Economic analysis paper-Jung Hoon Song (1).docx

8/10/2019 Economic analysis paper-Jung Hoon Song (1).docx

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What were the Causes of the Chinese Housing Bubble?

University of Minnesota-Twin Cities

Jung Hoon Song(4466868)


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INTRODUCTION:

The main issue that we will discuss in this paper is the Chinese housing bubble in recent

years. China is the one of the biggest countries in the world and China is growing really fast.

Housing prices in China have been rapidly increasing in recent years. In the graph represented in

figure 1 shows that the average housing sale price has been increasing in China from 2002 until

2011. From figure 1, we developed the following question: why have housing prices in China

been increasing and what are things that have been driving the housing bubble in China in recent

years.

The paper will be organized as follows; section one will deal with the historical

background of the Chinese housing market and establish that there is in fact a bubble in the

housing market. From the Section two to Section four, we examine some of the possible causes

of the housing bubble in China. In section two, we will examine how the willingness of Chinese

state owned enterprises to pay more for property drives up housing prices. Section 3 uses

regression analysis to analyze how urbanization trends in china could be increasing the demand

for housing. Section four examines how the lack of confidence in other investment opportunities

could be driving investors to the housing market. Finally section five offers the conclusion of the

paper, including the implications of housing bubble in China.

1) History of Chinese Housing Market and Measurement of the Bubble:

Since the beginning of their economic reform in 1978, Chinese housing policy has

experienced dramatic changes. Prior to 1978, Chinese housing was a part of a welfare allocation

system. During this period, the Chinese government allocated public housing through

government departments or companies under the central planning system. In that way, the

government directly controlled the production, allocation and pricing of housing. Since 1978,


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both economic and population growth has pushed the government to initiate the housing system

reform by gradually setting the housing market free. By 1998, China finally established a

market-based housing system. Since then, the housing market developed rapidly and the

construction contributed a great deal to ChinasGDP. However, in the last ten years, a housing

bubble began to appear in the real estate market, this economic bubble brought many problems to

this rapidly developing country.

Since 1998, the real estate market, the Chinese government, real estate developers, and real

estate speculators have been enjoying the high speed growth of real estate. However, now the

housing bubble has become a noticeable and potentially dangerous threat to the Chinese

economy.Generally speaking, a real estate bubble has three common indicators: the real estate

investment growth rate to GDP growth rate ratio, the real estate development loans to total loans

of financial institutions ratio, and finally the Home prices to Household income ratio.

1.1) The Real Estate Investment Growth Rate to GDP Growth Rate Ratio:

The real estate investment growth rate to GDP growth rate ratio indicates the extent of

the bubble in real estate investment. In accordance with international standards, this ratio should

generally not exceed 2. The larger the ratio, the more the real estate industry deviated from the

real economy, and the more investment demand and artificially high prices are forming.

According to the China Statistical Yearbook, between 2000 and 2011, China's GDP increased

from 8.9404 trillion yuan in 2000 to 47.1564 trillion yuan in 2011 (National Bureau of Statistics

of China). However, the total real estate investment rose from 0.49 trillion yuan surged to 7.5685

trillion yuan (National Bureau of Statistics of China). Between 2000 and 2011, China had a real

estate investment growth rate to GDP growth rate ratio greater than two for every year except

2005.The sum of the ratio of these 12 years is 34.58, which means the average ratio was 2.88 per


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year (National Bureau of Statistics of China). This shows that Chinese real estate investment is

overheating, and that property speculation obviously exists, which indicates that there is a strong

possibility that recent increases in Chinese housing prices are a part of a housing bubble.

1.2) The Real Estate Development Loans to Total Loans of Financial Institutions Ratio:

The real estate development loans to total loans of financial institutions ratio measures how

much support financial institutions are giving the real estate market. According to international

standards, this ratio should generally not exceed 2 percent. A real estate development loans to

total loans of financial institutions ratio that is larger than 2 percent indicates that banks,

investment companies, and other financial institutions are providing too much support to the real

estate market. If the rate exceeds 2.5 percent, it means that the housing bubble is pretty serious.

