehman overshooting, 

uneven recovery 

1600 

1400 

Lehman Brothers collapses 

(September 15, 2008) 

Index, 1941-43 = 10 

1200 

1000 

800 

600 

NBER recession shaded 

(March 9, 2009) 

2007 2008 2009 2010 2011 


Sources: Standard & Poor’s, Federal Reserve Bank of St. Louis; daily closing data through June 22, 2011 

21

Intra-day S&P 500 May 6, 2010 “Flash Crash” 

time domain: intraday 

−8.5% 


Source: http://www.marketoracle.co.uk/images/2010/Jun/flash-crash-4-2.jpg 

22

Kauai visitor arrivals, hurricane Iniki etc., 

time domain: half a decade 

160 

Monthly in thousands, s.a., log scale 

80 

40 

20 

Gulf 

War 

Iniki 

Iraqnaphobia* 

9/11 90 92 94 96 98 00 02 04 


Sources: Hawaii DBEDT; Bank of Hawaii; *semantic mahalo to Diane Swonk, Mesirow Finance 

23

Hawaii international arrivals variation in monthly 

standard deviations under four exogenous shocks 

“3-6 months” “3-6 months” 

2 

Kobe 

2 

1 

0 

-1 

Gulf War 

1 

0 

-1 

SARS 

9/11 

-2 

-2 

-3 

-3 

Early-1990s: rising arrivals trend 

q 0 

q 1 

q 2 

q 3 

q 0 

q 1 

q 2 

q 3 

Early-2000s: falling arrivals trend 


Sources: Arrivals data published by HTA and DBEDT; interval standard deviation estimates relative to 4.5–year trends 

by TZ Economics 

24

Hawaii daily passenger arrivals on flights from 

Japan—difference from this year to last 

Thousands per day, net 

(2011 minus 2010) 

2 

1 

0 

-1 

-2 

-3 

Sendai seismic 

event (March 11) 

-4 

10.12 11.01 11.02 11.03 11.04 11.05 11.06 


Source: Hawaii DBEDT; calculations on daily passenger counts by TZ Economics through June 7, 2011 

25

Double-dip your home price 

• A “double-dip” in asset valuations could just be somebody’s way of 

scaring the crap out of you so you’ll “tune in tomorrow”* 

• Or it could be the norm—complexity 

• You’re surrounded by exogenous impulses 

• These shocks generate complex paths: overshooting, oscillation, many 

different convergence trajectories from one equilibrium to another 

• Asset price dynamics—for stocks, bonds, commodities, houses—all 

exhibit these movements; it’s happening right now 

*See NBC Nightly News, Friday, July 8, 2011: http://www.msnbc.msn.com/id/3032619/vp/43691544#43691544 

(fast-forward to 1:11 in the clip) 


26

Two decades of Oahu home prices: under the cycle 

lie varying forcing factors and complex dynamics 

• Long-term home prices gravitate to an appreciate trend dictated by longrun 

returns in alternative asset classes (via arbitrage—capital and 

occupants are both mobile) 

• Each valuation cycle has its own character; each cycle is associated 

with a particular asset price adjustment path from one equilibrium 

“before the boom” to another equilibrium “after the boom” 

• A variety of complex trajectories are possible, in the same market in 

different cycles, or in different markets in the same cycle (even when all 

markets face the same constellation of macroeconomic drivers) 


27

Long run Oahu SF home prices: arbitrage to risk- 

free asset return plus a risk premium and expected 

inflation premium (higher pre-1980, lower post-1980) 

1000 

Annual in thousand $, s.a. (log scale) 

100 

Know your inflation history: a trend break in 

U.S. inflationary momentum in 1980, when 

monetary policy regime change was initiated 

by Jimmy Carter’s appointment of Paul Volker 

Oahu mean SF price 

Trend pre-1980 

Trend post-1980 

1960 1970 1980 1990 2000 2010 2020 


Sources: Hawaii DBEDT, TZ Economics; regressions of natural logs of mean home prices on time trend 1958-1983 and 

1976-2010. 

28

In the 1990s, median Oahu condo and SF home 

prices fell: possible convergence paths generated 

by a logistic model (see Appendix 1) 

200 

150 

100 

50 

Calibrated to early- 

1990s Oahu condo 

price movements 

200 

150 

100 

50 

400 

350 

300 

250 

200 

Calibrated to early- 

1990s Oahu SF home 

price movements 

400 

350 

300 

250 

200 


29

The actual 1990s Oahu path: smooth compression 

220 

200 

180 

160 

140 

120 

Thousand $, s,a, (log scale) 

Thousand $, s,a, (log scale) 

400 

380 

360 

340 

320 

300 

280 

100 

End of U.S. recession shaded 

91 92 93 94 95 96 97 98 99 00 

260 

End of U.S. recession shaded 

91 92 93 94 95 96 97 98 99 00 

Median condo price 

Median single-family home price 


Sources: Honolulu Board of Realtors, Standard & Poor’s; seasonal adjustment using Census X-12 ARIMA filter and trend 

extraction using Hodrick-Prescott filter by TZE 

30

Median Oahu home prices: mid-1990s sag, 

2000s overshoot and oscillation 

700 

Thousand $, s.a. (log scale) 

400 

200 

Single-family (right scale) 

Condominium (left) 

600 

500 

400 

300 

200 



100 

90 95 00 05 10 


Sources: Harvey Shapiro, Honolulu Board of Realtors; recession dates are NBER cyclical turning points (peak-trough); 

seasonal adjustment by TZE using Census X-12 ARIMA filter 

31

Median Oahu home prices: mid-1990s sag, 

2000s overshoot and oscillation 

700 


400 

200 

After the 1994 Fed 

tightening: smooth 

convergence to lower 

equilibrium prices 

Late in the 2000s housing 

“bubble:” overshooting, 

oscillatory convergence 

600 

500 

400 

300 


100 

90 95 00 05 10 



seasonal adjustment by TZE using Census X-12 ARIMA filter 

32

This cycle’s s median existing single-family home 

prices: “double-dip” or overshooting + oscillation? 

