Population Foreshadows Housing Bubbles and Busts1

October 4, 2012

Kazunori Yamaguchi, MRICS, CIIA
(Japan)Yamaguchi Real Estate Appraisal Service, Inc.
2-4-18, Shinmeicho, Higashimatsuyama, Saitama 355-0021, Japan
T +81 493 22 0567 / F +81 493 22 1179
kazunori@yamaguchi-rea.co.jp
www.yamaguchi-rea.co.jp

Key Words: Housing Bubble Bust, Working Age Population, Population Bonus, Population Onus

1th

A previous version of this paper was presented at the 26 Pan Pacific Congress.

1

Abstract

It has been over twenty years since the Japanese property bubble burst. In the USA, thehousing bubble that was caused by subprime mortgages exploded in 2006. In Europe as well,especially in the UK, Ireland, and Spain, housing prices reached their peak in 2007–2008. Where
the housing bubbles burst in these developed countries, whether by coincidence or inevitability,
the percentage of the working age population (15–64 years old/WAP) peaked at the same time.
In this paper, by using the UN 2010 World Population Prospects, I test interval estimation for thecoincidence of the year that the WAP percentage peaks and the housing bubble explodes. Next,
I employ regression analysis using the rate of change in housing prices and the rate of change in
the WAP and find that before the Lehman collapse the correlation of these two quantities was
statistically significant, whereas post-Lehman, it is not in general. However, the data for eachcountry in each year show that the correlations are statistically significant in most of the countries
whose housing prices rose dramatically. For the WAP-decreasing countries, a rise in housing
prices will happen only with local population density or printing more money in the future.

2

1.

Introduction

It has been over 20 years since the Japanese bubble collapsed. As a result of the “Lost 20,”
the public debt outstanding, which Japanese fiscal policy created, grew to 1 quadrillion yen(1,000,000,000,000,000 yen, which amounts to about 12.5 billion US dollars), or twice theGDP of Japan. In the case of the USA, the housing bubble burst in 2006, triggering theLehman collapse, which in turn brought about worldwide recession. More recently, Europe
has become a risk factor for the world economy owing to the Greek debt and the Eurozone
crisis. Each country has attempted to bolster its economy by using monetary policy (printing
more money or monetization) to bring about some respite from this common “illness.”
According to Reinhart and Rogoff [2009], in their book “This Time is Different,” no matter
how different the latest financial frenzy or crisis always appears, there are usuallyremarkable similarities with past experience from other countries and from history. They
also suggested that there is something about human nature that gives rise to bubbles. Alan
Greenspan, the last chairman of the US Federal Reserve Board, referred to “irrational
exuberance.” However, I am of the view that “rational exuberance” is responsible forbubbles.
Motani [2010] noted that Japan’s “Lost 20” was brought about in part by its decreasing
population, especially the working age population (15–64 years old/WAP). In this paper, I
focus on a dynamic population as the reason for “exuberance,” and explore the correlation
between housing bubbles and the working age population in developed countries first. Next,
I consider the effect of population on housing prices. Mankiw and Weil [1989] also
documented that changes in the number of births over time lead to large and predictablechanges in the demand for housing, and these changes in housing demand appeared tohave substantial impact on the price of housing. As we can predict population with a highdegree of reliability and public organizations estimate it, we can predict the housing priceusing population as an indicator, if the two quantities are related.

2.

The Aging Japanese Economy

Japanese property prices rose dramatically in the latter half of the 1980s on account of theJapanese bubble, peaked in 1991, and then fell. Figure 1 shows transitions in the urban
land price index (derived by the Japan Real Estate Institute, or JREI) in six large city areas.2
Comparing the indexes before the bubble (1983) with those at the peak, we see that theresidential index and the commercial index increased approximately threefold and fivefold,
2

Tokyo Metropolitan Wards, Yokohama, Nagoya, Kyoto, Osaka, and Kobe.