According to China Statistical Yearbookand China 's central bank announced data, from 2000 to

2011, China's total loans from financial institutions rose from 9.94 trillion yuan in 2000 to 58.2

trillion yuan in 2011 (People's Banks of China), an increase of 4.86 times. Meanwhile, total real

estate development loans rose from 138.5 billion yuan in 2000 to 2.72 trillion yuan in 2011, an

increase of 18.64 times (National Bureau of Statistics of China). In fact, during the period of

2000-2005, this ratio was below 2 percent, however, since 2006, it has been increasing rapidly to

a number of 4.67 percent in 2011 (National Bureau of Statistics of China).These data illustrate

that there is a huge credit bubble in Chinas real estate market since 2006. If this financial credit

bubble were to burst, it would bring unimaginable devastation to the Chinese economy.

1.3) Home prices to Household incomeRatio:

The home price to household income ratio measures the bubble on a price level. The indicator is

a single set of real estate sales price divided by the average annual household income. According

to international standards, a ratio between 4 and 6 is considered appropriate, and a ratio between


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3 and 6 is considered an appropriate ratio in developing countries. If the ratio is more than six, it

means it is very difficult for residents to buy a house, which means there could be a bubble in the

housing market. The greater the home prices to household income ratio, the greater the

likelihood of a housing bubble. According toChina Statistical Yearbookfrom 2000 to 2011,

the average annual household income of urban residents rose from 19,656 yuan (per capita

disposable income of urban residents 6,280 yuan times the average population of people per

household 3.13) in 2000 to 67,393 yuan ( per capita disposable income of urban residents 21,810

yuan times the average people per household population 3.09 ) in 2011 (National Bureau of

Statistics of China). This number would be even smaller if the income of rural residents was

included. Meanwhile, the average selling price of real estate from a single set went up from

175,320 yuan (1948 yuan / sq m 90 sq m) in 2000 to 484,290 yuan (5,381 yuan / sq m 90

square meters) in 2011 (National Bureau of Statistics of China). Accordingly, the index of a

single set of sales price to average annual household income of residents is 8.92 in 2000 and

7.19 in 2011 (National Bureau of Statistics of China). In large cities like Beijing, Shanghai,

Shenzhen and other cities, then the value is close to 20.

Since the home price to household income ratio has been consistently above 6 between

2000 and 2011, we can say with relative certainty that the Chinese housing market is

experiencing a bubble. In sections 2 through 5 of this paper, we will explore the possible causes

of the Chinese housing markets potential bubble.

2) SOEs Willingness to Pay More for Property Drives up Housing Prices:

One of thepossible driving forces behind Chinas real estate bubble could be state-owned

enterprises (SOEs). In China, state-owned enterprises are firms that are wholly owned by the

Chinese government. These firms could be overpaying for the property rights that they purchase


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which in turn, results in the price of the land for which they purchase the rights for becoming

inflated. In China, the central government owns all urban land, and leases the rights to use urban

land to various agents (Naughton 118). Property rights are granted for between 40 to 70 years

depending on what the land will be used for: 40 years for commercial use, 50 years for industrial

and mixed uses, and 70 years for residential use (Wu et al. 534).

Wu et al. (2011) finds that the transaction price for parcels of land in Beijing are about 27

percent higher when the land parcel is purchased by a state-owned enterprise controlled by the

central government (Wu et al. 536). Wu et al. (2011) analyzed transaction data for residential

land parcels in Beijing that were purchased either through public bidding or auction between

2003 and 2010 (534). Wu et al. (2011) found that the 309 residential parcels that were transacted

between 2003 and 2010 were purchased by 199 different firms. The majority of these firms (67

percent) were private firms, while the remaining 33 percent were SOEs. Furthermore, according

to Wu et al. (2011), the non-SOE developers purchased their residential parcels for a price that

was on average about 5000 yuan/m2less than the average purchasing price of the SOEs (535).

According to Wu et al. (2011), the central SOE developers tended to win the bigger

parcels [of residential land] and pay the highest prices (Wu et al. 535). This, in turn, resulted in

the transaction price being about 27 percent higher, thereby inflating the value of the land,

resulting in a higher price for the housing that was built on it. Wu et al (2011) suggests that one

of the possible reasons as to why SOEs are willing to pay significantly more for land rights is

because of a moral hazard arising from these entities believing they are too important to fail

(537). In other words, state-owned firms are willing to pay more for land, because they believe

that the government will protect them, if the acquisition does not prove to be profitable. It is

worth noting however, that Wu et al. (2011) only analyzed housing data for the Beijing market


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between 2003 and 2010. Nevertheless, assuming this trend holds for other urban markets in

China, it couldbe one of the factors behind Chinas housing bubblein urban areas.