900 

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

800 

700 

600 

500 

400 

300 


2005 2006 2007 2008 2009 2010 2011 

San Jose, Sunnyvale, Santa Clara 

Oahu 

Anaheim, Santa Ana, Irvine 

San Francisco, Oakland, Fremont 


Los Angeles, Long Beach, Santa Ana 

San Diego, Carlsbad, San Marcos 


Sources: Honolulu Board of Realtors, Realtors Association of Maui, National Association of Realtors; seasonal 

adjustment by TZE using Census X-12 ARIMA filter 

33

All these markets faced the same interest rates, 

homebuyer tax credits; each has unique price path 

900 

800 

700 

600 

500 

Oahu 

900 

800 

700 

600 

500 

400 

400 

Orange County, CA 

800 

06 07 08 09 10 11 12 

800 

06 07 08 09 10 11 12 

700 

700 

600 

600 

San Diego 

500 


500 

400 

400 

300 

05 06 07 08 09 10 11 12 

300 

05 06 07 08 09 10 11 12 


34

Maui existing single-family home sales median prices 

now smoothly converging to the low- to mid-$400ks 

800 

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

400 

200 

1995 2000 2005 2010 2015 

Pau by 2013? By 

“October 26, 2012?” 


Sources: Realtors Association of Maui; seasonal adjustment using Census X-12 ARIMA filter and trend extraction using 

Hodrick-Prescott filter by TZE 

35

Maui existing single-family home sales price 

distribution shifted to the left since the cyclical peak 

700 

Number of sales 

in $250,000 increments 

600 

500 

400 

300 

200 

100 

2006 

2010 

0 

0.0 0.5 1.0 1.5 2.0 2.5 3.0 

Million dollars 


Sources: Realtors Association of Maui; histogram generation by TZ Economics 

36

Maui existing-home days on market: not so much a 

glut of supply* as lackluster absorption 

Lehman 

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

200 

100 

Single-family 

Condominium 

Condo 

Single-family 

Recessions shaded 

02 04 06 08 10 12 

*Even with all those #@*^& foreclosures! 


Sources: Realtors Association of Maui; seasonal adjustment using Census X-12 ARIMA filter and trend extraction using 

Hodrick-Prescott filter by TZE 

37

Maui macroeconomic indicators: insufficient “lift, lift,” 

and self-inflicted wounds (“we( 

meant to do that”) 

• The legacy of the 2008 Aloha Airlines and ATA shutdowns still is felt in 

the relative lack of scheduled air seats to Maui from the mainland, and 

reduced interisland lift (Wiki: “Stackelberg competition”)* 

• Good timing on that fiscal austerity: no public job growth to complement 

the utter lack of private job growth 

• Unemployment rates are falling partly (largely?) because the labor force 

(the denominator in the unemployment rate) is shrinking as workers 

leave Maui (of course, they also came to Maui) 

• That wacky “workforce housing” ordinance is the gift that keeps on 

giving: x percent of zero is still zero 

• Hey, anti-Superferry n00bs: great job! Big, big help on the economy 

there. You’re doing a great job protecting the environment by checking 

all that luggage that comes into Maui…what’s that? you only check the 

luggage leaving Maui? Boy, you guys must really be concerned about 

the environment. Yo: pono this. 


* http://en.wikipedia.org/wiki/Stackelberg_competition 

38

Maui total visitor arrivals through May 2011 

Monthly in thousands, s.a. (log scale) 

240 

220 

200 

180 

160 

140 

Desert 

Storm 


9/11 

Aloha 

Lehman 

Sendai + weak spring travel 

demand (higher oil prices / 

air fares) 

90 95 00 05 10 

Sources: 


Hawaii Tourism Authority, Hawaii DBEDT; seasonal adjustment using Census X-12 ARIMA filter and trend 

extraction using Hodrick-Prescott filter by TZE 

39

Maui payroll employment: no recovery yet 

Monthly in thousands, s.a. (log scale) 

75 

70 

65 

60 

55 

50 

45 


9/11 

1995 2000 2005 2010 


Sources: Hawaii DLIR, DBEDT; seasonal adjustment using Census X-12 ARIMA filter by TZE 

40

Both private and public employment 

on Maui stagnant since 2009 

Monthly 000, s.a. (log scale) 

10.4 

10.0 

9.6 

9.2 

8.8 

8.4 

8.0 

66 

64 

62 

60 

58 

56 

54 

Private (right scale) 

Public (left scale) 

Recession shaded 

03 04 05 06 07 08 09 10 11 12 

Monthly 000, s.a. (log scale) 



41

Maui unemployment rate 

Monthly, percent, s.a. 

10 

9 

8 

7 

6 

5 

4 

3 

2 


1 

90 95 00 05 10 

Decrease partly 

attributable to labor 

force exit (outmigration 

back to 

Oahu or mainland) 



42

Maui new housing units authorized by permit: 

demand-constrained or supply-constrained? 

Quarterly units, s.a. (log scale) 

1000 

100 


1,500 per 

year 

10 

75 80 85 90 95 00 05 10 

< 300 per year? 

How’s that workforce housing 

ordinance working out? 


Sources: Maui County Building Department, Hawaii DBEDT; seasonal adjustment using Census X-12 ARIMA filter and 

trend extraction using Hodrick-Prescott filter by TZE 

43

Maui total real private building permit values 

Monthly in million 2006$, s.a. (log scale) 

100 

10 

Lehman 

Back To The Future 

75 80 85 90 95 00 05 10 15 


Sources: Maui County Building Department, Hawaii DBEDT; seasonal adjustment using Census X-12 ARIMA filter and 

trend extraction using Hodrick-Prescott filter by TZE 

44

The challenges for Maui 

• Overhang of distressed properties, delinquencies with mainland 

borrowers and lenders; foreclosure logjam and idiotic Act 48* 

• Rising absorption of existing inventory driven by low valuations is 

vulnerable to incipient interest rate normalization, exogenous 

“headwinds” on investor expectations (energy shocks; geofinancial risks) 

• Supply side constraints: Maui still has that wacky housing ordinance— 

growth-management policy when there is no growth 

• Air capacity: domestic lift back to the 1990s (“No Aloha”) 

• Otherwise, macro-fundamentally: Maui unemployment in 2012 will be 

the same as it was in 1997—the beginning of the last great decade 

• Demographic challenge: that late-1990s 50-something investor is now 

70 years old and ran her last triathlon when Kimo was Maui Mayor 

(define “active lifestyle” for Baby Boomers in the 20-teens) 

• What does Maui offer for skaters, gamers, and WoW addicts? 


* In the Q&A somebody asked about Act 48. I had no answer. I looked it up. My answer: Whiskey Tango Foxtrot. 

45

Pau 


46

Appendix 1: a simple model of asset price movements 


47

An example from the bio-economics of growth with 

a capacity constraint—the the logistic growth function 

Ever hear someone say that tourism in Hawaii risks exceeding the islands’ “carrying 

capacity?” What are they talking about? Consider the logistic growth function. 

