1

THE DETERMINANTS OF THE MORTGAGE CREDIT BOOM IN

CHINA

Santiago Carbo-Valverde

University of Granada

Francisco Rodriguez-Fernandez

University of Granada

Ming Qidagger

University of Granada

Abstract

In this paper we investigate the nexus between housing price and mortgage lending in

China We analyse a sample of 28 Chinese domestic commercial banks during the period

2003-2009 Our results suggest a reserve causality between mortgage lending and

housing prices The approaching urbanisation throughout China contributes substantively

to the credit boom We find a distinct behavior of the economical housing market in

which the housing price and mortgage credits affect each other adversely The national

inflation rate is of more consideration by potential high grade and economical house purchasers We also find that the monetary policy has a greater impact on the mortgage

lending than banksrsquo characters Raising bank reserves is a relatively effective instrument

to control the mortgage credit boom On the other hand the interest rate functions

better in tempering property prices Our findings have important policy implications and

account for the existing monetary policies which are implemented in China

Key words mortgage house price bank

JEL G12 R21 G21

daggerCorrespondingauthor Departamento de Teoriacutea e Historia Econoacutemica

Facultad de CCEE y Empresariales Universidad de Granada Campus Cartuja

sn E-18071 Granada Spain e-mail qimingcorreougres

2

1 Introduction

Over the past decade both the mortgage market and property sector have

experienced a dynamic change The current financial crisis which starts from

2008 and makes the whole banking industry suffer is largely attributed to the

meltdown of US subprime mortgage market The rapid growth of real house

prices and mortgage debts has been extensively documented across many

countries (Milesand and Pillonca 2008 Wolswijk 2006) Meanwhile China as

one of the largest emerging markets has witnessed soaring property price and

mortgage credit boom in the domestic market (Liang and Cao 2007) Figure 1

and figure 2 illustrate the movements of mortgage loans and property prices

in mainland China respectively In an international context it is a stylized fact

that the amount of bank lending is strong correlated to the property price

Many existing empirical studies highlight a strong correlation between bank

lending and property prices (Hofmann 2003 Gerlach and Peng2005

Fitzpatrick and McQuinn 2007 Davis and Zhu 2010) China is an interesting

example which also experienced mortgage boom and soaring property prices

She provides a representative figure of emerging property market which grows

and develops out of nothing during the past decades At the same time China

also experienced a tremendous reformation in the banking and house

financing sectors In this paper we focus on the relationship between the

outstanding mortgage loans and housing prices In addition we test the real

effects of bankrsquos characteristics and monetary policies

3

The development of Chinese commercial mortgage market began to

accelerate from 1998 in which year Peoplersquos Bank of China authorizes all

domestic commercial banks to issue household mortgage loans From then on

the Chinese government has embarked upon an effort to introduce

market-oriented mechanisms into property financing system eg financial

leverage through commercial banks All these measures have promoted and

contributed to the housing reform in mainland China (Zhang 2000)

Meanwhile commercial banks also recognize the residential mortgage credits

as health asset in their portfolio given the lower default rate of loans to

individual mortgagors than state-owned entrepreneurs (Deng and Fei 2008)

Accordingly the competition is brought about into house financing market

Although the marketised housing mortgage was first introduced in 1998 the

outstanding mortgage loans soared from 51 billion Yuan in 1999 to 6060

billion Yuan at the end of 2010 The mortgage to loan ratio for all financial

institutions increased from 059 in 1999 to 1265 at the end of 2010

During the same period the property prices increase with the volume of

outstanding mortgage loans during the period 1999-2009 except for 2008 in

which year the influence of financial crisis spread to China Except for

commercial mortgage loans from financial institutions Housing Provident

Fund (HPF) is an alternative way which provides the participants lower

interest rate than commercial banks1

1Since the HPF loan is often not sufficient most house buyers prefer to apply to both HPF and commercial

mortgage loansYeung andHowes (2006) reviewedthe role of the HPF infinancing affordable housing development

4

The Chinese mortgage market is featured in two aspects First a uniform and

adjustable mortgage rate is set by the Peoplersquos Bank of China for all

mortgagers2 Once a new mortgage rate is announced it applies to all types

of mortgages including existing ones Second there has been not yet mature

secondary mortgage market Until now only two Mortgage-Backed Securities

(MBS) have been issued in China3 Consequently Chinese mortgage holders

have limited alternative way to invest and then are sensitive to the change of

mortgage rate (Deng et al 2009) That accounts for the high rate of

prepayment in China

The paper is organized as follows Section 2 reviews theoretical and empirical

literature on the relationship between mortgage lending and property price

and highlights the major contributions of my work Section 3 describes the

data methodology and variables employed in the analysis The empirical

results are presented in section 4 Section 5 concludes this paper

2 Literature Review

Various models and empirical frames have been proposed to explain the close

relationship between bank lending movements and property prices which is

also supported by abundant cross-country evidence (Tsatsaronis and Zhu 2004

in China in detail 2 Since 2005 commercial banks can set individual rate which cannot be lower than 90 percent of the rate set by

the Peoplersquos Bank of China 3According to the report by the Peoplersquos Bank of China only China Construction Bank (CCB) are authorized to

issue MBS in the domestic inter-bank Bond Market amounting to yen3018 billion and yen 416 billion respectively

5

Eacutegert and Mihaljek 2007 Lacoviello and Minetti 2008 Goodhart and Hofmann

2008 Davis and Zhu2010) and the indications of individual countries such as

the USA (Capozza et al 2002) Spain (Gimeno and Martinez-Carrascal 2006)

Ireland (Fitzpatrick and McQuinn 2007) and China (Liang and Cao 2007)

Hofmann (2003) and Basurto et al (2006) suggest a bi-directional causality

between housing prices and bank credits However Goodhart and Hofmann

(2004) Gerlach and Peng(2005) find that the change of property prices have a

significant impact on bank lending without the real effect of bank credits on

property prices By contrast Liang and Cao (2007) investigate the case of China

and suggest a unidirectional causality from bank lending to property prices

which differs from previous empirical work about developed economies

Although the empirical analyses above are attractive and involve standard

ldquobubble bustersrdquo such as income or interest into their frameworks they are

hard to explain the real effects theoretically and rather ad hoc There are two

alternative models in the literature to systematically and theoretically

interpret the housing prices The first one is an inverted demand model which

includes the per capita housing supply and an index of credit conditioning as

additional explanatory variables and then provides better theoretical

foundations (Muellbauer and Murphy 1997 Meen 2002) This model is

appropriate for those countries in which the housing supply rockets up

considerably The alternative model is the house price-to-rent model (Ayuso

and Restoy 2006) It assumes the house rent-to-price ratio only depends on the

4

The Chinese mortgage market is featured in two aspects First a uniform and

adjustable mortgage rate is set by the Peoplersquos Bank of China for all

mortgagers2 Once a new mortgage rate is announced it applies to all types

of mortgages including existing ones Second there has been not yet mature

secondary mortgage market Until now only two Mortgage-Backed Securities

(MBS) have been issued in China3 Consequently Chinese mortgage holders

have limited alternative way to invest and then are sensitive to the change of

mortgage rate (Deng et al 2009) That accounts for the high rate of

prepayment in China

The paper is organized as follows Section 2 reviews theoretical and empirical

literature on the relationship between mortgage lending and property price

and highlights the major contributions of my work Section 3 describes the

data methodology and variables employed in the analysis The empirical

results are presented in section 4 Section 5 concludes this paper

2 Literature Review

Various models and empirical frames have been proposed to explain the close

relationship between bank lending movements and property prices which is

also supported by abundant cross-country evidence (Tsatsaronis and Zhu 2004

in China in detail 2 Since 2005 commercial banks can set individual rate which cannot be lower than 90 percent of the rate set by

the Peoplersquos Bank of China 3According to the report by the Peoplersquos Bank of China only China Construction Bank (CCB) are authorized to

issue MBS in the domestic inter-bank Bond Market amounting to yen3018 billion and yen 416 billion respectively

5

Eacutegert and Mihaljek 2007 Lacoviello and Minetti 2008 Goodhart and Hofmann

2008 Davis and Zhu2010) and the indications of individual countries such as

the USA (Capozza et al 2002) Spain (Gimeno and Martinez-Carrascal 2006)

Ireland (Fitzpatrick and McQuinn 2007) and China (Liang and Cao 2007)

Hofmann (2003) and Basurto et al (2006) suggest a bi-directional causality

between housing prices and bank credits However Goodhart and Hofmann

(2004) Gerlach and Peng(2005) find that the change of property prices have a

significant impact on bank lending without the real effect of bank credits on

property prices By contrast Liang and Cao (2007) investigate the case of China

and suggest a unidirectional causality from bank lending to property prices

which differs from previous empirical work about developed economies

Although the empirical analyses above are attractive and involve standard

ldquobubble bustersrdquo such as income or interest into their frameworks they are

hard to explain the real effects theoretically and rather ad hoc There are two

alternative models in the literature to systematically and theoretically

interpret the housing prices The first one is an inverted demand model which

includes the per capita housing supply and an index of credit conditioning as

additional explanatory variables and then provides better theoretical

foundations (Muellbauer and Murphy 1997 Meen 2002) This model is

appropriate for those countries in which the housing supply rockets up

considerably The alternative model is the house price-to-rent model (Ayuso

and Restoy 2006) It assumes the house rent-to-price ratio only depends on the

5

Eacutegert and Mihaljek 2007 Lacoviello and Minetti 2008 Goodhart and Hofmann

2008 Davis and Zhu2010) and the indications of individual countries such as

the USA (Capozza et al 2002) Spain (Gimeno and Martinez-Carrascal 2006)

Ireland (Fitzpatrick and McQuinn 2007) and China (Liang and Cao 2007)

