8/9/2019 2010 08 13 CreditSuisse China Property Policy Outlook 20100810
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DISCLOSURE APPENDIX CONTAINS ANALYST CERTIFICATIONS AND THE STATUS OF NON-US ANALYSTS. U.S.Disclosure: Credit Suisse does and seeks to do business with companies covered in its research reports. As a result,investors should be aware that the Firm may have a conflict of interest that could affect the objectivity of this report. Investorsshould consider this report as only a single factor in making their investment decision.
10 August 2010
Asia Pacific/China
Equity Research
Real Estate
China Property Policy Outlook: 2THEME
Grey income: who benefits if the wealth gapnarrows?
Figure 1: Grey income leads to a big swing in housing affordability in China
Housing price to annual income ratio
0
1
2
3
4
5
6
7
8
9
10
11
12
13
U S Korea Japan Singapore HK China * China** China*** China* ** *
P/I(x)Housing price to annual income ratio
0
1
2
3
4
5
6
7
8
9
10
11
12
13
U S Korea Japan Singapore HK China * China** China*** China* ** *
P/I(x)
Source: Government websites, Credit Suisse research * = using average income; ** =
average income with grey income included; *** = median income; **** = median income with
grey income included
Based on the grey income research sponsored by Credit Suisse, we conclude
that China will not only increase wages, but also optimise its tax system
developers that focus on mass market products and high asset turns, such as
China Vanke, should stand to win.
Grey income improved affordability, but only for the rich: The wealthgap skews housing affordability more significantly than indicated by the
official data.
Wealth gap led to a housing mix mismatch in major cities time tochange: In metropolitan areas such as Hong Kong, the widening wealth gap
has created social issues. However, public housing took up around 45% of
Hong Kongs total housing stock, and only less than 6% in China. Therefore,
we expect the government to use tax, among other things, to improve
affordability, and continue to suppress investment-purpose housing demand.
Asset turns will become more important for developers: We expect thatthe pace of land price appreciation will slow in China, and that the prevailing
business model of land hoarding for developers will no longer work. China
Vanke, COLI, and KWG remain our top picks in the sector.
Much of the data in this report is from the Credit Suisse Expert Insights
series: analysing Chinese grey income, published on 6 August 2010.
Research Analysts
Jinsong Du
852 2101 6589
Raymond Cheng, CFA
852 2101 6945
Ronney Cheung
852 2101 7472
Asia Property Research Analysts
Ernest Fong
(Head of Asia Property Research)
Anand Agarwal
(India)
Raymond Cheng
(China)
Jinson Du
(China)
Cusson Leung
(Hong Kong)
Gilbert Lopez
(Philippines)
Teddy Oetomo
(Indonesia)
Tricia Song(Singapore)
Chai Techakumpuch
(Thailand)
Tan Ting Min
(Malaysia)
Sidney Yeh
(Taiwan)
Research Assistants
Abhishek Bansal
(India)
Ronney Cheung
(China)
Joyce Kwock
(Hong Kong)
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China Property Policy Outlook: 2 2
Focus charts and tableFigure 2: Grey income does improve affordability ratio Figure 3: but wealth gap skews housing affordability
Gini index vs P/I ratio
US
ChinaJapan
SingaporeHK
China*
Korea
0123456789
10111213
0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Gini index
Price to income ratio
(x)Gini index vs P/I ratio
US
ChinaJapan
SingaporeHK
China*
Korea
0123456789
10111213
0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Gini index
Price to income ratio
(x)
Gini index vs P/I ratio - based on median income
China***
HK
China**
Korea
Japan
US
Singapore
0123456789
10111213
0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Gini index
Price to income
ratio (x)Gini index vs P/I ratio - based on median income
China***
HK
China**
Korea
Japan
US
Singapore
0123456789
10111213
0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Gini index
Price to income
ratio (x)
* = grey income used for calculation; Source: Government websites;
Credit Suisse estimates
** = using median offical income; *** = using median official income +
median grey income; Source: Government websites; Credit Suisse
estimates;
Figure 4: Investment-driven demand caused housing mix mismatch Shanghais large
unit sales volume as % of total correlates with property pricing changes
0
5
10
15
20
25
30
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10
%
-1,000
2,000
5,000
8,000
11,000
14,000
17,000
20,000
23,000
26,000Rmb/sqm
Above 150 sqm/ uni t ASP (RMB/sqm) - RHS
0
5
10
15
20
25
30
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-0
5
10
15
20
25
30
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10
%
-1,000
2,000
5,000
8,000
11,000
14,000
17,000
20,000
23,000
26,000Rmb/sqm
Above 150 sqm/ uni t ASP (RMB/sqm) - RHS
Source: CRIC, Credit Suisse Research
Figure 5: Those with higher asset turns stand to win
