Private and Public Discrepancy:
the Anatomy of Valuation in Market
By
-MAssACHUSET7S
INSTMOfE.
OF TECHNOLOGY
Jiayin Zhang
B.S., Tsinghua University (2005)
M.A., Tsinghua University (2008)
M.S., Massachusetts Institute of Technology (2013)
MAY
LIBRARIES
Submitted to the Sloan School of Management
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy in Management
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2014
C 2014 Massachusetts Institute of Technology. All rights reserved.
Signature redacted
Signature of Author:
J
Certified By:
MIT Sloan School of Management
May 9, 2014
Signature redacted
Ezra W. Zuckerman Sivan
Professor of Technological Innovation, Entrepreneurship, and Strategic Management
Thesis Supervisor
Accepted By:
Signature redacted
'I
5 2014
Ezra W. Zuckerman Sivan
Chair, PhD Program
MIT Sloan School of Management
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2
Private and Public Discrepancy:
The Anatomy of Valuation in Market
By
Jiayin Zhang
Submitted to the MIT Sloan School of Management on May 2,
2014 in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Management
ABSTRACT
The popular explanations of market bubbles, based on the classical economic
assumption that market prices incorporate market participants' private valuations,
argue that bubbles are caused by the collective delusion of individual participants who
have false beliefs of fundamental values. An emerging institutionalist approach of
research, in contrast, argues that bubbles can be produced even if rational investors
collectively have the resources to correct mispricing, implying that market price
doesn't necessarily incorporate true private beliefs. The primary analysis of my
dissertation tests the two competing explanations in the context of the Beijing real
estate market, where the collective delusion explanation seems particularly appropriate
since amateur participants dominate this market. However, my analysis of the unique
survey data shows systematic and precise evidence that bubble-era prices do not equal
the mean of private valuations, which strongly supports the institutionalist approach.
The second analysis of my dissertation is to answer the question that how market price
has been driven up in the circumstance that the majority of market participants regarded
the properties as overpriced. My results shed light on a novel explanation in the
institutionalist approach by showing that the market was driven by market participants
who were drawing incorrect inferences about other participants' beliefs-they
overestimated the degree of others' support to the price, though they personally did not
endorse the price. They therefore chose "dancing"-speculating but exiting from the
market before the burst of the bubbles-as the optimal strategy, but it is actually
suboptimal in such a situation and fuels the bubble. The third analysis of my
dissertation is to understand the logics of market participants' behaviors in depth by
examining their opinions on "popular theories"-the theories or models that were
widely used to justify the bubble-era price. My analysis shows that, first, these popular
theories reflect market participants' perceptions of the institutional influences on the
real estate market in this country. Second, market participants' perceptions of the
stability of the social and political institutions led them to be tolerant of market
inefficiency, though they had fully realized such inefficiency. Theoretical and policy
implications are discussed.
Thesis Supervisor: Ezra W. Zuckerman Sivan
Title: Professor of Technological Innovation, Entrepreneurship, and Strategic
Management
3
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4
Acknowledgements
I would like to express the deepest appreciation to my advisor and doctoral thesis
committee chair, Professor Ezra Zuckerman Sivan, who has the attitude and substance
of both a genius and a great educator. I am greatly indebted to him, for his superior
guidance on developing constructive, meaningful and delicate research. I have been
continually encouraged and inspired by him, not only in regard to research and
scholarship, but also to becoming a better and stronger person in general. He has
incredibly important influence on the path through which I turned from a newly
graduate to a junior scholar.
I owe my gratitude to the doctoral thesis committee members, Professor Yasheng
Huang, whose vision and supports are crucial for me to complete my dissertation;
Professor Ray Reagans, who provided extremely helpful suggestions at a number of
critical stages during my study at Sloan; and Professor Emilio Castilla, who has been a
prominent model for me of developing faultless research. Moreover, I would like to
thank Professor Kate Kellogg, for teaching me the hands-on experience of ethnography.
I am indebted to my colleagues, and the following persons who provided invaluable
comments on my research: Professor Roberto Fernandez, Professor Susan Silbey,
Professor Elena Obukhova, Professor Rodrigo Canales, Professor Christopher Yenkey,
Professor Roman Galperin, Professor Catherine Turco, Professor Evan Apfelbaum,
Professor Yanbo Wang, and the participants of the MIT Economic Sociology Working
Group, the MIT-Harvard Economic Sociology Seminar, the annual meetings of
American Sociological Association, and the annual meetings of Academy of
Management. I would like to thank the MIT Economic Sociology Program, the MIT
Center for International Studies, and the Chiang Ching-kuo Foundation for
International Scholarly Exchange for their financial supports on my dissertation.
In addition, I would like to thank Professors Jar-der Luo and Yuan Shen of Tsinghua
University and Professor Michael Sobel of Columbia University for the training and
support I have received before coming to the US. I also wanted to thank my parents for
their consistent encouragement and support during the past years. They have always
been my greatest backing, and I owe them too much. I would like to thank my husband,
Duo Li, with whom I started a family life in Cambridge, and who has been my priceless
companion and thoughtful partner since then.
5
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6
TABLE OF CONTENT
11
Chapter 1 Introduction....................................................................................
-----------------...............
........
12
15
17
......
19
M otivation .......................................................................................
Three Objectives..........................................................................
Agenda......................................................................................................--------.................
Chapter 2 Theories ........................................................................
19
Theories of Market Valuation ............................................................................
.19
........
.............................................................................
hypothesis
arket
m
Efficient
20
Collective Delusion Approach......................................................................................
21
Approach.............................................................................................
Institutionalist
24
Pluralistic Ignorance ......................................................................
Chapter 3 Background: China Housing Market ...............................................
---------------------....................
H istory ......................................................................................
Boom.........................................................................
Market
the
Factors that Drive
The Government Policy Intervention ..........................................................................
29
29
30
32
Chapter 4 Qualitative Evidence on the Private-and-Public Discrepancy......... 35
.......... 35
...............................................................................
Fieldwork
37
Public Valuation and Private Valuations in the Market..........................................
41
------------------------.......................
Hypotheses.............................................................--..........
Chapter 5 Surveys to Investigate Market Participants' Valuations ................. 43
- 44
Basic Design of the Survey.........................................................................
44
.......
.....
Obtain valuation gaps ............................................................................
45
Obtain opinions on popular theories.............................................................................
Obtain opinions on housing market in core region vs. peripheralregion .................... 47
48
Survey Platform............................................................................
49
.........................................
Groups
Reference
of
Complication
Pilot Survey and the
51
.........................................
Major Survey with Improvements.......................
participants...............................................................51
market
of
Clarify the categories
53
Obtain opinions on the most influential category .......................................................
category.................53
influential
most
the
in
valuation
Obtain opinions on the popular
Obtain opinions on the popular valuationwithin the respondent's category............... 54
Chapter 6 Primary Gap: Price Does Not Aggregate Private Valuations ......... 57
57
Results on Market Participants................................................................................
59
Valuation............................................
Private
Gap between Price and the Average
60
.....
..
Robustness Checks .........................................................63
Chapter 7 Secondary Gap: Pluralistic Ignorance...............................................
9 . .................. . .. . . .. . . .. . . . 63
mind)
participants'
market
(in
Who is driving market price
............ 64
Private vs. Popular Valuation...............................................................
65
........................
price
drive
who
those
and
participants
Pluralisticignorance between
66
Pluralisticignorance among investors........................................................................
67
....
Robustness Checks .................................................................
69
Chapter 8 Reexamination of "Popular Theories"...............................................
70
Boom...............................................................
Market
the
Two Models that Explain
70
The rise of afinanciallogic...........................................................................................
7
The business model createdby the central and local governments .............................
Market Participants' Perceptions of the Popular Models .........................................
Chapter 9 D iscussion and Conclusion................................................................
73
76
79
References..................................................................................................................83
Figures and Tables................................................................................................
91
Figures ...............................................................................................................................
Tables...............................................................................................................................
91
104
A ppendix A : Interview Protocols ..........................................................................
Interview protocol with housing m arket participants.................................................
Interview protocol with Real Estate Developers..........................................................
Interview protocol with Real Estate Brokers...............................................................
133
133
136
137
A ppendix B : Survey Q uestionnaires .....................................................................
139
Pilot Survey Questionaire ..............................................................................................
M ajor Survey Questionaire ...........................................................................................
139
156
8
INDEX OF FIGURES
Figure 1 The relationships between price, value and the mean of private valuations, implied
91
by three different theoretical approaches............................................................................
92
............................................................................
models
Figure 2 Different institutionalist
93
1991-2011..............
China,
in
housing
residential
Figure 3 Total sale of commercialized
from
government
central
the
by
launched
policies
Figure 4 Distribution of real estate market
.....-----------------................----- 94
2003 to 2013 ............................................................................
and perceived
valuation
popular
real
Figure 5 The relationships between market price,
95
............................................................
popular valuation, predicted by the two hypotheses
96
..............................................
Figure 6 Beijing districts and the distribution of population
97
...
....
Figure 7 The ring roads of Beijing.......................................................................
98
Figure 8 Market price does not aggregate private valuations ..............................................
99
.................
optimistic
are
Figure 9 Market participants believe that those who drive price
100
............................
Figure 10 Investors' opinions on the valuation popular among investors
101
Figure 11 The economic model created by the central and local governments ....................
102
Figure 12 Major logics behind the popular theories .............................................................
Figure 13 Private vs. (perceived) public agreements with the logics ................................... 103
INDEX OF TABLES
104
Table 1 Summ ary of "popular theories" .............................................................................
central
the
by
launched
market
estate
real
to
pertinent
Table 2 Summary of the policies
105
government, 2003-2013 ..................................................................
106
............................................
group
occupational
Table 3 The targeted interviewees in each
107
them...
in
selected
(branches)
bank
the
and
Table 4 The administrative districts of Beijing
108
....................................
region
peripheral
Table 5 The characteristics of the core region vs.
109
region)............
core
(for
groups
Table 6 The distribution of the respondents' occupational
110
region)..
peripheral
(for
groups
Table 7 The distribution of the respondents' occupational
111
Table 8 Categories of market participants ........................................................................
Table 9 The Correlations between different categories of market participants (dummy
112
variables; l=Yes) and individual characteristics ..................................................................
113
region)............
Table 10 Private valuations, by categories of market participants (for core
114
region)...
Table 11 Private valuations, by categories of market participants (for peripheral
115
Table 12 The factors that influence private valuations .........................................................
Table 13 Most influential types in shaping the market prices in short-term, identified by each
116
..........
type (for core region).....................................................................
each
by
identified
long-term,
in
prices
market
the
shaping
in
types
Table 14 Most influential
117
type (for core region)...........................................................................
each
by
identified
short-term,
in
prices
market
the
shaping
in
Table 15 Most influential types
... 118
type (for peripheral region).....................................................................
each
by
identified
long-term,
in
prices
market
the
Table 16 Most influential types in shaping
119
type (for peripheral region)..................................................................
participants
market
of
categories
by
Table 17 Private valuations and perceived valuations,
. ... 120
(for core region).......................................................................-----..
9
Table 18 The average of the gaps between private valuations and popular valuations of the
most influential category (for core region)...........................................................................
121
Table 19 Private valuations and perceived valuations, by categories of market participants
(for peripheral region)...........................................................................................................
122
Table 20 The average of the gaps between private valuations and popular valuations of the
most influential category (for peripheral region)..................................................................
123
Table 21 The average of the gaps between private valuations and popular valuations within a
category (for core region) .....................................................................................................
124
Table 22 The average of the gaps between private valuations and popular valuations within a
category (for peripheral region)............................................................................................
125
Table 23 Regressions of private valuation and popular valuation on categories of market
particip ants............................................................................................................................
126
Table 24 The factors that influence the popular valuations within self-category ................. 127
Table 25 Market participants' perception of "popular theories", raw data........................... 128
Table 26 Market participants' perception of "popular theories" (all of respondents, N=807)
..............................................................................................................................................
12 9
Table 27 Home buyers' perception of "popular theories" (N=469) ..................................... 130
Table 28 Switchers' perception of "popular theories" (N=181)...........................................
131
Table 29 Investors' perception of "popular theories" (N=1 17) ............................................
132
10
Chapter 1 Introduction
In the past ten years, the real estate markets in many Western countries experienced
remarkable booms followed by epic crashes. However, the housing prices in a few
countries, mainly in Asia, Oceania and Southern Africa, continued to soar even after
the notorious global financial crises in 2008. In particular, the average residential
housing price in the capital city, Beijing-increased at an average annual rate of 24.4%
from 2003 to 2007. The rate somewhat decelerated after the financial crisis, down to
8.5% in 2008 and 13.5% in 2009, but went back to 29.7% in 20101. In 2012, the priceincome ratio was estimated between 13.3 and 21, which means that the members of an
average family needed to spend the equivalent of 13.3 years' worth of their total
disposable income to buy their homes 2, according to the most conservative estimate.
This ratio is extremely high. By comparison, the world's average price-income ratios
were between 3 and 6; the peak values during the recent housing bubble of the most
expensive cities in the United States, such as New York and Los Angeles, were about
7 (Gao, 2010). Moreover, prices have been rapidly rising relative to rents in the city:
the price-to-rent ratio increased by almost three-quarters from 26.4 in 2007 to 45.9 in
2010 (Wu, Gyourko and Deng 2012), which means that in 2010, it would take a
homeowner 45.9 years on average to offset the expense of a house purchase via
expected rental income. The above data have led researchers to conclude that property
I These numbers are calculated based on the data of "China Statistical Yearbook (2004-2011)", which
is compiled by National Bureau of Statistics of China. By the constraints of the author's data, these
numbers show the nominal increase. However, Wu, etc. (2012) shows that deflated with the CPI series,
Beijing's growth rate is still closer to 20% per annum from 2003 to 2010.
2 This is calculated based on the average disposable household income and the average housing spaces
for a three-member family. See Wu, etc. (2012) for more information about calculating the price-income
ratio in Chinese housing market.
11
prices in Beijing and several other big Chinese cities constitute a "bubble" in that they
significantly exceed the property's fundamental value 3 (Wu, etc. 2012; Wang and
Zhang 2012).4
Motivation
Similar to the 1929 and earlier crises, events in the early
2 1st
century require
scrutiny of existing theoretical models and policies. Different approaches have been
proposed to understand financial bubbles. First, the efficient market hypothesis argues
that arbitragers would close any gap between price and value. Therefore, market prices
must reflect fundamental value, and bubbles are impossible. Other approaches
recognize that bubbles are possible and seek to explain why. The most prominent one
of such heterodox approaches argues that market participants may not recognize
arbitrage opportunities. Rather than understanding value precisely, market participants
are caught in a "collective delusion" (Mackay 1980[1841]) in which they overestimate
the assets they are purchasing. This has been a popular explanation for the recent
notorious global financial crisis (e.g. Akerlof and Shiller 2009; Fligstein and Goldstein
2010; Pozner, Stimmler, and Hirsch 2010; Reinhart and Rogoff 2009). And it seems
particularly appropriate for a property bubble such as that of Beijing, especially because
this market is full of amateur investors who likely do not have sophisticated valuation
skills or tools.
3 Or the present value of the future cash flow discounted by time and risk.
4 See Wu, etc. (2012) for more detailed calculations that lead to this conclusion. Their paper also
analyzes the data of other seven major cities in China, and shows that the housing bubbles in these cities,
especially those off the coast such as Shanghai, Hangzhou, Shenzhen, are also substantial.
12
However, when prices are extraordinarily high, such as the case currently in the
Beijing housing market, the collective delusion explanation seems improbable. Is it
possible that the majority of market participants do not recognize that Beijing property
is overpriced?
This question motivates an emerging line of research that provides an
alternative approach to explaining bubbles. This approach does not rely on the
assumption that the majority of market participants are led astray by collective delusion.
Described as an "institutionalist" perspective by Turco and Zuckerman (2014), this
approach argues that arbitrage opportunities may be unavailable due to institutional
limits or they may be too costly to be implemented, even if arbitragers identify these
opportunities and dominate the market. Key contribution to the institutionalist
perspective was made by Miller (1977) who points out that if short-selling (a type of
arbitrage in which investors profit from the decline of an asset's price in the future) is
restricted in a market, a minority of optimistic investors can inflate the price, but
arbitragers who recognize the mispricing will not be able to eliminate it. Abreu and
Brunnermeier (2003) provide a somewhat different institutionalist model. They argue
that rational arbitragers will not "attack a bubble" if they think that there is not a critical
mass of rational arbitragers to attack it at the same time. Rather, they choose to
"dance"-speculate and exit the market when the bubble is about to burst 5 , which is an
optimal strategy for them; however, by doing so they actually fuel the bubble (cf. Allen,
Morris, and Shin 2006; Delong, etc. 1999; see also Brunnermeier and Nagel 2004;
Temin and Voth 2004). Turco and Zuckerman (2014) propose a third institutionalist
5 The bubble will burst either because there is an exogenous shock or because the number of the
arbitragers who attack it simultaneously reaches a threshold.
13
model that is described as "suboptimal dancing". They argue that in the private equity
industry in the mid-2000s, where mispricing is widely noticed, financial professionals
erred in interpreting others' strategies-they think everyone else is going to dance as
what they will do. Dancing was in fact a suboptimal strategy in this model, as there was
not be enough liquidity for the dancers to exit in the end.
The institutionalist approach has distinct implications for the relationship
between price and private valuations. The collective delusion approach, as well as the
efficient market hypothesis, assumes that market price efficiently aggregates private
valuations. The problem, however, is that these valuations are biased by psychological
and social influence (Barberis and Thaler, 2005; Shiller, 2005). This implies that
market bubbles are intractable situations if we don't have effective ways to improve
investor learning. But the institutionalist approach implies that price does not
necessarily reflect private valuations. The core institutionalist point is that short-selling
constraints prevent the market from efficiently incorporating (skeptical) private beliefs.
Hence, market bubbles are potentially remediable if institutions strengthen participants'
capacity to act based on their private valuations. The problem is market failure, not
behavioral failure.
It is generally hard to adjudicate between the collective delusion and the
institutionalist approaches empirically, because to obtain data on private valuations
distinct from prices is quite difficult. To distinguish the two approaches, a critical test
is that whether during a market bubble price departs from the aggregate of private
valuations. If private valuations are equal to the market price, this implies that
collective delusion is responsible for the bubble. However, if the mean of private
14
valuations is lower than the price, institutionalist explanations are more tenable as they
suggest that the price doesn't reflect the true beliefs of market participants.
Three Objectives
The first objective of my dissertation project is to conduct the test - whether
bubble-era price departs from the aggregate of private valuations - in the context of
Beijing housing market. I choose this research site as it provides unique opportunities
to examine the two competing arguments at the same time. Turco and Zuckerman (2014)
provide the first study showing that private skepticism in price exists during a bubble
in a context where the collective delusion explanation is an unlikely explanation for the
bubble. However, the collective delusion explanation seems to be the most
straightforward explanation of the bubbles in Beijing housing market, as it is dominated
by amateur participants: most home buyers are families and individuals. Moreover,
Real estate private equity funds, run by financial professionals, who probably have the
highest expertise in analyzing the market, accounted for only about 2% of the market's
total investment by the end of 2011, and this number was as low as 1% before 2011
(Qin 2011, 2012). On the other hand, the institutionalist explanation could also be
applicable, as short selling in general is not available in this market.
The major data for this study were collected via originally designed surveys
administered to a sample of clients of a network of Beijing banks. For the first time,
my result shows systematic and precise evidence that bubble-era prices do not equal
the mean of private valuations. Rather, the vast majority of market participants regard
Beijing property as overpriced. My results strongly support the institutionalist approach.
15
The second objective of my dissertation is to understand that who has been
driving the market price in the circumstance that the majority of market participants
regarded the properties as overpriced. Through investigating this question, my survey
data also shed light on which of the three institutionalist models best characterizes the
dynamics underlying the Beijing housing bubble. As my analysis of the data show little
evidence that there exists a group of noise traders who believe that Beijing real estate
is appropriately priced, therefore both Miller's (1977) and Abreu and Brunnermeier's
approaches are not supported. Rather, as in Turco and Zuckerman (2014), the market
is driven by dancers who are behaving "suboptimally" in that they are drawing incorrect
inferences about other market participants. More specifically, I document a new type
of mistake made by market participants, which retained to their understanding of other
participants' valuations: they falsely think that the other participants believe that the
price should be even higher. This situation is similar to what scholars call "pluralistic
ignorance" (O'Gorman, 1975; 1986; Pentice and Miller, 1993; Centola, etc., 2005; Willer,
etc., 2009) -
the actors overestimate the degree of the others' supports for the price
though they personally don't endorse it. Since they mistakenly believe that the others
are deluded, market participants choose dancing as the optimal strategy, but actually
dancing is suboptimal in such a situation.
The third objective of my dissertation is to examine the logics behind market
participants' behavior in depth through their perceptions of the "popular theories".
Shiller (1990; see also Case and Shiller 2004, 2012) argues that market participants
could hold theories or models that they use to justify the price in a bubble, though these
theories or models are often deviated from the rational models created by economists.