Hofmann (2003) and Basurto et al (2006) suggest a bi-directional causality

between housing prices and bank credits However Goodhart and Hofmann

(2004) Gerlach and Peng(2005) find that the change of property prices have a

significant impact on bank lending without the real effect of bank credits on

property prices By contrast Liang and Cao (2007) investigate the case of China

and suggest a unidirectional causality from bank lending to property prices

which differs from previous empirical work about developed economies

Although the empirical analyses above are attractive and involve standard

ldquobubble bustersrdquo such as income or interest into their frameworks they are

hard to explain the real effects theoretically and rather ad hoc There are two

alternative models in the literature to systematically and theoretically

interpret the housing prices The first one is an inverted demand model which

includes the per capita housing supply and an index of credit conditioning as

additional explanatory variables and then provides better theoretical

foundations (Muellbauer and Murphy 1997 Meen 2002) This model is

appropriate for those countries in which the housing supply rockets up

considerably The alternative model is the house price-to-rent model (Ayuso

and Restoy 2006) It assumes the house rent-to-price ratio only depends on the

6

user costs and investigates the arbitrage between occupied and rental housing

market It is attractive to those countries such as the US in which rent is

market-oriented

Although the previous work using a bank-level data is not much Davis and Zhu

(2009) employ a sample of micro data from 904 banks in 17 countries during

the period 1989-2002 and find strong correlation between bank credits and

commercial property in those countries which experienced crises linked to

property loss by banks To my knowledge there has been no previous research

that investigates the Chinese case using a bank level data Our paper

supplements the existing literature in three ways First a unique bank-level

mortgage data - which consists of a sample of 28 Chinese domestic

commercial banks during the period 2003-2009 - is employed for the empirical

analysis Second we estimate the nexus between the mortgage credits boom

and four different housing prices Finally we include the urbalisation and

monetary policy indices to capture the effects of socio-demographic change

and regulatory constraints respectively

3 Data Description and methodology

31 Methodology

We aim to investigate the interrelation between bank mortgage and property

markets and test two hypotheses below

7

H1 Do the mortgage credits and housing prices tend to be closely associated

with each other

H2 Does The process of urbanisation have a positive effect on the mortgage

credits

In addition along with the credit boom the central bank has carried out

different monetary policies to intervene with the property and mortgage

market indirectly We also test the real effect of interest instrument and

required reserve ratio on both housing prices and mortgage lending In order

to test the hypotheses above we first propose the model of banksrsquo and

borrowersrsquo behavior in the context of house financing through mortgage

lending

Borrowerrsquos behavior

Assume there are two goods house occupation and a composite consumption

good We use Cobb-Douglas utility function to analyze borrowerrsquos behavior

1U X Y

Where U denotes the utility of a borrower X is the consumption of composite

good and Y is the house occupation of the household Considering the

monotonicity of utility function we take logarithmic transformation and α is

the indicator of house purchase intension

log log (1 ) logU X Y

Suppose the household use mortgage loan to finance the house occupation

8

and dispensable income to support the consumption of house service and

composite good Then we can get borrowerrsquos maximum utility under the

constraint of dispensable income

max log log (1 ) logX Y U X Y

st [1 ( )]X r i M I

M P Y

Where I represents the dispensable income of borrower r is the real interest

rate of individual banks i denotes the inflation rate M is the total mortgage

loans P is the house price Optimal allocation is solved through Lagrange

function

log (1 ) log [ (1 ) ]L X Y X r i P Y I

Thesis of the Lagrange function is shown as follows

0

1(1 ) 0

(1 ) 0

L

X X

Lr i P

Y Y

LX r i P Y I

By solving the linear equation system above with three unknowns which are P

M and λ respectively we get

(1 )

(1 )

IM P Y

r i

From the equation of M we argue that Mortgage demand increases with

9

borrowerrsquos income and house price but decreases with house purchase

intension and the interest rate In addition inflation has a positive impact on

mortgage demanding

Bankrsquos behavior

We assume the whole deposits of banks are interest-bearing assets She

allocates a proportion of its total Deposits on mortgage loans The total

mortgage lending is denoted as Y which is equal to consumersrsquo demanding of

mortgage loans The Loan_To_Deposit ratio is θ Then the mortgage can be

rewritten as Y D An individual bank enjoys an interest rate r on its

mortgage loans On the other hand she has to pay out the interest rate dr on

its deposits The lending business of individual bank incurs costs C which is

explained as a quadratic function 2( ) (1 )C Y c Y The individual bank aims to

maximize its total profits which is denoted as Then the maximization

function can be outlined as follows

2max [1 ] dr M N c Y r D

with M N P Y

D M

Then we plug M and D into our target function and get

2max [1 ( ) ] d

M

M Mr M N c r

N P

2

2( ) 0

( )

dr cN Mr

M N P

2( )

2

dr r N PM

c

10

then we replace by Y D and get

2 2 22( ) ( )

( )

2 2 22

d d d

d

r r D r M r Dr N P r N P N P

r M r D N PM D M DMMc c cM

cD

Now we can find that

dr M r D

D

is the net interest margin (NIM) of

individual bank and the denominator 2 2cM D c The optimal mortgage

supply provided by the bank increases with its NIM the number of mortgagers

(N) housing price (P) and decrease with its Loan_To_Deposit ratio (θ)

Econometric approach

Based on the theoretical analysis above we propose an empirical framework

to estimate the movements of mortgage lending as follows

1 1 1Mortgage Pop Interest Rate Inflation NIM LTD House Prices it it it it i t i t it itf RR

The index of potential mortgagers is Pop which is estimated by the

percentage of urban population to total population in China It also refers to

the effect of urbanization and internal migration Inflation and House Prices

are macroeconomic factors which have potential impact on the mortgage

market NIM LTD represents the Net Interest Margins and Loan to Deposit

ratio These variables reflect the characteristics of individual banks Interest

Rate and RR the Required Reserves of banks are proxies of monetary policy

constraints

11

Previous research also indicates that House price is rather an endogenous

variable and driven by market factors including creditmortgage rationing

Inverted demand equation is then widely used to estimate the house prices

We employ a simplified version of the house price model which is proposed by

Muellbauer and Murphy (1997) 4

( )HS Pop f I D

( )eP r P P

Where is the real user cost of housing services P is the real housing price

and eP P is the expected appreciation rate of housing price5 Muellbauer

and Murphy (1997) also pointed out that a proxy for creditmortgage

rationing is often included in the measurement housing price Substituting the

user cost into the housing stock function and involve the proxy of credit

conditions we can get the invert demand function to obtain the estimation of

P

( _ )eP f HS P P I credit rationing

Where HS eP P and I represent the housing stock expected appreciation

rate of housing price and the Income respectively Here in our frame we

employ the ratio of house price to house rental index to proxy the

appreciation rate Although the down payment ratio is a good and popular

index of credit rationing Chinese housing financing system has not

4Dougherty and Order investigate the measurement of the housing cost through two alternative approaches

They deduct the capital cost by maximizing household utility or implicit rent of the landlord and come up with the same results 5 Murphy also pointed that the long-run price-to-rent ratio can be estimated by real user cost and loan-to-value

ratio

12

experienced significant change of this ratio However the change of Mortgage

can partially reflect the bankrsquos credit rationing The increase of mortgage loans

is associated with loose credit rationing and lower interest rate Therefore we

employ the change of mortgage to proxy the credit rationing index Taking

into account the estimation of mortgage lending we form a simultaneous

equation system as follows to estimate mortgage loans and endogenous

housing prices Considering the bank-level mortgage loan data panel data

approach and two-stage least squaresInstrument variable method are

employed in our estimation

1 1 1

1 1

Mortgage Pop Inflation NIM LTD House Prices

House Price Mortgage Interest Rate

it it it it i t i t it

it it it it it i t

f RR

f HS Income RR appriciation

32 Data description

Compared with developed countries Chinese mortgage market has a relative

short history and therefore limited data available In this paper an unbalanced

panel of annual mortgage loans of Commercial Banks is employed for the

analysis 6 Our sample consists of all State-Owned-Banks (SOBs)

Joint-Stock-Banks (JSBs) and major City-Commercial-Banks (CCBs) The sum of

mortgage credits in our sample covers more than 80 of total mortgage

outstanding which makes our sample highly representative Therefore fixed

effect model is more appropriate to control for the individual heterogeneity

6Since the financial reform in Chinese banking sector and new enforcement of information disclosure by China

Bank Regulatory Commission the mortgage data for some banks in early years is not available

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

7

H1 Do the mortgage credits and housing prices tend to be closely associated

with each other

H2 Does The process of urbanisation have a positive effect on the mortgage

credits

In addition along with the credit boom the central bank has carried out

different monetary policies to intervene with the property and mortgage

market indirectly We also test the real effect of interest instrument and

required reserve ratio on both housing prices and mortgage lending In order

to test the hypotheses above we first propose the model of banksrsquo and

borrowersrsquo behavior in the context of house financing through mortgage

lending

Borrowerrsquos behavior

Assume there are two goods house occupation and a composite consumption

good We use Cobb-Douglas utility function to analyze borrowerrsquos behavior

1U X Y

Where U denotes the utility of a borrower X is the consumption of composite

good and Y is the house occupation of the household Considering the

monotonicity of utility function we take logarithmic transformation and α is

the indicator of house purchase intension

log log (1 ) logU X Y

Suppose the household use mortgage loan to finance the house occupation

8

and dispensable income to support the consumption of house service and

composite good Then we can get borrowerrsquos maximum utility under the

constraint of dispensable income

max log log (1 ) logX Y U X Y

st [1 ( )]X r i M I

M P Y

Where I represents the dispensable income of borrower r is the real interest

rate of individual banks i denotes the inflation rate M is the total mortgage

loans P is the house price Optimal allocation is solved through Lagrange

function

log (1 ) log [ (1 ) ]L X Y X r i P Y I

Thesis of the Lagrange function is shown as follows

0

1(1 ) 0

(1 ) 0

L

X X

Lr i P

Y Y

LX r i P Y I

By solving the linear equation system above with three unknowns which are P

M and λ respectively we get

(1 )

(1 )

IM P Y

r i

From the equation of M we argue that Mortgage demand increases with

9

borrowerrsquos income and house price but decreases with house purchase

intension and the interest rate In addition inflation has a positive impact on

mortgage demanding

Bankrsquos behavior

We assume the whole deposits of banks are interest-bearing assets She

allocates a proportion of its total Deposits on mortgage loans The total

mortgage lending is denoted as Y which is equal to consumersrsquo demanding of

mortgage loans The Loan_To_Deposit ratio is θ Then the mortgage can be

rewritten as Y D An individual bank enjoys an interest rate r on its

mortgage loans On the other hand she has to pay out the interest rate dr on

its deposits The lending business of individual bank incurs costs C which is

explained as a quadratic function 2( ) (1 )C Y c Y The individual bank aims to

maximize its total profits which is denoted as Then the maximization

function can be outlined as follows

2max [1 ] dr M N c Y r D

with M N P Y

D M

Then we plug M and D into our target function and get

2max [1 ( ) ] d

M

M Mr M N c r

N P

2

2( ) 0

( )

dr cN Mr

M N P

2( )

2

dr r N PM

c

10

then we replace by Y D and get

2 2 22( ) ( )

( )

