ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
Shadow Banking and Property Market 361
INTERNATIONAL REAL ESTATE REVIEW
2019 Vol. 22 No. 3: pp. 361 – 399
Shadow Banking and the Property Market in
China
Rose Neng Lai
Professor, Department of Finance and Business Economics, University of
Macau, Taipa, Macau, China. E-mail: RoseLai@um.edu.mo.
Robert Van Order
Oliver Carr Professor of Real Estate and Professor of Finance and Economics
at George Washington University, Washington D.C. E-mail: rvo@gwu.edu.
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 2007-2016, define the “fundamentalsof housing prices with
the Gordon dividend discount model, and use lagged rents, prices, real
and 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 bubbles; rather houses are like growth
stocks with house prices rapidly chasing growing rents. More importantly,
we find that house prices increase more quickly with the availability of
shadow banking funds, which have grown rapidly.
Keywords
Chinese Housing Market, Shadow Banking, Pooled Mean Group Estimation
362 Lai and Van Order
1. Introduction
Property values have increased rapidly in China in the last decade, and one of
the contributing factors have been public policy changes. The Chinese
government claimed that a housing bubble had been successfully “deflatedat
the end of 2014, and subsequently relaxed restrictions on second-home
purchases upon the emergence of an economic slowdown in 2015, with the aim
to boost the property market as a means of supporting the dampened economy.
Chinese households have been since buying more apartments, and developers
have been borrowing more to fund their construction projects. The ensuing
demand for funding has boosted the expansion of the shadow banking system.
Our interest in this paper is the role of that system in the growth of property
values.
As in Lai and Van Order (2017), one of the ways to study the dynamics of house
prices is to apply the Gordon dividend discount model, which makes use of
rental and interest rates to explain for long run house prices, to which actual
prices adjust over time. This leads to a well- defined long run equilibrium, but
with a less restrictive adjustment process. We use Pooled Mean Group (PMG)
(and Mean Group (MG)) estimation to separate long run from short run. We
study the Chinese housing market with a focus on shadow banking as the source
of funding. Our specification forces shadow banking to only affect short run
property value adjustment (momentum), but not long run fundamentals. We
exploit the fact that shadow banking policies are made at the national level, but
we use them to estimate the determinants of city by city price changes, as a way
of avoiding endogeneity problems and suggesting causality from shadow
banking to price changes.
To the best of our knowledge, we are the first to formally test the effects of
shadow banking on the property markets in China and model Chinese house
prices by decomposing the effects into long run fundamentals and short run
adjustments. We find that the cities tend to share long run fundamentals and
adjust relatively quickly to deviations from the fundamentals, and we do not
find bubbles. We also find that housing growth in the short run is related to the
availability of shadow banking funds, which have also grown rapidly. A policy
suggestion for the Chinese government is to focus on regulatory monitoring in
this funding sector. Not only can its contraction hurt property markets, its non-
performing loans can trigger contagion to the main banking system and
therefore the economy as a whole.
2. Shadow Banking in China
The Financial Stability Board (FSB) (2015) defines shadow banking as “credit
intermediation involving entities and activities outside of the regular banking
system”. An important element of being outside the regular banking system is
Shadow Banking and Property Market 363
the absence of deposit insurance coverage. The FSB points out that non-bank
credits contribute to financing the real economy, simultaneously becoming a
source of systemic risk when they are highly interconnected with the regular
banking system.
1
In China, shadow banking can be broadly defined as non-bank financing, such
as trust and entrusted loans, bankers acceptances, interbank entrusted loan
payments, microfinance companies, financial leasing, special purpose finance
companies associated with e-commerce, guarantees, pawn shops and unofficial
lenders, bond markets, trust beneficiary rights, and wealth management
products (WMPs), and interbank market activities (see Elliott et al. (2015) for
detailed descriptions of each of these sources of shadow banking). Some of the
non-bank channels, such as bond markets and interbank market activities,
should not be classified as shadow banking. For instance, the bonds here refer
to corporate bonds, which are not generally traded in the bond markets like
those in the US, whereas interbank market activities are really large
corporations that are using finance company subsidiaries to act like banks. The
most common source of shadow banking funds that is collected from the
general public comes from the WMPs that pool assets together. Most of the
assets are loans. Sharma (2014) and Hsu and Li (2015) are some examples of
the literature on shadow banking in China.
In response to the Global Financial Crisis, the Chinese central government
initiated a stimulus package in 2009, which was followed by unprecedented
growth in fixed asset investments that were increasingly funded by shadow
banking. Infrastructure projects constituted 72% of the stimulus package, of
which 30% was funded by the central government, while the rest was from local
governments.
2
Funding was also available to riskier borrowers, typically real
estate developers and local government financing vehicles, the corporate arm
of local governments, which helped local governments generate their high
GDPs through infrastructure construction.
The growth of shadow banking has been fueled by the fact that the five largest
banks in China, all state-owned, are only allowed to lend to the large state-
owned enterprises (SOEs) but not corporates and small and medium sized
enterprises (SMEs). Hence, the shadow banking sector provides lending needs
outside regulations. On the supply side, the lack of investment opportunities
(made up of only the stock markets, the very small bond market, and the very
hot real estate market) stimulates all sorts of WMPs that can generate returns
higher than the very low (sometimes even negative in real terms) deposit rates.
Furthermore, average investors are under the impression that these products are
1
Various issues of the “Global Shadow Banking Monitoring Reportby the Financial
Stability Board.
2
Reported by Sarah Hsu in “The Rise and Fall of Shadow Banking in China – How
shadow banking became the catch-all for riskierThe Diplomat, 2015 11 09, available
at http://thediplomat.com/2015/11/the-rise-and-fall-of-shadow-banking-in-china/
364 Lai and Van Order
safe because they are mostly sold by big banks, which supposedly have implicit
guarantees from the People’s Bank of China (the Chinese central bank),
ignoring the fact that these big banks are only intermediaries and do not provide
any guarantees.
While boosting local incomes, over-investment in infrastructure has generated
“ghost towns”, with roads and bridges that very few people use. It is reported
that shadow banking makes up 20%-41% of on-balance bank lending, without
which total lending would have declined by 16-29%.
3
The downturn of the
Chinese property market resulted in the lack of liquidity for developers. Yet the
Chinese government is still optimistic that the shadow banking sector in China
is only a small problem.
Table 1 shows that shadow banking is only 26% of the GDP in China, which
ranks 13th among the 26 jurisdictions according to the Financial Stability Board
(2015), relative to 82% in the US. A comparison between Figures 1 and 2 shows
that, unlike the US (Figure 1) where funding comes from various sources, (with
“Other Financial Institutionsas the dominant sector) banks in China (Figure
2) dominate funding supply. It should be noted, however, that by referring to
only the economic function-based measures of shadow banking, the FSB
might have underestimated the proportion of loans in the total loan system.
As shown in Table 2, shadow banking in China has grown very rapidly, from
1.6% of all assets in financial intermediation to 7.7%, thus becoming the third
largest sector in terms of size. Along with that, according to Elliott and Yan
(2013), there are large pools of bad loans that are not acknowledged by banks.
An example is the situation in Wenzhou, a small city that eventually prospered
from profitable SMEs and was able to obtain financing through various
channels of shadow banking, subsequently followed by widespread defaults
(after 2012) because of the economic slowdown. Sheng et. al. (2015) report that
the real estate sector makes up 18% of shadow banking assets as of 2013, which
is the third largest industry. Also based on their calculations, an estimate of 22%
to 44% of the non-performing loans in shadow banking will be brought back to
the banking system. In fact, it is also reported that there are RMB1.19 trillion
(USD1 RMB7 as at mid-2019) bad loans at the end of September 2015, up
from RMB842.6 billion at the end of 2014
4
, and a 22% non-performing loan
rate in the whole financial system at the end of 2016.
5
It seems that even though
the central government is willing to stabilize the market through intervention,
3
Ibid.
4
China’s December New Bank Loans Miss Expectations”, MarketWatch, January 15,
2016.
5
As reported by Peter Eavis in “Toxic Loans Around the World Weigh on Global Growth
in The New York Times on February 3, 2016.
Shadow Banking and Property Market 365
there is still the risk of bad-debt that leads to contagion throughout the financial
sector.
6
Figure 1 FSB Assets of Financial Institutions and Economic Function-
Based Shadow Banking Measure – USA
Panel A: In Billions USD
Panel B: In Percentage
Source: Financial Stability Board (2015)
6
See, for example, the Bloomberg reports in “Be Scared of China's Debt, Not Its Stocks
on January 7, 2016, available at http://www.bloombergview.com/articles/20160107/
bescaredofchinasdebtnotitscrashingstocks, and “Mid-tier Chinese banks piling up
trillions of dollars in shadow loansof Thomson Reuters on January 31, 2016, available
at http://www.reuters.com/article/china-banks-investment-idUSL8N 156053.
0.0
10,000.0
20,000.0
30,000.0
40,000.0
50,000.0
60,000.0
70,000.0
80,000.0
90,000.0
100,000.0
2002 2005 2008 2011 2014
Shadow
banking
Other Financial
Intermediaries (OFIs)
Public Financial
Institutions
Pension Funds
Insurance Companies
Banks
Central Banks
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2002 2005 2008 2011 2014
Shadow
banking
Other Financial
Intermediaries (OFIs)
Public Financial
Institutions
Pension Funds
Insurance Companies
Banks
Central Banks
366 Lai and Van Order
Figure 2 FSB Assets of Financial Institutions and Economic Function-
Based Shadow Banking Measure – China
Panel A: In Billions USD
Panel B: In Percentage
Source: Financial Stability Board (2015) and Lai and Van Order (2017)
0.0
5,000.0
10,000.0
15,000.0
20,000.0
25,000.0
30,000.0
35,000.0
40,000.0
45,000.0
2003 2005 2007 2009 2011 2013
Shadow
banking
Other Financial
Intermediaries (OFIs)
Pension Funds
Insurance Companies
Banks
Central Banks
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2003 2005 2007 2009 2011 2013
Shadow
banking
Other Financial
Intermediaries (OFIs)
Pension Funds
Insurance Companies
Banks
Central Banks
Shadow Banking and Property Market 367
Table 1 Shadow Banking, Other Financial Intermediaries (OFIs) and
Banks as Percentage of GDP of 26 Jurisdictions: End of 2014
Shadow banking
OFIs
Banks
Indonesia
1
8
54
Russia
4
5
109
Saudi Arabia
5
5
74
Argentina
6
6
30
Turkey
6
11
108
Singapore
10
90
607
Mexico
16
23
40
Italy
17
38
223
India
19
17
95
Hong Kong
20
85
817
Spain
21
69
267
Chile
23
31
106
China
26
29
271
South Africa
27
61
108
Australia
27
64
211
Brazil
33
60
91
Korea
48
100
205
Canada
58
147
228
Total
59
112
223
Japan
60
87
374
France
61
96
370
Germany
73
81
241
The Netherlands
74
838
326
United States
82
148
122
Switzerland
90
277
364
United Kingdom
147
326
601
Ireland
1190
1551
363
Notes: Banks = broader category of ‘deposit-taking institutions’; OFIs = Other Financial
Intermediaries; and Shadow Banking = economic function-based measure of
shadow banking.
Source: Financial Stability Board (2015)
368 Lai and Van Order
Table 2 Share of Shadow Banking Assets as Percentage of All
Financial Intermediations of 26 Jurisdictions: End of 2010
and 2014
End of 2010
End of 2014
United States
40.9
39.7
United Kingdom
13.0
11.4
China
1.6
7.7
Ireland
6.9
7.6
Germany
7.1
7.2
Japan
9.5
6.8
France
6.1
4.4
Canada
2.4
2.8
Brazil
2.0
1.9
Korea
1.3
1.8
The Netherlands
1.8
1.7
Switzerland
1.5
1.6
India
0.9
1.1
Australia
1.3
1.0
Italy
1.2
0.9
Spain
1.0
0.7
Mexico
0.5
0.5
South Africa
0.3
0.2
Hong Kong
0.1
0.2
Chile
0.1
0.2
Russia
0.1
0.1
Turkey
0.1
0.1
Saudi Arabia
0.1
0.1
Argentina
0.0
0.1
Singapore
0.2
0.1
Indonesia
0.0
0.0
Note: Shadow banking is based on an economic function-based measure.
Source: Financial Stability Board (2015)
3. The Chinese Housing Market
There have been many studies on the US housing bubble. Examples include
Black and Hoesli (2006), Chan et al. (2001), Chang et al. (2005), Coleman et
al. (2008), Hwang et al. (2006), and Wheaton and Nechayev (2008). However,
studies on the Chinese house price movements are scant, although studies on
the housing markets themselves are extensive. Deng et al. (2009) and Yang and
Chen (2014), among others, focus on the Chinese housing policy reform. Others
such as Wu et al. (2012) discuss the sustainability of its boom. The links
between house prices and land policies are studied in Cai et al. (2013), and Peng
and Thibodeau (2009). Ren et al. (2012) are one of the few to explicitly measure
Shadow Banking and Property Market 369
the extent of the Chinese house price run up. A very recent study is Glaeser et
al. (2017). Lai and Van Order (2018) compare the housing bubbles between
China and the US. To the best of our knowledge, there is no study on the effect
of shadow banking as a source of funding on Chinese house prices.
This paper studies the property markets in China over the past decade. We use
a variation of the Gordon dividend discount model as the common
representation of long run fundamentals, but the model allows a short run
momentum that can vary across cities. We are able to test and estimate a long
run fundamental model, as well as the short run adjustments and momentum
across cities. Associated with this is an estimation of how fast deviations from
the long run are corrected. For further analysis, we also classify cities into Tier
1 and Tier 2 cities (which are official classifications based on size and speed of
development) and coastal versus inland (because coastal cities are those that
have had more advanced development for a longer period of time). Our prior is