16
Case and Shiller (2004) regard the existence of popular theories in the U.S. housing
market in 2000s as the evidence of collective delusion. My analysis of both the
interview and survey data shows that, first, the popular theories reflected the influences
of the economic, social and political institutions on the market perceived by the market
participants. Second, market participants overestimated the importance of some
popular theories believed by others-they tended to believe that others were more
deluded and speculative than themselves. Third, their perception of the institutional
made them tolerant with the market inefficiency, though they had fully realized such
inefficiency. This part of analysis also depicts a larger picture of the institutional
influences on market.
Agenda
The rest of the dissertation is organized as follows. In Chapter 2, I discuss the
theoretical models of market valuation. In Chapter 3, I provide background on the
Chinese real estate market and depict the factors that contribute to the market boom. In
Chapter 4 I introduce the qualitative phrase of this study, and provide preliminary
evidence of the private doubts underlying the optimistic climate based on my interview
data. In Chapter 5, I discuss how to effectively collect data on market participants'
private valuations and their perceptions of the public valuations through survey design.
Next, Chapter 6 shows the first important piece of results-there is a significant gap
between the market price and the average market valuation. Such a result supports the
institutionalist model rather than the collective delusion model. In Chapter 7 I show the
evidence of pluralistic ignorance, and argue that the market price is driven by the
investors who falsely thought other market participants are more optimistic than
17
themselves. In Chapter 8 I examine market participants' perceptions of popular
theories. The dissertation concludes by drawing lessons on how the findings of this
study can be connected with diverse lines of research, and what the implications for
policy and financial practices are.
18
Chapter 2 Theories
Theories of Market Valuation
Academic research has developed alternative theories to explain how market
participants evaluate assets and why prices might become highly inflated relative to
their value-i.e., a bubble, which could be categorized into three approaches (Turco
and Zuckerman 2014): a) efficient market hypothesis, b) collective delusion approach,
and c) institutionalist approach. The efficient market hypothesis argues that price
accurately reflects an asset's value. Bubbles and crashes are not predicted by the
efficient market hypothesis. However, collective delusion approach argues that bubbles
can emerge when the majority of market participants misestimate the value of their
assets. In despite of different arguments on whether price reflects value, the efficient
market hypothesis and the collective delusion approach share the assumption that
market price aggregates the personal valuations of market participants. In contrast, the
emerging institutionalist approach suggests that price does not necessarily aggregates
personal valuations. In this section I compare the key arguments of the three approaches,
and analyze why the collective delusion approach and the institutionalist approach,
which have different practical and policy implications, are very likely to be confounded
with each other in empirical contexts. I then propose an empirical test to distinguish
collective delusion and institutionalist explanations during a market bubble.
Efficient market hypothesis
The efficient market hypothesis (EMH) (e.g. Fama 1976; Jensen 1978) has
emerged as a dominant theory since the mid-1960. According to EMH, a simple model
19
of financial markets consists two types of participants: "arbitrageurs" and "noise
traders". The former understands value accurately, who are also called as rational, or
informed investors, while the latter do not (Black, 1986; Shleifer and Summers, 1990).
Arbitrageurs seek profits by betting against noise traders and they thereby stabilize
prices. When noise traders push up prices, a common strategy for arbitrageurs to earn
profits is short-selling, and the basic way to implement it is as follows: first, borrowing
the asset that is overpriced and selling it at this high price, and then as the asset's price
goes down they buy it back and return it to the lenders. In this model, noise traders lose
money to arbitrageurs; they will eventually exit from the market or learn to be rational
investors. Through such a process the price accurately represents the value of the asset.
According to EMH, market price is wiser than any single individual, as it accumulates
all pieces of information in the market. It is foolhardy to question price.
Collective DelusionApproach
According to the collective delusion approach, bubbles are fueled because the
majority of market participants falsely believe that the price is right. Therefore, though
market price efficiently aggregates market participants' private valuations, it does not
reflect the value of the asset. Bubbles will burst when external information makes
market participants realize the true value of the asset.
Collective delusion approach has been supported by the research of behavioral
economists. This group of research argues that market participants tend to display
systematic biases when they form valuations. This is based on the findings of a series
of psychological experiments that argue that common judgmental biases exist among
human subjects. Moreover, many of these biases are relevant in financial markets (see
20
review in Barberis and Thaler, 2005). For instance, these biases include but are not
limited to: overconfidence, which leads people to bear more risk than a rational actor
would do (e.g. Fischhoff, Slovic and Lichtenstein, 1977; Alpert and Raiffa, 1982);
beliefperseverance, which indicates that people stick to an opinion too tightly or for
too long once they form it (e.g. Lord, Ross and Lepper, 1979), and optimism and
wishful thinking, which suggests that most people tend to form unrealistic views of their
ability and prospects (e.g. Weinstein, 1980; Buehler, Griffin and Ross, 1994).
In addition, another group of research that supports the collective delusion
approach emphasizes that actors are subject to social influences. For instance,
information-cascade theory argues that market participants demonstrate herding
behavior as they tend to anchor their choices to the preceding choices of others (in some
cases such a behavior can be completely rational). Such a tendency forms "bubble-like"
phenomena such as fads and fashion in markets (Anderson and Holt, 1997; Banerjee,
1992; Bikhchandari, Hirshleifer and Welch, 1992; Centola, Willer and Macy, 2005).
InstitutionalistApproach
Distinct from the collective delusion approach, research of the institutionalist
approach highlights the possibility that mispricing can persist even when rational
market participants dominate the market and collectively possess sufficient resources
to correct it. Several reasons could lead to such an outcome.
First, institutional constraints can make arbitrage opportunities unattractive or
unavailable for rational investors, though they have identified these opportunities. For
instance, short selling, which is often essential for arbitrage process, usually requires
21
large amount of capital and involve considerate risks. For instance, borrowing shares
of assets typically requires fees and interests, and sometimes borrowing is simply
impossible at any price. Furthermore, short-selling is simply not allowed in some
financial markets due to legal constraints. Miller (1977) shows that, if short-selling is
restricted in a market, a very small group of optimistic investors can push price at a
level higher than its value in a market filled by rational investors.
Furthermore, when noise traders dominate the market (or take a large position
in term of their resources), the price could depart from value in the relative long term,
and thus arbitrage will be too risky (and too costly) to be implemented. Therefore,
arbitrageurs will not be able to correct mispricing if they are risk-averse (De Long, et
al, 1990). In particular, a group of research argues that information asymmetry among
rational investors could impede their collective attack on mispricing. In most cases, to
successfully attack a bubble needs a critical mass of the participating arbitrageurs to
have the same understanding of the right timing to do it, and act at the same time. Yet
the mechanisms that foster coordination among arbitrageurs are often absent. Abreu
and Brunnermeier (2003; see also Allen, Morris and Postleawite, 1992) discuss one
possible coordinating failure of arbitrageurs. In their models, an arbitrageur will attack
a bubble only if the arbitrageurs who will attack simultaneously reach a threshold. If
not, this arbitrageur will continue riding the bubble, as noise traders also exist in the
models. They find that bubbles will persist for a substantial period unless an exogenous
event facilitates the coordination of the arbitrageurs.
Turco and Zuckerman (2014) suggest another type of coordination failure
among arbitrageurs, which derives from the false interpretation of other arbitrageurs'
22
strategies. They study the managers in the private equity industry during and after the
recent financial crisis, where few participants can be considered as "noise traders."
since they are all professionals When bubbles occur, the optimal strategy for these
managers is to sit out of the market, since there is no liquidity in a market for private
assets, so there will be no one who is going to buy their assets when the crash arrives.
However, they find that these managers were actually dancing, as they mistakenly
thought that other investors were dancing as well.
When a market is dominated by noise traders, an optimal strategy for
arbitrageurs may not be correcting mispricing but rather to engage in "dancing" speculating in the market and then exiting from it before the bubble is about to burst.
Dancing is particularly attractive if arbitrageurs believe that investor sentiment is
predictable. Evidence has been found to support the dancing behavior of sophisticated
investors. For instance, Temin and Voth (2004) study a case of a London bank, which
earned considerable profits by riding the famous South Sea Bubble.
Similarly,
Brunnermeier and Nagel (2004) shows that hedge fund managers invested heavily in
technology stocks in the upturn while avoided the downturn, suggesting that they might
prefer riding the bubbles than stabilizing prices. Rather than eliminating bubble, the
dancing behavior of arbitrageurs actually fuels bubbles. In Abreu and Brunnermeier
(2003)'s model, dancing is an optimal strategy as noise traders dominate the market,
and arbitrage is difficult for rational investors. However, in the case analyzed by Turco
and Zuckerman (2014), dancing was suboptimal as actually few professional investors
were deluded on value, so when dancer would not be able to exit the market
successfully as there would not be enough liquidity for them to do so.
23
We must now note an important implication from our comparison of the three
approaches. In particular, if a market consists both rational investors and noise traders,
these findings suggest that we can't infer the average valuation of market participants
from the market price. In other words, if price is overly high, it could be that the
majority of participants are deluded, as what the collective delusion approach suggests.
Alternatively, it could also be that a large proportion of rational investors didn't act
based on their real valuations. In particular, most people probably share the assumption
of the efficient market hypothesis and the collective delusion approach that price
efficiently aggregate private valuations. Therefore, if the institutionalist approach is
right in a context, the conditions that foster the market failure can further lead astray
the market participants-they may overestimate the optimistic valuations in the market.
In my dissertation, the primary analysis I would like to conduct is to examine
the collective delusion approach and the institutionalist approach in a bubble-era
market where both of the approaches are potential candidates for explaining the bubble.
As mentioned above, a key distinction between the two approaches is whether they
assume that the price aggregates private valuations (see Figure 1). Since we cannot rely
on price to infer private valuations, the only way to understand actual private valuations
is to directly collect data on them. In this study, I investigated private valuations
through first a qualitative approach, and then a quantitative approach, which are
included in Chapters 4 to 7.
Pluralistic Ignorance
Psychologists and sociologists have repeatedly found a phenomenon in many
social contexts: a majority of group members privately reject a norm, but they assume
24
incorrectly that most others accept it; as a result, they behave in the way that they think
the most others will do. Their behavior leads to the persistence of the norm that is
actually "unpopular" (Katz and Allport 1931; O'Gorman 1986; Miller and McFarland
1991; Centola, Willer and Macy 2005; Willer, Kuwabara and Macy 2009). The famous
examples of "pluralist ignorance" include teenagers' engagement in self-destructive
behaviors such as binge drinking, and the "flag waving" of congressional democrats
(Prentice and Miller 1993; Willer, Kuwabara and Macy 2009). Teenagers are not
personally fond of such behaviors, but they falsely believe others approve of them
(Prentice and Miller 1993). In the case of congressional democrats, they voted for the
war in Iraq to avoid being chided as unpatriotic in spite of private misgivings (Willer,
Kuwabara and Macy 2009).
In these social contexts pluralistic ignorance is often attributed to the heavy
social pressure for conformity. Such a pressure prevents social members to express
their private opinions. And the lack of voices furthermore increases the vulnerability
and cost of any member to express dissents. Such misinformation obscures, and even
conceals the desirability and possibility of change (Kuran 1995, p.106).
In markets, the private opinions of market participants are supposed to be
expressed unobstructively through price: the bull participants will buy in and lift the
price, while the bear participants will sell out and lower the price. Yet institutionalist
approach suggests that such a pricing mechanism can be dysfunctional: pessimistic
investors may not be able to express their opinions through acting on price. If the
mechanisms that make the market participants to directly exchange opinions is lacking
(and usually it is), the situation may resemble to that in the social contexts where
25
misinformation among group members aggravates their incorrect perceptions of other
members' opinions (Zuckerman 2010; Zhu and Westphal 2011).
Pluralistic ignorance is a different dynamic from those proposed by other
institutionalist literature (see Figure 2). The seminal work of Miller (1977) argues that
if short selling is unavailable, just a small group of optimistic investors can inflate the
price. Abreu and Brunnermeier (2003) sheds light on information asymmetry among
rational investors-if the rational investors are out of the knowledge about the timing
of other investors' action, they won't be able to coordinate to attack a bubble. Turco
and Zuckerman (2014) also highlight a type of information asymmetry among rational
investors. In particular, it shows a case where the rational investors can end up with
sub-optimal dancing. What Turco and Zuckerman found is that the professional
investors actually falsely thought other investors approve of the actions that they
personally desiredto do.
In a market dominated by unprofessional investors where profiting from short
selling is constrained, these investors, if they are not deluded, tend to believe that the
price is driven by deluded, optimistic participants as in Miller (1977) and Abreu and
Brunnermeier (2003). But if actually the majority of investors are not deluded, they
will fall into the situation of "pluralistic ignorance": market participants personally do
not regard the price as reasonable, but think other participants believe that the price is
right. This is not the case in Turco and Zuckerman (2014) because professional
investors were unlikely to think that their peers were deluded by value. However, if the
investors behave like those rational investors in Abreu and Brunnermeier (2003)choose to dance, as they believe there are deluded "noise traders", they will end up with
26
"suboptimal dancing" in Turco and Zuckerman (2014), because there is no liquidity for
them to exit from the market. In chapter seven of this dissertation, I will show my
analysis of pluralistic ignorance in this China real estate market.
27
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Chapter 3 Background: China Housing Market
History
The housing market in China is a nascent, emerging market, which did not exist until
1979. After the founding of the country in 1949, urban residential housing units were
nationalized by the state (the central government), and the state became the sole owner
and developer of urban housing. The housing units were developed according to annual
national economic plan. The allocation of residential housing was part of the welfare
system: housing units were allocated to households at low rent through their employers,
most of which were state-owned enterprises (SOEs).
In late 1970s, China began a series of economic reforms, which also included
reform of the urban housing sector. In 1979, the government began a pilot privatization
of state-owned housing, first in a few coastal cities and then expanded to more cities
inland. A limited housing market that mainly targeted foreigners and employees of nonSOEs gradually emerged, and the first private housing developer was founded in 1980
in Shenzhen. A milestone in the history of housing reform was the government's
approval of the 1988 Constitutional Amendment, which declared that the state would
remain the owner of urban land, but individuals could buy the right to use the land for
up to 70 years. This Amendment provided significant legal foundation for the
development of the private housing sector. In the next decade, the government
accelerated the privatization of urban residential housing by encouraging residents to
purchase the housing units allocated by their work units at low prices (usually at
production cost), and at the same time, a housing allowance was introduced into the
salary structure. In 1998, the State Council issued the
29
2 3 rd
Decree, which was critical
for the development of private housing market. The
2 3rd Decree
terminated the housing
allocation system of enterprises. After this, urban households need to buy or rent their
homes in housing markets (Gao 2010; Wu, etc., 2012).
Factors that Drive the Market Boom
The liberalization of the housing market eliminated barriers to the development of the
commercialized housing markets in China. Since 1998, commercialized housing
markets have experienced a significant boom, with dramatic growth after 2003 (see
Figure 3). An important factor that drives these markets is large-scale urbanization and
migration (Wu, etc., 2012; Chovanec, 2011). As big cities in general provide better
quality health, education and other public services, many Chinese migrated from rural
areas to urban areas, and from smaller cities to bigger cities. From 1990 to 2012, the
proportion of urban population in the country increased from 22% to 52.57%6. In
Beijing, in 2005 migrants bought 22.7% of newly built private housing units, while in
2009 the number increased to 33.5% (Wu, etc., 2012). In addition, the average age of
the people who apply for their first home mortgage is only 27 in China, much lower
than those of other major countries (Chinese Academy of Social Science, 2013). This
could be driven by the belief that an apartment is a necessity for marriage, as traditional
Chinese culture treats housing as a requisite part of betrothal gifts 7 . Furthermore, the
increasing gap between the rate of housing price growth and that of the income growth
may also encourage people to purchase their homes earlier rather than later.
6 The numbers are calculated by the author based on the data provided by China Statistical
Yearbook
(2004-2011), compiled by National Bureau of Statistics of China.
7 Now the common case is that the groom's family pay the down payment of the new couple's housing,
which is typically no less than 30% of the house's total price.
30
Nevertheless, the increase in urban residents only explains a small portion of
the dramatic increases of housing prices, according to recent research (Wu, etc., 2012).
The more immediate divers of the housing boom are the investors who bet on the uptrend in demand by buying multiple houses--some of them even buy dozens of
apartments at one time (Chovanec, 2011). Though an accurate estimate of how many
houses have been bought by investors is not yet available, the estimated vacancy rate
among new buildings sold is about 30% in Beijing (21st Century Business Herald,
2013), indicating the investment activities are extremely flourishing in the city.
One factor that drives the investment activities is the absence of attractive
alternative investment avenues for these investors. The domestic stock markets have
experienced significant volatility and provided unattractive average returns in recent
years. Also, most Chinese citizens are prevented from investing abroad because of the
strict controls on capital flows. The real estate market, in contrast, has been providing
returns with which few other markets can compete, and has not endured a sustained
downturn since its formation.
The belief that the government would endorse the real estate industry in the
event of a downturn is also a factor in fueling investors' confidence in the market, as
this industry has become the strongest engine of the nation's economic growth. A
supporting piece of evidence is that in 2009 the government increased the money
supply by about two-thirds as a response to the global financial crisis in 2008, starting
a lending boom in which huge amounts of capital flowed into the real estate industry.
In just two years, housing prices increased by 47% in the capital city. Moreover, as the
land is owned by the state, housing developers need to get a lease for using a land parcel
31
from the local government in a typical housing development project. Through such land
sales 8, local governments obtain about half of their income. It is believed that this also
encourages the government to support the growth of the real estate market.
The Government Policy Intervention
In the spring of 2010, however, skyrocketing prices had aroused wide-range
concern for the sustainability of economic development, and for social unrest. In fact,
it is hard to believe that people in these cities did not have any sense that the housing
prices were excessively high relative to basic housing prices fundamentals, such as
income and rent. As they were living in the city, they clearly felt the pressure induced
by the rapid increase of housing prices on their lives. The slogan that "a house ruins a
middle-class family" had become prevalent among urban residents. The younger
people who were in their twenties claimed "housing prices ruin our faith and dreams"
(Zhang and Jia, 2010). The mood was also captured and dramatized by the media; for
instance, a group of TV dramas and movies that revealed the life struggle under high
housing prices aroused widespread sympathy in recent years. Even governmental
officials publicly expressed the opinion that the housing prices were not adapting to the
country's income level.
The central government imposed a series of "constraint" policies in May 2010,
which was first implemented in huge cities such as Beijing and Shanghai, to cool down
the investing activities. The policies included limiting the number of units that a family
8 The local government charges a total price for
leasing the land at the beginning of the whole leasehold
period, which the developer has to pay immediately. After 2002, all land in urban areas for residential
and commercial use must be transacted through public auction or bidding. The income through land
auctions tripled from 2003 to 2009.
32
could buy and requiring proof of residency for home purchasing, stricter qualifications
for mortgages and larger down payments. These policies sent the clear signal that the
central government wanted to rein in growth in housing prices. However, the price did
not exhibit a significant downturn after the launch of these policies, and the general
expectation in the market did not seem materially different.
33
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34
Chapter 4 Qualitative Evidence on the Private-and-Public
Discrepancy
Fieldwork
In the spring of 2012, when the "constraint policies" had been in place in
Beijing for nearly two years and the price had somewhat leveled off, I conducted 29
interviews with a sample of housing market participants (76%) and professionals (24%)
in Beijing. 9
In these interviews, I asked a broad range of questions through which I tried to
understand how people with various backgrounds perceived and evaluated the housing
market, what housing meant for them, whether they were conscious of housing in
different social contexts, which factors mainly influenced their decisions on purchasing
housing, and what they thought the majority of others perceived and evaluated the
market. As this is an exploratory approach, I was open to find the dynamics that had
not been found or understood. Therefore, at this stage, the interview design aimed to
capture different possibilities rather than focused on verifying a particular explanation.
To select interviewees, I mainly targeted individuals from the middle and the
upper-middle strata of the city, who are the typical participants (or potential
participants) of the housing market. I also added two interviewees from the lower strata.
To make the investigation effective, I focused on sampling interviewees from a few
occupational groups rather than from the whole population. I chose the sample of the
market participants from five different occupational groups: 1) managers and bankers
9 Details on the method for these interviews, including how these interviewees were selected and what
the questions are, are included in the appendix.
35
in financial industry; 2) engineers in information technology industry; 3) entrepreneurs
and self-employed in media and public relationship industry; 4) professors and research
scientists in universities; 5) government staffs and officials. I chose these occupational
groups because they were all typical industries that produced middle and up-middle
classes in the city, but they were different in nature and together employed persons
across a wide range of backgrounds. Moreover, each category has unique
characteristics, which together add variations to the whole sample. For instance,
financial industry has employees with heterogeneous educational and income levels; in
contrast, the engineers in IT industry constitute a relatively homogenous group (most
employees have similar educational backgrounds and have similar paths of life stages).
