The Demand for Commercial Housing:
The Price and Income Elasticity Analysis in Shanghai
Name: Q. Wang
Student Number: 5912776
Faculty: Economics and Business
Supervisor: Thomas Buser
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Introduction
Housing system reform in China started in 1998 and carried out widespread since
2000. Since then, Chinese real estate economy, which is transferred from centrally
planned economy to market economy, steps into the most “tremendous” 10 years.
With the rapid development of real estate market for over a decade, housing
consumption behaviour has become one of the issues that draw the highly attention of
Chinese government and developers. Overall there is no convincing sign that a bubble
is formed. However when you look at certain areas along the coast, particularly, the
mass market of shanghai and Beijing，one could reasonably say that a bubble is
inflating (IMF, 2010). The real estate price in China has increased by more than 200%
in real terms between 2000 and 2009, from 2112yuan in 2000 to 4681yuan per square
meter in 2009 (National Bureau of Statistics of China). In 2009 alone, house price
index of the first-tier cities (i.e. Beijing, Shanghai, Guangzhou and Hangzhou) have
surged from 93 to 146, raised up to 62%. This growth rate in real estate value has
highly exceeded the drop in interest rate and the increase rate of personal income
between 2000 and 2009. Within the same time horizon, mortgage interest rate has
slightly fallen, from 6.21% annually in 2000 to 5.94% in 2009 (The People’s Bank of
China) while personal income levels have steadily moved upward (from average
$1000 annually in 2000 to $3600 in 2009)1.
Further, as a special commodity market, Chinese real estate market presents a
considerable noteworthy phenomenon which is quite against to the normal economic
laws, i.e., as the prices continue raising, the demand for real estate “abnormally”
retains a very strong trend. In another word, the continuously increasing house-price
fails to cast a chill over high purchasing demand, but on the contrary, it further
stimulates the demand of the market. In the view of catching possible explanations,
researches on the housing value fluctuation and its related impact factors have
1
Annual mortgage repayments are calculated with a 20-year mortgage with 100% financing based on the
corresponding commercial loan rate.
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important theoretical sense and practical value.
Within an economic framework, the key concept of assessment the housing demand
shifters lies in the estimation of income and price elasticity. They are essential
elements to analyse national (regional) welfare, plan economic development, design
and implement policies in almost all national regulation fields. Accordingly, analysis
of elasticity of housing demand can improve our understanding of housing market,
which is the prerequisite to draw up appropriate housing policies. Yet, effective data
are slowly updated in the recent 10 years in China, especially the related researches
on price elasticity of housing demand, though the development of Chinese real estate
market is highly dynamic.
Facing these facts, in this paper, we choose the top first-tier city, Shanghai, as research
object and investigate the factors that influence consumer behaviours of the housing
expenditure. To realise the objectives, time-series data from 2000 to 2009 are used to
estimate the income and price elasticity of households who buy the commercial real
estate in Shanghai. The objectives of this paper are (1) to briefly review the
income-demand and price-demand relationship in the major developed countries (US
and UK) following by the analysis of the previous studies in China, (2) to calculate
the latest income and price elasticity estimators for the model of commercial housing
demand in Shanghai based on time-series regression in 10 years, imposing the
restriction that the effect of relevant democratic factors are small, (3) to explore
possible explanations for the abnormal demand scenario of owner-occupied housing
market in Shanghai.
The rest of the paper is organised as follows. Section 2 introduces some focused
issues of income elasticity of the demand for housing, which are likely to bias the
accuracy of the relevant regression. Section 3 describes the elements that are possibly
included in our regression model. Section 4 contains an analysis of the data set used in
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the paper. Section 5 explains the empirical model and strategy. Section 6 holds the
findings and finally makes a conclusion of our study.
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Elasticity of Housing Demand
An early contribution by Allen c. Goodman (1986), Ermisch, Findlay and Gibb (1994)
give a general form of housing demand functions:
Q  Q( P, Y , Z )
where,
Q
-
Quantity of housing services demand,
P
-
Price of housing services,
Y
-
Real income of household,
Z
-
Other sociodemographic variables that affecting housing demand.