-
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
China Vanke COLI KWG Shimao GZ R&F CR Land Sino Ocean Hopson
Source: Company data, Credit Suisse estimates
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China Property Policy Outlook: 2 3
Figure 6: Valuation summary China developersShare Target +/- Mkt Current (Disc.)/ +12M (Disc.)/ Yield P/B Net
Price Price Cap NAV Prem. NAV Prem. Core PE (x) (%) (x) Gearing
Company RIC Rating (HK$) (HK$) (%) (US$b) (HK$/sh) (%) (HK$/sh) (%) FY09 FY10 FY11 FY09 FY10 FY10 (%)
COLI 0688.HK O 16.72 17.70 6 17.5 16.88 (1) 19.68 (15) 18.3 17.6 15.4 1.2 2.9 59.5
CR Land 1109.HK O 15.96 17.00 7 10.3 21.66 (26) 24.29 (34) 24.8 16.7 13.5 1.5 2.1 44.5
China Vanke - A (Rmb) 000002.SZ O 7.96 10.00 26 11.2 8.50 (6) 10.50 (24) 16.4 12.7 11.0 0.9 2.4 33.4
China Vanke - B 200002.SZ O 9.74 7.90 (19) 1.6 9.66 1 11.93 (18) 17.7 13.7 11.8 0.7 2.6 33.4Evergrande 3333.HK N 2.69 2.40 (11) 5.2 6.08 (56) 6.20 (57) 154.3 5.1 4.7 0.1 1.9 21.6
Greentown 3900.HK U 9.33 8.30 (11) 1.8 15.95 (42) 16.62 (44) 17.4 10.1 7.7 3.3 1.3 169.1
Guangzhou R&F 2777.HK N 12.68 9.80 (23) 5.3 15.80 (20) 17.80 (29) 14.3 9.9 8.7 3.2 1.8 109.7
Hopson 0754.HK N 10.74 9.90 (8) 2.0 22.00 (51) 24.80 (57) 6.4 5.7 4.3 2.3 0.6 33.4
Kaisa 1638.HK O 1.62 2.10 30 1.1 4.90 (67) 5.30 (69) 17.1 5.0 8.3 n.a 1.1 54.1
KWG 1813.HK O 5.46 5.90 8 2.0 10.10 (46) 10.70 (49) 18.0 10.6 9.2 0.9 1.2 70.2
Poly (A) 600048.SS O 12.61 17.30 37 7.4 16.70 (24) 19.20 (34) 11.9 12.1 9.1 1.3 1.6 66.5
Poly (Hong Kong) 0119.HK O 9.15 9.90 8 3.8 12.20 (25) 14.10 (35) 31.7 24.7 17.0 0.5 1.5 52.9
Shimao Property 0813.HK O 14.24 14.10 (1) 6.5 17.66 (19) 20.20 (30) 16.2 11.4 10.3 2.0 1.8 78.5
Sino Ocean 3377.HK O 5 .81 8.00 38 4.2 9.60 (39) 11.44 (49) 21.9 12.4 10.9 1.4 1.2 45.1
Prices are as of 9 August 2010;Source: Bloomberg;Company data, Credit Suisse estimates
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China Property Policy Outlook: 2 4
Grey income: who benefits if thewealth gap narrowsBased on the grey income research sponsored by Credit Suisse, we conclude that China
will not only increase wages, but optimise its tax system developers that focus on mass
market products and high asset turns, such as China Vanke, should stand to win.
Grey income has improved affordability, but only forthe rich
The grey income research (conducted by Dr Wang Xiaolu and sponsored by Credit
Suisse) enables us to compare Chinas real housing affordability with other countries.
We conclude that the consideration of grey income helps explain the strong demand for
housing despite the surging property prices the overall affordability may be better than
indicated by official government data.
However, the research also shows that the wealth gap skews housing affordability more
significantly than indicated by the official data. If we use the median urban income instead
of the average to calculate affordability, the affordability ratio will increase from 8x to 9.8
based on official income data. The difference between average and median income iseven bigger when we consider the effect of grey income the affordability ratio in this
case will increase from 4x to 7.5x.
Wealth gap has led to a housing mix mismatch inmajor cities
In metros such as Hong Kong, it is well documented that the widening wealth gap has
created social issues. However, public housing took up around 45% of Hong Kongs total
housing stock, and therefore cushioned the negative impact to a significant extend. Public
housing in China, however, currently takes up less than 6% of the total housing stock.
Therefore, the negative impact on housing in China should be much more significant, in
our view.
The Chinese government is committed to improving housing affordability by building more
public housing, raising average wages, and implementing changes in the tax system (such
as property tax). However, so far only wage increases have been implemented broadly.
To improve housing affordability, we believe China should not only raise wages, but also
optimise its tax system to narrow the wealth gap. For example, we expect the government
to implement property tax, which should increase the holding costs for property
investments of the wealthy.
With tax reform, asset turns will become moreimportant for developers
We believe that the positive impact of public housing should come only after it expands to
a large scale. Hence, in the near-to-medium term, we expect that the government will
continue to suppress housing demand for investment/speculation purposes. As a result,
we expect the pace of land price appreciation to slow in China.
Therefore, the prevailing business model of land hoarding for developers will no longer
work in China, in our view. Instead, we believe those focusing on high asset turns and a
diversified product mix should stand to win.
China Vanke, COLI, and KWG remain our top picks in the sector.