Assume the environment has a carrying capacity of “100,” a natural limit on how much 

a biological population can grow: k = 100 . Assume that the intrinsic growth rate of the 

population is the parameter r. The relationship between population next year x ( t +1) 

and today’s population x ( t) 

is: 

( t) ⎞ ⎟⎠ 

⎛ x 

∆ x ≡ [ x( t + 1 ) − x( t) 

] = rx() 

t ⎜1 

− , 

⎝ k 

If the population xt 

can be defined in continuous time, this is: 

dx ⎛ x ⎞ 

≡ = ⎜ − 

t 

x& rxt 1 ⎟ . 

dt ⎝ k ⎠ 

Now, solve for x () t , for all t , as a function of r , k , and ( 0) 

x ( x at time t = 0 ). 


48

Logistic growth function*, k = 100, r = 0.10 and r = 1.9 

120 

100 

80 

Crunchy-granola bliss 

120 

100 

80 

x(t) 

60 

x(t) 

60 

40 

40 

20 

0 

r = 0.10 

20 

r = 1.9 

0 

1 21 41 61 81 101 121 

1 21 41 61 

t (time) 

t (time) 

A population with a carrying capacity of k = 100 growing at a logistic 

growth rate of 10%, converges smoothly (left panel); a higher intrinsic 

growth rate yields more rapid, oscillatory convergence (right panel). 


*This slide is a replicate of slide 16 

49

Logistic growth function, k = 100, r = 2.1 and r = 2.8 

x(t) 

120 

100 

80 

60 

40 

20 

0 

r = 2.1 

20 

r = 2.8 

0 

1 21 41 61 81 101 121 

1 21 41 61 81 101 121 

t (time) 

t (time) 

Really high intrinsic growth rates (r) generate complexity: rapid 

convergence to k followed by bifurcation (left panel) or by chaotic 

motion (right panel)—deterministic, not stochastic movement! 

x(t) 

140 

120 

100 

80 

60 

40 


50

Appendix 2: mortgage delinquency 


51

Despite surge, Hawaii mortgage delinquency now 

subsiding as home prices prices settle 

Percent of mortgages outstanding, s.a. 

4 

3 

2 

1 

0 

Hawaii home prices (right) 

30 days past due (left) 

90 days past due (left) 


Data 

puka 

75 80 85 90 95 00 05 10 

800 

400 

200 

100 

50 

Index, 1980Q1 = 100, s.a. (log scale) 

Sources: 


Federal Housing Finance Agency, Mortgage Bankers, Association, Guy Sakamoto—Bank of Hawaii; seasonal 

adjustment by TZE 

52

Rank percent of loans 

90+ days 

past due 

Foreclosure 

inventory 

Serious delinquency 

rankings by state 

1 Florida 4.59 14.38 

2 Nevada 6.65 9.32 

3New Jersey 3.48 7.74 

4 Illinois 3.70 6.77 

5New York 3.77 5.33 

6 Arizona 4.23 4.81 

7 California 4.84 3.97 

8 Ohio 3.57 5.09 

9 Rhode Island 3.92 4.53 

10 Indiana 3.42 4.89 

11 Maryland 4.77 3.52 

12 Maine 2.78 5.48 

13 Georgia 4.64 3.43 

14 Mississippi 4.59 3.41 

15 Michigan 4.16 3.74 

16 Connecticut 3.31 4.36 

17 Delaware 2.94 4.29 

18 Hawaii 2.64 4.57 

19 South Carolina 3.15 4.04 

20 Louisiana 3.24 3.92 

21 Massachusetts 3.54 3.32 

4% 

3 

2 

1 

0 

Prices (right) 

30+ days (left) 

90+ days (left) 

Index, 1980Q1 = 100, 

s.a. (log scale) 

540 

520 

2006 2007 2008 2009 2010 2011 

Hawaii home price index and delinquency 

500 

480 

460 

440 


Sources: 

Federal Housing Finance Agency, Mortgage Bankers, Association, Guy Sakamoto—Bank of Hawaii; seasonal 

adjustment by TZE 

53

Mortgage delinquency varies widely: 

≥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/) 

54

Appendix 3: housing and monetary policy 


55

Housing and monetary policy: 

an exit strategy for the Federal Reserve? 

• That was then and this is now: one big change since the 1990s is that 

the Fed now contemporaneously announces policy shifts and articulates 

forward-looking language to help agents anticipate future policy 

• In 1994, unanticipated Fed tightening did not clarify the extent of inflation 

risk, it actually aggravated the perception of inflation risk, causing an 

increase in real long-term interest rates 

• [The subsequent 1999 tightening—post-LTCM—partly was intended to 

deflect “irrational exuberance” fueling an equity asset pricing bubble; it 

too raised real long-term interest rates] 

• The 2004 Fed tightening was articulated—“accommodation can be 

removed at a pace that is likely to be measured”—it did not aggravate 

inflation expectations and had no adverse real interest rate impacts 

• The end of QE2 (June 30, 2011) and start of Fed tightening (2012?) 

cannot occur until after medium-term Treasury yields rise somewhat 

(with adverse effects partly offset by narrowing mortgage spreads) 

possibly exerting drag on a revival of housing investment 


56

Fed funds rate increases (1994>, end-1998>, 2004>): 

the first two “unannounced; 

unannounced;” the last “transparent” 

9 

Percent 

8 

7 

6 

5 

4 

Each arrow between 

vertical lines denotes 

periods during which 

the Federal Open 

Market Committee 

(FOMC) was raising 

its target for the 

overnight interbank 

lending rate, the socalled 

fed funds rate. 

3 

2 

1 


0 

90 95 00 05 10 



Fed tightening dates indicate major move in effective Fed Funds rate or target 

57

Risk premiums and real interest rates: 2004 Fed 

“transparency” kept real interest rates from jumping 

7 

6 

Mortgage spread (right scale) 

Real mortgage rate (left) 

LTCM 

Crisis 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

Percentage points 

Percent 

5 

4 

3 


2 

90 95 00 05 10 


Sources: Federal Reserve Board; recession dates are NBER cyclical turning points (peak-trough); Fed tightening dates 

indicate major move in effective Fed Funds rate or target 

58

Median Maui and San Diego home prices, Fed tightening (arrows): 

1994/1989 “surprises” ignored; 2004 announced The End Is Near 

Maui (median, right scale) 

San Diego (CS, left) 

800 

Index Jan 2000 = 100, s.a. 

(log scale) 

400 

200 

100 

400 

200 


50 


94 96 98 00 02 04 06 08 10 12 


Sources: Realtor Association of Maui, Standard & Poors (seasonal adjustment by TZE); recession dates are NBER 

cyclical turning points (peak-trough); Fed tightening dates indicate move in effective Fed Funds rate target 

59

Monetary policy and the macroeconomic outlook 

• Monetary policy changes conditioned on actual economic performance 

• Talk is cheap: people now getting sound-bites predicting a “double-dip” 

recession are not a reflection of the majority view regarding the outlook 

• Examine the data—does the progress of economic reacceleration 

warrant a return to monetary policy normalcy? Eventually yes. 