The entrepreneurs and the self-employed in media and public relationship industry are
chosen because people with relatively low level of education (e.g. below college) can
enter into this industry and earn a relatively high level of income, a phenomenon that
is less common in other four occupational groups. The professors and research
scientists constitute a group that has extremely high level of education (almost Ph.D.).
Government is chosen because a very high proportion of its employees are from local,
well-established families. Besides the consumer groups of the market, I also
interviewed real estate professionals from the following occupational groups: 1) real
estate developers (project managers), 2) real estate fund managers, 3) real estate
brokers and 4) urban planners. The majority of these interviewees are in their thirties
or forties, with annual income from 60,000 to multiple millions RMB.
The subjects were recruited through the following "snowball" process: I first
contacted a few personal contacts in each group, and then asked them to refer me to
36
other individuals, who were told that a doctoral student from MIT would like to ask
them several questions about Beijing real estate market' 0 . The interviewees were not
the persons who I knew initially.
An interview was initiated in the following way: after the referee introduced me
to a prospective interviewee, I would send the prospective interviewee an email that
briefly introduced the aim and the format of the interview. The aim of the interview
was stated as understanding individuals' opinions on the real estate market and its
development. I contacted 31 potential interviewees and 29 of them accepted my
interviews (respond rate=93.5%). The interviews were open-ended, guided by the
interview protocols shown in Appendix A.
Public Valuation and Private Valuations in the Market
The most striking finding of my interview is that although the "constraint
policies" in Beijing were launched for nearly two years and the increase of housing
price has decelerated, highly optimistic sentiment still filled the market.
Most of my interviewees (97%) thought that the majority of people in the
market believed the market would be strong, and the price would continue to increase
in a long future:
10 As firms in financial and IT industries could be quite different from each other in size and ownership
structure, to avoid just getting interviewees from some specific backgrounds, I build stratified samples
of the firms in these two industries in Beijing according to firm size and firm ownership structure (stateowned, foreign, private), and try to get access to individuals in different strata.
37
"So the feeling is that if you buy houses, the earlier the better. If you have
enough money, just buy it, the price will increase for sure."" -A 25-year-old electronic
engineer
"All people around me think that the housing price can only increase. I don't
know if this is right. I don't see that people are worrying." -A 29-year-old professor
"In the short term, the price may fluctuate according to policies, but what people
believe in the market is that the housing price will go even higher in the long term." A 35-year-old project manager
"Most people think in general the trend of price is going up. People around me
recently talked more about houses than before because the price went down a little bit,
and it seemed that many people began to consider buying again."--A 29-year-old
governmental employee
"It is general believed that if the government feels the pressure of economic
growth, it may loosen the current (constraint) policy. Then the price may go up quickly
again." -A 44-year-old banker
The interviewees also mentioned a series of "popular theories" (Shiller, 1990;
Case and Shiller, 2004) that were believed to support the high price. The theories are
summarized in Figure 1.
Despite the optimistic sentiment perceived by almost all interviewees, it is
notable that 78% (72% of consumers and 86% of professionals) of the interviewees
who thought that most of others believe that the price was strong expressed personal
I
All these quotes were translated from Chinese to English by the author.
38
doubts on the reasonability of the price. They used the words such as "crazy",
"unbelievable", "unreasonable" and "ridiculous"" to describe the market. And many
of them provided me concrete reasons that why they were skeptical about the price:
"The magnitude of increases is inconceivable. I don't know, on earth, how
many people have the real demand. ...My feeling of the market is crazy, too crazy." A 31-year-old mechanical engineer
"Supply exceeds demand is the long-term trend. There is going to be the change
in population structure, and the large supply of public housing. Actually in 2009 the
price has reached the summit. Panic purchasing was common at that time. If there was
no constraint-policy, we should have seen that the price goes down." -A 43-year-old
fund manager
"Urbanization is a fact; and also it is true that people hold the opinions such as
'housing is necessary for family, dignity and status'. But all these have been reflected
by the current price. The price has reflected the expectations. I don't know a single
person who can afford a house but hasn't bought one." -A 41-year-old producer and
writer
However, among these private skeptics, most of them (6 1%) believed that what
"most people believe" is that prices would keep going up for a rather long time (more
than ten years). Among the rest, 22% said they were not sure about the future trend,
and only 17% thought that the reverse trend would happen in a short run. Only one
person said that he had carried out some plans based on his personal pessimistic belief
12 These words in Chinese are "MIR"
,
"$
4 1L
39
" ,
" TE
" and "jui"
.
for his personal investment. Ironically, he actually bought a house in 2011, when the
price was almost at the peak, as he said he wanted to please his girlfriend and her
family-"that is the only reason for me to buy the house, to make them happy", he said.
For himself, he bought some stocks that short Chinese Economy in the Hong Kong
stock market.
Unlike this person, few others appeared to realize that there might be financial
vehicles for them to act based on their personal valuations. Moreover, as these vehicles
were all indirect related to the real estate market and were basically offshore, it was
extremely difficult to get access to them and also to estimate the potential risks and
returns. Furthermore, simply selling houses could be a very risky and unrealistic option.
One of the interviewees pointed out: "since I am pessimistic about the market, I should
sell my apartments. But what if the price increases again? If I don't sell, and the price
decreases, I will be compared with the average people (who have the average loss). But
if the price increases again, I probably won't be able to buy my home back in the future.
My wife won't agree with me on this."
It is noticeable that some investors who had been actively purchasing houses
demonstrated substantial caution when talked about their investments, suggesting that
they were likely to conduct a "dancing" strategy in the market, though they hadn't sold
any apartments as the downturn hadn't come. For instance, one investor and his wife
had bought two houses in Beijing and three more in Chongqing since 2009. He told me:
"We should have (rented our houses to others) to earn rent, but it is not safe.
You know what I mean? You should be cautious. Actually when the price is so high,
40
you can't get much return (from rent). The only return that you can get is from the
further appreciation."
At least two patterns that I found in my qualitative data cannot be explained by
efficient market hypothesis and collective delusion approach. First, the majority of
interviewees, especially including those who currently own multiple houses, are
skeptical about the reasonability of the housing price. This implies that the market price
may not reflect the aggregate of private valuations. Second, though personally skeptical,
few interviewees question the overall optimistic sentiment in this market. This implies
that, the sentiment perceived to be "popular" may not reflect the real average of the
private valuations.
Hypotheses
Together, the qualitative data suggest two striking patterns: first, most
interviewees personally thought that the market price was too high. This is consistent
with the implication of the institutionalist approach rather than the collective delusion
approach (or the efficient markets hypothesis). Second, there is significant discrepancy
between interviewees' private valuations and the popular valuation perceived by them,
and it seems that these interviewees were not aware of this discrepancy. This implies
that market participants may overestimate others' valuations of the market, which
encourages their investing activity in the market. This implies a particular variant of
the institutionalist approach.
Two hypotheses follow from the findings (see Figure 5):
Hypothesis 1: Property prices are higher than the average of market
participants'privatevaluations of the property in the Beijing housing bubble.
41
Hypothesis2: Significantgaps exist between the average of marketparticipants'
private valuations and the popular valuationperceived by participantsin the Beijing
housing bubble.
To test these hypotheses, I designed a survey to collect data on market
participants' private valuations and the popular valuation perceived by them. The next
chapter introduces the method in detail.
42
Chapter 5 Surveys to Investigate Market Participants'
Valuations
Research in finance heavily depends on the data of transactions that reflect the
buying and selling behaviors of market participants. However, these transaction data
are not sufficient to understand the valuations of market participants. Especially when
market bubbles happen, the pessimistic market participants may "sit out" of the market
without any actions, so neither the price nor the records of transactions reflect the
valuations of these participants. Moreover, some of the pessimistic participants may
choose to "dance"-keeping investing till they believe that the market is going to crash.
As a result, the price and the transaction data may temporarily reflect the opposite of
these participants' true private valuations.
Sentiment analysis has been used to understand market participants' opinions,
which mainly relies on the computational analysis of subjective information. In
particular, sentiment analysis gains its popularity with the rise of social media such as
blogs and social networks. However, sentiment analysis can just provide the polarity
of the given text information, such as classifying the information into qualitative
categories such as "positive", "negative" and "neutral". Moreover, it is very difficult to
distinguish private valuations and public valuations based on sentiment analysis.
For investigating my research questions, survey appears to be a method that can
be particularly useful. Case and Shiller (2004, 2012) have conducted surveys since
2003 in several cities of the United States, in order to collect data on the opinions of
the house purchasers. However, in their surveys they didn't distinguish the private
43
valuations from the public valuation. Therefore, my hypotheses cannot be tested based
on their data.
In my study I obtain market participants' precise valuations through originally
and carefully designed survey questions. Through the survey I will be able to: 1)
distinguish market participants' opinions on value from those on price, and 2)
distinguish market participants' private valuations and the valuations they perceive as
popular. The details of the survey are introduced in the following sections.
Basic Design of the Survey
Three sections comprise the survey. The first section asks respondents about their
information sources, knowledge, and opinions regarding housing market. The second
section is a scenario design in which the respondents will go through a series of
questions, through which I try to obtain data on their valuations and their perceptions
of popular valuations. The third section is about respondents' background information,
and their past and expected experience in the housing market.
Obtain valuationgaps
According to the two hypotheses, I aim to examine two types of gaps through
the survey data. The first is whether significant gap exists between the actual market
price and the price regarded as reasonable by market participants privately. The second
gap is whether significant gap exists between the price regarded by market participants
privately as reasonable and the price that market participants believe the majority of
other participants regard as reasonable (see the demonstration of the two gaps in Figure
5).
44
For obtaining the first gap, I provide the actual market price as the reference,
and I ask respondents, compared with the reference price, what do they personally think
the reasonable price should be. By using the term "reasonable price", I aim to obtain
respondents' opinions on value. I also collect data on how respondents would like to
predict the price change, both in short-term (in twelve months) and in long-term (in ten
years).
At the same time, I try to understand how respondents perceive the popular
valuations in the market. I ask them what they think the popular opinions are about
value (reasonable price), as well as about (the future change of) the price in twelve
months and in ten years. I explain that the "popular opinion" means what they consider
as believed by the majority of residents in the city.
The order of the questions, i.e. if I first ask respondents their private opinions or
popular opinions, may influence respondents' answers. To control the effects of
ordering, I counterbalanced the two types of orders in the pilot survey: in half of the
questionnaires private valuations were asked first, and in the other half the perceived
popular valuations were asked first. However, my analysis of the pilot survey does not
show significant differences in the answers between these two types of orders.
Obtain opinions on popular theories
Shiller (1990) argues that market participants could hold theories or models that
they use to justify the price in a bubble, and these theories or models are often deviated
from economists' models. The popular theories greatly influence market participants'
beliefs and behavior, and even the existence of these theories could be a sign of real
estate bubble (e.g. Case and Shiller, 2004; Shiller, 2007, 2008, 2009). In the real estate
45
surveys conducted by Case and Shiller, they asked the respondents to what extent they
agree on some popular opinions on real estate at the time when they did the surveys
(Case and Shiller, 2004). For instance, these popular theories include: "when there is
simply not enough housing available,price becomes unimportant", "realestate is the
best investment for long-term holders, who canjust buy and hold through the ups and
downs ", "Housingprices are very unlikely to fall; at least not likely to fall for long",
etc. (Case and Shiller 2004; 2012).
My qualitative research suggests that similar beliefs were also popular among
the Chinese real estate market participant. I summarize the popular theories in the
China real estate market, which are demonstrated by Table 1. However, what we don't
know are to what extent market participants personally agree with these theories, and
to what extent they consider others accept these theories. To investigate these, I design
two types of questions in the survey:
First, I ask respondents that personally, to what extent they agree with the
statements included in Table 1 (in order to better understand respondents' opinions and
to make the questions on statements #7 and #8 symmetric, I add two statements in the
survey: 1) the government desires that the housing prices decrease in a steady way, and
2) regardless of its desire, the government has the ability to ensure the stability of the
housing market). The respondents are asked to choose a number on a 9-point scale,
from "extremely disagree" at the left end, to "not sure" in the middle, and "extremely
agree" at the right end.
46
Second, I ask respondents, in their opinions, what the proportion of population
that agrees with each of those statements is. And respondents select a percentage on a
scale from 0% to 100%.
Also I develop a strategy based on which I can compare respondents' answers
to the two types of questions. For every respondent, I calculate a z-score for each item,
which measures the deviation of the response to this item from the mean of responses
across all items. Through z-scores I standardize individuals' responses. For each item,
I can get a mean of the z-scores of all respondents. The average z-score reflects the
relatively importance of these statements 1) regarded by respondents privately, and 2)
accepted by the public, according to the respondents' opinions. A test between 1) and
2) would reflect whether this item's importance in the two groups could be considered
as significant or not.
Obtain opinions on housing market in core region vs. peripheralregion
Beijing is large city. In different regions, the nature of the markets could be
different. Based on my fieldwork and interviews, I found that a basic distinction that
the market participants made is between the housing market inside the fourth ring
roads (core region) and that outside the fourth ring road (peripheral region). The
positions of the two regions are shown in Figure 6 and Figure 7. The characteristics of
the housing markets in the two regions are summarized in Table 5. The core region,
whose size is 3.5 times as big as Manhattan, occupies 1.8% of the whole Beijing city.
Land supply in this region is extremely limited, and the residential buildings are mainly
second-hand. However, the residential buildings in this region are highly valued, and
47
have been major targets for investors, especially those from other regions of the
country. The residential housing market in the peripheral region is mainly for whitecollar works to buy their homes. It is also attractive for local investors who would like
to stock up multiple properties.
In the survey I would like to obtain respondents' opinions on both the market
in the core region and in the peripheral region.
Survey Platform
The survey platform, which is composed of 128 banks (or bank branches) 13, is
established and managed by the National Bureau of Statistics of China (NBSC). NBSC
uses this platform to collect data on this city's middle and upper-middle class's
opinions on the prices of consumer goods. The original goal of building this survey
platform is to obtain data for calculating CPI. The 128 bank branches are selected
according to the geographic distribution of the targeted population. In each of the
administrative districts of Beijing (see Figure 6 and Table 4), a certain number of banks
(or bank branches) are selected based on the size of population and the income level of
this district.
In a survey, the NBSC staff evenly distributes the questionnaires to the 128
bank branches. At least one staff in each bank branch, usually the hall manager, is
trained to conduct surveys. What they usually do is to ask the clients, who are waiting
in the line for the bank' service, "would you like to participate in a survey of NBSC
about XX". In one or two sentences they briefly introduces the topic and the purpose
13
It includes all types of banks.
48
of the survey. According to NBSC's past records, the response rate to hall managers is
about 30%.
This platform is very appropriate for this study because 1) the targeted
population of this platform, the middle and upper middle class, constitutes the
mainstream participants in the Beijing housing market; 2) this platform samples the
residents of the city, which provides an important advantage for this study, as it does
not select respondents on their past experience with the real estate market.
Pilot Survey and the Complication of Reference Groups
From July 14th to August
4 th
2012, I conducted the first round of survey via the
NBSC survey platform. One thousand and eight questionnaires were sent out, and in
total 660 responses were received. Among them, 443 were entirely completed.
The analysis of the data afterwards suggested a problem might exist in the
survey questions. When I asked respondents what they think the popular valuation was,
I aimed to get their opinions on the general market expectation perceived by them. In
the pilot survey, I asked them to choose the value that they considered as believed by
the majority of the residents in the city.
One potential problem is whether the respondents considered the majority of
residents as the typicalparticipantsin the housing market. If not, their answers to this
question would be irrelevant with the general market expectation perceived by them.
The second potential problem is that the references that the respondents used when they
thought about the opinions of the majority of the residents might not be consistent.
The second problem is relatively easier to be analyzed, as the pilot survey
questionnaire includes the following question:
49
"When you think about the popular belief, the most important channel for you
to get the reference is:
A. Through the daily conversations with family, friends and colleagues
B. Through the opinions other people expressed on the public online forums.
C. Through real estate sales, brokers and other real estate institutions
D. Through the popular belief published by the media
E. Just my guess"
The original purpose of this question was to get a sense of respondents' sources
of information based on their social interaction. The result is that 30% of respondents
choose A, 20% choose B, 24% choose C, 16% choose D and 10% choose E.
Respondents' answers to this question suggested that market participants infer
the popular valuation by referring to two different major groups. This was also
supported by my interviews with a few respondents after the survey. One group was
the "people around me", i.e. those with whom the interviewees usually interacted and
who were similar to interviewees in terms of social-economic status. The other group
was those regarded by the interviewees as the most active, influential market
participants who were driving the housing price. The survey design should be able to
distinguish these two groups, and the survey should ask the respondents to estimate the
average valuation in each of the two groups.
In the design for the major survey, I come up a series of strategies to overcome
the reference issues in the survey design. As the length of the survey is constrained by
the time that a bank client is in general available (which is about fifteen minutes), the
survey had to be as succinct as possible. Thus the survey design must achieve the
50
balance between succinctness and effectiveness. In the section below, I introduce the
strategies in detail.
Major Survey with Improvements
To overcome the problems of reference, I came up with a series of strategy to
improve the survey design:
" Clarify the categories of market participants;
* Obtain opinions on who are the most influential market participants on price;
Obtain opinions on the popularvaluation among the most influentialparticipants
0 Obtain opinions on the popular valuation among the respondents' peers.
These steps are introduced one by one below.
Clarify the categories of market participants
Instead of asking respondents their opinions on the valuation popular among
the majority of residents, I provided them a clear definition of market participants.I
then ask them whether they regard themselves as market participants. This first step is
to clarify the role of the respondent plays in the market.
Market participants were defined as including both the individuals who have
bought or sold houses in the market, and the individuals who are likely to buy and sell
houses in the future in the survey. In particular, the real estate market includes a variety
of market participants with distinct incentives and economic capabilities, who can have
differential understanding of the market. Based on my fieldwork, I developed a
typology of market participants. For instance, one of my interviewees said:
51
"For different individuals, it (the meaning of housing) is different. ...For the
persons whose income is low, they probably won't be able to considerabout (housing).
It is basically impossible for them to purchase houses in Beijing. They can only rent
houses.. .For the middle-class, they are entangled by housing... Some of them obtained
houses before the housing price rocketed--they inherited them 14. For (the rest of) the
middle class, they find that after they buy houses, they have no money left... Forthese
people, theyjust seek to own their housesfor living. If you ask them to invest, in houses
or in other products, they simply don't have the economic ability. For the up-middle
class, the situation is different. Besides their requirements for living, they also invest.
For the people on the very top, all these things are not problems. They commonly own
several houses in different cities." -A 45-year-old scientist
I divided market participants into two large groups-home purchasers and
investors. Each group is further divided into a few sub-categories. The description of
all five different categories of market participants is presented in Table 8.
Furthermore, I asked respondents which of the five types they identified with
most strongly 5 . 99.5% of the respondents in my sample regard themselves as one type
of market participants.
Similar to the pilot survey, respondents' private valuations were obtained
according to the following procedure. In the surveys, respondents were asked about
their personal assessments of the current average housing price of the core (or the
peripheral) region in Beijing. A reference price was provided, which was the actual
14 They inherited from their parents who obtained houses through
the housing reform.
1I also leave space for respondents to specify her type if none of
the five categories describes herself.
No respondent specified another type.
52
average market price. Respondents were asked what they think the reasonableaverage
housing price should be, compared with the actual price. To answer this question,
respondents each selected a percent on a scale, which indicates the percent higher or
lower than the reference price.
Obtain opinions on the most influential category
The different categories of market participants, apparently, have unequal power
to influence the marginal change of housing price. And the valuation of this most
powerful group determines the general market expectation. So it is important to clarify
which of the five is the most influential category in respondents' mind, and then I could
ask respondents the valuation that they regarded as popular in this category. The
question I asked was which category of market participants had the most influential
power to influence the housing prices, for short-term (in 12 months) and long-term (in
ten years), respectively.
Obtain opinions on the popular valuation in the most influential category
Based on what the respondent selected as the most influential category, I
furthermore asked, "on average, what do the participants of this category that you
chose as the most influential category think the reasonable average residential housing
price (in region X) in Beijing"? In particular, if this category is the same as the
respondent's self-identified category, he or she would not allowed to answer this
question, because there is one specific question that asks respondents' opinions on the
valuation popular within their categories (see the following section). Instead, I asked
what they thought secondary influential category was, and what was the popular
53
valuation in the secondary category. I call the popular valuations that I obtained such a
way the between-category popular valuations.
Obtain opinions on the popularvaluation within the respondent's
category
The last step is to obtain the respondents' estimate of the popular valuation
among their peers, i.e. within the category they self-identify with. I asked the following
question: "in your mind, among the types of individuals that you categorized yourself,
what is the popular belief about the reasonable average residential housing price (in
region X) in Beijing?" I also explained in the survey that "popular belief' meant the
one believed by the majority of people in this category. I call this the within-category
popular valuation.