The wealth gap skews
housing affordability more
significantly than indicated
by official data
Public housing in China
currently took up less than
6% of the total housing
stock
We expect that the pace of
land price appreciation will
slow in China, and that the
prevailing business model of
land hoarding for developers
will no longer work
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10 August 2010
China Property Policy Outlook: 2 5
Grey income has improvedaffordability, but only for the richThe grey income research (conducted by Dr Wang Xiaolu and sponsored by Credit
Suisse) enables us to compare Chinas real housing affordability with that of other
countries. We conclude that the consideration of grey income helps explain the strong
demand for housing despite the surging property prices the overall affordability may be
better than indicated by official government data.
However, the research also shows that the wealth gap skews housing affordability more
significantly than indicated by official data. If we use the median urban income instead of
the average to calculate affordability, the official income data results in an affordability ratio
of 9.8x instead of 8x. The difference is even bigger when we consider the effect of grey
income the affordability ratio in this case is 7.5x instead of 4x.
This helps explain why the government is committed to doubling average wages to narrow
the wealth gap, in our view.
Figure 7: Chinas proposed solutions to its housing issue
Middle class
Low-income population
Investors/speculators
Property tax(near-term)
Double wages
(five-year plan)
Public housing
(long term but start now?)
Middle class
Low-income population
Investors/speculators
Property tax(near-term)
Double wages
(five-year plan)
Public housing
(long term but start now?)
Source: Credit Suisse Research
How skewed is the measurement of housingaffordability?
Due to historical reasons, the method for calculating affordability ratios differs from country
to country. Nevertheless, the key difference has been in using average income versus
median income for the calculation.
After we published our brief introductory note on the impact of grey income on Chinas
housing affordability on 20 July 2010, many investors inquired about the extent to which
the wealth gap skewed real affordability in China. To answer this question, we need to
introduce the Gini index.
We conclude that the grey
income consideration helps
explain the strong demand
for housing despite of the
surging property prices
However, the research also
shows that the wealth gap
skews housing affordability
more significantly than
indicated by official data
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China Property Policy Outlook: 2 6
Figure 8: Gini index (or Gini coefficient) worldwide
Source: Wikipedia
The Gini index (or Gini coefficient) is a measure of the inequality of a distribution, a value
of 0 expressing total equality and a value of 1 maximal inequality. It is commonly used as
a measure of inequality of income or wealth. Worldwide, Gini coefficients for income range
from approximately 0.25 (Denmark) to 0.70 (Namibia), although not every country has
been assessed.
The figure below shows that, based on the official average urban income from Chinas
National Statistics Bureau (NSB), Chinas current affordability ratio is 8x (that is, it takes
eight years average income to buy an average residential property unit). This is lower
than the figure for city states such as Singapore (probably not a relevant comparison), but
significantly higher than the figure for large and developed continental nations such as the
U.S. However, if we consider the impact of grey income, Chinas national affordability ratio
drops to 4x similar to that in the U.S.
Figure 9: Gini index versus price-to-income ratio based
on average income
Figure 10: Gini index versus price-to-income ratio based
on commonly used local methods
Gini index vs P/I ratio
US
ChinaJapan
SingaporeHK
China*
Korea
0123456789
10111213
0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Giniindex
Price to income ratio
(x)Gini index vs P/I ratio
US
ChinaJapan
SingaporeHK
China*
Korea
0123456789
10111213
0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Giniindex
Price to income ratio
(x)
Gini index vs P/I ratio (publicised)
Japan
China
US
Korea
HK
Singapore
China*
0
123456789
10111213
0.2 0 .25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Gini index
Price to income
ratio (x)Gini index vs P/I ratio (publicised)
Japan
China
US
Korea
HK
Singapore
China*
0
123456789
10111213
0.2 0 .25 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Gini index
Price to income
ratio (x)
* = using average official income + grey income; Source: Government
websites; Credit Suisse estimates
* = using average official income + grey income; Source: Government
websites; Credit Suisse estimates
The grey income data also implies that Chinas Gini index may be much higher than the
official data. Based on the official data, Chinas Gini index is between 0.45-0.5, roughly
equal to the U.S. but higher than other developed countries such as Japan and Europe. If
the effect of grey income is included, Chinas Gini index is likely to be more than 0.55
similar to that of many South American countries.
Based on the official data,
Chinas current affordability
ratio was 8x. However, it
drops to 4x if we consider
the impact of the grey
income
The grey income data also
implies that Chinas Gini
index may be much higher
than the official data
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China Property Policy Outlook: 2 7
Indeed, the grey income research shows that the average per capita disposable income
of the top 10% of the urban population should be 26x that of the bottom 10% much
higher than the 9x in the official data.
Figure 11: Comparing Dr Wangs study to the official data(2008) Per capita disposable income (Rmb per annum)
Urban population distribution Official Data (1) Wang's study (2) (2)/(1) %
Bottom 10% 4,754 5,350 113
10-20% 7,363 7,430 101
20-40% 10,196 11,970 117
40-60% 13,984 17,900 128
60-80% 19,254 27,560 143
80-90% 26,250 54,900 209
Top 10% 43,614 139,000 319
Total 16,885 32,154 190
Source: Prof. Wang's Study
The wealth gap thus skews housing affordability more significantly than indicated by the
official data. If we use median urban income instead of the average to calculate
affordability, the official income data results in an affordability ratio of 9.8x instead of 8x.
