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The Driving Factors of China’s Housing Prices Pre- and after 2012

Journal of Mathematical Finance, 2021, 11, 255-266
ISSN Online: 2162-2442
ISSN Print: 2162-2434
10.4236/jmf.2021.112015 May 17, 2021 255
Journal of Mathematical Finance
The Driving Factors of China’s Housing Prices
Pre- and after 2012
Haishan Li1*, Ting Pan2, Qianqian Tang2, Zhengxun Tan2*
1School of Shipping Economics and Trade, Guangzhou Maritime University, Guangzhou, China
2Department of Finance, Business School, Hunan Normal University, Changsha, China
While GDP and the money supply (M2),
the two key demand fundamentals
of Chinas housing prices, shifted gears and decelerated after 2012, China
housing prices maintained high growth rates, specifically during 2015-2018
To explain the puzzling phenomenon, we use the TVP-
VAR model to
compare the time-varying features of the factors driving up the housing
prices. Our results depict the interesting finding that before 2012, ma-
cro-fundamental factors such as M2 and per capita GDP were the k
ey drivers
of housing prices. But after 2012,
shadow banking had gained explanatory
power alongside M2 and per capita GDP.
Housing Price, Shadow Banking, TVP-VAR Model
1. Introduction
The issue of housing prices has always been a topic of great concern. Scholars at
home and abroad have tried to explain the growth and fluctuation of housing
prices from various angles, including the supply-side [1] and the demand-side
[2]. In China, the issue of housing prices is of particular concern because the
country has maintained an astonishing constant rise in housing prices for more
than three decades ever since the 1980s reform of the housing supply mechan-
ism. Scholars have analyzed the factors of the rising housing prices one after
another and put forward various explanations. Income and interest rates [3], the
government’s ability to control land supply [4] and among other factors are
found to be important elements that influencing different levels of housing pric-
es in China.
Most studies of housing price determination confirmed the explaining power
How to cite this paper:
Li, H.S., Pan, T.,
Q.Q. and Tan, Z.X. (2021) The Driv
Factors of China’s Housing Prices
after 2012
Journal of Mathematical Finance
, 255-266.
February 24, 2021
May 14, 2021
May 17, 2021
Copyright © 20
21 by author(s) and
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
Open Access
H. S. Li et al.
10.4236/jmf.2021.112015 256
Journal of Mathematical Finance
of GDP and money supply as the most essential determinants. For example, Tan
et al.
[5] found that the fundamental value of housing prices is determined by
macroeconomic factors of GDP and money supply. Li and Chiang [6] discov-
ered an equilibrium relationship between housing prices and GDP. Oikarinen
et al.
[7] concluded that GDP growth is the crucial factor of house prices, and
particularly Aizenman
et al.
[8] suggested that house price appreciations are
positively associated with economic growth in 19 countries. Evidence also
showed significant influence of monetary policy on house prices in various
countries, including Sweden [9], US [10], South Africa [11] and Kazakhstan
[12]. Housing price booms are typically proceeded by a period of easing mone-
tary policy [13]. Looser lending condition in the sub-prime mortgage market ex-
plained the run up in house price in the US between 1998 and 2005 [14]. Greiber
and Setzer [15] believed that expansionary monetary policy offers abundant li-
quidity and rises in housing prices.
The housing price rise synchronously with GDP and money supply in China
until the year 2012, which well proved the fundamental role of GDP and money
supply in determining the housing prices stated by the existing literature, what
puzzling us however, is that when Chinas economy shifted gear and the curren-
cy release slowed down after 2012, housing prices still maintained high rate of
growth, especially during the period 2015-2018 (see in Figure 1). As far as we
know, no scholars have conducted research on this confusing phenomenon and
given a reasonable explanation. There is no article covering whether there are
different effects of shadow banking on Chinas real estate market among differ-
ent periods either.
This paper aims to explain the mystery of the deviating house prices from the
perspective of shadow banking money supply. Money supply among other ma-
croeconomic factors is frequently proved to be the key influencing factor of
housing prices around the world [12] [16], it is also one of the very important
factors booming the Chinese real estate market. As confirmed by Wang
et al.
