２０【論文】

The Dynamic Effects of Money Supply and Bank
Lending on Commercialized Building Prices in China
Fengyun LIU［キーワード］Money Supply, Total Lending, Real Estate Development Loans,
House Purchasing Loans, Commercialized Building Prices［JEL 分類番号］E５１，R３
１，C３2

１

cities. Although housing prices finally appeared

Introduction

to be on a downward trend, the policies that

Owing to the introduction of market‑

forbade the purchasing of houses and lendingoriented housing reforms in 1998, the average

involved too much administrative power to becommercialized building price in China rosesustainable. In the long run, monetary policy is

sharply from 2,063 Yuan/square meter in 1998

the most effective method to regulate theto 4,681 Yuan/square meter in 2009. The highhousing prices. During these years, both longgrowth rate of real estate prices, particularly

and short‑term deposit interest rates (thein the recent years, has created the risk of aprice of money) have been determined by the

bubble in the Chinese economy. To regulate

central bank, with only limited fluctuations.

and control real estate prices, the Chinese

However, money supply, as the official

government has announced a series of policies,

intermediary target of monetary policy sinceparticularly tight monetary policies, since 2003.

1996 (See Xia, 2001), has been drasticallyDespitepricesincreasing. There is apprehension that sharp

remained persistently high until 2011, the year

upswings in the money supply have been

when the most stringent regulatory policies─

driving

restrictions on the quantity of houses purchased

Therefore, we focus on the quantitative aspect

by per family along with the prohibition ofof monetary policy─the money supply─torelative loans１─were implemented in someexamine its role in housing price fluctuations.

of

these

efforts,

housing

theincreaseinhousing

prices.

Some problems are imperative and need to be───────────１

The unified policies issued by the central
government are as follows: Local families are
not permitted to purchase three or morehouses, and non‑local families are not permitted
to purchase two or more houses. Moreover,loans are not available for a family s third
house. Furthermore, some cities announced
even stricter policies. For example, only one

addressed immediately. What were the effects
of money supply on housing prices? How did
money supply influence housing prices? To
what extent were housing prices affected after
a shock to the money supply? Consequently,───────────house is available for each local family in Beijing.

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

２１

this paper aims to shed light on the dynamic

explanatory variables for the instability ofeffects of money supply on housing prices inhousing prices in Korea. Their results are in

Chinavectorline with the quantity theory of money, which

autoregression (VAR) model, and proposes

states that when other factors are fixed, an

policy suggestions for regulations and controls

increase in the money supply will lead to an

on housing prices.

increase in price levels.

based

on

the generalized

Most of the related literature examines

Furthermore, an excessive money supply

developed countries, with a focus on monetary

has been noticed by some studies on China. Yi

policy shocks rather than money supply(1995) uses the logarithmic form of the Fisher

shocks, and introduces interest rates as aequation under the assumption of fixed

proxy

For

monetary velocity in the traditional quantity

instance, the short‑term interest rate istheory of money to prove that money supply

employed by Sims (1992), Carlino and DeFina

2 was over supplied by 5.4% from 1984 to(1998), Bruneau and Bandt (2003), Yang et al.

1994. Hu (2011) and He and Fan (2010) state(2010) and Musso et al. (2011) for the USA andthat money has been oversupplied in China incountries in the EU zone, by Sousa and Zaghini

most years since 1980. Xiong (2007) asserts(2007) for the G‑5 area (the USA, the EU Zone,

that excessive liquidity leads to the expansion

Japan, the UK, and Canada), and by Bjornlandof banking credit, increases investment on theand Jacobsen (2010) for Norway, Sweden, andreal estate market, and finally generates a

the UK. Because the nations investigated insurge in housing prices. Moreover, Hu (2009)

these studies are developed countries wherestates that money supply has considerable

the interest rate serves as one of the official

effects on housing prices. Therefore, it isintermediary targets of monetary policy, thenecessary and appropriate to focus on theinterest rate flexibly reflects the money supply

influence of money supply on housing prices.

formonetary

policyshocks.

and demand conditions in these highly market‑
oriented markets.

Although some related papers examine theeffects of money supply on housing prices inOn the other hand, money supply, as a

China, most of them focus on monetary policy

quantitative indicator of money, has been

shocks rather than money supply shocks.

considered only by a few international papers

Furthermore, money supply is only one of thewith regard to its effects on housing prices.

many proxies for monetary policy, as reported

Based on the VAR model, Lastrapes (2002)

by Han et al. (2007), Wang and Guo (2007) andfinds that both real housing prices and housing

Gao and Wang (2009). Although transmission

sales respond positively to money supply

from monetary policy to the housing market

shocks in the USA in the short run, on thehas been adequately discussed, thus far, thegrounds that the increase in the money supply

mechanism through which money supply

reduces interest rates and user costs, andinfluences housing prices has rarely been

results in the promotion of housing demand.

studied. Moreover, the Chinese financial system

Yu and Lee (2010) show that the money supply,in which most financing activities are conductedcorporate bond returns, and the number ofindirectly, has rarely been considered. The

building construction permits and orders

Chinese financial market has been developing

received for building construction are essential

since the establishment of the Shanghai

２２

中国経済研究第１０巻第１号

Securities Exchange in 1990 and participants

have not been studied in detail. Moreover,in economic activities, therefore, mainly rely onbank financing for both housing supply andbanks for financing. The Specialized Statisticalhousing demand has never been empiricallyAnalysis

Bankinganalyzed in China. As a result, this paper isRegulatory Commission (2005) declares thatmeaningful because it fills some gaps in thisapproximately 60% of the capital in the real

field of study and provides some guidance on

estate industry comes from banks. Moreover,research, quantitative monetary policy, andchanges in the money supply transmitted fromhousing

the central bank to the economy mainly flow

especially China.

Team

of

the

China

pricesindeveloping

countries,

through the banking system, and the multiplier

Furthermore, the interest rate, which is theeffect of the banking system could magnify theprice index of money, is not completely

influence of changes in money supply onmarket‑oriented in China and its effects on

economic activities. Wu (2008) asserts that the

housing prices have been challenged by many

real economy can be better explained whenstudies. By analyzing empirical data from 1998:

both the credit scale and the money supply are

3M to 2004: 8M, Song (2006) finds that interest

considered, rather than when only the money

rate has no significant impact on housing

supply is considered. Furthermore, many

prices. Interest rate leverage is impeded

researchers state that the expansion of

because the regulated interest rate goesbanking credit causes an increase in housing

against the marketization of economy. Gao andprices, such as Hofmann (2004) for 20 countries,

Wang (2009) assert that with the increase inKim (2004) for Korea, Liang and Cao (2007) for

loans flowing into the real estate industry, theChina, and Davis and Zhu (2009) for 15

rise in interest rates can not reduce housing

industrialized countries. Zhou (2005) proposes

prices. Xu et al. (2012), however, argue thatthat in the context of institutional transition,

movements in the real interest rate areuncertainty,

asymmetry,negatively related to changes in housing

financial institutions provide excessive loans toprices, and their effects on housing prices are

the real estate industry, resulting inspeculative

greater than those of the money supply. Thus,

behaviors from investors and consumers andthis paper introduces real interest rate into

the subsequent drastic fluctuations in housing

VAR models to determine its role.

andinformation

prices. Therefore, this paper also adopts a

The remainder of this paper is organized as

quantitative credit variable─the total lending

follows. Section 2 analyzes the mechanisms ofof financial institutions─to present the money

how money supply influences housing prices.

supply process.

