ISSN (Print) 0473-453X

Discussion Paper No. 1130

ISSN (Online) 2435-0982

FINANCIAL CONDITIONS,LOCAL COMPETITION,
AND LOCAL MARKET LEADERS:
THE CASE OF REAL ESTATE DEVELOPERS

Ying FanCharles Ka Yui Leung
Zan Yang

April 2021

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Financial Conditions, Local Competition, and Local Market
Leaders: The Case of Real Estate DevelopersΩ
Ying Fan, Charles Ka Yui Leung and Zan Yang ΞThis version: April 2021
Abstract:

This paper studies whether (and how) corporate decisions are affected by internal factors (such as
the financial conditions of own company) and external factors (such as the actions of local
competitors) in an imperfectly competitive environment. We study the listed real estate developers
in Beijing as a case study. Our hand-collected dataset includes transaction-level information
booked indicators (such as profitability, liability, and liquidity) and un-booked financial indicators(political connections). Our multi-step empirical model shows that both the firm's financial
conditions and her competitors' counterparts are essential but play different roles in the output
design, pricing, and the time-on-the-market (TOM). Internal versus external factors' relative
importance relates to the degrees of market concentration in a nonlinear manner. Local market
leaders' existence alters the small firms' strategy and leads to higher selling prices and slower
selling pace in the local market. Our findings survive various robust checks.
Keywords: corporate financial status; output market decision; internal and external driven; realestate developers; housing supply
JEL Classification: G11, R30, L10Ω

Acknowledgement: The authors are grateful to many friends for discussion over the years, and especially YenJong Chen, Yuichiro Kawaguchi, Chiu-Yu Ko, Tommy Leung, Joe Ng, Shane Su, Edward Tang, Byron Tsang,
Kerry Vandell, especially an anonymous referee and David Cook (editor), seminar participants in ASSA meeting(CEANA), IAAE meeting, GCREC meeting, TEA meeting, Tsinghua University, for helpful comments, and City
University of Hong Kong, Tsinghua University for financial support. Leung’s travel has received support from the
National Taiwan University through Grant no. 110L900201, and Ministry of Technology MOST 109-2634-F-002045, MOST 110-2634-F-002-045, and from ISER in Osaka University through JSPS KAKENHI Grant Number JP
20H05631. Part of this research is conducted when Leung visits the Hoover Institution, whose hospitality isgratefully acknowledged. The usual disclaimer applies.
Ξ
Correspondence: Fan: Department of Building and Real Estate, Hong Kong Polytechnic University, Hong Kong.
Email: yingfan@polyu.edu.hk; Leung: Department of Economics and Finance, City University of Hong Kong,
Kowloon Tong, Hong Kong. Email: kycleung@cityu.edu.hk ; Yang: Hung Lung Center of Real Estate, Tsinghua
University, Beijing, China. Email: zanyang@tsinghua.edu.cn .

1. Introduction
This paper shows that the following factors matter in pricing and the competition amongfirms: the firm's financial condition, the financial condition of local rivals, and the existence ofa price leader in the local market. It is in sharp contrast with what the paradigm of a perfectlycompetitive market would predict. 1 In effect, we combine the insights of the finance-IO
literature and the literature of local competition. The former emphasizes the interactions offinancial decisions (e.g., the leverage ratio) and the equilibrium product prices and output levels(Brander and Lewis, 1986). The latter would study the impacts of a local price leader, such as
Wal-Mart (Jia, 2008; Holmes, 2011).2
Empirically, this paper overcomes at least two challenges. First, with the development oftechnologies and different internet platforms, the local market boundary is not clear. For
instance, consider a geographically isolated small town, which has only one bookstore.
However, residents in that small town may use Amazon or another internet platform to orderbooks and audio items. This paper focuses on housing, which is non-tradeable. We can also
identify the local competitors and obtain the corresponding information for our analysis, further
explained later. Second, the debt ratio cannot capture all the financial decisions (Glazer, 1994;
Maksimovic, 1988). The debt structure (such as short-term versus long-term debt) matters.
Other financial instruments, such as warrants, convertible bonds, etc., can also affect the
product market competition. In light of these contributions, this paper includes a wide range offinancial condition variables (FCV) to capture the potentially complex relationship between
firms' internal finance and product market behaviors.
Our research focuses naturally ties us to several strands of the literature. First, this paper
complements the literature on spatial competition (SC), which overlaps urban economics andIO (Borla, 2012; Brekke et al., 2010; Pennerstorfer, 2017). Our empirical findings on how realestate developers compete locally would complement the SC literature, which tends to betheoretical.
Second, since this paper employs listed real estate developer data, it contributes to theurban economics literature, emphasizing the housing demand. Studies on housing supply are
limited. 3 Several authors have recently shown that regulatory constraints would limit the
housing supply and drive up the house prices (Green et al., 2005; Hilber and Vermeulen, 2016;
Jackson, 2016; Saiz, 2010). Those studies are typically at the city level. This paper
complements these studies with a firm-level analysis. In particular, we would argue that themarket structure for real estate development in Beijing is oligopolistic. The decisions made by
the developers, including the product design (such as the average height of a real estatedevelopment), pricing, etc., are influenced by both the financial conditions of the developers,
as well as the competitors (i.e., other developers) within the same district. Hence, while all
Beijing developers may face the same set of regulatory constraints, they may behave differently.
1 Among others, see Mas-Colell et al. (1995) for a detailed analysis.
2 Many have examined both the theoretical robustness and empirical validity of such effect (Campello, 2003; Chevalier, 1995;

Fresard, 2010). Parsons and Titman (2009) surveyed the literature on the relationship between debt financing and corporate
strategy and confirmed the leverage ratio's importance on corporate decisions.
3 Among others, see Green and Malpezzi (2003), Leung et al. (2020), Leung and Ng (2019). The housing supply literature
considers real estate development a real option, inspired by Titman (1985). Except for Quigg (1993), that literature seems to
be mainly theoretical (Grenadier, 1996; Lee and Jou, 2010; Wang and Zhou, 2006).
2

We concentrate our attention on the real estate developers (REDR) in Beijing and study
whether their decisions are driven by internal factors (such as the financial conditions) andexternal factors (such as the financial conditions of local competitors). Several academic merits
may be worth attention. First, Beijing is China's capital city and has 21 million people,
according to 2016 official statistics. Such population size is comparable to some member
countries in the United Nations and some large metropolitan areas such as Los Angeles and
New York. The lessons we learn from Beijing may shed light on other cities and countries.
Second, as housing is non-tradeable, it mitigates the concerns of competitions from different
places through the internet. In a way, comparing the importance of internal versus external
factors among competing real estate developers may provide a relatively "clean" test.
Third, many REDR in Beijing is listed in the stock market. In contrast, many REDR in
advanced economies are private companies, and hence, they may not disclose the vital
information critical for academic research. This research takes advantage of corporate
information disclosure in the stock market requirement and uses it in our empirical analysis.
Notice that other firms could influence a firm's decisions when a market deviates from theperfect competition paradigm. Hence, we may need the information about a firm and its rivals
to analyze a company's behaviors in a micro-level analysis (Leung and Tang, 2015; Wang andZhou, 2000, 2006; Williams, 1993). Our sample of listed real estate developers enables us toobserve one firm's financial conditions and rivals. Our fruitful hand-collected dataset includes
information obtained from the listed mother companies, all of the "daughter companies" that
we can identify, some of which are private, and the housing units' trading information. Besides,
we have access to variables that reflect the "corporate financial status," including both bookedindicators (profitability, liability, and liquidity) and un-booked indicator (political connections),
as well as local economic conditions. Thus, the information of developers, projects, and land
is efficiently matched. We then investigate these developers' principal decisions, namely, landacquisition, project development, and sales. Employing a sample of listed REDR may alsoenrich our understanding of how oligopolistic firms compete with financial conditions. Noticethat these REDR are listed firms and hence should have better access to external funding. If
the listed REDR behave as financially constrained, likely, those non-listed firms are even moreso.
Fourth, real estate developers share some similarities with other corporations. For instance,
real estate developers need to make decisions sequentially in the production process, in which
earlier decisions ("upstream") may potentially create valuable discretionary "downstream"
opportunities (Herath and Park, 2002; Miles et al., 2010). More specifically, developers takethe risk and evolving cost and revenue in the locational choice and land acquisition stage. Afterthe land acquisition, developers need to decide what to develop, how much to develop, andwhat prices the units would be sold (Wang and Zhou, 2000; Williams, 1991). These decisions
could depend on the site's location and the developers' preference, how products are being
offered, or would soon be provided by other developers locally (Dong and Sing, 2013). 4
Intuitively, given the differences in physical/social landscape, real estate projects areheterogeneous by nature, and developers might have significant market power (Salop, 1979).
4 The location choice of developers may be similar to the choice of location for multinationals. See Baldwin (2006), Okubo etal. (2010), among others.
3

To mitigate the potential competitive pressure from other developers, a developer has
incentives to shape a real estate development (RED) to make it even more differentiated fromthose produced by the competitors and to increase her market power (Gnagey, 2012; Leung etal., 2014; Somervile, 1999). Also, a developer can adjust the prices and the timing mechanism
to maximize her profit. These facts highlight the necessity to control the multi-stage decision
chain, from land acquisition and project development to the market transaction in a sequential
manner. Our multi-step empirical model thereby mitigates several econometric issues. They
include the endogeneity and non-linearity in the real estate development process and thepricing-timing process. Thus, this study may throw light on how corporates compete, given theinternal factors (such as the financial status) and external factors (such as the rivals).
To our knowledge, our paper is the first to provide empirical evidence in comparing theimpact of corporate internal and external financial status on both pricing and output decisionsof REDR. On average, we find that both the corporate and its competitors' financial indicators
are significant in the output design and marketing stages. We also find that the presence of aprominent developer would influence how other developers compete. Thus, our results suggestthat incorporating financial information may be necessary to understand the local competition.
Our findings are robust to changing the measurement of financial status and controlling the
spatial-temporal co-movement of housing prices and time on the market. Second, since existing
empirical studies on real estate markets disproportionally focus on the demand side, our paper
could enhance our understanding of housing supply.
The organization of the paper is as follows. Section 2 provides background information on
the case market and literature review of the real estate developer decision-making mechanism.
We then describe the data and the multi-step empirical strategies used in the study. Section 4
presents the empirical results for the role of internal and external financial status in thedeveloping decision. Section 5 presents our conclusions.
2. A Brief Review of the LiteratureThis section provides a review of the different strands of literature from which this paper
is built. They include research on the relationship between financial conditions and corporate
decisions and the housing supply studies in urban development.
There is a voluminous literature on the relationship between firms' financial conditions andtheir product market behaviors. Brander and Lewis (1986) articulated a two-stage duopoly
game that corporate financial status could have effects on rivals in an imperfectly competitiveenvironment. Many have examined both the theoretical robustness and empirical validity ofsuch a result (Benoit and Krishna, 1991; Bolton and Scharfstein, 1990; Campello, 2003;
Chevalier, 1995; Fresard, 2010; Matsa, 2010; Oechssler and Schuhmacher, 2004). Parsons andTitman (2009) surveyed the literature on the relationship between debt financing and corporate
strategy and confirmed the leverage ratio's importance on corporate decisions. This paper
complements the literature in several ways. We include several financial condition variables(FCV) in the empirical analysis and show that these FCV explain the developers' behaviors.
Our multi-step procedures enable us to identify how different FCV affect different stages ofreal estate development.
4