2 2 22

d d d

d

r r D r M r Dr N P r N P N P

r M r D N PM D M DMMc c cM

cD

Now we can find that

dr M r D

D

is the net interest margin (NIM) of

individual bank and the denominator 2 2cM D c The optimal mortgage

supply provided by the bank increases with its NIM the number of mortgagers

(N) housing price (P) and decrease with its Loan_To_Deposit ratio (θ)

Econometric approach

Based on the theoretical analysis above we propose an empirical framework

to estimate the movements of mortgage lending as follows

1 1 1Mortgage Pop Interest Rate Inflation NIM LTD House Prices it it it it i t i t it itf RR

The index of potential mortgagers is Pop which is estimated by the

percentage of urban population to total population in China It also refers to

the effect of urbanization and internal migration Inflation and House Prices

are macroeconomic factors which have potential impact on the mortgage

market NIM LTD represents the Net Interest Margins and Loan to Deposit

ratio These variables reflect the characteristics of individual banks Interest

Rate and RR the Required Reserves of banks are proxies of monetary policy

constraints

11

Previous research also indicates that House price is rather an endogenous

variable and driven by market factors including creditmortgage rationing

Inverted demand equation is then widely used to estimate the house prices

We employ a simplified version of the house price model which is proposed by

Muellbauer and Murphy (1997) 4

( )HS Pop f I D

( )eP r P P

Where is the real user cost of housing services P is the real housing price

and eP P is the expected appreciation rate of housing price5 Muellbauer

and Murphy (1997) also pointed out that a proxy for creditmortgage

rationing is often included in the measurement housing price Substituting the

user cost into the housing stock function and involve the proxy of credit

conditions we can get the invert demand function to obtain the estimation of

P

( _ )eP f HS P P I credit rationing

Where HS eP P and I represent the housing stock expected appreciation

rate of housing price and the Income respectively Here in our frame we

employ the ratio of house price to house rental index to proxy the

appreciation rate Although the down payment ratio is a good and popular

index of credit rationing Chinese housing financing system has not

4Dougherty and Order investigate the measurement of the housing cost through two alternative approaches

They deduct the capital cost by maximizing household utility or implicit rent of the landlord and come up with the same results 5 Murphy also pointed that the long-run price-to-rent ratio can be estimated by real user cost and loan-to-value

ratio

12

experienced significant change of this ratio However the change of Mortgage

can partially reflect the bankrsquos credit rationing The increase of mortgage loans

is associated with loose credit rationing and lower interest rate Therefore we

employ the change of mortgage to proxy the credit rationing index Taking

into account the estimation of mortgage lending we form a simultaneous

equation system as follows to estimate mortgage loans and endogenous

housing prices Considering the bank-level mortgage loan data panel data

approach and two-stage least squaresInstrument variable method are

employed in our estimation

1 1 1

1 1

Mortgage Pop Inflation NIM LTD House Prices

House Price Mortgage Interest Rate

it it it it i t i t it

it it it it it i t

f RR

f HS Income RR appriciation

32 Data description

Compared with developed countries Chinese mortgage market has a relative

short history and therefore limited data available In this paper an unbalanced

panel of annual mortgage loans of Commercial Banks is employed for the

analysis 6 Our sample consists of all State-Owned-Banks (SOBs)

Joint-Stock-Banks (JSBs) and major City-Commercial-Banks (CCBs) The sum of

mortgage credits in our sample covers more than 80 of total mortgage

outstanding which makes our sample highly representative Therefore fixed

effect model is more appropriate to control for the individual heterogeneity

6Since the financial reform in Chinese banking sector and new enforcement of information disclosure by China

Bank Regulatory Commission the mortgage data for some banks in early years is not available

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

8

and dispensable income to support the consumption of house service and

composite good Then we can get borrowerrsquos maximum utility under the

constraint of dispensable income

max log log (1 ) logX Y U X Y

st [1 ( )]X r i M I

M P Y

Where I represents the dispensable income of borrower r is the real interest

rate of individual banks i denotes the inflation rate M is the total mortgage

loans P is the house price Optimal allocation is solved through Lagrange

function

log (1 ) log [ (1 ) ]L X Y X r i P Y I

Thesis of the Lagrange function is shown as follows

0

1(1 ) 0

(1 ) 0

L

X X

Lr i P

Y Y

LX r i P Y I

By solving the linear equation system above with three unknowns which are P

M and λ respectively we get

(1 )

(1 )

IM P Y

r i

From the equation of M we argue that Mortgage demand increases with

9

borrowerrsquos income and house price but decreases with house purchase

intension and the interest rate In addition inflation has a positive impact on

mortgage demanding

Bankrsquos behavior

We assume the whole deposits of banks are interest-bearing assets She

allocates a proportion of its total Deposits on mortgage loans The total

mortgage lending is denoted as Y which is equal to consumersrsquo demanding of

mortgage loans The Loan_To_Deposit ratio is θ Then the mortgage can be

rewritten as Y D An individual bank enjoys an interest rate r on its

mortgage loans On the other hand she has to pay out the interest rate dr on

its deposits The lending business of individual bank incurs costs C which is

explained as a quadratic function 2( ) (1 )C Y c Y The individual bank aims to

maximize its total profits which is denoted as Then the maximization

function can be outlined as follows

2max [1 ] dr M N c Y r D

with M N P Y

D M

Then we plug M and D into our target function and get

2max [1 ( ) ] d

M

M Mr M N c r

N P

2

2( ) 0

( )

dr cN Mr

M N P

2( )

2

dr r N PM

c

10

then we replace by Y D and get

2 2 22( ) ( )

( )

2 2 22

d d d

d

r r D r M r Dr N P r N P N P

r M r D N PM D M DMMc c cM

cD

Now we can find that

dr M r D

D

is the net interest margin (NIM) of

individual bank and the denominator 2 2cM D c The optimal mortgage

supply provided by the bank increases with its NIM the number of mortgagers

(N) housing price (P) and decrease with its Loan_To_Deposit ratio (θ)

Econometric approach

Based on the theoretical analysis above we propose an empirical framework

to estimate the movements of mortgage lending as follows

1 1 1Mortgage Pop Interest Rate Inflation NIM LTD House Prices it it it it i t i t it itf RR

The index of potential mortgagers is Pop which is estimated by the

percentage of urban population to total population in China It also refers to

the effect of urbanization and internal migration Inflation and House Prices

are macroeconomic factors which have potential impact on the mortgage

market NIM LTD represents the Net Interest Margins and Loan to Deposit

ratio These variables reflect the characteristics of individual banks Interest

Rate and RR the Required Reserves of banks are proxies of monetary policy

constraints

11

Previous research also indicates that House price is rather an endogenous

variable and driven by market factors including creditmortgage rationing

Inverted demand equation is then widely used to estimate the house prices

We employ a simplified version of the house price model which is proposed by

Muellbauer and Murphy (1997) 4

( )HS Pop f I D

( )eP r P P

Where is the real user cost of housing services P is the real housing price

and eP P is the expected appreciation rate of housing price5 Muellbauer

and Murphy (1997) also pointed out that a proxy for creditmortgage

rationing is often included in the measurement housing price Substituting the

user cost into the housing stock function and involve the proxy of credit

conditions we can get the invert demand function to obtain the estimation of

P

( _ )eP f HS P P I credit rationing

Where HS eP P and I represent the housing stock expected appreciation

rate of housing price and the Income respectively Here in our frame we

employ the ratio of house price to house rental index to proxy the

appreciation rate Although the down payment ratio is a good and popular

index of credit rationing Chinese housing financing system has not

4Dougherty and Order investigate the measurement of the housing cost through two alternative approaches

They deduct the capital cost by maximizing household utility or implicit rent of the landlord and come up with the same results 5 Murphy also pointed that the long-run price-to-rent ratio can be estimated by real user cost and loan-to-value

ratio

12

experienced significant change of this ratio However the change of Mortgage

can partially reflect the bankrsquos credit rationing The increase of mortgage loans

is associated with loose credit rationing and lower interest rate Therefore we

employ the change of mortgage to proxy the credit rationing index Taking

into account the estimation of mortgage lending we form a simultaneous

equation system as follows to estimate mortgage loans and endogenous

housing prices Considering the bank-level mortgage loan data panel data

approach and two-stage least squaresInstrument variable method are

employed in our estimation

1 1 1

1 1

Mortgage Pop Inflation NIM LTD House Prices

House Price Mortgage Interest Rate

it it it it i t i t it

it it it it it i t

f RR

f HS Income RR appriciation

32 Data description

Compared with developed countries Chinese mortgage market has a relative

short history and therefore limited data available In this paper an unbalanced

panel of annual mortgage loans of Commercial Banks is employed for the

analysis 6 Our sample consists of all State-Owned-Banks (SOBs)

Joint-Stock-Banks (JSBs) and major City-Commercial-Banks (CCBs) The sum of

mortgage credits in our sample covers more than 80 of total mortgage

outstanding which makes our sample highly representative Therefore fixed

effect model is more appropriate to control for the individual heterogeneity

6Since the financial reform in Chinese banking sector and new enforcement of information disclosure by China

Bank Regulatory Commission the mortgage data for some banks in early years is not available

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

9

borrowerrsquos income and house price but decreases with house purchase

intension and the interest rate In addition inflation has a positive impact on

mortgage demanding

Bankrsquos behavior

We assume the whole deposits of banks are interest-bearing assets She

allocates a proportion of its total Deposits on mortgage loans The total

mortgage lending is denoted as Y which is equal to consumersrsquo demanding of

mortgage loans The Loan_To_Deposit ratio is θ Then the mortgage can be

rewritten as Y D An individual bank enjoys an interest rate r on its

mortgage loans On the other hand she has to pay out the interest rate dr on

its deposits The lending business of individual bank incurs costs C which is

explained as a quadratic function 2( ) (1 )C Y c Y The individual bank aims to

maximize its total profits which is denoted as Then the maximization

function can be outlined as follows

2max [1 ] dr M N c Y r D

with M N P Y

D M

Then we plug M and D into our target function and get

2max [1 ( ) ] d

M

M Mr M N c r

N P

2

2( ) 0

( )

dr cN Mr

M N P

2( )

2

dr r N PM

c

10

then we replace by Y D and get

2 2 22( ) ( )

( )

2 2 22

d d d

d

r r D r M r Dr N P r N P N P

r M r D N PM D M DMMc c cM

cD

Now we can find that

dr M r D

D

is the net interest margin (NIM) of

individual bank and the denominator 2 2cM D c The optimal mortgage

supply provided by the bank increases with its NIM the number of mortgagers

(N) housing price (P) and decrease with its Loan_To_Deposit ratio (θ)