3) Regression Analysis:

In this section, we will use regression analysis to analyze the relationship between

urbanization trends, income per capita for urban and rural residents, and the average sale price of

urban residential real estate between 2002 and 2011. For the purposes of this paper, a variable

will be considered statistically significant if it has a significance level of 5 percent or less (i.e.

a p-value of 0.05 or less).

The regression analysis for this paper uses the following estimation equations:

(1)

(2)

In equation 1, the dependant variable) is the natural logarithm of the real (i.e. inflation

adjusted) average sale price of residential real estate in major Chinese cities in year t. The

average is calculated using the sale price data from 35 major Chinese cities. In figure 1, it can be

observed that, after adjusting for inflation, the average sale price of residential real estate in

urban areas in China increased dramatically by over 60 percent between 2002 and 2011 from an

average of 2,950.89 RMB/m2to an average of 7,439.30 RMB/m

2.

The independent variable in equation 1 is the natural logarithm

of the total urban population of China in year t as a function of the natural logarithms of per

capita urban income and per capita rural income . The implications of this

independent variable are that changes in both per capita urban income and per capita rural

income affect total urban population , which in turn, affects the average sale price of urban

residential real estate .


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In equation 2, the independent variables and are the natural logarithms of

per capita annual urban income in year t and per capita annual rural income in year t respectively.

Meanwhile, is the natural logarithm of the total urban population of China. Finally, in

both equation 1 and equation 2, is the error term that is meant to represent any variables that

are not included in the regression equations. The data for ,, and comes from the National

Bureau of Statistics of China and the data for comes from the World Banks World

Development Indicators. In sub-sections 2.1 and 2.2 we will offer a more in depth description of

the independent variables used in this regression, while sub-section 2.3 summarizes the

regression results.

3.1) Urbanization Trends in China could be Increasing the Demand for Housing:

Since the beginning of their economic reform in 1978, China has urbanized at a very

rapid pace (Naughton 127). Figure 2 illustrates Chinas urban populationas a percent of the total

population between 1960 and 2012. Between 1960 and 1978, Chinas urban population was

relatively stable, only increasing slightly from just above 16 percent of the total population to

just fewer than 19 percent of the total population in a span of almost 20 years. Indeed, Chinas

urban population was completely stable at 17.4 percent for a span of six years from 1970 through

1975. However, after 1978, Chinas urban population grew rapidity and steadily. As of 2012, the

majority of Chinas population isconsidered urban.

One of the factors contributing to Chinas rapid post-1978 urbanization was the

relaxation of Chinas aggressive population control measures that were prevalent in the 1960s

and 1970s (Naughton 128). During this period, access to urban residence permits [were]

jealously guarded, and almost no farmers were allowed to move to the city(127). However,

since the 1980s, access to urban residence permits (urban hukou), which give the holder the right


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to live in an urban area permanently, have been substantially liberalized (124).Furthermore,

due to the demand for factory labor by export-oriented cities in the 1990s, it became easier for

migrant workers to stay and work in urban areas indefinitely without a formal urban hukou(125).

The Chinese governments liberalization of population control policies have allowed

rural-urban migration to increase dramatically leading to an increase in Chinas urban population.

This increase in urban population has increased the demand for residential housing in urban areas

causing an increase in housing prices thereby contributing to Chinas housing bubble.Indeed,

Wu et al. (2011) observe that one of the key factors underpinning the demand for housing in

Chinas major markets is a strong urbanization trend [] in 2009, about one-third of the newly-

built private housing units were purchased by migrants [from rural China] (Wu et al. 534).

In figure 3, it can be observed that increases in urban population between 2002 and 2011

are positively correlated at about 98 percent with increases in the average sale price of residential

real estate in urban areas. This high correlation between urban population and the sale price of

residential real estate implies that there is the possibility that increases in urban population are

statistically significant to housing prices in urban China. However, observing urbanization trends

alone does not address why Chinese citizens want to migrate to urban areas in the first place. In

section 2.2 we will discuss the role economic opportunity and per capita income play in

incentivizing Chinese citizens to migrate to urban areas.