The difference is even bigger when we consider the effect of grey income the
affordability ratio in this case is 7.5x instead of 4x.
Figure 12: Gini index versus price-to-income ratio (based on median income)
Gini index vs P/I ratio - based o n median income
China***
HK
China**
Korea
Japan
U S
Singapore
01
2345678
910
111213
0.2 0.25 0 .3 0.35 0 .4 0.45 0 .5 0.55 0 .6
Gini index
P rice to income
ratio (x)Gini index vs P/I ratio - based o n median income
China***
HK
China**
Korea
Japan
U S
Singapore
01
2345678
910
111213
0.2 0.25 0 .3 0.35 0 .4 0.45 0 .5 0.55 0 .6
Gini index
P rice to income
ratio (x)
** = using median official income; *** = using median official income + median grey income; Source:
Government websites; Credit Suisse estimates
Figure 13: Grey income leads to a big swing in housing affordability in China
Housi ng pri ce to annual income ratio
0
1
2
3
4
5
67
8
9
10
11
12
13
US Ko rea Japan Singapore HK China * China** China*** Chin a****
P/I (x)Housi ng pri ce to annual income ratio
0
1
2
3
4
5
67
8
9
10
11
12
13
US Ko rea Japan Singapore HK China * China** China*** Chin a****
P/I (x)
* = using average income; ** = average income with grey income included; *** = median income; **** =
median income with grey income included; Source: Government websites, Credit Suisse research
Research shows that
average per capita
disposable income of the
top 10% of the urban
population should be 26x
that of the bottom 10%
much higher than the 9x in
the official data
The wealth gap thus skews
housing affordability more
significantly than indicated
by the official data
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China Property Policy Outlook: 2 8
Wealth gap has led to a housing mixmismatch in major citiesIn metros such as Hong Kong, it is well documented that the widening wealth gap has
created social issues. However, public housing took up around 45% of Hong Kongs total
housing stock, and therefore cushioned the negative impact to a significant extend. Public
housing in China, however, currently takes up less than 6% of the total housing stock.
Therefore, the negative impact on housing in China should be much more significant, in
our view.
The Chinese government is committed to improve housing affordability by building more
public housing, raising average wages, and implementing changes in the tax system (such
as property tax) to improve housing affordability. However, so far, only wage increases
have been implemented broadly.
To improve housing affordability, we believe China should not only raise wages, but
optimise its tax system to narrow the wealth gap as well. For example, we expect the
government to implement property tax, which should increase the holding costs for
property investments of the wealthy.
Figure 14: Public housing constitutes 45% of the total housing stock in Hong Kong
versus less than 6% in China
0
5
10
15
20
25
30
35
40
45
50
Hong Kong China
%
Source: CEIC, Credit Suisse estimates
The higher the unit prices, the bigger the unit size
In Chinas major cities, the 2009 property price surge resulted in more transactions of
larger size property units. The widening wealth gap should be one of the reasons, in our
view although the official data shows that China has a Gini ratio similar to Hong Kongs
(0.45-0.5), the grey income research implies that the Gini ratio should be a lot higher
(0.55-0.6) for China as a whole, and even higher for major cites specifically.
Property ASPs are more correlated with locations rather than unit sizes. However, as
shown in the two figures below, the volume of large-size property sales as a percentage oftotal sales volume in Shanghai and Shenzhen correlates closely with property ASP
changes.
Public housing in China
currently took up less than
6% of the total housing
stock
The volume of large-size
property sales as apercentage of total sales
volume in Shanghai and
Shenzhen correlates closely
with property ASP changes
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Figure 15: Shanghais large unit sales volume as % of total correlates with property
pricing changes
0
5
10
15
20
25
30
Jan-
09
Feb-
09
Mar-
09
Apr-
09
May-
09
Jun-
09
Jul-
09
Aug-
09
Sep-
09
Oct-
09
Nov-
09
Dec-
09
Jan-
10
Feb-
10
Mar-
10
Apr-
10
May-
10
Jun-
10
Jul-
10
%
-1,000
2,000
5,000
8,000
11,000
14,00017,000
20,000
23,000
26,000Rmb/sqm
Above 150 sqm/ uni t ASP (RMB/sqm) -RHS
0
5
10
15
20
25
30
Jan-
09
Feb-
09
Mar-
09
Apr-
09
May-
09
Jun-
0
5
10
15
20
25
30
Jan-
09
Feb-
09
Mar-
09
Apr-
09
May-
09
Jun-
09
Jul-
09
Aug-
09
Sep-
09
Oct-
09
Nov-
09
Dec-
09
Jan-
10
Feb-
10
Mar-
10
Apr-
10
May-
10
Jun-
10
Jul-
10
%
-1,000
2,000
5,000
8,000
11,000
14,00017,000
20,000
23,000
26,000Rmb/sqm
Above 150 sqm/ uni t ASP (RMB/sqm) -RHS
Source: CRIC, Credit Suisse Research
Figure 16: Shenzhens large unit sales volume as % of total also correlates with property
pricing changes
Shenzhen
0
5
10
15
20
25
30
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10
%
-1,000
2,000
5,000
8,000
11,000
14,000
17,000
20,000
23,000
26,000
Rmb/sqm
Above 140 sqm / unit ASP (RMB/sqm) -RHS
Shenzhen
0
5
10
15
20
25
30
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Shenzhen
0
5
10
15
20
25
30
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10
%
-1,000
2,000
5,000
8,000
11,000
14,000
17,000
20,000
23,000
26,000
Rmb/sqm
Above 140 sqm / unit ASP (RMB/sqm) -RHS
Source: CRIC, Credit Suisse Research
Figure 17: % second home purchase in ChinaIncome group More than one home (as a % of total)
No income 28.57%
Rmb 1 - 7,000 4.38%
Rmb 7001 - 10,000 4.03%Rmb 10,001 - 17,000 9.72%
Rmb 17,00126,500 12.50%
Rmb 26,50134,000 14.20%
Rmb 34,00175,000 23.24%
Rmb 75,001400,000 37.01%
> Rmb 400,000 64.47%
Source: Prof. Wang's survey
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China Property Policy Outlook: 2 10
Figure 18: Average housing size per capita in ChinaIncome group Average GFA per capita (sq.m.)