Econometric approach

Based on the theoretical analysis above we propose an empirical framework

to estimate the movements of mortgage lending as follows

1 1 1Mortgage Pop Interest Rate Inflation NIM LTD House Prices it it it it i t i t it itf RR

The index of potential mortgagers is Pop which is estimated by the

percentage of urban population to total population in China It also refers to

the effect of urbanization and internal migration Inflation and House Prices

are macroeconomic factors which have potential impact on the mortgage

market NIM LTD represents the Net Interest Margins and Loan to Deposit

ratio These variables reflect the characteristics of individual banks Interest

Rate and RR the Required Reserves of banks are proxies of monetary policy

constraints

11

Previous research also indicates that House price is rather an endogenous

variable and driven by market factors including creditmortgage rationing

Inverted demand equation is then widely used to estimate the house prices

We employ a simplified version of the house price model which is proposed by

Muellbauer and Murphy (1997) 4

( )HS Pop f I D

( )eP r P P

Where is the real user cost of housing services P is the real housing price

and eP P is the expected appreciation rate of housing price5 Muellbauer

and Murphy (1997) also pointed out that a proxy for creditmortgage

rationing is often included in the measurement housing price Substituting the

user cost into the housing stock function and involve the proxy of credit

conditions we can get the invert demand function to obtain the estimation of

P

( _ )eP f HS P P I credit rationing

Where HS eP P and I represent the housing stock expected appreciation

rate of housing price and the Income respectively Here in our frame we

employ the ratio of house price to house rental index to proxy the

appreciation rate Although the down payment ratio is a good and popular

index of credit rationing Chinese housing financing system has not

4Dougherty and Order investigate the measurement of the housing cost through two alternative approaches

They deduct the capital cost by maximizing household utility or implicit rent of the landlord and come up with the same results 5 Murphy also pointed that the long-run price-to-rent ratio can be estimated by real user cost and loan-to-value

ratio

12

experienced significant change of this ratio However the change of Mortgage

can partially reflect the bankrsquos credit rationing The increase of mortgage loans

is associated with loose credit rationing and lower interest rate Therefore we

employ the change of mortgage to proxy the credit rationing index Taking

into account the estimation of mortgage lending we form a simultaneous

equation system as follows to estimate mortgage loans and endogenous

housing prices Considering the bank-level mortgage loan data panel data

approach and two-stage least squaresInstrument variable method are

employed in our estimation

1 1 1

1 1

Mortgage Pop Inflation NIM LTD House Prices

House Price Mortgage Interest Rate

it it it it i t i t it

it it it it it i t

f RR

f HS Income RR appriciation

32 Data description

Compared with developed countries Chinese mortgage market has a relative

short history and therefore limited data available In this paper an unbalanced

panel of annual mortgage loans of Commercial Banks is employed for the

analysis 6 Our sample consists of all State-Owned-Banks (SOBs)

Joint-Stock-Banks (JSBs) and major City-Commercial-Banks (CCBs) The sum of

mortgage credits in our sample covers more than 80 of total mortgage

outstanding which makes our sample highly representative Therefore fixed

effect model is more appropriate to control for the individual heterogeneity

6Since the financial reform in Chinese banking sector and new enforcement of information disclosure by China

Bank Regulatory Commission the mortgage data for some banks in early years is not available

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

10

then we replace by Y D and get

2 2 22( ) ( )

( )

2 2 22

d d d

d

r r D r M r Dr N P r N P N P

r M r D N PM D M DMMc c cM

cD

Now we can find that

dr M r D

D

is the net interest margin (NIM) of

individual bank and the denominator 2 2cM D c The optimal mortgage

supply provided by the bank increases with its NIM the number of mortgagers

(N) housing price (P) and decrease with its Loan_To_Deposit ratio (θ)

Econometric approach

Based on the theoretical analysis above we propose an empirical framework

to estimate the movements of mortgage lending as follows

1 1 1Mortgage Pop Interest Rate Inflation NIM LTD House Prices it it it it i t i t it itf RR

The index of potential mortgagers is Pop which is estimated by the

percentage of urban population to total population in China It also refers to

the effect of urbanization and internal migration Inflation and House Prices

are macroeconomic factors which have potential impact on the mortgage

market NIM LTD represents the Net Interest Margins and Loan to Deposit

ratio These variables reflect the characteristics of individual banks Interest

Rate and RR the Required Reserves of banks are proxies of monetary policy

constraints

11

Previous research also indicates that House price is rather an endogenous

variable and driven by market factors including creditmortgage rationing

Inverted demand equation is then widely used to estimate the house prices

We employ a simplified version of the house price model which is proposed by

Muellbauer and Murphy (1997) 4

( )HS Pop f I D

( )eP r P P

Where is the real user cost of housing services P is the real housing price

and eP P is the expected appreciation rate of housing price5 Muellbauer

and Murphy (1997) also pointed out that a proxy for creditmortgage

rationing is often included in the measurement housing price Substituting the

user cost into the housing stock function and involve the proxy of credit

conditions we can get the invert demand function to obtain the estimation of

P

( _ )eP f HS P P I credit rationing

Where HS eP P and I represent the housing stock expected appreciation

rate of housing price and the Income respectively Here in our frame we

employ the ratio of house price to house rental index to proxy the

appreciation rate Although the down payment ratio is a good and popular

index of credit rationing Chinese housing financing system has not

4Dougherty and Order investigate the measurement of the housing cost through two alternative approaches

They deduct the capital cost by maximizing household utility or implicit rent of the landlord and come up with the same results 5 Murphy also pointed that the long-run price-to-rent ratio can be estimated by real user cost and loan-to-value

ratio

12

experienced significant change of this ratio However the change of Mortgage

can partially reflect the bankrsquos credit rationing The increase of mortgage loans

is associated with loose credit rationing and lower interest rate Therefore we

employ the change of mortgage to proxy the credit rationing index Taking

into account the estimation of mortgage lending we form a simultaneous

equation system as follows to estimate mortgage loans and endogenous

housing prices Considering the bank-level mortgage loan data panel data

approach and two-stage least squaresInstrument variable method are

employed in our estimation

1 1 1

1 1

Mortgage Pop Inflation NIM LTD House Prices

House Price Mortgage Interest Rate

it it it it i t i t it

it it it it it i t

f RR

f HS Income RR appriciation

32 Data description

Compared with developed countries Chinese mortgage market has a relative

short history and therefore limited data available In this paper an unbalanced

panel of annual mortgage loans of Commercial Banks is employed for the

analysis 6 Our sample consists of all State-Owned-Banks (SOBs)

Joint-Stock-Banks (JSBs) and major City-Commercial-Banks (CCBs) The sum of

mortgage credits in our sample covers more than 80 of total mortgage

outstanding which makes our sample highly representative Therefore fixed

effect model is more appropriate to control for the individual heterogeneity

6Since the financial reform in Chinese banking sector and new enforcement of information disclosure by China

Bank Regulatory Commission the mortgage data for some banks in early years is not available

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

11

Previous research also indicates that House price is rather an endogenous

variable and driven by market factors including creditmortgage rationing

Inverted demand equation is then widely used to estimate the house prices

We employ a simplified version of the house price model which is proposed by

Muellbauer and Murphy (1997) 4

( )HS Pop f I D

( )eP r P P

Where is the real user cost of housing services P is the real housing price

and eP P is the expected appreciation rate of housing price5 Muellbauer

and Murphy (1997) also pointed out that a proxy for creditmortgage

rationing is often included in the measurement housing price Substituting the

user cost into the housing stock function and involve the proxy of credit

conditions we can get the invert demand function to obtain the estimation of

P

( _ )eP f HS P P I credit rationing

Where HS eP P and I represent the housing stock expected appreciation

rate of housing price and the Income respectively Here in our frame we

employ the ratio of house price to house rental index to proxy the

appreciation rate Although the down payment ratio is a good and popular

index of credit rationing Chinese housing financing system has not

4Dougherty and Order investigate the measurement of the housing cost through two alternative approaches

They deduct the capital cost by maximizing household utility or implicit rent of the landlord and come up with the same results 5 Murphy also pointed that the long-run price-to-rent ratio can be estimated by real user cost and loan-to-value

ratio

12

experienced significant change of this ratio However the change of Mortgage

can partially reflect the bankrsquos credit rationing The increase of mortgage loans

is associated with loose credit rationing and lower interest rate Therefore we

employ the change of mortgage to proxy the credit rationing index Taking

into account the estimation of mortgage lending we form a simultaneous

equation system as follows to estimate mortgage loans and endogenous

housing prices Considering the bank-level mortgage loan data panel data

approach and two-stage least squaresInstrument variable method are

employed in our estimation

1 1 1

1 1

Mortgage Pop Inflation NIM LTD House Prices

House Price Mortgage Interest Rate

it it it it i t i t it

it it it it it i t

f RR

f HS Income RR appriciation

32 Data description

Compared with developed countries Chinese mortgage market has a relative

short history and therefore limited data available In this paper an unbalanced

panel of annual mortgage loans of Commercial Banks is employed for the

analysis 6 Our sample consists of all State-Owned-Banks (SOBs)

Joint-Stock-Banks (JSBs) and major City-Commercial-Banks (CCBs) The sum of

mortgage credits in our sample covers more than 80 of total mortgage

outstanding which makes our sample highly representative Therefore fixed

effect model is more appropriate to control for the individual heterogeneity

6Since the financial reform in Chinese banking sector and new enforcement of information disclosure by China

Bank Regulatory Commission the mortgage data for some banks in early years is not available

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

12

experienced significant change of this ratio However the change of Mortgage

can partially reflect the bankrsquos credit rationing The increase of mortgage loans

is associated with loose credit rationing and lower interest rate Therefore we

employ the change of mortgage to proxy the credit rationing index Taking

into account the estimation of mortgage lending we form a simultaneous

equation system as follows to estimate mortgage loans and endogenous

housing prices Considering the bank-level mortgage loan data panel data

approach and two-stage least squaresInstrument variable method are

employed in our estimation

1 1 1

1 1

Mortgage Pop Inflation NIM LTD House Prices

House Price Mortgage Interest Rate

it it it it i t i t it

it it it it it i t

f RR

f HS Income RR appriciation

32 Data description

Compared with developed countries Chinese mortgage market has a relative

short history and therefore limited data available In this paper an unbalanced

panel of annual mortgage loans of Commercial Banks is employed for the

analysis 6 Our sample consists of all State-Owned-Banks (SOBs)

Joint-Stock-Banks (JSBs) and major City-Commercial-Banks (CCBs) The sum of

mortgage credits in our sample covers more than 80 of total mortgage

outstanding which makes our sample highly representative Therefore fixed

effect model is more appropriate to control for the individual heterogeneity

6Since the financial reform in Chinese banking sector and new enforcement of information disclosure by China