• Before (1990s) when the Fed removed monetary policy accommodation 

it was a surprise, causing both long-term and short-term rates to rise 

• Now (2000s) the Fed removes accommodation transparently, leaving 

long-term rates relatively unchanged while short-term rates rise 

• Transparency means: we know their forecast, whether policy is 

conventional (Fed Funds target) or unconventional (quantitative easing) 

how much unconventional accommodation there will be (QE2) and when 

it will end (June 30), how long conventional accommodation may last 

(“an extended period”) and what conditions will warrant its removal. 


60

U.S. real GDP indexed to cyclical peaks 

Index of real GDP: peak quarter = 1.000 

1.050 

Previous 10 recessions* 

Carter/Reagan double-dip 

Great Recession 

1.000 

0.950 

-6 -4 -2 0 2 4 6 8 10 12 14 

Actual 2011Q1p real GDP 

*Recessions since World War II 


Source: Professor Robert Hall, Stanford University and Chair, NBER Dating Committee; Bureau of Economic Analysis, 

U.S. Department of Commerce; includes 2011Q1P data and forecasts though 2011Q4 

61

October 2010 NABE real growth forecasts, 

with actual 2010 fourth quarter data 

Quarterly annualized growth in percent 

8 

4 

0 

-4 

-8 

Crisis 

begins 


P 

Lehman 

fails 

T 

Actual 

NABE Oct 2010 

Top 5 

Bottom 5 

NABE 

forecast 

published 

October 10, 

2010 

06 07 08 09 10 11 

Source: 


Bureau of Economic Analysis, National Association for Business Economics; advance 2010Q4 data released 

January 27, 2011 include revisions to prior quarters 

62

May 2011 NABE real growth forecasts, 

with actual 2011 first quarter data 

8 

P 

T 

Quarterly annualized growth in percent 

4 

0 

-4 

-8 

Crisis 

begins 


Lehman 

fails 

Actual 

NABE May 2011 

Top 5 

Bottom 5 

NABE 

forecast 

published 

May 16, 2011 

06 07 08 09 10 11 12 

Source: 

Bureau of Economic Analysis, National Association for Business Economics; preliminary 2011Q1 data 

released May 26, 2011 include revisions to prior quarters 


63

Federal Reserve real GDP growth projections 

released with the June 2011 FOMC statement 


Source: Federal Reserve Board minutes of the Federal Open Market Committee meeting January 25-26, 2011 

(http://www.federalreserve.gov/monetarypolicy/files/fomcminutes20110126.pdf) 

64

Quantitative easing and the Fed’s s balance sheet 

Securities 

Other assets 

SIFIs 

TALF 

Term Auction 

CP funding 

Central banks 

Lehman 

QE1 

QE2 

2.5 

2.0 

1.5 

1.0 

Treasury 

Currency 

Deposits 

Lehman 

QE1 

QE2 

2.5 

2.0 

1.5 

1.0 

0.5 

0.5 

0.0 

A-07 F-08 A-08 F-09 A-09 F-10 A-10 F-11 

Federal Reserve Assets 

0.0 

A-07 F-08 A-08 F-09 A-09 F-10 A-10 F-11 

Federal Reserve Liabilities 


Sources: Federal Reserve Board (http://www.federalreserve.gov/monetarypolicy/bst_recenttrends.htm), balance 

sheet data through week of June 2, 2011; calculations by TZE 

65

Why quantitative easing? Lesson from the 1930s: 

offset collapse of M1 money multiplier (m)( 

Lehman 

+ c 

m = 1 money multiplier 

r + e + c 

c = currency/deposit ratio 

e = excess reserves/deposit ratio 

r = required reserves/deposit ratio 


Source: Federal Reserve Bank of St. Louis (http://research.stlouisfed.org/fred2/series/MULT) 

66

Monetary accommodation and interest rates after 

the Dot.com Recession and the Great Recession 

Percent 

7 

6 

P T 

“Under these circumstances 

policy accommodation can be 

removed at a pace that is 

likely to be measured.” 

Percent 

7 

6 


P T 

“economic conditions…are 

likely to warrant exceptionally 

low levels for the federal 

funds rate for an extended 

period.” 

5 

4 

10-y T-Note 

5 

4 

10-y T-Note 

3 

2 

Fed funds 

3 

2 

But 

when? 

1 

0 


99 00 01 02 03 04 05 

1 

0 

Fed funds 

06 07 08 09 10 11 12 

Dot.com 

Great 


Sources: Federal Reserve Board, Federal Reserve Bank of St. Louis; TZ Economics 

67

U.S. Treasury (risk-free) constant-maturity yields: 

1990s Fed tightening shot long-term rates upward; 

2004 tightening was neutral w.r.t. . long-term rates 

Percent 

10 

8 

6 

4 

2 

Fed 

tightens 

Fed 

tightens 

Fed 

tightens 

NBER recessions shaded 

0 

90 95 00 05 10 

TY30 

TY20 

TY10 

TY7 

TY5 

TY3 

TY2 

TY1 

TMO6 

TMO3 

TMO1 

FEDF 


Sources: Federal Reserve Board, Federal Reserve Bank of St. Louis; TZ Economics 

68

Recent U.S. Treasury (risk-free) constant-maturity yields 

6 

P T 

5 

4 

LR equilibrium? 

Percent 

3 

2 

1 

0 


06 07 08 09 10 11 12 

10-year 

7-year 

5-year 

Fed funds (overnight) 


Sources: Federal Reserve Board, monthly data include June 20, 2011 observation; TZ Economics 

69

Nominal U.S. Treasury yield curves 

5 

Jul 06 

Jul 07 

4 

Percent 

3 

2 

Jul 08 

Jul 09 

Jul 10 

June 21, 2011 

1 

FF 

0 

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 21, 2011 

70

FOMC Statement June 22, 2011: 

no easing, no tightening 

• “The economic recovery is continuing at a moderate pace, though 

somewhat more slowly than the Committee had expected” 

• “The slower pace of the recovery reflects in part factors that are likely to be 

temporary, including the damping effect of higher food and energy prices 

on consumer purchasing power and spending as well as supply chain 

disruptions associated with the tragic events in Japan” 

• “The Committee expects the pace of recovery to pick up over coming 

quarters and the unemployment rate to resume its gradual decline [and] 

that inflation will subside to levels at or below those consistent with the 

Committee's dual mandate” 

• “Economic conditions…are likely to warrant exceptionally low levels for the 

federal funds rate for an extended period.” 