Section 3 discusses the general situation of the

To explore how credit variables influence

Chinese financial system and commercialized

housing prices, this research further discusses

building market. Section 4 explains the databanking credit for the suppliers and consumers

and methodology. Section 5 presents thein the housing market by introducing real

results of empirical analysis in different sample

estate development loans and house purchasing

periods. Section 6 outlines the conclusions,loans, respectively. This is an innovationsuggestions, contributions, and limitations.

because to the best of our knowledge, thus far,the effects of banking credit on housing prices

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

２

Mechanisms of how Money Supply
Influences Housing Prices

In theory, money supply influences housing
prices primarily based on the three mechanisms
presented below.
２．
１ Interest Rate Mechanism
According to Keynesianism, in efficientfinancial markets, changes in monetary policies
influence economic activities through interest
rates. The increase in money supply reduces
interest rates, which in turn, reduces thefinancing costs for real estate development
companies and housing buyers, and thisultimately affects housing prices. However, in
China, interest rates are still largely fixed by
the central bank. As a result, the interest rate
leverage is hampered and money supply canhardly influence housing prices through theinterest rate mechanism, as demonstrated bySong (2006) and Gao and Wang (2009).
２．
２ Banking Credit Mechanism
Changes in the money supply could influencecommercial banks abilities to provide loans toreal estate development companies and housing
buyers, and therefore, influence housing prices.
Mohanty and Philip (2008) find that the mosteffective channel for the transmission ofmonetary policy in China is the quantitative‑
based banking credit channel. Leung and Lu

２３(2011) state that the Chinese banking sector
holds excessive reserves for loan expansion,
which has two main effects: First, these banks
can absorb the increase in interest rates byreducing the profit margins on their lending.
Second, because of their political and market
power, it is easier for the large‑scale state‑owned enterprises to obtain loans from state‑owned banks. Finally, Zhou (2006) states thatthe money supply is the driver of fluctuations inasset prices in China because it bears a strong
influence on loans, which amplifies its influenceon asset prices through credit intermediaries.
２．
３ Other Mechanisms
Besides the interest rate and banking credit
mechanisms, money supply can also influence
the housing market directly. As stated in thequantity theory of money, an increase in themoney supply will inflate the price levels ofboth financial and physical assets. Yao andZhang (2011) find that an increase in money
supply, which is also known as excess liquidity,
effectively determines the formation of inflation
expectations and consequently, inflation. In
other words, money supply can directly
influence the housing market and housing
prices. Moreover, the inflation caused by theincrease in money supply amplifies the construction costs for real estate development
companies, and thereby reducing housing

Fig. 1. Mechanism of how Money Supply Influences Housing Prices in China

２
４

中国経済研究第１０巻第１号

supply and raising housing prices. Furthermore,

the ratio of the value of domestic equities (thatan increase in the money supply could

are traded on domestic exchanges) to GDP toencourage investments in the real estate

present market size (P.9). We calculated theindustry, raising the demand for housing and,Bank Assets/GDP and Market Capitalization/

thus, housing prices.

GDP ratios in China based on the definitions of

The mechanisms through which the money

these ratios provided by Demirg ‑Kunt andsupply influences housing prices in China are

Levine (1999); these ratios are presented inshown in Figure 1. The field surrounded by the

Table 1. With the development of the market

dotted line is the focus of this paper.

sector, the Market Capitalization/GDP ratio

３

increased from 1994 to 2009 and reached an

The Chinese Financial System andCommercialized Building Market

abnormally high point of 1.23 because of the
sharp upsurge in stock prices in 2007, although

３．
１ The Chinese Financial System

the average value, 0.36, was low. The Bank

３．
１．
１ Bank‑based Financial System

Assets/GDP ratio, however, was greater, withan average value of 1.64, which is approximately

Since the establishment of the People s Bankof China in December 1948, the Chinese

4.6 times the value of the Market Capitalization/

financial system has experienced a series of

GDP ratio. These data suggest that althoughreforms. The banking system is the mostthe market continued to account for a small

important part of the financial system, despite

portion, the banking sector accounted for a

the fact that other financial instruments suchmajor portion of the financial system.

as securities have also been developing.

Table 2 describes the average values of the

Demirg ‑Kunt and Levine (1999) compare

Bank Assets/GDP and Market Capitalization/

the financial systems of 150 countries, andGDP ratios in other countries in the 1990s, asdevelope some indicators to evaluate thecalculated by Demirg ‑Kunt and Levine

structure and development of financial systems.

(1999). In the USA and Great Britain, whose

They use

Banks Assets/GDP, which is thefinancial systems were market‑based, theratio of the total domestic assets of deposit

Market Capitalization/GDP ratio was similar

money banks divided by GDP, to measure theto the Bank Assets/GDP ratio. This shows that

overall size of the banking sector (P.6), andthe market was similar in terms of size to the

adopt

banking sector. In contrast, in Germany andMarket Capitalization/GDP, which is

Table 1 Market Capitalization/GDP and Bank assets/GDP ratios in China
Time1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Ave.

M.C./GDP 0.08

0.06

0.14

0.22

0.23

0.30

0.49

0.40

0.32

0.31

0.23

0.18

0.41

1.23

0.38

0.71

0.36

B. A./GDP 1.15

1.17

1.26

1.38

1.46

1.53

1.55

1.54

1.81

1.88

1.88

1.93

1.92

1.85

1.84

2.15

1.64

Source: China s Finance and Banking Almanac (1994‑2010)

Table 2 Average Ratios of Market Capitalization/GDP and Bank Assets/GDP
in Other Countries in 1990s
Country

USA

Great Britain

GermanyJapan

Market Capitalization/GDP

0.80

1.13

0.24

0.79

Bank Assets/GDP

0.73

1.16

1.21

1.31

Source: Demirg ‑Kunt and Levine (1999).