Bank Regulatory Commission the mortgage data for some banks in early years is not available

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

13

Next we carefully select conventional explanatory variables for our empirical

analysis so as to ensure our estimation is robust to omitted variables The

variables included in the empirical framework are described in groups as

follows The macro data which is collected from the Peoplersquos Bank of China

and National Bureau of Statistics of China Mortgage loans bank specific

variables and house prices are collected from ALMANAC OF CHINAS FINANCE

AND BANKING which is the official publication of Peoplersquos Bank of China

Dependent variables

We are more interested in the movements of mortgage loan and housing

prices within a given year Therefore the change of mortgage loans of

individual banks and housing prices are employed as the dependent variable

In addition log transformation of mortgage loans is employed In order to

avoid heteroscedasticity

Macroeconomic explanatory variables

Although GDP is a popular variable to assess the macroeconomic situation

(Liang and Cao 2007 Davis and Zhu 2010) a large body of literature explores

the impact of disposable income as an alternative McQuinn and OrsquoReilly

(2007) Hill and Gan(2008) established the relationship between income and

house price from both theoretical and empirical sides7 Urban disposable

7Other existing researches also corroborate the potent influence of household income on the dynamics of

housing prices see Jud and Winkler 2002 Gimeno and Martinez-Carrascal 2006 Bourassa and Hoesli2006

14

Income is appropriate to analyse the movement of property prices We

employ the national average deposable income for SOBs and JSBs given their

nationwide business line and local average disposable income for CCBs

considering their local market focus In addition urban disposable income is

log transformed in order to avoid heteroscedasticity problem8

Urbanization is another factor of our interests that may drive the movement of

mortgage credits Over the past decade China has experienced a dramatic

process of industrialisation and urbanisation And Comparing with

industrialisation Chinas urbanisation developed more rapidly (Zheng et al

2007) More and more new immigrants moved into cities Taking into account

more potential home buyers a higher volume of mortgage loans is expected

We use the percentage of urban population to total population to capture the

effect of urbanisation

House stock refers to the number of existing residential units It is also

considered as the response to the land-use regulation through the elasticity of

new housing supply (Quigley and Raphael 2005) In our empirical analysis we

employ the number of house sales to proxy the housing stocks Furthermore

the expected rate of appreciation of house prices reflects consumersrsquo

Nevertheless some other literatures are not in favor of this point of viewGallin (2006) analyzes a panel of 95 US metropolitan areas over 23 years and does not find evidence of long-run relationship between the property prices and income in level This result is also proved by Tsatsaronis and Zhu (2004) who find little impact of household income on housing prices in their cross-country analysis 8No evidence of long-run relationship between the level of property prices and level of income is found by in

Gallin (2006) Therefore we intend to investigate this issue by involving log transformation of income (Ermini and Hendry2008)

15

purchase intention of houses In this empirical framework the ratio of house

price index to rental price index is used to proxy the house prices appreciation

And it is lagged one period to reduce simultaneity

Bank specific explanatory variables

The interest rate as an indicator of monetary policy is another widely

accepted explanatory variable9 It is calculated as the ratio of total interest

revenue over to interest bearing assets of individual banks A declining interest

rate environment contributes to the increasing demand for real estate and

housing investment (Greenand Shoven1986 Kearl1979 Tsatsaronis and Zhu

2004) In terms of Chinese housing market Liang and Cao (2007) suggest that

the property price movements have no sensitivity to the change of the real

interest rate By involving the rate of interest we reinvestigate to what extend

the raising interest rate can affect the mortgage credits and property prices

The required reserves are required to hold by commercial banks in the form of

deposits at the central bank Over the past decade along with the soaring

property prices and mortgage credit boom the central bank has raised the

required reserve ratio several times from 6 in 1999 to 20 in 2011 It is

viewed as another frequently used monetary policy other than the interest

9Hofmann (2003) concludes that real interest rate is more closely associated with property prices than with bank

lendingWolswijk (2006) and Eacutegertand Mihaljek (2007) confirm the negative effect of interest rate on houses prices in 15 EU countries and Eastern Europe

15

purchase intention of houses In this empirical framework the ratio of house

price index to rental price index is used to proxy the house prices appreciation

And it is lagged one period to reduce simultaneity

Bank specific explanatory variables

The interest rate as an indicator of monetary policy is another widely

accepted explanatory variable9 It is calculated as the ratio of total interest

revenue over to interest bearing assets of individual banks A declining interest

rate environment contributes to the increasing demand for real estate and

housing investment (Greenand Shoven1986 Kearl1979 Tsatsaronis and Zhu

2004) In terms of Chinese housing market Liang and Cao (2007) suggest that

the property price movements have no sensitivity to the change of the real

interest rate By involving the rate of interest we reinvestigate to what extend

the raising interest rate can affect the mortgage credits and property prices

The required reserves are required to hold by commercial banks in the form of

deposits at the central bank Over the past decade along with the soaring

property prices and mortgage credit boom the central bank has raised the

required reserve ratio several times from 6 in 1999 to 20 in 2011 It is

viewed as another frequently used monetary policy other than the interest

9Hofmann (2003) concludes that real interest rate is more closely associated with property prices than with bank

lendingWolswijk (2006) and Eacutegertand Mihaljek (2007) confirm the negative effect of interest rate on houses prices in 15 EU countries and Eastern Europe

16

rate in China We explore in our paper whether the change of bank required

reserves has an impact on the movements of properties and mortgage credits

And we will compare the real effect of interest rate and required reserve ratio

We use one period lag transformation to reduce simultaneity

Loan_to_asset ratio is an important measure of balance sheet risk of individual

banks Since banks can not grant credit at all costs they need to conduct credit

audit and monitoring effectively in order to maintain low levels of

non-performing loans and increase margins (Abreuand Mendes 2001) In spite

of a positive relationship between Loan to deposit ratio and bank profitability

a high loan to deposit ratio is a warning sign of bankrsquos loan risk and then

discourage credit expansion In order to reduce simultaneity one period

lagged Loan_to_asset ratio is employed to capture the real effect of banksrsquo risk

taking behavior NIM is referred to as the net yield on interest-earning assets

and an indicator of profitability of a bankrsquos lending activities10 In addition

NIM is also a proxy of bank performance and profitability for Chinese banking

sector (Matthews 2009) We would like to test the favor of individual banks on

mortgage profits

Property prices as explanatory variables

The growth of four different property prices is employed in the empirical

10

Hanweck and Ryu (2005) capture NIM dynamics in response to unanticipated credit and find negative relationship between NIM and expected credit loss

17

framework They are condominium price the average real estate price and an

indicator of the whole property market residential price the indicator of solo

residential market high_grade houses price a measure of luxury and high-end

real estate market and economic houses price the price of affordable houses

for low-and-medium wage households Crone and Voith(1992) compare five

popular methods to estimate house price appreciation and conclude that the

mean sales price is least affected by the sample size and more accurate than

other estimating methods We aim to investigate the impact of different prices

on the change of mortgage separately The same as disposable income and

mortgage outstanding property prices are converted to logarithm form in

order to avoid heteroscedasticity problem Figure 4 illustrates the movements

of property prices in four separate real estate market

4 Results

41 Mortgage loans

The determinants mortgage lending are illustrated in table 3 from which we

test our hypotheses proposed in section 31

H1 Do the mortgage credits and housing prices tend to be closely associated

with each other

Our findings so far suggest that the volume of outstanding mortgage loans

increases with housing prices expect for the economic housing market which

is not surprising and in line with most recent evidence of developed countries

17

framework They are condominium price the average real estate price and an

indicator of the whole property market residential price the indicator of solo

residential market high_grade houses price a measure of luxury and high-end

real estate market and economic houses price the price of affordable houses

for low-and-medium wage households Crone and Voith(1992) compare five

popular methods to estimate house price appreciation and conclude that the

mean sales price is least affected by the sample size and more accurate than

other estimating methods We aim to investigate the impact of different prices

on the change of mortgage separately The same as disposable income and

mortgage outstanding property prices are converted to logarithm form in

order to avoid heteroscedasticity problem Figure 4 illustrates the movements

of property prices in four separate real estate market

4 Results

41 Mortgage loans

The determinants mortgage lending are illustrated in table 3 from which we

test our hypotheses proposed in section 31

H1 Do the mortgage credits and housing prices tend to be closely associated

with each other

Our findings so far suggest that the volume of outstanding mortgage loans

increases with housing prices expect for the economic housing market which

is not surprising and in line with most recent evidence of developed countries

18

However we argue that the elasticity of condominium (and residential) price

to mortgage credits is around 17 which is much higher than that of other

countries 11 The condominium and residential prices illustrate a similar

impact on the mortgage lending It is simply because after the housing

reformation in 1998 all work units are not allowed to provide welfare houses

to employees As a result the fully market-oriented residential houses

contribute the majority of condominium supply in housing market and their

prices also follows similar movements On the other hand we find a contrary

result for economical house price which has a negative impact on mortgage

movements Since the economic houses focus on the market of medium amp

low income households and new migrants the increased price will weaken

the affordability of some potential buyer and vice versa The fallen price is

favored by more potential buyers most of whom need to rely on the

mortgages to finance their house purchasing

H2 Does the process of urbanisation has a positive effect on the mortgage

credits

The results of our estimation also support our second hypothesis Over the

past two decades China has been witnessing rapid urbanisation more and

more new immigrants choose to settle down in cities This massive internal

migration has brought about more potential home buyers and contributed to

11

The impact of housing price on mortgage credits is tested to be 05 for Ireland 062 for Spain and 075 for European Area (Fitzpatrick and McQuinn 2007 CarboValverde and Francisco Rodriguez 2010 Annett 2005)

19

the mortgage credit boom in China This impact of urbanisation is appreciable

which is reflected by the greater correlation coefficients of population than

other variables in our results Another interesting feature is that the change of

urban population has a more significant impact on the economic housing

market Since the purchasing power of new migrants is not as strong as city

natives the economical houses are of more concern to them and are more

sensitive to the change of urban population

In terms of monetary policies we found the interest rate is increasing with the

mortgage loans It tells us that the raising interest rate have little effect on

controlling the credit boom in China By contrast a negative correlation

between banksrsquo mortgage loans and required reserves for condominium and

residential markets is observed However the required reserves can neither

curb the credit boom for High_Grade and economic housing markets It

implies that although bankers were bracing for a slowdown in home loans

raising required reserves is not a good enough reason for them to curb their

loan to the High_Grade and economic houses mortgages which they view as

health asset in their portfolio Then we argue that compared with interest

tool the required reserve ratio is a relative effective instrument in controlling

the mortgage boom

20

CPI is the index of composite consumption goods12Increased CPI implies a

higher living expenditure which undermines the individualrsquos risk taking

intention of long-term mortgage loans And its impact on High_Grade and

economic housing markets is more significant In addition we found another

notable phenomenon is neither the net interest margin nor the loan to deposit

ratio affects the banksrsquo mortgage loans It tells us that the lending behavior of