Income Elasticity Estimators
Most of the analysts agree that price and income are most important forces that drive
housing consumption. In particular, there is a heated argument in 1980s concerning
permanent and current income in relation to housing expenses, which arose just right
after the American economist Milton Friedman posting the permanent income
hypothesis (PIH) theory in the beginning of 1970s. Some emphasise that permanent
income should be used as an appropriate income variable (Muth, 1960). By reviewing
the previous reprehensive works, Leeuw (1971) and Mayo (1980) conclude that
current income elasticity of housing is lower than appropriate measure permanent
income elasticity, since the “true” permanent income elasticity is biased downward by
the transitory income.2 In the UK, for instance, by making use of family expenditure
survey data (instead of permanent income), Clark and Jones (1971) hold the overall
income elasticity is in the range of 0.85 and 0.95, whereas Vipond and Walker (1972)
2
The asymptotic expectation of estimated current income elasticity, ῆy, will be related to the permanent income
elasticity, ηy, by E (ῆy) = ηyPy. where Py is the ratio of the variance of permanent and current income:
Py =
𝛔𝟐𝐲𝐩
𝛔𝟐𝐲
=
𝛔𝟐𝐲𝐩
𝛔𝟐𝐲𝐩+𝛔𝟐𝐲𝐭
< 𝟏,
where y is current income, yp is permanent income and yt is transitory income. Because yp and yt are uncorrelated,
the variance of current income, y, is equal to the sum of variance of permanent and transitory income. It is clear
that if the variance of transitory income is small, the bias of the estimated current income elasticity will be small.
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get a even lower result between 0.4 and 0.6. After correcting the transitory income
bias, Byatt et al. (1973) raise the results to the region 0.75-1.25. In the US, earlier
works by Duesenberry and Kisten (1953), Maisel and Winnick (1960) present a long
term income elasticity varied between 0.15 and 0.5. However, by reviewing the works
adopted permanent income, de Leeuw (1971) obtains the income elasticity range from
1.25 to 1.46 for house-owners. Later, contrary voices are firstly thrown out of the
study of Goodman and Kawai (1984), where analysts show that the major impact of
permanent income may be in the tenure choice, rather in the demand stage. By
controlling for tenure choice (which means by controlling for employment rates,
availability of mortgage etc.), the permanent and current incomes are approximately
the same. As to the present paper, limited by accession of permanent income in the
target region, we take tenure choice as one of omitted variables in form of our
estimation so that annual disposable income (current income) is valid to be adopted
(or say there is no explicit concern with tenure choice in our study).
By the means of cross-sectional analysis, Chinese researchers estimate that income
elasticity of commodity real estate in Chinese first-tier cities is in the range of
0.45-0.9: a recent paper by Xu and Liang (2008) pronounces the long run income
elasticity of commercial housing demand in Hangzhou is 0.48. In Guangzhou, the
southern gateway of China, open-market real estate is substantially mature. In spite of
this, the results of regression analysis show that in Guangzhou, the income elasticity
of housing demand is pretty low (less than 0.1) (Li, 2000). The abnormal results come
about probably because of the Chinese commercial real estate market instability in the
beginning of the new system of housing economy. For Beijing, Zheng and Liu (2005)
get the income elasticity value of 0.86. Comparing with the demand inelastic of the
previous two metropolises, income elasticity in Beijing appears much higher. Even so,
Zheng and Liu claim that income in developed countries has even higher effect on
housing demand. By and large, earlier efforts have produced income elasticity of
housing consumption for all the metropolitan areas in China except Shanghai.
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Considering the highly primary nature of elasticities quantification, research on
income elasticity in Shanghai is urgently needed.
Price elasticity Estimators
Earlier efforts by Case and Shiller (2003), Himmelberg et al. (2005) show that most of
the housing-price increase can be explained in terms of demand factors. As a
significant parameter for assessment of relevant policy effectiveness, estimates of
price elasticity of housing demand are few so that many related questions are still
opening. One representative work published by Ermisch et al. (1994) sorts out the
price elasticity studies into two types – those which view housing as consumption
durable, i.e., housing is treated as a set of services that can satisfy households needs
such as shelter, entertainment, etc, and those which take the environmental services
into account so that hedonic pricing method3 is often brought into play. From this
study, we take on the perspective of Ermisch et al by estimating the price elasticity of
housing demand as a composite commodity
In the literature on examining price elasticity of housing demand, Ermisch et al (1994)
yield the results between -0.5 to -0.8 in developed countries, which are more
collective than the measures of income elasticity. In the UK, price elasticity is
approximately 0.4~0.5. Furthermore, the analysts suggest that analysing price-demand
relation should use individual household data because of the absence of a uniform
price for housing services. From researching on the Spanish housing market,
Javier
et al (2008) conclude that among developed countries, Spanish real estate market is
unique since it has both one of the highest percentages of owned housing and one of
the lowest proportions of dwellings for rent. Comparing with Britain and the U.S., the
income and price elasticity in Spain are estimated at a lower end in the literature, with
3
This method is applied to reveal effect of environmental which attributes in changed in the local real estate
pricing. In this view, the estimating cost is normally related to :

The overall quality of the environment in terms of air pollution, water pollution and noise

Environmental amenities which include aesthetic sights and closeness to recreational sites such as parks,
beaches, etc.