3

respectively. Using economic indicators to delve further into the Japanese bubble, I foundthat the period during which the real GDP growth exceeded the real interest rate coincidedwith the period of the Japanese bubble (Figure 2). This observation is also true for theAmerican housing bubble.
After the Japanese bubble, Japan suffered a long spell of deflation, which Motani [2010]
described as “a disease of an internal economy, a contraction of internal demand, so to
speak, the aging of the economy” (p.46, own translation). Of more serious concern, thebaby boomers (the generation born between 1947 and 1949), who lifted the Japanese
economy, have reached retirement age, so we can predict that this “contraction” will become
even more severe. Figure 3 presents a graph showing the percentage of elderly Japanese.
The proportion of Japanese aged 65 years or older rose with each year to reach 23.1
percent in 2010 (and 23.3 percent in 2011, the latest figure available). This means that one
in four people in Japan is an elderly person. Figure 4 is a scatter diagram of the percentage
of elderly people and the percentage change in residential land prices over the previous
year 3 by prefecture. Using a linear regression, we see a declining straight line. Thissuggests that the higher the percentage of elderly people, the larger the rate of decline inhousing prices. The coefficient of determination R2

is 0.3865, which is relatively significant.

The correlation coefficient between the percentage of elderly people and the rate of change
in housing prices was strongly positive in 1995 as a reaction to the bubble (Table 1). In otherwords, housing prices fell sharply in urban areas where they had risen sharply, while theyfell slightly in provinces where they had risen slightly. However, the correlation coefficient
turned negative in 2005. Housing prices in prefectures with high percentages of elderly
people (that is, in the provinces) fell sharply, with the negative correlation becoming evenmore significant in 2010.

Table 1
Correlation coefficient between distribution of population aged 65 years and over,and rate of change in housing prices
Year

Correlation Coefficient

1995

+0.78

2000

+0.72

2005

–0.38

2010

–0.61

Source: Ministry of Internal Affairs and Communications

3

I used the percentage change in the Land Market Value Publication administered by the Ministry of Land, Infrastructure,
Transport, and Tourism (MLIT).

4

(MIC); Ministry of Land, Infrastructure, Transport, andTourism (MLIT)

3.

The Housing Bubble and the Percentage of the Working Age Population

3.1.

Strange Coincidence

We observe that the correlation coefficient between the percentage of elderly people
and the rate of change in housing prices turned negative recently. Moreover, the United
Nations (UN)4 has noted that the number of elderly will continue to increase (Figure 5).
In this connection, accompanied by the continued increase in the number of elderly
persons, another point of interest is the drastic decrease of the WAP. Although the WAP
numbered 80 million in 2010, the UN predicts it will fall to 55 million in 2050, whichtranslates to a rate of decrease exceeding 30 percent.
I also discovered a strange coincidence while studying the percentage of the WAP. First,
let us consider Figure 6, which shows the transitions in the quantity representing WAP
growth minus total population growth. Although we can see that the WAP growth waslarger than the total population growth in the bubble economy of the 1980s, we observe
the contrary when the bubble burst. Next, let us consider Figure 7, which is a graph ofthe transitions in the percentage of the WAP and the housing prices 5 in Japan.
Observing the peaks of the WAP percentage and housing price graphs, we may note a
strange coincidence. (Strictly speaking, Japanese housing prices peaked in 1991, while
the percentage of the WAP peaked in 1992.) Similarly in the USA, the percentage of the
WAP and housing prices peaked in 2005 and 2006, respectively. Subsequently, thesubprime mortgage bubble exploded (Figure 8). Figure 9 and 10 also show evidence ofthe housing bubble bursts in Ireland and Spain, respectively. However, as Figure 11
shows, in the UK, while the percentage of the WAP peaked in 1950, housing prices
peaked in 2007, closer to the second peak in the WAP percentage (in 2008). Indeed, we
can identify some common coincidences among all these countries. Similar to thepresent study, Nishimura [2011] also identified some strange coincidences in the“Inverse Dependency Ratio (which indicates how many people of working age it takes toprovide for one dependent person).” Unfortunately for Japan, the decline goes beyond
this percentage and also concerns the WAP itself, which peaked in 1995. The decreaseof the WAP coincides with the rapid increase of elderly persons. The change from the45

I used the medium variant.
I used the urban land price index (derived by JREI).

5

era of population bonus (an increase in the WAP) to that of population onus (a decreasein the WAP) was accompanied by a rapid increase in the number of elderly persons. In
this context, I fully agree with Motani’s [2010] observation that the WAP decline marks
the start of a crisis that happens not once in 100 years, but once in 2000 years.

3.2.