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

２５Japan, which have bank‑based financial

value of 16.59%) exceeded the growth rate ofsystems, the Market Capitalization/GDP ratios

the nominal GDP during most years in thiswere approximately 6 times and twice the size

period, and experienced similar fluctuations asof the Bank Assets/GDP ratio, illustrating thatthe growth rate of M2. Xiong (2007) contends

the banking sector was larger than the market

that the excessive liquidity leads to thesector.

expansion of banking credit, increases invest-

Based on the above analyses, the Chinese
financial system is similar to those of Germany
and Japan, where the banking sector accounts

ment in the real estate market, and ultimately
leads to the surge in housing prices.
Japan experienced high economic growth

for a much greater portion of the financialfrom 1955 until the burst of thesystem than market. This differs from the

1989, when the country entered into an era offinancial systems of the USA and Great Britain.

economic recession. The growth rates of the

３．
１．
２ Money Supply

nominal GDP, M2, and total bank lending inbubble inWith economic development, a massive

Japan from 1980 to 1998 are shown in Figure 3.

supply of money has emerged in the Chinese

Before 1990, the average growth rates of both

economy. As described in Figure 2, from 1999

M2 (9.15%) and total lending (9.04%) wereto 2010, the average growth rate of the money

much higher than that for nominal GDP

supply (M2) (17.57%) was greater than that of(5.14%). Subsequently, the growth rates of M2

the nominal GDP (11.26%). This implies thatand total lending slowed down and were only

the supply of money surpassed its demand inslightly higher than the nominal GDP growth

the economy. The high money supply resulted

rate. Hence, there was an abundant money

in a huge amount of liquidity and strongly

supply and liquidity in Japan throughout thestimulated soaring asset prices. Moreover, theperiod of the bubble in the 1980s.

growth rate of total lending (with an average

Consequently, the situation in China during

Fig. 2. Growth Rates of Nominal GDP, M2, and Total Lending in China (%)
Notes: 1. M0: Cash currency in circulation; M1: M0 + current deposits; M2: M1 + Quasi-money (timedeposits + saving deposits + other deposits).
2. Benchmark year = 2000.
3. Nominal GDP = real GDP+GDP Deflator
Source: The People s Bank of China and the Chinese Statistical Yearbook (2010).

２
６中国経済研究第１０巻第１号

Fig. 3. Growth Rates of Nominal GDP, M2 and Total Lending in Japan (%)
Notes: 1. M0: Cash currency in circulation; M1: M0 + deposit money; M2: M1+ Quasi-money (time deposits +fixed savings + installment savings + non-resident yen savings + foreign currency deposits).
2. Until 1991, excluding trust subsidiaries and foreign trust banks. This is why the growth rate of total
lending suddenly rose to 22.33%.
3. Benchmark year = 1990.
4. Nominal GDP = real GDP+GDP Deflator
Source: The Statistical Bureau of Japan.

Fig. 4. Growth Rates of Total Lending, Commercialized Real Estate Loans, Real Estate
Development Loans, and House Purchasing Loans in China (%)
Source: The People s Bank of China and the Report of Chinese Monetary Policy Performance.

the past decade is similar to that of Japan inthrough the banking system. Figure 4 shows

the 1980s in terms of excessive money supply.

the growth rates of total lending, commerciali-

Therefore, it is advisable for China to pay more

zed real estate loans, real estate developmentattention to its high level of liquidity to avoid

loans, and house purchasing loans in China

the formation of an economic bubble, whichfrom 2004 to 2010. The growth rate of

had occurred in Japan.

commercialized real estate loans was much

３．
１．
３ Expansion of Banking Credit to the Reallarger (with an average difference of 8.38% andEstate Industry

a maximum difference of 38.54% in 2006) thanSince China has a bank‑based financial

the growth rate of total lending during thesystem, money supply enters economy mainly

period, except in 2008. Booms in real estateThe Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

２７

Fig. 5. Growth Rates of Total Lending and Real Estate Loans in Japan (%)
Source: The Statistical Bureau of Japan.

development loans occurred in 2006 and 2009,

estate industry and stimulated the housing

and booms in house purchasing loans occurred

price bubble.

in 2004, 2007, and 2009. It can be concluded

The above analysis shows that China isthat there was an expansion of bank credit tocurrently facing a situation that is comparable

the real estate industry. Many researchers

to that faced by Japan in the 1980s. The large

state that the amplification of bank credit

amount of bank credit entering the real estate

causes the increase in housing prices; theseindustry has increased housing prices, which isresearchers includes Hofmann (2004), whovery risky for China.

discusses the situation for 20 countries; Kim

３．
１．
４ Monetary Policies on Housing Price(2004), who studies the case of Korea; LiangControls and Regulations

and Cao (2007), who study the case of China;

The Chinese government started controllingand Davis and Zhu (2009), who investigate theand regulating the housing market in 2003, andcase of 15 industrialized countries.

ever since, a series of monetary policies have

Figure 5 illustrates the growth rates of total

been issued. Table 3 shows that monetarylending and real estate loans in Japan frompolicy became increasingly constrictive from1980 to 2005. The growth rate of real estate2003 to 2011, except in the second half of 2008

loans was much higher than that of total

to the end 2009, when monetary policies werelending in the 1980s, with an averageunrestricted to stimulate the economy todifference of 9%. In 1986, real estate loans

recover from the global financial crisis. The

peaked and were 24% higher than the growthmoney supply, however, has remained highrate of total lending. Ultimately, a large

since 2003 (See Figure 2).

amount of bank credit flowed into the real
Table 3 Monetary Policies from 2003 to 2011
Time

Monetary Policies
Increase reserve requirement ratios by 1% once; Eliminate the preferential housing interest rate on

2003high‑grade housing, villas, and second commercial housing, and appropriately increase down
payments.

２８

中国経済研究第１０巻第１号

Increase various deposit and lending interest rates by 0.27% once; Widen the lending interest rate
2004

float bands and permit the deposit interest rate to float down; Self‑owned funds in real estatedevelopment projects should not be smaller than 35% of total investments; Monthly payments shouldbe less than half of the income of the buyers.

2005

Cancel the preferential housing interest rate; Increase the down payments to 30% in regions withsoaring housing prices; Restrict loans from the Trust to real estate development corporations.
Increase reserve requirement ratios by 0.5% twice; Raise diverse lending interest rates by 0.27%

2006

once; Regulate the details on the area of the housing and increase the percentage of small‑size andnew housing down payments; Restrict the inflow of foreign funds into the housing market.
Boost reserve requirement ratios by 0.5% 10 times; Raise different deposit and lending interest rates

2007

by 0.27% 6 times; Amplify down payments for second houses to 40% and increase mortgage interest
rates by 10%.

The first

Raise reserve requirement ratios 5 times; Loosen regulation on development projects forhalf year of

economically affordable housing.

2008
The second

Decrease the reserve requirement ratios and interest rates twice; Reduce various lending interest

half year ofrates 5 times and various deposit interest rates 4 times; Decrease down payments to 20% and the

2008
2009

prescribed minimum mortgage interest rates to 0.7 times the benchmark lending interest rate .
Apply preferential interest rate policies to 4 state‑owned banks; Decrease the ratio of self‑owned
funds to loans on commercial housing projects.
Increase reserve requirement ratios by 0.5% 6 times; Raise various deposit and lending interest rates
twice; Secondary housing down payments should not be less than 50% and mortgage interest rates

2010

should not be less than 1.1 times the benchmark interest rate; The down payment for first houses
above 90 square meters should not be less than 30%; Stop offering mortgages for the purchase ofthird houses and for non‑local residents.
Increase the reserve requirement ratios by 0.5% 6 times, then lower reserve requirement ratios by
0.5% once in November; Boost various deposit and lending interest rates thrice; Forbid the purchase2011of the third houses for local families and the secondary houses for non‑local families, and make therelated mortgage loans unavailable; Secondary housing down payments should not be lower than
60%.

Source: The People s Bank of China and Almanac of China s Finance and banking (2003‑2010).

