2010 08 13 CreditSuisse China Property Policy Outlook 20100810

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

[email protected]

Raymond Cheng, CFA

852 2101 6945

[email protected]

Ronney Cheung

852 2101 7472

[email protected]

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)


2/18

10 August 2010

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


(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

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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

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30

Jan-09

Feb-09

Mar-09

Apr-09

May-09

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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


3/18

10 August 2010


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


4/18

10 August 2010


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


5/18

10 August 2010


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




Double wages

(five-year plan)

Public housing


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


6/18

10 August 2010


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


(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


(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


7/18

10 August 2010


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


8/18

10 August 2010


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


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


9/18

10 August 2010


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

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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-

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Feb-

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%

-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


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

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Feb-10

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Apr-10

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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

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Jun-09

Jul-09

Aug-09

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Oct-09

Nov-09

Dec-09

Jan-10

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Mar-10

Apr-10

May-10

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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


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


10/18

10 August 2010


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


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


11/18

10 August 2010


Figure 20: Chinas proposed solutions to its housing issue

Middle class




Double wages(five-year plan)

Public housing


Middle class




Double wages(five-year plan)

Public housing


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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10 August 2010


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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10 August 2010


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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10 August 2010


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

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26

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-10


HK$



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10 August 2010


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


Rmb


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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10 August 2010


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.

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Commission is the commission rate or the amount agreed with a customer when setting up an account or at anytime after that.

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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10 August 2010


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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2010 08 13 CreditSuisse China Property Policy Outlook 20100810