Interval Estimation of Housing Bubble Busts

Following the observations recorded in Figure 7–11, I performed a statistical check to
confirm whether the peak years for housing prices and for the percentage of the WAP
coincide. In this case, I check the interval estimation for the population mean of thedifference between the year that the housing bubble burst and the year that thepercentage of the WAP peaked. Note that the population variance is unknown.
The IMF [2009] defines busts as periods when the four-quarter trailing moving averageof the annual growth rate of the asset price, in real terms, falls below a particularthreshold. The threshold is set at –5 percent for real house prices and –20 percent forreal stock prices.6 Applying this technique to data for real stock and real house prices
identifies 47 house price busts and 98 stock price busts from 1970 to 2008. If we limit
our consideration to one property bust in each country that has undergone such multiple
property busts, the list of 47 house price busts condenses to 16 observations. Using
these 16 observations, I checked the interval estimation for the population mean of thedifference between the year that the housing bubble burst and the year that thepercentage of the WAP peaked. Considering a 95 percent confidence level, we can says2
s 2 
Pr  x  t 0.025 n  1
   x  t 0.025 n  1
 0.95

n
n 

2

Here, x refers to the sample mean, n to the number of samples, s to the sample
variance, and  to the population mean. When we substitute actual numbers, we get
65.39665.396  , 2.063  2.131
2.063  2.131
   2.247, 6.372
16
16  

In other words, we can assert with a 95 percent confidence level that the housing
bubbles burst within an 8-year interval surrounding the peak in the WAP percentage,that is, 2 years before and 6 years after the WAP percentage peak. Now, if we use the6

To be clear, a bust occurs when the following condition holds:
g t  3  g t  2  g t 1  g t

x4
where g is the growth rate of the asset price and x is the relevant threshold (–5 for house prices and –20 for stock prices).
If the condition holds, then the periods t–3 through t are labeled as a bust (IMF [2009]).

6

second peak of the WAP percentage seen in 2008 for the UK (Figure 11), we obtain thefollowing estimates.


31.19631.196  , 0.563  2.131
0.563  2.131
   2.414, 3.539
16
16  
This interval estimation shows that the bursting of a housing bubble in these developed
countries may occur within a span of 6 years, a time period that also contains a peak in
the percentage of the WAP. That is, the statistical checks verify that both eventsoccurred at about the same time.

4.

Housing Prices and the Working Age Population

4.1. Correlation between the Percentage Change in Housing Prices and the Percentage
Change in the WAP

Next, in order to find the correlation between housing prices and the WAP, I use a panel
data analysis. I consider the simple regression modely    x  u , and try toestimate the parameters by an ordinary least squares (OLS) method, using housing
prices in 20 countries researched by the Organization for Economic Co-operation andDevelopment (OECD).

Table 2
Correlation between percentage change in housing prices and percentage change in WAPhp    wap
hp : Percentage change in housing prices7 (Multiplication)
wap : Percentage change in WAP
2000–201123.79**

(2.26)



2.56**

R2

0.28(2.64)

t-statistic in brackets. * significant at 10%; ** significant at 5%; ***7

I used the “real” rate of change in housing prices. As the OECD provides Italian data up to 2010 only, for data pertaining
to 2011, I used the latest percentage change from the previous year (March 31, 2012, The Economist “Downdraft”)
deflated by the private consumption deflator administered by OECD [2012].

7

significant at 1%.
Source: UN [2010]; OECD [2012].

According to this result,  and  are statistically significant at the 5 percent level,
but the coefficient of determination R2

is 0.28, a little low. Therefore, both arestatistically significant to some extent, but we cannot state positively that the WAP
determines housing prices.

4.2. Correlation between the Percentage Change in Housing Prices and the Percentage
Change in the WAP before and after the Lehman collapse

In this period (2000–2011), the Lehman collapse, a major economic event, occurred,
and the structural readjustment pressed the worldwide housing market for greatchanges. I divided the data into the following two periods: (1) before the Lehman
collapse (for a middle term in 2000–2007), a period characterized by an overheatedeconomy in the global property market, and (2) after the Lehman collapse (for a shortterm in 2008–2011). I try to estimate the parameters by an OLS as in 4.1.

Table 3
Correlation between percentage change in housing prices and percentage change in WAP
before and after the Lehman collapsehp    wap
hp : Percentage change in housing prices (Multiplication)
wap : Percentage change in WAP




R

2

Before (2000–2007)

After (2008–2011)30.02***–7.94*(3.16)(–1.77)

4.44***

0.67(3.68)(0.42)

0.43

0.01

t-statistic in brackets. * significant at 10%; ** significant at 5%; ***significant at 1%.
Source: UN [2010]; OECD [2012].