3.2) Changes in Per Capita Income Incentivized Rural-Urban Migration:

The annual per capita income of Chinese citizens living in urban areas has increased

exponentially since the beginning of Chinas economic reform in 1978. In figure 4, it can be

observed that nominal urban income per capita was relatively low in 1978 at only 343 yuan per

person, but has since increased to almost 22 thousand yuan per person in 2011. However, during


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the same period, nominal rural income has not grown by nearly as much, increasing from about

133 yuan per person to only about 6,977 yuan per person. In figure 5, it can be observed that

after adjusting for inflation, the same trend can be observed between 2002 and 2011 with per

capita urban annual income being substantially higher than per capita rural annual income.

As previously mentioned in section 2.1; the Chinese governments relaxation ofits

population control policies in the 1980s, combined with the increased need for factory labor in

the 1990s, which made it easier for workers to stay in urban areas without an urban hukou, made

it easier for rural residents to move to urban areas. The fact that urban areas offer a higher

income incentivized rural residents to take advantage of the relaxed government policies and

move to urban areas. According to Naughton (2007), by migrating they[rural migrants]

substantially increase their income-generating potential and begin to work their way upward

(Naughton 129). This increase in urban population, which was brought about by increases in

urban per capita income, increased the demand for housing which lead to increases in housing

prices. In figure 6 it can be observed that per capita urban annual income is 99 percent correlated

with urban population, indicating that there is a strong possibility that per capita urban income is

statistically significant to total urban population.

3.3) Regression Results:

Figure 7 summarizes the results of the regression analysis of equation 2. In figure 7 it can

be observed that per capita urban annual income is statistically significant at the 5 percent

significance level with a p-value of 0.00206. However, per capita rural annual income is not

statistically significant at the 5 percent significance level with a p-value of 0.57535. This means

that there is about a 42 percent chance that the null hypothesis holds and equation 2s equals

zero which means that has no affect on total urban population . This makes sense


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considering how little per capita rural annual income has changed between 2002 and 2011.

During this period, has only increased by about 3,754 yuan, while has increased by about

11,782 yuan in the same period of time. When compared to , has been relatively stagnate, so

it makes sense that it would been statistically insignificant to total urban population.

In figure 8, we remove from equation 2 due to its lack of statistical significance. The

revised estimation equation for can be written as follows:

(2a)

In figure 8 it can be observed that is still statically significant at the 5 percent significance

level with a p-value of 3.20e-11. According to figure 8, an increase in per capita urban annual

income of one yuan results in an increase in Chinas total urban population of about 0.4 percent.

The multiple R-squared for equation 2a is 0.9967, implying that about 99 percent of the variance

in can be explained by . These results reinforce our assertion that increases in per

capita urban annual income incentivizes rural residents to move to urban areas thereby increasing

Chinas rural population.

Figure 9 summarizes the results of the regression analysis of equation 1. In figure 9 it can

be observed that is statistically significant to the average sale price of urban residential real

estate at the 5 percent significance level with a p-value of 3.81e-08. According to figure 9, if

Chinas urban population increases by one person, the average sale price of urban residential real

estate will increase by about 3.17 percent. The multiple R-squared for equation 1 is and 0.98,

which implies that about 98 percent of the variation observed in the average sale price of urban

residential real estate can be explained by increases in Chinas total urban population. These

results reinforce our assumption that increases in Chinas urban population are driving a demand

for housing, which in turn, has resulted in increased hosing price in urban areas in China.


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In short, based on the results of our regression analysis we found that increase in per

capita urban annual income between 2002 and 2011 have incentivized rural residents to take

advantage of the Chinese governments relaxed population control policies, and move to urban

areas. This has resulted in a steady increase in Chinas urban population which has driven up the

demand for urban housing. Increases in urban housing demand have resulted in dramatic

increases in the average sale price of urban residential real estate which has helped to create the

economic bubble that is observed in the Chinese real estate market today.