Chinese commercial banks is more market-oriented rather not risk- or

profit-adjusted

42 House Prices

The determinants of housing price are illustrated in table 4 We can find that

the mortgage loan has a significant impact on the property prices The effect

on condominium and residential prices is positive which implies the

procyclical behavior of credits boom and condominium prices as a general

However the prices of High_Grade and Economical Houses have illustrated an

entirely different response to mortgage credits The economic houses in China

are also referred to as capped-price houses whose price is more

administrative interference rather not fully market-oriented And the market

of High_Grade properties features the high rigidity The movements of these

two special property markets cannot meet the boom of mortgage credits

12

According to the National Statistic Bureau of China property price is not calculated into CPI

21

Although the raising reserve ratio has little effect the interest rate moves

negative to the condominium prices and plays an active role in restraining the

housing prices We do not find significant impact of Housing Stock expected

appreciation rate of housing price and CPI on the movements of housing

prices

5 Conclusion

We use a bank level data set and panel data approach to gain insight in the

factors determining the growth of mortgage lending and housing prices Based

on Two-Stage Least SquaredInstrumental Variable approach the evidence of

relationship between house prices and mortgage credits is found We

document the positive impact of urbanization effect on the growth of

mortgage lending In terms of monetary policy we find negative effects of

required reserves on the change of mortgage credits and the interest rate on

property prices but not vice versa Our findings have important policy

implications and account for the existing monetary policies which are

implemented in China

22

Appendix

Description of Variables

Variables Descriptions

Macroeconomic

Disposable Income To assess the domestic macroeconomic situation

and house affordability

Interest rate Five-year commercial interest rate is employed as

an indicator of Monetary Policy

Population The percentage of urban population to total in

China It is an proxy of urbanisation

Expected appreciation rate of

housing price

we employ the ratio of house price index to house

rental index to proxy the appreciation rate

House Stock

We use the number of house sales to proxy the

housing stocks

Bank Specific Variables

Interest Rate It is an indicator of monetary policy and calculated

as the ratio of total interest revenue over to interest

bearing assets of individual banks

Required Reserves Minimum reserves by commercial banks in the form

of deposits at the central bank It is another

indicator of monetary policy

Loan To Deposit ratio (LTD) ex ante assessment of bank lending decision

Net Interest Margin (NIM)

ex post measure of bankrsquos interest spread

Property prices

Condominium price The average real estate price and an indicator of the

whole property market

Residential price The indicator of solo residential market

Highgrade houses price A measure of luxury and high-end real estate

market

Economic houses price The price of affordable houses for low-and-medium

wage households

23

Table 1

Summary statistics of bank mortgage loans by group ()

Bank Type 2004 2005 2006 2007 2008 2009

SOBs 7939 7327 6756 6437 7222 6697

JSBs 254 789 1623 2063 2247 2070

CCBs 000 017 269 301 361 305

SUM 8192 8133 8648 8800 9830 9072

Table 2

Summary statistics of variables

Variable Mean Std Dev Min Max

Income(log) 958 032 860 1027

Interest (log) 165 025 044 214

Population(log) 912 159 681 1104

Mortgage(log) 1013 217 459 1368

Loan Deposit Ratio() 6573 777 4672 8631

NIM() 268 052 105 429

Condominium price(log) 831 037 763 953

Residential price(log) 826 038 753 949

High grade price(log) 891 032 806 1000

Economical price(log) 758 027 694 838

24

Table 3

The determinants of Mortgage lending

(1) (2) (3) (4)

mortgage mortgage mortgage mortgage

Condominium 1728 - - -

price (494) - - -

Residencial - 1817 - -

price - (576) - -

High_grade - - 0542 -

price - - (204) -

Economical - - - -2012

price - - - (-214)

Urbanization 4613 4085 6306 4122

(247) (227) (318) (230)

Intere rate 0441 0488 0226 0485

(229) (265) (110) (073)

CPI -1335 -1308 -2625 -2060

(-097) (-099) (-178) (-212)

NIM 0176 0237 -00139 1239

(113) (158) (-009) (157)

Loan_To_Deposit 00895 00579 00691 -0241

ratio (031) (021) (022) (-024)

Required Reserves -0245 -0303 00385 1118

(-181) (-233) (028) (196)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

19

the mortgage credit boom in China This impact of urbanisation is appreciable

which is reflected by the greater correlation coefficients of population than

other variables in our results Another interesting feature is that the change of

urban population has a more significant impact on the economic housing

market Since the purchasing power of new migrants is not as strong as city

natives the economical houses are of more concern to them and are more

sensitive to the change of urban population

In terms of monetary policies we found the interest rate is increasing with the

mortgage loans It tells us that the raising interest rate have little effect on

controlling the credit boom in China By contrast a negative correlation

between banksrsquo mortgage loans and required reserves for condominium and

residential markets is observed However the required reserves can neither

curb the credit boom for High_Grade and economic housing markets It

implies that although bankers were bracing for a slowdown in home loans

raising required reserves is not a good enough reason for them to curb their

loan to the High_Grade and economic houses mortgages which they view as

health asset in their portfolio Then we argue that compared with interest

tool the required reserve ratio is a relative effective instrument in controlling

the mortgage boom

20

CPI is the index of composite consumption goods12Increased CPI implies a

higher living expenditure which undermines the individualrsquos risk taking

intention of long-term mortgage loans And its impact on High_Grade and

economic housing markets is more significant In addition we found another

notable phenomenon is neither the net interest margin nor the loan to deposit

ratio affects the banksrsquo mortgage loans It tells us that the lending behavior of

Chinese commercial banks is more market-oriented rather not risk- or

profit-adjusted

42 House Prices

The determinants of housing price are illustrated in table 4 We can find that

the mortgage loan has a significant impact on the property prices The effect

on condominium and residential prices is positive which implies the

procyclical behavior of credits boom and condominium prices as a general

However the prices of High_Grade and Economical Houses have illustrated an

entirely different response to mortgage credits The economic houses in China

are also referred to as capped-price houses whose price is more

administrative interference rather not fully market-oriented And the market

of High_Grade properties features the high rigidity The movements of these

two special property markets cannot meet the boom of mortgage credits

12

According to the National Statistic Bureau of China property price is not calculated into CPI

21

Although the raising reserve ratio has little effect the interest rate moves

negative to the condominium prices and plays an active role in restraining the

housing prices We do not find significant impact of Housing Stock expected

appreciation rate of housing price and CPI on the movements of housing

prices

5 Conclusion

We use a bank level data set and panel data approach to gain insight in the

factors determining the growth of mortgage lending and housing prices Based

on Two-Stage Least SquaredInstrumental Variable approach the evidence of

relationship between house prices and mortgage credits is found We

document the positive impact of urbanization effect on the growth of

mortgage lending In terms of monetary policy we find negative effects of

required reserves on the change of mortgage credits and the interest rate on

property prices but not vice versa Our findings have important policy

implications and account for the existing monetary policies which are

implemented in China

22

Appendix

Description of Variables

Variables Descriptions

Macroeconomic

Disposable Income To assess the domestic macroeconomic situation

and house affordability

Interest rate Five-year commercial interest rate is employed as

an indicator of Monetary Policy

Population The percentage of urban population to total in

China It is an proxy of urbanisation

Expected appreciation rate of

housing price

we employ the ratio of house price index to house

rental index to proxy the appreciation rate

House Stock

We use the number of house sales to proxy the

housing stocks

Bank Specific Variables

Interest Rate It is an indicator of monetary policy and calculated

as the ratio of total interest revenue over to interest

bearing assets of individual banks

Required Reserves Minimum reserves by commercial banks in the form

of deposits at the central bank It is another

indicator of monetary policy

Loan To Deposit ratio (LTD) ex ante assessment of bank lending decision

Net Interest Margin (NIM)

ex post measure of bankrsquos interest spread

Property prices

Condominium price The average real estate price and an indicator of the

whole property market

Residential price The indicator of solo residential market

Highgrade houses price A measure of luxury and high-end real estate

market

Economic houses price The price of affordable houses for low-and-medium

wage households

23

Table 1

Summary statistics of bank mortgage loans by group ()

Bank Type 2004 2005 2006 2007 2008 2009

SOBs 7939 7327 6756 6437 7222 6697

JSBs 254 789 1623 2063 2247 2070

CCBs 000 017 269 301 361 305

SUM 8192 8133 8648 8800 9830 9072

Table 2

Summary statistics of variables

Variable Mean Std Dev Min Max

Income(log) 958 032 860 1027

Interest (log) 165 025 044 214

Population(log) 912 159 681 1104

Mortgage(log) 1013 217 459 1368

Loan Deposit Ratio() 6573 777 4672 8631

NIM() 268 052 105 429

Condominium price(log) 831 037 763 953

Residential price(log) 826 038 753 949

High grade price(log) 891 032 806 1000

Economical price(log) 758 027 694 838

24

Table 3

The determinants of Mortgage lending

(1) (2) (3) (4)

mortgage mortgage mortgage mortgage

Condominium 1728 - - -

price (494) - - -

Residencial - 1817 - -

price - (576) - -

High_grade - - 0542 -

price - - (204) -

Economical - - - -2012

price - - - (-214)

Urbanization 4613 4085 6306 4122

(247) (227) (318) (230)

Intere rate 0441 0488 0226 0485

(229) (265) (110) (073)

CPI -1335 -1308 -2625 -2060

(-097) (-099) (-178) (-212)

NIM 0176 0237 -00139 1239

(113) (158) (-009) (157)

Loan_To_Deposit 00895 00579 00691 -0241

ratio (031) (021) (022) (-024)

Required Reserves -0245 -0303 00385 1118

(-181) (-233) (028) (196)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

20

CPI is the index of composite consumption goods12Increased CPI implies a

higher living expenditure which undermines the individualrsquos risk taking

intention of long-term mortgage loans And its impact on High_Grade and

economic housing markets is more significant In addition we found another

notable phenomenon is neither the net interest margin nor the loan to deposit

ratio affects the banksrsquo mortgage loans It tells us that the lending behavior of