[17], a positive co-movement and causality between money supply growth and
housing returns exist. However, the money supply considered by the general li-
terature is the currency from the formal financial system and the shadow bank-
ing money from the informal financial system is ignored. In the aftermath of an
unprecedented stimulus of multitrillion RMBs injected by the Chinese
Figure 1. Quarter year-on-year growth of real GDP, M2 and Hp.
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government to combat the 2008 financial crisis, the Peoples Bank of China soon
tighten the M2 supply between 2009 and 2015 to counteract the over flooded
money generated by the previous stimulus plan [18]. Paradoxically, although the
contractionary monetary policy had exerted an expected loan, it stimulated sha-
dow banking and encouraged banks to bring shadow banking products onto
their balance sheets in the form of risky non-loan assets. The share of shadow
banking loans and bank loans increased steadily to around 20% in 2013-2015 as
estimated by [18].
The existing studies that focus on the nexus of money supply and housing
prices in China have not addressed the role played by shadow banking which
may be appropriate when the scale of the shadow banking money is small and
negligible but leading to biased research conclusion when it reaches to a large
size. The prevalence of credit supplied by non-bank financial institution via the
shadow banking system had played an important role in the US and the UK after
the financial crisis [19]. The Financial Stability Board [20] in China as well
pointed out that non-bank credits contribute to financing the real economy. As
far as we know, only Lai and Order [21] have formally tested the relationship
between shadow banking and asset prices so far in China. Different from Lai and
Order [21], however, our paper further explains the changes in the factors af-
fecting Chinas housing prices pre- and after 2012 with shadow banking pers-
To be specific, our paper contributes to the literature in several ways. First, as
far as we know, we are the first to discover the above mentioned mystery that
house prices in China deviated from the fundamental factors of GDP and money
supply. Although we believe that the growth of house prices should slow down
with GDP and money supply when the two fundamentals decelerated after the
year 2012, the truth is contrary to what we expected which greatly provokes our
interest; Second, we provide a reasonable explanation by taken shadow banking
into consideration of the money supply. Despite the reduction of generalized
money supply after the tightening monetary policy, the endogenous money
supply which includes shadow banking currency did not decrease and flowed
into the real estate market, by which we believe housing prices is supported; Fi-
nally, we do a serious of empirical experiments to prove the role play by shadow
banking money in replacing economic growth and bank loan supply and get
some inspiring results. 1) Before 2012, GDP and money supply were two impor-
tant factors driving housing prices up in China, but the explanation for them
became weaker after 2012. 2) In contrast, shadow banksinterpretation of house
prices was weak before 2012 but gradually strengthened after then. 3) Shadow
banking influences real estate market prices directly as well as through expand-
ing the endogenous money supply.
2. Empirical Methods and Data
2.1. Empirical Method
Compare with the constant coefficient VAR model, the TVP-VAR model with
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time-varying parameters can describe two different forms of impulse response
functions, which are equal-interval impulse response function and time-point
impulse response function. Thus, this paper adopts the vector auto-regressive
model with time-varying parameters and random fluctuations (TVP-VAR) pro-
posed by Nakajima (2011), the TVP-VAR model can be written in the form as
t t t t tt
yX A
= +Σ
( )
~ 0,
is a
( )
vector of observable variables;
( )
t k t ts
XI y y
= ⊗
refers to the Kronecker product. The coefficients
, and the parameters
are all time varying. Specifically,
a lower triangular matrix with a diagonal element of 1 and
( )
t t kt
Following Primiceri (2005), We stack the lower left hand side of
to get,
( )
21 31 . 1
, ,,
t kk
α αα α
( )
t t kt
hh h
jt jt
1, ,jk=
1, ,
ts n= +
. And the time varying parameter obeys the following random
walk process:
1t tt
= +
1t tt
+= +
1t t ht
h hu
= +
0 00
00 0
00 0
 
 
 
 
 
( )
1 00
( )
1 00
( )
1 00
s hh
h Nu
In order to calculate the Bayesian posterior distribution needed for the model,
we first estimate the time-varying parameters by the Monte Carlo Marcov Chain
(MCMC) method.