that the housing bubble would be larger in Tier 1 and/or coastal cities.
3.1 Data
We use monthly house price and rental series for 65 cities over the period of
2005 2014 obtained from CityRE Data Technology Co. Ltd,
7
which is the first
to compile comprehensive data on housing for sale and lease for over 290 cities
and areas in China starting from 2003. Numerous studies such as Ren et al.
(2012) use house price and rental indices from the CEIC Data. However, the
CEIC database only covers 35 cities. Monthly data on the sales price indices of
newly constructed residential buildings for 70 cities from 1997 onwards can be
obtained from the National Bureau of Statistics (NBS) of China; however, the
data were no longer collected after 2010. The property price index of the China
Real Estate Index System (CREIS) can also be used, especially after the
termination of the housing index in 2010. Note that it might be a challenge to
compare house price and rent series if the underlying representative housing
units are not comparable, for instance, due to improved quality over time.
Fortunately, such an effect is not likely to have significant impact on the
empirical results due to the relatively large volume of transactions, and the
relative homogeneity of housing within cities in China.
We use the 5-year Chinese government bond rates obtained from the National
Interbank Funding Center as a proxy of nominal long-term risk-free rate, and
from which we also obtain real interest rates by using the consumer price index
(CPI) from the NBS of China. We also proxy risk relative to government bonds
with the 5 year AAA corporate bond yields (obtained from the China Central
7
Details of CityRE Data Technology Co., Ltd. can be obtained from
http://www.cityre.cn/en/ or http://www.cityhouse.cn. They claim to operate the largest
real estate data set in China.
370 Lai and Van Order
Depository & Clearing Co., Ltd.), so that the yield spread is the 5-year corporate
bond yields minus the 5-year Chinese government bond yields.
3.2 Impact from Shadow Banking
The shadow banking data, classified as “loans from non-banking financial
institutions”, are reported by the NBS, with a monthly frequency that spans the
period of January 2006 to December 2015. These are nationwide data, and the
only public source of data on shadow banking. We cannot rule out the existence
of a significant amount of funds from informal banking via many informal
channels that are unfortunately not systematically and officially recorded. To
control for the basic factors in the real estate sector, we also include the house
price and the rental indexes from the CPI series published by the NBS as well
as interest rate, proxied by nominal lending rates for housing loans issued by
the People’s Bank of China.
In order to study how shadow banking affects investments in the real estate
sector in China, we include data on “housing completed”, “housing soldand
“housing starts”, and land purchases and investment. All are aggregate monthly
data from the NBS, some of which start as early as March 1998, while data that
start as late as March 2007 and March 2008 are on land purchases and
investment. The “Housing Completed category is proxied by “Floor Space
Completed”, “Floor Space Completed: Commodity Building Residential”,
“Floor Space Completed – Residential”, and “Floor Space Completed: 40 cities
– Residential”. “Housing Soldis proxied by “Floor Space Sold: Residential:
Presale”, “Floor Space Sold: Residential: Existing Units”, “Floor Space Sold:
Residential”, “Building Sold: Residential”, “Building Sold: Residential:
Presale”, and “Building Sold: Residential: Existing Units”. “Housing Starts
are proxied by “Floor Space Started: Commodity Building: Residentialand
“Floor Space Started: 40 cities: Residential”. Finally, “Land Purchases and
Investmentis proxied by “Land Area Purchased”, “Real Estate Investments:
Residential”, “Real Estate Investments: New Increase”, “Real Estate
Investments”, “Real Estate Investments: Land Transactions”, “Land Area
Purchased: 40 citiesand “Real Estate Investments: Residential: 40 cities”.
All of the variables are presented in percentage changes. We perform unit root
tests and cannot reject that the log differenced (percentage change) data are
stationary. As expected, the rental rates do not have much influence on the
demand and supply measures of real estate because the rental market is very
small, and hence, we rerun the regressions without rental rates on the right-hand
side. The signs of the explanatory variables are mostly as expected. That is,
interest rates have negative effects while funds from shadow banks have
positive effects on the housing market. Other variables that do not seem to have
the expected signs are not significant anyway. Table 3 shows the results with
various proxies of real estate demand and supply.
Shadow Banking and Property Market 371
Table 3 Regression of Measures of Real Estate Supply and Demand
on Shadow Banking Loans
Panel A: Buildings and Floor Space Sold
Buildings Sold
Proxy 1
Proxy 2
Proxy 3
Housing Price
2.819***
4.79
8.443**
Shadow Banking
1.185***
1.361***
1.250***
Interest rates
-0.242**
-0.372**
-0.420***
Constant
0.022
-0.009
-0.008
Observations
100
70
70
Adjusted R-squared
0.664
0.638
0.689
Floor Space Sold
Proxy 1
Proxy 2
Proxy 3
Housing Price
2.731***
1.516
3.206***
Shadow Banking
1.312***
1.426***
1.277***
Interest rates
-0.248**
-0.192*
-0.268***
Constant
0.005
0.003
0.005
Observations
100
100
100
Adjusted R-squared
0.69
0.662
0.691
Panel B: On Floor Space Started and Completed
Floor Space Started
Proxy 1
Proxy 2
Housing Price
1.813***
2.776***
Shadow Banking
1.095***
1.275***
Interest rates
-0.169***
-0.218**
Constant
-0.003
-0.024
Observations
72
100
Adjusted R-squared
0.653
0.744
Floor Space Completed
Proxy 1
Proxy 2
Proxy 3
Proxy 4
Housing Price
-1.652
-2.062**
-1.961*
-3.424***
Shadow Banking
1.012***
0.878***
0.937***
0.373**
Interest rates
0.059
0.107
0.076
0.222**
Constant
0.106***
0.086***
0.094***
0.146***
Observations
100
100
100
72
Adjusted R-squared
0.552
0.509
0.534
0.195
372 Lai and Van Order
Panel C: On Land Purchases and Real Estate Investments
Land Purchases
Proxy 1
Proxy 2
Housing Price
2.338***
2.264***
Shadow Banking
1.404***
1.090***
Interest rates
-0.117
-0.157**
Constant
-0.033*
0.019
Observations
100
81
Adjusted R-
squared
0.779
0.558
Real Estate Investments
Proxy 1
Proxy 2
Proxy 3
Proxy 4
Proxy 5
Housing Price
3.320***
1.868**
-2.214**
3.387***
2.278***
Shadow Banking
1.313***
1.298***
0.668***
1.292***
1.032***
Interest rates
-0.248**
-0.202**
0.118
-0.245**
-0.164**
Constant
0.002
-0.008
0.108***
0.006
0.035*
Observations
100
79
100
100
81
Adjusted R-
squared
0.713
0.739
0.42
0.707
0.593
Note : *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.
All variables except interest rates are differenced log values to obtain percentage
changes of the values. Interest rates are differenced values.
It can be seen from Panel A of Table 3 that high housing prices and availability
of shadow banking funds can increase the volume of buildings and floor space
sold, while interest rates have negative effects as expected. In particular, an
increase of 1% in the shadow banking funds increase, say, the floor space of
existing units (Proxy 2) by 1.426%, or the floor space of presale units (Proxy 3)
by 1.277%. Similarly, available funds and surging house prices can encourage
construction, as demonstrated by Floor Space Started in Panel B. While the
variables also exert effects on Floor Space Completed, they are negatively
affected by housing price, which is not reasonable. Nevertheless, note first that
they are mostly not significant, and more importantly, the house price variable
is not lagged, and therefore should not be a factor that would deter construction
which has already taken place. Panel C also shows interesting results in that
there will be real estate investments and land purchases as long as there is
funding and housing price continues to increase; therefore, interest rates do not
seem to be very important when considering investments by developers.
We use lagged shadow banking variables to represent situations where funding
was needed for construction one or two years beforehand in order for units to
be completed and sold . Alternatively, funds from shadow banking might be
needed immediately for purchases of housing units, the completion of which
would be triggered by high property prices one or two years ago. We repeat the
regression in two sets of tests with six-month, 1-year, 18-month, and 2-year lags,
Shadow Banking and Property Market 373
given that most housing buildings can be completed in around two years.
Similar lags are applied to all explanatory variables. The first set includes lags
for all variables, while the second set includes lags for all variables except
shadow banking funds. The rationale for the former is that current investment
decisions can be attributed to observations of a good market over the past period.
The rationale of the latter is that construction decisions are often made
beforehand, while funding is needed immediately to stimulate purchases. The
results are shown in Table 4 (tests with various lags generate similar results and
therefore not all are provided). Interestingly, shadow banking funds are the most
significant explanatory variable, regardless whether we use no lags, six-month,
1-year, 18-month, or two year lags. Moreover, when shadow banking is
considered, even a dominating factor such as housing price index sometimes
shows a negligible influence.
From Panels A and B of Table 4, it is clear that real estate investments and land
purchases are affected by the lagged availability of funds. Interestingly, housing
price is not a strong consideration for land purchases, probably because
developers always favor stacking up land banks for any option for construction
whenever housing prices become favorable. Panel C shows that the property
sales market is affected by house price and interest rate variables lagged by six
months, while shadow banking funds are not lagged; that is, testing whether
past information on house prices and interest rates trigger more purchases if
funds are available today. Again, while the variables are correctly signed, only
current funding availability is important. In terms of the decisions of the
developers, commencement of construction (i.e. Floor Space Started) and land
purchases are mostly based on availability of funding. While this is not very
convincing, it should be noted that our sample period covers a market boom
period when investors and developers were optimistic about the market, but
also when there were government restrictions and policies that curbed the
market, and the housing prices were once affected. Developers were apparently
willing to invest as long as there was funding.
We also study if there is an increased demand from shadow banking because of
increased demand and supply of real estate investments. The results are shown
in Table 5. All of the variables that represent real estate investments, building
and development, and housing completed and sold exert significant and positive
effects on shadow banking. In other words, both demand and supply of real
estate trigger demand for more shadow banking funds. While it is logical that
higher interest rates also attract a larger supply of shadow banking funds, it is
also logical to interpret that lower housing prices attract more buying and
therefore increase the demand for shadow banking funds.
In general, the results confirm that shadow banking might be an important,
although perhaps endogenous, factor in real estate investment in China. To
check for robustness, we also test for the presence of autocorrelation of the
regression residuals in the above tests. All of the regression results show no
autocorrelation. Next, we include shadow banking funds as a short term
374 Lai and Van Order
variable that affects the model for the pricing of housing units across cities in
China.
Table 4 Regression of Measures of Real Estate Supply and Demand
on Lagged Explanatory Variables, Including Shadow
Banking Loans
Panel A: Land Purchases and Real Estate Investments with 1-year Lag
Variables
Land Purchases
Proxy 1
Proxy 2
Housing Price
1.943
1.618*
Shadow Banking
1.218***
0.968***
Interest rates
-0.186
-0.202**
Constant
-0.001
0.038*
Observations
90
81
Adjusted R2
0.57
0.435
Real Estate Investments
Proxy 1
Proxy 2
Proxy 3
Proxy 4
Proxy 5
Housing Price
3.050***
1.146
-1.943*
3.010***
2.256***
Shadow Banking
1.296***
0.994***
0.632***
1.284***
0.960***
Interest rates
-0.225**
-0.213
0.136
-0.216*
-0.161*
Constant
0.004
0.049*
0.108***
0.006
0.045**
Observations
90
79
90
90
81
Adjusted R2
0.699
0.483
0.4
0.702
0.535
Panel B: Land Purchases and Real Estate Investments with 2-year Lag
Variables
Land Purchases
Proxy 1
Proxy 2
Housing Price
1.959
1.182
Shadow Banking
1.100***
0.814***
Interest rates
-0.12
-0.052
Constant
0.015
0.062**
Observations
80
72
Adjusted R2
0.576
0.336
Real Estate Investments
Proxy 1
Proxy 2
Proxy 3
Proxy 4
Proxy 5
Housing Price
2.926***
1.879
-2.245**
2.902***
2.369***
Shadow Banking
1.253***
0.930***
0.595***
1.246***
0.932***
Interest rates
-0.206*
-0.064
0.138
-0.204*
-0.167*
Constant
0.009
0.058**
0.110***
0.011
0.048**
Observations
80
79
80
80
72
Adjusted R2
0.707
0.509
0.41
0.708
0.531
Shadow Banking and Property Market 375
Panel C: Buildings and Floor Space Sold with 6-month Lag in Price Index
and Lending Rates but No Lag for Shadow Banking Funds
Buildings Sold
Proxy 1
Proxy 2
Proxy 3
Housing Price
0.217
0.591
0.094
Shadow Banking
1.153***
1.335***
1.221***
Interest rates
-0.169
-0.206*
-0.173
Constant
0.034*
0.007
0.019
Observations
100
70
70
Adjusted R2
0.642
0.611
0.624
Floor Space Sold
Proxy 1
Proxy 2
Proxy 3
Housing Price
0.536
1.091
0.356
Shadow Banking
1.282***
1.404***
1.244***
Interest rates
-0.155
-0.187
-0.146
Constant
0.016
0.009
0.018
Observations
100
100
100
Adjusted R2
0.669
0.66
0.659
Panel D: On Floor Space Started and Land Purchases with 6-month Lag
in Price Index and Lending Rates but No Lag for Shadow Banking Funds
Floor Space Started
Land Purchases
Proxy 1
Proxy 2
Proxy 1
Proxy 2
Housing Price
0.207
0.561
0.801
0.031
Shadow Banking
1.022***
1.251***
1.397***
1.017***
Interest rates
-0.031
-0.079
-0.119
-0.157*
Constant
0.013
-0.014
-0.029*
0.033
Observations
72
100
100
81
Adjusted R2
0.597
0.715
0.767
0.538
Note : *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.
All variables except interest rates are differenced log values to obtain percentage
changes of the values. Interest rates are differenced values.
4. Modeling House Price Growth via Pooled Mean Group
This section follows Lai and Van Order (2017) in developing a model for
property value changes over time. In equilibrium, rent, which is essentially the
current dividend from the property, should equal the risk-adjusted interest rate
and expected capital gains over the period. Then, given an information set, t,
the equilibrium condition for holding the property at time t is given by
8
8
See Lai and Van Order (2010).
376 Lai and Van Order
 