３．
２ The General Situation of the Chinese

Figure 6 provides the total area of commercia-

Commercialized Building Market

lized buildings sold in China from 1998 to 2009.

３．
２．
１ Components of Commercialized Buildings

The area of sold commercialized buildings has

Chinese Statisticalbeen increasing since 1998, except for 2008, theAs reported by the

Yearbook ,commercializedbuildings

areyear of the outbreak of the financial crisis.

defined as houses constructed by real estatedevelopment companies for selling and renting.
They comprise residential buildings, officebuildings, houses for business use, and others２.
───────────
２ Because of the characteristics of the Chinese
real estate market, houses are not referred to in
the same terms as in other countries. Residential───────────buildings in China are commercialized buildings
used for living. Office buildings are buildings
used for official affairs. Houses for business use
are buildings used for sales transactions such as
shopping malls or stores. Others includecommercialized buildings except residentialbuildings, office buildings and houses for business
use.

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

２９Fig. 6. Area of Commercialized Buildings Sold in China (10,000 sq. m.)
Source: Chinese Statistical Yearbook (2010).

Fig. 7. Average Prices of Commercialized Buildings in China (Yuan/sq. m.)
Source: Chinese Statistical Yearbook (2010).

Notably, the area of sold residential buildings

growth rates of the average prices ofaccounted for approximately 90% of the area ofcommercialized

sold commercialized buildings.

fluctuated lesser than those of office buildings,

３．
２．
２ Prices of Commercialized Buildings

houses for business use and others. The

andresidentialbuildings

Figure 7 illustrates the average selling

growth rates of the average commercialized

prices of commercialized buildings in Chinabuilding price (17.76%) and the average

from 1998 to 2009. The average prices for all

residential building price (18.71%) from 1999 to

the four types of commercialized buildings

2009 were higher than the corresponding

began growing at a faster rate in 2003. The

growth rate of CPI (3.9%) except in 2008, andaverage selling price of office buildings was thethe difference was the highest in 2004. The

highest, followed by that of houses for businessgrowth rate of the average price of houses foruse,business use was greater than the growth ratecommercializedbuildings,

residentialbuildings, and others, in that order.

of CPI from 1999 to 2009, except in 2000 and

Figure 8 compares CPI with the growth

2008, with the greatest difference of 29.29% inrates of different types of commercialized

2005. The growth rate of the average price ofbuilding prices in China from 1999 to 2009. The

office buildings peaked at 36.89% in 2004; ever３０

中国経済研究第１０巻第１号

Fig. 8. Growth Rates of Commercialized Building Prices and CPI in China (%)
Source: Chinese Statistic Yearbook (2010).

since the growth rate of office buildings has

housing price index data from 1999 to 2011 inbeen higher than that of the CPI. Interestingly,

China. Another indicator─the average selling

the growth rates of the prices of different
types of commercialized buildings increased

price─could compensate for this shortcoming.
The average commercialized building pricesubstantially around 2003 and became much(AP) and the average residential building pricehigher than the CPI level.

(RP) are used to represent the aggregate

４

Data and Methodology

commercialized building price level and theresidential building price level, respectively.

The literature review in Section 1 indicates

Money supply 2 (M2) represents the totalthat there is a close relationship among money

amount of money in the economy. The total

supply, bank credit, and housing prices in

lending of financial institutions (TL) encompa-

China. The theoretical analysis in Section 2

sses the gross amount of credit issued bydescribes how money supply influences housingbanks. Real estate development loans (DL) andprices, and, based on related literature, thehouse purchasing loans (PL) indicate thebank credit mechanism seems to be significant

financial support of banks to providers andin China. Section 3 proves that the bank‑based

consumers in the real estate market, respecti-

financial system, excessive money supply, andvely. This is one of the innovations of thisexpansion of bank credit to the real estate

paper because existing studies have notindustry accompany the sharp increase in

examined the influence of banking credit on

housing prices. Therefore, based on existing

housing prices in such detail by empiricallyliterature, theoretical mechanisms, and current

introducing these two variables. Because theChinese situation, we adopted seven variables

deposit interest rate is directly controlled by(with their abbreviations in parentheses) in our

the central bank with limited fluctuations, theempirical analysis, as illustrated below.

real interest rate (RI), which is defined as the

Because the calculation method for the

one‑year deposit nominal interest rate minus

housing price index was reformed twice, in

the inflation rate (CPI), is adopted to represent

2005 and 2011, there is no consistent successive

the monetary price in the following analysis.

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

３１

All data are from the official website of thethe DL and PL data are only available on a

People s Bank of China, Report of Chinese

quarterly basis after 2003Q2. The monthly

Monetary

Chinesedata are used in the models for the earlier

Academy of Social Sciences Database, and thesample period to obtain adequate sample size.

PolicyPerformance,

Tsinghua Financial Database. The variables,

The quarterly data for all the variables are

except for the RI, are expressed in logarithmic

available from 2003Q2 to 2011Q3, and theirform, seasonally adjusted using the X11

augmented Dickey‑Fuller (ADF) tests results

method, and expressed as LM2, LTL, LDL,

on stationary are shown by Table 4. Series RI,

LPL, LAP and LRP.

LAP, and LRP appear to be I(0), whereas series

The sample is divided into two periods,

LM2, LTL, LDL, and LPL appear to be I(1).

1999:1M to 2003:3M and 2003:2Q to 2011:3Q

The log difference of money supply 2 (DLM2),(2003:4M to 2011:9M). Many studies were

log difference of total lending (DLTL), log

conducted on the Chinese housing market

difference of real estate development loans

after the market‑oriented reforms in 1998.

(DLDL), log difference of house purchasing

However, this study is the first to attempt toloans (DLPL), difference of real interest rate

separate the sample into two periods for(DRI)３, log level of average commercialized

empirical analysis. There are four reasons forbuilding price (LAP) and the log level of theusing 2003:3M as a cut‑off for the sample

residential building price (LRP) then enter theperiod. First, the Chinese financial system has

VAR models. The empirical analysis of thisexperienced substantial reforms since 2003,

sample period is necessary because all

evidenced by the establishment of the China

available data on these variables is required

Banking Regulatory Commission (CBRC) infor exploring the affecting mechanism of M2,

April 2003. The importance of these reforms is

TL, DL, and PL on the housing price.

also proved by the following empirical analysis.
Second,theChinesegovernment

began

implementing policies to regulate housing
prices in the second quarter of 2003. Third, theprices of commercialized buildings in Chinabegan increasing sharply since 2003. Fourth,

The data on DL and PL are not available
from 1999:1M to 2003:3M. Therefore, to obtain───────────
３

Because the monthly series RI is I(1) and DRI
enters the monthly models, the quarterly
models also use DRI to maintain homogeneity.

Table 4 Results of ADF Test (2003:2Q to 2011:3Q)
The Original SeriesFirst Difference Series

Series（C,T,P）

ADF
Test Statistic

Prob.

Series（C,T,P）

ADF
Test Statistic

Prob.