4) Investment Uncertainty:

The most commonly cited reason for the housing bubble in China has been investor's

lack in the stock market (Bloomberg News). Considering the fact that the Chinese populace does

not have a wide array of vehicles for investment (Bloomberg News), the stock market and the

real estate sector have an inverse relationship with regard to investment opportunities. Even

though China's recovery from the global recession of 2008 was one of the quickest, if not the

quickest, the Chinese people are not quite convinced that the returns on investment would ever

outpace the vast increases in home prices and the returns that come with these price increases

(Bloomberg News), thus investors have been putting their money into real estate development.

Since just before the Chinese new year of 2013, the Shanghai Composite Index slumped by 5.6

percent (Bloomberg News). This is reflective of the fact that 80% of the Chinese market is driven

by individual retail investors, and thus market movement can be greatly influenced by policy

announcements and simple speculation since a large proportion of the population (approx 90%)

read the same news reports, so major policy announcements can trigger large emotional market

reactions (Bloomberg News). According to Qinwei Wang, economist at Capital Economics in

China, this recent slump in Q1 2013 seems to have been sparked by reports that Beijing will


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further tighten rules on mortgages and enforce a profit tax on the sales of used homes with the

government tightening monetary controls due to concerns about rapid credit growth, considering

the fact that the stock of loans increased by 50% from 2008 to 2012 (Christian Dreger and

Yanquan Zhang). While this attempt to reign in rising housing prices might have been mildly

successful, the problem it creates is that a slowdown in real estate also translates to a reduction in

GDP growth rate.

5) Strategies to Reduce Soaring Prices:

As stated above, a negative effect of restricting the increase of real estate prices is a

slowdown in economic growth (Dreger et al). While the 79 listed property developers listed on

the SHCOMP make up only about 3.1% of the total stock market cap, the real estate sector has a

tremendous impact with regard to its impact on the exchange, and the Chinese economy as a

whole. This is due to the residual effect the real estate market has on other industries such as

those providing basic construction materials like steel and cement as well as the financial sector.

The demise of property developers would lead people to start worrying that they (developers)

won't be able to repay their loans to the banks. (Dreger and Yang)

The central governments attempts to cool the market have had limited success nationally,

but in large cities like Beijing and Shanghai it had done little to deter high-income individuals

from purchasing multiple properties, with properties in both cities rising by more than ten

percent from July 2012 to July 2013 while the Shanghai Composite Index dropped by a little

more in the same period (Bloomberg News).

A decent proportion of the real estate purchased in China is paid for in cash, and this

greatly reduces the possibility of a US-type mortgage crisis (Bloomberg News). Recently though,

the increase in credit availability driven by an increase in construction output has people worried


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about what could happen with the tighter regulations being proposed by the government.

Speculators believe that these mortgage restrictions could damage the value of their investment

holdings, in the form of various pieces of property (Dreger and Yang).

CONCLUSION:

We figured out the details about the historical background of the Chinese housing market

and the real estate bubble. For section one, we had three different factors to analyze in order to

establish that there is a housing bubble in China. These factors are the real estate investment

growth rate to GDP growth rate ratio, the real estate development loans to total loans of financial

institutions ratio, and the home price to household income ratio. After analyzing these three

ratios, we concluded that there is in fact a housing bubble in China.

The three factors that we examined that could be driving the housing bubble in China

were SOEs willingness to pay more for property, urbanization trends in China, and the lack of

confidence in other investment opportunities. SOE developers tended to overpay for residential

land, thus causing the transaction price to be about 27 percent higher, thereby inflating the value

of the land, resulting in higher prices for the housing. Rapidly growing per capita urban income

incentivized rural residence to move to urban areas driving demand for housing and contributing

to the housing bubble in China. China has experienced a reasonable loss of investor confidence

in the stock market; due to the fact that the Chinese populace does not have a wide array of

vehicles for investment and the stock market and the real estate sector have an inverse

relationship, a reduction of stock value increases investment in housing, thus further increasing

prices.


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BIBLOGRAPHY:

Bloomberg News. 2013. No Confidence in China Market Inflates Housing Bubble.Bloomberg,

September 16, 2013. http://www.bloomberg.com/news/2013-09-15/no-confidence-in-china-

markets-inflates-housing-bubble.html.

Dreger, Christian, and Yanqun Zhang. 2010. Is there a bubble in the Chinese Housing Market?

German Institute for Economic Research.