According to this result, before the Lehman collapse,  and are statisticallysignificant at the 1 percent level and the coefficient of determination R
82

increases to0.43. This means that the relationship between housing prices and the WAP isstatistically more significant, whereas post-Lehman it falters because of the confusion
created by the financial crisis. We can also attribute this finding to the short term of theperiod.
In Table 4, I also use multiple regression analysis to explore some other factors that
may have been related to housing prices.

Table 4Multiple regression analysis between percentage change in housing prices and economic
indicators

hp    1wap   2 gdp   3 irhp : Percentage change in housing prices (Multiplication)
wap : Percentage change in WAP gdp : Percentage change in GDP growth (Multiplication)
 ir : Percentage change in interest rate (Remainder)

WAP
GDP8
Interest Rate9
Adjusted R

2

Before (2000–2007)

After (2008–2011)

4.27**–1.14(2.46)(–0.86)0.12

1.74**(0.13)(2.57)

3.54

–0.25

(0.30)(–0.21)

0.33

0.40

t-statistic in brackets. * significant at 10%; ** significant at 5%; *** significant
at 1%.
Source: UN [2010]; OECD [2012].

Before the Lehman collapse, the correlation with the WAP is statistically significant,whereas post-Lehman, the correlation with GDP, in place of the WAP, is statisticallysignificant. However, as it has been only four years since the collapse, this observation
may just reflect temporary economic confusion. According to this result, in so far as we
consider 20 countries in general, we can say that the WAP is statistically significant in an
upward trend, but not in a downward trend.

89Real gross domestic product administered by OECD [2012].
Long-term interest rates administered by OECD [2012].

9

4.3. Correlation between the Percentage Change in Housing Prices and the Percentage
Change in the WAP for each country in each year

Next, in order to find the correlation between housing prices and the WAP in somedetail, I consider the simple regression modely    x  u for percentage changes

in housing prices and the WAP for each country in each year. I try to estimate theparameters, using the OLS method.

Table 5
Correlation between percentage change in housing prices and percentage change in WAP
for each country in each year

hp    wap
hp : Percentage change in housing priceswap : Percentage change in WAP
Country

2000–2011





R2

Australia

8.50(0.81)–1.92(–0.29)0.01

Belgium

0.35(0.17)

9.50*(2.05)

0.30

Canada1.88(0.30)

3.15(0.59)

0.03

Denmark

3.62(0.69)–5.26(–0.22)0.01

Finland3.35*(1.96)–3.48(–0.61)

0.04

France–3.04(–0.67)

16.56*(2.04)

0.29

Germany

0.15(0.07)

4.69(0.58)

0.03

Greece–4.40(–1.46)38.40**(2.61)

0.40

Ireland–18.91***(–3.58)12.83***(4.15)

0.63

Italy

2.81

(1.28)–1.27(–0.19)

0.01

Japan

–4.82***(–4.38)–3.38*(–1.91)

0.27

Korea

4.69

(0.86)–5.11(–0.54)0.03

Netherlands–4.02*(–1.88)19.94***(3.28)

0.52

New Zealand–19.31**(–2.76)17.93***(3.51)

0.55

Norway8.39(1.64)–2.80(–0.59)

0.03

Spain

–17.83***(–6.92)18.32***(9.01)

0.89

Sweden4.01(1.14)

2.55(0.48)

0.02

Switzerland

3.31(1.79)–1.74(–0.70)0.05

United Kingdom–11.04(–1.31)26.09*(1.96)

0.28

United States–13.91***(–3.69)14.49***(4.12)

0.63

10

t-statistic in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.
Source: UN [2010]; OECD [2012].

The results are statistically significant for the countries that have experienced housing
bubbles or rises of over 10 percent per year in housing prices (for example, Greece,
Netherlands, and New Zealand). These countries also exhibit wide fluctuations, because
the numerical values of  (regression coefficients) are over 10. That is to say, housing
prices changed by over 10 times the growth in the WAP for the corresponding period.
Furthermore, in Spain, the coefficient of determination R2

is 0.89, which means thatthe percentage change in housing prices is almost entirely explained by the percentagechange in the WAP.