LM2

（C,T,1）‑2.387625

0.3786

DLM2（C,0,0）‑3.217596

0.0281

LTL（C,T,1）‑2.070502

0.5419DLTL（C,0,0）‑3.651415

0.0101LDL（C,T,0）‑1.167058

0.9010DLDL（C,0,0）‑4.755407

0.0006

LPL（C,T,1）‑1.814486

0.6741DLPL（C,0,0）‑8.412051

0.0000

RI(C,0,7)‑4.809282

0.0007

DRI(C,0,0)‑3.378037

0.0194

LAP（C,T,3）‑3.763212

0.0331

‑

‑

‑

‑

LRP

（C,T,3）‑4.090519

0.0161

‑

‑

‑

‑

３
２

中国経済研究第１０巻第１号

a sufficient sample size, the monthly data on

Sims and Zha, 2006). However, the traditional

M2, TL, RI and AP are adopted. Table 5unrestricted VAR has inherent problems. As

describes their ADF test results on stationary.

Pesaran and Shin (1998) contend,

Although Series LAP is I(0), series LM2, LTL

lying shocks to the VAR model are orthogona-

and RI are found to be I(1). As a result, DLM2,

lized using the Cholesky decomposition before

DLTL, DRI and LAP enter the VAR models.

impulse responses, or forecast error variancethe under-

To ensure an accurate comparison is madedecompositions are computed. This approach

between the two periods, the same variables

is not, however, invariant to the ordering of theand data frequency as those for 1999:1M tovariables in the VAR (P.1). Consequently, the2003:3M are employed throughout 2003:4M tostructural VAR model is developed by

2011:9M. The monthly data on the M2, TL, RI

Bernanke (1986), Blanchard and Quad (1989),

and AP are introduced, and the results of ADF

Sims (1986), and Blanchard and Watson (1986).

tests on stationary are illustrated by Table 6.

Dekker et al. (2001) state

Series LM2, LTL, and RI are I(1); however,restrictions on the covariance matrix of theseries LAP is I(0). As a result, DLM2, DLTL,

structural errors and the contemporaneous

DRI and LAP enter the VAR models.

and/or long‑run impulse responses to them-

imposing a priori

Sims (1980) proposes VARs to conduct aselves (P.6). Nevertheless, the number ofdynamic analysis of a system in which changes

restrictions positively relates to the number of

to a particular variable are affected by changes

variables, and it is sometimes difficult toto other variables, the lags of those variables,

impose a priori assumptions because of theand changes in its own lags. The VAR

complex economic situation. The generalized

technique has been broadly used in theapproach to VAR is advanced by Koop et al.

analysis of monetary policy and asset markets(1996) for nonlinear dynamic systems and by(Sims, 1992; Dekker at el., 2001; Lastrapes, 2002;

Pesaran and Shin (1998) for linear systems, toTable 5 Results of ADF Test (1999:1M to 2003:3M)
The Original SeriesFirst Difference Series

Series（C,T,P）

ADF
Test Statistic

Prob.

Series（C,T,P）

ADF
Test Statistic

Prob.

LM2

（C,T,0）‑2.606334

0.2794

DLM2（C,0,1）‑7.870254

0.0000LTL（C,T,0）0.601531

0.9993

DLTL（C,0,0）‑5.089698

0.0001

RI（C,0,0）‑1.928556

0.3169DRI（C,0,0）‑6.482454

0.0000LAP（C,T,0）‑6.096147

0.0000‑‑‑‑

Table 6 Results of ADF Test (2003:4M to 2011:9M)
The Original SeriesFirst Difference Series

Series（C,T,P）

ADF
Test Statistic

Prob.

Series（C,T,P）

ADF
Test Statistic

Prob.

LM2

（C,T,0）‑1.347478

0.8701

DLM2（C,0,0）‑8.518202

0.0000LTL（C,T,1）‑1.332113

0.8741DLTL（C,0,0）‑7.270554

0.0000

RI（C,0,0）‑2.316292

0.1692

DRI（C,0,0）‑4.534248

0.0004

LAP（C,T,0）‑5.270226

0.0002‑‑‑‑

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

overcome the shortcomings of the orthogona-

３３

In model (2),lized approach and the structural VAR. It has
been employed by various studies such as Wen(2001), Dekker et al. (2001), and Ewing and

The second step is the modeling for theThompson (2008). Guided by these scholars,period from 1999:1M till 2003:3M. Because ofthis paper utilizes the generalized VAR

data limitations, only four monthly series aretechnique.

adopted. These are DLM2, DLTL, DRI and

An m‑dimensional and p‑order vector
autoregressive model is presented as follows.
(1)
where

LAP. The relationship among them is explainedby model (3) using a 2‑lag length５.
In model (3),vectorThird, to compare the empirical results ofof endogenous variables, jointly determined bythese two periods, VAR model (4) is set using

its own lags and the lags of other variables,

the monthly data of DLM2, DLTL, DRI, and

is a

are

LAP from 2003M4 to 2011M9. Therefore, theis anvector for the fixed effects,

matrix of

results of models (4) and (3) are comparable

unobserved shocks (disturbances). The matrix

because they use the same variables and data

forms offrequency. Model (4) has the same 2‑lag length６

coefficient matrices, andis anare presented below.

as VAR model (3).
In model (4),The VAR models described above are
The empirical analysis is conducted in threeestimated using the Eviews 6.0 software andsteps. The first step involves the modeling forsuccessfully pass the AR root test, which

the period of 2003:2Q to 2011:3Q. Because theimplies that the VAR models are stable. The

data on all the variables are available, it isimpulse response analysis based on thepossible to analyze the detailed transmission

estimated VARs could be used to trace out themechanism from the M2 to TL, DL and PL,

dynamic responses of each variable to theand finally to AP and RP. There are two VAR

innovations in a particular variable in themodels. Model (1) is for the AP, where

system.

variables of DLM2, DLTL, DLDL, DLPL, DRI
and LAP are introduced. Model (2) is for theRP which comprises the DLM2, DLTL, DLDL,

５

Results of Impulse Response Analysis

５．
１ Period from 2003:2Q to 2011:3Q

DLPL, DRI, and LRP variables. Both have a 1‑

The results of the generalized impulse

lag length４.

response functions of model (1), in which LAP

In model (1),is introduced and model (2), in which LRP is───────────５───────────
４ The principles of LR, FPE, AIC, SC, and HQ
hint a 1‑lag length for VAR models (1) and (2).

The principles of LR, FPE, and AIC hint a 2‑
lag length for VAR model (3).
６ The principles of LR, FPE and AIC hint a 2‑
lag length for the VAR model (4).