2.2. Variables and Data
Among the many factors that affect housing prices, we first consider economic
growth and money supply, for they are recognized as the most fundamental and
most Chinese-specific factors affecting housing prices. Over the past few dec-
ades, China's economic growth has led the world, and domestic housing prices
remain stubbornly high. Topics of housing prices and economic growth attract
much attention, and the relationship between the two has been confirmed by a
large number of literatures [6] [22] [23]. Similarly, the nexus of money supply
and housing prices in China are also empirically proved by dozens of papers [24]
[25]. As pointed out by Zhao [26], following a series of moderately loose mone-
tary policies, Chinas money supply (M2) has grown fivefold in the past decade,
leading to a persistent upward growth in housing prices. We agent economic
growth by GDP per capita and denote it as gdp, besides, proxy money supply by
endogenous money supply or generalized money supply which is denoted as
m2e or m2.
The impact of loans and interest rates on housing prices should not be neg-
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lected either, since the main way of purchasing commercial housing is residential
mortgage loans. Many papers have analyzed the relationship between housing
prices and mortgage loans, and find that easily available credit has been one of
the common explanations for the housing boom, too [27] [28]. Interest rate is
also reckoned as a house price determinant, and low interest rate substantially
boosts housing demand and prices [29]. Thus, we include r and loan referring to
interest rate on loans of five or more years and the amount of credit loans re-
spectively. Finally, discovered by Lai and Order [21], unprecedented growth in
fixed asset investments were increasingly funded by shadow banking after the
stimulus package initiated by the Chinese central government in response to the
Global Financial Crisis, we therefore incorporate shadow banking loan in our
research system denoted as sb and calculate as the sum of entrusted loans, trust
loans and undiscounted bank acceptance.
In terms of housing prices, according to the mainstream practice of the existing
researches, the average commercial housing prices calculated as the sales volume
(yuan) divided by the sales area (square meters) are adopted and denoted as hp.
We estimate the TVP-VAR model using quarterly data from the first quarter
of 2002 to the fourth quarter of 2019. Its noteworthy that we calculate money
supply in another way instead of m2 by the rule introduced by Chen
et al.
and get m2e to reflect the real endogenous supply of money. All the data come
from China’s national bureau of statistics and are seasonally adjusted and loga-
rithmically differentiated. The empirical analysis of this paper is divided into
three parts. To empirically study the impact of monetary policy on housing
prices, we established System 1 denoted as
( )
1, 2, , ,
t t tt t t
y gdp m r loan hp
in the
first part. Next, in the second part, variable of shadow banking is included to fur-
ther study its impact on housing prices where System 2 and System 3 expressed as
( )
2,2,, ,
t t tt t t
y gdp m e r loan hp
( )
, ,, ,
t tt tt t
y gdp sb r loan hp
are intro-
duced. Lastly, System 4 estimated as
( )
, ,2,, ,
t t t tt t t
y gdp sb m e r loan hp
in part
3 empirically analyzes the influence of shadow banking on monetary policy reg-
ulation of housing prices.
3. Empirical Results
The impulse response functions between variables are analyzed in this section.
Since both the model parameters and structural shocks are time-varying, the
impulse responses between the variables must also be time-varying. We firstly
depict the impulse response graph of different lag phases in the entire sample
period to catch the time-varying feature (diagram a), and then employ the lag
phase impulse response in different periods to make comparison (diagram b).
We show the dynamic profiles of the housing prices when propagated a
one-SD positive shock by other variables in the lag phase 1, 3 and 5 representing
the short-, mid- and long-term respectively. Three observation points, namely,
the first quarter of 2004, the first quarter of 2012 and first quarter of 2018 are
chosen in diagram b. We select 2004, 2012 and 2018 as the cut off points based
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on the truth that the real estate market deviated from the economic growth ever
since 2012, and the housing prices are rising faster, even as the Chinese govern-
ment intensifies its efforts to crack down on property prices. These three time
points also correspond to the periods when shadow banking began to develop,
developed rapidly and developed steadily. An examination of these three periods
is helpful in studying the time-varying feature at the reform time points as well
as deeper learning the interacting mechanism of shadow banking, monetary
policy and real estate market.