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the very inelastic value of -0.04 for the ownership demand (Javier et al, 2008). Similar
as the characteristics in Spanish real estate market, dwellings in China are highly
intended to get the ownership of accommodations. Accordingly, the paper only sheds
light on the ownership housing market, ignoring the housing consumption behaviours
of tenants.
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Impact Factors on Demand of Commodity Housing
There are many factors affecting housing market demand. This study holds that five
major factors are particularly prominent in Chinese real estate market. They are
resident disposable income, real price of houses, demographic changes, expectation of
buyers and related policies.
Disposable income is the total personal income subtracts personal taxes. Subject to
the budget constraint, a typical household can maximise its utility with a mix of
housing and non-housing related consumption (Daniel and Mark, 2006). In Chinese
real estate market, income is merely correlated with the demand of housing until the
latest 10 years, when Chinese real estate market is reformed from state-owned to
market-oriented. Normally, “housing price-to-income ratio” is used to present the
housing payment capacity of a family. Higher housing price-to-income ratio means
lower buyers are able to afford on housing. For a certain housing price, an increasing
disposable income would on the same time help to enhance the housing purchasing
power and vice versa. In this view, housing demand is supposed to be positively
related with the personal disposable income.
Housing price has always been the most primary impact factor for housing demand.
Since house is a special commodity, according to John and Anthoney (1997), housing
investors benefit from two aspects: own consumption (i.e., housing does provide a set
of servicing such as shelter, sanitation and entertainment area) (Daniel and Mark,
2006) which means no capital benefit can be achieved in future, or further investment
like any other investment properties hunting for capital gains later. To the extent
housing is a consumption-good, increasing of housing price is supposed to depress the
demand of housing, subject to a certain budget constraint; on the contrary, a lower
housing value will encourage people stepping into housing market. However,
continuously climbing housing prices do simultaneously push up housing demand
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because of wealth effect4. With development of real estate industry, investors expect
for an appreciation potential, resulting in a large amount of capital inflow in housing
market. The rapid growth in house prices will finally rise out of market and the real
estate bubble inflates fast (Chai and Oh Dong, 2012). Earlier effects by Kim and Suh
(1993), Gallin (2003) and Daniel et al (2006) define a bubble as a price boom caused
by a transitory demand shock. In other words, in normal circumstances, if demand of
a housing market is pushed up by house prices instead of personal real income, then
we can say the real estate bubble is forming in this market.
Demography-induced-change is another important impact factor in housing demand.
Commonly examined demographics include total population quantity, population
density and population structure. Since countries are provided with their specific
demographic patterns in a specific historical period, housing demands would be
primarily affected by different demography-induced changes in different countries. In
the U.S, according to Mankiw’s well-known research in 1989, the substantial increase
in housing demand and real housing price in the 1970’s are caused by the entry of the
Baby Boom generation into its housing-buying years. Afterwards, in the 1990’s, house
demand growth rate almost hits bottom since the Baby Bust generation is on its house
consumption period (Mankiw and David, 1989). In China, recent 20-year’s waves of
internal migration, primarily rural to urban, small cities to modern metropolis, reflect
a huge transition in urbanising housing conditions. Enlarging population density in
big cities will undoubtedly change the normal housing structures and push up the
housing demand in migration cities (Weiping, 2002). In this study, however, we keep
demography-induced factors constant since the only aim of the study is to find the
effects of housing-price and income on housing demand instead of the disturbance of
other error terms.
Like all the other investment properties, expectation on future return has a substantial
4
Wealth Effect: economic term refers to an increase (decrease) in spending that accompanies an increase
(decrease) in perceived wealth.