The literature on multi-stage housing production is also a source of inspiration for this paper.
The location choice, project decision, and marketing strategy are often considered criticalstages in RED decisions (Miles et al., 2010; Weiss, 2002). As an echo of the famous saying,"location, location, and location," some authors estimate that the locational choice can affectover 95% of the follow-up decisions of a project and is also positively correlated with the risk
rating and profitability of a project (Gregor, 2015).
The third strand of literature that is related to our work concerns the project design of RED.
In principle, both the demand and supply sides would influence the housing structure designand land use patterns. Developers play a core role in the formation of scale, density, style, andstructure of the project (Peiser, 1990; Weiss, 2002), though consumers and local governments
affect by inducing or regulating the project (Maruani and Amit-Cohen, 2011). By investigating
projects in Shanghai, Lai et al. (2008) confirm that if a developer reaches a specific scale, it
can freely decide its developing strategy, which ensures developers' critical role in the processof project location choice and design.
This paper is also related to the literature on "price leadership" (Deneckere and Kovenock,
1992; Ono, 1982). In that literature, researchers have established conditions under which somefirms would be disproportionally influential in setting the market prices. In this paper, we canidentify the competitors in each local market ("district"). We examine whether the large
developers are "price leaders" in practice.
This paper complements the previous literature on the pre-marketing stage of housing
supply in several ways. First, we highlight the importance of considering financial constraints.
For real estate developers (REDR), both assets (such as land) and products (such as housing
units) are "collateralizable'' or pledgable. Our sample focuses on listed REDR, which are
expected to have better access to external funding sources. If even the listed REDR are"financially constrained," many other firms would more likely to be financially constrained. In
this paper, we employ several measures of "financial constraint" and test whether the REDR in
our sample is indeed constrained. Real estate development is a multi-stage process, and wehave access to the actions by the REDR in different stages. Our multi-step empirical strategy
can indicate whether the REDR are financially constrained and at which stage they are
constrained if they are indeed constrained. Also, since we have all the listed REDR information,
we can examine whether their rivals may affect the REDR and at which stage their opponents
exert those influences. Moreover, as we have explained, housing is non-tradeable goods. Nonlocal competition can be comfortably neglected in our analysis.
This paper may also contribute to the literature on the marketing stage of housing supply,
which studies the relationship between the housing price and time-on-the-market (TOM). Most
studies in that literature focus on the secondary market, where buyers and sellers engage in asearch-and-matching process (e.g., Allen et al., 2005; Anglin, 1997; Knight, 2002; Leung andTse, 2017; Yavas, 1992). On the other hand, this paper focuses on the asymmetric primarymarket, where sellers (i.e., real estate developers) and buyers (often households) arguably havevery different bargaining power (BP) may be overlooked. Intuitively, if REDR were financiallydistressed, they would be more willing to accept lower prices to sell faster. The capital
appreciation expectations of developers may also increase TOM (Ong et al., 2000). We
5examine whether (and if so, to what extent) a real estate developer's financial conditions andits rivals affect the selling price and TOM. We believe this point may be relatively new because
there are typically many potential sellers in the secondary market. Hence, empirical works often
implicitly assume each seller as a price-taker and do not consider the sellers' strategicinteractions.
Based on our discussion, here are our hypotheses.
1.
The financial conditions matter. More specifically, the coefficients of the company's
financial condition variables and its rivals are expected to be statistically significant.
2.
Leaders matter. More specifically, we examine whether some large real estatedevelopers' presence influences the local market prices and transaction patterns.
Moreover, if multiple factors are found statistically significant, we provide an empiricaldecomposition on how different factors can explain pricing behaviors.
3. Data and Empirical Design
3.1 Data Descriptions
While we provide a more detailed description of the dataset in the appendix, it is worth
giving some highlights here. This paper employs data from different sources. Our
macroeconomic data comes from the Beijing Statistical Yearbook (2007-2009) and includes
permanent and external population, land area, population density, disposable income per capita,
fixed assets, and real estate investment. 5 The nominal variables are deflated by the annual
consumer price index (CPI).
Our second category of data is housing-related. The micro-level housing data are drawn
from the City Housing Index Database, established by the Ministry of Construction (MOC)
and the Department of Housing and Urban and Rural Construction Committee in Beijing. We
focus on development projects by listed firms, as the corresponding financial information isreadily available. After the usual data-cleansing procedures,6 we obtain 59,451 residential unittransaction records from 2006 to 2008. The sample contains information on the project name,unit-selling price, selling time, and issue date of the pre-sale license, in addition to the physicalattributes of the project and unit, such as project area, the total number of floors in the building,unit area, and the height of the unit. We used GIS Mapping to acquire the location andneighborhood attributes, including the distance to CBD, the nearest subway station, hospitals,
and schools.
Geographically, Beijing is divided into different "rings." Roughly speaking, the higher the
ring's number, the further the ring is located from the central business district (CBD). We,
therefore, divide our sample into four sub-samples, namely, inside the second ring (the "core
5 “Permanent population” refers to the population with the registered citizenship (hokou).
6 We removed observations with incomplete information on one of the following categories: location, project design, andtransaction details. We also removed projects that are not developed by listed (A-share) real estate firms in China.
6

function zone''), inside the fifth ring (the "expanded function zone"), inside the sixth ring (the"development zone") and outside the sixth ring (the "ecological zone"). Figure 1 provides avisualization of the geographical distribution of the fifteen districts. First, we aggregate theminto four Functional Zones or Regions. Within our sampling period, most of the new-developed
projects are located inside the fifth ring. It is probably due to the following facts: (1) the corefunction zone is already well developed, (2) the infrastructure and amenities in the ecologicalzone is under-supplied, and (3) the land leasing policy imposed by the government that may
not be driven by purely economic reasons.

Figure 1 Distribution of Four Functional Zones in Beijing

The oligopolistic nature of the Beijing housing market is well illustrated in its distributionof sales volume. In the appendix, we show that the top 10 developers account for about one
half of the total sales volume from 2006 to 2008. We naturally turn to the industrial organization
literature and measure market concentration. We first calculate the Herfindahl–HirschmanIndex (HHI), which is one of the most popular measures in this regard.7 During this period,
HHI reaches 0.26, which indicates that the market is highly concentrated. The market is moreconcentrated in the inner city as well as the outer suburban. This result is intuitive. The land
cost in the inner city is very high. And in the outer sub-urban, the market demand is highly
uncertain. It discourages financially weak developers from entering those markets. (We will
provide a more systematic analysis on the location choices of the developers).
Furthermore, the four zones' major developers are all different, which suggests that different
developers might have distinct "comparative advantages" in each zone. The comparative
7 HHI is a widely used measure of the concentration of industry. It is beyond the scope of this paper to review the related

literature. Among others, see Tremblay and Tremblay (2012) and the reference therein. The formula of HHI is simple andstated as follows:swhere s is the market share of firm i in the market, and N is the number of firms. Conventionally, a value of HHI below 0.01
may indicate a highly competitive industry, below 0.15 may indicate an un-concentrated industry, between 0.15 to 0.25 may
indicate moderate concentration. Above 0.25 may show high concentration.
7

advantage might be economically (such as local knowledge in a specific area) or politically(such as the top executives being more politically connected to the local government officials).8
More discussion and analysis related to such market concentration will be presented later.
Our third source of data is financial. The RESSET Financial Database provides us the listeddevelopers' information, including the earnings per share, current ratio, interest cover ratio,
inventory turnover ratio, debt-to-asset ratio, PE ratio, and yearly market capitalization. These
indices reflect profitability, competitiveness, and the debt-paying ability of the firm. 9 The
recent literature also demonstrates the potential benefits of political connectedness (Chan et al.,2012; Li et al., 2007, 2008; Lambert and Volpin, 2017). We refer them as un-booked financial
status or political economy variables in this paper. We include additional information such as
the firm's corporate governance information, including the governance background of the firm's
board members, supervisors, and executive leaders, etc., as a proxy of the firm's politicalconnections. Following Faccio (2006), we identify firms as politically connected if one of the
firm's board members, supervisors, or executives used to have an official governmental
background. They include the National People's Congress (NPC) deputy, Chinese People's
Political Consultative Conference (CPPCC) member, and officials at national, provincial, city,or county levels. Thus, for each project, we can match the mother company's financial status
and political connection status at the listing time. Table 1 provides a quick summary of thefinancial and political economy variables of the firms. Several remarks are in order. Recall thatthe interest cover ratio is a company's earnings before interest and taxes (EBIT), divided by thecompany's interest expenses for the same period. The top 10 developers have a significantly
higher interest cover ratio, which is about 140. The full sample average is only about 40.
Moreover, the PE ratio of the top 10 developers is 76, compared to the full sample average,
which is less than 6. We may say that the top 10 developers have some advantages in terms offinance. On the other hand, it seems that the top 10 developers are less politically connected at
the national and provisional level, but more connected than the average at the city and county
level.

8 Notice that the comparative advantage might be economically (such as local knowledge in a certain zone), or politically (suchas the top executives being more politically connected to the local government officials).
9 Our choice of variables is guided by the literature and constrained by data availability. For a survey of the literature, see
Shleifer and Vishny (1997), among others.
8

Table 1 Descriptive Statistics of Financial and Political Economy Variables of Listed Developers
Variablecurrt
intcvrdbastrtperatio
ebitdanpc_cppcc

Definition
Financial Attributes (booked)
current ratio
interest cover ratio
debt-to-assets ratio
PE ratio
earnings per share
Political Economy Indicators
dummy variable equals to 1 if thecorporate management level used tohave a NPC and CPPCC background

Mean
(Full
Sample)Std. Dev.
(Full
Sample)Mean(Top 10developers)

Std. Dev.
(Top 10developers)

1.600742
39.38115
57.51366
5.260944
2.37E+08

2.072399
260.5568
32.52184132.6877
4.76E+08

1.05806
144.4856.85389
76.25905
1.62E+08

0.520694
567.0533
20.84452
60.5248
8.87E+07

0.292370

0.454855

0.169015

0.374781

0.084841

0.278648

0.020798

0.142714

0.138616

0.345548

0.020798

0.142714

0.136059

0.342855

0.177759

0.382326

0.419111

0.493418

0.546689

0.497837

0.209261

0.406785

0.169015

0.374781

0.673249

0.469029

0.569864

0.495116

10

official_1_2
official_3_4

official_5_6

official_7_8

official_9_12
official_otherdummy variable equals to 1 if thecorporate management level used tohave a national official backgrounddummy variable equals to 1 if thecorporate management level used tohave a provincial or ministerial
official backgrounddummy variable equals to 1 if thecorporate management level used tohave a city-level or bureau-level
official backgrounddummy variable equals to 1 if thecorporate management level used tohave a county-level official
backgrounddummy variable equals to 1 if thecorporate management level used tohave a township official backgrounddummy variable equals to 1 if thecorporate management level used tohave an other official background

3.2 Multi-Step Design and Multicollinearity Solution
As we have discussed above, the decisions of Chinese real estate developers (location
selection, project design, pricing, and timing) could be influenced by her financial conditions
and the conditions of her rivals, among other factors. We, therefore, build a simple multi-step
empirical model, which reflects such a sequential decision process.

10 “Management level” denotes firm’s board members, supervisors and executive layers. NPC denotes National People’s
Congress and CPPCC denotes Chinese People’s Political Consultative Conference (CPPCC).
9

Figure 2 Multi-Step Empirical Process

Step I
In the first step, firms make location choices. As consumer demand for housing differs
across locations, developers would have different willingness to pay for land slots in various
regions or "zones" in Beijing. Because land prices vary across zones within Beijing, it is also
possible that the financial conditions could constrain the locational choice. We, therefore,
include the financial condition as a crucial empirical determinant of that decision. Implicitly,
this formulation assumes that financial strength (or financial constraint) could impact landacquisition. We observe "location anchoring" of developers, which means that among 64 listed
firms in our dataset, 71.88% of them choose to develop projects in only one district during oursampling period. Only two firms in our sample invest in more than two regions in Beijing.
Thus, instead of diversifying their investments, most developers display strong "locational
preference" and tend to "focus" on specific areas. 11 This fact may be due to government
regulations, or developers’ preference, or that most developers are so financially constrainedthat they cannot afford to develop more than one project within Beijing. In any case, we use a
multiple logit model as follows.12

11 Generally, location choice reflects both financial status and the inherent attribute of the land. Hence, a conditional logit
model is more favored to combine the information on both sides. However, due to the phenomenon of "location anchoring,"
we cannot separately identify the pure effect of regional-macroeconomic variables from the firm's financial status. Therefore,
we omit the macroeconomic variables in the first step. We include the macroeconomic variables in the regressions in Step 2
and Step 3 to control the impact on project design and pricing-timing mechanism.
12 It has been suggested to us that the land heterogeneity could be embedded in the location choice. Consider instead thatdifferent developers have different projects (un-observed) in mind. Then, "given" the characteristics of land, the developer
will decide whether a particular piece of land would "fit-in" those project plans, and if that piece of land does, the developer
will attempt to acquire it. Hence, there might be an endogenous issue in location choice and project design. This problem has
been studied with the Principal Component Analysis and Propensity Score Method by some authors. We nevertheless use thecurrent "recursive" empirical strategy instead. The reasons are clear. First, to complement existing studies, it may beinteresting to use an alternative approach to examine whether specific findings still stand.
Moreover, most land characteristics are dummy variables based on subjective classification; the synthesized index could lose
robustness. It seems to us that "project design" could be changed significantly when the regulations suddenly change. And
there are examples reported in the media. On the other hand, whether a piece of land with specific characteristics is available
or not seems to be out of the control for most, if not all, real estate developers.
10denotes the financial conditions of developers in period .
whererefers to the year dummies of transaction. We also control the firm and year-month fixed effect,
and the standard error is clustered at the project level. 13
denotes the weighted
financial conditions of the developer’s local competitors. More specifically, for each developer,
using two different weighting methods. The first method
1,2, …, we calculate/ , whereis the total value of housing units sold by
is value-based weighting,∑developer i in period t, andis the total value of transactions in period t. Thus,
developers who sell less but more expensive units could carry a higher weight than those who
trade more affordable units in the index. The second method is transaction-based weighting,/ , whereis the number of housing units sold by developer i in period t, and∑is the total number of transactions in period t. Thus, developers that are associated.
with more dealings would get more weight in