At first, System 1 denoted as
( )
, 2, , ,
t t tt t t
y gdp m r loan hp
is introduced to
analyzed how economic growth and monetary policy affect housing prices and
the results are displayed in Figures 2-5.
Firstly, as showed in Figure 2(a), the impulse responses of per capita GDP on
housing prices are positive in the lag phase 1, 3 and 5. In Figure 2(b), the im-
pulse responses of per capita GDP to housing prices at different time periods al-
so present positive changes. The response of housing prices to GDP per capita
was initially strong, but began to weaken in 2012 and was even weaker in 2016.
Figure 2. Impact of per capita GDP on housing prices ((a), (b)).
Figure 3. Influence of broad money supply on housing price ((a), (b)).
Figure 4. Influence of bank credit on housing prices ((a), (b)).
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Figure 5. Influence of interest rate on housing price ((a), (b)).
This results indicate that GDP per capita has been one of the major momentum
behind the fast rising housing prices in China in the past decade, in recent years
however, with the transformation of economic and the deceleration in the
economic growth, per capita GDP is playing a weaker and weaker role in pro-
moting housing prices.
Similar to GDP, the impulse of money supply on housing prices also show
time-varying features (See in Figure 3(a)). In line with the 4 trillion yuan bailout
plan implemented by the Peoples Bank of China in response to the financial cri-
sis in 2008, the influence of money supply on housing prices has kept rising from
2005 to 2009 and peaked in 2009. But the loose monetary policy turned to tight
soon after m2 reached its peak. As a result, the promotion of m2 to housing
prices has weaken since 2010 and even turned negative after 2012. The impulse
responses in the time periods figure (Figure 3(b)) also confirm the above con-
clusion with a significantly greater positive response to house prices in the first
quarter of 2004 than in the first quarter of 2012 and 2018. Likewise, the positive
impact of bank credit on housing prices has also gradually waning and turned
negative after 2015 (See in Figure 4(a) and Figure 4(b)). Finally, as can be seen
from Figure 5, the response of housing prices to interest rates is positive and
time lagged, therefore, raising interest rates will not bring house prices under
control quickly in the short term.
The above results suggest that economic development and money supply are
the primary impetus of prosperity in Chinese real estate market. What sur-
prising is that, house price increased independently even when economic
growth decelerated as well as money supply tightened after 2012 and especially
after 2016. In explanation of this paradox, we further incorporate System 2
( )
,2,, ,
t t tt t t
y gdp m e r loan hp
into our analysis, in which, the endogenous
money supply estimated by Chen
et al
. (2018) is used to replace the generalized
money supply. The resulting impulse response diagrams are shown in the fol-
lowing (Figure 6).
See from Figure 6(a), the impacts of the endogenous money supply on hous-
ing prices increased gradually in the lag phase 1, 3 and 5. The hysteresis res-
ponses of the three chosen time periods react basically the same too, reaching the
peak in the second lag period and gradually converging to steady. The impulse
response effect in the first quarter of 2018 is greater than that in the first quarter
of 2004, indicating a great role play by endogenous money supply in pushing up
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Figure 6. Influence of endogenous money supply on housing price ((a), (b)).
Chinese housing prices after the year 2012. As the endogenous money supply
different from the generalized money supply in it contains shadow banking cur-
rency, we further introduce System 3
( )
3, ,, ,
t tt tt t
y gdp sb r loan hp
below to
make a more in-depth analysis of shadow banking.
As shown in Figure 7(a), when a one-SD shock is propagated by the shadow
banking money supply, the housing prices react positively in the lag phase 1, 3
and 5 and the effect is increasing over time. In Figure 7(b), the influences of
shadow banking on real estate prices in the three different time periods are
basically the same, exempt that the positive impact in the first quarter of 2018 is
greater than that in the first quarter of 2012 and the first quarter of 2004. This
shows that although the tight monetary policy has reduced bank credit and
broad money supply, as a supplement to traditional credit, shadow banking has
not decreased, and most of the shadow banking funds have flowed to the real
estate market, thus promoting the prosperity of the real estate market, and even
assuming the supporting role of economic growth and money supply for real es-
tate. After 2012, shadow banking is an important reason for the rise of housing
prices in China.