No income 46
Rmb 1 - 7,000 31
Rmb 7001 - 10,000 31
Rmb 10,001 - 17,000 35
Rmb 17,00126,500 39
Rmb 26,50134,000 39Rmb 34,00175,000 48
Rmb 75,001400,000 65
> Rmb 400,000 99
Source: Professor Wangs survey
In the meantime, migrant workers (and professionals) first home and average working
classes upgrade (both for smaller units) remain the main drivers of Chinas self-use
housing demand. Indeed, Credit Suisses annual consumer survey shows that first-home
demand is on the rise.
Figure 19: Purpose of purchasing property in the coming one year first home demand
is on the rise
53 5347
40
23 2927
36
1312
17 18
11 6 9 6
0
10
20
30
40
50
60
70
80
90
100
2006 2007 2008 2009
Upgrad e Fir st-ti me b uyer Investme nt Othe rs
53 5347
40
23 2927
36
1312
17 18
11 6 9 6
0
10
20
30
40
50
60
70
80
90
100
2006 2007 2008 2009
Upgrad e Fir st-ti me b uyer Investme nt Othe rs
Source: Credit Suisse Annual Consumer Survey
Therefore, there is clearly a mismatch between supply and the self-use demand in China.
The government is raising wages to narrow thewealth gap but tax may be more effective
The Chinese government is committed to improving housing affordability by building more
public housing, raising average wages, and implementing changes in the tax system (such
as property tax) to improve housing affordability. However, so far only wage increaseshave been implemented broadly.
Migrant workers (and
professionals) first home
and average working
classes upgrade (both for
smaller units) remain the
main drivers of Chinas self-
use housing demand
Among the housing
affordability solutions, so far
only wage increases havebeen implemented broadly
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Figure 20: Chinas proposed solutions to its housing issue
Middle class
Low-income population
Investors/speculators
Property tax(near-term)
Double wages(five-year plan)
Public housing
(long term but start now?)
Middle class
Low-income population
Investors/speculators
Property tax(near-term)
Double wages(five-year plan)
Public housing
(long term but start now?)
Source: Credit Suisse Research
So far in 2010, 30 provinces already raised the minimum wages, with an average increase
of more than 20%. According to the Ministry of Human Resources and Social Security,
other provinces will follow within this year.
Figure 21: Wage increases implemented so far this yearCity Range Min. hourly rate Avg. % change
Shanghai 1120 9 16.7
Zhejiang 800-1100 6.5-9 15.3
Guangdong 660-1030 6.4-9.9 21.1
Beijing 960 11 20
Jiangsu 670-960 5.4-7.2 13.1
Tianjin 920 7.8 12.2
Shandong 600-920 6.5-9.6 21.2
Hebei 690-900 6.9-9 18
Mongolia 680-900 6.1-8.1 n.a
Liaoning 650-900 6-8.5 29
Fujian 600-900 6.5-9.6 24.5
Hubei 600-900 6.5-9 28.9
Helongjiang 600-880 5.5-7.5 n.a
Sichuan 650-850 6.8-8.9 13
Shanxi 640-850 7-9.3 15.5
Hunan 600-850 6-8.5 27.8
Hainan 630-830 5.9-7.2 n.a
Yunnan 680-820 6.0-8.0 n.a
Jilin 680-800 5.2-6.3 22.9
Henan 600-800 6.8-9 n.a
Shaanxi 580-760 5.8-7.6 22.7
Tibet 680-730 6-6.5 n.a
Anhui 500-720 7.5 27.25Jiangxi 500-720 4.7-6.8 12
Ningxia 605-710 6.8-7.2 n.a
Chongqing 520-680 n.a n.a
Guangxi 460-670 3.5-5 n.a
Guizhou 550-650 5.9-6.9 n.a
Gansu 500-620 5.2-6.5 n.a
Qinghai 580-600 6.3-6.5 n.a
Average 20.06
Source: Government websites
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However, history shows that an appropriate tax system should be more effective in
optimising the housing product mix. In fact, during the pilot housing program in Yantai in
the 1980s, and the wider pilot housing program in major cities such as Shanghai in the
1990s, the tax system has consistently proven to be an effective tool for better aligning
resources during the shortage in housing supply.