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impact on consumer behaviour. Since last century, real estate has been an important
component of large institutional investment portfolios. Well-located, high quality real
estate may provide good returns and current income. Moreover, the consistent and
relatively predictable cash flows (like rent from future tenant) associated with real
estate may provide a high degree of confidence when pooling a large amount of future
liability into investment (Richard, 2002. pp3-4). Accordingly, rational real estate
investors put money into the housing market with the expectation of future return that
upon thorough analysis, having a high degree of security for the principle amount, as
well as security returns. In contrast, the expectation of gain without thorough analysis
and enough security principle amounts is called speculation. In China, with the
fascinating economic growth and the process of urbanisation accelerated, Chinese real
estate market is developing fast. Thus many investors have the optimistic expectancy
that house-price in big-city like Shanghai will keep growing. Prompted by benefits,
more and more individual investors plunge into real estate market through mortgages
instead of amount principle as back-up. According to a latest research by Chai and Oh
Dong Noon (2012), the house prices-to-income ratio in Shanghai is more than “15”
though internationally the safety value for this index is “7” in developing countries.
The expansion of real estate speculation gradually brings Chinese market to the edge
of the bust of real estate bubble.
In this view, the policy-maker (Chinese government) is supposed to play an important
role
on
house-price
regulation
since
policy
adjustment
is
effective
on
direction-guiding and price-control. One of the most remarkable examples on
restraining real estate speculation in China is enhancing mortgage rate three times in a
row in 2011, which helps to control the real estate investment growth rate within 2%
last year.
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Description of the Data
The data used in this study are extracted from the following sources: Shanghai
Statistical Yearbook 2000-2009, National Bureau of Statistics of China (For a more
detailed breakdown, please check Appendix). The evolution of commercial housing
(difference from the own build housing and rent housing) prices per square meter,
income per capita, registered unban population in Shanghai between 2001 and 2009
are used to evaluate price elasticity and income elasticity of commodity housing
demand.
From Figure 1, we observe that the demand for commercial houses is positively
related to the house-price – increasing when the growth rate of houses value
strengthens and decreasing when it slows down. Especially in the latest three years
between 2007 and 2009, this closely positive relation presents extremely obvious. As
we studied before, demand for normal commodity decreases when its price increases.
From the diagram we can see that residents prefer stepping into the real estate market
when the prices at the high point. This phenomenon reflects the secularity of
commercial housing market comparing with the other normal commodity markets.
Comparison of growth rate between real estate price and demand
6000
4000
2000
0
2000
-2000
2002
2004
2006
2008
2010
Year
Growth rate of average value [Yuan/m^2/year]
Growth rate of demand of commercial housing [*10^4m^2/year]
Figure 1: Relationship between real estate value and housing demand
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The relationship between real estate demand and residents disposable income is less
clear-cut. From Figure 2, we observe two distinct segments: in the beginning of the
time period, the housing demand closely tails with the change of per capita disposable
income between 2000 and 2003. After then, however, the residential income growth
enters into a relatively stable stage while the demand for commercial houses
experiences a series of dramatic fluctuations starting from early 2003. It is noteworthy
that even in 2009 when the income growth further slows down comparing to the
growth rate in 2008, housing demand however reaches the peak during the whole
sample period. This observation suggests that the continuously increasing real estate
value urges buyers to hold a higher prices expectation in future. Driver by the wealth
effect, buyers are eager to set foot in real estate market at its high point even though
the huge expenses are quite beyond their payment capacities.
Comparison of growth rate between disposible income and housing demand
4000
2000
0
2000
-2000
2002
2004
2006
2008
2010
Year
Growth rate of resident disposable income [Yuan/year]
Growth rate of demand of commercial housing [*10^4m^2/year]
Figure 2: Relationship between disposable income and commodity housing demand
In the following part, I would like to put the commercial housing demand, house-price
and resident disposable income together in order to further observe their relationship
as a whole. From Figure 3, we find out that the demand for commercial housing is
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less related to the wave tendency of disposable income. Instead, the line of housing
demand goes up and down by closely following the change of housing value. This
phenomenon suggests that within our sample period, the price elasticity of housing
demand in Shanghai real estate market plays a more significant role than the income
elasticity of housing demand. This observation may lead many to infer that Shanghai
commercial housing market is experiencing a real estate bubble. In the following part,
we take advantage of the econometric software to test all of our observations in
practical.