Chinese commercial banks is more market-oriented rather not risk- or

profit-adjusted

42 House Prices

The determinants of housing price are illustrated in table 4 We can find that

the mortgage loan has a significant impact on the property prices The effect

on condominium and residential prices is positive which implies the

procyclical behavior of credits boom and condominium prices as a general

However the prices of High_Grade and Economical Houses have illustrated an

entirely different response to mortgage credits The economic houses in China

are also referred to as capped-price houses whose price is more

administrative interference rather not fully market-oriented And the market

of High_Grade properties features the high rigidity The movements of these

two special property markets cannot meet the boom of mortgage credits

12

According to the National Statistic Bureau of China property price is not calculated into CPI

21

Although the raising reserve ratio has little effect the interest rate moves

negative to the condominium prices and plays an active role in restraining the

housing prices We do not find significant impact of Housing Stock expected

appreciation rate of housing price and CPI on the movements of housing

prices

5 Conclusion

We use a bank level data set and panel data approach to gain insight in the

factors determining the growth of mortgage lending and housing prices Based

on Two-Stage Least SquaredInstrumental Variable approach the evidence of

relationship between house prices and mortgage credits is found We

document the positive impact of urbanization effect on the growth of

mortgage lending In terms of monetary policy we find negative effects of

required reserves on the change of mortgage credits and the interest rate on

property prices but not vice versa Our findings have important policy

implications and account for the existing monetary policies which are

implemented in China

22

Appendix

Description of Variables

Variables Descriptions

Macroeconomic

Disposable Income To assess the domestic macroeconomic situation

and house affordability

Interest rate Five-year commercial interest rate is employed as

an indicator of Monetary Policy

Population The percentage of urban population to total in

China It is an proxy of urbanisation

Expected appreciation rate of

housing price

we employ the ratio of house price index to house

rental index to proxy the appreciation rate

House Stock

We use the number of house sales to proxy the

housing stocks

Bank Specific Variables

Interest Rate It is an indicator of monetary policy and calculated

as the ratio of total interest revenue over to interest

bearing assets of individual banks

Required Reserves Minimum reserves by commercial banks in the form

of deposits at the central bank It is another

indicator of monetary policy

Loan To Deposit ratio (LTD) ex ante assessment of bank lending decision

Net Interest Margin (NIM)

ex post measure of bankrsquos interest spread

Property prices

Condominium price The average real estate price and an indicator of the

whole property market

Residential price The indicator of solo residential market

Highgrade houses price A measure of luxury and high-end real estate

market

Economic houses price The price of affordable houses for low-and-medium

wage households

23

Table 1

Summary statistics of bank mortgage loans by group ()

Bank Type 2004 2005 2006 2007 2008 2009

SOBs 7939 7327 6756 6437 7222 6697

JSBs 254 789 1623 2063 2247 2070

CCBs 000 017 269 301 361 305

SUM 8192 8133 8648 8800 9830 9072

Table 2

Summary statistics of variables

Variable Mean Std Dev Min Max

Income(log) 958 032 860 1027

Interest (log) 165 025 044 214

Population(log) 912 159 681 1104

Mortgage(log) 1013 217 459 1368

Loan Deposit Ratio() 6573 777 4672 8631

NIM() 268 052 105 429

Condominium price(log) 831 037 763 953

Residential price(log) 826 038 753 949

High grade price(log) 891 032 806 1000

Economical price(log) 758 027 694 838

24

Table 3

The determinants of Mortgage lending

(1) (2) (3) (4)

mortgage mortgage mortgage mortgage

Condominium 1728 - - -

price (494) - - -

Residencial - 1817 - -

price - (576) - -

High_grade - - 0542 -

price - - (204) -

Economical - - - -2012

price - - - (-214)

Urbanization 4613 4085 6306 4122

(247) (227) (318) (230)

Intere rate 0441 0488 0226 0485

(229) (265) (110) (073)

CPI -1335 -1308 -2625 -2060

(-097) (-099) (-178) (-212)

NIM 0176 0237 -00139 1239

(113) (158) (-009) (157)

Loan_To_Deposit 00895 00579 00691 -0241

ratio (031) (021) (022) (-024)

Required Reserves -0245 -0303 00385 1118

(-181) (-233) (028) (196)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

21

Although the raising reserve ratio has little effect the interest rate moves

negative to the condominium prices and plays an active role in restraining the

housing prices We do not find significant impact of Housing Stock expected

appreciation rate of housing price and CPI on the movements of housing

prices

5 Conclusion

We use a bank level data set and panel data approach to gain insight in the

factors determining the growth of mortgage lending and housing prices Based

on Two-Stage Least SquaredInstrumental Variable approach the evidence of

relationship between house prices and mortgage credits is found We

document the positive impact of urbanization effect on the growth of

mortgage lending In terms of monetary policy we find negative effects of

required reserves on the change of mortgage credits and the interest rate on

property prices but not vice versa Our findings have important policy

implications and account for the existing monetary policies which are

implemented in China

22

Appendix

Description of Variables

Variables Descriptions

Macroeconomic

Disposable Income To assess the domestic macroeconomic situation

and house affordability

Interest rate Five-year commercial interest rate is employed as

an indicator of Monetary Policy

Population The percentage of urban population to total in

China It is an proxy of urbanisation

Expected appreciation rate of

housing price

we employ the ratio of house price index to house

rental index to proxy the appreciation rate

House Stock

We use the number of house sales to proxy the

housing stocks

Bank Specific Variables

Interest Rate It is an indicator of monetary policy and calculated

as the ratio of total interest revenue over to interest

bearing assets of individual banks

Required Reserves Minimum reserves by commercial banks in the form

of deposits at the central bank It is another

indicator of monetary policy

Loan To Deposit ratio (LTD) ex ante assessment of bank lending decision

Net Interest Margin (NIM)

ex post measure of bankrsquos interest spread

Property prices

Condominium price The average real estate price and an indicator of the

whole property market

Residential price The indicator of solo residential market

Highgrade houses price A measure of luxury and high-end real estate

market

Economic houses price The price of affordable houses for low-and-medium

wage households

23

Table 1

Summary statistics of bank mortgage loans by group ()

Bank Type 2004 2005 2006 2007 2008 2009

SOBs 7939 7327 6756 6437 7222 6697

JSBs 254 789 1623 2063 2247 2070

CCBs 000 017 269 301 361 305

SUM 8192 8133 8648 8800 9830 9072

Table 2

Summary statistics of variables

Variable Mean Std Dev Min Max

Income(log) 958 032 860 1027

Interest (log) 165 025 044 214

Population(log) 912 159 681 1104

Mortgage(log) 1013 217 459 1368

Loan Deposit Ratio() 6573 777 4672 8631

NIM() 268 052 105 429

Condominium price(log) 831 037 763 953

Residential price(log) 826 038 753 949

High grade price(log) 891 032 806 1000

Economical price(log) 758 027 694 838

24

Table 3

The determinants of Mortgage lending

(1) (2) (3) (4)

mortgage mortgage mortgage mortgage

Condominium 1728 - - -

price (494) - - -

Residencial - 1817 - -

price - (576) - -

High_grade - - 0542 -

price - - (204) -

Economical - - - -2012

price - - - (-214)

Urbanization 4613 4085 6306 4122

(247) (227) (318) (230)

Intere rate 0441 0488 0226 0485

(229) (265) (110) (073)

CPI -1335 -1308 -2625 -2060

(-097) (-099) (-178) (-212)

NIM 0176 0237 -00139 1239

(113) (158) (-009) (157)

Loan_To_Deposit 00895 00579 00691 -0241

ratio (031) (021) (022) (-024)

Required Reserves -0245 -0303 00385 1118

(-181) (-233) (028) (196)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

22

Appendix

Description of Variables

Variables Descriptions

Macroeconomic

Disposable Income To assess the domestic macroeconomic situation

and house affordability

Interest rate Five-year commercial interest rate is employed as

an indicator of Monetary Policy

Population The percentage of urban population to total in

China It is an proxy of urbanisation

Expected appreciation rate of

housing price

we employ the ratio of house price index to house

rental index to proxy the appreciation rate

House Stock

We use the number of house sales to proxy the

housing stocks

Bank Specific Variables

Interest Rate It is an indicator of monetary policy and calculated

as the ratio of total interest revenue over to interest

bearing assets of individual banks

Required Reserves Minimum reserves by commercial banks in the form

of deposits at the central bank It is another

indicator of monetary policy

Loan To Deposit ratio (LTD) ex ante assessment of bank lending decision

Net Interest Margin (NIM)

ex post measure of bankrsquos interest spread

Property prices

Condominium price The average real estate price and an indicator of the

whole property market

Residential price The indicator of solo residential market

Highgrade houses price A measure of luxury and high-end real estate

market

Economic houses price The price of affordable houses for low-and-medium

wage households

23

Table 1

Summary statistics of bank mortgage loans by group ()

Bank Type 2004 2005 2006 2007 2008 2009

SOBs 7939 7327 6756 6437 7222 6697

JSBs 254 789 1623 2063 2247 2070

CCBs 000 017 269 301 361 305

SUM 8192 8133 8648 8800 9830 9072

Table 2

Summary statistics of variables

Variable Mean Std Dev Min Max

Income(log) 958 032 860 1027

Interest (log) 165 025 044 214

Population(log) 912 159 681 1104

Mortgage(log) 1013 217 459 1368

Loan Deposit Ratio() 6573 777 4672 8631

NIM() 268 052 105 429

Condominium price(log) 831 037 763 953

Residential price(log) 826 038 753 949

High grade price(log) 891 032 806 1000

Economical price(log) 758 027 694 838

24

Table 3

The determinants of Mortgage lending

(1) (2) (3) (4)

mortgage mortgage mortgage mortgage

Condominium 1728 - - -

price (494) - - -

Residencial - 1817 - -

price - (576) - -

High_grade - - 0542 -

price - - (204) -

Economical - - - -2012

price - - - (-214)

Urbanization 4613 4085 6306 4122

(247) (227) (318) (230)

Intere rate 0441 0488 0226 0485

(229) (265) (110) (073)

CPI -1335 -1308 -2625 -2060

(-097) (-099) (-178) (-212)

NIM 0176 0237 -00139 1239

(113) (158) (-009) (157)

Loan_To_Deposit 00895 00579 00691 -0241

ratio (031) (021) (022) (-024)

Required Reserves -0245 -0303 00385 1118

(-181) (-233) (028) (196)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

23

Table 1

Summary statistics of bank mortgage loans by group ()