• “The Committee will complete its purchases of $600 billion of longer-term 

Treasury securities by the end of this month and will maintain its existing 

policy of reinvesting principal payments from its securities holdings” 


Sources: Federal Reserve Board (http://www.federalreserve.gov/newsevents/press/monetary/20110622a.htm) 

71

Notes from Bernanke’s s press conference 

June 22, 2011 

• The “burn-off” in the Fed’s securities portfolio has been put on hold: QE2 

ends on June 30, and the balance sheet will remain stable for now 

(reinvesting principal and interest) 

• QE2 did achieve something: a reduction in the probability of expected 

deflation implied by TIPS yields 

• Not reinvesting principal and interest would be the first step in an exit from 

policy accommodation 

• No timetable for initiating the exit has been determined 

• The FOMC’s “extended period” is not intended to be deliberately opaque, 

but it cannot be said exactly how long it would be 

• Bernanke conjecture: the extended period would be “at least” two or three 

FOMC meetings, with the emphasis on at least 

• Other communication tools are available to indicate the direction of Fed 

policy and timetable for exit from monetary policy accommodation 


Sources: Federal Reserve Board (http://www.federalreserve.gov/monetarypolicy/fomcpresconf20110622.htm) 

72

Appendix 4: Inflation (no mo da kine?) 

• Remember the idea behind inflation targeting: 

• The real side of the economy will achieve employment goals if 

inflation risk can be diminished 

• Credible monetary policy-making increases the potency of 

countercyclical fiscal and monetary policy tools (especially at the 

zero bound for nominal interest rates)—small doses suffice 

• In principle, keeping your eye on the prize involves low and stable 

core inflation 

• Now concluded, QE2 should be seen through the lens of incipient 

deflation: what do we learn from Japan’s Lost Decade? Don’t let 

deflationary expectations take root (the risk in summer 2010) 

• Talk is cheap: we can see expectations revealed in securities pricing 


73

Long-term inflation expectations implied by Treasury 

yields (including unobservable inflation risk premia) 

3 

Jul 06, 07, 08 

Percentage points, difference 

between nominal U.S. Treasury 

yields and real (TIPS) yields 

2 

1 

0 

-1 

June 21 2011 

Jul 09, 10 

Feb 2009 

Nov 2008 

e 

σ π 

= 0.5% 

≈ 1.5−2.0% 

“target” 

-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 21, 2011 

74

Federal Reserve economic projections 

Mistaken date published in original 

(actual release date June 22, 2011) 


Source: Federal Reserve Board (http://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20110622.pdf) 

75

The spoiler: real crude petroleum spot prices 

(West Texas Intermediate) 

160 

Monthly, in April 2011 $/bbl (log scale) 

80 

40 

20 

50 60 70 80 90 00 10 


Source: Federal Reserve Bank of St. Louis (http://research.stlouisfed.org/fred2/series/MCOILWTICO?cid=32358); deflation 

using U.S. consumer price index for all urban consumers (CPI-U) by TZ Economics 

76

U.S. consumer price inflation rates 

6 

Monthly s.a. percent change y-o-y 

5 

4 

3 

2 

1 

0 

-1 

-2 


Headline 

Core 

Transitory commodity 

price-based impulse 





-3 

00 02 04 06 08 10 


Source: BLS, U.S. Department of Labor 

77

Honolulu inflation varies around the U.S. urban 

average over the business cycle (booms and busts) 

Semiannual; percent change y-o-y 

8 

6 

4 

2 

0 

Honolulu 

U.S. city average 


-2 

85 90 95 00 05 10 


Source: BLS, U.S. Department of Labor 

78

Overall Oahu Phillips Curve 1983-2010: 

inverse relationship between inflation and unemployment 

Honolulu inflation rate (%) 

8 

6 

4 

2 

0 

Boom 

Semiannual data 

1983-2010 

Bust 

-2 

1 2 3 4 5 6 7 8 

Oahu unemployment rate (%) 


Source: Bureau of Labor Statistics, Hawaii DLIR; seasonal adjustment using Census X-12 ARIMA filter, regressions 

on natural log of contemporaneous unemployment rate by TZE 

79

Oahu Phillips Curves since the 1970s: decrease in 

long-term inflation expectations shifted curve down 

Honolulu inflation rate (%) 

8 

6 

4 

2 

0 

-2 

"Japan" 1983-98 

"Subprime" 1998-2009 

Phillips curves (fitted) 

1 2 3 4 5 6 7 8 

Regression of Honolulu inflation rate ( pˆ 

t 

) 

on the Oahu unemployment rate ( u ) at time t. 

pˆ 

[ ln( )] 

= 12.67574 − 6.22218⋅ 

(1983-1998) 

(0.094243) (0.63310) 

2 = 0.763018 

t u t 

AdjR 

F = 96.59607 

D . W. 

= 0.786456 

pˆ 

[ ln( )] 

= 10.30540 − 5.83584 ⋅ (1998-2009) 

(0.994873) (0.732546) 

2 = 0.751378 

t u t 

AdjR 

F = 6.346547 

D . W. 

= 0.736671 

Oahu unemployment rate (%) 


Source: Bureau of Labor Statistics, Hawaii DLIR; seasonal adjustment using Census X-12 ARIMA filter, regressions 

on natural logs of contemporaneous unemployment rates by TZE 

80

Oahu Phillips Curve since early-1990s: 

commodity price shocks pushing out the trade-off 

Inflation rate (%) 

6 

5 

4 

3 

2 

1 

π * 

Boom 

2008H1 

2008H2 

2010 

Assumed long-term 

non-accelerating 

inflation equilibrium: 

( π ) 

* ,u * 

Volatile global petroleum 

and commodity prices over 

last two years have exerted 

transitory inflation impulses 

0 

-1 

u * 

Bust 

2009 

2 3 4 5 6 

Unemployment rate (%) 


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

81

Another trade-off: inverse relationship between Oahu 

house price change, inventory remaining (1994-2010) 

40 

Single-family 

40 

Condominium 

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

30 

20 

10 

0 

-10 

ˆ 

p H 

[ ln( ( −5) 

)] 

= 32.80695 −15.36972⋅ 

MRI 

(1.61912) (0.82424) 

AdjR 

2 = 0.6514993 

F = 347.7148 

D . W. 

= 1.218097 

% chng P(condos) (y-o-y) 

30 

20 

10 

0 

-10 

ˆ 

p H 

[ ln( ( −5) 

)] 

= 39.11897 − 20.23304⋅ 

MRI 

(1.48397) (0.96947) 

AdjR 

2 = 0.785419 

F = 435.5696 

D . W. 