4.4. Japan after the Housing Bubble Burst

Japan experienced the biggest bubble and it has been over 20 years since this bubble
exploded. However, in the above result, Japan’s coefficient of determination R

2

is a

little low (0.27), so we cannot use these results as forecasting or explanatory models.
Nevertheless, according to the graphs in this paper (Figure 7), there is no doubt that
housing prices (indexes) and the percentage of the WAP have decreased in the sameway. In order to ascertain the correlation between the two quantities after the bubble
burst, we employ regression analysis using Japanese housing prices (indexes) and thepercentage of the WAP over 20 years since the bubble burst. Table 6 presents thefindings.

Table 6
Correlation between housing prices (indexes) and percentage of WAP after the Japanese bubble
bust10hp    pw
hp : Housing prices (indexes)
pw : % of WAP
1990–2010–498.18**



(–5.74)880.61***
10(6.83)

I sourced figures for 1990, 1995, 2000, 2005, and 2010 from census data administered by MIC.

11

R2

0.94

t-statistic in brackets. * significant at 10%; ** significant at 5%; ***significant at 1%.
Source: MIC [2010]; JREI [2012].

There is clear evidence of a correlation, because and  are statisticallysignificant at the 5 percent level and the coefficient of determination R

2

is very high(0.94). In above panel data analysis, after the Lehman collapse (for a short term in
2008–2011), the correlation between the percentage change in housing prices and thepercentage change in the WAP is not statistically significant. However, when we
consider the Japanese case which represents the long-term point of view, the correlation
between the housing prices (indexes) and the percentage of the WAP is statisticallysignificant. The UN predicts that the percentage of Japanese WAP will continue to drop
until 2055 and the WAP itself will decrease until 2100. Under these circumstances, it ishighly possible that Japanese housing prices will also decline in the foreseeable future.

5.

Conclusion

Generally speaking, housing prices are almost determined by actual demand. The
population, especially the working age population, has a great impact on this price formation.
According to the panel data analysis in the developed countries, the rate of change inhousing prices and the rate of change in the WAP were statistically significant factors before
the Lehman collapse, whereas post-Lehman their importance falters in general. However,when I tried to check the data for each country in each year, I discovered that the two factors
are statistically significant in most of the countries whose housing prices rose dramatically.
In these cases, it is highly possible for a housing bubble to burst when the percentage of the
WAP peaks.
In addition, for those countries, including Japan, where the WAP will decrease, we canpredict that houses, offices, and land itself will be in oversupply, and will therefore undergo
further depreciation.

6.

Discussion

Table 7 provides a summary of the years in which the percentage of the WAP, the WAP
itself, and the population peaked or will peak in developed countries.

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Table 7
Summary of peak years for various elements% of WAP

WAP

Population

Peak

Peak

Peak

Australia

2008

na

na

Belgium

1950

na

na

Canada

2008

2078

na

Denmark

1993

2010

na

Finland

1984

2010

na

France

1988

2099

na

Germany

1986

1997

2005

Greece

1999

2010

2044

Ireland

2005

na

na

Italy

1991

2010

2018

Japan

1992

1995

2009

Korea

2013

2015

2029

Netherlands

1990

2010

na

New Zealand

2008

na

na

Norway

2010

na

na

Spain

2004

2024

2049

Sweden

1964

na

na

Switzerland

1988

2012

2032

United Kingdom

1950

2047

na

United States

2005

na

na

Country

Source: UN [2010].
“na” implies the population continues to grow in the country.

According to Table 7, the percentage of the WAP peaked earlier in Europe than elsewhere.
As Thomas Robert Malthus (1766–1834) pointed out at the end of the 18th century:

By encouraging the industry of the towns more than the industry of the country,
Europe may be said, perhaps, to have brought on a premature old age.

It is my belief that the European economic bloc (the EU and the Euro) were born at least
partly out of the necessity to address the problems posed by its dynamic population.
Considering the EU or the Euro as one entity, we note that the WAP peaked in 2010, which13also happened to coincide with the Greek debt problem and the Eurozone crisis. Therefore,
population (WAP) peaks foreshadow busts. Furthermore, the additional hardship brought
about by the decrease in the WAP is likely to intensify the economic crisis in Europe evenfurther.
Meanwhile, the situation in Japan has been worsening, with a decrease not just in the WAP
but also in the total population. In fact, the situation may go beyond the “Lost 20” to becomea “Lost 30” or even a “Lost 50.” Figure 12 shows the total Japanese population in blocks of
100 years each. We can see that it increased drastically after the Meiji Restoration(Japanese revolution). Worryingly, this figure closely resembles the graph of the bubble
economy. Malthus [1798] said:

Necessity, that imperious all pervading law of nature, restrains them within theprescribed bounds. The race of plants and the race of animals shrink under thisgreat restrictive law. And the race of man cannot, by any efforts of reason,
escape from it.