*
*
*
In November, a total of 1400 questionnaires were sent out and 807 complete
responses were received. Half of the questionnaires ask respondents about their
valuations on the residential housing prices in the core region of Beijing, i.e. the region
within the fourth ring road in Beijing. And the other half asks about their opinions about
the prices in the peripheral region in Beijing. Respondents were randomly assigned to
answer either of two types of questionnaires. I received 407 complete responses for the
first half, and 400 for the second half.
To test hypothesis 1 and 2, I analyzed the survey data in the following manner.
First, on the aggregate level, I examine whether the average of the respondents'
personal beliefs is significantly different from the market price. Next, I compare the
respondents' valuation beliefs with the two types of popular valuation. I further discuss
54
the implications of these results. The strategies of analysis and the results are
demonstrated in the following three chapters.
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56
Chapter 6 Primary Gap: Price Does Not Aggregate Private
Valuations
In this and the next chapter I present the major findings of this research. These results
are based on the analysis of the major survey data collected in the November of 2012.
In this chapter I focus on testing hypothesis 1, i.e. whether significant gap exists
between market price and the average of market participants' private valuations, and in
the next chapter I focus on testing hypothesis 2. I mainly take the results for the market
in the core region (inside the fourth ring road of Beijing) as the example to present my
analysis and results. The results for the peripheral region (outside the fourth ring road
of Beijing), which are consistent with those for the core region, are also included and
introduced in tables and figures.
Results on Market Participants
Consistent with the previous surveys conducted through this platform by NBSC,
the respondents in my surveys were at and above the average income level of the middle
class in the city. The average annual personal income (after tax) of my surveys'
respondents was 114,000 RMB (the median was 100,000 RMB). The average annual
personal income of the Beijing middle class at the time was about 70 thousands RMB
(Lu, Zhang and Tang 2010). Seventy percent of my respondents were above this level.
Almost all respondents had college degrees (the persons who had below college
degrees only accounts for 0.98%), and 38.6% of them have post-graduate degrees. In
addition, these respondents were from diverse occupational groups (see the distribution
of occupational groups in Table 6 and Table 7). Most of the respondents were
professionals in their industries. In particular, more than 6% of the respondents are in
57
the "super elite" occupational groups, e.g. who are the principal managers (or officials)
in enterprises (or governments) (category 1-4 in Table 6 and Table 7).
The first important piece of results is how respondents (self-) identify with the
different categories of market participants. Next, I present the distribution of the
different categories of market participants that the respondents identify with in the
samples.
Among the 807 respondents who complete the survey, 803 of them identify
with one of the five categories-first home purchasers, switchers, investors, external
investors or speculators (see Table 8 for their descriptions). According to my definition
of market participants, these 803 respondents either have bought or sold houses in this
city, or plan to buy or sell in the future. Only four respondents don't regard themselves
as market participants. These four respondents are excluded from the following
analysis.
Taking the sample for the core region of Beijing as an example. Table 8 shows
the distribution of market participants. Among the 403 respondents, 265 (65.8) regard
themselves as home purchasers, 61 (15.1%) as switcher, 63 (15.6%) as investor, 10
(2.5%) identify as external investor, and 4 (1%) as speculators. Table 9 shows the
correlations between different categories of market participants and a series of variables
that helps to describe the features of different categories. The results suggest thatfirst
home purchasersare relatively young, unmarried, earn less, less educated and don't
own any property. Switchers are more likely to be "Beijingers" who probably have
inherited or obtained properties from their parents. Investors have almost opposite
characteristics from home purchasers;they are rich, older, married, very well educated
58
and own properties. External investors are like investors in term of their high income
and ownership of property, but they are not as highly educated as investors, and are
more divergent in their age and marital status. The sample for the peripheral region of
Beijing shows similar distribution, but more respondents in this sample identify with
switchers rather than first home purchasers. In this sample that includes 400
respondents, 206 (51.5%) regard themselves as home purchasers, 120 (30%) as
switcher, 54 (13.5%) as investor, 11(2.75%) identify as external investor, and 1
(2.35%) as speculators.
Gap between Price and the Average Private Valuation
Hypothesis 1 suggests that market price does not reflect the average valuation
of market participants. Table 10 shows the average private valuations in all of the five
categories of market participants among the sample for core region housing market.
The percent in the table means that, according to the respondents' opinions, how much
the reasonable average market price should be higher (if the percent is positive) or
lower (if the percent is negative) than the reference price given in the survey
questionnaire. For example, the mean of the private valuation of home purchasers,8.45, means that on average home purchasers thought the reasonable price should be
8.45% lower than the reference price.
The results show that all categories' average private valuations are negative,
and significantly different from zero, except for the category of speculators(this could
be due to the small sample size of this category). This suggests that the actual market
price went beyond the level that the market participants regarded as reasonable.
Moreover, except for speculators, all other categories' average private valuations are
59
not statistically different from each other. This suggests that when participants valuated
the market privately, they actually exhibited consensus that prices were too high for the
core region market. In particular, these include the people who currently have houses,
and the people who potentially would like to sell houses in the future. Moreover, the
distribution of the private valuations, in terms of whether a respondent thought the
reasonable price was lower than (private valuation<O), equals to (private valuation=O)
and higher than (private valuation>O) the actual market price, suggests that the majority
of market participants thought the current price was too high.
The results for the peripheral region market are show in Table 11. Similarly to
the results for the core region, all categories' average private valuations are
significantly smaller than zero. Figure 8 shows the results for the core and the
peripheral regions together (speculators are not included in this figure as there are too
few of them, leading to insignificant results). An interesting patter is that the first home
purchasers, switchers and investors seem to regard that the housing price in the core
region are more unreasonablethan the price in the peripheral region, while the external
investors have the opposite opinions. The reason could be the market in the core region
is more likely to attract external investors than that in the peripheral region; the external
investors who currently do not live in Beijing may regard the housing in the peripheral
region has much less value than those who live in the city.
Robustness Checks
Existing research suggests that psychological factors could influence market
participants' valuations. For instance, the participants who own houses and want to sell
houses may overestimate the value of their properties, while the participants who want
60
to buy houses may tend to think the market price is too high. However, my results show
that the majority offour categories(except for speculators), which include participants
with all kinds of variations, all think the price is too high. This suggests that such a
valuation is not driven by a specific group with certain characteristics. To further verify
this, I examine the relationships between a series of variables and participants' private
valuations. These variables include gender, age, education level, occupation, marital
status, birth of place, the region that currently live in, etc.. In particular, I examine
whether two characteristics of a market participant affects her or his private valuation:
1) the number properties that this respondent owns, and 2) whether this respondent
plans to buy or sell houses. Table 12 shows the results of regressions of the pertinent
variables on private valuations. For the core region market, almost all variables,
including the ownership of properties, have no significant effects on private valuation
except for one variable-whether the respondent plans to sell houses within five years.
Other variables being constant, the respondents who have the plans to sell houses have
more optimistic private valuations than those do not. This could indicate that these
people were planning to speculate in this market. Nevertheless, my data show that, on
average, these people still have negative valuations. For the peripheral region market,
the financial professionals have more optimistic valuations than others, and
respondents who have higher annual household income are more optimistic. The
respondents who have plan to sell within five years, however, have more negative
valuations than others. These could be because the market in the peripheral region had
been considered as more speculative than that in the core region. But the financial
61
professionals and high-income individuals were more optimistic about this market.
However, on average the financial professionals' valuation is still negative.
In sum, my results support hypothesis 1, which lends credence to the core idea
behind the institutionalist approach. An important factor behind the Beijing housing
bubble appears to be the fact that, due to the absence of a vehicle to act on their negative
views on housing prices, such negative sentiment is not being incorporated into prices.
In such a situation, how would market participants behave and what drives their
behavior? In the next two chapters I investigate these two questions in depth.
62
Chapter 7 Secondary Gap: Pluralistic Ignorance
In chapter six I present the result that market price does not aggregate private valuations
of market participants. Actually, the average private valuation is significantly below
the market price. Such a result, however, raises an important question: since the
majority of market participants don't believe that the price is right, why the price still
keeps going up?
Who is driving market price (in market participants'
mind)?
According to Miller's model in institutionalist approach, one possibility is that
there exists a small group of optimistic investors who drive the market price. It seems
that speculators might be such a group in this context, since it is the only group in my
samples who do not have significantly negative valuations. If other market participants
believe that speculators determine the market price, they may keep purchasing even if
they personally do not endorse the price. Thus, the first question that I explore is: who
is driving market price, according to market participants' opinions.
Table 13 and Table 14 show the distribution of the respondents' opinions
regarding which is the most influential categories in both the short term (in 12 months)
and the long term (in 10 years), respectively, for the housing market in the core region.
Overall, the majority of respondents consider investors as the most powerful category
in influencing market prices, in both the short term and the long term. The nextpowerful category is switchers, followed by home purchasers,speculatorsand external
investors in the short term, and by speculators,external investors and home purchasers
in the long term. In particular, more than 80% of investors also regard themselves as
63
the most influential category in both the short and the long term. These results suggest
that respondents actually regard the market in the core region as mainly driven by local
investors. Table 15 and Table 16 show the results for the housing market in the
peripheral region. Similarly, investors are considered as the most influential
participants on market price in the peripheral region, but switchers are as almost equally
important as investors on influencing the price in short term.
According to the results of private valuations that I demonstrate in the previous
chapter, switchers and investors, on average, think the market price is too high. These
two groups, in fact, are not optimistic groups. But how do other groups of market
participants thought about these two groups' valuations? Do others thought them were
optimistic? In the following sections, I demonstrate the analysis of popular valuations
perceived by market participants.
Private vs. Popular Valuation
To recall, the popular valuations of other categories were obtained in the survey
in the following way: each respondent was asked which category of market participants
had the greatest power to influence the market price, and if this category was not the
respondent's own category, how she thought the average participant of that category
evaluated the market. However, if the most influential category was the respondent's
own category, she was asked which was the secondary most influential category and
what the average valuation among the secondary powerful category was. These
questions aimed to reveal how market participants perceived the public valuations
among the most powerful participants, which was highly relevant with the general
market expectation perceived by the market participants.
64
Pluralisticignorancebetween participantsand those who drive price
Results of perceived popular valuation among the most influential category are
in Table 17, for the core region market. A straightforward conclusion is that all the
perceived popular valuations are positive, and this makes a striking contrast to those
negative valuations that the participants privately have. This result shows that no matter
which categories market participants regarded as highly powerful in influencing the
market prices, they always thought those people were optimistic. But this was in fact
not a correct understandingof what others really thought.
Furthermore, Table 18 shows the average gap between a respondent's private
valuation and the same individual's perceived public valuation among the most (or the
secondary most) influential category identified by this individual. The gap is calculated
by this individual's private valuation minus the popular valuation perceived by her. The
gaps in the table are also all negative, suggesting that on average, respondents
personally were more pessimistic than what they think those active market participants
are.
The results for peripheral region market are shown in Table 19 and Table 20.
The patterns are completely consistent with those for the core region. In Figure 9 I
summarize the perceived popular valuation of each category, by those who regarded
this category as the most influential. These results, together with those shown in Figure
8, support the hypothesis 2-there are significant gaps between the real popular
valuation (the average private valuation) and the valuation perceived by market
participants as popular.
65
Pluralisticignorance among investors
In particular, most "investors" regard themselves as the most influential
category. How did investors perceive the valuations of other investors? In the survey,
respondents were asked what the valuation prevalent within their categories was. I thus
obtained the investors' opinions on other investors' valuations, and the results are
shown in Table 21 for the core region and Table 22 for the peripheral region. Their
perceptions of the popular valuations within their categories were largely negative,
much closer to the real average valuations than their perception of other categories'.
However, the investors thought their peer investors had less negative valuations than
themselves, and such a result is significant for the core region market. This suggests
that investors thought their peers were more optimistic themselves about the core
region housing market. Such a result also supports pluralistic ignorance among
investors.
In a word, these results show that respondents systematically thought that other
categories in the market were overly optimistic, and second, they personally were
pessimistic than what they thought the influential market participants were. This
implies that, though market participants felt the bullish climate in the market, they
themselves were largely skeptical about it. In particular, overall the results suggest that
investors-who largely determined the market prices-actually exhibited pessimistic
opinions on market prices, though they believed that their peer investors were more
optimistic than themselves. But other categories of market participants thought the
investors were optimistic. This could be responsible for the seemingly optimistic
66
expectation in the market, even though most market participants privately thought the
price was unreasonable. These results support hypothesis 2.
Robustness Checks
An alternative interpretation of the results above is that market participants
always tend to think that housing prices are too high and that others are foolish to want
to pay such high prices. In the last chapter I have pointed out that this seems unlikely
in this market of a variety of participants, who have different purposes and incentives
to participate in this market. Moreover, Table 21 and Table 22 show a pattern that is
inconsistent with this alternative explanation: the "home purchasers," in contrast to
"investors," personally had significantly more positive valuations than their peers.
A possible explanation for this pattern is that media have dramatized the
complaints on the high housing prices of the relatively low-income groups, especially
those who did not own a property. To further investigate this explanation, I conduct
simple regressions of respondents' private valuations and public valuations on a set of
indicator variables for which category they identify with. The results are presented in
Table 23. These results confirm that home purchasers perceive a more negative popular
valuation in their category compared with other categories, while investors perceive a
more positive popular valuation in their category.
I further examine whether the difference between home purchasers and
investors are driven by their differences in characteristics, such as wealth and
ownership of properties. Thus I add more control variables, and the results are shown
in Table 24. Model 1 repeats the results on private valuation, which I have analyzed in
chapter six. Model 2&3 suggest that the individuals who were born in Beijing, who
67
own one property, or more than two properties, and who are the owners of private
enterprises perceive more optimistic public valuation. In particular, the effects of home
purchasers disappear after I add the control variables. But the effects of investors
remain significant. This suggests that investors' overestimates of other investors'
valuation is not due to their personal characteristics but due to some features specific
to the their group. These results support that the patterns I found cannot be simply
attributed to the psychological factors pertinent with the relative positions of
participants in this market.
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Chapter 8 Reexamination of "Popular Theories"
Case and Shiller (2004; 2012; see also Shiller 1990) show that with the growth of the
housing bubble, market participants demonstrate an increasing tendency to believe in
the popular theories that justify the price. Many of these "theories", however, are
proved to be just delusion after the burst of bubble. During my fieldwork in China, I
also found that media, professionals, investors, as well as ordinary people frequently
referred several statements to justify the rocketing increase of the price. I call these the
"Chinese popular theories". I summarize the popular theories I found in Table 1.
In this chapter I aim to examine these popular theories in depth. A proposition
is that, if market participants overestimated others' endorsement of price, they might
also overestimate the importance of the factors in the popular theories that were
believed by others. Moreover, do market participants treat all the theories in the same
way? Do they privately believe in some of them but deny others?
My investigation paves the way for a more comprehensive understanding of the
logics that drive the market participants' behaviors. My analysis shows that, first, the
popular theories were rooted in the changes of the institutional logics in the society,
which are a reflection of people's perceptions of these changes. They are not just
psychological biases. Second, market participants made inaccurate inferences on how
others consider the importance of these popular theories. They tended to regard others
as more deluded and speculative, and these inaccurate inferences could aggravate their
anxiety of purchasing houses. Third, their perception of the stability of the social and
political institutions makes them tolerant with market inefficiency, though they have
well realized such inefficiency. In the sections below I first introduce the changes in
69
the institutional logics with the real estate market boom, and next I analyze market
participants' perceptions of the popular theories.
Two Models that Explain the Market Boom
The rise of a financial logic
With the privatization and the boom of the housing sector, a financial logic
gains it prevalence in this society: housing is the most important financial asset for a
family, which is almost necessary to ensure the family's security and stability.
Housing was used to be part of the welfare for urban residents in China before
the economic reform that was initialized in 1979. Allocation of housing was executed
via work units-employers put workers into a queue according to their administrative
ranks and seniority, and what the workers could do was to wait for houses available to
them.
A consequence of the economic reforms, including the liberalization of the
housing sector, was that the close attachments of workers to their employers ("Danwei"
in Chinese) dissolved. A significant portion of social welfare, such as housing, became
people's own responsibility. The rapid shift from a very stable social structure to a more
uncertain one raises the anxiety that a family may lose its life security and relative
social status. The economic growth, inflation, and the increase in economic inequality,
outpaced the developments in more advanced systems that provide appropriate public
welfare and financial services for this post-reform society. People have been confronted
with a question that how to make the growth of their personal wealth not left behind
70
the growth of the nation. For many people, the answer seems to be apparent: buying
houses. As my interviewees pointed out:
"Your apartment is your big asset; lots of down payment, (and) lots of loans.
It is an important part of family wealth; it is the greatest asset. Some day you may need
a cash in." -A 29-year-oldmale dataprocessingengineer
"Housing helps you to accumulate wealth. Otherwise you can't accumulate
wealth." - A 32-year-old male bank manager
Cultural factors also interact with the rising financial logic. "An tu zhong qian"
( "-±
il
" in Chines), which means "to be resting in the homeland and avoid
moving" is a traditional Chinese value that has been formed in the ancient time.
Although people nowadays move frequently, they still tend to treat the place where
they own a house as their "home".
One of interviewees, who was living in an apartment rented from his employer
in Beijing, told me that:
"What is home? For me, what is home? Is the place I am currently living my
home? No. It is at most a 'dormitory'. When one of my friends talked with me, who
rented an apartment with his wife-he never say I am going home, but I am going
where I live. He distinguishes these two things clearly. For me, what is home? At the
mention of 'home', I still think my home in Hunan (province). It is there, steadily. That
original house is my home. I am at Beijing, and if someday I buy an apartment here
and I live there, it is my home. "-A 29-year-old male researcher
71
Other interviewees expressed the similar ideas:
"My house provides the sense of belonging. When you rent your apartments
you feel quite insecure."-A 32-year-oldbank manager
"It (the ownership) is something that turns you into a Beijinger, which makes
you, in this city, someone from the capital. You will have a foothold that really belongs
to you. And to able to return home is the most important thing." -A 30-year-oldfemale
entrepreneur
If owning a house (or usually an apartment in this city) is necessary for someone
who wants to be a permanent resident in the city, and if the housing price is expected
to keep increasing in the future, it is rational to buy houses as soon as someone can. To
purchase earlier means less payment and more accumulation of wealth in the future.
Moreover, the importance of housing for a family, for either traditional or
practical reasons (or both), makes purchasing an apartment almost the precondition for
marriage. The request for purchasing a home is often raised up by the girls' families
before marriage. Most of my male interviewees, no matter whether they had married
or not, mentioned this to me as they thought it had been a very common belief in the
society. As one interviewee mentioned:
"Now the primary thing for marriage is purchasing a house. This is requested
more and more severely. Essentially two reasons (for purchasing houses): the first is
that the Chinese tradition leads people to, ultimately, buy a house, and second, this is a
fashion, which is formed by the social atmosphere. The atmosphere is the most
important thing." -A 23-year-oldreal estate broker
72
Another interviewee interpreted the phenomena in the following way:
"It is not valuing 'house' itself, it is just because now if you own a house your
(life) situation would be much better. So it has become a symbol. Previously people
heavily value your employer, and your family background. (In Mao's era) if you were
a landowner's child, everything would be miserable for you in the rest time of your life.
Essentially their nature is the same; there are just different symbols: now it is housing,
and it was family background." - A 29-year-oldresearcher
The rising of the financial logic, and the value that housing brings for marriage
and life in the city, could lead people to believe in the importance of housing. However,
behind these reasons there is another great force that could significantly contributed to
the formation of the market.
The business model created by the centraland local governments
In just twenty years, China turned from an agricultural economy stripped with
Soviet-style heavy industry to the country covered by shinning skyscrapers, ports,
expressways and apartment complexes. A hidden contributor of such a rapid
urbanization is a business model created by governments.
Local governments in China are required to provide infrastructure and services,
but they have been short of revenues after 1994, when a budget law was launched in
that year. According to this law, local governments are prevented from running deficits
and selling bonds, and they also cannot introduce their own taxes. The central
government transfers some of tax revenue to local governments, which is often
73
inadequate. Since 1994, the local governments' share of revenues has kept falling,
which jumps from 78% in 1993 to 45% in 2002 (Sanderson and Forsythe, 2013).
Yet the local governments own the most valuable resource in the process of
urbanization-the land. The question is how to turn this unpriced resource into revenue.
Since 2003, the central policy bank begins the trial model with local governments (see
Figure 11): it provides low-interest loans to the local governments through the
government-backed companies, which are also named "local government financial
vehicles", and the local governments use the money to develop infrastructures, build
roads, stadiums, and parks, making the previous barren land seemingly desirable places
for working and living. Then the real estate developers come in. The local governments
lease the plots of land in and around the area they newly develop to real estate
developers, and charge them a significant amount of land-leasing fees. The leasing is
usually done through an auction. By using land-leasing fee the local governments pay
back the debts they owe the central policy bank. The real estate developers then develop
the land, build office buildings and residential housing, and sell them to customers.