Figure 3: Relationship of housing value, disposable income and housing demand
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The Empirical Model and Strategy
In economics, elasticity is a general term for ratio change. The measures concerning
income elasticity5 and price elasticity6 of housing consuming are often considerably
applied by researchers and policy makers in real estate economics. In the analysis of
consumers’ commodity housing demand, it is often assumed that a 1% increase in
income leads to a certain percentage increase in the housing quantity demanded. And
we call this percentage increase in demanding resulting from 1% increase in income
as income elasticity. In our study, we have:

Q
Q
Y
Y
where,
Q
-
Total housing demand in Shanghai;
Y
-
Personal disposable income;
η
-
Income elasticity of housing demand. When η > 1, the demand for housing
is called relatively elastic, and conversely, if 0 < η < 1, then demand is relatively
inelastic.
Similarly, we will label the amount of demand ratio change caused by 1% increase in
price as λ. So we have:

P
P
Y
Y
where,
P
-
Real house-price;
λ
-
Price elasticity of housing demand.
If |λ| > 1, the demand for housing is called relatively elastic, and conversely, if 0 < |λ|
< 1, then demand is relatively inelastic.
5
Income elasticity of demand measures the responsiveness of the demand for a good to a change in the income
of the people demanding the good
6
Price elasticity of demand is a measure used to show the responsiveness of the quantity demanded of a good
or service to a change in its price
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Limited by the emphasis of the study, the individual housing consuming in this study
will only depend on house prices, income and population in Shanghai. Consider a
simple representative model of commercial housing demand, we get:
H  P  Y 
where,
H
-
Housing consuming per capita (H = Q/N)
Q
-
Amount of houses needed,
N is the amount of registered population in Shanghai; α is a positive constant number;
P represents house-price; Y is the average disposable income; λ, η are power exponent.
When λ<0, η>0, the function explains the relationship among H, P and Y.7
A log-linear8 solution to the model would relate the log per capita house needed to
the logarithm of the other relevant variables:
ln H   0   ln P   ln Y
where,
0
-
Constant term (  0  ln  0 )
In the log-log model,
1% change in independent variable is associated with λ% (η %) change in dependent
variable (James and Mark, 2007, p272)9. Thus, λ is the price elasticity of housing
Assume a power exponential function f(x) = xa , when a>0, the function is monotone increasing, i.e., when η>0,
income is positively related with the housed needed per capita; conversely, when a<0, the function is monotone
decreasing, i.e., when λ<0, house-price is negatively related with house needed per capita
7
8
In empirical work elasticity is the estimated coefficient in a linear regression equation where both the
dependent variable and the independent variable are in natural logs.
9
Apply the conceptΔY = f(X1 + ΔX1, X2, … , Xk ) - f(X1 , X2, … , Xk ); thus ln(H + ΔH) − lnH =[βο + λ ln(P +
ΔP) + η ln(Y + ΔY)]-[βο + λ ln(P) + η ln(Y)]=λ[ln(P + ΔP) − ln(P)]+η[ln(Y + ΔY) − lnY]. Application of the
Δx
approximation of ln(x + Δx) − ln(x) ≅ , to both sides of the equation yields:
x
ΔH⁄
ΔH
H = 100 ∗ ( ⁄H) = percentage change in H
λ=
ΔP⁄
percentage change in P
100 ∗ (ΔP⁄P)
P
ΔH⁄
ΔH
H = 100 ∗ ( ⁄H) = percentage change in H
η=
ΔY⁄
percentage change in Y
100 ∗ (ΔY⁄Y)
Y
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demand; η is the income elasticity of housing demand.
According to all the analysis above, the consumer demand model of housing in the
study comes to:
ln H   0   ln Pi   ln Yi   i
where,
μi represents other individual demand shifters those are also likely to influence
housing demand. Assume the error term μi has conditional mean zero given Pi, Yi: E
(μi｜Pi, Yi) = 0
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Empirical Results
The aim of this study is to estimate the relationship between housing demand and
income (house-price) in Shanghai by making use of the Ordinary Least Square (OLS)
regression. We begin this section by testing the heteroskedasticity of the model in
order to keep the regression unbiased and effective.
One important assumption in OLS is that given regressor Xi (P and Y in our case), the
variance of the conditional distribution of μi is constant, i.e. var (μi | Xi = x) = σ2 (σ is
constant), and in particular does not depend on x. Otherwise, the error term is
heteroskedastic. This assumption is a statement about the “other factors” contained in
μi and then asserts that “other factors” are unrelated to regressors. With
heteroskedasticity, the OLS estimators remain unbiased and asymptotically normal
but no longer hold the effective least variance. Accordingly, the study first implements
White Test to test the heteroskedastic of our model:
H1: Var (μi | Xi = p, y) is different.