Japan may be a small place but has a large population of more than 100 million people tosupport.
It was a long time ago that Japan became the world’s second largest economy. The era
when we Japanese were gloried in the population bubble with “Japan as Number One” will
never return, just as the property bubble that expanded three or five times in a few years will
never return. From now on, we Japanese should aim for a small but shining country. To dothat, we must better the level of education and drastically change the manner in which ourcountry competes with other countries, that is, not by quantity but by quality.

14

References

Carmen M. Reinhart and Kenneth S. Rogoff, “This Time Is Different” Princeton Univ. Press,
September 11, 2009.

Kousuke Motani, “The Real Face of Deflation,” Kadokawa Publishing Co., Ltd., June 10, 2010.

Robert J. Shiller, “Irrational Exuberance,” Princeton Univ. Dept. of Art, illustrated edition, March
15, 2000.

Takako Ide and Toru Kurahashi, “The Property Bubble & the Economy,” Nippon Hyoron Sha Co.,
Ltd., September 25, 2011.

Thomas Robert Malthus (1798), “An Essay on the Principle of Population,” J. Johnson (London),
http://129.237.201.53/books/malthus/population/malthus.pdf

Working Papers

Kiyohiko G. Nishimura, “This Time May Truly Be Different: Balance Sheet Adjustment underPopulation Ageing,” Remarks at the 2011 American Economic Association Meeting, January 7,
2011.

Mankiw, N.G., Weil, D.N. (1989). The Baby Boom, the Baby Bust, and the Housing Market.
Regional Science and Urban Economics, 19, 235–258.

Masaaki Shirakawa, “Bubbles, Demographic Change and Natural Disasters,” Opening Speech
at 2011 Annual International Conference, June 1, 2011.

OECD (2012), OECD Economic Outlook, Vol. 2012/1, OECD Publishing.
http://dx.doi.org/10.1787/eco_outlook-v2012-1-en

Prakash Kannan, Pau Rabanal and Alasdair Scott, “Macroeconomic Patterns and Monetary
Policy in the Run-Up to Asset Price Busts,” IMF Working Paper, November 2009.

Yuji Mori, “The Impact of the Aging on the Property Market,” Daiwa Institute of Research,
December 29, 2011.

15

100

Sep‐36
Jul‐38May‐40Mar‐42
Jan‐44
Nov‐45
Sep‐47
Jul‐49May‐51
Mar‐53
Jan‐55
Nov‐56
Sep‐58
Jul‐60May‐62
Mar‐64
Jan‐66
Nov‐67
Sep‐69Jul‐71
May‐73
Mar‐75
Jan‐77
Nov‐78
Sep‐80Jul‐82
May‐84
Mar‐86
Jan‐88
Nov‐89Sep‐91
Jul‐93
May‐95
Mar‐97
Jan‐99Nov‐00
Sep‐02
Jul‐04May‐06
Mar‐08Jan‐10Figure 1 Magnitude of the Japanese Bubble(Six Large City Areas/Mar 2000=100)
Residential
Commercial

600

Source: CRI(1936‐1954); JREI(1955‐)

500
Sep‐90, 525.4

400

300
Sep‐90, 231.5

200

Mar‐83, 104.6
Mar‐83, 76.2

0

Copyright © 2012 Kazunori YAMAGUCHI(Japan)1

Figure 2 Indicator of Bubble
Real GDP growth

Real interest rate

8

6

4

2

% 0

‐2

‐4

‐6

Source: WB‐8

Copyright © 2012 Kazunori YAMAGUCHI(Japan)2

Figure 3 % Distribution of population aged 65 & over
25.0%
23.1%

20.0%

15.0%

10.0%

5.0%

Source : MIC
0.0%
1920

1930

1940

1950

1955

1960

1965

1970

1975

1980

1985

Copyright © 2012 Kazunori YAMAGUCHI(Japan)

1990

1995

2000

2005

2010

3

Figure 4 Regression Analysis
Elderly ratio & Residential Land Price % change on
previous year by prefecture
2.0%