This seems to be a workable model that could benefit multiple parties. But local
governments became addicted to it for boosting economic growth. As the whole model
is backed by the governments, the local government financial vehicles sucked up the
investments from all kinds of sources. From nothing a market could be created. As one
urban planner pointed out:
"An empty plot of land, with little value, and suddenly the local government
wanted to build something on this plot, as it could not find other places available. So it
began to play with the 'concept'. (For instance,) It let the high-speed train to stop by,
74
and once it has the high-speed train station it can hype up the concept, 'new business
district around the high-speed station'. Anyway, it can say anything."
As long as the housing prices go up, this model will work out. The former
governor of the central policy bank, Chen Yuan, said that half a century of fast
development in China could be realized with urbanization at its core. He said in 2005
"urbanization is the most important and enduring motive force in stimulating
consumption and investment in China's domestic economy today" (Yuan 2005). And
the country leaders also endorse such an idea. It seems that the model will be executed
for a rather long time.
For ordinary people, this is a process that re-distributes wealth: apparently,
those who own apartments can see the appreciation of their properties, while those who
don't own get nothing. In order to become a beneficiary of the country's growth, people
need to invest in housing.
But this model involves potential risks. Do people realize the risks? I ask this
question to a real estate private equity manager who has more than ten years'
experience in the real estate market. And he said:
"People would like to believe, as long as the 'constraint policy' is loosen and
(the central government) lets the real estate industry to run freely, the economy will
grow. This is a positive feedback loop. Once the bubble cannot grow bigger, and the
economy is deformed, people may question it."
"So we haven't reached this time point? How far are we from it?" I asked.
With a short pause, he said: "people don't have choices; they can only believe
this story (of urbanization). The central task of the local governments is to do the
75
business about building cities. For the mass of ordinary people, you do not need to do
other businesses, you just follow me and speculate in houses."
Similarly, another real estate professionals told me: "the general belief that the
housing price will not go down is not led by 'market confidence' that we generally talk
about, but because people think the government won't let it go down. ... Some
fundamental parts of governments rely on it (the real estate market), and they won't
allow it to change a lot. Some people say that the price will go down because they
worry that the government won't be able to control it. It is difficult to know to what
extent the government can resist the power of market. I don't think people 'listen to'
the government', it (what they are doing) is like forced behavior-in this circumstance
they need to behave in that way."
Market Participants' Perceptions of the Popular Models
Based on my interview date, I categorized the popular theories into three
groups, which is shown in the three circles in Figure 12. The first group reflects the
rising financial logic in this market, the second group reflects people's beliefs about
the government's role in this market, and the last group reflects the (perceived)
speculative sentiment in this market.
Through the survey questions I obtained information on two things about the
sample of market participants: 1) to what extent they personally agree with each of the
popular theories, and 2) to what extent they believe each of the popular theories is
agreed by other participants. These data also reflect the respondents' opinions on the
relative importance of these theories privately, and how they consider the relative
importance believed by the majority of people. Figure 13 shows the results based on
76
the original data. Privately, respondents think that the most important factors are the
importance of housing for marriage (marriage) and life (life), as well as the
government's desire of the steady increase of the market (gov rise). However, the
theories that they regard as the most popular among other people are the government's
desire (gov rise) and a statement commonly believed by the deluded participants (Case
and Shiller 2004): housingprice are very unlikely to fall (Notit_drop). This suggests
that participants tend to think that others are deluded (at least more deluded than
themselves).
I also examine the significance of the rank differences between the private
group and the public group, by using the analysis method I created based on z-score,
which I introduced in chapter five. I did the analysis for different categories of
participants, respectively, and the results are largely consistent (see results in Table 26
to Table 29). Figure 13 demonstrates the results in a simple way: privately participants
valued housing for marriage and life (as long-term investment), as the housing market
was backed by governments, but they thought other participants treated this market as
a government-backed speculative arena. In particular, this is even true for the group of
investors in this market (see Table 29). The misperception of others' incentives in this
market enhances their feeling of "unpowered" in the current situation, as one of
interviewee said: " if the grand banquet backed by the government keeps going, the
speculators will make the price higher and higher, and my hope to buy a house becomes
less and less in at least ten years, or twenty years. But I still need my life in this ten
years or twenty years. I would like to buy my home to make my family happy." In sum,
77
market participants' perception of the institutional stability makes them to be tolerant
with the market inefficiency, though they fully realize the inefficiency.
78
Chapter 9 Discussion and Conclusion
In this section I review the contributions of this study, and discuss broader implications
for financial market literature and other relevant literature.
The first contribution of this study is that it shows the first systematic and
precise evidence that the price does not reflect the average valuation of market
participants in a market bubble. This finding is consistent with the institutionalist
approach on financial markets. In particular, the data that directly reflect the valuations
of market participants are in general unavailable in existing financial literature, leading
distinguishing the collective delusion explanations and the institutionalist explanations
almost impossible. This study, however, is based on the unique data collected by me,
which reflects the bubble-era valuations of market participants.
The second contribution of this study is that it sheds light on a variation of the
institutionalist models. All of the three major institutionalist models, proposed by
Miller (1977), Abreu and Brunnermeier (2003), and Turco and Zuckerman (2014),
reflect the core dynamics underlying this market. First, as in Miller (1977), short
selling is limited in Beijing housing market. However, I didn't find evidence showing
that a minority of optimistic participants who were driving the prices existed in the
market. Rather, the investors, i.e. those who were believed to drive the housing prices,
actually thought the properties were overpriced. Furthermore, different from what
Abreu and Brunnermeier (2003) assume in their model, I didn't find that the market
participants in Beijing housing market could be divided into arbitrageursand noise
traders.In fact, the majority of market participants behaved like "dancers"-they chose
to keep buying in even though they knew that the price had surpassed the value, which
79
is consistent with Turco and Zuckerman (2014)'s finding in the US private equity
industry. But I find a difficult reason that drove the market participants to do so. In
Turco and Zuckerman's study, the fund managers were correct in understanding others'
valuations-they knew that other fund managers also recognized the bubble. They
chose to dance because they believed that other managers would choose dancing, too.
However, in the context that I study, the market participants erred in understanding
others' valuations-they tended to believe that others were more deluded than
themselves.
The third contribution of this study is that I examine the logics behind market
participants' behavior in depth through their perceptions of the "popular theories".
Shiller (1990; see also Case and Shiller 2004, 2012) argues that market participants
could hold theories or models that they use to justify the price in a bubble, though these
theories or models are often deviated from the rational models created by economists.
My analysis of both the interview and survey data shows that, first, the popular theories
reflected the influences of the economic, social and political institutions on the market
perceived by the market participants. Second, market participants overestimated the
importance of some popular theories believed by others-they tended to believe that
others were more deluded and speculative than themselves. Third, their perception of
the institutional made them tolerant with the market inefficiency, though they had fully
realized such inefficiency.
Case and Shiller (2004, 2012) paves the way to study financial markets by
directly investigating market participants' opinions. Nevertheless, their method doesn't
80
distinguish market participants' opinions on price from those on value. My survey
design enables me to test both the collective delusion and the institutionalist
approaches.
Pluralistic ignorance has been found by psychologists and sociologists in widerange contexts where the social pressure for conformity is heavy and opportunities for
exit and voice are constrained. However, market is defined as providing abundant
opportunities for voice based on the mechanism of pricing: the bull participants will
buy in and lift the price, while the bear participants will sell out and lower the price.
But such a pricing mechanism is likely to be dysfunctional, and a reason for the
dysfunction is the information asymmetry among market participants (Abreu and
Brunnermeier 2003; Turco and Zuckerman 2014). If the mechanisms that make the
market participants to directly exchange opinions is lacking, the situation may resemble
to that in the social contexts where misinformation among group members aggravates
their incorrect perceptions of other members' opinions (Zuckerman 2010; Zhu and
Westphal 2011).
Besides the literature on financial markets, this research also contributes to a
wide range of other literature. For instance, for the social valuation literature, it
highlights the construction of publics by political and market institutions as an
intermediary step of social valuation. For the social contagion and epidemic literature,
it sheds light on the possible complex dynamics underlying the herd-like behavior. In
particular, the same kinds of dynamics may be also behind the "bubble and bust"
phenomena that we observe in many industries and management practices.
81
This research has policy implications for a broad range of financial practices,
and more broadly, any practices relevant with valuation and evaluation, which
constitutes an essential part of social life. In particular, it improves our understanding
about the causes of inefficiency in markets, and potentially assists us in developing
sound policies to avoid unpleasant bubbles. It emphasizes that, especially in the market,
institutional arrangements should provide incentives and opportunities for participants
to be able to exert their influences on the market. Moreover, the results suggest that the
analysts and the practitioners of markets and other social systems should pay more
attention on latent private valuations; otherwise, the risk in these systems could be
greatly overlooked.
82
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Figures and Tables
Figures
Figure 1 The relationships between price, value and the mean of private valuations, implied by three
different theoretical approaches.
Efficient Market
Hypothesis
Collective Delusion
Approach
Price
Price \,
0
Gap
Mean of
-----
Private
Valuations
Valuations
Price
/
Value
Institutionalist
Approach
--
-4
Private
Valuations
M ean of
4
Pri vate
Valuations
91
Figure 2 Different institutionalist models
Miller (1977): price is driven
by a minority of optimistic
investors.
Institutionalist
approach:
Price does not
aggregate private
valuations
Abreu and Brunnermeier
(2003): "synchronization
problem" among rational
investors
Informat ion asymmetry
imong rational investors
impedes their collective
attack Cn mispricing
-1----
Turco and Zuckerman (2014):
professional (and rational)
investors erred in interpreting
others' strategies
K I
Zhang (2014):
Rational investors overestimate
others' endorsement of the price
92
19931
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
1991
19921
0D~
-)
0
0
0
0C
0C
0>
0)
CD
00)
0
0
0>
a0(
0
C0D
0D
C0
E-
00
Total sale of commercialized buildings (100
million Yuan)
0)
0
a'
Floor space of commercialized residential
buildings sold(million square meters)
0~'
0
0
0
0
0
0
t~~)
W1
C
0
CD
CD
Figure 4 Distribution of real estate market policies launched by the central
government from 2003 to 2013
16
14
12
10
8
6
4
2
0
2003
2004
2005
2006
2007
2008
N Cooling down policy
94
2009
2010
2011
Stimulating policy
2012
2013
Figure 5 The relationships between market price, real popular valuation and perceived
popular valuation, predicted by the two hypotheses
Perceived popular
valuation
-I-
Market Price
Significant
gap predicted
by HI
Real popular valuation
(Average of private
valuations)
95
Significant
I"Wgap predicted
by H2
Figure 6 Beijing districts and the distribution of population
DISTRICT-DENSITY (1km')
2 KIcIWh
Dis1tict
3 ShiJkwighan Diwtia
-'
a-,ft.
f.t~w) go,
Source of the map: http://beijingconflict.wordpress.com/maps/
96
Figure 7 The ring roads of Beijing
[
Shijin shan
Sihuan
(The fourth ring road)
Xicheng
C haoy ang
Dongcheng
Xuanwu Chongwen
14gtl
Source: http://www.chinatouristmaps.com/provinces/beijing.html
97
Figure 8 Market price does not aggregate private valuations
Baseline (zero): market price
ors
E3
Gap between price and
the mean of private
valuations
.79%***
W.94%***
-5%***
-8.45%***
a For core region
-8.17%***
-8.00%
-12.27%***
N For peripheral region
Different from baseline: ***p<0.001; **<0.01; *<O.1
98
-8 .23%***
Figure 9 Market participants believe that those who drive price are optimistic
n For core region
N For peripheral region
43.14%***
31.73%***
25.33%***
20.54%***
11 .26%*E
10.36%*
11.09%*
6.32%**
4.73%
4.73%**\
0.
First home
purchasers
Switchers
Investors
Baseline (zero): market price
External Investors
Different from baseline: * *p<0.001; **<0.01; *<O.l
99
Speculators
Figure 10 Investors' opinions on the valuation popular among investors
Popular valuation
perceived by investors
among themselves
Average private
valuation of investors
-0.79%
-3.15%
U
For core region
\ For peripheral region
Different from baseline: ***p<0.001; **<0.01; *<0.1
Note: The difference between I and 2 for core region is significant at the 0.1 level.
100
Figure 11 The economic model created by the central and local governments
Other sources
of investments
State policy
and
commercial
banks
Loan
I
Income from leasing
Deposi ts
Re-distribution
of "growth
bonus" to wealth
families
Urban
expansion and
investments in
infrastructure
Localgovernment
financing
vehicles
(LGFVs)
land
Construction and
sale of
residential
housing
Appreciation of
properties
101
Land
appreciation
Land leasing to
real estate
developers
Figure 12 Major logics behind the popular theories
Housing is a
family's most
important
financial asset.
The government
wants the housing
price to steadily
increase.
The housing
market is largely
speculative.
Marriage
Gov rise
Gov ab rise
NoL drop
Supply
L ife
102
Figure 13 Private vs. (perceived) public agreements with the logics
Private
No-It_drop
Gov-rise
Marriage
Life
Investment
Gov-rise
Public
Marriage
Life
Investment
Gov-rise
Gov-rise
No-It drop
103
Tables
Table 1 Summary of "popular theories"
No.
Label
Summary of the quotes
1
Marriage
For the couple that plans to get married, owning a house is a
requirement for marriage.
2
Life
3
Investment
For a person/family who would like to take root in Beijing,
owning a house in the city is necessary.
Real estate is the best investment for long-term holders.
4
NoLtDrop
Housing prices are very unlikely to fall.
5
Supply
There is simply not enough housing available.
6
Econ
In the next many years, economic development will still largely
depend on the growth of real estate industry.
7
Gov-rise
The government desires that the housing prices increase in a
steady way.
8
Govabrise
Regardless of its desire, the government has the ability to ensure
the stability of the housing market.
Source: Interviews conducted by author in spring, 2012
104
Table 2 Summary of the policies pertinent to real estate market launched by the central government, 2003-2013
Year
Cooling Down Policy
Finance &
Land supply
taxation
&
intervention
construction
intervention
Total
number
of
policies
Instructional
policy
2003
2004
1
2
0
0
0
1
2005
5
2
3
2006
10
2
2007
6
0
2008
13
2009
2010
2011
2012
2013
3
15
3
2
3
Sum of
cooling
down
policy
Instructional
policy
0
1
0
0
2
1
0
0
5
3
10
0
6
0
6
0
1
4
2
7
0
1
9
0
1
1
1
2
2
0
0
1
4
1
0
2
3
15
3
1
3
0
0
0
0
0
5
105
Stimulating Policy
Finance &
Land supply
taxation
&
intervention
construction
intervention
Sum of
stimulating
down
policy
0
1
0
0
0
0
0
0
0
0
6
0
6
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
Table 3 The targeted interviewees in each occupational group
eof
R
(nnuli
RMB)
Migration
Status of
employees
Ownership of
25+t35+
50,000 to
500,000+
Mixed
State-owned,
foreign, private
Around 30
120,000 to
200,000+
Mixed (mainly
non-local)
Foreign,
private
3000
ie
rvt
60,000+ to
300,000+
Mixed (mainly
non-local)
Public
universities'
Occupational
group
Range of
Education
an(o
Age (or life
stage)
analysts and
managers
College to
PhD
IT engineers
(High end)
Entrepreneurs
Typically
masters
Below
and the self-
lowcollege
25± to 35+
employed
Professors and
Research
PhD
30+
College to
PhD, typically
masters
Around 30
Scientists
Government
staffs and
officials
5ollege
0 000 to
50 000 to
150,000
Note: Most Chinese universities are public and owned by the state.
106
Mostly
local
the
enterprises
State-owned
Table 4 The administrative districts of Beijing and the bank (branches) selected in them
District
Description
Size(km2)
Population(k)
Dongcheng
Traditionally
42
960
Xicheng
core region
51
431
465
306
1320
2110
1830
1040
Shijingshan
86
360
Tongzhou
912
650
Shunyi
1021
570
Fangshan
19
1994
7
770
Daxing
1040
590
Changping
1352
510
Huairou
2128
280
Pinggu
1075
400
1455
240
Miyun
2227
430
Yanqing
1993
280
Total
16578
12340
Haidian
Chaoyang
Expanding core
Fengtai
region
FangshanNewly
developed region
Mentougou
Ecologically
reserved region
107
Typical banksrselected in
State-owned big banks,
e.g. Bank of China,
Industrial and
Commercial Bank of
China, China Merchants
Bank
Local banks and saving
offices
Table 5 The characteristics of the core region vs. peripheral region
Core
Peripheral
(Inside the fourth ring road)
(Mainly between the fourth and the sixth ring
road)
v
V
1.8% of whole Beijing area
V
11.9%of whole Beijing area
26% of urban residents
/
74% of urban residents
Core business area
/
Expanded business area
Limited land supply
V
Potentially abundant land supply
Mainly secondary housing market and
/
Market of new housing
high-end housing market
/
Public housing
108
Table 6 The distribution of the respondents' occupational groups (for core region)
Categorical
ID
1
Percentage
Occupation
Frequency
Principal officials
Principal managers of State owned
Enterprises
1
(%)
0.25
3
0.74
Cumulative
Percentage (%)
0.25
8
1.97
4
Owners of Private owned Enterprises
Executive managers of Foreign or
Joint owned Enterprises
0.99
2.96
27
6.65
9.61
5
Scientists & Researchers
33
8.13
17.73
Engineers
Agricultural and medical
professionals
17
4.19
21.92
13
3.2
25.12
8
Economic Professionals
23
5.67
9
Financial Professionals
17
4.19
30.79
34.98
10
Legal Professionals
28
6.9
41.87
11
Government officers
14
3.45
45.32
12
53
13.05
58.37
22
5.42
63.79
14
Educational professionals
Professionals in Literature, Art and
Physical industry
Professionals in Media and Culture
industry
22
5.42
69.21
15
Religious
7
1.72
70.94
16
Clerks
46
11.33
82.27
17
Service
12
18
Agricultural producer
2.96
2.96
85.22
12
19
Workers
11
2.71
20
Entrepreneurs
14
3.45
21
Self-employed
10
2.46
22
Students
13
3.2
406
100
2
3
6
7
13
Total
Total
109
88.18
90.89
94.33
96.8
100
Table 7 The distribution of the respondents' occupational groups (for peripheral region)
Frequency
0
4
10
0.00
1.01
2.51
Cumulative
Percentage
(%)
0.00
1.01
3.52
10
14
13
12
26
20
22
13
55
23
12
13
44
22
18
25
16
12
14
2.51
3.52
3.27
3.02
6.53
5.03
5.53
3.27
13.82
5.78
3.02
3.27
11.06
5.53
4.52
6.28
4.02
3.02
3.52
6.03
9.55
12.81
15.83
22.36
27.39
32.91
36.18
50.00
55.78
58.79
62.06
73.12
78.64
83.17
89.45
93.47
96.48
100.00
Percentage
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Occupation
Principal officials
Principal managers of State owned Enterprises
Owners of Private owned Enterprises
Executive managers of Foreign or Joint owned
Enterprises
Scientists & Researchers
Engineers
Agricultural and medical professionals
Economic Professionals
Financial Professionals
Legal Professionals
Government officers
Educational professionals
Professionals in Literature, Art and Physical industry
Professionals in Media and Culture industry
Religious
Clerks
Service
Agricultural producer
Workers
Entrepreneurs
Self-employed
Students
Total
398
110
(%)
Table 8 Categories of market participants
Core region
Category
Home
purchasers
purchasers
Switchers
Investors
Investors
Percent
Those who buy their first
home for their families.
Those who sell the
current home and switch
to a bigger/better place.
Those who keep their
265
65.76%
206
51.50%
61
15.14%
120
30.00%
home and buy other
housing units as longterm investments
Those who don't live in
the city but buy one or
more houses in the city
for long-term
investments.
Those who have bought
63
15.63%
54
13.50%
10
2.48%
11
2.75%
multiple houses and also
sold multiple houses
within just a few years.
4
0.99%
1
2.35%
403
100%
400
100%
SubStbcategor
First Home
External
Inestors
Speculators
Peripheral region
No. in
Percent
the
sample
No. in
the
sample
Total No. of
Observations
111
Table 9 The Correlations between different categories of market participants (dummy variables;
1=Yes) and individual characteristics
Correlation
First Home
Purchasers
Switchers
Investors
External
Investors
Annual Household Income
(log form)
Male
-0.2585*
0.0219
-0.0795
-0.049
0.3849*
0.0187
0.1443*
0.011
Age
-0.355*
-0.0326
0.4848*
0.0957
Live in the core region
0.0203
-0.0498
0.0334
-0.0272
Married
-0.1118*
-0.0727
0.2157*
0.0769
Born in Beijing
-0.0765
0.1145*
0.042
-0.1839*
Graduate
-0.2221*
0.0569
0.2995*
-0.109*
Don't own any property
0.3368*
-0.1186*
-0.2944*
-0.1398*
Own one property
Own more than one
-0.1802*
0.1591*
0.0617
0.1009*
properties
-0.2803*
-0.0756
0.4197*
Note: I don't include speculators in the table as there are too few of them in the sample.