Hypothesis 1 (H1) states that the variance of the conditional distribution of μi, given
regressors Xi, is not constant. In other words, the errors of the regression (1), μ i, are
heteroskedatic. Results of the test could be collected from Figure 1. After computing
the p-value and rejecting the hypothesis at the 5% significant level if the p-value is
less than 0.05, we get p-value of Obs*R-squared = 0.6488>0.05. Accordingly, we
cannot reject the null hypothesis that the regression (1) is not heteroskedastic. In other
words, the error term μi is homoscedastic. Referring to our study, the testing clarifies
that (the variance of) μi does not depends on the house-price (P) and disposable
income per capita (Y), which means the OLS estimators will not be unbiased by
omitted variable in our case.
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Table 1: White test
F - statisitics
*
Obs R-squared
Scaled explained SS
0.331505
0.865209
0.197470
Prob. F(2,7)
Prob. Chi-square(2)
Prob. Chi-square(2)
0.648800
0.906000
The study needs to estimate the coefficients λ, η of the regression model calculated by
using a population data from 2000 to 2009. Fortunately, these coefficients can be
estimated using ordinary least squares10. Because OLS is the dominant method used
in practical, it has become the common language for regression analysis throughout
economics and the social sciences more generally.
In this study, houses needed per capita (H) are treated as dependent variable;
house-price and disposable income per capita are independent variables. After taking
the logarithm of the dependent and independent variables by applying OLS, the
coefficients λ and η have been displayed in Table 2:
Table 2: Income elasticity and price elasticity of housing demand in Shanghai (2000 2009)
Variables
Coef.
Std. Error
t-statistic
Prob.
Log(P)
0.901014
0.636423
2.415747
0.099800
Log(M)
0.676039
0.845145
1.999908
0.050100
C
1.212220
0.797582
2.019869
0.072300
R-square
0.493072
Mean dependnet var.
0.382213
Ajusted R-square
0.348236
S.D.dependent var.
0.257634
S.E.of regression
0.207993
Akaike info criterion
-0.059304
Sum squared resid
3.296511
F-statisitic
3.404340
Prob (F-statisic)
0.092749
From Table 2, p-value associated with t-statistic is log (P) =0.0998, or 9.98%. That is,
under a significant level of 10%, if the null hypothesis is λ = 0, we get
10
The estimators of the coefficients ß0, ß1... ßk that minimize the sum of squared mistakes in
2
expression∑ni=1(Yi − b0 − b1 X1i − ⋯ − bk Xki ) are called the ordinary least squares estimators of ß0, ß1... ßk.
19 | P a g e
p-value=0.0998<0.1, it is reasonable to reject the null hypothesis that house-price is
unrelated with the housing demand. Similarly, p-value associated with t-statistic is log
(Y) =0.0501<0.1. We shall reject null hypothesis, η=0, under 10% significant level.
As well, disposable income per capita has an impact on house-needed. Accordingly, it
would be effective to read the coefficient estimators from Table 2 under 10%
significant levels: the price elasticity of housing demand in Shanghai (λ) is 0.901; the
income elasticity of housing demand in Shanghai (η) is 0.676
Analysis on λ = 0.901. From the calculated result, the houses consuming is relatively
inelastic on house value (0 < λ < 1). That is, falling house-price cannot stimulate an
increase in turnover but arouse an negative expectation for further declining price;
conversely, by controlling all the other conditions, for every 1% increase on
house-price, the quantity of housing demand per capita will present an 0.901%
increase. It means an increase on house value give all the investors a positive
expectation on climbing trend in near future. In order to get more profit, investors get
involved themselves into housing market, which push the house-price continuously
rising.
In normal situation, the price elasticity of demand is negative11, but in our study we
can see the housing demand is positively related to house-price in Shanghai real estate
market. That is, the investors prefer spending higher expenses on house-consuming.
That’s because the climbing house-price produces not only budget constraint effect
but also the Wealth effect. On one hand, the increase of housing expenses depresses
the purchasing power on housing, subject to unchanged income standard, thus the
housing consumption would deflates; on the other hand, the assessed value of their
houses increasing could make people perceive themselves to be richer. Accordingly,
even under huge budget pressure, rational investors would dip the fingers in the real
11
Giffen good is an exception. In normal situations, as the price of a good rises, the substitution effect causes
consumers to purchase less of it and more of substitute goods. In the Giffen good situation, the income effect
dominates, leading people to buy more of the good, even as its price rises. In other words, the income elasticity
of demand is negative. In this study, we get η=0.676>0, which means house is not Giffen good.