３４中国経済研究第１０巻第１号

adopted from 2003:Q2 to 2011:Q3, are shown inin the first quarter. After a 1% positive shock

Figures 9 and 10, respectively.

to DLTL, the responses of DLM2 reach

Following a 1% positive shock to DLM2, theapproximately 0.95% in the first quarter. This

response of LAP peaks at 2.77% in the third

implies that there is a mutual causal relationshipquarter, whereas the response of LRP peaks at

between DLM2 and DLTL. A shock to DLM2

3.31% in the second quarter. When a 1%

has a positive influence on DLTL, and vicepositive shock to DLTL occurs, the LAP

versa.

response rises to 1.92% in the third quarter,

Moreover, DLPL responds more significantly

whereas the LRP response peaks at 2.60% in

to a 1% positive shock to the money supply

the second quarter. The responses of LAP andthan DLDL, peaking at approximately 1.08%

LRP to a 1% positive shock in DLPL reach theand 1.04% respectively in the second quarter inhighest point in the first quarter at 0.72% andboth models (1) and (2). Moreover, DLPL

1.14%, respectively. Nevertheless, the response

responds more drastically than DLDL to a 1%of LAP (‑0.47% in the 7rd quarter) to a 1%

positive total lending shock, at approximately

positive shock to DLDL is stronger than that of

1.23% in the second quarter and 1.07% in the

LRP (‑0.25% in the 1st quarter). Both LAP and

first quarter, respectively. Thus, the upswings

LRP respond negatively to a 1% positive shockin DLM2 and DLTL generate increases in bothto the real interest rate in the first two

DLDL and DLPL.

quarters and reach the lowest points of ‑1.19%

Interestingly, DLM2 and DLTL both respondand ‑1.00% respectively in the first quarter;

positively to a 1% positive real interest rate

however, they respond positively after theshock, peaking at 0.48% and 0.49% respectively

third quarter, with the largest values of 1.30%

in the second quarter. This suggests that theand 1.20% respectively in the 7th quarter. Thus,

increase in the DRI causes the growth in the

DLM2 has the strongest effects on housing

DLM2 and DLTL, which could increaseprices, followed by the DLTL, DLRI, DLPL,

housing prices. This is why a positive realand DLDL. Moreover, the LRP responds moreinterest rate shock results in an increase in thestrongly to shocks in DLM2, DLTL and DLPL

commercialized and residential building prices

thanfrom the third quarter onwards.

thenon‑residentialbuildings

price.

However, it responds more weakly to shocks
in DRI and DLDL.

５．
２ The Period from 1999:1M to 2003:3M
The results of the generalized impulse

Furthermore, there is a positive relationshipresponse functions of model (3) for the period

between DLM2 and DLTL. In models (1) andfrom 1999:1M to 2003:3M are described in(2), the responses of DLTL to a 1% positive

Figure 11.

money supply shock７ are the largest at 1.45%

The response of LAP to a 1% positive money───────────
７ a 1% positive money supply shock in thispaper means a 1% positive shock to DLM2, that
is, a one‑ positive‑ standard‑ deviation innovation to the increment on the logarithmic money
supply. This is also the case for the a 1%
positive total lending shock, real estate development loans shock, house purchasing loans shock,and real interest rate shock.

supply shock is the largest at 0.26% in the third
month. LAP peaks at 1.84% in the first monthfollowing a 1% positive total lending shock.
The response of DLTL peaks at 0.11% in thesecond month following a 1% positive money
supply shock, and DLM2 has the strongest
response of 0.17% in the second month

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

３５

Fig. 9. Results of the Generalized Impulse Response Functions of Model (1) (LAP, 2003:2Q to
2011:3Q)
following a 1% positive total lending shock.

are the largest, followed by the DRI, and,

LAP, DLM2 and DLTL respond negatively

finally, the DLM2 from 1999:1M to 2003:3M.

to the real interest rate shock, and reach theInterestingly, compared with model (1), theminimum values of ‑1.02% in the first month,relationship between DLTL and DLM2 is‑0.24% in the third month and ‑0.18% in themuch weaker, and the response of LAP to asecond month, respectively. Thus, an increase

money supply shock is paltrier in model (3).

in DRI could lower LAP, DLM2, and DLTLThis leads us to believe that the blockedfrom 1999 to 2003.

transmission between money supply and bankConsequently, the effects of DLTL on LAP

credit before 2003 led to the minor effects of

３
６

中国経済研究第１０巻第１号

Fig. 10. Results of the Generalized Impulse Response Functions of Model (2) (LRP, 2003:2Q to
2011:3Q)
the money supply on commercialized building

period from 2003:4M to 2011:9M.

prices. However, it is possible that the data

When there is a 1% positive shock to theadopted in models (1) and (3) differ inmoney supply, the response of LAP peaks atfrequency and cause the variance in the

1.94% in the third month. When a 1% positive

results. Further analysis is conducted tototal lending shock occurs, the LAP has theresolve this problem.

strongest response of 1.37% in the third month.

５．
３ Period from 2003:4M to 2011:9M

After a 1% positive shock to the money

Figure 12 shows the results of the generalized

supply, the response of DLTL is largest at

impulse response functions of model (4) for the

0.62% in the first month. The response of

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

３７

Fig. 11. Results of the Generalized Impulse Response Functions of Model (3) (LAP, 1999:1M to
2003:3M)

Fig. 12. Results of the Generalized Impulse Response Functions of Model (4) (LAP, 2003:3M to2011:9M)

３
８中国経済研究第１０巻第１号

DLM2 to a 1% positive total lending shockfunctions of model (3) with that of model (4).

reaches the highest point of 0.52% in the firstAccording to Table 7, LAP responds moremonth.

strongly to a total lending shock than a money

Interestingly, following a 1% positive real

supply shock in the model for the period frominterest rate shock, the response of LAP is

1999:1M to 2003:3M. In other words, before

negative (‑0.25%) in the first month, but

2003, the influence of total lending on commer-

positive after the second month, peaking at

cialized building prices was powerful; however,

0.56%. Moreover, the responses of the DLM2

the effect of money supply on commercialized

and the DLTL to a 1% real interest rate shockbuilding prices was limited. Nonetheless, after

are positive after the second month, reaching

2003, the influence of total lending decreased,

maximum values of 0.16% and 0.17%, respecti-

whereas the effect of money supply became

vely. Therefore, an increase in DRI positively

much more significant. These results imply

influences the DLM2 and DLTL, and, thus, thethat with the successive reforms in theprices of commercialized buildings. This is also

Chinese financial system, the money supply,

proved by the former quarterly models (1) andwhich became the official intermediary target(2).

of monetary policies, has gained more impor-

Accordingly, the DLM2 has the strongest

tance in the commercialized building market.

influence on LAP, followed by DLTL and DRI

Now, money supply plays a more significant

for the period from 2003:4M to 2011:9M. Thisrole than the former official intermediary

result is similar to that of models (1) and (2) fortarget, that is, total lending. Since China has a

the period from 2003:2Q to 2011:3Q.

bank‑based financial system, total lending

５．
４ Comparison

continues to bear a powerful influence on

Tables 7 and 8 compare the key data on the
results of the generalized impulse responsecommercialized building prices, although its
significance has decreased slightly.