112
0.0687
Table 10 Private valuations, by categories of market participants (for core region)
Private Valuation
Max
Distribution of Private Valuation
Private
Private
Private
Valuation<0 Valuation=0 Valuation>0
Self-Identified Type
N
Mean
SD
Min
First Home purchasers
265
-8.45%
12.250
-30%
30%
61.13%
23.77%
15.09%
57.38%
27.87%
14.75%
30.16%
11.11%
Switchers
61
-8.03%
11.521
-30%
10%
Investors
63
-8.17%
10.484
-30%
10%
58.73%
External investors
10
-8.00%
19.889
-30%
30%
60.00%
10.00%
30.00%
Speculators
4
2.50%
9.574
-10%
10%
25.00%
25.00%
50.00%
Total
403
-8.23%
12.085
-30%
30%
60.55%
25.31%
15.14%
113
Table 11 Private valuations, by categories of market participants (for peripheral region)
Private Valuation
Self-Identified Type
N
Distribution of Private Valuation
Mean
SD
.
Mmn
Max
Private
Valuation<O
Private
Valuation=0
Private
Valuation>O
First Home purchasers
206
-7.52%
12.03
-40%
10%
55.34%
29.61%
15.05%
Switchers
120
-5.29%
12.41
-40%
20%
52.50%
20.83%
26.67%
Investors
54
-6.94%
9.88
-30%
15%
59.26%
29.63%
11.11%
External investors
11
-12.27%
11.26
-20%
10%
72.73%
18.18%
9.09%
Speculators
9
-2.22%
8.33
-10%
10%
44.44%
33.33%
22.22%
Total
400
-6.79%
11.83
-40%
20%
55.25%
26.75%
18.00%
114
Table 12 The factors that influence private valuations
Private Valuations
Peripheral region
Core region
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Home
buyers(reference)
2.556
Switchers
0.463
Investors
0.272
0.138
-0.279
-4.464
Ex-investors
Born in Beijing
Annual Household
Income (Log form)
Own zero property
(reference)
-1.801
-1.752
-2.087
0.904
1.217
1.001
0.109
0.046
0.122
2.071*
2.137*
2.184
Own one property
Own at least two
properties
2.414
2.271
2.417
-2.626
-2.179
-2.204
3.738
2.656
2.658
0.261
1.494
1.587
Male
-1.548
-1.553
-0.318
-0.279
Age
-0.14
-1.593
-0.0483
-0.251
1.152
1.053
-0.0407
1.062
-0.229
Live in Core Region
-0.506
-0.528
In Marriage
Below College
(reference)
-1.919
-2.229
-1.96
1.027
1.133
-0.473
-0.306
-0.31
0.726
College
-1.201
0.205
-1.951
-2.337
1.901
1.941
1.911
-1.091
2.525
2.897
2.782
-0.819
-0.567
-1.394
-0.468
4.826*
4.827*
4.811*
-0.499
-0.292
-1.68
-1.928
-1.955
-2.156
2.73
5.082
3.322
4.349
2.84
5.939
1.436
1.855
1.906
1.809
3.280**
3.129**
-2.838*
-2.912*
-5.54
-6.015
-6.672
-6.722
-5.469
-5.927
-5.935
-6.043
-5.266
-5.434
-5.509
399
397
397
381
376
376
0.02
0.037
0.047
0.03
0.043
0.06
Graduate
Financial Professional
Owners of Private
Enterprise
Officials
Plan to buy (within 5
years)
Plan to sell in (within 5
years)
Constant
Observations
R-squared
R-sauared
-2.652
115
Table 13 Most influential types in shaping the market prices in short-term, identified by
each type (for core region)
1
N (percentage)
Home
Self-identified type
3
4
2
External
5
6
Speculators
Total
0(0%)
0(0%)
0(0%)
0(0%)
Switchers
Investors
Switchers
36(13.58%)
46(17.36%)
11(18.03%)
26(42.62%)
0(0%)
3(4.76%)
Investors
130(49.06%)
12(19.67%)
54(85.71)
External Investors
20(7.55%)
33(12.45%)
10(16.39%)
2(3.28%)
0(0%)
6(9.52%)
8(80%)
0(%)
0(%)
4(100%)
47(11.66%)
77(18.92%)
206(50.61%)
30(7.37%)
47(11.55%)
265
61
63
10
4
403(100%)
Home purchasers
Speculators
No
onf
Observations
purchasers
Investors
0(0%)
2(20%)
Note: Each column shows the distribution of this type's opinions on who is the most influential type in 12
months. For instance, column 1 shows that 130 out of 265 (49.06%) home purchasers think investors are
the most influential type. In total, 206 out of 403 (50.6 1%) respondents think investors are the most
influential type.
116
Table 14 Most influential types in shaping the market prices in long-term, identified by
each type (for core region)
Self-identified type
N (percentage)
Home
purchasers
Switchers
2
3
4
5
6
Switchers
Investors
Investors
External
Speculators
Total
6(2.3%)
8(13.11%)
2(3.28%)
0(0%)
0(0%)
16(4.06%)
37(14.18%)
1(1.64%)
1(10%)
51(83.61%)
9(90%)
0(0%)
0(0%)
59(14.97%)
231(58.63%)
1
Home
purchasers
Invetors
Investors
External
Investors
Speculators
144(55.17)
20(32.79%)
27(44.26%)
33(12.64%)
4(6.56%)
0(0%)
0(0%)
1(100%)
38(9.64%)
41(15.71%)
2(3.28%)
7(11.48%)
0(0%)
0(0%)
50(12.69%)
No. of
Observations
261
61
61
10
1
394(100%)
Note: Each column shows the distribution of this type's opinions on who is the most influential type in ten
years.
117
Table 15 Most influential types in shaping the market prices in short-term, identified by
each type (for peripheral region)
Self-identified type
1
N (percentage)
Home
purchasers
2
3
Switchers
Investors
Home purchasers
36(17.48%)
18(15%)
Switchers
77(37.38%)
51(42.5%)
Investors
External Investors
61(29.61%)
14(6.8%)
38(31.67%)
8(6.67%)
Speculators
18(8.74%)
5(4.17%)
8(14.81%)
7(12.96%)
34(62.96%)
4(7.41%)
1(1.85%)
206
120
54
No
onf
Observations
118
5
6
Investors
3(27.27%)
0(0%)
Speculators
Total
6(66.67%)
7(63.64%)
1(9.09%)
0(0%)
1(11.11%)
0(0%)
1(1 1.11%)
71(17.75%)
136(34.00%)
141(35.25%)
27(6.75%)
25(6.25%)
11
9
400(100%)
4
External
1(11.11%)
Table 16 Most influential types in shaping the market prices in long-term, identified by
each type (for peripheral region)
Self-identified type
1
Home
2
3
4
5
6
External
Speculators
Total
45(11.31%)
115(28.89%)
162(40.70%)
43(10.80%)
29(7.29%)
398(100%)
Switchers
Investors
Invetors
Investors
Home purchasers
purchasers
22(10.68%)
19(15.83%)
3(5.56%)
2(18.18%)
Switchers
65(31.55%)
30(25%)
12(22.22%)
2(18.18)
N (percentage)
Investors
79(38.35%)
46(38.33%)
30(55.56%)
5(45.45%)
External Investors
15(7.28%)
19(15.83%)
7(12.96%)
2(18.18%)
Speculators
24(11.65%)
4(3.33%)
1(1.85%)
0(0%)
1(11.11%)
6(66.67%)
2(22.22%)
0(0%)
0(0%)
206
120
54
11
9
No. of
Observations
Observations
119
Table 17 Private valuations and perceived valuations, by categories of market participants (for core region)
A
B
Average
private
valuation
C
D
E
F
Average valuation of category X perceived by
of category
X
Category(X)
No. of
Obs
First home
First
home
purchasers
Switchers
Investors
External
Investors
Speculators
4.73%
10.00%a
NA
20.00%a
5.67%
11.00%
1.29%
NA
NA
265
-8.45%
Switchers
Investors
61
63
-8.03%
-8.18%
10.53%
6.65%
5.92%
External
10
-8.00%
10.59%
12.40%
2.50%a
Investors
Speculators
Total
4
403
2.50%
-8.23%
7.77%
7.94%
9.00%
7.57%
12.67%
10.33%
purchasers
30.00%a
NA
3.44%
22.50%
Note: "NA" means no data is available. For instance, no external investor thinks home purchasers are the most influential market participants, and also no
external investor thinks home purchasers are the secondary most influential market participants. Therefore, (row 1, column 7) is NA. a: These numbers are based
on the perceived public valuations of the secondary most influential category.
120
Table 18 The average of the gaps between private valuations and popular valuations of the most influential category (for core region)
Gap between a respondent's private valuation and popular valuation
Mean (N)
First home
purchasers
Switchers
-17.46%***(1 1)
First home purchasers
External
Investors
Investors
-25.00%t(6)
a
-28.64%t(3)
Switchers
-18.42%***(45)
Investors
-16.78%***(121)
-11.75%***(12)
External Investors
-24.12%***(17)
-14.40%***(10)
-6.25%**(12)
Speculators
-14.77%***(30)
-24.00%f(2)
-36.50%***(10)
Speculators
NA
-15.00%(2)
-16.00%*(2)
NA
-12.71%t(7)
NA
-20.00%(1)
a
a
a
NA
-20.00%**(3)
-13.44%*(9)
-26.98%***(13)
-15.00%***(35)
-17.43%***(213)
Total (Gap between types)
Note: The gap is calculated by an individual's private valuation minus her public valuation. "NA" means no data is available.
a: These numbers are based on the perceived public valuations of the secondary most influential category.
t-test: ***p<0.001; **p<0.01; *p<0.05tp<0.1
121
Table 19 Private valuations and perceived valuations, by categories of market participants (for peripheral region)
A
Category(X)
No. of
Obs
Average
private
valuation of
category X
C
B
D
F
E
Average valuation of category X perceived by
First
home
purchasers
First home
-7.52%
206
purchasers
-5.29%
42.76%
120
Switchers
-6.94%
16.67%
54
Investors
External
33.21%
11
-12.27%
Investors
-2.22%
0.29%
Speculators
9
-6.79%
28.48%
400
Total
Note: "NA" means no data is available. For instance, no external
therefore, (row 2, column 7) is NA.
122
Switchers
Investors
External
Investors
Speculators
18.75%
22%
13.33%
NA
44.83%
42.70%
41.67%
NA
16.00%
30%
20%
31.25%
27.50%
44.00%
30.00%
NA
NA
25.00%
15.00%
31.25%
35.61%
investor thinks switchers are the most influential market participants,
Table 20 The average of the gaps between private valuations and popular valuations of the most influential category (for peripheral
region)
Gap between a respondent's private valuation and popular valuation
Mean (A)
Frchoses
First home purchasers
Switchers
-51.32%***(76)
Investors
-25.09%***(57)
Switchers
Investors
Entetna
Speculators
-30.00%**(16)
-35.00%**(5)
-21.67%(3)
NA
-51.67%**(6)
NA
-40.00%(1)
-26.00%(5)
-20.00%(1)
-48.38%***(37)
NA
External Investors
-33.21%***(14)
-36.25%**(8)
-37.5%t(4)
Speculators
Total (Gap between
-8.53%*(17)
-36.22%***(164)
-50.00%*(5)
-30.00%(1)
NA
-42.58%***(66)
-41.56%***(16)
-24.38%*(8)
-30.00%(2)
types)
Note: The gap is calculated by an individual's private valuation minus her public valuation. "NA" means no data is available.
t-test: ***p<0.001; **p<0.01; *p<0.05tp<O.l
123
Table 21 The average of the gaps between private valuations and popular valuations within a category (for core region)
Popular Valuation Within Self-identified
Type (SIT)
Gap between private and popular
opinions within SIT
Self-Identified Type
First-time purchasers
(ref.)
N
mean
sd
min
max
mean
sd
265
-14.51%
21.70186
-80%
50%
6.06%***
24.33231
-60%
80%
Switchers
61
-13.11%
20.04503
-50%
30%
5.08%
25.53579
-50%
60%
min
max
Investors
63
-0.79%
30.60158
-60%
100%
-7.38%*
34.82511
-120%
70%
External investors
10
-16.00%
6.992059
-20%
0%
8.00%
22.0101
-20%
50%
Speculators
4
-7.5.00%
15
-20%
10%
10.00%
14.14214
0%
30%
3.90%***
26.63712
-120%
80%
Total
403
-12.12%
23.26096
-80%
100%
The significance of the gaps between the private and the public valuations is tested
by t-test: ***p<0.001; **p<0.01; *p<0.05
124
Table 22 The average of the gaps between private valuations and popular valuations within a category (for peripheral region)
Popular Valuation Within Self-identified
Type (SIT)
max
min
sd
mean
Self-Identified Type
N
First-time purchasers
(ref.)
Switchers
206
-9.78%
19.52
-80%
120
-5.21%
15.14
Gap between private and popular
opinions within SIT
max
min
sd
mean
30%
2.26%t
24.36
-50%
30%
-0.08%
18.67
-50%
90%
-3.80%
25.12
-100%
50%
-50%
90%
Investors
54
-3.15%
22.91
-50%
100%
External investors
11
-16.36%
13.62
-30%
10%
4.09%
19.60
-30%
40%
Speculators
9
4.44%
5.27
0%
10%
-6.67%
10.00
-20%
10%
Total
400
-7.38%
18.74
-80%
100%
0.59%
22.58
-100
90
The significance of the gaps between the private and the public valuations is tested by -test: ***p<0.001; **p<0.01; *p<0.05
125
Table 23 Regressions of private valuation and popular valuation on categories of market participants
Private Valuation
Home Buyer
Switcher
Investor
External
Investor
Constant
Model 1
-0.663
Model 2
Model 3
Popular Valuation (within category)
Model 4
Model 5
-6.973**
0.227
Model 6
-8.260***
Observations
403
403
***p<0.001; **p<0.01; *p<0.05
Model 8
-1.17
0.0607
-7.790***
Model 7
13.43***
-8.235***
0.232
-8.232***
-7.536***
-11.94***
-14.22***
0.232
-8.232***
403
403
403
403
403
403
126
Table 24 The factors that influence the popular valuations within self-category
Private
Valuation
Model 1
Model 2
Public Valuation (within Category)
Model 5
Model 4
Model 3
Model 6
-1.017
Home Buyer
-2.666
Switcher
8.439**
Investor
Private Valuation
-0.0555
-0.0561
-0.0555
-0.0538
Born in Beijing
-1.793
6.052**
5.952**
5.887**
6.151**
5.657**
Annual Household Income (Log form)
-0.528
2.086
2.056
2.091
1.791
1.604
Own one property
1.882
8.318**
8.423**
8.218**
8.771**
8.090**
Own two properties
4.039
0.0623
0.286
-0.0606
0.317
-2.268
Own more than two properties
-0.973
47.32***
47.27***
46.78***
46.96***
45.06***
Male
-1.609
0.474
0.385
0.394
0.278
0.158
Age
-0.123
0.251
0.244
0.222
0.255
0.0697
Live in Core Region
1.19
3.039
3.105
3.112
3.016
2.835
College
-1.842
-8.368
-8.47
-8.426
-8.146
-7.276
Graduate
-0.371
-11.31**
-11.33**
-11.34**
-10.89**
-10.79**
Financial Professional
-0.942
0.223
0.171
0.202
0.134
-0.159
Owners of Private Enterprise
0.229
19.39**
19.40**
19.09**
20.59**
19.57**
Officials
2.356
-3.603
-3.472
-3.52
-3.472
-5.098
Constant
-1.64
-26.80**
-26.89**
-25.50**
-26.63**
-21.89**
0.383
0.164
0.722
5.739**
(Reference: own zero properties)
(Reference: Below College)
Incremental F-test
***
R-squared
0.019
0.188
0.189
0.19
0.191
0.201
No. of Observations
399
399
399
399
399
399
p<0.001, ** p<0.05, * p<0.01
127
Table 25 Market participants' perception of "popular theories", raw data
Private Opinion
Sorted
No. of
Obs
Mean
(range: 1-9)
Public Opinion
SD
Min
Max
Sorted
No.
of
Mean (%)
(range: 0-
Obs
100%)
SD
Min
Max
(%)
(%)
Marriage
Life
Govrise
807
807
807
6.00
5.82
5.68
1.14
1.13
1.05
3
3
3
9
9
8
Gov rise
No_Lt Drop
Life
807
807
807
60.52
60.02
59.37
13.87
14.99
14.00
20
20
20
90
90
90
Investment
807
5.62
1.14
3
8
Supply
807
59.12
15.32
20
90
Supply
807
5.62
1.24
2
9
Gov drop
807
59.01
13.89
20
90
Econ
807
5.56
1.18
3
8
Econ
807
58.46
13.46
20
90
Govdrop
NoLtDrop
807
807
5.50
5.43
1.06
1.16
2
3
9
8
Marriage
Govab drop
807
807
58.31
57.81
13.18
14.18
20
20
90
90
Govabdrop
807
5.40
1.01
3
8
Investment
807
56.37
14.43
20
90
Govabrise
807
5.29
1.03
2
9
Gov ab rise
807
54.56
14.16
20
90
128
Table 26 Market participants' perception of "popular theories" (all of respondents,
N=807)
t-test
Public Opinion
Private Opinion
Z-score Mean
SD
Z-score Mean
SD
t
Marriage
0.377
0.998
-0.006
0.935
7.811***
Life
0.184
0.999
0.062
1.002
2.426**
4.194***
-0.828
-1.241
-5.070***
-1.877*
0.038
1.003
Supply
Econ
NoLtDrop
Govrise
0.013
-0.062
-0.135
0.092
1.094
1.022
1.054
0.908
-0.163
0.056
-0.002
0.131
0.179
0.984
1.018
0.933
1.037
1.012
Govdrop
-0.065
0.932
0.05
0.991
-2.424**
Govabrise
-0.258
0.923
-0.273
0.978
0.331
Govab drop
-0.185
0.893
-0.034
1.031
-3.134***
Invest
***<O.001 **<o.01 *<O.1
129
Table 27 Home buyers' perception of "popular theories" (N=469)
Private Opinion
Public Opinion
t-test
Z-score
Z-score Mean
SD
Marriage
Life
Invest
0.405
0.174
0.033
1.004
0.996
1.016
0.014
0.075
-0.154
Supply
Econ
NoLtDrop
Govrise
Gov drop
-0.019
-0.022
-0.105
0.063
-0.091
1.079
1.008
1.052
0.92
0.923
Govabrise
-0.247
Govabdrop
-0.191
SD
t
0.078
-0.002
0.109
0.219
0.015
0.919
1.035
0.966
1.001
0.899
1.027
1.012
1.029
6.020***
1.566
3.002**
-1.410
-0.305
-3.121**
-2.582**
-1.654*
0.898
-0.288
0.976
0.682
0.937
-0.067
1.042
-1.955*
Mean
130
Table 28 Switchers' perception of "popular theories" (N=181)
Public Opinion
Private Opinion
Z-score
t-test
SD
t
-0.055
0.062
1.011
0.961
3.059**
0.829
1.004
-0.182
0.999
2.109*
0.035
-0.12
1.14
0.981
0.062
1.022
-0.240
-0.088
0.984
-0.333
No Lt Drop
-0.153
1.04
0.248
0.962
-3.734**
Govrise
0.163
0.922
0.113
1.016
0.495
Z-score Mean
SD
Marriage
L fe
0.26
0.155
1.003
0.988
Invest
0.049
Supply
Econ
Mean
Gov drop
Govabrise
-0.023
-0.219
0.97
1
0.075
-0.26
0.908
1.006
-0.997
0.388
Gov ab drop
-0.148
0.836
0.027
1.049
-1.719*
131
Table 29 Investors' perception of "popular theories" (N=1 17)
Private Opinion
Z-score Mean
Public Opinion
t-test
Z-score
SD
Mean
SD
t
Marriage
0.419
1.021
-0.011
0.945
3.110**
Life
Invest
0.16
0.016
1.069
1.007
0.103
-0.204
Supply
Econ
NoLtDrop
Govrise
Gov drop
Govabrise
Govabdrop
0.08
-0.014
-0.235
0.127
0.017
-0.3
-0.27
1.097
1.123
1.056
0.812
0.886
0.899
0.779
-0.059
0.089
0.049
0.158
0.109
-0.236
0.003
0.936
0.982
1.059
1.011
1.139
0.969
0.944
0.98
0.979
0.402
1.708*
0.942
-0.751
-2.044*
-0.267
-0.801
-0.567
-2.29*
132
Appendix A: Interview Protocols
Interview protocol with housing market participants
The interview protocol is a general guide for conducting interviews. During an
interview, the interviewer (I) may focus on part of the questions, add extra questions
and change the order of questions flexibly according to the interviewee's emotional and
verbal responses. The important questions are marked by *, which the interviewer
should try the best to ask.
For home buyers:
Introduction
-
Thanks for your time and willingness to participate
-
We would like to talk about your life in Beijing and the main
challenges to settle down and raise a family here.