 
1
/ / 1|
t t t t t t t ht
R P i E P P i
 
      
(1)
where Pt is the price of a constant quality house, Rt is net rental income, which
is the imputed net rent of the property in the case of owner-occupied housing,
it is the risk-adjusted hurdle rate, which can be thought of as a long term nominal
rate, α is constant depreciation, and πht is expected house price growth. Equation
(1) applies to a particular location. We suppress location notation until we
conduct the estimation later.
House price can thus be found from Equation (1) given expected future house
prices. Since future prices depend on future rents, current price depends on
future prices, which in turn depends on future rents through the expected
present value:
0( / | ) lim (1/ | )
t t i t i t t i t
i
P E R I E I
 
 
(2)
where the discount factor is It = 1+ it, such that It+i is the discount rate for an i-
period loan at time t. Assuming that the second term approaches zero and
dividing through by Rt, the expected present value formulation becomes:
0
/ (1/ | )
t t t i t
i
P R E D

(2’)
where
(1 )/ (1 *)
t i t i t i
Di
 
 
, and πt+i* is the expected rate of growth of
rent from period t to period i. If D and the growth rate of rent are constant in
the long run, then the reciprocal of Equation (2’) will converge to (1), which
gives the long run fundamentals.
The advantage of this approach is that it does not require the development of a
housing demand and supply model, but only a model of how the expectations
are formed. In other words, the model must allow transaction costs to facilitate
gradual adjustments of Equation (2’). In general, house prices adjust to shocks
slowly, and therefore are less efficient. Glaeser and Nathanson (2017) develop
a house pricing model in which traders are “almostrational. That is, small
mistakes can lead to large forecasting errors, such that forecasted prices based
on past prices have short run momentum (positive feedback), long run mean
reversion, and excess volatility. Our estimated models generate all of these
phenomena, with the long run model being the Gordon dividend model.
4.1 Long Run Specification
Theory suggests that in the long run, prices and rents are expected to move
together and depend on real interest rates, which, according to the Gordon
model, should be:
Shadow Banking and Property Market 377
tt t t
t
Rir
P
 