Therefore, to improve housing affordability, we believe China should not only raise wages,
but also optimise its tax system to narrow the wealth gap. For example, we expect the
government to implement property tax, which should increase the holding costs forproperty investments of the wealthy.
For more details on the property tax, please refer to our report, China Property Policy
Outlook 1, published on 14 June 2010.
We expect the government
to implement a property tax,
which should increase theholding costs for property
investments of the wealthy
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With tax reform, asset turns willbecome more important fordevelopers
We believe the positive impact of public housing should come only after it expands to alarge scale. Therefore, in the near-to-medium term, we expect the government to continue
to suppress the investment/speculation-driven housing demand. With this, we expect the
pace of land price appreciation to slow in China.
Therefore, the prevailing business model of land hoarding for developers will no longer
work in China, in our view. Instead, we believe that those focusing on high asset turns and
a diversified product mix should stand to win.
Figure 22: An assessment of developers asset turns using 2010E contracted sales /
total assets as a proxy
-
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
China Vanke COLI KWG Shimao GZ R&F CR Land Sino Ocean Hopson
Source: Company data, Credit Suisse estimates
Although these changes may happen only gradually, we believe the upcoming property-
related policies should change the valuation of the China property sector within the next
year or two. Those with a relatively high turnover, or strong brand name, or both, should
stand to outperform those mainly dependent upon financial leverage and a large land bank.
China Vanke, COLI, and KWG remain our top picks in the sector.
We expect that the pace ofland price appreciation will
slow in China, and that the
prevailing business model of
land hoarding for developers
will no longer work
China Vanke, COLI, and
KWG remain our top picks
in the sector
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Companies Mentioned (Price as of 06 Aug 10)China Overseas Land & Investment (0688.HK, HK$16.60, OUTPERFORM [V], TP HK$17.70)China Resources Land Ltd (1109.HK, HK$15.92, OUTPERFORM [V], TP HK$17.00)China Vanke Co Ltd-A (000002.SZ, Rmb8.00, OUTPERFORM [V], TP Rmb10.00)
Disclosure AppendixImportant Global Disclosures
Jinsong Du, Raymond Cheng, CFA & Ronney Cheung each certify, with respect to the companies or securities that he or she analyzes, that (1) theviews expressed in this report accurately reflect his or her personal views about all of the subject companies and securities and (2) no part of his orher compensation was, is or will be directly or indirectly related to the specific recommendations or views expressed in this report.
See the Companies Mentioned section for full company names.
3-Year Price, Target Price and Rating Change History Chart for 0688.HK
0688.HK Closing
Price
Target
Price Initiation/
Date (HK$) (HK$) Rating Assumption
15-Aug-07 14.68 17.94
9-Jan-08 15.04 21.27
24-Mar-08 11.56 20.78
4-Aug-08 X
9-Sep-08 11.52 13.84 N
3-Nov-08 8.95 10.899
6-Jan-09 11.9 12.1124-Mar-09 12.48 11.99
19-Jun-09 15.94 16.3
18-Aug-09 17.46 17.4
11-Jan-10 16.7 21 O X
19-May-10 14.42 17.7
18
21 21
14
1112 12
1617
21
18
4-Aug-08 11-Jan-10
O
N
6
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Closing Pr ice Target Price Initiation/Assumption Rating
HK$
O=Outperform; N=Neutral; U=Underperform; R=Restricted; NR=Not Rated; NC=Not C overed
3-Year Price, Target Price and Rating Change History Chart for 1109.HK1109.HK Closing
Price
Target
Price Initiation/
Date (HK$) (HK$) Rating Assumption
15-Aug-07 13.24 15.4 O
17-Sep-07 15.1 16.66
30-Oct-07 18.1 22.27
4-Dec-07 19.72 23.874-Aug-08 X
9-Sep-08 8.58 16.232
3-Nov-08 7.94 11.914
6-Jan-09 10 13.65
25-Feb-09 9.1 13.646
30-Mar-09 11.52 14.6
18-May-09 15.26 R
28-May-09 15.4 O
19-Jun-09 15.84 22.7
3-Aug-09 19.32 23.65
21-Sep-09 17.46 24.15
29-Oct-09 18.22 24.51
11-Jan-10 16.88 24 X
19-May-10 14.04 17
15
17
22
24
16
12
14 1415
2324 24
25 24
17
4-Aug-08 11-Jan-10
ORO
6
8
10
12
14
16
18
20
22
24
26
10-Aug
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Closing Pr ice Target Price Initiation/Assumption Rating
HK$
O=Outperform; N=Neutral; U=Underperform; R=Restricted; NR=Not Rated; NC=Not C overed
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3-Year Price, Target Price and Rating Change History Chart for 000002.SZ
000002.SZ Closing
Price
Target
Price Initiation/
Date (Rmb) (Rmb) Rating Assumption
28-Jul-09 14.24 17 O X
12-Oct-09 10.85 15
22-Feb-10 9.26 12
19-May-10 7.26 9.5
5-Aug-10 7.99 10
17
15
12
10 10
28-Jul-09
O
5
10
15
20
25
10-Aug
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10-Jun
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Closing Pr ice Target Price Initiation/Assumption Rating
Rmb
O=Outperform; N=Neutral; U=Underperform; R=Restricted; NR=Not Rated; NC=Not C overed
The analyst(s) responsible for preparing this research report received compensation that is based upon various factors including Credit Suisse's totalrevenues, a portion of which are generated by Credit Suisse's investment banking activities.Analysts stock ratings are defined as follows:Outperform (O): The stocks total return is expected to outperform the relevant benchmark* by at least 10-15% (or more, depending on perceivedrisk) over the next 12 months.