Warm-up session
The interviewer begins the conversation by talking with the interviewee about his or her
experiences and life in Beijing in order to make the intervieweefeel relaxed and
comfortable. The interviewer will try to ask the following questions during the
conversation or smoothly switch to the following questions.
I.
General on status*
People vary in the extent to which they care about things like the how much
money someone has, their job, how much education they have, where they live,
or what they own. In what contexts in today's Beijing do people really care
about these things, and which of them do you think is most important? Can you
give me some examples?
II.
Place to live and perception on housing*:
-
Does having your own house important in Beijing?
-
How about renting a house in the city?
-
What does renting mean to you?
133
-
What does the ownership of housing mean to you?
-
What type of neighborhood do you prefer?
-
What does the neighborhood/community mean to you?
-
Tell me something about the persons you know (for instance, friends
or colleagues). What are their opinions on these questions? Gould
you provide me a few examples?
III.
General opinions on housing market
-
In simple words, how will you describe the housing market in the
three years, in five years and in ten years?
-
In your opinion, how will the market look like in one year, in three
years and in five years? Why do you think so?
IV.
Detailed Information on housing purchases
a. For the individualswho have bought (at least) one house
-
When did you start to pay attention on housing market?
-
How did you make the decision on the purchase*? /What are the
crucial factors that influence your decision?
-
How did you raise the money*?
-
When did you buy it*?
-
Where is its location*?
-
How will you describe it and its community* (this question and two
above can help the researcherto roughly estimate the price of the
housing)?
-
(Only for multiple houses buyers) how did you make decisions on
subsequent purchases*?
-
Is there any change in your life after your purchase(s)*?
-
Do you plan to sell it at some time point?
-
Do you plan to buy additional ones?
b. For the individualswho haven't bought any houses
-
Do you expect to buy a house? Why?
-
What does housing mean for you*?
134
-
V.
What are the most important factors for choosing a house*?
Information on backgrounds* (may get through a short paper or online survey
which takes 3-5 minutes)
-
Which industry are you in?
-
How will you describe your education background?
-
Where did you go to college?
-
Where is your hometown?
-
Where do you live now?
-
How many years have you spent in city X?
-
If you don't mind me asking, how old are you?
-
Are you married?
-
How will you describe your father's education background and
occupation?
-
How much did you pay for the house you bought*?
135
Interview protocol with Real Estate Developers
Introduction
- Thanks for your time and willingness to participate
- We would like to talk about your work and your opinions on the
housing market and its future in city X.
Before interview, the interviewer will collect the background information
about the developer as much aspossible
Questions
I.
II.
About home buyers
-
How will you describe your customers?
-
What does housing mean for them?
About investments
-
How do you estimate the demand in the city?
-
What are the crucial factors that influence your decision on investing
in a location?
III.
What are the crucial factors for you to determine the price?
About the real estate market
-
How will you describe the housing market in the past ten years?
-
How will the market look like in one year, in three years and in five
years?
In your opinion, how do the consumers perceive the current condition of the market?
How do they predict the market?
136
Interview protocol with Real Estate Brokers
Introduction
- Thank for your time and willingness to participate
-We would like to talk about your work and your opinions on the
housing market and its future in Beijing.
Questions
I.
About clients:
-
How long have you ben working in this industry?
-
How will you describe your typical client?
-
Could you describe a typical deal is made?
-
According to your experiences, what are the most crucial factors that
influence the customers' decisions?
II
-
In your opinion, what does housing mean for your clients?
-
Would you like to share some stories that impress you*?
About the real estate market:
-
How do you estimate the demand in the city?
-
How will you describe the housing market in the past three, five and
ten years?
In your opinion, how will the market look like in one year, in three years and in five
years?
137
Page intentionallyleft blank
138
Appendix B: Survey Questionnaires
Pilot Survey Questionaire
Survey on Real Estate Market in Beijing'"
[GeneralSurvey Description:by NSB in unifiedformat]
The questions of this survey are organized in three sections. Please answer questions in all three
sections. When you are working on section two or three, please don't go back to previous sections
again.
Section One
1.
If there is any way you got information on housing market, what are your most important
information sources? Please choose all options that apply to you, or specify the sources that
are not listed.
ElIV news
ENewspapers and Magazines (including electronic edition)
LlWebsites (Soufun.com, other)
Elocial network media (Xiaonei.com, Kaixin.com, other)
ELBlogs and Weibo
L3illboards
EReal Estate brokers
LConnections in relative industries
ElColleagues, business partners and friends
LFamily members and close friends
EPublic market reports
LPrivate market reports
If any others, please specify:
2. In conversations with family members, friends and other associations over the last a few
years, conditions in the housing market were discussed...
1) Never
2) 1-2 times per week
3) 3-4 times per week
4) Almost every day
3. With whom of the following do you most frequently discuss housing market?
16 A few questions repeat what Case and Shiller did in their 1988 and 2003 survey on US housing market
(Case and Shiller, 2004). These questions may be slightly revised according to this particular context.
139
Family members
1)
2) Friends
3) Colleagues
4. According to your personal opinion, what is the reasonableaverage residential housing
price in Beijing? On the scale provided below, please circle a percent, which represents
the degree that you think the reasonableaverage price should be lower or higher than the
average price of the first quarter of 2012. For instance, in the following sample, SOLID
and DASHED circles represent two different choices. SOLID circle represents the
reasonable price is 20% lower than (or, 80% of) the given average price by the end of
March 2012. DASHED circle represents the reasonable price is 30% higher than (or 130%
of) the given average price by the end of March 2012.
Sample:
i
100%
i
i
80%
i
I
-60%
I
I
I
-40%
(ii
-20%
I
I
0
I
I I
I1 (i: %
20%
40%
I
60%
The average price inside of Sihuan is 36,720 yuan per in 2
You think the reasonable average price inside of Sihuan should be
yuan.
100%
80%
60%
-40%
-20%
0
20%
40%
60%
I
I
80%
80%
-60%
-40%
-20%
0
20%
40%
60%
100% >100%
than 36,720
80%
The average price outside of Sihuan is 23,760 yuan per in 2
You think the reasonable average price outside of Sihuan should be
yuan.
100%
I
100% >100%
than 23,760
80%
100% >100%
What is the most important factor that influences your judgment of the reasonability of
housing price?
1) The city's average or median income level
2) Residential rent price
3) Supply-demand relationship
If any others, please specify:
For question 5-8, on the scale, please circle a percent that represents the change in
price. Let negative numbers denote decrease of price, positive numbers denote increase of
price, and zero denotes unchanging of price. For instance, in the following sample, SOLID
and DASHED circles represent two different choices. SOLID circle represents the price of
property decreases by 30%. DASHED circle represents the Price of property increases by
15%
140
Sample:
80%
100%
80%
60%
40%
20%
0
20%
40%
60%
100% >100%
5. Do you own any property? If No, please go to question 7 directly.
If YES, how much of a change do you expect there to be in the price of your
property over the next 12 months? If you own multiple properties, please consider the
one you most often live in.
100%
80%
60%
40%
20%
0
20%
40%
60%
-80%
100% >100%
6. On average over the next 10 years, how much do you expect the price of your property to
change each year?
80%
100%
60%
80%
60%
40%
20%
0
-20%
-40%
100% >100%
7. According to your personal opinion, how much of a change do you expect in the average
residential housing price of Beijing over the next 12 months? Please consider the housing
inside of Sihuan and outside of Sihuan respectively. If you think there is no difference
between the two regions, just choose the checkbox "The same as above" for "Housing
outside of Sihuan". This instruction also works for the similar questions below.
Housing Inside of Sihuan:
i
100%
i
i
80%
i
I
-60%
i
i
-40%
i
i
20%
I
i
0
i
i
i
20%
i
i
i
i
i
i
40%
60%
80%
100% >100%
40%
60%
80%
100% >100%
Housing Outside of Sihuan: E The same as above. Or,
100%
80%
60%
-40%
20%
0
20%
8. According to your personal opinion, on average over the next 10 years, how much do
you think the average residential housing price of Beijing to change each year?
Housing Inside of Sihuan:
100%
80%
60%
-40%
20%
0
20%
Housing Outside of Sihuan: 0 The same as above. Or,
141
40%
60%
80%
100% >100%
100%
WY%
-60%
-40%
-20%
0
20%
60%
40%
80%
100% >100%
9. Please select the extent to which you agree or disagree with the following statements:
1) For the couple that plans to get married, owning a house is a requirement for
marriage.
Extremely disagree
1
Not sure
Disagree
2
3
4
Extremely agree
Agree
5
I
I
I
I
6
7
8
9
2) For a person/family who would like to take root in Beijing, owning a house in the
city is necessary.
Extremely disagree
Disagree
Not sure
Extremely agree
Agree
I
I
I
I
I
I
I
1
2
3
4
5
6
7
I
8
I
9
3) In the next many years, economic development will still largely depend on the
growth of real estate industry.
Extremely disagree
Disagree
Not sure
Agree
Extremely agree
I
I
I
I
I
I
I
I
I
1
2
3
4
5
6
7
8
9
4) When there is simply not enough housing available, price becomes unimportant.
Ext remely disagree
1
Disagree
2
3
Not sure
4
Agree
5
6
7
Extremely agree
8
5) Real estate is the best investment for long-term holders, who can just buy and hold
through the ups and downs.
Extremely disagree
1
Disagree
2
3
Not sure
4
Agree
5
6
7
Extremely agree
8
9
6) Housing prices are very unlikely to fall; at least not likely to fall for long.
Extremely disagree
Disagree
Not sure
Agree
Extremely agree
I
I
I
I
I
I
I
I
I
1
2
3
4
5
6
7
8
9
7) The central government desires to make the housing prices steadily decrease.
Extremely disagree
1
Not sure
Disagree
II
2
II
3
4
5
Extremely agree
Agree
I
I
6
7
I
I
8
9
8) Regardless of its desire, the central government has the ability to ensure housing
prices to decrease steadily.
Extremely disagree
I
1
Disagree
Not sure
I
I
I
2
3
4
5
Agree
I
6
7
Extremely agree
|
8
9
9) Regardless of its desire, the central government has the ability to ensure housing
prices to increase steadily.
Extremely disagree
1
Disagree
2
3
Not sure
4
5
142
Agree
6
7
Extremely agree
8
9
Questions 11-13 ask you about the popular beliefabout the housing price in Beijing.
Please choose the value that you consider as believed by the majority of people.
10. In your mind, what is the popular belief about the reasonableaverage residential housing
price in Beijing? On the scale provided below, please circle a percent, which represents
the degree that you think the reasonable average price should be lower or higher than the
average price of the first quarter of 2012.
2
The average price inside of Sihuan is 36,720 yuan per m
The popular belief about the reasonable average price inside of Sihuan is
36,720 yuan.
0
20%
80%
60%
40%
100'
120%
140%
160%
180%
The average price outside of Sihuan is 23,760 yuan.
The popular belief about the reasonable average price outside of Sihuan is
than
200% >200%
% of
23,760 yuan.
0
20%
80%
60%
40%
100"1
120%
140%
160%
180%
200% >200%
In your opinion, what is the most important factor for the majority of people to judge the
reasonability of housing price?
1) The city's average or median income level
2) Residential rent price
3) Supply-demand relationship
If any others, please specify:
11. In your mind, what is the popular belief about the change in the average residential
housing price of Beijing over the next 12 months?
Housing Inside of Sihuan:
100%
80%
60%
40%
Housing Outside of Sihuan:
100%
80%
60%
40%
20%
I
0
20%
40%
60%
80%
100% >100%
40%
60%
80%
100% >100%
The same as above. Or,
20%
0
20%
12. In your mind, on average over the next 10 years, what is the popular beliefabout the
change in the average residential housing price of Beijing each year?
Housing Inside of Sihuan:
143
100%
-80%
-60%
-40%
-20%
0
20%
40%
60%
80%
100% >100%
40%
60%
80%
100% >100%
Housing Outside of Sihuan: E The same as above. Or,
100%
-80%
-60%
-40%
-20%
0
20%
13. In your opinion, what is the proportion of population that agrees on the following
statements? Among the people who agree, how many of them strongly agree with the
statement? For instance, in the following sample, you think 80% of the population agrees
with a statement. And half of these people who agree strongly agree on this statement. So
you circle 80% for question a, and 40% (80%*(1/2)=40%) for question b.
Sample:
a. What is the proportion of population that agrees on the following statement?
0
II
10%
20%
30%
fj
50 "
40%
60%
70%
80%
I
100%
90%
b. What is the proportion of population that strongly agrees on the following
statement?
i
I
i
I
(
i
I
I
0
10%
20%
30%
50
60%
70%
80%
90%
100%
1) For the couple that plans to get married, owning a house is a requirement for
marriage.
Agree:
i
I
i
i
I
i
i
i
i
i
I
0
10%
i
Strongly Agree:
I
i
0
10%
20%
20%
30%
40%
50 i
40%
50 '
70%
60%
70%
Ii
i
30%
60%
I
80%
90%
100%
90%
100%
Ii
80%
I
2) For a person/family who would like to take root in Beijing, owning a house in the
city is necessary.
Agree:
I
I
I
I
I
I
I
I
I
0
10%
i
Strongly Agree:
i
i
0
10%
20%
20%
30%
40%
50
30%
40%
50
60%
iI
I
60%
70%
i
70%
80%
90%
100%
90%
100%
i
I
80%
3) In the next many years, economic development will still largely depend on the
growth of real estate industry.
Agree:
I
0
I
10%
I
20%
I
30%
I
50
40%
Strongly Agree:
144
I
1
60%
I
70%
I
80%
90%
I
100%
i
i
0
10%
I
ii
20%
30%
i
50
40%
i
i
60%
70%
i
80%
i
90%
i
100%
4) When there is simply not enough h :using available, price becomes unimportant.
Agree:
i
i
I
|
I
I
i
i
I
i
100%
70%
90%
60%
50
80%
0
10%
20%
30%
40%
Strongly Agree:
o
10%
20%
30%
40%
I
I
50
60%
70%
80%
90%
100%
5) Real estate is the best investment fcr long-term holders, who can just buy and hold
through the ups and downs.
Agree:
I
I
I
I
IIII
0
10%
20%
30%
50
40%
6"%
70%
80%
90%
100%
Strongly Agree:
i
I
0
10%
20%
30%
I
i
40%
50
i
60%
i
70%
I
80%
I
90%
100%
6) Housing prices are very unlikely to fall; at least not likely to fall for long.
Agree:
I
i
0
10%
I
i
20%
50
i
60%
30%
40%
I
I
I
30%
40%
50
b%
I
70%
80%
70%
80%
90%
i
100%
Strongly Agree:
I
I
0
10%
20%
I
I
90%
I
100%
7) The central government desires to make the housing prices steadily decrease.
Agree:
0
10%
20%
30%
40%
50
60%
70%
80%
90%
100%
30%
40%
50
60%
70%
80%
90%
100%
Strongly Agree:
0
10%
20%
8) Regardless of its desire, the central government has the ability to ensure housing
prices to decrease steadily.
Agree:
0
10%
20%
30%
40%
50
40%
50
60%
70%
80%
90%
70%
80%
90%
100%
Strongly Agree:
SI
0
10%
I
I
20%
30%
I
I
I
60%
I
100%
9) Regardless of its desire, the central government has the ability to ensure housing
prices to increase steadily.
Agree:
145
40%
50
I||
30%
I
60%
70%
80%
90%
100%
30%
40%
50
60%
70%
80%
90%
I
0
10%
i
Strongly Agree:
ii
0
10%
20%
20%
i
100%
14. Please check the box which best describes your knowledge of the items
I don't know
the meaning of
this item
Price-to-rent ratio
Price-to-income ratio
Vacancy rate
El
El
D
I know the
meaning of this
item but I have
no idea about its
value in this city
El
D
ED
I know the
meaning of this
item and I know
its rough value in
the city
E
D
E
15. Assume you are considering whether to buy a house in the city. Are the following items
important for you to know before you make the decision?
The total investment in
fixed assets
The land purchased and
developed
The total sale of new
houses
The amount of security
housing
Required reserve ratio
Foreign exchange reserve
Interest rate
The recent changes in this
item
LEssential
W1ood to know
Lllnnecessary
LElssential
W1ood to know
Elinnecessary
LEssential
E[Good to know
ElJnnecessary
LEssential
EGood to know
ilJnnecessary
lE]ssential
W]ood to know
ElJnnecessary
Lssential
Dood to know
Lllnnecessary
[lssential
WGood to know
[llJnnecessary
146
The expected change in this
item
LEssential
W1ood to know
EJnnecessary
[lEssential
[W3ood to know
Elinnecessary
lEssential
[W3ood to know
Elinnecessary
Essential
W13ood to know
Elinnecessary
[lVssential
W1ood to know
ElJnnecessary
ElEssential
W1ood to know
Elinnecessary
ElEssential
Eood to know
Elinnecessary
Economic growth rate
Inflation
Urbanization rate
Elissential
ElGood to know
DJnnecessary
Essential
[lGood to know
EJnnecessary
ELssential
Eood to know
OUnnecessary
147
LEssential
[Iiood to know
ElJnnecessary
LEJssential
[i3ood to know
ElJnnecessary
LElssential
Elood to know
ElUnnecessary
Section Two
In this section, you will answer some questions relevant to some concrete market participant(s)
and apartment(s) in Beijing housing market.
[A general,short description of Bejing housing market]
The housing price in Beijing increased very rapidly before 2010. After the constraint-policy was
implemented in May 2010, the housing price became relatively stable.
[Experimental One]
The Central Bank of China reduced the requiredreserve ratio twice, respectively in May and in
June, in order to stimulate economic growth. In May, the volume of new housing sale reaches the
record high infifteen months. In June, the prices of new housing have begun to increase.
[Experimental Two] .
In the NationalPeople's Congress held in this spring, the central government expressed the idea
that the pattern of economic development should be changed. The Premiersaid that the housing
price was farfrom revertingto reasonablelevels and the regulationon housing market wouldn't
be loosened. On June 18, the officials ofMOHURD 7 emphasized that the tight mortgage policy
will continue to be strictly implemented.
[Control: Blank]
Here is some information of an apartment that is on sale.
A 90 m2 two-bedroom apartment
Between Sihuan and Wuhuan
Within two kilometers from Subway Station
Age<3 years
The asking price for this apartment is 2.75 million yuan. The average asking price for a
similar house ranges from 2.5 million to 3 million yuan. The renting for such an
apartment is typically 5000 yuan per month.
Ming Li and Jun Wang are two individuals who are considering purchasing this
apartment. Their background information is provided below. In your mind, how will Ming Li and
Jun Wang assess this apartment's present value, its value in twelve months and in ten years?
Please provide us your best guesses.
[Scenario #1]
Ming Li is a 28-year-old married male who works as a programmer with an annual income
(including salaries and investments) 180,000 yuan. Ming comes from Wuhan.
[Dimensions: Two
17
Ministry of Housing and Urban-Rural Development of the People's Republic of China
148
Marital Status: marriedor plans to get marriedrecently
Family Background: local (Beijing) or non-local (Wuhan)
1. What do you think is the maximum price that Ming Li would like to pay for the apartment
at present (in wan yuan (10,000 yuan), the same for the below)?
275
250
225
200
175
150
<150
>400
400
375
350
325
300
2. What do you think is Ming Li's expectation of this apartment's value in 12 months?
I
i
<150
i
f
i
i
i
275
250
i
i
i
i
i !
i I i
225
200
175
150
i
i
i
i
i
375
350
325
300
>400
400
3. On average over the next 10 years, what is Ming Li's expectation of the change in this
apartment's value each year? For instance, if you think the value of the apartment will
increase 200,000 yuan each year on average, circle +20 (wan yuan) on the following scale.
Sample:
<l
100
100
< 100
0
6
80
60
00
+20
0
20
40
+40
+60
8
+60
+80
00
1
+100
>100
[Scenario #2]
Jun Wang is a 35-old-man who moved to Beijing in early 2000s. Jun currently owns three
properties in Beijing.
[No manipulationof variables in this scenario]
1. What do you think is the maximum price that Jun Wang would like to pay for the
apartment at present?
i
<150
150
i
i
175
200
i
225
i
I
i
250
275
i
300
i
i
325
i
i
350
i
i
375
i
400
>400
2. What do you think is Jun Wang's expectation of this apartment's value in 12 months?
<150
150
175
200
225
250
275
149
300
325
350
375
400
>400
3. On average over the next 10 years, what is Jun Wang's expectation of the change in this
apartment's value each year?
< 100
100
-80
-60
-40
20
0
+20
+40
+80
+60
+100
>100
Regarding to your personal condition,
1.
What is the maximum price that you would like to pay for the apartment at present?
i
<150
150
i
i
175
I
i
200
i
i
225
i
I
250
i
I
275
i
i
300
i
i
325
i
i
i
i
i
I
350
375
400
>400
350
375
400
>400
2. What do you think is this apartment's value in 12 months?
<150
150
175
200
225
250
275
300
325
3. On average over the next 10 years, what is your expectation of the change in this
apartment's value each year?