Step II
The second step is to investigate the determinants of residential design by a structural model.
As "design" is a multi-dimensional subject, and given the data availability, we can only focus
on the building height and unit area as proxies of the developer's project design. However, fora specific land with a given land area, the average floor and unit area of a given project areendogenously determined due to the FAR (Floor Area Ratio) limit and Building Height
Restriction.14 We apply three-stage least square (3SLS) to estimate the simultaneous equation
system. More specifically, the empirical model is shown as follows.
Stage 1
_
2
_
2
where
_
and
_
represents the building's total floors and the average area(squared meters) of the project developed by developer in region
during period
is the weighted financial conditions of the developer ’s local competitors in.
region and period .
includes macroeconomic variables, which is common to all regions,and region-specific variables,15 as well as the project’s location attributes, such as the distanceandare exclusive vectors,
to CBD, the nearest subway station, hospitals, and schools.
denotes the project’s average floor anddenotes the total construction area of
where
13 Notice that in each real estate development (RED) project, there are several units. Units within the same RED project sharethe same average building height and average area information. Yet each of them has a potentially unique selling price andselling time. Thus, the variable resid is not independent. We need to control for such clustering in the regression.
14 According to the Regulation for the Management of Construction Land Plot Ratio issued by Housing and Urban-Rural
Development of the People's Republic of China, a developer is forbidden to exceed the ceiling FAR (Floor Area Ratio) and
Building Height determined by the urban planning department. Under this regulation, there are two extreme situations.
Situation A: build a house with a big unit area but low building height. Situation B: build an apartment with a small unit but
high building height. Thus, we could consider that the average unit area and building height being jointly decided.
15 The macroeconomic variables include GDP, fixed assets investment, and real estate investment. The region-specificvariables include the number of subway lines, population structure (permanent, external, registered, move in-out ratio), andemploy status (number of labors, registered urban unemployed people). We also include household variables (disposable
income per capita, labor compensation per capita, and consumption per capita).
11the project.
2 refers to the time dummy of the transaction as well as the district
dummy of the project. After stage 1, we obtain the OLS projects of the endogenous variables
_
and
_
.
Stage 2
_
2

_
_
_

2

We retain the residuals to form a consistent estimate of the covariance matrix of the
disturbances.
Stage 3We then employ GLS estimation with the covariance matrix in Stage 2 as the optimalon project design.
weighting matrix and analyze the impact ofStep IIIThe last step is to identify the empirical determinants of pricing and timing mechanism by
simultaneous estimation. The time difference between listing time and selling time, namely
TOM, is involved as the proxy of the selling pace of developers. It reflects the waiting cost ofdevelopers and consumers in the searching process. To study the transaction price changes ratiowith the monthly extension of TOM, reflecting the difference of the marginal change rate, andreduce the influence of different variance problem, the semi-logarithm Hedonic model isapplied for exploring the correlations between TOM and selling price. The endogeneity
between TOM and selling price cannot be neglected, as it is a critical tradeoff experienced by
the sellers. Previous studies have confirmed that prices TOM. On the other hand, housingtransaction price depends not only on its physical properties and location attribute but also on
TOM (see Clauretie and Daneshvary, 2009; Yavas, 1992).
To solve the endogenous problem, we estimate another simultaneous equation system asfollows.
_
2
2
where
_
and
represents the unit transaction price (per square meters) andthe time on market (in days) of the unit located in the project developed by developer in region
andare a pair of exclusive vectors.
denotes information
during period .
indicateson the unit level, which includes the floor and area of the unit. In contrast,
information on the project level, which includes the project’s average floor and total
construction area of the project. To get more consistent estimates of pricing-timing mechanismand firm’s financial status variables, we involve both of the estimated residual terms in step12

and) together with other explanatory variables into the third step regression.
two (We further employ three stage least square to estimate the structural model.
PCA Process: Financial Status Variables and Macro Variables
We have a long list of explanatory variables. Therefore, the potentially high correlations of
explanatory variables in each step of our multi-step procedure cannot be ignored. In the
appendix, we show that many of our variables are significantly correlated to one another. Thesehigh correlations are more frequently found among the financial status indicators andmacroeconomic variables. Such multi-collinearity problem might induce distortions in ourestimation or the interpretations of the results. To tackle this problem, we follow the literature
to adopt the principal component analysis (PCA) (Leung et al., 2006). As a statistical procedure,
PCA transforms the original correlated variables into a set of orthogonal variables called theprincipal components (PC).
Moreover, we can attribute to each PC the share of the total variations of the original set it
causes. In the usual practice, researchers focus on the few PCA that account for most of theoriginal data variations. Hence, PCA can effectively mitigate the multi-collinearity problem
and reduce the dimensionality of the right-hand-side variables simultaneously (Huang et al.,2015; Jolliffe, 2005). In our study, we select the high-correlated variables to establish ascandalized matrix (, where denotes the number of residential unit samples,while indicates the variable size). Then, we calculate the correlation matrix R of standardized| 0 ), we obtain the eigenvalue ( ) andmatrix Z . Solving the eigen-equation ( |Reigenvector ( ), and determine the number of principal components ( ). We get the value ofprincipal components by calculating
1,2, … , .
PCA is applied twice in our paper. First, we extract principal components of financial
condition. The calculated eigenvalues and eigenvectors of principal components process ineach step are shown in Table 2a and Table 2b. In 11 raw proxy variables of booked and unbooked financial status indicators, the PCA process helps us extract five principal components.
We label them as profitability, liability, liquidity, PClocal, and PCcentral, as each of the PC isstrongly correlated to a different set of variables.

13

Table 2a Principal Components of Financial Variables (Eigenvalue and Covariance Proportion)
Component
Eigenvalue
Difference
Proportion
Cumulative
Comp1
0.574262
0.19410.19412.13509Comp2
0.1619990.14190.336
1.56083
Comp3
1.39883
0.144230.1272
0.4632Comp4
0.217235
0.11410.57721.2546Comp5
1.037360.229915
0.09430.6715Comp6
0.807449
0.034322
0.07340.7449Comp7
0.7731270.025053
0.07030.8152Comp8
0.748074
0.084515
0.068
0.8832Comp9
0.663559
0.200298
0.06030.9435
Comp100.463261
0.305437
0.04210.9857
Comp110.157824.
0.0143
1

Table 2b Principal Components of Financial Variables (Eigenvectors)
Variable
Comp4
Comp5
Comp1 Comp2 Comp3

stdcurrt
stdintcvr
stddbastrt
stdperatio
stdebitda
stdnpc_cppcc
stdofficial_1_2
stdofficial_3_4
stdofficial_5_6
stdofficial_7_8
stdofficial_9_12

0.0431-0.12470.00570.07690.10730.1164-0.0177
0.39350.53270.45140.55580.04180.00480.45740.0225-0.10290.5626-0.57210.0602-0.0254
0.1952-0.3029

0.23950.35380.0722
0.52810.4693-0.2835-0.3657-0.2794
0.0965-0.0541
0.09330.13320.5138-0.4688
0.3599-0.51150.137
0.02310.22530.00670.1309-0.1415-0.8382
0.16380.04670.29720.15470.13330.1356-0.0865-0.2166
0.2444
0.077

Unexplained

0.162
0.43250.3881
0.34240.29770.32270.2816
0.48310.33140.41790.1538

Note: All variables have been standardized to eliminate the influence of dimensionality. Bold types indicate that
this weight is the highest among Comp1, Comp2 and Comp3.

We test the correlation between our five financial status indicators with some integrationindex constructed in the existing literature. Financial dependency, which is a measurement of
the demand in external finance, could be regarded as a combined measurement of scale,cashibility, solvency, and the profitability of developers (Claessens et al., 2012). Following themethodology developed by Rajan and Zingales (1998) and Claessens et al. (2012), we calculate
the financial independence index as an alternate proxy explaining changes in the ex-post“performance” of real estate firms (i.e., profits, sales, and investments). Two measures of afirm's intrinsic dependence on external finance are employed in the robustness tests, namely) and intrinsic dependence
intrinsic dependence on external finance for investment ( _16). The data is collected from the China
on external finance for working capital ( _Stock Market Trading Database (CSMAR database), which provides the listed firm’s financial
status together, including capital expenditures, cash flows, inventories, etc. We merge CSMAR
quarterly information with our developer database and construct the following approximationsof a firm's intrinsic dependence on external finance for capital investment:

16 There are other metrics for a firm's reliance on external finance, or the extent of the firm may be financially constrained thatare used in the literature. However, we are constrained by data availability to employ those metrics.
14

_
_

365 ∗

According to Table 2c, our five indicators of financial status show some correlations withtraditional definitions. However, correlations are not exceptionally high. Notice that our
financial status variables include the booked variables like profitability, liability, and liquidity
and some un-booked variables like political connections. Thus, our indicators could containmore abundant information on corporate financial status than the current literature's
singularized index. We use FD_INV and FD_WK as alternative measurement of financial
position and compare the results with our findings in the robustness check.
Table 2c Correlation between Our Measurement and Financial Dependence Identified by Literature
Variable
FD_INV
FD_WK
HFD_WK

profitability
liability
liquidity
pccentral
pclocal-0.0745-0.0040.10750.0143-0.0375-0.2287-0.018-0.04490.3701-0.3056-0.02310.026-0.02610.1872-0.055

Note: FD_INV denotes “intrinsic dependence on external finance for investment,” while FD_WK denotes“intrinsic dependence on external finance for working capital.” HFD_WK denotes the “intrinsic dependence on
external finance for working capital” calculated using history information (i.e., an average of FD_WK in the past
three years). Due to the data deficiency before 2007, we could not calculate the historical “actual firm use of
external finance for investment (FD_INV).” As an alternative test, historical information of “actual firm use of
external finance for working capital (FD_WK)” is used as a comparison.

Second, we extract components of local and macroeconomic information. The calculated
eigenvalues and eigenvectors of the principal components process in each step are shown in
Table 3a and Table 3b. In 18 raw proxy variables of geological and macroeconomic status, the
PCA process helps us to extract three principal components macro-fundamental indicator(macro), geography indicator (geography), and household indicator (micro).

15

Table 3a Principal Components of Macro Variables (Eigenvalue and Covariance Proportion)
Component
Comp1
Comp2
Comp3
Comp4
Comp5
Comp6
Comp7
Comp8
Comp9
Comp10
Comp11
Comp12
Comp13
Comp14
Comp15
Comp16
Comp17
Comp18Eigenvalue
11.4155
3.396221.21471
0.949698
0.494603
0.434437
0.245308
0.198424
0.173354
0.124445
0.109516
0.0887391
0.075093
0.0428978
0.0202896
0.00884153
0.00537074
0.00234198

Difference
8.01925
2.181510.265008
0.455094
0.0601661
0.189129
0.0468846
0.0250695
0.0489089
0.0149295
0.02077660.0136461
0.0321952
0.0226082
0.0114481
0.00347079
0.00302876
0.00209627

Proportion
0.60080.17870.06390.05
0.026
0.02290.01290.01040.00910.00650.00580.00470.004
0.00230.00110.00050.00030.0001

Cumulative
0.60080.7796
0.84350.89350.91950.94240.95530.96570.97490.98140.98720.99180.99580.998
0.99910.99960.99991

Table 3b Principal Components of Macro Variables (Eigenvectors)
Variable
Comp1
Comp2
Comp3
Unexplained
d_cbd
d_sub
d_h1
d_middle
d_highn_subway
perma_pop
exeter_pop
registered_pop
move_in_out
no_employed
no_unemployedgdp
fix_investment
real_estate_investment-0.1412-0.1838-0.1307-0.1698-0.14220.26670.26860.25380.28190.21330.27950.26130.28530.2787
0.27110.43550.26780.44770.38980.44410.07640.18880.22030.12510.07180.13570.03910.13510.123
0.089

0.06080.067
0.11330.10980.1314-0.0826-0.2127-0.2277-0.1468-0.063-0.0549-0.10240.0463-0.0721-0.07960.13390.37710.12010.15390.09160.18050.03029
0.06634
0.039810.47180.06444
0.22360.02904
0.078350.1481

personal_disposable_income
0.1988-0.0301
0.063040.7015personal_consumption
0.2504-0.1186
0.074220.42320.25770.045
0.1309personal_income_employed
0.349
Note: All variables have been standardized to eliminate the influence of dimensionality. Bold types indicatethat this weight is the highest among Comp1, Comp2 and Comp3.

Following the correlation test after the PCA process, all the high correlations are
successfully eliminated (see Appendix). Indeed, since PCA is easily affected by the relative
scaling of the original variables, we also employ the Kaiser-Meyer-Olkin (KMO) test tomeasure of sampling adequacy and the applicability of the PCA process in each step. We pass
the test, and the details are omitted due to limited space.

16

4 Empirical Results
This section presents the empirical results. The presentation follows the natural order:
results from the pre-marketing stage first (i.e., the location choice and project design), followedby results from the marketing stage (i.e., pricing and timing decisions). Then we present a
diagnosis of the drivers in the marketing stage. We will also provide several robustness checks.
4.1 Pre-Marketing StageIn this sub-section, we present the results in both the location sorting and the project design.
Table 4a shows the impact of the developer's financial status on project location choice.