Finally, on the basis of the above analysis, this paper comprehensively consid-
ers the influence of both the shadow banking currency and the monetary polices
by introducing System 4 as
( )
, ,2,, ,
t t t tt t t
y gdp sb m e r loan hp
According to Figure 8(a), the impulse response of endogenous money supply
(m2e) to shadow banking (sb) is basically positive, which is to say, the endo-
genous money supply increases with the expansion of shadow banking. While
showed by Figure 8(b), the influence of shadow banking on endogenous money
supply was positive at all three time points, peaked around the first period, le-
veled off around the eighth period, and had the largest positive impact in the
first quarter of 2018. In conclusion, shadow banking has a positive amplify effect
on money supply, thats why the promoting effect of the generalized money
supply which excludes shadow banking money is weakening, while on the oppo-
site, the effect of the endogenous money supply which includes shadow banking
money is gradually enhancing.
In Figure 9(a), the responses of interest rate(r) in lag phase 1, 3 and 5 to sha-
dow banking are all positive, which is to say shadow banking pushes up the in-
terest rate. And this effect keeps increasing before the year 2012 but decreasing
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Figure 7. Impact of shadow banking on housing prices ((a), (b)).
Figure 8. Impact of shadow banking on endogenous money supply ((a), (b)).
Figure 9. Impact of shadow banking on interest rates ((a), (b)).
Figure 10. Impact of shadow banking on house prices ((a), (b)).
there after. Similar conclusion is drawn from Figure 9(b) that the positive re-
sponse of interest rate to shadow banking shocks in 2012 is greater than that in
2004. The high-risk and high-yield nature of shadow banking makes its interest
rate much higher than that in the formal banks’, thereby, pushes up the market
interest rate and then affects the effectiveness of monetary policy.
The impulse response analysis is later applied to estimate the effects of shadow
banking and endogenous money supply on housing prices. The diagrams
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Figure 11. Impact of endogenous money supply on house prices ((a), (b)).
showed in Figure 10(a) and Figure 11(a) demonstrate that the responses of
housing prices to shadow banking and endogenous money supply are positive in
the lag phase 1, 3 and 5. In addition, the impulse responses in different time
periods (see in Figure 10(b) and Figure 11(b)) are also positive and gradually
strengthened along time. These again, not only confirm the finding in the above
but also the robustness of our results.
4. Conclusion
When GDP and the money supply (M2), the two key demand fundamentals of
China’s housing prices, shifted gears and decelerated after 2012, the housing
prices maintained high growth rates, specifically in the period of 2015-2018. To
address these puzzling phenomena, we explicitly examine the factors affecting
housing prices and make comparisons between the per- and after 2012 periods
by using the TVP-VAR model. We do empirical analysis in different lag phases
and different time periods, specifically, we consider the effects of different levels
of money supply on housing prices, including the generalized money supply and
the endogenous money supply with or without shadow banking currency. The
empirical results show that before 2012, macro-fundamental factors such as
money supply and per capita GDP are the key factors driving the rise of housing
prices. But after 2012, shadow banking became a key driver instead of money
supply and per capita GDP. These results suggest that there may be a bubble in
the Chinese housing prices after 2012. In order to stabilize the housing prices,
the Chinese government should closely monitor shadow banking in preventing
the flow of shadow bank loans from entering the real estate market.
Tan is grateful for the Hunan Provincial Innovation Foundation for Postgra-
duate (No. CX20200492). Li appreciates for the research fund granted by De-
partment of Education of Guangdong Province (No. 2018WQNCX136).
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this
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[1] Chow, G.C. and Niu, L. (2010) Demand and Supply for Residential Housing in Ur-
ban China.
Journal of Financial Research
, 44, 1-11.
[2] Deng, Y., Gyourko, J. and Wu, J. (2012) Land and House Price Measurement in
China. National Bureau of Economic Research, Cambridge.