= 1.545932 

-20 

0 4 8 12 16 20 24 

-20 

0 4 8 12 16 20 24 

Months of inventory remaining (lagged 5) 

Months of inventory remaining (lagged 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 

82

Oahu months of inventory remaining, 

through May 2011 

Months, [inventory(t) / sales(t –1)], s.a. 

20 

16 

12 

8 

4 

0 

Condominium 

Single-family 

98 00 02 04 06 08 10 


Source: Honolulu Board of Realtors; seasonal adjustment by TZE 

83

Appendix 5: (for tourism) remember: markets have 

two sides and most shocks are transitory 


84

Recession (demand) and lift (supply): 

Hawaii monthly domestic visitor arrivals 

520 

480 


P T 


440 

400 

360 

320 

Additional 

Aloha Airlines & 

lift 2010 

ATA shutdowns 

Lehman 

Brothers 

03 04 05 06 07 08 09 10 11 12 


Sources: Hawaii DBEDT, HTA; seasonal adjustment using Census X-12 ARIMA filter by TZE 

85

Monthly air seats operated to Hawaii 


Domestic (right scale) 

International (left) 

260 

240 

220 

200 

180 

Aloha/ATA 

shutdown 

P T 


04 05 06 07 08 09 10 11 

680 

640 

600 

560 

520 

480 




86

Hotel industry performance indicators 


80 

Constant 2010$, s.a., log scale 

220 

200 

180 

160 

140 

Occupancy (right scale) 

Real room rate (left) 

94 96 98 00 02 04 06 08 10 12 

70 

60 

Percent, s.a. 


Sources: Hospitality Advisors LLC; seasonal adjustment using Census X-12 ARIMA filter by TZE 

87

Hawaii monthly international visitor arrivals: 

shocks matter but are transitory 

240 

200 


160 

120 

Y2K 

9/11 

SARS 

H1N1-A 

Sendai 

80 

00 02 04 06 08 10 



88

Conditional annualized quarterly seasonally- 

adjusted Japanese visitor arrivals volatility 

1.4 

1.2 

9/11 

1.0 

Before wars were broadcast live 

Gulf 

SARS 

0.8 

0.6 

0.4 

H1N1-A 

0.2 

0.0 

75 80 85 90 95 00 05 10 


Sources: Arrivals data originally published by HVB, later DBEDT, raw data source here is UHERO; seasonal adjustment 

and calculation of threshold autoregressive conditional heteroskedasticity standard deviations by TZE 

89

Pre-/post 

/post-Sendai daily passenger arrivals growth 

Hawaii daily arriving passenger counts 

growth rates in percent vs. comparable days of week, one year earlier 

Flight origin: Domestic Canada Japan Other 

January-February 9.1 no data 6.2 20.9 

March 1-10 8.1 - -6.3 40.4 

March 11-31 1.9 - −24.2 24.5 

April 1-11 4.2 - −25.2 34.9 

April 12-19 (pre-Easter) 17.8 - −29.4 49.6 

April 20-27 4.8 - −24.4 35.2 

April 28 - May 8* 3.6 - −15.4 43.8 

May 9-31 2.2 −22.3 39.7 

June to-date † -0.9 −18.4 38.4 

Post-Sendai † 3.7 - −22.6 36.2 

Shares (Jan-Mar 10) 75.7% 18.5% 5.8% 

*Golden Week in Japan 

† Through Tuesday, June 6 for domestic, Monday, June 7 for international 


Sources: Raw data from DBEDT (http://hawaii.gov/dbedt/info/economic/data_reports/special/daily-pax-update.xls); 

compilation of interval year-over-year growth rates by TZE 

90

Appendix 6: the investment cycle is bottoming 


91

Hawaii real private building permit values 

800 

Monthly, million 2010 $, s.a., log scale 

400 

200 

100 

50 

75 80 85 90 95 00 05 10 


Sources: Hawaii DBEDT, County building departments, Bureau of the Census; deflation, seasonal adjustment using 

Census X-12 ARIMA filter and trend extract using Hodrick-Prescott filter by TZE 

92

Appendix 7: balance pragmatism against ideology, 

an illustration from federal fiscal imbalance 


93

Restoring federal fiscal balance 

“We do have to solve the fiscal situation and that’s probably going to take some compromise that’s a 

little hard to see right now. I think the real problem is that if one side wants no revenue increase in 

the form of tax increases then all future-oriented investment has to come out of some current 

program or redistributive part of the government’s operation, and those are Draconian choices. 

We should probably have increased taxes for a period of time to get past a deficit that we’ve created. 

We also don’t want to destroy whatever recovery we have, which is fragile. So there’s a debate 

going on about what the withdrawal rate should be. 

It’s encouraging that there’s serious debate going on about the fiscal situation now. …Even in 

Congress now, even if you think…it doesn’t look like a very well-organized debate, at least they’re 

debating something important…based on principles that various sides believe in, and that’s good. I 

mean, if this were a situation of not so benign neglect I think you and I would be very worried. 

I think there’s two streaks in America. We have an ideological streak, and I don’t mean that on one 

side or the other, both, and we have a pragmatic streak. And I think they come and go. We need 

this pragmatic streak to come out.” 

Michael Spence, recipient of the 2001 Nobel Memorial Price in Economic Sciences 

(with George Akerlof and Joseph Stiglitz) 

on Charlie Rose (May 27, 2011) http://www.charlierose.com/guest/view/7162 


94

Fiscal imbalance 

• The last time the discretionary portion of the federal budget imbalance was in 

full-employment surplus was before the 2001 recession 

• History suggests the probable zone of compromise for federal government 

receipts and outlays is 18-20% of GDP 

• Pragmatism requires that all available fiscal tools be used, whether for shortterm 

full-employment balance or long-term balance—e.g. Douglas Holtz-Eakins’ 

(ex-CBO) social security fix: 

1. Raise the social security payroll tax 

2. Increase the retirement age 

3. Decrease social security benefits 

4. Means-test benefits 

5. Move the system toward hybrid accounts (SS + IRA/401k) 

• It’s impossible to get to full-employment federal budget balance without higher 

marginal tax rates, lower spending, and sacrifice of Sacred Cows* 

• It’s hard to argue that a reset to 2000 parameters would be a bad thing 


*like the tax deductibility of mortgage interest expense 

95

U.S. federal budget deficit as a percent of GDP 

4 

2 



0 

-2 

-4 

-6 

-8 

-10 

70 75 80 85 90 95 00 05 10 15 


Source: Congressional Budget Office, Budget and Economic Outlook Fiscal Years 2011 Through 2021 

(http://cbo.gov/ftpdocs/120xx/doc12039/BudgetTables[1].xls) 