Table 7 Key Data on the Results of the Generalized Impulse Response Functions in Two Periods(Monetary Quantitative Shock)
Largest Response

LAP to money
supply shock

LAP to total
lending shock

DLTL to money
supply shock

DLM2 to total
lending shock

1999M1:2003M3
Model (3), Figure 11

0.26%

1.84%

0.11%

0.17%

2003M4:2011M9
Model (4), Figure 12

1.94%

1.37%

0.62%

0.52%

Table 8 Key Data on the Results of Generalized Impulse Response Functions in Two Periods(Monetary Price Shock)
Largest Response

LAP to real interest
rate shock

DLM2 to real interest
rate shock

DLTL to real interest
rate shock

1999M1:2003M3
Model (3), Figure 11‑1.02%‑0.24%‑0.18%

2003M4:2011M9,
Model (4), Figure 12

0.56%

0.16%

0.17%

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

３９Moreover, it is worth noting that theperforming assets in 1999, and the banking

response of DLTL to a money supply shock

system was facing a very difficult situation (Heand the response of DLM2 to a total lending

and Yin, 2010). This explains why the increase

shock were much smaller in the period fromin the deposit interest rate failed to increase

1999:1M to 2003:3M. In other words, for the

total lending and money supply, and consequen-

period from 2003:4M to 2011:9M, changes intly, housing prices. After 2003, the conversion

money supply generated greater movementsof state‑owned banks into joint‑stock companies

in total lending, and the variations in total

stimulated business effectiveness. With exce-

lending caused larger fluctuations in thessive money, the banks had excess liquidity,

money supply. Through their consecutive

which promoted loans to the real estate

mutual causation circulation, the effects ofindustry and ultimately stimulated growth inmoney supply on commercialized building

housing prices. Similarly, Shen et al. (2011) findprices were amplified several times. Thisthat bank credit has a considerable influence onprocess is known as the money creation of

housing prices, and that the RI for the realbank credit. Apparently, it was a smootherestate industry positively influences housingtransmission between money supply and total

prices. Also, Gao and Wang (2009) suggest thatlending that generated the increase in thethe increases in interest rates accompanied bymoney multiplier and, in turn, amplified thethe expansion of bank credit to the real estate

influence of the money supply on commerciali-

industry can not lower housing prices. Xu et al.

zed building prices after 2003. Li (2007) and Lu(2012) emphasize the minus effects of the RI on

and Zhu (2011) also indicate that the money

housing prices. However, they neglect themultiplier has grown up in recent years.

relationship between money supply and RI by

As described in Table 8, the responses of

introducing

them

into

separatemodels.

LAP, DLM2, and DLTL to a real interest rate

Consequently, they fail to determine that anshock were negative before 2003, and became

increase in RI stimulates growth in money

positive, with a one‑month lag, after 2003. This

supply and bank lending, and therefore,

is similar to the results of Wang (2012), who

housing prices. This is the main reason why

argues that the increase in the deposit interest

they obtain the conflicting result that increases

rate leads to the expansion of bank credit

in RI generate a considerable decline inbased on data from 2004 to 2010. Because thehousing prices.

increase in the deposit interest rate stimulates

The reforms in financial system since 2003

the growth in deposits and increases theled to the increase in the credit multiplier andbanks costs, banks have to explore more

the opposite influences of RI on the prices ofloans to make profits when other financial

commercialized buildings between the two

products are not sufficient for investment. This

periods. The International Monetary Fundprocess increased the total lending and money(IMF) and the World Bank (2012) report, The

supply, and consequently, the influence of RI

banking system has undergone tremendous

on commercialized building prices changed

change in China in the recent period and thisfrom negative to positive after 2003. However,process is still ongoing. The CBRC is abefore 2003, the capital adequacy ratio of banks

relatively young agency, having been created

was low because of the insulation of non‑in 2003 from the PBC as part of the major

４０

中国経済研究第１０巻第１号

banking sector reform instituted by thebuilding prices and residential building prices

Chinese authorities. In addition to the streng-from 2003:2Q to 2011:3Q. The study alsothening of financial sector regulation andcompared the influences of the money supply

supervision, these reforms have also led to theand total lending on commercialized building

conversion of four large state‑owned banks

prices during 1999:1M to 2003:3M with those

into joint‑stock companies; consolidation ofduring 2003:4M to 2011:9M. The main findings,

rural credit cooperatives; restructuring of

explanations, and suggestions are provided

joint‑stock banks and securities companies;

below.

and reform of the insurance sector … The

６．
１ Explanations for the Empirical Results

CBRC was established in 2003 as a stand‑alone

The empirical results suggest that a positive

prudential authority and is widely credited

shock to money supply, TL and PL would leadwith having made significant achievements in

to an increase in commercialized building

its short existence. (P.5&P.7). Fan (2010) states

prices, while a positive shock to DL wouldthat the establishment of the CBRC leads todecrease the prices of commercialized buildings.

the creation of a new financial regulatory

First, as the official intermediate target ofsystem in which the CBRC, the China

monetary policy, money supply has the most

Securities Regulatory Commission (CSRC), andsignificant impact on housing prices among

the China Insurance Regulatory Commission

quantitative financial variables after 2003.

(CIRC) work in coordination and each body has

Therefore, money supply should be the mostits own clearly defined responsibilities. Cao

important long‑term consideration. Interesting-(2008) also divides the reform of the Chinesely, however, from 1999M1 to 2003:3M, thefinancial system into two periods: from 1978 to

response of commercialized building prices to2003 and after 2003. The first stage of thea money supply shock was minor. The change

reform focuses on independence from govern-

was caused by the smoother transmission

ment financing, whereas the second stage

between money supply and bank credit, andfocuses on the structural conversion of

the higher money multiplier in the subsequent

financialcorporations.

stage, which amplified the effects of the money

Consequently, the financial system reforms

organizations

andsupply through the transmission of bankafter 2003 remarkably improved the credit

credit. The changes and reforms in themultiplier and the transmission between

financial system strengthened the transmission

money supply and bank lending, which

between the money supply and bank credit.

amplified the amount of money flowing into theConsequently, the effects of the money supply

economy and, ultimately, magnified the effects

on commercialized building prices have largelyof the money supply on commercialized

increased.

building prices.

６

Second, the responses of commercialized

Conclusions

building prices to a positive total lending shock
are large in both sample periods. The

Using four generalized VAR models, thisexpansion of bank credit has been an influential

paper empirically analyzed the transmission

factor for commercialized building pricemechanism of how money supply, bankincreases since the market‑oriented reform oflending, DL and PL influenced commercialized

the housing market. Since China has a bank‑

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

４１

based financial system and the funds in thecritical to consider the lags; otherwise, thereal estate industry come primarily from

effects will not be as expected.

banking credit, an expansion in banking credit

Fifth, the influence of the RI on commerciali-

certainly influences housing prices. This is also

zed building prices is smaller than those of the

supported by Liang and Cao (2007), who findmoney supply and total lending. After 2003, theunidirectional causality running from bankrise in the RI even generated increases in thelending to property prices in China. Therefore,

money supply and total lending, thereby

to prevent housing prices from increasing

increasing commercialized building prices as

drastically, total lending needs to be controlled.

well. Therefore, the prices of commercialized

Third,

theeffects

of

a

real

estatedevelopment loans shock are the smallest. The

buildings could not be lowered by increasing
interest rates.

promotion of real estate development loans

Finally, residential buildings prices are more

could reduce construction costs and, subsequ-sensitive to shocks in money supply, total

ently, stimulate growth in the housing supply,

lending and PL than the non‑residential

which would theoretically lower housing price.