[3] Zhu, H., Li, Z. and Guo, P. (2018) The Impact of Income, Economic Openness and
Interest Rates on Housing Prices in China: Evidence from Dynamic Panel Quantile
Applied Economics
, 50, 4086-4098.
[4] Glaeser, E.L. and Nathanson, C.G. (2017) An Extrapolative Model of House Price
Journal of Financial Economics
, 126, 147-170.
[5] Tan, Z.X., Liu, J. and Chen, P. (2021) The Trend and Cycle Components of China’s
Housing Prices: A New Decomposition Method.
Applied Economics
[6] Li, J. and Chiang, Y.H. (2012) What Pushes Up Chinas Real Estate Price?
tional Journal of Housing Markets and Analysis
, 5, 3-22.
[7] Oikarinen, E., Bourassa, S.C., Hoesli, M.,
et al.
(2018) US Metropolitan House Price
Journal of Urban Economics
, 105, 54-69.
[8] Aizenman, J., Jinjarak, Y. and Zheng, H. (2016) House Valuations and Economic
Growth: Some International Evidence. National Bureau of Economic Research,
Cambridge, No. w22699.
[9] Yang, Z., Wang, S. and Campbell, R. (2010) Monetary Policy and Regional Price
Boom in Sweden.
Journal of Policy Modeling
, 32, 865-879.
[10] Lastrapes, W.D. (2002) The Real Price of Housing and Money Supply Shocks: Time
Series Evidence and Theoretical Simulations.
Journal of Housing Economics
, 1,
[11] Gupta, R., Jurgilas, M. and Kabundi, A. (2010) The Effect of Monetary Policy on
Real House Price Growth in South Africa: A Factor-Augmented Vector Autoregres-
sion (FAVAR) Approach.
Economic Modelling
, 27, 315-323.
[12] Kondybayeva, S.K. and Ishuov, Z.S. (2013) The Effect of Monetary Policy on Real
House Price Growth in the Republic of Kazakhstan: A Vector Autoregression Anal-
World Applied Sciences Journal
, 22, 1384-1394.
[13] Ahearne, A.G., Ammer, J., Doyle, B.M.,
et al.
(2005) Monetary Policy and Housing
Prices: A Cross-Country Study. FRB International Finance Discussion Paper 841.
[14] Wheaton, W. and Nechayev, G. (2008) The 1998-2005 Housing Bubbleand the
Current “Correction”: Whats Different This Time?
Journal of Real Estate Research
30, 1-26.
[15] Greiber, C. and Setzer, R. (2007) Money and Housing: Evidence for the Euro Area
and the US.
[16] McCarthy, J. and Peach, R.W. (2002) Monetary Policy Transmission to Residential
H. S. Li et al.
10.4236/jmf.2021.112015 266
Journal of Mathematical Finance
FRBNY Economic Policy Review
, 8, 139-158.
[17] Wang, X.Q., Hao, L.N., Tao, R.,
et al.
(2020) Does Money Supply Growth Drive
Housing Boom in China? A Wavelet-Based Analysis.
Journal of Housing and the
Built Environment
, 35, 125-141.
[18] Chen, K., Ren, J. and Zha, T. (2018) The Nexus of Monetary Policy and Shadow
Banking in China.
American Economic Review
, 108, 3891-3936.
[19] Bordo, M.D. and Landon-Lane, J. (2013) Does Expansionary Monetary Policy
Cause Asset Price Booms; Some Historical and Empirical Evidence (No. w19585).
National Bureau of Economic Research, Cambridge.
[20] Financial Stability Board (2015) Global Shadow Banking Monitoring Report 2015
(November 2015).
[21] Lai, R.N. and Van Order, R. (2019) Shadow Banking and the Property Market in
International Real Estate Review
, 22, 361-399.
[22] Xu, T. (2017) The Relationship between Interest Rates, Income, GDP Growth and
House Prices.
Research in Economics and Management
, 2, 30-37.
[23] Yu, H. (2010) Chinas House Price: Affected by Economic Fundamentals or Real
Estate Policy?