96

Procyclical component of U.S. federal budget deficit 

attributable to automatic stabilizers as share of GDP 

2 

Automatic stabilizers 

Deficit without stabilizers 


0 

-2 

-4 

-6 

-8 


70 75 80 85 90 95 00 05 10 15 




97

Federal outlays as a percentage of GDP 


Source: Karl Case, Ray Fair and Sharon Oster, Principles of Macroeconomics, 10 th ed. (2012 forthcoming) Prentice Hall 

98

Federal personal income tax/taxable income (%) 


Source: Karl Case, Ray Fair and Sharon Oster, Principles of Macroeconomics, 10 th ed. (2012 forthcoming) Prentice Hall 

99

Federal government revenue and outlays 

as a percent of GDP through 2010 

26 

24 

Outlays 

Revenues 

Percent of GDP 

22 

20 

18 

I’m just going to guess 

that, pragmatically 

speaking, 18-20% is “it” 

16 

Vertical shaded areas denote recessions 

14 

70 75 80 85 90 95 00 05 10 15 




100

CBO projections were scary 

after and before Obama 


Source: Congressional Budget Office, Analysis of the President’s Budgetary Proposals for Fiscal Year 2012, 

http://www.cbo.gov/ftpdocs/121xx/doc12130/04-15-AnalysisPresidentsBudget.pdf 

101

Appendix 8: income distribution changes 

now confound U.S. economic policy options 

• The distribution of income in Hawaii and the U.S. has been widening 

since the mid- to late-20 th century 

• The rich are getting richer and the poor are getting richer (partly 

because technology is improving so fast) 

• But the rich have gotten richer way, way faster than everybody else 

• Implication: high income/high net worth households not only do not 

need a tax cut, they need to pay more in taxes 


102

U.S. income distribution 2007 

from the Survey of Consumer Finances 

% 

Histogram of the 2007 SCF income 

distribution (and of its smoothed 

kernel density estimates) 

Income 


Source: Javier Diaz-Gimenez, Andy Glover and Jose-Victor Rios-Rull, “Facts on the Distributions of Earnings, Income, and Wealth in the United States: 2007 

Update,” Federal Reserve Bank of Minneapolis Quarterly Review vol. 34, no. 1 (February 2011), pp. 2-31 http://www.minneapolisfed.org/research/qr/qr3411.pdf 

103

“The Hollowing Out of the Middle Class [sic]” 

Source: Mary C. Daly, “The ‘Shrinking’ 

Middle Class?” FRBSF Economic Letter 97- 

07 (March 7, 1997) 

www.frbsf.org/econrsrch/wklyltr/el97-07.html 

A: 75 percent of the poverty line 

B: two times the poverty line 

C: five times the poverty line 

Economic 

mobility 

The “middle class” comprises families 

between B and C (2X to 5X poverty) 

92 percent of the loss in the middle class 

shifted higher, 7 percent shifted lower—the 

rise in income inequality occurred “through 

disproportionate increases in economic wellbeing, 

not through ‘immiserization.’” 


Note: definitional changes in the 2000 census prevented strict extension of these comparisons, but 2010 census data 

should permit renewed comparison, 1999-2009 

104

U.S. Lorenz Curves 

Cumulativeshare of household income (%) 

100 

80 

60 

40 

20 

0 

1967 

2009 

The Lorenz curve depicts the 

cumulative distribution function of 

income, showing for each quintile 

of households (x-axis) the 

proportion of total income (y-axis) 

accruing cumulatively to those 

households. For example, for 2009 

3.4% of income was received by 

the lowest fifth of households in the 

U.S, and 8.6% was received by the 

second fifth. On the Lorenz curve, 

the lowest received 3.4% of 

income and the two lowest 

received 12% (3.4 + 8.6) of 

income. A perfectly equal income 

distribution would be indicated by 

the 45º line (the diagonal). 

Lowest 2 nd 3 rd 4 th Highest 

Quintile 


Source: U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplements. For information on 

confidentiality, error, and definitions, see www.census.gov/apsd/techdoc/cps/cpsmar10.pdf 

105

Lorenz curves and Gini ratios 

Cumulativeshare of household income (%) 

100 

80 

60 

40 

20 

0 

A 

B 

The Gini ratio (or Gini coefficient) is 

calculated as the ratio of the areas 

A/(A+B) in the Lorenz curve, as 

depicted in the graph to the left. An 

economy with a perfectly equal income 

distribution, with each quintile earning 

20% of income, would have a Lorenz 

curve depicted as the diagonal, and a 

Gini ratio of zero (since, in this case, 

the area A = 0). The Gini ratio can 

range from 0 to 1 (often multiplied by 

100). It summarizes the degree of 

income inequality. A high Gini ratio, 

such as 60 in Bolivia, indicates 

relatively unequal income distribution; 

ratios like 25 in Sweden and 26 in 

Norway reflect greater income equality. 

Lowest 2 nd 3 rd 4 th Highest 

Quintile 


http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20238991~menuPK:4921 

38~pagePK:148956~piPK:216618~theSitePK:430367,00.html describes related measures 

106

Changes in U.S. employment structure 

• Shifts in industry mix from goods- to services-producing 

• Shifts in occupational mix even more complex 

• “Barbell” growth at ends of spectrum: managerial, professional, unskilled 

labor; but mid-skilled tiers are in relative decline 

• Human capital theory: skills get paid, lack of skills makes you absolutely 

worse off 

• Technological shifts reward skills-technology complementarity 

• Globalization has further leveled the spatial playing field 

• Global shift from inter-industry to intra-industry trade: components 

• Nontradeables do OK (who does your hair?) 


107

Changes in real hourly earnings by educational 

attainment and sex, 1997-2007 


Source: David Autor (MIT) “Impending Labor Market Challenges” presented at the AEA meetings (January 2011) 

http://econ-www.mit.edu/files/6347 

108

Longer-term returns to education were positive: 

returns to under-education education were negative 

Figure 6 Men’s s Earnings by 

Education 

Figure 7 Women’s s Earnings by 

Education 

Definitions: 

PG post-graduate 

CG college graduate 

SC some college 

HSG high-school graduate 

HSD high-school dropout 

Source: Zvi Eckstein and Navy Nagypal, “The Evolution of US Earnings Inequality: 1961-2002” FRB Minneapolis 

Quarterly Review 28-02 (December 2004) http://minneapolisfed.org/research/QR/QR2822.pdf 


109

Increasing income inequality 

• U.S. highest income quantiles’ shares have been rising since the 1970s, 

reversing an early-20 th century trend towards greater income equality 

• Top decile shares have nearly doubled—a phenomenon not universal 

among advanced countries (and therefore not technologically-based) 