17

Table 4a Regression of Developer’s Financial Status and Location Selection

Value-based weighting
profitability
liability
liquidity
pccentral
pclocalprofitability-i
liability-i
liquidity-i
pccentral-i
pclocal-i
Transaction-based weighting
profitability
liability
liquidity
pccentral
pclocalprofitability-i
liability-i
liquidity-i
pccentral-i
pclocal-i(1)Region 2/(2)
Region 3/(3)
Region 4/(4)
Region 3/(5)
Region 4/(6)
Region 4/

Region 1

Region 1

Region 1

Region 2

Region 2

Region 3

0.913***(0.187)-0.0330(0.156)-1.650***(0.239)0.706***(0.128)-0.769***(0.0800)-0.376**(0.183)-0.504***(0.151)
0.236(0.234)-1.065***(0.125)0.396***(0.0793)-0.145(0.217)0.558***(0.178)-0.105(0.266)-0.798***(0.148)-0.650***(0.0860)
0.637***(0.212)-1.113***(0.173)-0.909***(0.261)-0.525***(0.146)
0.391***(0.0852)0.571(0.475)
1.538**(0.644)-0.0492(0.433)-0.985**(0.406)0.209(0.311)0.595(0.457)-2.321***(0.626)-1.173***(0.393)-2.069***(0.383)0.354(0.296)-1.057***(0.136)
0.591***(0.134)
1.545***(0.148)-1.504***(0.106)0.119***(0.0409)
1.013***(0.133)-0.609***(0.131)-1.145***(0.145)0.541***(0.105)-0.00433(0.0407)-0.341(0.440)1.571**(0.630)1.601***(0.372)-1.691***(0.389)0.978***(0.302)
0.971**(0.422)-1.817***(0.612)-1.409***(0.326)-1.004***(0.365)-0.0413(0.286)0.716(0.446)0.980(0.627)
0.0560(0.351)-0.187(0.396)0.860***(0.303)-0.0419(0.427)-1.208**(0.609)-0.264(0.303)-1.544***(0.373)-0.0369(0.287)0.925***(0.189)-0.0604(0.160)-1.627***(0.241)0.728***(0.129)-0.766***(0.0806)-0.387**(0.185)-0.478***(0.155)0.213(0.236)-1.087***(0.127)0.393***(0.0799)55170-0.135(0.218)0.553***(0.183)-0.0586(0.269)-0.790***(0.151)-0.646***(0.0865)0.628***(0.214)-1.109***(0.178)-0.955***(0.263)-0.532***(0.148)0.387***(0.0858)
55170

0.566
(0.484)1.489**(0.656)-0.0206(0.440)-0.951**(0.409)0.195(0.315)0.601(0.465)-2.274***(0.638)-1.203***(0.399)-2.103***(0.386)0.368(0.299)55170-1.060***(0.135)0.614***(0.137)1.568***(0.150)-1.519***(0.109)0.120***(0.0410)1.015***(0.133)-0.631***(0.134)-1.168***(0.146)
0.555***(0.107)-0.00575(0.0409)55170-0.359(0.448)1.550**(0.641)1.606***(0.378)-1.679***(0.392)0.961***(0.305)0.988**(0.430)-1.796***(0.624)-1.415***(0.333)-1.015***(0.300)-0.350(0.338)
55170

0.701
(0.453)0.936(0.637)0.0380(0.358)-0.161(0.399)0.840***(0.306)-0.0268(0.435)-1.165*(0.620)-0.248(0.310)-1.571***(0.305)-0.210(0.339)55170

Obs
Note: This table presents the impact of a firm’s financial status on the probability project’s location choice(across Region 1, Region 2, Region 3 and Region 4 defined in Table 3), controlling for year fixed effect andproject’s cluster effects on 55170 transaction records. Standard errors are in parentheses: *p<0.10, **p<0.05,*** p<0.01.
18

We find developers with higher profitability locate their projects within ring 5, as there is atradeoff between land price and housing demand. Real estate development in the inner city isexpensive, as both the land price and demolition compensation are high. On the other hand,commuting time in suburban areas, such as in ring 6, is long, and public facilities such as
hospitals and schools tend to be under-supplied. The housing demand in those areas is uncertain.
Our empirical results indicate that developers with high profitability prefer inner-city locations,leaving the risky development to less profitable competitors.
Developers with higher liquidity values, i.e., better debt-paying ability, prefer projects
located inside ring 2. This result is also intuitive. First, the land price within ring 2 is
exceptionally high. Moreover, unexpected liquidity needs, for instance, from other projects,
may emerge in the development process. Thus, other things being equal, developers with better
financial strength and capital turnover ability would locate in this area. We also find thatdevelopers with higher liability will show a higher possibility to choose the land locating
outside ring 5 and ring 6. As we have explained, the development projects in sub-urban areas
are riskier as the housing demand may be more volatile. With limited liability, real estatedevelopers with more debt may find themselves optimal to choose risky projects.17
Regarding un-booked financial status (political connections on both central and local levels),
we find that developers with more robust relationships with the central government will be
more likely to develop projects in inner-city zones. In contrast, developers with tighter local
political connections will be more likely to develop projects in outer city zones, primarily
suburban areas. In other words, developers with lower liquidity, higher liability, and higher
central political connections favor areas located inside ring 2 and ring 5. In contrast, sites
located in suburban areas are more favored by developers with more debt burden and higher
local political connections. Our results are robust to changing the value-based weighting totransaction-based weighting.
It is noteworthy that, developer's location choice may influence developers' financial status.
We then replace the indicators with historical information of financial and political connection
status back to two years before the listing time as a robustness check.18 Table 4b shows that theresults are similar, which suggests some degree of robustness of our results.

17 For instance, see Vereshchagina and Hopenhayn (2009) for related analysis.
18 Unlike western countries, the timing of land development is considerably shorter in China. Based on the residential landparcel transaction data from the Beijing Municipal Bureau of Land and Resources, the average duration from land acquisition
to housing presale is around 10 months, while the duration form land acquisition to construction completion is still less than
20 months from 2003 to 2015 (Yang and Wu, 2018). Thus, the current project's land acquisition process is less likely to interferewith the developer's financial information two years ago.
19

Table 4b Regression of Developer’s Financial Status and Location Selection (Historical Information)

Value-based weighting
history_profitability
history_liability
history_liquidity
history_pccentral
history_pclocal
history_profitability-i
history_liability-i
history_liquidity-i
history_pccentral-i
history_pclocal-i
Transaction-based weighting
history_profitability
history_liability
history_liquidity
history_pccentral
history_pclocal
history_profitability-i
history_liability-i
history_liquidity-i
history_pccentral-i
history_pclocal-i(1)
Region 2/(2)
Region 3/(3)
Region 4/(4)
Region 3/(5)
Region 4/(6)
Region 4/

Region 1

Region 1

Region 1

Region 2

Region 2

Region 3

0.484***(0.113)-0.297(0.209)0.824***(0.117)-0.142(0.145)-0.768***(0.0917)-0.182(0.114)1.911***(0.213)0.238*(0.127)-0.417***(0.143)0.443***(0.0915)-0.110(0.117)-0.914***(0.213)
1.102***(0.119)-1.362***(0.166)-0.500***(0.0993)-0.0220(0.119)2.945***(0.217)0.102(0.130)-0.487***(0.164)0.234**(0.0991)

0.703***(0.180)-2.228***(0.342)
1.564***(0.212)-2.781***(0.347)-1.352***(0.277)2.140***(0.178)2.283***(0.334)-1.284***(0.252)-0.247(0.332)
1.136***(0.274)-0.594***(0.0372)-0.616***(0.0581)0.278***(0.0260)-1.220***(0.104)0.268***(0.0455)
0.160***(0.0393)1.034***(0.0603)-0.136***(0.0342)-0.0699(0.103)-0.209***(0.0456)

0.219(0.154)-1.931***(0.278)0.739***(0.181)-2.639***(0.329)-0.584**(0.266)2.321***(0.153)0.371(0.266)-1.523***(0.223)0.170(0.314)0.693***(0.263)0.813***(0.154)-1.314***(0.276)
0.461**(0.181)-1.419***(0.324)-0.852***(0.267)
2.162***(0.153)-0.663**(0.264)-1.387***(0.223)0.240(0.309)0.902***(0.264)0.485***(0.113)-0.314(0.210)0.823***(0.117)-0.136(0.146)-0.770***(0.0924)-0.182(0.114)
1.931***(0.214)0.239*(0.127)-0.423***(0.144)0.445***(0.0922)49618-0.107(0.117)-0.931***(0.214)
1.102***(0.119)-1.356***(0.167)-0.502***(0.1000)-0.0244(0.119)2.967***(0.218)0.103(0.130)-0.493***(0.165)0.237**(0.0998)49618

0.693***(0.181)-2.219***(0.343)
1.579***(0.211)-2.776***(0.350)-1.357***(0.280)2.154***(0.179)2.275***(0.335)-1.303***(0.252)-0.253(0.336)
1.142***(0.277)49618-0.592***(0.0373)-0.617***(0.0582)
0.278***(0.0260)-1.220***(0.104)0.267***(0.0456)
0.158***(0.0393)
1.035***(0.0605)-0.136***(0.0343)-0.0705(0.103)-0.208***(0.0457)
49618

0.207
(0.155)-1.905***(0.278)0.755***(0.180)-2.640***(0.332)-0.587**(0.268)2.336***(0.154)0.343(0.267)-1.543***(0.222)0.170(0.317)0.697***(0.265)49618

0.800***(0.155)-1.288***(0.276)0.477***(0.180)-1.421***(0.327)-0.854***(0.270)2.178***(0.154)-0.692***(0.265)-1.406***(0.222)0.241(0.312)0.905***(0.267)49618

Obs
Note: This table presents the impact of firm’s financial status on the probability project’s location choice,controlled for year fixed effect and project’s cluster effects on 49618 transaction record (we lose someobservations due to limitations in historical information). Standard errors are in parentheses: *p<0.10,**p<0.05, *** p<0.01.
20Recall that the second stage of real estate development concerns the project design. Here we
present the results relating to the project design and the financial conditions of the developers.
Our econometric approach considers that the simultaneous choices on the unit area and project
height are endogenous. Table 5 exhibits the crucial determinates of project dual decision inbuilding height and unit area design. Column (1) and (3) exhibit the result using simultaneous
estimation under two weighting systems. We find that after controlling for the simultaneity
issues, the coefficients of profitability and liability are significantly positive in both regressions.
Companies with higher financial solvency and profitability tend to build larger and high-rise
residential units. Companies with a smaller scale, lower financial solvency, and profitability
are more inclined to produce small-sized and medium-rise residential units. These findings are
consistent with the conjecture that larger and high-rise units are more profitable, but may also
be riskier, as fewer people would be able to afford them. Thus, developers need to have"precautionary saving" to build those units and be profitable. Further, there is a significant
negative interaction between the average unit area and building height, consistent with theconstruction constraints above-mentioned.

21

Table 5 Regression of Developer’s Financial Status and Project Design

lpro_floor

value -based weighting(1)(2)
lunit_area
lpro_floortransaction-based weighting(3)(4)
lunit_area
lpro_floor-0.246***(0.00803)-0.246***(0.00803)

lunit_area
profitability
liability
liquidity
pccentral
pclocalprofitability-i
liability-i
liquidity-i
pccentral-i
pclocal-igeography
macro
micro

0.193***(0.0164)0.0427***(0.0165)-0.0247(0.0160)
0.0733***(0.0145)0.0147**(0.00667)-0.132***(0.0160)-0.0312*(0.0160)
0.00107(0.0154)-0.0514***(0.0142)0.0102(0.00662)
0.0385***(0.00184)-0.00239(0.00234)-0.0495***(0.00608)
55170-5.613***(0.435)1.085***(0.127)0.249***(0.0942)-0.162*(0.0908)0.453***(0.0913)0.0880**(0.0386)-0.753***(0.110)-0.164*(0.0902)0.0278(0.0865)-0.300***(0.0833)
0.0557(0.0372)
0.186***(0.0127)-0.0113(0.0132)-0.181***(0.0309)55170

0.193***(0.0164)0.0427***(0.0165)-0.0247(0.0160)
0.0733***(0.0145)0.0147**(0.00667)-0.132***(0.0160)-0.0312*(0.0160)
0.00107(0.0154)-0.0514***(0.0142)0.0102(0.00662)
0.0385***(0.00184)-0.00239(0.00234)-0.0495***(0.00608)
55170-5.614***(0.435)
1.093***(0.128)0.260***(0.0964)-0.157*(0.0915)
0.461***(0.0927)
0.0912**(0.0388)-0.761***(0.111)-0.175*(0.0924)0.0224(0.0873)-0.308***(0.0847)
0.0526(0.0373)0.186***(0.0127)-0.0114(0.0132)-0.181***(0.0309)55170

Obs
Note: This table presents the impact of a firm’s financial status on the project’s design (building height andunit area), controlled for region fixed effect, year fixed effect and project’s cluster effects on 55170 transaction
records. Standard errors are in parentheses: *p<0.10, **p<0.05, *** p<0.01.