< 100
100
-80
-60
-40
20
0
150
+20
+40
+60
+80
+100
>100
Section Three
Please answer questions in this section according to your personal background.
Age:
Gender (circle one): F M
Birthplace (Province + City/Town):
Address of Hukou (Province + City):
Years of Residence in Beijing:
Engaged
Marital Status: _Single
Education:
Divorced
Married
Widowed
Below High School
High School
College
Master & MBA
Ph.D.
Please indicate the graduation year of your highest degree:
Occupation (choose one from the following list):
[Standardlistfrom NSB]
Annual Household Income (in wan yuan):
i
<1
1
I
i
10
I
I
20
i
i
30
i
i
40
i
i
50
i
i
60
151
80
1
1
100
110
2
>i2
120
>120
1. Do you plan to buy house(s) in six months?
a. Yes
b. No
2. Do you plan to sell house(s) in six months?
a. Yes
b. No
3. How many houses have you ever bought in Beijing?
a. 0
b. 1
c. 2
d. >2
If you have ever bought, please answer the following questions:
1) When did you buy the house(s) (Please circle all years in which you have bought one or
more houses)?
i
Befote
2000
i
00
'01
'02
I
iI
i
i
i
'03
'04
'05
'06
'07
I
'08
I
i
i
'09
'10
11
'12
2) In deciding to buy your house(s), did you think of the purchase as a requirement for
marriage?
a. It was a major consideration
b. In part
c. Not at all
3) If you selected (a) or (b) for the above question, did you buy the house with your spouse?
Yes
No.
How did you and your spouse split the expense of buying the house?
a. I pay more than 50%
b. Half and half
c. I pay less than 50%
4) Have your spouse, or his or her family, influence your decisions pertinent to purchase?
Please choose the degree of importance of their opinions on your purchasing decisions.
Extrerrely unimportant
I
I
1
2
unimportant
Not sure
I
3
I
4
important
I
5
Extremely important
I
6
7
8
9
If you choose a number > 5 for the above question, which aspect(s) of your decision have they
influenced (choose all options that apply to you)?
a. Whether to buy a house for the marriage
b. The timing to buy the house
c. The location of the house
d. The size of the house
152
e. The community of the house
If others, please specify_
5) In deciding to buy your house(s), did you think of the purchase as an investment?
a. It was a major consideration
b. In part
c. Not at all
4. How many houses have you ever sold in Beijing?
1) 0
2) 1
3) 2
4) >2
If you have ever sold, please answer the following questions:
1) When did you sell the house(s) (Please choose all years in which you have sold one or
more houses)?
Before
I
'00
i
i
i
i
I
i
i
'01
'02
'03
'04
'05
'06
'07
i
a
i
i
i
'08
'09
'10
'11
'12
2000
2)
a.
b.
c.
d.
What is the major reason for you to sell the house(s)? (Choose all that applies to you)
I wanted to live in a new house.
I wanted to invest in other house(s).
I wanted to invest in other fields.
I just wanted to get the cash.
3)
a.
b.
c.
The price you finally settle on was most often...
Above the asking price
Equal to the asking price
Blow the asking price
4) If you had not been able to sell your property for the price that you received, what would
you have done?
a. Left the price the same and waited for a buyer
b. Lowered the price step by step hoping to find a buyer
c. Lowered the price till I found a buyer
d. Taken the house off the market
5. What types of properties have you ever owned in Beijing (choose all types that you have ever
owned)?
a. <90 m 2 apartment
b.
c.
d.
e.
90-144 m 2 apartment
>144 m2 apartment
Townhouse
Courtyard House
153
f. Villa
g. Commercial housing
h. Hotel
If others, please specify:
6. What is the type of property that you most often live in?
a. < 90 m 2 apartment
b.
c.
d.
e.
f.
90-144 m2 apartment
>144 m2 apartment
Townhouse
Courtyard House
Villa
154
Note: Type 2
A 135 m2 two-bedroom apartment
Between Sihuan and Wuhuan
Within two kilometers from Subway Station
Age<3 years
The asking price for this apartment is 4.1 million yuan. The average asking price for a
similar house ranges from 3.8 million to 4.40 million yuan. The renting for such an
apartment is typically 10,000 yuan per month.
Ming Li is a 28-year-old married male who works as a banker with an annual income (including
salaries and investments) 270,000 yuan. Ming comes from Wuhan.
Total number of categories:
2(marital Status)*2(hometown)*3(political factors)*2(two types of housing)=24
24*16=384
155
Major Survey Questionaire
Survey on Real Estate Market in Beijing"
[GeneralSurvey Description:by NSB in unifiedformat]
The questions of this survey are organized in three sections. Please answer questions in all three
sections.
Section One
16. If there is any way you got information on housing market, what are your most important
information sources? Please choose all options that apply to you, or specify the sources that
are not listed.
LIrV news
ENewspapers and Magazines (including electronic edition)
LlWebsites (Soufun.com, other)
Liocial network media (includes Blogs and Weibo)
[i3illboards
LReal Estate Sales Offices
EReal Estate brokers
LConnections in relative industries
[Tolleagues, business partners and friends
EFamily members and close friends
LPublic market reports
LiPrivate market reports
If any others, please specify:
17. In conversations with family members, friends and other associations over the last a few
years, conditions in the housing market were discussed...
5) Never
6) 1-2 times per week
7) 3-4 times per week
8) Almost every day
18. With whom of the following do you most frequently discuss housing market?
4) Family members
5) Friends
6) Colleagues
A few questions repeat what Case and Shiller did in their 1988 and 2003 survey on US housing market
(Case and Shiller, 2004). These questions may be slightly revised according to this particular context.
18
156
When you answer the rest of questions in section one, please consider the residential
housing market inside Sihuan in Beijing.
/** In the alternative questionnaire, all "inside Sihuan" will be replaced by "outside
Sihuan"**/
19. According to your personal opinion, what is the reasonableaverage residential housing
price inside Sihuan in Beijing? On the scale provided below, please circle a percent,
which represents the degree that you think the reasonableaverage price should be lower
or higher than the average price of the first quarter of 2012. For instance, in the following
sample, SOLID and DASHED circles represent two different choices. SOLID circle
represents the reasonable price is 20% lower than (or, 80% of) the given average price by
the end of March 2012. DASHED circle represents the reasonable price is 30% higher
than (or 130% of) the given average price by the end of March 2012.
Sample:
-100%
-60%
-80%
-40%
-20%
0
80%
60%
40%
20%
100% >100%
2
The average price inside of Sihuan is 36,720 yuan per m .
than 36,720
You think the reasonable average price inside of Sihuan should be
yuan.
-100%
-60%
-80%
-40%
-20%
0
80%
60%
40%
20%
100% >100%
20. According to your personal opinion, how much of a change do you expect in the average
residential housing price inside Sihuan in Beijing over the next 12 months?
I I I Ii
i i i i i i i i i I i: i i i
-100%
-60%
-80%
-40%
-20%
0
80%
60%
40%
20%
100% >100%
21. According to your personal opinion, what will be the total increase (or decrease) in the
average residential housing price inside Sihuan in Beijing in ten years? Please write down
a percentage, which represents the total increase (or decrease) relative to the current price.
22. Please select the extent to which you agree or disagree with the following statements:
10) For the couple that plans to get married, owning a house is a requirement for
marriage.
1
2
3
4
5
Extremely agree
Agree
Not sure
Disagree
Extremely disagree
6
7
8
9
11) For a person/family who would like to take root in Beijing, owning a house in the
city is necessary.
1
2
3
4
5
157
Extremely agree
Agree
Not sure
Disagree
Extremely disagree
6
7
8
9
12) In the next many years, economic development will still largely depend on the
growth of real estate industry.
Extremely disagree
1
Disagree
2
3
Not sure
4
5
Agree
6
7
Extremely agree
8
9
13) When there is simply not enough housing available, price becomes unimportant.
Extremely disagree
1
Not sure
Disagree
2
3
Extremely agree
Agree
I
I
I
I
I
I
4
5
6
7
8
9
14) Real estate is the best investment for long-term holders, who can just buy and hold
through the ups and downs.
Disagree
Extremely disagree
Not sure
Extremely agree
Agree
1
2
3
4
5
6
7
8
9
15) Housing prices are very unlikely to fall; at least not likely to fall for long.
Extremely disagree
1
Disagree
2
3
Not sure
4
5
Agree
6
7
Extremely agree
8
9
16) The central government desires to make the housing prices steadily decrease.
Extremely disagree
-
I
1
2
Disagree
I
I
3
4
Not sure
I
i
5
6
Agree
i
i
7
8
Extremely agree
i
9
17) The central government desires to make the housing prices steadily increase.
Extremely disagree
I
1
Disagree
II
2
3
Not sure
Agree
Extremely agree
I
I
I
I
I
I
4
5
6
7
8
9
18) The central government has the ability to ensure housing prices to decrease
steadily.
Extremely disagree
I
I
1
2
Disagree
i
i
3
4
Not sure
Agree
Extremely agree
i
i
i
i
i
5
6
7
8
9
19) The central government has the ability to ensure housing prices to increase
steadily.
Extremely disagree
1
Disagree
2
3
Not sure
4
5
Agree
6
7
Extremely agree
8
9
Questions 8-16 ask you about the typical marketparticipantsin the housing market of Beijing, and
how do you think they will evaluate the market prices in the short term and in the long term.
Market participants include both those who have bought or sold houses and those who are likely
to buy or sell houses in the housing market. Here are five typical types of market participants:
A. Those who buy their first homes for their families;
B. Those who want to sell their current home and switch to a bigger/better one;
C. Those who want to keep their home and buy others as long-term investments;
D. Those who have bought multiple houses and also sold multiple houses within just a few
years.
158
In your mind, do you think these four types cover all the important types of market participants
in the market? If not, please describe a fifth important type.
E.
23. If you have to categorize yourself in one of the four (or five, if you specify the fifth type)
categories, which one would you pick?
E.
D.
C.
B.
A.
If none of the above describes you, please specify your type:
F:
If you haven't bought or sold any house in the city and absolutely have no plan to buy or
sell house in the city, please check G below.
G. (non-market participants)
24. In your mind, among the types of individuals that you categorized yourself in question 8
(one in A-G)-we call this your type, what is the popular belief about the reasonable
average residential housing price inside Sihuan in Beijing? When you think about the
popular belief, please choose the value believed by the majority of people of your type.
On the scale provided below, please circle a percentage, which represents the degree that
you think the reasonableaverage price should be lower or higher than the average price of
the first quarter of 2012 (36,720 yuan).
The popular belief among your type about the reasonable average price inside of Sihuan is
of 36,720 yuan.
0
20%
40%
60%
80%
1000
120%
140%
160%
180%
200% >200%
25. Among your type, what is the popular belief about the change in the average residential
housing price inside Sihuan in Beijing over the next 12 months?
100%
-80%
60%
-40%
0
20%
40%
20%
60%
80%
100% >100%
26. Among your type, what is the popular belief about the total increase (or decrease) in the
average residential housing price inside Sihuan in Beijing in ten years? Please write down
a percentage, which represents the total increase (or decrease) relative to the current price.
27. In your opinion, which type of market participants (A-F), collectively, has the most power
to influence the housing prices inside Sihuan in the short-term, e.g. over the next 12 months?
A.
B.
C.
D.
159
E.
F.
If this type is not the same as your type, please answer question 13 and 14 below.
28. In your opinion, on average, what do the participants of the type you picked in question 12
think the reasonableaverage residential housing price inside Sihuan in Beijing?
This type of participants, on average, think the reasonable average price inside of Sihuan
is
of 36,720 yuan.
-4
0
i
20%
40%
60%
80%
1001.
120%
140%
160%
180%
200% >200%
29. In your opinion, on average, what do the participants of the type you picked in question 12
think the change in the average residential housing price inside Sihuan in Beijing over the
next 12 months?
1 I% i
100%
i
80%
i %I
-60%
i
-40%
i
i
-20%
0
20%
40%
60%
80%
100% >100%
30. In your opinion, which type of market participants (A-F), collectively, has the most power
to influence the housing's prices inside Sihuan in the long-term, e.g. in ten years?
A.
B.
C.
D.
E.
F.
If this type is not the same as your type, please answer question 16 below.
31. Among the type you picked in question 15, what do the participants of the type you picked
in question 12 think the total increase (or decrease) in the average residential housing
price inside Sihuan in Beijing in ten years is? Please write down a percentage, which
represents the total increase (or decrease) relative to the current price.
32. Please check the box which best describes your knowledge of the items
I don't know
the meaning of
this item
Price-to-rent ratio
Price-to-income ratio
Vacancy rate
El
El
El
I know the
meaning of this
item but I have
no idea about its
value in this city
D
ED
D
I know the
meaning of this
item and I know
its rough value in
the city
D
D
D
33. Assume you are considering whether to buy a house in the city. Are the following items
important for you to know before you make the decision?
160
The land purchased
and developed
The total sale of new
houses
The amount of
security housing
Required reserve ratio
Foreign exchange
reserve
Economic growth rate
Inflation
Urbanization rate
The recent changes in this item
ElEssential Eiood to know Elinnecessary EDon't
know
ElEssential Good to know EILnnecessary LDon't
know
LEssential M3ood to know ELUnnecessary ELDon't
know
LEssential Miood to know ELJnnecessary ELDon't
know
ElEssential Liood to know lJnnecessary ELDon't
know
LE)ssential EGood to know ELUnnecessary ELDon't
know
EEssential Liood to know EUnnecessary EDon't
know
LEssential Good to know ELUnnecessary ElDon't
know
34. In your opinion, what is the proportion of population that agrees with the following
statements? For instance, in the following sample, you think 80% of the population agrees
with a statement. So you circle 80% for question.
Sample:
c. What is the proportion of population that agrees on the following statement?
o
10%
20%
30%
50
40%
60%
70%
80%
90%
100%
10) For the couple that plans to get married, owning a house is a requirement for
marriage.
i
i
Ii
Ii
i
i
i
I
0
11)
0
10%
20%
30%
50'
40%
60%
70%
80%
90%
100%
For a person/family who would like to take root in Beijing, owning a house in the
city is necessary.
10%
20%
30%
50
40%
60%
70%
80%
90%
100%
12) In the next many years, economic development will still largely depend on the
growth of real estate industry.
i
i
i
i
I
i
i
i
0
10%
20%
30%
50
40%
6{%
70%
8"%
90%
100%
13) When there is simply not enough housing available, price becomes unimportant.
0
10%
20%
30%
50
40%
161
6b%
70%
80%
90%
100%
14) Real estate is the best investment for long-term holders, who can just buy and hold
through the ups and downs.
I
0
i
i
10%
20%
i
30%
i
i
40%
so
i
60%
Ii
70%
80%
i
90%
100%
15) Housing prices are very unlikely to fall; at least not likely to fall for long.
I
a
I
10%
I
I
20%
30%
40%
50
I
60%
70%
80%
I
90%
I
100%
16) The central government desires to make the housing prices steadily decrease.
I
0
I
10%
I
20%
I
30%
I
I
40%
50
I
60%
I
I
70%
80%
I
90%
I
100%
17) The central government desires to make the housing prices steadily increase.
I
I
I
0
10%
20%
I
I
I
I
I
70%
80%
90%
1001%
0
10%
20%
30%
40%
50
60%
70%
80%
90%
100%
18) Regardless of its desire, the central government has the ability to ensure housing
prices to decrease steadily.
I
i
i
I
i
I%
%
%
i
30%
40%
s0
60%
19) Regardless of its desire, the central government has the ability to ensure housing
prices to increase steadily.
I
I
I
I
i
iI
I
0
10%
20%
30%
50
40%
162
60%
70%
80%
90%
100%
Section Two
Please read the following information about an apartment that is on sale.
A 90 m2 two-bedroom apartment
Between Sanhuan (the third ring road) and Sihuan (the fourth ring road)
The asking price for this apartment is 4.5 million yuan. The average asking price for a
house at the similar location ranges from 4.0 million to 4.9 million yuan.
Ming Li, comes from Hubei, is working as a computer programmer in Beijing. Li wants to
buy his first house in the city. The above information on the apartment is provided to him.
In your mind, how will Ming Li assess this apartment's current value, its value in twelve
months and in ten years? Please provide us your best guesses.
** Factor manipulated: occupation: banker or computer programmer *
35. In your mind, how much money would Ming Li like to pay for the apartment at present?
I I
<350
I
a
370
350
i
i
I
3
I
i
i
I
I
i
I
I
470
450
430
410
390
i
I
i
I
i
I
M
i
I
I
I
1
I
550
530
510
490
>550
months?
of this apartment's value in 12
36. What do you think is Ming Li's expectation
I
I
a
a
I
I
I
I
I
I
I
I
I
I
<350
350
I
I
370
I
I
390
I
I
I
I
410
I
430
I
4
450
I
4
470
550
530
510
490
>550
37. What is Ming Li's expectation of this apartment's value in ten years? Please write down a
percentage, which represents the total increase (or decrease) relative to the current price.
Regarding to your personal condition,
38. In your mind, how much money would you like to pay for the apartment at present?
i
I
i i i i
I
i i
i
I
i i i
i5
550 >550
530
510
490
<350
350
370
390
410
430
450
470
39. What do you think is this apartment's value in 12 months?
I
I
I
I
I
I
I
I
t~ I
I
t
I
I
I
F
F
I 3
I
I
I
I
I
I
4
7
490
470O
450
430
410
390
370
350
<350
I
k
I
I
510
I
tI
I
k
I
530
I
1
550
1
>550
40. What is your expectation of this apartment's value in ten years? Please write down a
percentage, which represents the total increase (or decrease) relative to the current price.
163
/**The following is the alternative for the other subsample:
A 90 m2 two-bedroom apartment
Between Wuhuan (the fifth ring road) and Liuhuan (the sixth ring road)
The asking price for this apartment is 1.9 million yuan. The average asking price for a
house at the similar location ranges from 1.4 million to 2.4 million yuan.
Jun Liu is working as a banker in Beijing. Liu-has owned one house in the city, and wants
to buy another one as long-term investment. The above information on the apartment is provided
to him.
In your mind, how will Jun Liu assess this apartment's value in twelve months and in ten
years? Please provide us your best guesses.
164
Section Three
Please answer questions in this section according to your personal background.
41. Gender (circle one): F M
Age:
42. Birthplace (Province + City/Town):
43. Address of Hukou (Province + City):
44. Years of Residence in Beijing:
45. Marital Status: __Single Engaged Married Divorced
46. Education:
A. Below High School
B. High School
C. College
D. Master & MBA
E. Ph.D
47. Occupation (choose one from the following list):
[Standardlistfrom NSB]
48. Annual Household Income (in wan yuan):
<1
1
10
20
30
40
so
60
165
80
100
Widowed
110
120
>120
8. Do you plan to buy house(s) in twelve months?
c. Yes
d. No
9. Do you plan to sell house(s) in twelve months?
c. Yes
d. No
10.
Do you plan to buy house(s) in five years?
a. Yes
b. No
11.
Do you plan to sell house(s) in five years?
a. Yes
b. No
12.
Have you settled down, or decided to settle down in the city?
a. Yes
b. No
13.
How many houses do you currently own in Beijing?
a. 0
b. 1
c. 2
d. >2
14.
How many houses have you ever bought in Beijing?
e. 0
f. 1
g. 2
h. >2
If you have ever bought, please answer the following questions:
6) When did you buy the house(s) (Please circle all years in which you have bought one or
more houses)?
Before
2000
'00
'01
'02
'03
'04
'05
'06
'07
i
I
'08
'09
'10
'11
'12
7) In deciding to buy your house(s), did you think of the purchase as a requirement for
marriage?
d. It was a major consideration
e. In part
f. Not at all
8) In deciding to buy your house(s), did you think of the purchase as an investment?
d. It was a major consideration
166
e. In part
f. Not at all
15.
5)
6)
7)
8)
How many houses have you ever sold in Beijing?
0
1
2
>2
If you have ever sold, please answer the following questions:
5) When did you sell the house(s) (Please choose all years in which you have sold one or
more houses)?
1~
I
Before
'00
'01
'02
'03
'04
'Os
'06
'07
'08
'09
'10
'11
'12
2000
16.
What types of propertie s have you ever owned in Beijing (choose all types that you have
ever owned)?
i. <90 m2 apartment
j. 90-144 m2 apartment
k. >144 m 2 apartment
1. Townhouse
m. Courtyard House
n. Villa
o. Commercial housing
p. Hotel
If others, please specify:
The last four questions...
1.
I prefer being
a)
b)
c)
d)
e)
2. Being distinctive is
a) Not at all,
b) Slightly,
c) Moderately,
d) Very
e) Extremely
3. I
different from other people.
No,
Slightly
Moderately
Very
Extremely
important to me.
intentionally do things to make my self different from those around me.
a) Never,
b) Seldom,
c) Sometimes,
167
d) Often,
e) Always
4. I havea
a)
b)
c)
d)
e)
need for uniqueness.
Weak,
Slight
Moderate,
Strong
Very stron
168