• The top 1 percent of households’ share of total income has grown from 

.10 to .20 of the nation’s total (the share of the other 99 percent of 

households has shrunk from .90 to .80) 

• Fully two-thirds of all the income created in the U.S. in the 2002-2007 

economic expansion accrued to the top 1 percent of U.S. households 

• Effective tax rates for the highest income households have fallen twice 

as much—in percentage points—as for other quantiles 

• It is simply not possible to close the U.S. federal budget imbalance 

without some reversal of these tax and income trends as part of a 

meaningful expenditure reduction strategy 


110

Gini ratios for selected states since 1916 

.65 

.60 

.55 

Hawaii 

California 

Colorado 

Gini coefficient 

.50 

.45 

.40 

.35 

.30 

20 30 40 50 60 70 80 90 00 10 


Source: Mark W. Frank, “Inequality and Growth in the United States: Evidence from a New State-Level Panel of Income 

Inequality Measure,” Economic Inquiry vol. 47, no. 1 (2009) pp 55-68; http://www.shsu.edu/~eco_mwf/unequal/Frank_All.csv 

111

Highest quantiles’ income shares since 1916 

Share of total income (IRS filings) 

.5 

.4 

.3 

.2 

.1 

.0 

Hawaii 

California 

Top10percent 

Top 1 percent 

20 30 40 50 60 70 80 90 00 10 


Source: Mark W. Frank, “Inequality and Growth in the United States: Evidence from a New State-Level Panel of Income 

Inequality Measure,” Economic Inquiry vol. 47, no. 1 (2009) pp 55-68; http://www.shsu.edu/~eco_mwf/unequal/Frank_All.csv 

112

Decomposed top decile U.S. income shares, 1913-2006 


Sources: Emmanual Saez, “Striking It Richer: The Evolution of Top Incomes in the United States,” update (March 15, 

2008) http://elsa.berkeley.edu/~saez/saez-UStopincomes-2006prel.pdf 

113

The top 0.1 percent income share and composition, 

United States, 1916-2007 


Sources: Anthony B. Atkinson, Thomas Piketty, and Emmanual Saez, “Top Incomes in the Long Run of History,” Journal of 

Economic Literature 49:1 (2011) pp. 3-71 ftp://emlab.berkeley.edu/pub/users/saez/atkinson-piketty-saezJEL10.pdf 

114

Top 1 percent share: English speaking countries 

(U-shaped) 1910-2005 




115

Top 1 percent share: middle Europe and Japan 

(L-shaped) 1900-2005 




116

Total effective federal tax rates by income quantile 

Year 

Lowest 

Quintile 

Second 

Quintile 

Middle 

Quintile 

Fourth 

Quintile 

Highest 

Quintile Top 1% 

1979 8.0 14.3 18.6 21.2 27.5 37.0 

1980 7.7 14.1 18.7 21.5 27.3 34.6 

1981 8.3 14.7 19.2 22.1 26.9 31.8 

1982 8.2 13.8 17.9 20.6 24.4 27.7 

1983 9.1 13.7 17.5 20.1 23.9 27.7 

1984 10.2 14.6 18.0 20.4 24.3 28.2 

1985 9.8 14.8 18.1 20.4 24.0 27.0 

1986 9.6 14.8 18.0 20.5 23.8 25.5 

1987 8.7 14.0 17.6 20.2 25.8 31.2 

1988 8.5 14.3 17.9 20.6 25.6 29.7 

1989 7.9 13.9 17.9 20.5 25.2 28.9 

1990 8.9 14.6 17.9 20.6 25.1 28.8 

1991 8.4 14.2 17.6 20.5 25.3 29.9 

1992 8.2 13.7 17.4 20.2 25.6 30.6 

1993 8.0 13.5 17.3 20.2 26.8 34.5 

1994 6.6 13.1 17.3 20.4 27.4 35.8 

1995 6.3 13.4 17.3 20.5 27.8 36.1 

1996 5.6 13.2 17.3 20.3 28.0 36.0 

1997 5.8 13.6 17.4 20.5 28.0 34.9 

1998 5.8 13.0 16.8 20.4 27.6 33.4 

1999 6.1 13.3 16.9 20.5 28.0 33.5 

2000 6.4 13.0 16.6 20.5 28.0 33.0 

2001 5.1 11.5 15.3 18.9 26.7 32.8 

2002 4.7 10.8 14.8 18.3 26.0 32.8 

2003 4.6 9.8 13.8 17.4 25.0 31.7 

2004 4.3 9.9 14.1 17.3 25.2 31.4 

2005 4.3 10.1 14.2 17.5 25.8 31.6 

2006 4.3 10.2 14.2 17.6 25.8 31.2 


Sources: The New York Times, Congressional Budget Office 

117

Top percentile share and average income growth 

in the United States 




118

An example: one complication 

“But another factor I would emphasize is that since 1981 when we first promoted 

[the ‘flat tax’] plan, there’s been a dramatic widening of the income distribution in 

the U.S. That means that the idea of the poor paying the same tax rate just 

seems less viable than it was when the income distribution was tighter. 

The division between a small number of winners in the modern economy, mostly 

businessmen and lawyers, as opposed to most other people, has grown 

significantly. 

…While there are a lot of good ideas in flat tax reform, it wouldn’t be remotely 

practical to do it with a single positive tax rate now. So I play around with systems 

that have, say, two brackets. The ‘not-so-flat’ tax. But of course that doesn’t have 

quite the simple appeal that the ‘flat tax’ did.” 

Professor Robert Hall, Stanford University 


Source: Federal Reserve Bank of Minneapolis, “Interview with Robert Hall” The Region (June 2010) 

(http://www.minneapolisfed.org/pubs/region/10-06/hall.pdf) 

119

Appendix 9: Hawaii is no longer on the way to anywhere 

With or without APEC, it’s on us to rebuild Hawaii’s economic ties in the region 


120

By Pythagoras’ Theorm: 

a 2 + b 2 = c 2 ; 

so it is always true that: 

c < ( a + b ); 

Who goes the long way? 

Ping Pong, China 

W 

Califrisco 

E 

(c) 

(a) 

(b) 

Hawaii

Appendix 10: FOMC forecasts from earlier in 2011 


122

Federal Reserve real GDP projections from minutes 

of the January 2011 FOMC meeting 


Source: Federal Reserve Board minutes of the Federal Open Market Committee meeting January 25-26, 2011 


123

Federal Reserve real GDP projections from minutes 

of the April 2011 FOMC meeting 


Source: Federal Reserve Board minutes of the Federal Open Market Committee meeting April 26-27, 2011 


124

125 page Presentation - REALTORS® Association of Maui, Inc.

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?