   
(3)
where rt is the real rate. This suggests a coefficient of unity for the real rate. It
is possible that for tax reasons, money illusion or inability to borrow against
human capital that a coefficient of unity for the real rate is unlikely. Hence, we
consider a more general formula:
tt
t
Rc
P
(4)
where
t i t t i t r t
c i i r
 
 
is the “cap ratefor housing.
Our tests include both long-run and short-run behaviors. The long-run tests are
to check whether property prices converge to rent divided by the cap rate as in
Equation (4), and, if so, the speed of convergence. For the short-run, the tests
are about the nature of the deviations from the long-run phenomenon, and
whether the coefficients make sense. We use long run risk free rates for i; α
represents depreciation and long run expected future rent growth; and risk
adjustments with one for each city, which are assumed to be invariant over time.
We take Equation (4) as our representation of long run fundamentals. We do not
define short run fundamentals; rather we analyze how short run deviations
move over time. In general,
r
is expected to be close to 1, and
to be around
2%. We have no presuppositions about
i
.
4.2 Dynamic Heterogeneous Panel Estimation
We assume that R/P depends on a complicated lagged function of past levels of
R, P and i. We decompose the relationship into long-run and short-run effects
by using the PMG and MG estimation models developed in Pesaran et al. (1997,
1999).
9
Our hypothesis is that t contains only past rents, prices, interest rates
and shadow banking indicators, and that prices ultimately adjust to
fundamentals.
Traditionally, economic analysis has focused on long run relationships among
the dependent variables and the regressors. PMG estimation allows us to
identify long run relationships (Equation (4)) and short run dynamics separately;
the intercepts that reflect the fixed effect, short run coefficients and error
variances are allowed to differ across cities, but long run coefficients are
constrained to be the same. MG estimation is different in that the long run
coefficients are also allowed to vary across cities.
9
See Ott (2014) for a study that uses PMG on house price dynamics in the Euro area.
378 Lai and Van Order
Our model can be represented by:
,
, , , , ,
1 0 1
,,
q
ln
c t j
c,t kk
c j c j c t j c c t
j j k
c t c t j
R
Rx
PP
 
 
 
 
(5)
where
,
c,t
ct
R
P
is property rent to price ratio in city c at time t
c captures the city specific fixed effects
xkc,t-j is the kth of n regressors for city c
δkc, j is the coefficient of the kth regressor for city c
λc,j are scalars
εc,t are city specific errors
c represents panels or cities, i = 1,2,…,N
t represents time in quarters, t = 1,2,…,T
j is an indicator of lags;
j = 0,1,2,…, l for lagged dependent variable
j = 0,1,2,…, q lags for regressors
Letting
R
P
, Equation (5) can be written as:
, , 1 , , ,
01
qnkk
c t c c t c j c t j c c t
jk x
 


 

(6)
which when written in error correction form, yields:
, , 1 , , , ,
1 0 1
q
nn
k k k k
c t c c t c c t c j c t j c c t
k j k
β x δ x
 

 

 


 
(7)
where
(1 )
cc

 
,
,0
(1 )
k
c
k
ct
.
Equation (7) is used for the MG estimation model, which allows us to restrict
some of the parameters inside the brackets to be zero so that we can obtain
a long run specification that looks like the Gordon model, as given in Equation
(4), but with fewer restrictions on the short run adjustment parameters across
cities. Among the items inside the brackets in Equation (7) are long run fixed
effects, αc, and note that
/
c c c
 
.
The coefficients (one for each city) before the brackets,
c, denote the speed of
the reversion to the long run, after short run deviations. The adjustment outside
the brackets is the momentum (or mean reversion), which will disappear if the
model is not explosive.
Shadow Banking and Property Market 379
For the PMG, we assume homogeneous long run relations; i.e., βck = βk for all
cities, but we continue to allow long run adjustment speeds and constant terms
to vary across cities. Then:
, , 1 , , , ,
1 0 1
q
nn
k k k k
c t c c t c t c j c t j c c t
k j k
β x δ x
 

 

 


 
(8)
The double summation term in Equations (7) and (8) can include lagged
changes in the dependent variable, that is, in R/P. We measure the level of
momentum with the sum of these coefficients. If there is momentum, we expect
the sums of the coefficients to be positive; a negative sum implies short run
mean reversion.
Note that the model requires long run rents and prices to grow at a constant rate
within each city in the long run
10
, but
c allows the growth rates to vary across
cities in the long run, which in turn causes the long run level of R/P to differ
across cities. The long run equilibrium is given by:
1/
nkk
c c c c
k
βx
 