Frontiers of Economics in China
, 5, 25-51.
[24] Lin, X.H. (2014) The Relationship between Incremental Change of M2 and Real Es-
tate Price Volatility: Theory and Empirical Analysis. Working Paper, East China
Normal University, Shanghai.
[25] Shi, W. (2016) An Empirical Study of the Interaction between Money Supply and
Asset Price Based on Granger Causality Test in Frequency Domain.
Journal of Hefei
University of Technology
Social Sciences
), 4, 25-31.
[26] Zhao, G.L. (2015) Endogenous Research and Empirical Test on Chinese Money
Review of Economy and Management
, 5, 133-139.
[27] Hofmann, B. (2003) Bank Lending and Property Prices: Some International Evi-
[28] Fitzpatrick, T. and McQuinn, K. (2004) House Prices and Mortgage Credit: Empir-
ical Evidence for Ireland. Research Technical Paper 5. Bank of Ireland.
[29] Mayer, C. and Sinai, T. (2009) US House Price Dynamics and Behavioral Finance.
Policy Making Insights from Behavioral Economics. Federal Reserve Bank of Bos-
ton, Boston.
Scholars have focused on macroeconomic fundamentals and speculative value to understand China's housing boom; however, this study empirically finds that great economic uncertainty and bleak economic prospects increase housing prices. We study this flight-to-housing effect in a two-regional model with housing. The results indicate that a bubble burst shock contributed to the safe-asset shortage in China following the 2008 global financial crisis, prompting households' demand for housing assets as stores of value and generating a housing boom. The financial market collapse and economic slowdown intensified the shortage and fueled the housing boom. Moreover, our findings suggest that the “housing purchase restriction policy” cannot curb the housing boom; the policy transfers the boom from one region to the other. This study provides a systematic framework to understand China's housing boom from the new standpoint of housing as a safe asset.
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This paper provides new insights into the dynamic relationships between money supply growth and housing boom, through the application of wavelet analysis. Specifically, in the time domain, the results highlight the existence of positive co-movement and causality between money supply growth and housing returns, during different sub-periods. In the frequency domain, the co-movements occur especially over the short and medium term. Our results are basically in line with the general equilibrium model developed by Frankel (Am J Agric Econ 68:344–348, 1986) and Li (The theory and empirical research on housing price overshooting under the impact of money supply in China, 2012). After controlling for the simultaneous impact of inflation and interest rates, the correlations become considerably strong in the short term since the early 2000s, which proves for an increasingly close interaction between money supply growth and real estate market. The results provide further evidence of the value of steady money supply growth for the maintenance of healthy and effective operation of the real estate market. Moreover, appropriate signal released by monetary authorities is rapidly assimilated by the real estate market, which highlights that policy adjustments should focus on the short term horizons in order to improve policy pertinence and flexibility.
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This paper studies the evolution of property values and the connections between shadow banking and property markets in China. We use Pooled Mean Group estimation to analyze Chinese house prices in 65 cities from 2007-2014, defining the " fundamentals " of housing prices with the Gordon dividend discount model, and using lagged rents, prices, real, nominal interest rates, and shadow banking activity as short term explanatory factors. We find that the cities tend to share long run fundamentals and adjust relatively quickly to deviations from the fundamentals; we do not find anything close to bubbles. We also find that house prices grow more rapidly with availability of shadow banking funds, which have grown rapidly. A policy implication for the Chinese government is to focus on regulatory monitoring in this funding sector.
The paper proposes a novel hybrid method that extends previous work incorporating the fractionally cointegrated vector autoregressive and the permanent-transitory decomposition model. Using the hybrid method, we investigate whether the rapid rise in China’s housing prices is a trend or cyclical fluctuation as well as whether there exists a housing price bubble. The findings indicate there exists a fractional cointegration relationship between housing prices and macroeconomic fundamentals in China, and that the fundamental value of housing prices is determined by macroeconomic factors. In particular, the upward trend in China’s housing prices has shifted and decelerated since 2012, in line with GDP and money supply. Moreover, we provide evidence of the existence of a housing price bubble in China since 2012, particularly during the 2015–2018 period. We demonstrate that the hybrid model has a superior forecasting performance and decomposes the trend and cycle components more accurately than the conventional methods when estimating a system of possibly fractional cointegration relationship.