Neutral (N): The stocks total return is expected to be in line with the relevant benchmark* (range of 10-15%) over the next 12 months.Underperform (U): The stocks total return is expected to underperform the relevant benchmark* by 10-15% or more over the next 12 months.*Relevant benchmark by region: As of 29th May 2009, Australia, New Zealand, U.S. and Canadian ratings are based on (1) a stocks absolute totalreturn potential to its current share price and (2) the relative attractiveness of a stocks total return potential within an analysts coverage universe**,with Outperforms representing the most attractive, Neutrals the less attractive, and Underperforms the least attractive investment opportunities.Some U.S. and Canadian ratings may fall outside the absolute total return ranges defined above, depending on market conditions and industryfactors. For Latin American, Japanese, and non-Japan Asia stocks, ratings are based on a stocks total return relative to the average total return ofthe relevant country or regional benchmark; for European stocks, ratings are based on a stocks total return relative to the analyst's coverageuniverse**. For Australian and New Zealand stocks a 22% and a 12% threshold replace the 10-15% level in the Outperform and Underperform stockrating definitions, respectively, subject to analysts perceived risk. The 22% and 12% thresholds replace the +10-15% and -10-15% levels in theNeutral stock rating definition, respectively, subject to analysts perceived risk.**An analyst's coverage universe consists of all companies covered by the analyst within the relevant sector.Restricted (R): In certain circumstances, Credit Suisse policy and/or applicable law and regulations preclude certain types of communications,including an investment recommendation, during the course of Credit Suisse's engagement in an investment banking transaction and in certain other
circumstances.Volatility Indicator [V]: A stock is defined as volatile if the stock price has moved up or down by 20% or more in a month in at least 8 of the past 24months or the analyst expects significant volatility going forward.
Analysts coverage universe weightings are distinct from analysts stock ratings and are based on the expectedperformance of an analysts coverage universe* versus the relevant broad market benchmark**:Overweight: Industry expected to outperform the relevant broad market benchmark over the next 12 months.Market Weight: Industry expected to perform in-line with the relevant broad market benchmark over the next 12 months.Underweight: Industry expected to underperform the relevant broad market benchmark over the next 12 months.*An analysts coverage universe consists of all companies covered by the analyst within the relevant sector.**The broad market benchmark is based on the expected return of the local market index (e.g., the S&P 500 in the U.S.) over the next 12 months.
Credit Suisses distribution of stock ratings (and banking clients) is:Global Ratings Distribution
Outperform/Buy* 47% (63% banking clients)Neutral/Hold* 40% (59% banking clients)Underperform/Sell* 12% (53% banking clients)Restricted 2%
*For purposes of the NYSE and NASD ratings distribution disclosure requirements, our stock ratings of Outperform, Neutral, and Underperform most closely correspond to Buy,Hold, and Sell, respectively; however, the meanings are not the same, as our stock ratings are determined on a relative basis. (Please refer to definitions above.) An investor'sdecision to buy or sell a security should be based on investment objectives, current holdings, and other individual factors.
Credit Suisses policy is to update research reports as it deems appropriate, based on developments with the subject company, the sector or themarket that may have a material impact on the research views or opinions stated herein.
Credit Suisse's policy is only to publish investment research that is impartial, independent, clear, fair and not misleading. For more detail please refer to CreditSuisse's Policies for Managing Conflicts of Interest in connection with Investment Research:http://www.csfb.com/research-and-analytics/disclaimer/managing_conflicts_disclaimer.html
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Credit Suisse does not provide any tax advice. Any statement herein regarding any US federal tax is not intended or written to be used, and cannotbe used, by any taxpayer for the purposes of avoiding any penalties.