(9)
Recall that the last term in Equation (9), which is the negative of the ratio of the
constant term in Equation (8) (short run constant term) divided by the correction
speed (which is negative), is the long run constant term, αc. This allows for
differences in risk premia and growth.
Since the PMG and MG estimations are autoregressive distributed lag (ARDL)
models, the series in the models must be stationary or cointegrated. Hence, we
run unit root tests on our time series.
We perform cointegration analysis tests developed by Westerlund (2007) to
confirm the presence of long-run relationships among the time series. If long
run cointegration exists, then we can find the long-run and short-run effects
among the variables by using the MG and PMG models. All of the variables
pass the tests (lengthy results are omitted). The Hausman test can be used to
check if a common long run coefficient is present (that is, if the null hypothesis
of the common coefficients between the MG and PMG is not rejected, then the
common coefficients should be adopted).
4.3 Specifics of Data and Models
Before discussing the tests, we provide some observations of the data and an
explanation of the tests. Figure 3 shows the average rent and house price across
10
We also try to relax this condition by adding a linear time trend, common to all cities
inside the brackets in Equation (8). The results are similar, and therefore omitted here.
380 Lai and Van Order
cities over time. A key observation from Figure 3A is that in the aggregate, the
slopes of the two series are close to each other, which means that despite rapid
growth, there might not be a bubble. Figure 3B shows the rent divided by house
price, which is placed on the left side of our regression. The plotted curve does
fluctuate but not nearly as much as in the US, which is plotted in Figure 3C,
and shows a large departure of the house price from rent during the upswings
and downturns around the Great Recession. Note in Figure 3B that the raw data
have rents relative to prices as values like 0.003, which means 30 basis points
per month (data are in monthly frequency). In our regressions, we multiply the
rent to house price by 1200, so that the above is now 3.60 (% per year). This
makes those return data comparable to our interest rate data, and we can test for
whether the coefficient of the real or nominal interest rate is equal to one.
Figure 3 Aggregate Prices and Rents
Panel 3A: Averages of Monthly Rent and Average Price
Panel 3B: Ratio of Average Monthly Rent to Average Price (in %)
15
17
19
21
23
25
4000
5000
6000
7000
8000
9000
10000
11000
01/01/2008
01/06/2008
01/11/2008
01/04/2009
01/09/2009
01/02/2010
01/07/2010
01/12/2010
01/05/2011
01/10/2011
01/03/2012
01/08/2012
01/01/2013
01/06/2013
01/11/2013
01/04/2014
01/09/2014
01/02/2015
01/07/2015
01/12/2015
01/05/2016
price_avg rent_avg
2.0
2.5
3.0
3.5
4.0
4.5
01/01/2008
01/06/2008
01/11/2008
01/04/2009
01/09/2009
01/02/2010
01/07/2010
01/12/2010
01/05/2011
01/10/2011
01/03/2012
01/08/2012
01/01/2013
01/06/2013
01/11/2013
01/04/2014
01/09/2014
01/02/2015
01/07/2015
01/12/2015
01/05/2016
RPratio_avg
Shadow Banking and Property Market 381
Panel 3C: Rent to House Price in the US
We group the cities in our sample into different categories bubble versus non-
bubble, coastal versus inland, and Tier 1 versus Tier 2 versus Other Tiers.
Bubble cities are those with a price growth over rent growth that is higher than
the 65-city average (2.59% for the 78-city average, and 2.75% for the 65-city
average). We classify coastal/inland cities because, according to Yang and Chen
(2014), for instance, there is a lower ownership rate in the eastern regions (i.e.
the coastal cities) because of the more expensive housing. This means that the
two groups of cities might be subject to different regimes. Lastly, Tier 1 cities
are made up of the four largest cities Beijing, Shanghai, Guangzhou, and
Shenzhen. Other smaller and more remote cities are classified as other tier cities.
We try two sets of long run fundamental models. Model A includes various
combinations of real interest rates, 5-year bonds, and 5-year bonds minus rent
growth rates. Model B uses real interest rates as the only long run variable,
which forces the model to converge to a strong (no money illusion) version of
the Gordon Model. In both models, the lagged dependent variables are included
to capture momentum. Interest rates are shown in Figure 4.
Other variables used in the models are the lagged shadow banking funds and
the lagged yield spread. Lagged 5-year bonds, and 5-year bonds minus rent
growth rates alternate in different models. We use both monthly and quarterly
data. With our monthly data, we use up to six lags, a maximum of half a year.
We also try to omit shorter lags for shadow banking funds to omit immediate
funding effects. For the quarterly data, we include up to four lags, which
represents one whole year. We also try to omit shorter lags for shadow banking
funds. All tests show that the PMG outperforms the MG results, thus implying
that all of the cities share the same coefficients for the long run fundamental
variables. Hence, we only show the PMG results here (the MG results are
available upon request). The models that work best are those with monthly data
with three lags, that is, lags from one month up to one quarter.
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1980 Q1
1981 Q3
1983 Q1
1984 Q3
1986 Q1
1987 Q3
1989 Q1
1990 Q3
1992 Q1
1993 Q3
1995 Q1
1996 Q3
1998 Q1
1999 Q3
2001 Q1
2002 Q3
2004 Q1
2005 Q3
2007 Q1
2008 Q3
2010 Q1
2011 Q3
2013 Q1
382 Lai and Van Order
Figure 4 Movement of Interest Rates and Housing Prices
Panel 4A: Various Rates (in %) Used for the Tests
Panel 4B: Plot of Real Interest Rates (in %), Housing Price (in RMB),
and Change in Housing Price (in %)
One of the concerns is that shadow banking is endogenous. In our model, the
dependent variables are by city, but the shadow banking variable is nationwide.
Hence, while the shadow banking variable might be influenced by national data,
it is unlikely that individual cities (65 to 78 of them) have influenced the
national level of shadow banking. As a result, we feel justified in assuming that
shadow banking is exogenous in the city-by-city equations.
-6
-4
-2
0
2
4
6
8
5Yr Government Bond 5Yr Corporate Bond
Real Rates
-5
0
5
10
15
0
2000
4000
6000
8000
10000
2007M3
2007M9
2008M3
2008M9
2009M3
2009M9
2010M3
2010M9
2011M3
2011M9
2012M3
2012M9
2013M3
2013M9
2014M3
2014M9
Housing Price (Primary Axis)
Real Rates (Secondaryd Axis)
Change in Price (Secondaryd Axis)
Shadow Banking and Property Market 383
5. Results
The results of Model A are shown in Panel A of Table 5, while those of Model
B are in Panel B. All of the variables, both long term and short term, have
coefficients with the correct signs as expected. All of the long term variables
are significant, thus implying that the proposed fundamental model works in
the case of the Chinese housing markets. The error correction coefficients,
which show the speed of reverting to the long term fundamental from the short
term deviation, range between about -0.16 and -0.21. This is very fast. Since
this is monthly data, the coefficients imply correction from short term deviation
takes about 5 to 6 months to return to a long run relationship. Short term lagged
yield spreads do not show a very strong and persistent effect, while 5-year
bonds and 5-year bonds minus rent growth are mostly significant. Lagged rent
to price ratios actually have negative effects which not only suggest that there
is no bubble but many mean reversions during the short run. Hence, our model
is not one of bubbles; rather it appears that prices chase rents and adjust rather
quickly. Note that this does not mean that house prices are stable; the stability
depends on the variations in rents.
All of the models show significant short run effects of shadow banking on price
changes.
11
We can also test whether the long run effects of the real rates on rent
to price is unity. For instance, the effects are around 1.2 in Model A, and close
to one in Model B in the first two panels and around a half in the third panel.
We note, however, the very strong long run effects of the nominal rates in Model
A. Hence, while our model is somewhat consistent with the Gordon Model of
the long run dependence on real rates, it is too ambiguous to warrant serious
consideration. Perhaps this is because the data set does not cover a very long
time period even though many cities are included.
We next compare and contrast how different cities react to the availability of
shadow banking funds. In particular, we group cities into bubble versus non-
bubble cities, coastal versus inland cities, as well as Tiers 1, 2, and others (see
Appendix A for the list of cities with various classifications). We identify bubble
cities in two ways. First, they have to have housing price growth rates higher
than the mean growth rate for the period of 2007-2014 (the housing boom
period). Second, they are the cities in which housing price growth minus rent
growth rates are above the mean for the period of 2007-2014. These three
categories overlap in that many bubble cities are also coastal cities, and the Tier
1 cities fall in the former two groups. The sum of the coefficients of three lags
of change in shadow banking funds as the short run variables for different city
classifications is shown in Table 7. Note that negative coefficients imply
positive effects on price relative to rent, as the dependent variables in the PMG
estimations are rent to price ratios.
11
Note that because the dependent variable is the reciprocal of the price to rent ratio,
negative shadow banking effect means increasing effects on house price growth.
384 Lai and Van Order
Table 5 Regression of Shadow Banking on Various Independent Variables
Part A
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Model 9
Model 10
House Price
-1.801
-1.485
-3.906*
0.615
0.859
0.81
-0.111
-1.640**
-0.923
-1.936***
Interest Rate
0.198
0.219
0.267
0.051
0.032
0.045
0.144*
0.188***
0.152**
0.203***
Building Sold
Total Residential
0.564***
Existing units
0.473***
Presale
0.545***
Floor Space
Completed
Total
0.545***
Residential &