Naughton, Barry. 2007. The Chinese Economy Transitions and Growth. Cambridge,

Massachusetts: The MIT Press.

National Bureau of Statistics of China. 2013. Peoples Republic of China.

http://data.stats.gov.cn/index(accessed November 7, 2013).

The People's Bank of China. 2013. People's Republic of China. http:// www.pbc.gov.cn(accessed November 24, 2013).

The World Bank. 2013. The World Bank Group. Data.Worldbank.org (accessed November 7,

2013).

Wu Jing, Joesph Gyourko, and Yongheng Deng. 2011. Evaluating Conditions in Major Chinese

Housing Markets.Regional Science and Urban Economics, 42: 531-542.


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APPENDIX:

Figure 1 illustrates the trend in the average sale price of residential real estate in urban areas between 2002 and 2011.

Prices have been inflation adjusted and are expression in terms of 2011 RMB. The average sale price was calculated

by averaging the sale price data of 35 major Chinese cities for a given year. The data used to calculate average sale

price comes from the National Bureau of Statistics of China. The data for the Chinese consumer price index used to

adjust prices for inflation comes from the World Banks World Development Indicators.

2800

3300

3800

4300

4800

5300

5800

6300

6800

7300

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

AverageSalePriceofUrbanResidentialReal

Estate(RMB/sqm)

Figure 1


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Figure 2 illustrates trends in the proportion of the total population that is considered urban between 1960 and 2012.

The data for urban population as a percent of total population comes from the World Banks World Development

Indicators.

Figure 3 illustrates the relationship between the average sale price of residential real estate and the total population

that is considered urban. Prices have been inflation adjusted and are expression in terms of 2011 RMB. The

correlation between the two is about 98 percent. The data for the average sale price of residential real estate comes

from the National Bureau of Statistics of China and the data for urban population as percent of the total population

comes from the World Banks World Development Indicators. The CPI data used to adjust prices for inflation

comes from the World BanksWorld Development Indicators.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

1960 1970 1980 1990 2000 2010

UrbanPopulation(%ofTotalPopulation)

Figure 2

2000

3000

4000

5000

6000

7000

4.90E+08 5.40E+08 5.90E+08 6.40E+08 6.90E+08A

verageSalePriceofResidentialRe

al

Estate(RMB/sqm)

Total Urban Population

Figure 3


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Figure 4 illustrates the trends in nominal urban and rural annual income per capita between 1978 and 2011. The data

for urban annual income per capita comes from the Nation Bureau of Statistics of China.

Figure 5 illustrates the trend in urban annual income per capita between 2002 and 2011. Both urban and rural

income are adjusted for inflation and expressed in terms of 2011 RMB. The data for urban annual income per capita

comes from the Nation Bureau of Statistics of China. The CPI data used to adjust per capita income for inflation

comes from the World Banks World Development indicators.

300

5300

10300

15300

20300

1978 1983 1988 1993 1998 2003 2008

PerCapitaAnnualIncome(Yua

n)

Figure 4

Per Capita Urban Annual Income

Per Capita Rural Annual Income

3000

5000

7000

9000

11000

13000

15000

17000

19000

21000

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

UrbanAnnualperCapitaIncome(Yu

an)

Figure 5


Per Capita Rural Annual Income


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Figure 6 illustrates the relationship between Chinas total urban population and per capita urban annual income. Per

capita urban annual income has been inflation adjusted and are expressed in terms of 2011 dollars. The correlation

between the two is about 99 percent. The data for per capita urban annual income comes from the National Bureau

of Statistics of China. The CPI data used to adjust price for inflation and the data for urban population comes from

the World Banks World Development Indicators.

4.75E+08

5.25E+08

5.75E+08

6.25E+08

6.75E+08

10,000 12,000 14,000 16,000 18,000 20,000 22,000

UrbanPopulation


Figure 6


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Figure 7:

Figure 7 summarizes the regression analysis of equation 2. The results were generated using the statistical

programming language R.

Figure 8:

Figure 8 summarizes a modified version of the regression analysis of equation 2 used. In this version we do not take

into per capita rural income. The results were generated using the statistical programming language R.


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Figure 9:

Figure 9 summarizes the regression analysis of equation 1. The results were generated using the statistical

programming language R.

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