buildings prices, whereas they are lessHowever, the conductive effects were not sosensitive to shocks to RI and DL. Therefore,

remarkable. Therefore, it could be illustrated

the extent to which the same monetary policythat the housing supply has been much smaller

influences different types of houses differs.

than the housing demand in China. The

６．
２ Policy Implications

increase in supply caused by financial support

Although monetary policies have beenfrom the banks could not satisfy the demand

implemented to control housing prices sincefor housing. To meet the most basic and2003, money supply, total loans and commercial

broadest housing demands, banking credit hasreal estate loans have still been ascending

to be offered for the development of common

substantially. A considerable amount of funds

housing such as economically affordable

and significant liquidity are continuing to flow

housing rather than villas, office buildings andinto the real estate industry, which has largely

houses for business use.

stimulated the demand for commercialized

Fourth, a house purchasing loans shock hasbuildings, thereby increasing their prices. The

immediate effects on commercialized building

considerable increase in money supply andprices; however, the influences of shocks toexpansion of banking credit to the real estatemoney supply, total lending and real estateindustry is what led to the powerfully growthdevelopment loans lag around the 3rd or 4thof commercialized building prices. This explains

quarters. As a result, it is most effective tothe strange phenomenon that occurred before

control PL to decrease housing prices in the

2011: the greater the number of monetary

short run. Liao and Dai (2007) also assert thepolicies on housing prices, the more theexistence of reinforced interdependency bet-

upswings in commercialized building prices.

ween housing prices and PL. Nevertheless, to

Nevertheless, in 2011, the policies forbidding

ensure that housing prices fluctuate in a safe

secondary or third house purchases andrange in the long run, attention has to be paid

related loans effectively limited speculative

to the money supply, TL and DL. Accordingly,

housing demand, which, in turn, led to thewhen determining monetary policy, it isslight decrease in commercialized building

４２

中国経済研究第１０巻第１号

under the assumption of perfect financial

prices.
The strict policies with excessive arbitrary

markets, multiple financial instruments ought

political power introduced in 2011 cannot be

to be developed to meet the investment needssustained in the long run because it will

for excessive liquidity.

threaten the stability of the economy. Therefore,

Additionally, time lags and differing policies

it is vital for the Chinese government toon various commercialized buildings should be

resolve the long‑term regulatory and control

considered when monetary policies are used tomechanisms on housing prices and adopt

regulate commercialized building prices.

indirect policy instruments rather than arbi-

６．
３ Contributions and Limitations
This paper demonstrated the various dyna-

trary direct controls.
Because the expansion of funds and liquidity

mic influences exerted by different quantitative

to the real estate industry is one of importantmonetary variables on commercialized building

reasons for the rise in commercialized building

prices. Regarding the analysis of the trans-

prices, it might be effective to use different

mission mechanism on how money supply,

types of monetary instruments to decrease itbank lending, DL and PL influence commercia-

from the source and absorb it by other means.

lized and residential building prices, our study

On the one hand, to use different types ofis the first to introduce DL and PL in empiricalmonetary instruments to decrease the sharp

analysis. Furthermore, the study explains thesurges in money supply, total loans, andreasons for the sharp increases in commercia-

commercial real estate loans to prevent funds

lized building prices during the last 10 years; itand liquidity from flowing into the real estatealso explains why monetary policies wereindustry. According to the monetarists, forineffective before 2011 but effective in 2011.

example, Friedman and Schwartz (1963), theThis might clarify mixed results obtained bymoney supply should be set as the exogenous

former studies in this field. Moreover, this isvariable. Bruner and Meltzer (1997) state thatthe first study to determine that the financial

the central bank can control the base money

system reforms strengthened the transmission

andflexible

between the money supply and banking credit

institutions and procedures. In China, He (2006)

the

money

supplythroughafter 2003, and, accordingly, boosted the effects

conducts a theoretical analysis and indicatesof the money supply on commercializedthat endogenous money does not represent thebuilding prices. However, previous studies do

uncontrollability of money supply. Moreover,

not divide the sample period before and after

through an empirical analysis, He (2006) finds

2003 and are not concerned with the impact ofthat the money supply can be controlled inchanges in the financial system on housing

China, and its controllability is even larger

prices. Future studies in this area should

than that in Germany and Switzerland.

consider the financial system context such as

Furthermore, Zeng and Li (2007), Huang (2010),

financialand Zhao (2012) note that money supply is stillcharacteristics of financial activities.

systemstructure,reforms,

andan effective and competent intermediate

Owing to data limitations, the influences oftarget of monetary policy. As a result, weDL and PL are not examined in the earlier

could not exclude the influence of the central

stage. Because the money supply is a nationalbank on the money supply. On the other hand,variable that cannot be segregated by areas,

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

this paper did not compare the effects of themoney supply on commercialized building
prices among different regions and areas in
China.

Acknowledgements
We extend our heartfelt gratitude toProfessor Yan Shanping (Editor) and the threeanonymous referees for their excellent suggestions. Moreover, we would like to thank Prof.
Shuji Matsuno for his guidance. We would also
like to acknowledge the valuable comments
provided by Prof. Kazuo Inaba and Prof.
Mitsuo Yamada at the 6th Seminar of the
Econometric Analysis Research Society in
2011, and those provided by Associate Prof. Go
Yano and Prof. Yoshio Kojima at the 11th
Annual Conference of the Japanese Research
Association for Chinese Economy. A Part ofthis work was supported by Prof. Yu and his
project team for the humanities and social
science research planning fund from the
Chinese Ministry of Education (10XJA790010).
Comments ara welcome to liufengyun888@hot
mail.com.
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（Graduate School of Economics,
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The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China

４７

The Dynamic Effects of Money Supply and Bank Lending
on Commercialized Building Prices in China
Fengyun LIU (Graduate School of Economics, Ritsumeikan University)
Keywords: Money Supply, Total Lending, Real Estate Development Loans,
House Purchasing Loans, Commercialized Building Prices
JEL Classification Numbers: E51, R31, C32
Based on the characteristics of the Chinese financial system, this paper analyzes thetransmission mechanism of how money supply, bank lending, real estate development loans, andhouse purchasing loans influenced the commercialized and residential building prices during theperiod from 2003:2Q to 2011:3Q. The study also compares the influences of money supply and total
lending on commercialized building prices during the period from 1999: 1M to 2003: 3M with thoseduring the period from 2003:4M to 2011:9M. The impulse response functions of the four generalized
VAR models show that, besides different time lags, a positive money supply shock leads to thelargest increase in commercialized building prices, followed by a positive shock to total lending andhouse purchasing loans, whereas a positive real estate development loans shock causes a slightdecline in commercialized building prices. Residential building prices are more sensitive to a shock
to money supply, total lending, and house purchasing loans than non‑residential building price,whereas they are less sensitive to a shock to real estate development loans. The real interest rate
shock positively influenced the commercialized building prices after 2003:3M; however its effectwas smaller than that of shocks to money supply and total lending. Interestingly, the response ofcommercialized building prices to a money supply shock was much stronger from 2003:3M to2011:9M than that from 1999:1M to 2003:3M because the financial system reforms after 2003
strengthened the transmission between the money supply and banking credit and, thus, enhanced
the effects of money supply on commercialized building prices.