Commercial
0.571***
Residential
0.566***
40-city Residential
0.186**
Floor Space Sold
Total Residential
0.529***
Existing units
0.470***
Presale
0.543***
Constant
0.052***
0.071***
0.064***
0.029*
0.048***
0.038**
0.133***
0.056***
0.062***
0.056***
Observations
100
70
70
100
100
100
72
100
100
100
Adj. R-Squared
0.667
0.633
0.672
0.551
0.5
0.529
0.125
0.694
0.67
0.693
Note : *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.
All variables except interest rates are differenced log values to obtain percentage changes of the values. Interest rates are differenced values.
384 Lai and Van Order
Shadow Banking and Property Market 385
Part B
Model 11
Model 12
Model 13
Model 14
Model 15
Model 16
Model 17
Model 18
Model 19
House Price
-1.363***
-1.787***
-1.439**
-1.540***
-1.988***
-1.125**
0.957
-2.041***
-1.659***
Interest Rate
0.170***
0.175***
0.106**
0.154***
0.188***
0.160***
0.040
0.189***
0.162***
Floor Space Started
40-city Residential
0.600***
Residential &
Commercial
0.585***
Land Area Purchased
Total
0.555***
40-Cities
0.516***
Real Estate
Investments
Total
0.544***
Land Transactions
0.573***
New Increase
0.604***
Residential-Total
0.548***
Residential-40 Cities
0.604***
0.577***
Constant
0.052***
0.071***
0.064***
0.029*
0.048***
0.038**
0.133***
0.056***
0.056***
Observations
100
70
70
100
100
100
72
100
100
Adj. R-Squared
0.667
0.633
0.672
0.551
0.5
0.529
0.125
0.694
0.693
Note : *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.
All variables except interest rates are differenced log values to obtain percentage changes of the values. Interest rates are differenced values
Shadow Banking and Property Market 385
386 Lai and Van Order
It can be seen from all three PMG models with the best explanatory power (i.e.
highest likelihood) that shadow banking has its largest impacts in the coastal
and Tier 1 cities. Exceptions are the results from the “bubblecities defined as
price growth rates higher than mean growth rates, which can nevertheless be
ignored. This is because “bubble cities (defined as cities with a house price
growth minus rent growth that is greater than the mean) would be more
meaningful since the rental markets in major cities are much larger, and
therefore rents tend to grow faster. Another observation is that the variations of
the sums of coefficients (i.e. maximum minus minimum) are in general larger
in non-bubble cities as well as non-tier cities. This implies that shadow banking
as a source of investment funds in these cities tends to vary a lot. The fact that
the means are generally higher in “bubble”, coastal, or Tier 1 cities is mostly
due to more investments drawn into those cities, where normal banking funds
would be available to only very large developers or State Owned Enterprises.
That is why other developers and investors have to rely on shadow banking. As
a result, more shadow banking funds of all forms would be available in those
cities, which subsequently push up housing prices. The sum of all short run
coefficients are provided in the Appendix B for reference.
Lai and Van Order (2017) mainly focus on testing whether the bubbles of the
housing markets in the US were explosive by checking if the residuals from the
regression estimates are highly autocorrelated, and the variances of the residual
autoregression equations differ between bubble and non-bubble cities. Since the
Chinese housing markets have also been described as having large bubbles that
have not exploded yet, we repeat their tests. In particular, we refer to PMG
Models A3, A4, and B3 as reported in Table 6, and attempt the autoregressive
modeling of the residuals with various lags. None of the equations show much
autocorrelation in the residuals (results are shown in Panel A of Table 8). This
and the low sums of the coefficients of lagged rent to price mean that bubbles
are not evident in the Chinese housing markets. Rather, there is momentum in
the short run that is nowhere near explosive. It should be noted that the Chinese
government has undertaken several policy changes to boost and curb the
markets at different stages of the market boom and bust. Such effects on
housing markets have been studied by, for instance, Cao et al. (2018). They
could be largely responsible for the lack of observed bubbles. Since we test the
effects of shadow banking on housing markets, and not the overall stabilization
policy, we do not try to separate stabilization policy from stabilization due to
the underlying market structure.
We further test if the variances of these models are different. Note that since the
residuals are not autocorrelated, the variances of these autoregressive models
are really the variances of the PMG models. We show the sums of the
coefficients of the lagged error terms in Panel B of Table 8. Also listed in the
same panel are the variances of the residuals from these autoregressive
equations. While large residual variances might be sources of bubbles in
housing markets, their small magnitudes show that there are no such sources of
bubbles in our sample cities. Nevertheless, bubble cities have smaller variances
Shadow Banking and Property Market 387
than non-bubble cities, coastal cities have larger variances than inland cities,
and Tier 1 cities have smaller variances than Tier 2 cites which are in turn
smaller than the other tier cities. This shows that our models are able to explain
those major cities (bubble and Tier 1 cities) with higher precision. To further
test if the variances from the models with different lags are indeed different, we
run the Goldfeld-Quandt test as shown in Panel D. Finally, we check the
differences in variances across city classifications with the Goldfeld-Quandt
test again, and the results are also shown in Panel D. Both show that these
models are different both across lags and cities, which imply that cities in
different categories do possess unique characteristics.
Table 6 Pooled Mean Group Estimation for Rent to Price Ratio
Panel A: Model A
Model A1
Model A2
Model A3
Model A4
Long run variables
Real Interest Rates
1.20***
1.20***
0.96***
1.20***
Tbond_5y
2.88***
31.2***
3.62***
15.7***
T5y_rentg
-10.68***
Short run variables
Error Correction
-0.17***
-0.17***
-0.14***
-0.14***
ΔR/Pt-1
-0.25***
-0.21***
-0.24***
-0.23***
ΔR/Pt-2
-0.17***
-0.15***
-0.15***
-0.153***
ΔR/Pt-3
0.015
-0.02
0.003
-0.00
Δ Shadow Bankt
-3.48**
-2.26*
-0.60
-0.60
Δ Shadow Bankt -1
-3.73*
-4.56***
-3.72***
-0.4.08***
Δ Shadow Bankt -2
-4.80***
-5.04***
-4.20***
-4.32***
Δ Shadow Bankt -3
-1.68**
-2.04***
-2.04***
-2.26***
ΔYield Spread t
0.48
0.36
-0.12
-0.12
ΔYield Spread t-1
0.48
0.36
-0.48
-0.48
ΔYield Spread t-2
-0.60
-0.96***
-1.32***
-1.32***
ΔYield Spread t-3
-1.32**
-1.32***
-1.32***
-1.32***
Δ5Yt
-1.68***
9.00***
7.68***
Δ5Yt-1
0.24
3.48***
2.64**
Δ5Yt-2
-0.84**
2.64**
1.80
Δ5Yt-3
-1.08***
-0.24
-0.72
Δ5Yt - RentGt
-2.26***
-10.2***
-9.12***
Δ5Yt-1 - RentGt-1
0.24
-3.48***
-2.64**
Δ5Yt-2 - RentGt-2
-0.84***
-3.48***
-2.76***
Δ5Yt-3 - RentGt-3
-0.72***
-0.72
-0.24
Constant
0. 37***
0. 35***
0. 27***
0. 22***
Observations
3,113
3,007
3,007
3,007
Number of groups
65
65
65
65
Log likelihood
16111
15980
16603
16606
Hausman Test
0.19
1.08
1.01
2.32
p-value
0.9102
0.5831
0.6022
0.5078
Note : *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.
388 Lai and Van Order
Panel B: Model B
Model B1
Model B2
Model B3
Long run variables
Real Interest Rates
0.96***
0.96***
0.48***
Short run variables
Error Correction
-0.16***
-0.16***
-0.13***
ΔR/Pt-1
-0.21***
-0.24***
-0.22***
ΔR/Pt-2
-0.15***
-0.16***
-0.15***
ΔR/Pt-3
-0.01
0.01
0.02
Δ Shadow Bankt
-2.16*
-3.48**
-0.60
Δ Shadow Bankt -1
-4.08***
-3.48*
-3.12**
Δ Shadow Bankt -2
-4.92***
-4.56***
-3.84***
Δ Shadow Bankt -3
-1.56***
-1.32*
-1.44**
ΔYield Spread t
0.24
0.38
-0.24
ΔYield Spread t-1
0.48
0.60
-0.36
ΔYield Spread t-2
-0.84**
-0.48
-1.08***
ΔYield Spread t-3
-1.44***
-1.32**
-1.44***
Δ5Yt
-1.44***
9.00***
Δ5Yt-1
0.48
3.36***
Δ5Yt-2
-0.60
2.76**
Δ5Yt-3
-0.84***
-0.36
Δ5Yt - RentGt
-2.04***
-10.08***
Δ5Yt-1 - RentGt-1
0.48*
-0.3.12***
Δ5Yt-2 - RentGt-2
-0.48**
-3.24***
Δ5Yt-3 - RentGt-3
-0.60***
-0.36
Constant
0. 47***
0. 49***
0. 40***
Observations
3,007
3,113
3,007
Number of groups
65
65
65
Log likelihood
15945
16092
16562
Hausman Test
1.13
0.07
0.24
p-value
0.2876
0.7973
0.622
Note : *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.
Table 7 Sum of Short Run Coefficients for Shadow Banking Loans
from PMG Three-lag Model (without Adjustment Rent to
Price Units)
Panel A: Model A3
Bubble Price
Bubble
Price-Rent
Coastal City
Non-
bubble
Bubble
Non-
bubble
Bubble
Non-
coastal
Coastal
Mean
-0.0106
-0.0063
-0.0066
-0.0109
-0.0080
-0.0105
Maximum
0.0224
0.0204
0.0205
0.0224
0.0224
0.0205
Minimum
-0.0606
-0.0498
-0.0606
-0.0498
-0.0474
-0.0606
No. of cities
37
28
32
33
44
21
(Continued…)
Shadow Banking and Property Market 389
(Panel A Continued)
Tier
Tier 1
Tier 2
Other Tiers
Mean
-0.0117
-0.0063
-0.0110
Maximum
-0.0022
0.0224
0.0186
Minimum
-0.0202
-0.0474
-0.0606
No. of cities
4
31
30
Panel B: Model A4
Bubble Price
Bubble
Price-Rent
Coastal City
Non-
bubble
Bubble
Non-
bubble
Bubble
Non-
coastal
Coastal
Mean
-0.0115
-0.0067
-0.0075
-0.0112
-0.0082
-0.0120
Maximum
0.0222
0.0197
0.0197
0.0222
0.0222
0.0174
Minimum
-0.0610
-0.0499
-0.0610
-0.0499
-0.0480
-0.0610
No. of cities
37
28
32
33
44
21
Tier
Tier 1
Tier 2
Other Tiers
Mean
-0.0121
-0.0070
-0.0115
Maximum
-0.0032
0.0222
0.0170
Minimum
-0.0200
-0.0480
-0.0610
No. of cities
4
31
30
Panel C: Model B3
Bubble Price
Bubble
Price-Rent
Coastal City
Non-
bubble
Bubble
Non-
bubble
Bubble
Non-
coastal
Coastal
Mean
-0.0087
-0.0060
-0.0051
-0.0099
-0.0071
-0.0085
Maximum
0.0274
0.0273
0.0274
0.0228
0.0273
0.0274
Minimum
-0.0536
-0.0478
-0.0536
-0.0490
-0.0490
-0.0536
No. of cities
37
28
32
33
44
21
Tier
Tier 1
Tier 2
Other Tiers
Mean
-0.0112
-0.0058
-0.0089
Maximum
-0.0001
0.0228
0.0274
Minimum
-0.0199
-0.0490
-0.0536
No. of cities
4
31
30
Notes: “Bubblecities are those with price growth greater than the mean during 2007-
2014.
“Bubble Price-Rentcities are those with price growth minus rent growth greater
than the mean during 2007-2014.
390 Lai and Van Order
Table 8 Residual Autoregressive Models from Model A4
Panel A Coefficients of the Autoregressive Models