4.2 Marketing StageAt the marketing stage, the housing units are completed. The developers now decide atwhat price and at what pace the units be sold. As a preview of the results, we find strong
evidence that liquidity demand influences the simultaneous decision of price of sale and TOM,
consistent with previous sections' results. Table 6a exhibits the results on the price, and Table
6b displays the results on the TOM. Our results confirm the expected tradeoff between the twovariables.
22

Table 6a Regression of Developer’s Financial Status and the effect of TOM on prices(1)
3 Stage Least
SquaresValue-based weighting
TOM

profitability
liability
liquidity
pccentral
pclocalprofitability-i
liability-i
liquidity-i
pccentral-i
pclocal-i-0.000726(0.000784)-0.308***(0.00723)
0.0307**(0.0123)
0.0234*(0.0124)0.0536***(0.0113)0.00579(0.0102)-0.00236(0.00481)-0.00957(0.0121)-0.0298**(0.0121)-0.0552***(0.0108)-0.0298***(0.00999)
0.00264(0.00484)(2)
TOM
Quintile(≤0.25)(3)
TOM
Quintile(0.25 to 0.5)

(4)
TOM
Quintile(0.5 to 0.75)(5)TOM
Quintile(>0.75)0.160***(0.0315)-0.581***(0.0161)-0.326***(0.0589)-0.191***(0.0445)-0.00850(0.0545)-0.0197(0.0317)-0.00789(0.0101)
0.296***(0.0583)
0.147***(0.0431)0.0288(0.0537)-0.00759(0.0314)0.00578(0.00988)-0.142***(0.0405)-0.424***(0.0117)-0.0989**(0.0386)-0.133**(0.0528)-0.0410**(0.0200)
0.0990***(0.0220)0.0317***(0.00918)
0.137***(0.0381)
0.162***(0.0518)0.0498**(0.0194)-0.155***(0.0218)-0.0350***(0.00973)-0.140***(0.0198)-0.208***(0.0169)
0.105***(0.0366)0.00142(0.0424)
0.0954**(0.0400)-0.0349(0.0324)0.0687***(0.0100)-0.0999***(0.0358)
0.000697(0.0413)-0.0891**(0.0388)0.0494(0.0338)-0.0602***(0.0103)0.0140***(0.00233)-0.108***(0.0189)0.0246(0.0273)-0.0341(0.0251)0.147***(0.0503)-0.0547*(0.0282)-0.100***(0.0148)0.0196(0.0265)0.0395(0.0243)-0.228***(0.0476)
0.0628**(0.0275)0.120***(0.0143)23

Transaction-based weighting
TOM-0.000721
0.160***-0.142***-0.140***0.0140***(0.000784)(0.0315)(0.0405)(0.0198)(0.00233)-0.308***-0.581***-0.424***-0.208***-0.108***(0.00723)(0.0161)(0.0117)(0.0169)(0.0189)
profitability
0.0309**-0.326***-0.100***0.103***
0.0225(0.0124)(0.0592)(0.0387)(0.0368)(0.0278)
liability
0.0243*-0.189***-0.135**0.000271-0.0394(0.0127)(0.0456)(0.0530)(0.0427)(0.0257)
liquidity
0.0537***-0.00943-0.0417**
0.0929**0.159***(0.0114)(0.0546)(0.0200)(0.0403)(0.0516)
pccentral
0.00507-0.0201
0.0999***-0.0360-0.0631**(0.0103)(0.0319)(0.0220)(0.0326)(0.0291)
pclocal-0.00281-0.00827
0.0317***0.0682***-0.104***(0.00483)(0.0101)(0.00918)(0.0101)(0.0149)
profitability-i-0.00973
0.297***
0.139***-0.0975***
0.0218(0.0122)(0.0586)(0.0382)(0.0360)(0.0271)-0.0306**
0.145***
0.165***
0.00179
0.0449*liability-i(0.0123)(0.0442)(0.0519)(0.0416)(0.0249)
liquidity-i-0.0553***
0.02970.0505***-0.0868**-0.240***(0.0109)(0.0539)(0.0195)(0.0391)(0.0489)
pccentral-i-0.0291***-0.00723-0.156***0.0506
0.0711**(0.0101)(0.0316)(0.0218)(0.0340)(0.0283)0.003100.00616-0.0350***-0.0597***
0.124***
pclocal-i(0.00486)(0.00989)(0.00974)(0.0104)(0.0145)
Obs
55170
14117
13677
13582
13794
Note: This table presents the impact of a firm’s financial status on the pricing-timing mechanism. The
dependent variable is lunit_price, controlled for region fixed effect, year fixed effect and project’s cluster
effects on 55170 transaction records. Column (1) exhibits the simultaneous regression results in the cohortof the shortest duration of sales (lowest 25% TOM). Column (2) exhibits the simultaneous regression resultsin the cohort of the sub-shortest duration of sales (TOM located in the interval 25%-50%). Column (3)exhibits the simultaneous regression results in the cohort of the sub-longest duration of sales (TOM locatedin the interval 50%-75%). Column (4) exhibits the simultaneous regression results in the cohort of the longest
duration of sales (highest 25% TOM). Standard errors are in parentheses: *p<0.10, **p<0.05, *** p<0.01.

24

Table 6b Regression of Developer’s Financial Status and the effect of price on TOM

Value-based
weightinglunit_priceprofitability
liability
liquidity
pccentral
pclocalprofitability-i
liability-i
liquidity-i
pccentral-i
pclocal-i
Transactionbased weighting
lunit_priceprofitability
liability
liquidity
pccentral
pclocalprofitability-i
liability-i
liquidity-i(1)
3 Stage Least
Squares(2)
Price Quintile(≤0.25)(3)
Price Quintile(0.25 to 0.5)

(4)Price Quintile(0.5 to 0.75)(5)Price Quintile(>0.75)8.311***(0.285)
1.068***(0.0806)
0.777***(0.275)-1.202***(0.278)1.238***(0.251)0.837***(0.227)0.692***(0.107)-1.292***(0.269)1.217***(0.270)-1.092***(0.241)0.271(0.223)-1.240***(0.107)0.112***(0.0358)
0.0149(0.0114)0.283***(0.0935)
0.113(0.0704)
0.154*(0.0861)-0.00538(0.0505)-0.0507***(0.0157)-0.342***(0.0921)-0.134**(0.0682)-0.0729(0.0853)-0.0235(0.0501)-0.00354(0.0158)

0.398***(0.0459)
0.00969(0.00927)-0.379***(0.0681)-0.377***(0.0979)-0.136***(0.0370)0.203***(0.0389)0.120***(0.0158)
0.361***(0.0673)
0.353***(0.0960)
0.123***(0.0361)-0.180***(0.0385)-0.139***(0.0162)0.139(0.182)0.417***(0.0407)
0.104(0.166)-0.249(0.191)0.806***(0.172)-0.910***(0.122)0.00387(0.0471)-0.138(0.162)0.227(0.186)-0.751***(0.168)1.037***(0.119)0.0759(0.0475)

4.314***(0.505)0.0678(0.177)-0.812*(0.419)0.525(0.386)-1.087(0.773)0.943**(0.433)-0.806***(0.230)1.200***(0.406)-0.713*(0.373)0.0567(0.737)-0.497(0.423)0.288(0.229)8.329***(0.286)
1.064***(0.0806)
0.784***(0.278)-1.203***(0.284)1.243***(0.253)0.881***(0.230)0.717***(0.108)-1.300***(0.272)
1.219***(0.276)-1.097***0.112***(0.0358)
0.0149(0.0114)0.285***(0.0940)0.112(0.0722)0.153*(0.0863)-0.00421(0.0508)-0.0508***(0.0157)-0.344***(0.0926)-0.133*(0.0699)-0.0725

0.398***(0.0459)
0.00967(0.00927)-0.379***(0.0683)-0.376***(0.0982)-0.135***(0.0371)
0.202***(0.0389)0.120***(0.0159)
0.361***(0.0674)
0.352***(0.0963)
0.122***

0.137(0.182)0.417***(0.0407)0.103(0.167)-0.245(0.192)0.813***(0.173)-0.917***(0.122)0.00357(0.0472)-0.136(0.163)0.224(0.187)-0.758***4.326***(0.505)0.0645(0.177)-0.826*(0.428)0.580(0.395)-1.163(0.794)
1.046**(0.447)-0.785***(0.233)
1.214***(0.415)-0.770**(0.381)0.135

25

(0.244)(0.0855)(0.0362)(0.169)(0.758)0.228-0.0246-0.179***1.044***-0.600(0.226)(0.0504)(0.0385)(0.120)(0.437)
pclocal-i-1.266***-0.00348-0.139***0.0761
0.266(0.107)(0.0158)(0.0162)(0.0475)(0.233)
Obs
55170
13450
14009
13817
13894
Note: This table presents the impact of a firm’s financial status on the pricing-timing mechanism. The
dependent variable is TOM, controlled for region fixed effect, year fixed effect and project’s cluster effects
on 55170 transaction records. Column (1) exhibits the simultaneous regression results in the cohort of thelowest transaction price (lowest 25% unit_price). Column (2) exhibits the simultaneous regression results inthe cohort of the sub-lowest transaction price (unit_price located in the interval 25%-50%). Column (3)exhibits the simultaneous regression results in the cohort of the sub-highest transaction price (unit_pricelocated in the interval 50%-75%). Column (4) exhibits the simultaneous regression results in the cohort of
the highest transaction price (highest 25% unit_price). Standard errors are in parentheses: *p<0.10, **p<0.05,*** p<0.01.
pccentral-i

Combining with our previous results, we also find developers with stronger connections
central and local government wait a long time on the market. Notice that political connections
might signal for Soft Budget Constraint or implicit guarantee. In other words, firms withstronger political relationships might have an extra resource when needed (Faccio, 2006).
Therefore, these developers could have more bargaining power and the ability to wait longer.
As a mirror image, we find that when the developers with better politically-connectedcompetitors reduce their transaction price and TOM. Since their rivals might have more
information on the future policy changes and better resources, developers find it optimal toadopt a "quick sale with discounted price" strategy.
Some casual observations suggest that the relationship between TOM and price might benonlinear. For instance, financially constrained households are forced to purchase relatively
cheap housing units and may not discount much along the time dimension. The "middle class"
who can choose housing units of different sizes may be more sensitive to the tradeoff between
TOM and price. For affluent households, a budget constraint may be less binding. Instead, theymight be more selective regarding characteristics, including the location, the architecturaldesign, even the neighbors, or their peers' perception. These are the considerations from thedemand side. On the supply side, recall that the top 10 developers are financially more
"adequate" than an average developer. Thus far, all the results presented have also confirmedthat the corporate's financial conditions would significantly affect her behavior. Putting theseobservations together, it may be reasonable to conjecture that the relationship between TOM
and price is indeed nonlinear.
To formally examine this hypothesis, we divide the TOM and transaction price of the unit
into for quintiles, respectively (Quintile (≤0.25), Quintile (0.25 to 0.5), Quintile (0.5 to 0.75),
and Quintile (>0.75)). According to the comparisons of the different cohort, we find whiletransaction price, in general, has a positive impact on TOM, TOM has a U-shape impact on thetransaction price. This empirical result may be related to the "Fishing Strategy" and "Negative
Herding" effect discussed in the literature. Sellers can either sell the house immediately withcertainty at the current market price, or fish for a better price by waiting for high-price bidders.
This fishing strategy will induce high transaction price co-existence with long TOM (Stein,
1995; Leung and Tse, 2017). Also, potential buyers may regard TOM as a sign of unobserved
information like housing quality (Taylor, 1999). Since longer TOM could induce buyer's
26

suspicion of a flaw in the house, the seller may have an incentive to post an inordinately low
initial price to make an early sale and avoid consumer "herding." Our results show that thenegative herding effect is associated with the initial transaction in line with existing literature,
while the fishing effect is reflected in longer waiting times. Similarly, the impact on price on
TOM is much weaker at the highest Quintile, suggesting at the high-end market, factors otherthan price may be more critical in determining the Time-On-the-Market of housing units.
4.3 Empirical Determinants of the Developers’ behavior at the Marketing StageWe begin with the observation that, in an oligopolistic market, firms would respond tointernal factors (such as liability, liquidity, and political connection) and external factors (suchas the pricing behaviors of rival firms). Developers who have higher liquidity levels andstronger political relationships with the local government can afford a slower pace of sales andsell their property at higher transaction prices. Developers who have the limited risk-bearing
capacity and faces inventory financing constraints and debt service obligations may be forced
to sell at lower prices to accelerate the sales, which is often specified as the liquidity constraint
effect (Chevalier and Scharfstein, 1995; White, 1992). To investigate such possibility, in thissection, we first compare the different driven forces of internal and external financial status by
calculating the marginal effect of latent variables, i.e., internal financial status and externalfinancial status. Figure 3 provides a visualization.
On average, we find that our empirical framework can explain about 60% of the developers'
pricing behavior, in which internal and external factors have roughly equal contributions.
However, our previous quantile regressions suggest that different variables' explanatory power
may vary across different price quantiles. Figure 3(a) confirms the same idea. In particular, ourmodel's explanatory power declines in regions 2 and 3, where external factors seem to be lesscritical. On the other hand, our model explains almost 90% of the pricing behaviors in region
4, where amenities tend to be under-provided, and internal factors contribute nearly 70%.
Simultaneously, Figure 3(b) shows that our model explains less than 10% of TOM acrossdevelopers on average. As in the case of the transaction price, heterogeneity seems to be thekey to this unsatisfactory result. It is clear that our model again explains almost 90% of thevariations of TOM across developers within region 4. This time, however, it is the external
factors which make up nearly all the contributions. The model's performance in explaining
TOM in region 1 is about 60%, which is comparable to the performance in explaining
transaction prices in the same area. Our econometric model cannot explain the prices inregions 2 and 3; our model also fails to account for the TOM in those regions.

27

100.0%100.0%

90.0%

90.0%

80.0%

80.0%

70.0%

70.0%

60.0%

60.0%

50.0%

50.0%

40.0%

40.0%

30.0%

30.0%

20.0%

20.0%

10.0%

10.0%

0.0%

0.0%
average Region Region Region Region
effect1
2
3
4Internal Factors

External Factors

average Region Region Region Region
effect1
2
3
4

UnexplainedInternal Factors(a)External Factors

Unexplained(b)

Figure 3 Decomposition of Internal and External Effects in Pricing and Timing
Note: Figure (a) and (b) present the comparison of decomposition of internal and external effects (using
value-based weighting system). The dependent variable in (a) is unit selling price, while the dependent
variable in (b) is TOM. We apply three stage least square to estimate the simultaneous regression, controlled
for region fixed effect, year fixed effect and project’s cluster effects on 55170 transaction records. The results
are almost the same using transaction-based weighting system.