20 | P a g e
estate market in order to better off in terms of personal wealth after then. This
consumption behaviour, however, does not promise safety of the initial investment
along with the return on the principal sum since all the intent of potential returns are
based on a financial expectation instead of thorough analysis. In the period of
economic expansion, people tend to have a “naïve” expectation that the prosperity
will be ongoing and risk takers will finally benefit from it. This speculation behaviour
pushes the formation of a hottest real estate market in the last 10 years while houses
price continuously increase to an incredible level. This unstable factor is further likely
to bring real estate bubble to Shanghai’s real estate market.
Analysis on η = 0.676. The income elasticity of housing demand in Shanghai real
estate market is 0.676. That is, a 1% increase in personal disposable income may push
up 0.676% in housing demand. First of all, it would be emphasized that the result has
been slightly biased by data selection: in the regression, we use residential disposable
income, which we can get from Shanghai Statistical Yearbook, as the regressor. But in
today’s China, invisible income has spread to all economic activities. And statistical
data of this part of income is possibly got from nowhere. In this view, the real income
per capita should be higher than the data we use in the regression. Moreover, the
paper uses housing transaction area instead of real housing consuming area (normally,
they are not equal to each other) as housing demand data to regress the income
elasticity of housing demand, which will again imply a slightly bias on the regression
result. Fortunately, our regression result could still be properly explained in practice.
As we mentioned before, η=0.676<1 implies the houses demand is inelastic in terms
of income in Shanghai real estate market. Furthermore, in economics, as long as
income elasticity of one commodity demand is larger than 0 but smaller than one; the
good is categorized as necessary good. Accordingly, commodity houses are still a kind
of necessary consumption in Shanghai real estate market.
After analysis the effects of house-price and income on housing demand market in
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Shanghai respectively, we may further measure the degree of real estate bubble by
putting these two independent variables together and calculating “housing
price-to-income ratio”. Chai and Oh Dong Noon (2012) address that price-to-income
ratio is used to measure family payment capacity. Higher price-to-income ratio
presents a lower purchasing power. With the consideration of the characteristic
housing system and grey income in China, the safety value of housing
price-to-income ratio is international realized as 8 (while the safety value in normal
developing countries is 7). Figure 4 shows the housing price-to-income ratio of
Shanghai, Hong Kong and average value in China from 2000 to 2009. We can see in
10 years, the average ratio of housing price-to-income in Shanghai is around 9.8,
which is far beyond the value of Hong Kong and Chinese average value (6.1 and 6.6
respectively). Furthermore, the changing curve of Shanghai is above the safety value
from 2003 and reaches the peak at 14.6 in 2009. Noteworthily, the ratio soars rapidly
since 2008 in Shanghai due to financial crisis in the US (Chai and Oh Dong Noon,
2012). Such a high housing price-to-income ratio and low payment capacity in
Shanghai jeopardize the development of economics and implies a bubble in real estate
market of Shanghai.
Figure 4: Housing price-to-income ratio of Shanghai, Hong Kong and average value
in China.
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Conclusion
In this paper, we have analysed the commercial housing demand in Shanghai, using
10-year consensus data from 2000 to 2009. From the result of regression, we have
argued that the process of speculation in the real estate market in Shanghai is to a
large extent, which can be explained by the relatively inelastic demand in terms of
income and the abnormal price elasticity of demand estimates obtained from
time-series analysis. By restricting the type of tenure, we focus on examining the
consumption activities of households who have the ownership and ignoring the
relative behaviors of tenants.
We estimate a time effect linear regression with two regressors. It is noticeable that
we hold the sociodemocratic factors as omitted variables in this paper though they
substantially influence the housing consumption. In order to correct the omitted
variable bias, in this case, we firstly test the heteroskedasticity of the model.
Fortunately, the results of regression testing are consistent with the general diagram
analysis of the data. That is, the results obtained confirm the inelastic response in
commercial housing demand in the face of disposable income (η = 0.676). Particularly,
the testing of price elasticity presents the abnormally same direction movement
between expense and house demand (λ = 0.901). From analysis above, we conclude
that the bubble is inflating in commercial real estate market in Shanghai.
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