Overall
Bubble (Price-Rent)
Non-Bubble (Price-Rent)
Residual
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
1 Lag
0.0176
0
-0.0099
0.0464*
-0.0178
-0.0629
-0.0078
0.015
0.0231
2 Lags
0.0293
0.0303
0.0251
0.0942
0.0371
-0.03
3 Lags
-0.0367
0.0018
-0.0633*
-0.1175*
-0.0123
0.1046*
4 Lags
0.0247
0.0009
0.0971
5 Lags
0.1249**
-0.0022
0.2860***
Obs
2,501
1,493
495
1,287
769
255
1,214
724
240
Adj. R2
-9.74E-05
0.000181
0.0051
0.00134
0.000642
0.0075
-0.00077
-0.00222
0.0696
Tier 1
Tier 2
Other Tiers
Residual
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
1 Lag
0.0358
-0.0094
0.1399
0.0500*
0.0192
-0.0935
-0.0113
-0.0158
0.0255
2 Lags
-0.0736
0.0115
0.0587*
0.0772
0.0132
0.003
3 Lags
-0.0236
0.1371
-0.0188
-0.0457
-0.0583
0.0439
4 Lags
-0.480**
0.1473**
-0.0514
5 Lags
0.0285
0.0135
0.220***
Obs
158
94
31
1,227
735
244
1,116
664
220
Adj. R2
-0.00513
-0.0279
0.0737
0.00162
0.000441
0.0197
-0.00077
-0.00089
0.0283
Coastal
Non- Coastal
Residual
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
1 Lag
-0.005
-0.046
-0.103
0.0294
0.0213
0.0573
2 Lags
-0.0332
0.1175
0.0591**
-0.0305
3 Lags
-0.0897*
-0.0766
-0.0137
0.0225
4 Lags
0.0112
0.0463
5 Lags
0.0838
0.158***
Obs
824
492
163
1,677
1,001
332
Adj. R2
-0.00119
0.00337
0.00228
0.000248
0.00182
0.015
Note: *, ** and *** denote significance at the 10%, 5% and 1% levels respectively.
390 Lai and Van Order
Shadow Banking and Property Market 391
Panel B: Sum of Coefficients and Variances of the Autoregressive Models
Overall
Bubble (Price-Rent)
Non-Bubble (Price-Rent)
Sum
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
Coeff.
0.0176
-0.0074
0.1718
0.0464
-0.056
-0.0875
-0.0078
0.0398
0.4808
Sig. Coef
0
0
0.1249
0.0464
-0.0633
-0.1175
0
0
0.1046
Variance
1.24E-06
1.22E-06
1.12E-06
1.12E-06
1.12E-06
1.06E-06
1.37E-06
1.33E-06
1.10E-06
Tier 1
Tier 2
Other Tiers
Sum
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
Coeff.
0.0358
-0.1066
-0.1631
0.05
0.0591
0.0988
-0.0113
-0.0609
0.2411
Sig. Coef
0
0
-0.4801
0.05
0.0587
0.1473
0
0
0
Variance
1.05E-06
1.35E-06
6.56E-07
1.09E-06
1.01E-06
1.08E-06
1.43E-06
1.44E-06
1.17E-06
Coastal
Non- Coastal
Sum
1 lag
3 lags
5 lags
1 lag
3 lags
5 lags
Coeff.
-0.005
-0.1689
0.0329
0.0294
0.0667
0.2538
Sig. Coef
0
-0.0897
0
0
0.0591
0
Variance
1.30E-06
1.39E-06
1.29E-06
1.21E-06
1.13E-06
1.02E-06
Notes: “Coeff.means sum of coefficients; “Sig. Coefmeans sum of significant coefficients.
Shadow Banking and Property Market 391
392 Lai and Van Order
Panel C: Goldfeld-Quandt Tests of Variance of Residuals from Autoregression Models
Overall
Bubble
(Price-Rent)
Non-Bubble
(Price-Rent)
Coastal
Non-
Coastal
Tier 1
Tier 2
Other Tiers
Model A4
1 & 5 lags
1.646***
1.678***
1.627***
1.789***
1.568***
2.205***
1.550***
1.6907
3 & 5 lags
4.584***
4.853***
4.143***
5.151***
4.324***
3.758***
5.063***
4.236***
1 & 3 lags
2.786***
2.892***
2.546***
2.879***
2.757***
1.705***
3.267***
2.506
Model B3
1 & 5 lags
1.677***
1.742***
1.630***
1.796***
1.616***
2.223***
1.584***
1.724
3 & 5 lags
4.626***
5.023***
4.071***
4.973***
4.484***
3.647***
5.224***
4.210***
1 & 3 lags
2.758***
2.884***
2.498***
2.769***
2.775***
1.641***
3.299***
2.442
Model A3
1 & 5 lags
1.660***
1.703***
1.634***
1.801***
1.584***
2.217***
1.565***
1.705
3 & 5 lags
4.551***
4.750***
4.148***
5.107***
4.296***
3.746***
4.999***
4.240***
1 & 3 lags
2.741***
2.790***
2.539***
2.837***
2.713***
1.690***
3.194***
2.488
392 Lai and Van Order
Shadow Banking and Property Market 393
Panel D: Goldfeld-Quandt Tests of Variance of Residuals of Different City
Classifications
Bubble (p-r)
vs. non-
Bubble (p-r)
Coastal vs.
non-
Coastal
Tier 1 vs.
Tier 2
Tier 1 vs.
Other
Tiers
Tier 2 vs.
Other
Tiers
Model A3
1 lag
1.3135***
2.1625***
7.4781***
5.2374***
1.4278***
3 lags
1.2602***
2.4586***
10.5935***
6.8152***
1.5544***
5 lags
1.1471
2.5708***
5.6036***
4.6272***
1.211
Model A4
1 lag
1.2959***
2.1877***
7.5468***
5.2216***
1.4453***
3 lags
1.2569***
2.4957***
10.7368***
6.8087***
1.5769***
5 lags
1.1064
2.6060***
5.6016***
4.6322***
1.2093*
Model B3
1 lag
1.3369***
2.1429***
7.3524***
5.0559***
1.4542***
3 lags
1.2511***
2.3823***
10.3197***
6.5190***
1.5830***
5 lags
1.0836
2.3769***
5.1327***
4.3800***
1.1718
Notes: The Goldfeld-Quandt Test is a test for statistical differences between two
fundamental equations.
*, ** and *** denote significance at the 10%, 5% and 1% levels respectively
(compared to an F-value of 1.3).
6. Conclusions
This is perhaps the first study that incorporates the availability of shadow
banking funds on real estate prices in China, as well as using PMG estimations
of house price dynamics to analyze the role of fundamentals and adjustment to
them. The question lies in determining their causality. The issue is similar to the
case in the US on whether the increase in private label securities is the cause,
or result, of the house price bubble. That we use aggregate shadow banking data
but local house prices is an attempt to manage the endogeneity of shadow
banking policy with respect to economy-wide variables. The strong link
between shadow banking and housing price movements suggests important
implications for the effects of a collapse in the shadow banking market.
We further analyze pricing dynamics by classifying cities into bubble versus
non-bubble, coastal versus inland, and Tier 1 versus Tier 2 and other tier cities.
We find that shadow banking indeed helps to improve the liquidity of
developers who cannot easily borrow from the major banking channels,
particularly in non-bubble cities, which are mostly also inland cities/non-Tier 1
cities. This is particularly remarkable as anecdotal evidence suggests that
shadow banking is an essential source of funding in second tier cities. As for
the long run, we find that housing in China is priced like a growth stock (high
P/E ratio) with different expected growth rates across cities, as in Lai and Van
Order (2018), and cities tend to share common long run fundamentals and
394 Lai and Van Order
adjust relatively quickly to deviations from them, without bubbles. Prices
appear to be rapidly chasing growing rents. Indicators of shadow banking
activity have a positive effect on house price growth. The data are consistent
with the notion that a one-percentage point increase in the real rate leads to
approximately a one-percentage point change in the rent to price ratio, but the
data are too thin to take this argument seriously.
That there is no evidence of bubbles does not mean Chinese property is not
risky. Growth stocks are risky because small changes in expected growth of
earnings (in this case rents) can lead to large changes in value. Alternatively,
one might argue that there is a rent bubble, or perhaps a lending bubble, that is
causing high prices. However, rents and lending activity are not prices of traded
assets; they are at best considered as factors in risk assessment. Fear of shadow
banking collapse is reasonable; nevertheless, the property market in China is
risky but not doomed to crash.
Acknowledgement
We thank Brent Ambrose and John Glascock and the participants at the
American Real Estate and Urban Economics Association 2017 Annual
Conference and the Fellows Forum at the 2019 Asian Real Estate Society
Annual Conference for their helpful comments. This research is funded by the
Research Committee of the University of Macau with grant number: MYRG
2015-00085-FBA.
References
Black, P.A. and Hoesli, M. (2006). House Prices, Fundamental and Bubbles.
Journal of Business Finance and Accounting, 33, 1535-1555.
Cai, H., Henderson, V. and Zhang, Q. (2013). China's Land Market Auctions:
Evidence of Corruption? The RAND Journal of Economics, 44(3): 488-521
Cao, X., Huang, B. and Lai, R.N. (2018). The Impact of Exogenous Demand
Shock on the Housing Market: Evidence from the Home Purchase Restriction
Policy in the People’s Republic of China, Asian Development Bank Institute
(ADBI) Working Paper Series, No. 824 (March 2018).
Chan, I., Lee, S. and Woo, K. (2001). Detecting Rational Bubbles in the
Residential Housing Markets in Hong Kong. Economic Modelling, 18, 61-73.
Shadow Banking and Property Market 395
Chang, Y., Cutts, A. and Green, R. (2005). Did Changing Rents Explain
Changing House Prices During the 1990s? Geroge Washington University
School of Business Working Paper.
Coleman, M.I., LaCour-Little, M. and Vandell, K.D. (2008). Subprime Lending
and the Housing Bubble: Tail Wags Dog? Journal of Housing Economics, 17(4):
272-290
Deng, L., Shen, Q. and Wang, L. (2009). Housing Policy and Finance in China:
A Literature Review. Retrieved from
http://www.chinaplanning.org/Publications/Lan Deng - Housing Policy and
Finance In China.pdf
Elliott, D. J. and Yan, K. (2013). The Chinese Financial System: An
Introduction and Overview. John L. Thornton China Center at Brookings.
Elliott, D., Kroeber, A. and Qiao, Y. (2015). Shadow Banking in China: A
Primer. Economics Studies at Broookings. The Brookings Institution.
Financial Stability Board. (2015). Global Shadow Banking Monitoring Report
2015 (November 2015).
Glaeser, E., Huang, W., Ma, Y. and Shleifer, A. (2017) A Real Estate Boom with
Chinese Characteristics. Journal of Economic Perspective, 31(1): 93-116
Glaeser, E.L. and Nathanson, C. (2017) An Extrapolative Model of House Price
Dynamics. Journal of Financial Economics, 126(1): 147-170
Hsu, S. and Li, J. (2015). The Rise and Fall of Shadow Banking in China.
Political Economy Research Institute, Working Paper Series Number 375.
University of Massachusetts Amherst.
Hwang, M., Quigley, J. and Son, J. (2006). The Dividend Pricing Model: New
Evidence from the Korean Housing Market. Journal of Real Estate Finance
and Economics, 32, 205-228.
Lai, R.N. and Van Order, R. (2010). Momentum and House Price Growth in
the U.S: Anatomy Of A Bubble. Real Estate Economics, 38(4), 753-773.
Lai, R.N. and Van Order, R. (2017). U.S. House Prices over the Last 30 Years:
Bubbles, Regime Shifts and Market (In)Efficiency. Real Estate Economics,
45(2), 259-300.
Lai, R. N. and Van Order, R. (2018). A Tale of Two Countries: Comparing the
US and Chinese Bubbles Housing Markets, Journal of Real Estate Finance and
Economics, forthcoming.
396 Lai and Van Order
Ott, H. (2014). Will Euro Area House Prices Sharply Decrease? Economic