We study how monetary policy in China influences banks' shadow banking activities. We develop and estimate the endogenously switching monetary policy rule that is based on institutional facts and at the same time tractable in the spirit of Taylor (1993). This development, along with two newly constructed micro banking datasets, enables us to establish the following empirical evidence. Contractionary monetary policy during 2009-2015 caused shadow banking loans to rise rapidly, offsetting the expected decline of traditional bank loans and hampering the effectiveness of monetary policy on total bank credit. We advance a theoretical explanation of our empirical findings.
Using data for 70 U.S. metropolitan areas, this study explores spatial heterogeneity in house price dynamics. We use recent advances in panel econometrics that allow for spatial heterogeneity, cross-sectional dependence, and non-stationary but cointegrated data. We test for spatial differences and analyze the relationship between the price elasticity of housing supply and the income elasticity of prices, as well as bubble size and duration. The long-term elasticity of house prices with respect to aggregate personal income averages 0.81, but varies considerably across metropolitan areas. The long-term income elasticity generally is greater in the more supply-inelastic metropolitan areas, and we show that bubble size and duration are inversely related to supply elasticity. Also short-term momentum and reversion dynamics show substantial spatial heterogeneity.
The rapid growth of housing prices has attracted the attention of the whole of society in China. This article adopts the dynamic panel quantile regression to investigate the impact of income, economic openness and interest rates on housing prices in China, based on the panel data of 35 major cities from 2002 to 2012. Compared with previous studies, we can more precisely and reasonably discuss the impact of these variables on different levels of housing prices. The empirical results indicate that the impact of independent variables on housing prices is heterogeneous across quantiles. Specifically, the impact of income is positive and significant across quantiles, and the impact becomes greater at the 90th and 95th quantiles. Economic openness has a positive and significant effect at the 5th–80th quantiles, which support the Balassa–Samuelson effect, but it is insignificant at the 90th and 95th quantiles. The impact of interest rates is positive and significant at low quantiles, but the impact is negative and insignificant at high quantiles. Furthermore, we also find that the coefficients of interest rates at various quantiles are smaller. In addition, the population has a significant positive effect across quantiles. Finally, we provide important policy implications.
A model in which homebuyers make a modest approximation leads house prices to display three features present in the data but usually missing from rational models: momentum at one-year horizons, mean reversion at five-year horizons, and excess longer-term volatility relative to fundamentals. Approximating buyers assume that past prices reflect only contemporaneous demand, just like professional economists who use trends in housing prices to infer trends in housing demand. Consistent with survey evidence, this approximation leads buyers to expect increases in the market value of their homes after recent house price increases.
em>This paper will analyse the relationship between interest rate, income, GDP growth and house prices. First, the control power of interest rate for the prices is limited. Second, people’s income increases, thus that also increases the demand for housing. But house prices are too high and will cause buying pressure. Third, the real estate industry’s growth and GDP growth have inseparable relationship, they interact with each other.</em
The money supply can be changed in the economic system by the monetary methods; as a result it leads to a change in the values of the key macroeconomic parameters. The work estimates the influence of monetary policy on actual growth of prices per square meter of housing in the Republic of Kazakhstan with the help of factor-augmented vector autoregression model FAVAR, using a data set comprising 76 quarters for the period from 1994:01 to 2012:04. Most aspects of the model are defined from statistical data on the economy of the Republic of Kazakhstan. The increment rate of the CPI ratio, the index of actual effective exchange rate for a group of CIS and non-CIS countries (24 countries), the average monthly rated salary per one employee, the st nd rd th M3 money supply and rated house prices (1st,2nd,3st,4th class of comfort) have been calculated. A comparative analysis of the consequences of monetary policy options for the Republic of Kazakhstan with data for the Russian Federation and Republic of Belarus, shows that monetary policy in the Republic of Kazakhstan meets the standards of the leading countries.