Residential Land Price % change on previous year

Source : MIC; MLIT

0.0%
15.0%

16.0%

17.0%

18.0%
Okinawa

‐2.0%

19.0%

Aichi
20.0% 21.0% 22.0%
Tokyo

23.0%

24.0%

25.0%

26.0%

27.0%

28.0%

29.0%

30.0%

31.0%

Saitama‐4.0%
y = ‐0.4426x + 0.0764
R² = 0.3865

‐6.0%

‐8.0%

‐10.0%

Tokushima

Akita

Kochi

Elderly ratio

Copyright © 2012 Kazunori YAMAGUCHI(Japan)4

Figure 5 Population Composition
0

1950

1990

20,000,000

29,080,255(35.4%)

40,000,000

60,000,000

49,052,792(59.7%)

80,000,000

100,000,000

140,000,000

4,066,423(4.9%)

85,257,174(69.7%)

22,387,143(18.3%)

120,000,000

14,606,867(11.9%)

0‐14
15‐64
65+

2010

16,902,861(13.4%)

2050

14,503,735(13.4%)

80,926,012(64.0%)

55,446,339(51.1%)

28,707,047(22.7%)

38,598,603(35.6%)

Source: UN

Copyright © 2012 Kazunori YAMAGUCHI(Japan)5

Figure 6 Indicator of Bubble Part.2(Working Age Population Growth – Total Population Growth)
0.8%

0.6%

0.4%

0.2%

0.0%

‐0.2%

‐0.4%

‐0.6%

‐0.8%

Source: UN‐1.0%

Copyright © 2012 Kazunori YAMAGUCHI(Japan)6

Figure 7 Working Age Population & House Price(Japan)% Distribution of Working Age Population

Urban Land Price Index (Residential)

72.0

140
1990, 69.69

70.0

120

1990, 123

68.0

100

66.0
64.0

80

% 62.0
60

60.0
58.0

40

56.0
20
54.0

Source: MIC; CRI; JREI
52.0

0
1940

1945

1950

1955

1960

1965

1970

1975

1980

1985

Copyright © 2012 Kazunori YAMAGUCHI(Japan)

1990

1995

2000

2005

2010

7

Figure 8 Working Age Population & House Price(USA)% Distribution of Working Age Population

CS Home Price (Composite 10)250.0067.5%
2005, 67.2%

2006, 224.56

67.0%

200.0066.5%
150.0066.0%
100.00
65.5%

50.00

65.0%

Source: UN; S&P
64.5%

0.00
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Copyright © 2012 Kazunori YAMAGUCHI(Japan)8

Figure 9 Working Age Population & House Price(Ireland)% Distribution of Working Age Population

Average House Prices (New Houses/Whole country)
350,000

70.0%
2005, 68.4%

2007, 322,634

68.0%

300,000

66.0%
250,000
64.0%
62.0%

200,000

60.0%

150,000

58.0%
100,000
56.0%
50,00054.0%

Source: UN; Environment, Community and Local Government
52.0%
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010

0

Copyright © 2012 Kazunori YAMAGUCHI(Japan)9

Figure 10 Working Age Population & House Price(Spain)% Distribution of Working Age Population

Average Price per m2 of Private Housing
2500

69.0%
2004, 68.8%

68.8%

2008, 2094.7
2000

68.6%
68.4%

1500
68.2%
68.0%
1000
67.8%
67.6%

500

67.4%

Source: UN; Banco de España
67.2%

0
1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Copyright © 2012 Kazunori YAMAGUCHI(Japan)

2007

2008

2009

2010

2011

10

Figure 11 Working Age Population & House Price(UK)% Distribution of Working Age Population

House Price Index (All Houses)

67.0%

12000.0
2008, 66.2%

1952, 66.3%

2007, 9729.5

66.0%

10000.0

65.0%
8000.064.0%
6000.063.0%
4000.062.0%
2000.061.0%

Source: UN; Nationwide
60.0%

0.0

Copyright © 2012 Kazunori YAMAGUCHI(Japan)11

Figure 12 Movement of Total Population in Japan
140

Source: IPSS; UN
120

100

Million

80

60

40

20

0
A.D.14

350

600

1000

1340

1500

1600

1700

1800

1900

2000

2100

Year

Copyright © 2012 Kazunori YAMAGUCHI(Japan)12