Modelling, 42, 116-127.
Peng, L. and Thibodeau, T.G. (2012). Government Interference and the
Efficiency of the Land Market in China, Journal of Real Estate Finance and
Economics, 45(4), 919-938.
Pesaran, M.H., Shin, Y. and Smith, R.P. (1997). Pooled Estimation of Long-
Run Relationships in Dynamic heterogeneous Panels. Working Paper
Pesaran, M.H., Shin, Y. and Smith, R.P. (1999). Pooled Mean Group Estimation
of Dynamic Heterogeneous Panels. Journal of the American Statistical
Association, 94, 621-634.
Ren, Y., Xiong, C. and Yuan, Y. (2012). House Price Bubbles in China. China
Economic Review, 23, 786-800.
Sharma, S.D. (2014). Shadow Banking, Chinese Style. Economic Affiars, 34(3),
340-352.
Sheng, A., Edelmann, C., Sheng, C. and Hu, J. (2015). Bringing Light Upon
The Shadow: A Review of the Chinese Shadow Banking Sector. Oliver Wyman
& Fung Global Institute.
Westerlund, J. (2007). Testing for Error Correction in Panel Data. Oxford
Bulletin of Economics and Statistics, 69, 709748.
Wheaton, W. and Nechayev, G. (2008). The 1998-2005 Housing ‘Bubble’ and
the Current ‘Correction’: What’s Different this Time? Journal of Real Estate
Research 30:1-26.
Wu, J., Gyourko, J. and Deng, Y. (2012). Evaluating Conditions in Major
Chinese Housing Markets. Regional Science and Urban Economics, 42(3):
531-543
Yang, Z. and Chen, J. (2014). Housing Reform and the Housing Market in
Urban China. In Housing Affordability and Housing Policy in Urban China (pp.
15-43). Springer Briefs in Economics.
Shadow Banking and Property Market 397
Appendices
Appendix A List of 78 Cities in the Sample
City
Bubble
Coastal
Tier
City
Bubble
Coastal
Tier
Anqing
Shantou
*
*
Baoding
*
Shaoxing
Beihai
*
2
Shenyang
*
2
Beijing
*
1
Shenzhen
*
*
1
Bengbu
Shijiazhuang
2
Changchun
*
2
Suzhou
*
Changde
Taiyuan
2
Changsha
2
Taizhou
Changzhou
Tangshan
*
Chengdu
2
Tianjin
*
*
2
Chongqing
*
2
Urumqi
*
Dalian
*
2
Weifang
*
Dongguan
*
*
Weihai
*
Foshan
*
Wenzhou
*
*
2
Fuzhou
*
*
2
Wuhan
*
2
Guangzhou
*
*
1
Wuxi
*
2
Guiyang
2
Xiamen
*
*
2
Haikou
*
*
2
Xi'an
*
2
Hangzhou
2
Xining
2
Harbin
2
Xuzhou
*
Hefei
*
2
Yancheng
*
*
Huzhou
Yangzhou
*
Jiaxing
Yantai
*
Jilin
*
Zhengzhou
*
2
Jinan
*
2
Zhuhai
*
Jinhua
Zibo
Kunming
2
Shantou
Lanzhou
2
Shaoxing
*
Nanchang
2
Shenyang
Nanchong
Shenzhen
Nanjing
*
2
Shijiazhuang
Nanning
2
Suzhou
Nantong
*
Taiyuan
*
2
Ningbo
*
*
2
Taizhou
*
Qingdao
*
*
2
Tangshan
Qinhuangdao
*
Tianjin
Quanzhou
*
Urumqi
*
2
Rizhao
Weifang
Shanghai
*
*
1
Weihai
*
*
Note: Due to missing data, our tests are also based on 65 cities, which do not include the
13 cities on the bottom right of the table. Shaded are the four Tier 1 cities.
398 Lai and Van Order
Appendix B Sum of Significant Short-run Coefficients for Individual
Cities from PMG Estimation of Model A4
Bubble versus Non-bubble Cities (classified by price growth minus rent growth)
Panel A: Non-Bubble Cities
City
Error
Correct-
ion
Sum of
Δ(R/P)
Sum of Δ
Shadow
Bank
Sum of
Δ
Spread
Sum of
Δ5Y
rate
Sum of Δ
(5Y Rate
– Rent
Growth)
Constant
Changsha
-0.1126
-0.7644
-0.0063
-0.0015
0.0024
-0.0078
0.0029
Guiyang
-0.1466
-1.3375
-0.0095
-0.0217
0.0643
-0.0651
0.0046
Yantai
-0.2121
-0.7142
-0.0105
-0.0073
-0.0014
-0.0006
0.0025
Beihai
-0.0096
0.2451
-0.0275
-0.0012
0.0271
-0.0297
-0.0012
Bengbu
-0.2321
-0.429
0.0151
-0.0043
0.0104
-0.0159
0.004
Chengdu
-0.0903
-0.0528
-0.0199
0.0013
0.0033
-0.0073
0.0013
Dalian
-0.2779
0.0789
0.0174
-0.0006
0.0095
-0.011
0.0065
Harbin
-0.3119
-0.7486
-0.0094
-0.0193
0.0174
-0.0195
0.0091
Jinhua
-0.1282
-0.8079
-0.0232
0.0041
0.0055
-0.0124
0.0005
Kunming
-0.2546
-0.9534
-0.0107
-0.0037
0.0144
-0.0159
0.0044
Lanzhou
-0.0256
-0.1349
-0.006
-0.0035
0.014
-0.0164
0.0002
Nanchang
-0.0742
-0.7484
0.0036
0.0075
0.0127
-0.0182
0.0011
Nanchong
-0.2512
-0.0579
0.017
-0.0013
0.007
-0.0136
0.0037
Nanning
-0.0902
-0.2988
0.0002
-0.0082
0.0062
-0.0094
0.0023
Nantong
-0.0352
-1.1987
-0.0099
-0.0015
0.0139
-0.013
0.0005
Qinhuangdao
-0.5426
0.0583
0.0005
-0.0011
-0.0195
0.018
0.0054
Quanzhou
-0.0601
-1.2077
-0.0090
-0.0081
0.0258
-0.0268
0.0016
Shijiazhuang
0.0099
-0.6356
0.0197
-0.0096
0.0042
-0.0041
-0.0003
Weihai
-0.2452
0.0892
-0.061
0.0027
0.0158
-0.0173
0.0015
Zibo
-0.2032
-0.4254
-0.0021
0.0014
0.0026
-0.0067
0.0022
Anqing
-0.0725
0.4811
-0.0084
-0.0006
-0.0085
0.009
0.0007
Changde
-0.2479
0.3327
-0.0024
0.0002
0.0112
-0.0162
0.0058
Changzhou
-0.0948
-0.8634
-0.0270
0.0019
0.0161
-0.0228
0.0021
Hangzhou
-0.2147
0.9152
-0.0244
0.0015
0.0007
-0.0075
0.0014
Huzhou
-0.1731
0.0787
-0.0254
-0.0034
0.0005
-0.0006
0.0021
Jiaxing
-0.0796
-0.3421
-0.0099
-0.0028
-0.0109
0.0048
0.0015
Rizhao
-0.1669
-0.4414
-0.0233
-0.0016
0.001
-0.0031
0.0006
Shaoxing
-0.4128
-0.6861
0.0025
-0.0044
-0.0038
-0.003
0.0036
Taiyuan
-0.1203
-0.1278
0.0055
-0.0023
0.0107
-0.0118
0.0022
Taizhou
-0.1686
-1.3656
0.0123
-0.0105
0.021
-0.0195
0.0016
Xining
-0.4072
-0.358
0.0161
-0.0144
0.0128
-0.0154
0.0114
Zhuhai
-0.0418
-0.6482
-0.0243
-0.0012
-0.0178
0.0157
0.0004
Shadow Banking and Property Market 399
Panel B: Bubble Cities
City
Error
Correct-
ion
Sum of
Δ(R/P)
Sum of
Δ
Shadow
Bank
Sum of
Δ
Spread
Sum of
Δ5Y
rate
Sum of Δ
(5Y Rate
– Rent
Growth)
Constant
Qingdao
-0.0560
0.1124
-0.0010
0.0018
0.0033
-0.0070
0.0005
Changchun
-0.0395
-0.3366
-0.0152
-0.0011
0.0072
-0.0105
0.0002
Jinan
-0.1337
-0.1401
-0.0204
0.0011
0.0169
-0.0194
0.0013
Shantou
-0.1559
-1.8098
-0.0499
0.0072
0.0363
-0.0476
0.0036
Suzhou
-0.0422
-0.3962
-0.018
-0.0109
0.0301
-0.0286
0.0005
Weifang
-0.0718
-0.7851
-0.0162
-0.0002
0.0024
-0.0065
0.0008
Xiamen
-0.0322
-0.3512
-0.012
-0.0049
-0.0077
0.0050
-0.0001
Xi'an
-0.0685
-0.6129
-0.0073
-0.0032
0.0346
-0.0354
0.0014
Chongqing
-0.3375
-1.4195
-0.0228
-0.0083
0.0803
-0.0849
0.0076
Dongguan
-0.0893
-0.5404
0.0161
-0.0071
0.0207
-0.0277
0.0025
Foshan
-0.2111
-0.7477
-0.0293
-0.0023
-0.0305
0.0275
0.0042
Fuzhou
-0.1081
-0.3706
-0.0164
-0.0004
0.0074
-0.0092
0.0011
Guangzhou
-0.0185
-0.0815
-0.0032
-0.0022
0.0107
-0.0072
-0.0002
Haikou
-0.0304
-0.4868
0.0077
-0.0081
0.0292
-0.0291
0.0013
Jilin
-0.1305
-0.3070
-0.0055
0.0027
0.0052
-0.0091
0.0025
Nanjing
-0.2480
-0.6407
0.0050
-0.0126
0.0224
-0.0221
0.0026
Ningbo
-0.0664
0.4866
-0.0137
0.0029
0.0007
-0.0038
0.0005
Shanghai
-0.0495
-0.4857
-0.0200
-0.0028
0.0068
-0.0093
0.0000
Shenyang
-0.0415
-1.1229
0.0222
-0.0077
0.0262
-0.0272
0.0016
Shenzhen
-0.0101
0.5447
-0.0166
0.0096
-0.0343
0.0323
-0.0002
Tangshan
0.0073
0.1926
0.0164
-0.0059
0.0138
-0.0148
-0.0004
Tianjin
-0.0133
-0.2947
-0.0196
0.0079
0.0115
-0.0160
-0.0003
Wenzhou
-0.1267
0.0563
-0.0043
-0.0020
-0.0088
0.0066
0.0003
Wuhan
-0.2260
-0.1896
-0.0100
0.0000
0.0016
-0.0078
0.0046
Urumqi
-0.1436
-0.7475
-0.0387
-0.0027
0.0122
-0.0159
0.0018
Wuxi
-0.2133
-0.4083
-0.0078
-0.0070
0.0172
-0.0169
0.0033
Xuzhou
-0.1794
-0.6709
-0.0101
-0.0068
0.0214
-0.0213
0.0023
Zhengzhou
-0.0961
-0.1679
-0.0018
-0.0050
0.0167
-0.0175
0.0016
Baoding
-0.2461
-0.2766
-0.0362
0.0078
-0.0069
1E-04
0.0021
Beijing
-0.0725
0.4200
-0.0087
0.0012
0.0090
-0.0127
0.0004
Hefei
-0.1805
-0.7416
-0.0480
0.0005
0.0308
-0.0293
0.002
Yancheng
-0.2243
-0.1542
0.0059
-0.0024
0.0116
-0.0141
0.0037
Yangzhou
-0.1106
0.3070
0.0088
-0.0015
-0.0515
0.0459
0.0014
... 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 affecting China's housing prices pre-and after 2012 with shadow banking perspective. ...
... 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 affecting China's housing prices pre-and after 2012 with shadow banking perspective. ...
... Thus, we include r and loan referring to interest rate on loans of five or more years and the amount of credit loans respectively. 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. ...
Article
Full-text available
The recent surge in property values in China has been similar to the surge in the U.S before the crash in 2007. This raises concerns about whether China is destined to have a crash as well. We estimate similar models of property values for the two countries, in order to compare price dynamics side by side. We find little in common between them. In the U.S. the adjustment process appears prone to “bubbles” in the sense of strong momentum, but Chinese prices have been generally mean reverting, without momentum. This suggests that the recent price rise in China has had more to do with scarcity than with irrational exuberance.
Chapter
Full-text available
The housing market in Urban China has experienced many important changes since the Communists came to power in 1949. In general, we can divide China’s housing reform into three significant stages: the pre-reform process (between 1949 and 1978), the reform process (from 1978 to 1998) and the post-reform process (after 1998).
Article
The cause of the "housing bubble" associated with the sharp rise and then drop in home prices over the period 1998-2008 has been the focus of significant policy and research attention. The dramatic increase in subprime lending during this period has been broadly blamed for these market dynamics. In this paper we empirically investigate the validity of this hypothesis vs. several other alternative explanations. A model of house price dynamics over the period 1998-2006 is specified and estimated using a cross-sectional time-series data base across 20 metropolitan areas over the period 1998-2006. Results suggest that prior to early 2004, economic fundamentals provide the primary explanation for house price dynamics. Subprime credit activity does not seem to have had much impact on subsequent house price returns at any time during the observation period, although there is strong evidence of a price-boosting effect by investor loans. However, we do find strong evidence that a credit regime shift took place in late 2003, as the GSE's were displaced in the market by private issuers of new mortgage products. Market fundamentals became insignificant in affecting house price returns, and the price-momentum conditions characteristic of a "bubble" were created. Thus, rather than causing the run-up in house prices, the subprime market may well have been a joint product, along with house price increases, (i.e., the "tail") of the changing institutional, political, and regulatory environment characteristic of the period after late 2003 (the "dog").