Given the dramatic difference in our model performance among different regions, we thenturn to the more disaggregate geographical classification and investigate whether the "modeof local competitions" varies across regions (regions with and without top 10 developers). Since
the location choice is not random, we modify our empirical model in this section. Morespecifically, we first calculate the probability of having at least one of the top 10 developers ina given district. In the second stage, we re-estimate the pricing and TOM equations, taking theresidual term from the first stage as a proxy for some top 10 developers' location preference,
and again employ three-stage least square to estimate the structural model. Table 7a shows the
distribution of real estate development projects across the 15 districts in Beijing during oursampling period. Notice that the top 10 real estate developers have never been involved in some
districts, namely, districts 1, 4, 6, 9, 10, 12. We can, therefore, propose the following test
procedures.

28

Table 7a Distribution of real estate projects across the 15 districts in Beijing
District No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Total

District Name
Changping
Chaoyang
Chongwen
Daxing
Dongcheng
FangshanFengtai
HaidianHuairou
Miyun
Shijingshan
ShunyiTongzhou
Xicheng
XuanwuNon-big10
5
31
1
3
1
2
8
12
2
1
0
5
5
1
1
78

Big10
0
5
4
0
1
0
2
2
0
0
1
0
1
2
3
21

Total
5
36
5
3
2
2
10
14
2
1
1
5
6
3
4
99

More formally, in the first stage, we recognize that the location choice is not random andwe estimate the following model,
10

Φ

1|
Φ
10

where dummy variable
10 denotes whether there is a project developed by the top 10
is a vector of explanatory variables that might affect the top
developer in a given district.
10 developers' location choice, including the geographic, macro, and micro factors of the
district, and year fixed-effects. is a vector of unknown parameters estimated by maximum
likelihood. Φ is the cumulative distribution function of the standard normal distribution.
is the estimated probability of having a top 10 developer in a given district whileis the residual of estimation, reflecting some idiosyncratic locationpreference or not included in our dataset.
In the second stage, we re-estimate the pricing and TOM equations, taking the residual
term from the first stage, as a proxy for the top 10 developers' location preference.
_
2

2
29

and
are a pair of exclusive vectors.
denotes information on the unit level,
where
including the unit's floor and area. In contrast,
conveys information on the project level,
consisting of the project's average floor and total construction area. To get more consistentestimates of pricing-timing mechanism and firm's financial status variables, we involve bothand) and other explanatory variables into
the estimated residual term in step two (the third step regression. We again employ a three-stage least square to estimate the structuralmodel.
Table 7b reports the results. Several observations are in order. First, the slopes are muchsteeper in districts without the top 10 developers. For instance, the coefficient of TOM onlog(unit price) is around -0.002 in districts that involve some real estate projects by the top 10
developers. In contrast, the counterpart in districts without any project by the top 10 developers
is around -0.035.
Similarly, the coefficient of log(unit price) on Tom is around 5 in districts that involvesome real estate projects by the top 10 developers. In contrast, the counterpart in districts
without any project by the top 10 developers is around 30. Second, the project design
preference matters. Recall that in stage two, we estimate the project design against financial
and) can be interpreted as
and geographical variables. Hence, the residuals terms (indicators of the developers' project design, which are not captured by our observed variables.
Notice that the coefficients of these second-stage residual terms in the log(unit price) and TOM
equations are -0.399 and 1.207 in districts with the top 10 developers, respectively. In
comparison, the counterparts in districts without any of the top 10 developers are -0.794 and2.362. All of these coefficients are 1% significant.
Third, the top 10 developers' presence does not only change the sensitivities of the house
price and TOM to each other, to the project design but also directly impact the house price andTOM. Recall that the variable resid(Probtop10) can be interpreted as the top 10 developers'
location preference. In districts with the top 10 developers' presence, the coefficients of thatlocation preference variable in the house price and TOM equations are -0.272 and -0.725,
respectively. In districts without these prominent developers' involvement, the corresponding
coefficients become -0.364 and -17.39, respectively. Thus, in districts without the top 10
developers, other things being equal, houses would be sold at lower prices and faster. Noticethat we use the 3-stage least square method to mitigate the endogeneity. We, therefore, may not
be able to provide the usual adjusted . Fortunately, we can still produce an estimate about
how much our model explains the variations of the house price and TOM across districts.

30

Table 7b Pricing and TOM influenced by financial conditions and local competition
Districts with big10
Districts without big10
lunit_price
TOM
lunit_price
TOM
(1)
(2)
(3)
(4)
TOM
-0.00254***-0.0351***(0.000856)(0.00393)
lunit_price5.483***
28.57***(0.289)(1.735)-0.399***1.207***-0.229***1.894***(0.00903)(0.0905)(0.0139)(0.175)-0.272***0.725-0.364***-17.39***(0.0337)(0.747)(0.0609)(0.704)
profitability
0.144***-1.057***0.443***-28.72***(0.0131)(0.292)(0.0941)(3.078)liability-0.0153
0.408
0.189*-20.66***(0.0130)(0.286)(0.101)(3.151)liquidity
0.01272.406***-0.588***21.11***(0.0120)(0.261)(0.0913)(3.193)
pccentral
0.104***-1.303***-0.219***18.79***(0.0110)(0.244)(0.0469)(1.520)pclocal0.0363***0.310***-0.0830**
0.0192(0.00510)(0.113)(0.0336)(1.040)-0.0932***0.0107-0.221**
16.33***
profitability-i(0.0127)(0.282)(0.0869)(2.740)0.00802-0.114-0.15016.61***
liability-i(0.0125)(0.277)(0.0958)(2.975)-0.0324***-2.031***0.248***-6.311**liquidity-i(0.0114)(0.250)(0.0873)(2.793)-0.0741***
1.178***
0.0978**-12.86***
pccentral-i(0.0106)(0.236)(0.0440)(1.353)-0.00296-1.279***-0.00532
5.220***
pclocal-i(0.00514)(0.112)(0.0341)(1.029)region effectcontrolledcontrolledcontrolledcontrolled
year effectcontrolledcontrolledcontrolledcontrolled
project’s cluster
controlledcontrolledcontrolledcontrolled
N
12370
12370
37250
37250
Note: This table presents the compassion between districts with top 10 developers and districts without top
10 developers, focusing on the impact of a firm’s financial status on the pricing-timing mechanism. The
dependent variable in columns (1) and (3) is lunit_price, while the dependent variable in columns (2) and (4)is TOM, controlled for region fixed effect, year fixed effect and project’s cluster effects. All the rival variables,such as profitability-i, liability-i, etc., are value-weighted. Standard errors are in parentheses: *p<0.10,**p<0.05, *** p<0.01.

Figure 4 demonstrates some intuitive results. Therefore, in districts without the top 10
developers’ participation, competition is among relatively small developers. We find thatinternal factors majorly drive the pricing strategy while external factors drive timing strategy.
However, in districts with "market leader(s)," i.e., involving at least one of the top 10
developers, the contributions of internal and external factors are approximately the same inpricing. Notice also that the explanatory power on our model's pricing is comparable in districts
with top 10 developers (about 50%) and districts without (about 40%).
On the other hand, the explanatory power on TOM is very different. In districts without top
31

10 developers, our model can explain more than 80% of the variations and attribute mostly tothe external factors, suggesting that the developers may compete intensively to sell their units.
In districts that involve at least one of the top 10 developers, our model can explain less than
20% of TOM variations across real estate projects. The presence of “market leaders” may leadfirms to adopt much more complicated strategies that can be captured by our empiricalframework. While a full account would be left to future research, we consider this sharp
contrast between local markets with and without “leaders” a potential important stylized fact
for real estate economics and industrial organization research.
100.0%

100.0%

90.0%

90.0%

80.0%

80.0%

70.0%

70.0%

60.0%

60.0%

50.0%

50.0%

40.0%

40.0%

30.0%

30.0%

20.0%

20.0%

10.0%

10.0%

0.0%

0.0%

average effect District withDistrict
big10
without big10
Internal Factors

External Factors

average effectUnexplainedInternal Factors(a)District with District without
big10
big10
External Factors

Unexplained(b)

Figure 4 Decomposition of Internal and External Effects in Pricing and Timing(with and without Big 10)
Figure (a) and (b) present the comparison of the decomposition of internal and external effects (using avalue-based weighting system). The dependent variable in (a) is the unit selling price, while the dependent
variable in (b) is TOM. We apply three-stage least square to estimate the simultaneous regression,
controlled for region fixed effect, year fixed effect, and project’s cluster effects on 55170 transaction
records. The results are almost the same using a transaction-based weighting system, and hence notpresented here.

4.4 Robustness Check: Spillover Effects
Thus far, our analysis presumes that when a developer considers her actions, she will takeher rivals' financial conditions in consideration (section 3.2). Implicitly, we have assumed thatthose financial condition variables (FCV) would dictate opponents' actions. In practice, theactions taken by a developer might be influenced by some other factors, including some
idiosyncratic shocks that we, as researchers, cannot observe. In those situations, including the
FCV alone may not be enough. There may be additional state variables that are important tothe actions taken by other developers. The literature contains a discussion on the spillover effectof other developers’ behaviors under asymmetric information, such as aggressive pricing andfishing strategy (for instance, see Pesaran et al., 2004). Thus, in this section, we include proxies
for other developers' pricing variables and timing variables at period t into our structural
pricing-timing model and re-estimate.
More specifically, for each developer,
1,2, … we construct two variables, which are theand timing strategies
ofweighted average of the unit prices_
32developers other than developer in region at time . Hence, for different developers, theandare different. Limited by the data availability, wevariables_
consider using these weighted average variables operational and a “constrained optimal”
empirical strategy. 19 Since theory per sec does not give us clear guidance about how toconstruct these indices, we also use value-based weighting and transaction-based weighting
and. Based on the empirical model in step
methods for calculating_
and) into aIII, we further involve the spillover effect variables (_structural model. We apply three-stage least square to estimate the simultaneous regression.
_
_

2
_

2

As mentioned above,andare a pair of exclusive vectors, whileandareestimated residual term in step II. Three stages least square is adopted to estimate thesimultaneous regression.
According to Table 8, our principal conclusions do not change, and the formerly identified
interactions between lunit_price and TOM are robust. Meanwhile, the negative coefficient oflunit_price-i on TOM indicates that the booming regional market where other developers aretaking a high-pricing strategy will accelerate the target developer's selling pace. This result is
consistent with the conventional wisdom that housing units produced by different developers
are substitutes, and when other developers are posting high prices, the consumers switch to thetarget developer and hence accelerate the selling pace. We also find a significant positive effecton developers’ pricing mechanism. If other developers extend the time-on-marketofto get higher transaction prices, the target developer will also raise its market price. This result
is also consistent with the textbooks' pricing strategies because when the competitors chargehigh prices, the target developer can also increase its price, perhaps to a smaller extent, withoutworrying that customers would be lost to its competitors.20 The statically significance of theandwe obtain, when we already include the FCV of othervariables_developers.

19 An alternative that has been suggested to us is to write down an oligopoly game and estimate empirically. Since we only
have three years of data and a limited amount of explanatory variables, such “ideal” strategy may not be feasible with thecurrent data set.
20 Among others, see Tirole (1988), Tremblay and Tremblay (2012) and the reference therein.
33

Table 8 Spillover Effect and Pricing-Timing Mechanism
value-based weighting
transaction-based weighting
(1)
(2)
(3)
(4)
lunit_price
TOM
lunit_price
TOM
TOM
0.00893***0.00872***(0.00118)(0.00118)
lunit_price5.059***
5.062***(0.229)(0.230)_0.118***-1.656***0.121***-1.665***(0.00247)(0.0455)(0.00248)(0.0465)-0.0144***0.954***-0.0141***0.955***(0.00119)(0.00594)(0.00119)(0.00594)-0.310***0.755***-0.310***0.754***(0.00706)(0.0623)(0.00705)(0.0623)
profitability
0.01870.181
0.01950.182(0.0123)(0.217)(0.0124)(0.218)liability
0.00589
0.309
0.005850.298(0.0125)(0.218)(0.0127)(0.223)liquidity
0.0216*0.970***
0.0215*0.985***(0.0113)(0.197)(0.0114)(0.199)pccentral
0.01300.922***
0.01160.940***(0.0103)(0.179)(0.0104)(0.181)
pclocal-0.00164
0.184**-0.00268
0.194**(0.00481)(0.0843)(0.00481)(0.0846)
profitability-i-0.00525-0.168-0.00616-0.168(0.0121)(0.212)(0.0122)(0.214)
liability-i-0.00858-0.318-0.00854-0.308(0.0121)(0.212)(0.0124)(0.217)liquidity-i-0.0227**-0.796***-0.0224**-0.811***(0.0109)(0.190)(0.0109)(0.191)
pccentral-i-0.0355***-0.605***-0.0342***-0.623***(0.0101)(0.176)(0.0102)(0.179)pclocal-i
0.00263-0.386***0.00366-0.395***(0.00481)(0.0843)(0.00482)(0.0847)
Obs
55170
55170
55170
55170
This table presents the impact of the spillover effect on the pricing-timing mechanism. We apply threestage least square to estimate the simultaneous regression. The dependent variable in column (1) and (3) islunit_price. In contrast, the dependent variable in columns (2) and (4) is TOM, controlled for region fixed
effect, year fixed effect, and project’s cluster effects on 55170 transaction records. Standard errors are inparentheses: *p<0.10, **p<0.05, *** p<0.01.