See the Companies Mentioned section for full company names.Price Target: (12 months) for (0688.HK)Method: Our 12-month target price of HK$17.70 for China Overseas Land & Investment is at 10% discount to our 12-month forward net asset value(NAV) estimate using a 12% discount factor.Risks: Risks to our earnings, rating and HK$17.70 target price for China Overseas Land & Investment include 1) completion delays or 2) any furtherunexpected tightening measures by the government, which could dampen buying sentimentPrice Target: (12 months) for (1109.HK)Method: Our 12-month target price of HK$17 for China Resources Land (CRL) is set at par to our 12-month forward NAV (net asset value) estimate(HK$17/share) including the assumption of a asset injection by its parent holding company. CRL is one of a few Chinese property companies thathas a sizeable exposure in investment properties and a proven track record in managing commercial assets, making it the prime beneficiary of yieldcompression. Thus we believe deserves to trade at a scarcity premium.Risks: Key risks to China Resources Land achieving our HK$17 12-month target price include: 1) completion delays, 2) unexpected macro policiestargeting the property sector in China and 3) uncertainties on potential asset injections from its parent company. Currently CR Holdings is on ourrestricted list and we are not permissible to comment on any transactions related to the company.Price Target: (12 months) for (000002.SZ)Method: Our target price of Rmb10 is based on 10% discount to its 12 mth NAV, due to its diversified presence and good sales track recordRisks: Risks to our target price of Rmb10 are demand conditions: if demand in China slows down, China's earnings and growth should be negativelyaffected. Changes in the company's financial conditions and other macro changes.
Please refer to the firm's disclosure website at www.credit-suisse.com/researchdisclosures for the definitions of abbreviations typically used in thetarget price method and risk sections.
See the Companies Mentioned section for full company names.The subject company (0688.HK, 000002.SZ, 1109.HK) currently is, or was during the 12-month period preceding the date of distribution of thisreport, a client of Credit Suisse.Credit Suisse provided investment banking services to the subject company (0688.HK, 000002.SZ, 1109.HK) within the past 12 months.Credit Suisse has managed or co-managed a public offering of securities for the subject company (0688.HK, 1109.HK) within the past 12 months.Credit Suisse has received investment banking related compensation from the subject company (0688.HK, 1109.HK) within the past 12 months.Credit Suisse expects to receive or intends to seek investment banking related compensation from the subject company (0688.HK, 000002.SZ,1109.HK) within the next 3 months.
Important Regional Disclosures
Singapore recipients should contact a Singapore financial adviser for any matters arising from this research report.
The analyst(s) involved in the preparation of this report have not visited the material operations of the subject company (0688.HK, 1109.HK,000002.SZ) within the past 12 months.
Restrictions on certain Canadian securities are indicated by the following abbreviations: NVS--Non-Voting shares; RVS--Restricted Voting Shares;SVS--Subordinate Voting Shares.Individuals receiving this report from a Canadian investment dealer that is not affiliated with Credit Suisse should be advised that this report may notcontain regulatory disclosures the non-affiliated Canadian investment dealer would be required to make if this were its own report.For Credit Suisse Securities (Canada), Inc.'s policies and procedures regarding the dissemination of equity research, please visithttp://www.csfb.com/legal_terms/canada_research_policy.shtml.
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To the extent this is a report authored in whole or in part by a non-U.S. analyst and is made available in the U.S., the following are importantdisclosures regarding any non-U.S. analyst contributors:The non-U.S. research analysts listed below (if any) are not registered/qualified as research analysts with FINRA. The non-U.S. research analystslisted below may not be associated persons of CSSU and therefore may not be subject to the NASD Rule 2711 and NYSE Rule 472 restrictions on
communications with a subject company, public appearances and trading securities held by a research analyst account. Jinsong Du, non-U.S. analyst, is a research analyst employed by Credit Suisse (Hong Kong) Limited. Raymond Cheng, CFA, non-U.S. analyst, is a research analyst employed by Credit Suisse (Hong Kong) Limited. Ronney Cheung, non-U.S. analyst, is a research analyst employed by Credit Suisse (Hong Kong) Limited. Ernest Fong, non-U.S. analyst, is a research analyst employed by Credit Suisse Taipei Branch. Anand Agarwal, non-U.S. analyst, is a research analyst employed by Credit Suisse Securities (India) Private Limited. Cusson Leung, CFA, non-U.S. analyst, is a research analyst employed by Credit Suisse (Hong Kong) Limited. Gilbert Lopez, non-U.S. analyst, is a research analyst employed by Credit Suisse (Hong Kong) Limited Philippines Branch. Teddy Oetomo, non-U.S. analyst, is a research analyst employed by PT Credit Suisse Securities Indonesia. Tricia Song, non-U.S. analyst, is a research analyst employed by Credit Suisse Singapore Branch. Chai Techakumpuch, non-U.S. analyst, is a research analyst employed by Credit Suisse Securities (Thailand) Limited. Tan Ting Min, non-U.S. analyst, is a research analyst employed by Credit Suisse Securities (Malaysia) Sdn Bhd.
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Sidney Yeh, non-U.S. analyst, is a research analyst employed by Credit Suisse Taipei Branch. Abhishek Bansal, non-U.S. analyst, is a research analyst employed by Credit Suisse Securities (India) Private Limited Joyce Kwock, non-U.S. analyst, is a research analyst employed by Credit Suisse (Hong Kong) Limited.For Credit Suisse disclosure information on other companies mentioned in this report, please visit the website at www.credit-suisse.com/researchdisclosures or call +1 (877) 291-2683.Disclaimers continue on next page.
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Asia Pacific/China
Equity Research
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