Bank Type 2004 2005 2006 2007 2008 2009

SOBs 7939 7327 6756 6437 7222 6697

JSBs 254 789 1623 2063 2247 2070

CCBs 000 017 269 301 361 305

SUM 8192 8133 8648 8800 9830 9072

Table 2

Summary statistics of variables

Variable Mean Std Dev Min Max

Income(log) 958 032 860 1027

Interest (log) 165 025 044 214

Population(log) 912 159 681 1104

Mortgage(log) 1013 217 459 1368

Loan Deposit Ratio() 6573 777 4672 8631

NIM() 268 052 105 429

Condominium price(log) 831 037 763 953

Residential price(log) 826 038 753 949

High grade price(log) 891 032 806 1000

Economical price(log) 758 027 694 838

24

Table 3

The determinants of Mortgage lending

(1) (2) (3) (4)

mortgage mortgage mortgage mortgage

Condominium 1728 - - -

price (494) - - -

Residencial - 1817 - -

price - (576) - -

High_grade - - 0542 -

price - - (204) -

Economical - - - -2012

price - - - (-214)

Urbanization 4613 4085 6306 4122

(247) (227) (318) (230)

Intere rate 0441 0488 0226 0485

(229) (265) (110) (073)

CPI -1335 -1308 -2625 -2060

(-097) (-099) (-178) (-212)

NIM 0176 0237 -00139 1239

(113) (158) (-009) (157)

Loan_To_Deposit 00895 00579 00691 -0241

ratio (031) (021) (022) (-024)

Required Reserves -0245 -0303 00385 1118

(-181) (-233) (028) (196)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

24

Table 3

The determinants of Mortgage lending

(1) (2) (3) (4)

mortgage mortgage mortgage mortgage

Condominium 1728 - - -

price (494) - - -

Residencial - 1817 - -

price - (576) - -

High_grade - - 0542 -

price - - (204) -

Economical - - - -2012

price - - - (-214)

Urbanization 4613 4085 6306 4122

(247) (227) (318) (230)

Intere rate 0441 0488 0226 0485

(229) (265) (110) (073)

CPI -1335 -1308 -2625 -2060

(-097) (-099) (-178) (-212)

NIM 0176 0237 -00139 1239

(113) (158) (-009) (157)

Loan_To_Deposit 00895 00579 00691 -0241

ratio (031) (021) (022) (-024)

Required Reserves -0245 -0303 00385 1118

(-181) (-233) (028) (196)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

25

Table 4

Determinants of Housing prices

(1) (2) (3) (4)

Condominium Residencial High_Grade Economical

Mortgage 0814 0744 -1608 -0361

(192) (254) (-192) (-343)

Income 0554 0414 2154 1016

(097) (081) (209) (433)

Intere rate -0417 -0394 0602 0153

(-184) (-222) (138) (225)

CPI 1085 1065 -6148 -2812

(066) (079) (-188) (-404)

Housing 0351 0120 -2223 -1273

appreciation (035) (014) (-131) (-173)

Required -0199 -0129 0285 -00122

reserves (-095) (-075) (080) (-023)

Housing Stock -0469 - - -

Condominium (-135) - - -

Housing Stock - -0347 - -

Residencial - (-145) - -

Housing Stock - - 0651 -

High_Grade - - (163) -

Housing Stock - - - -00817

Economical - - - (-121)

N 78 78 78 73

Notet statistics in parentheses plt010 plt005 plt001

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

26

Figures

2000

3000

4000

5000

Avera

ge P

ropert

y P

rice (

in Y

uan)

0

1000

2000

3000

4000

5000

Mort

gage O

uts

tandin

g (

in B

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage average_property_price

Data source Peoples Bank of China and Nationa Bureau of Statistics of China Annual data 1999-2009

Figure 1 Mortgage Outstanding and Averate Property Price

24

68

10

12

Mo

rtg

ag

e t

o t

ota

l lo

an

(

)

0

1000

2000

3000

4000

5000

Mort

gage (

in M

illion Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

mortgage Mortgage to total loan ratio

Data Source Peoples Bank of China annual data 1999-2009

Figure 2 Mortgage outstanding and Mortgage to total loan ratio

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

27

36

912

15

18

01 Jan 06 01 Jul 06 01 Jan 07 01 Jul 07 01 Jan 08 01 Jul 08 01 Jan 09 01 Jul 09 01 Jan 10 01 Jul 10

Note Big banks refer to five State-Owned banks and the Postal saving bank

Reserve Ratio for Big Banks Reserve Ratio for Medium amp Small Banks

Commercial Mortgage Rate HPF loan Rate

Data source The Peoples Bank of China

Figure 3 Movements of Required Reserve Ratio and Mortgage Rates

0

2000

4000

6000

8000

10000

Pro

pert

y P

rices in r

eal te

rm (

in Y

uan)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Note Property prices are in real term

average_property_price residential_price

high_grade_hosue_price economic_house_price

Data source Nationa Bureau of Statistics of China

Figure 4 Movements of Property Prices

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

28

Reference

Annett A (2005) ldquoHouse prices and monetary policy in the euro areardquo

Chapter III inEuro area policies selected issues IMF Country Report No

05266

Ayuso J and F Restoy (2006) House prices and rents An equilibrium asset

pricing approach Journal of Empirical Finance Vol 13 pp371-388

Eacutegert B and D Mihaljek (2007) Determinants of House Prices in Central and

Eastern Europe Comparative Economic Studies Vol 49 pp367ndash388

Capozza D P Hendershott C Mack and C Mayer (2002) Determinants of

Real House Price Dynamics NBER Working Paper No W9262

Carbo-Valverde S and F Francisco-Rodriguez (2010) The Relationship

between Mortgage Markets and House Prices Does Financial Instability Make

the Difference CenFIS Working Paper 10-02

Crone T and R Voith (1992) Estimating house price appreciation A

comparison of methods Journal of Housing Economics Volume 2 pp 324-338

Davis EP and H Zhu (2009) Commercial property prices and bank

performance The Quarterly Review of Economics and Finance Vol 49 Issue 4

pp 1341-1359

Davis EP and H Zhu (2010) Bank lending and commercial property cycles

Somecross-country evidence Journal of International Money and Finance

29

Deng L Q Shen and L Wang (2009) Housing Policy and Finance in China A

literature Review Prepared for US Department of Housing and Urban

Development

Deng Y and P Fei (2008) The Emerging Mortgage Markets in China In D

Ben-Shaher C K Y Leung amp S E Ong (Eds) Mortgage Market Worldwidepp

1-33

Deng YH D Zheng and C Ling (2005) An Early Assessment of Residential

Mortgage Performance in China The Journal of Real Estate Finance and

EconomicsVol 31 pp 117-136

Dougherty A and R Order (1982) Inflation Housing Costs and the

Consumer Price Index The American Economic Review Vol 72 pp 154-164

Ermini L and D Hendry (2008) Log Income vs Linear Income An Application

of the Encompassing PrincipleOxford Bulletin of Economics and Statistics Vol

70 Issue s1 pp 807-827

Fitzpatrick T and K McQuinn (2007) House prices and mortgage credit

Empirical evidence for Ireland Manchester School Vol 75 Issue 1 pp 82-103

Hanweck G and L Ryu (2005) The Sensitivity of Bank Net Interest Margins

and Profitability to Credit Interest-Rate and Term-Structure Shocks Across

Bank Product Specializations FDIC Working Paper No 05-02

30

Gerlach S and W Peng (2005) Bank lending and property prices in Hong

Kong Journal of Banking amp Finance Vol 29 Issue 2 pp 461-481

Gimeno R and C Martinez-Carrascal (2006) The Interaction between House

Prices and Loans for House Purchase The Spanish Case Banco de Espana

Research PaperNo WP-0605

Goodhart C and B Hofmann (2004) Deflation Credit and Asset Prices

HKIMR Working Paper No 132003

Goodhart C and B Hofmann (2008) House prices money credit and the

macroeconomy Oxford Review of Economic Policy Vol 24 pp180ndash205

Green J and J Shoven (1986) The Effects of Interest Rates on Mortgage

Prepayments Journal of Money Credit and Banking Vol 18 pp 41-59

Matthews K (2009) Bank Productivity in China 1997-2007 An Exercise in

Measurement HKIMR Working Paper No252009

Hill R J and Q Gan (2008) A New Perspective on the Relationship Between

House Prices and Income UNSW Australian School of Business Research

PaperNo 2008 ECON 13

Hofmann B (2003) Bank Lending and Property Prices Some International

Evidence HKIMR Working Paper No 222003

Kearl JR (1979) Inflation Mortgage and HousingThe Journal of Political

31

Economy Vol 87 pp 1115-1138

Lacoviello M and R Minetti (2008) The credit channel of monetary policy

Evidence from the housing market Journal of Macroeconomics Vol 30 pp

69-96

Liang Q and H Cao (2007) Property prices and bank lending in China

Journal of Asian Economics Vol 18 Issue 1 pp 63-75

McQuinn K and G OrsquoReilly (2007) A Model of Cross-Country House Prices

Research Technical Papers from Central Bank amp Financial Services Authority of

Ireland (CBFSAI) No 5RT07

Meen G (2002) The time series behavior of house prices A transatlantic

divide Journal of Housing Economics Vol 11 pp1-23

Miles D and V Pillonca (2008) Financial Innovation and European Housing

and Mortgage MarketsOxford Review of Economic Policy Vol 24 Issue 1 pp

145-175

Muellbauer J and A Murphy (1997) Booms and Busts in the UK Housing

Market The Economic Journal Vol 107 pp 1701-1727

Quigley J and S Raphael (2005) Regulation and the High Cost of Housing in

California The American Economic Review Vol 95 pp 323-328

32

Tsatsaronis K and H Zhu (2004) What drives housing price dynamics cross

countryevidenceBIS Quarterly Review March

Wolswijk G (2006) Determinants of Mortgage Debt Growth in EU Countries

European Journal of Housing Policy Vol 6 Issue 2 pp 131-149

Yeung S and R Howes (2006) The role of the housing provident fund in

financing affordable housing development in China Habitat International Vol

30 Issue 2 pp 343-356

Zhang X Q (2000) The Restructuring of the Housing Finance System in Urban

ChinaCities Vol 17 Issue 5 pp 339-348

Zheng L Q Huang T Lu W Zhou (2007) The Process and Problems of

Industrialization and Urbanization in China The Status of the Tenth Five-Year

Plan and Recommendations for the Eleventh Five-Year Plan Chinese Economy

Volume 40 pp 6-30

Zhu H (2006) The Structure of Housing Finance Markets and House Prices in

Asia BISQuarterly Review 55-70