Meanwhile, we find a robust correlation between FCV and their marketing strategies afterand). Higher internal
controlling for “direct” spillover effect (i.e.,_liquidity levels and political connections could raise the transaction price and postpone thetransaction pace. In contrast, higher profitability, liquidity, and political connection level of
their competitors will reduce the transaction price and accelerate the transaction pace.
Now, we conduct another robustness check on the measurement of the financial conditionsof the developers. In the previous sections, we construct our financial condition variables (FCV)
by adopting the principal component (PCA) method. There are other alternatives in theliterature. In this section, we replace our PCA-based measures with more standard metrics andcheck whether our results would be changed. Following the methodology developed by Rajan
34

and Zingales (1998) and Claessens et al. (2012), we calculate the financial independence index,
namely intrinsic dependence on external finance for investment (FD_INVi) and inherent
dependence on external funding for working capital (FD_WKi). For the interest of space, we
present only a summary here and the details in the appendix. Consistent with our former
findings, there is a significantly negative correlation between financial dependence and theprobability to locate their project between ring 2 and ring 5; developers with lower financial
dependence will build more extensive and high-rise residential units. Meanwhile, controlling
for the financial dependence of real estate firms, the correlation between the selling price and
TOM is also robust for the whole sample, quantile, and spillover effect regressions.
Table 9 exhibits the summary of explanatory power comparison in the internal and externalfinancial dependence index. Intuitively, we find a higher unexplained share using the financial
dependence variable compared to our indicating system. Thus, our approach of using a morecomprehensive measurement of financial status, including booked indicators of profitability,
liability, and liquidity, as well as the un-booked indicators like political connection, may
provide a better understanding of the competitions among the real estate developers comparedwith the standard measures suggested by existing literature. On average, we also find the results
using financial dependence variables are consistent with our previous findings. Overall,external variables dominate internal variables in explaining the pricing-timing mechanism ofdevelopers. However, when focusing on the regional heterogeneity, the conclusion slightly
deviates from our findings. Concerning pricing strategy, the deviation majorly comes from the
sub-urban regions. While regarding the timing strategy, the departure majorly comes from theinner-city regions. Part of the reason could be developers' spatial distribution with different
non-booked financial status (political connections on both central and local levels). As we
discovered before, developers with a more profound association with the central government
will be more likely to develop inner-city zones.
In contrast, developers with higher local political connections will be more likely todevelop projects in outer city zones. According to our former results, developers withstronger political relationships with the local government tend to sell their property at higher
transaction prices and wait a long time on the market. Since the traditional financial statusmeasurement does not capture these political economy variables, their estimated impacts
could be different from ours.

35

Table 9 Decomposition of Internal and External Effects(when alternative measures of financial condition variables are used)
Average
Region 1
Region 2
Region 3
financial dependence (FD_INV)
pricing regressions
Internal Factors
3.9%
2.9%
4.8%
9.1%
External Factors
52.0%
45.1%
35.9%
10.8%
Unexplained
44.0%
52.0%
59.2%
80.0%
timing regressions
Internal Factors
0.5%
1.9%
3.6%
8.2%
External Factors
0.5%
45.4%
11.1%
9.3%
Unexplained
99.0%
52.7%
85.4%
82.5%
financial dependence (FD_WK)
pricing regressions
Internal Factors
10.7%
0.0%
10.9%
0.4%
External Factors
41.9%
48.2%
17.6%
17.2%
Unexplained
47.4%
51.8%
71.5%
82.5%
timing regressions
Internal Factors
0.2%
2.2%
4.4%
0.6%
External Factors
1.1%
43.4%
4.9%
17.4%
Unexplained
98.8%
54.5%
90.7%
82.1%

Region 4

0.0%
90.6%
9.5%
11.0%
77.1%
11.9%

22.6%
67.9%
9.4%
65.9%
22.0%
12.1%

Note: This table presents the comparison of decomposition of internal and external effects (using valuebased weighting system). We apply three-stage least square to estimate the simultaneous regression,
controlled for region fixed effect, year fixed effect and project’s cluster effects on 55170 transaction records.
Due to the limitation of the database, we lose the information of FD_INV before 2007, which narrows down
the valid sample size into 25330. Similarly, due to the limitation of the database, we lose the information of
FD_WK in some real estate firms, which narrows down the valid sample size into 55049. The results isalmost the same using transaction-based weighting system.

Also, we use the information on a firm's history during each year between 2006 and 2008.
We define individual firms' actual use of external finance for working capital and investment(actual firm use of external finance for investment and actual firm use of external finance forworking capital) in a similar way as above. The result is consistent with our former findings
and not shown here.
5 Conclusion
This paper provides a systemic examination of the relative importance of internal versus
external factors in corporate decision-making, using the listed real estate developers in Beijing
as a "case study." Based on the micro-level data we compile; this paper builds a multi-step
empirical framework that mimics the developers' decision-making process and controls several
endogenous issues. In general, we find robust evidence that the company's financial conditions
and that of the rival companies are essential empirical determinants but play different roles inlocation, design, pricing-timing decisions of development. We also identify the location patternof developers. Developers with higher liability are more likely to develop in suburban areas,
while developers with higher liquidity tend to choose to build in the inner city more likely;
such spatial heterogeneity in the driven force hints the tradeoff between risk consideration incost and demand. Regarding project design, we confirm that companies with higher financial
36

solvency in terms of liquidity and liability and better profitability, tend to build larger, highrise residential units. In contrast, companies with lower financial capacity and profitability are
more inclined to construct small, medium-rise residential units.
Concerning pricing-timing decisions, there is strong evidence that liquidity demand
influences the simultaneous choice of selling price and TOM, while the interactions between
TOM and transaction price are non-linear: TOM has a U-shaped effect on the transaction price,
while transaction price has a positive impact on TOM. We further provide evidence on the
behavioral interactions between different developer clusters. The booming regional market
where other developers use high-pricing strategies accelerates the selling pace of the target
developer; meanwhile, if other developers extend their time on the market to get higher
transaction prices, the target developer will also raise his selling price.
Our findings also suggest that the degree of competition may be location-dependent. The
relative importance of internal (i.e., the financial conditions of the own company) versus
external factors (i.e., the rival firms) varies along city center to suburban areas, as the degreeof market concentration also varies across markets. Also, since the top ten developers are muchlarger than the other developers, our district-level analysis reveals that the existence of thoselarge developers as "market leaders" would significantly affect small developers' strategy andfurther shape the local market situation. Other things being equal, in districts without any
participation of the top ten developers, competing firms tend to respond more vital to one
another's actions. In contrast, the presence of at least one of those large developers would leadto higher selling prices and a slower selling pace. These results hold even when theendogeneity of location choice and product design are taken into considerations. Hence, ourresults are consistent with the notion that the existence of some "market leader" would
significantly change the strategies of relatively small firms in the local market and have asignificant “general equilibrium effect.” Therefore, our results provide support to both theories
that emphasize the interactions between corporate financial decisions and product market
competition and models that highlight the importance of "market leader." Our results sustain aseries of robustness checks.
There are many possibilities for future research. First, one can go beyond Beijing's city andcompare the situation in other cities and even countries. Second, this study only covers a shortperiod so that future research can extend to this dimension. In particular, a comparison ofdeveloper behavior under different policy regimes would be interesting to pursue. Besides,
since our paper shows that not only her financial conditions would affect the firm's practice,
but the financial conditions of her rivals also matter. Hence, any policy attempt to “relax thefinancial constraints” of firms may have non-trivial implications on the market structure andfuture output and prices. More research efforts may be needed along these lines.

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Science and Urban Economics, 42, 531-543.
Wu, J., Gyourko, J., & Deng, Y. (2016). Evaluating the risk of Chinese housing markets: what we know and what
we need to know. China Economic Review, 39, 91-114.
Yavaş, A. (1992). A simple search and bargaining model of real estate markets. Real Estate Economics, 20(4),533-548.

42

Appendix
This appendix has several sections.
Appendix A provides additional information about the variables, such as different correlation tables.
Appendix B provides a summary of the major governmental interventions in the Chinese housing market.
Appendix C provides the results of the robustness check using financial dependence indicators.
Appendix D outlines our decomposition procedures.
Appendix E provides more details about the Beijing housing market.

43

Appendix A. Additional information of the variables
Table A1 Correlations before Principle Component Analysis
lyrmc

currt

intcvrinvtrtrrat

dbastrtperatio

d_cbd

d_sub

d_h1

d_middle

d_highn_subway

perma_pop

exeter_pop

lyrmc
curt

1
-0.0338

1

intcvr

-0.1149

0.0341

1

invtrtrrat
dbastrt-0.03240.0228-0.0475-0.03480.0482-0.0823

1
0.003

1

peratio0.13780.044

0.2269-0.0353-0.06871d_cbd-0.0063-0.0321-0.1199-0.1832-0.1340.00611

d_sub-0.0074

0.0025-0.0643-0.0815-0.03710.05010.84191

d_h1

0.01730.0124-0.0927-0.1397-0.0306

0.02420.87720.93451

d_middle
d_high-0.0030.0122-0.0061-0.0042-0.092-0.0966-0.1162-0.1419-0.0954-0.1073

0.006
0.01730.91640.92820.91310.90270.92910.93071
0.97941n_subway-0.0074

0.04120.073-0.1754

0.2182-0.0145-0.5141-0.5249-0.4108-0.4881-0.45961

perma_pop
exeter_pop

0.031
0.01040.08210.08030.02690.0191-0.2835-0.29020.23210.2159-0.034-0.0413-0.4825-0.4632-0.5497-0.5442-0.4581-0.4407-0.5478-0.524-0.5118-0.48160.90970.92781
0.9935

1

registered_pop

0.0383

0.09810.0397-0.2484

0.2474-0.034-0.4926-0.5509-0.4758-0.5646-0.5346

0.85630.9826

0.9631

move_in_out
no_employed-0.03290.08130.135
0.09260.01230.0498-0.1651-0.2520.19320.2386-0.0675-0.0356-0.3276-0.4639-0.4423-0.5288-0.395-0.4547-0.4659-0.5526-0.4301-0.51110.51550.81260.74120.95610.71010.9354

no_unemployed

0.09280.0437

0.0565-0.14970.21440.0116-0.486-0.5027-0.431-0.5089-0.4850.84590.83

0.8239gdp
fix_investment

0.07450.04350.10360.0276

0.05950.051-0.2189-0.21430.23160.2152-0.0272-0.0112-0.4403-0.4838-0.4954-0.4783-0.4025-0.3716-0.496-0.4639-0.454-0.4332

0.87650.97380.94310.907

0.93840.9198real_estate_investment

0.0046

0.02660.0474-0.2369

0.1967-0.0278-0.4972-0.556-0.4613-0.5397-0.4974

0.92810.91430.9237

personal_disposable_income

0.17490.10980.0215-0.0092

0.11160.0038-0.3216-0.3808-0.3556-0.3931-0.34540.43770.53230.5328personal_consumption
personal_income_employed

0.12330.1032

0.06650.0207

0.09390.0713-0.10910.07720.22080.1535-0.0056
0.0026-0.4314-0.6006-0.4244-0.6434-0.3395-0.5935-0.4354-0.6554-0.3934-0.6180.82020.712

0.79630.70430.80290.704344

registered_p
opregistered_pop
move_in_out
no_employed
no_unemployedgdp
fix_investment
real_estate_investment
personal_disposable_incomepersonal_consumption
personal_income_employed

Table A1 Correlations before Principle Component Analysis (continued)
move_in_out no_employe no_unemplo
gdp
fix_investme real_estate_idyednt
nvestment

personal_dis
posable_income

10.8331
0.981
0.781
0.95170.84480.86530.56931
0.82210.406
0.74880.481
0.56780.48781
0.7681
0.97220.81310.84170.67341
0.79220.82710.80210.4582

1
0.86920.86720.7181

1
0.9454
0.463

1
0.482

1

0.79430.70380.54020.491

0.84720.751

0.76090.69050.921
0.79420.82220.71030.79910.71640.7686
0.841445

personal_consumption

1
0.8075personal_inc
ome_employ
ed1

lyrmccurrt
intcvrinvtrtrrat
dbastrtperatio
geography
macro
micro

lyrmc
1
-0.0338-0.1149-0.03240.02280.13780.0484-0.0206
0.1932

currt

Table A2 Correlations after Principle Component Analysis
intcvrinvtrtrrat
dbastrtperatio1
0.0341-0.0475-0.03480.044
0.06480.07710.03071
0.0482-0.08230.22690.0746-0.0769-0.0001

1
0.003-0.0353-0.1659-0.5066
0.18161-0.0687
0.21180.0336-0.000746

1-0.0252-0.0058
0.0382

geography

macro

micro

1
0
0

1
0

1

In the paper, we use the principal components. Here we present additional results to justify this approach.
Table A3 shows that the booked and non‐booked financial variables are significantly correlated.

currt
intcvrdbastrtperatio
ebitdanpc_cppcc
official_1_2
official_3_4
official_5_6
official_7_8
official_9_12

currt
1
0.0341*-0.0347*0.0440*0.0083-0.0171*-0.0391*-0.1177*
0.01180.0964*-0.0469*

intcvrTable A3 Pearson Correlation between booked and non-booked financial variables
dbastrtperatio
ebitdanpc_cppcc
official_1_2
official_3_4
official_5_6

1-0.0823*0.2269*-0.0322*-0.0453*-0.0248*-0.0580*-0.0490*-0.0588*-0.0706*

1-0.0687*
0.1178*0.2259*0.0568*-0.2460*-0.0747*
0.0363*-0.0156*1
0.0739*-0.0280*0.2199*-0.1554*-0.0535*
0.0374*0.0873*1-0.1778*0.7005*-0.1375*-0.0795*-0.0090.00591
0.0290*-0.1951*0.2090*-0.0405*
0.2616*47

1-0.0198*-0.0777*-0.2278*-0.0697*1
0.0240*-0.1489*-0.1261*10.3074*0.2476*

official_7_8

official_9_12

10.3173*1

Table A4 shows that we can find most of the booked and non-booked financial variables have Variance
Inflation Factors (VIF) significantly higher than 5, indicating a higher probability of causing
multicollinearity problems, which cannot be mitigated by directly dropping several proxies.
Table A4 Variance Inflation Factors with and without PCA
value-based weighting
transaction-based weighting
VariableTOM

lunit_price
VIF
1/VIF

VIF

1.28

0.782766

currt
intcvrdbastrt5.49
85.15
168.0538.96

0.182246
0.011744
0.00595
0.025669

2.13
1.12
85.12
168.02
38.96

peratio
ebitdanpc_cppcc
official_1_2
official_3_4

117.015.01
4.59
377.05379.920.008546
0.199680.217948
0.002652
0.002632

official_5_6
official_7_8
official_9_12
vcurrtvintcvr

21.59
20.35
38.64
84.93
167.74vdbastrt
vperatio
vebitdavnpc_cppcc

TOM
1/VIF

lunit_price
VIF
1/VIF

VIF

TOM
1/VIF

1.28

0.7841550.470020.891292
0.011748
0.005952
0.025665

5.48
87.14
169.2442.6

0.182538
0.011475
0.005909
0.023475

2.13
1.12
87.11
169.2142.61

0.469969
0.895263
0.011479
0.00591
0.02347

116.354.86
4.75
376.88380.760.008595
0.20579
0.210314
0.002653
0.002626

120.64856.87
4.58
569.88403.330.008289
0.001167
0.218289
0.001755
0.002479

119.96856.574.74
569.8
404.230.008336
0.0011670.210750.001755
0.002474

0.046323
0.049129
0.025877
0.011775
0.005962

21.71
20.43
38.7
84.93
167.730.046053
0.048949
0.025839
0.011775
0.005962

21.81
20.48
39.46
86.92
168.930.045846
0.048834
0.02534
0.011505
0.00592

21.94
20.56
39.52
86.92
168.920.045583
0.048648
0.025305
0.011505
0.00592

37.95
115.042.39
4.91

0.026351
0.008693
0.417975
0.203706

37.94
114.7
2.39
4.93

0.026354
0.008718
0.417958
0.202752

41.58
118.64842.784.86

0.02405
0.008429
0.001187
0.205692

41.58
118.28842.824.88

0.024049
0.008454
0.001186
0.204781

vofficial_1_2
vofficial_3_4
vofficial_5_6
vofficial_7_8
vofficial_9_12

365.83377.26
22.02
21.32
38.8

0.002733
0.002651
0.045412
0.04691
0.025772

365.73378.02
22.11
21.53
38.81

0.002734
0.002645
0.045235
0.046446
0.025766

554.43400.79
22.24
21.43
39.62

0.001804
0.002495
0.044957
0.046656
0.025237

554.35401.58
22.33
21.65
39.63

0.001804
0.002490.044782
0.046182
0.025234

unit_floor
unit_area

1.15
6.37

0.868081
0.15708

1.65
1.43

0.607614
0.698185

1.15
6.36

0.8681250.157121.64
1.43

0.608718
0.698119

lunit_price48

Appendix B. Major government intervention in the Chinese
housing market (2005– 2014)
This table provides a summary of the major government intervention that have significant effects on housing
market in China during our sampling period.
Date2006.5.29

Policy or AgentThe General Office of the StateCouncil: “Opinions on Adjusting
Housing Supply Structure to
Stabilize Housing
Prices”(GOSC[2006]No.37)

2006.7The Chinese Ministry of Housing
and Urban- Rural Development:“Several Opinions about
Implementing of New Constructed
Housing Ratio”
MHURD[2006]No.165
Notice of State Taxation
Bureau on Levying Income
Tax on Transferring UsedHouse

2006.72007. 92007.112008.10

2008.122009.5People’s Bank of China and China
Banking Regulatory Commission:
NoticeofStrengthening
theManagementofCommercial
Property Credit Loans (CBRC [2007]
No. 359).
National Development and Reform
Commission (NDRC), Ministry of
Finance
People’s Bank of China: Notice ofExtending the Downward Movementof Interest Rates for Loans to
Residential
PremisesofaCommercial Nature for Individuals in
Support of First Time Purchase of
Ordinary Residential Premises by
Residents (PBOC [2008] No. 137).
The General Office of the StateCouncil: several option on healthy
development of real estate market(GOSC [2008] No.131)
The General Office of the StateCouncil: “A Notification on the
Adjustment of Capital Proportion of
Fixed Assets Investment Project”

Basic Contents
The so-called “Guo Liu Tiao” aims to curb overrapid housing prices through supply adjustment.
During the 11th 5-Year- Plan period, the weight ofhouses with in-suite floor space of under 90 m2 must
be over 70% of the total development andconstruction area. As for transfer within 5 years ofhouse purchase, business tax will be levied based on
the total sales income; the proportion of the first
installment of a personal house mortgage shall not
be lower than 30%. The first installment shallaccount for 20% for self-residing house purchasedwith insuite floor space less than 90 m2.
It regulated that units with floor area less than 90
square meters must cove 70% of the total floor areain all newly registered or constructed projects.

When levying personal income tax on housetransfer, the actual transaction price shall be thetransfer income. When the house transaction price,declared by the taxpayer, is lower than the market
price, without a proper reason, the levying institution
shall have the right to verify his transfer income.
Tighten credit to the property sector: raise theminimum down-payment ratio to 40% and theminimum mortgage rate to 110% of the benchmark
rate for second mortgages. Minimum down-payment
ratio and mortgage rates are higher for third
mortgage loans.
Cross“Ordinary
Residential
Property
Development” from the list of industries opening toforeign investment. Restrict foreign investment insecond-hand house transactions.
Reduce the down payment requirements from 30%
to 20% and to adjust the lower limit of the lending
rate for residential properties to 70% of thebenchmark lending rate.

Extend preferential policies for first-home purchases
to second-home purchases. Shorten the holding
period to enjoy business tax exemption from 5 years
to 2 years.
Reduced the minimum capital requirement for the
first time since 2004.

49

2010.1

2010.42010.92010. 92010.11(GOSC [2009] No. 27)
State Council: The Circular on
Promoting the Stable and Healthy
Development of the Real Estate
Market (SC [2010] No. 4).
Ministry of Housing and Urban and
Rural Development, Ministry of
Finance, People’s Bank of China andChina
Banking
Regulatory
Commission: Notice of Issues
Relating to Standardizing Different
Residential Mortgage Loan Policies.
(MOHUR and MF [2010] No. 179)
Ministry of Land andResources,
Ministry of Housing and
Urban-Rural Development
ML and MOHUR [2010] No. 151.
Ministry of Housing and Urban-rural
Construction (MOHUR and PBOC
[2010]No. 275)
Ministry of Land and Resources,
State Administration of Foreign
Exchange (MOHUR [2010] No. 186)

2010.12

Ministry of Land and Resources(MLS [2010] No. 204)2011.1The General Office of the StateCouncil (GOSC [2011] No. 1)

2011.52012.2-5

National Development and Reform
Commission: “A Notification on theissue of “Regulation of Selling
Commercial Housing with Definite
Price” (NDRC[2011] No.548)
People’s Bank of China

2012.6People’s Bank of China

2014.9The people's bank of China, theChina banking regulatorycommission, "the notice on further
do a good job of housing financial
services to establish"

2014.11

The people's bank of China, the China
banking regulatory commission

The minimum down payment of mortgage loan for
additional residential property shall be 40% of theproperty value.
The minimum down payment for the first purchaseof residents will be increased to 30% and allcommercial banks shall suspend granting loans tocustomers purchasing a third or subsequent
residents. For those who purchase a second
residential property, the down payment shall not beless than 50% of the value and the interest rate shallnot be less than 1.1 times of benchmark interest rate.
Prohibiting developers from bidding for new plots ofland if they have land that has been purchased forproperty development and yet left idled for a year.
Restrict land supply to high-end housing and makeit available only if land supply for low-end housing
has been fully met
Impose home purchase restriction rules, limiting thenumber of houses that individuals or households areallowed to purchase.
Limit foreign individuals’ purchase of residential
property for own use to one. Allow overseas
institutions to purchase non-residential property
only in the city where it is registered.
Require local governments to report unusual landtransactions (i.e. land-transaction deals closed eitherwith a price 50% higher than the auction starting
price).
Require local governments to set price control
targets for new housing units (targets will be
released within the first quarter of 2011).
Collect business tax, currently at 5.5%, based on thehome purchase price, rather than on the difference
between the cost base and selling price, for all
secondhand homes sold less than five years after
purchase. Raise down-payment for mortgages on
second home to at least 60%.
Listed price is required to be announced for the
public. No further extra cost is required by thedeveloper.
During this period, deposit reserve rate of financial
institutions decreased twice and each time isdecreased by 0.5%.
The benchmark one year deposit and lending interest
rate decreased by 0.25%. The interest rate provident
funds is adjusted from 0.50% to 0.4% in the current
year.
For the first housing consumer, the minimum down
payment is adjusted to 30%, the interest rate of loan
was limited to 0.7 times of benchmark rate. For those
who have one set of housing, and have paid loan isequally cheated as the first buy in mortgage. MBS
and REITS are encouraged.
The benchmark one – year lending interest rate was
deceased by 0.4%, and deposit rate was decreased
by 0.25%. The financial institutions deposit rate
floating range limit by 1.1 times of the benchmark
deposit rate from the previous 1.2 times.
50Appendix C. Results of the robustness check using financial
dependence indicators
Table C1 Developer’s Financial Dependence and Location Selection (Robustness Check)

Value-based weighting
FD_INV
FD_INV-i
year effectproject’s cluster
Obs
FD_WK
FD_WK-iyear effectproject’s cluster
Obs
Transaction-based weighting
FD_INV
FD_INV-i
year effectproject’s cluster
Obs
FD_WK
FD_WK-iyear effectproject’s cluster
Obs(1)
Region 2/
Region 1(2)
Region 3/
Region 1(3)
Region 4/
Region 1(4)
Region 3/
Region 2(5)
Region 4/
Region 2(6)
Region 4/
Region 3

0.00225***(0.000187)-0.0119***(0.000657)
controlledcontrolled
25330
0.0476***(0.00523)
0.0293***(0.00374)
controlledcontrolled
55049

0.00204***(0.000187)-0.0116***(0.000657)
controlledcontrolled
25330
0.0448***(0.00526)
0.0179***(0.00380)
controlledcontrolled
55049

0.000349(0.000836)
0.0164***(0.00300)
controlledcontrolled
25330
0.0273*(0.0142)-0.00707(0.0134)
controlledcontrolled
55049-0.000214***(0.0000338)
0.000293***(0.0000862)
controlledcontrolled
25330-0.00283***(0.000728)-0.0113***(0.000816)
controlledcontrolled
55049-0.00190**(0.000829)
0.0282***(0.00297)
controlledcontrolled
25330-0.0203(0.0133)-0.0363***(0.0129)
controlledcontrolled
55049-0.00169**(0.000829)
0.0280***(0.00297)
controlledcontrolled
25330-0.0175(0.0133)-0.0250*(0.0129)
controlledcontrolled
55049

0.00225***(0.000187)-0.0119***(0.000656)
controlledcontrolled
25330
0.0476***(0.00523)
0.0293***(0.00374)
controlledcontrolled
55049

0.00204***(0.000186)-0.0116***(0.000656)
controlledcontrolled
25330
0.0448***(0.00526)
0.0179***(0.00380)
controlledcontrolled
55049

0.000357(0.000835)
0.0164***(0.00300)
controlledcontrolled
25330
0.0273*(0.0142)-0.00706(0.0134)
controlledcontrolled
55049-0.000214***(0.0000338)
0.000293***(0.0000862)
controlledcontrolled
25330-0.00283***(0.000728)-0.0113***(0.000816)
controlledcontrolled
55049-0.00189**(0.000828)
0.0283***(0.00296)
controlledcontrolled
25330-0.0203(0.0133)-0.0363***(0.0129)
controlledcontrolled
55049-0.00168**(0.000828)
0.0280***(0.00296)
controlledcontrolled
25330-0.0175(0.0133)-0.0250*(0.0129)
controlledcontrolled
55049

Note: This table presents the impact of firm’s financial dependence index on the probability project’s location
choice, controlled for year fixed effect and project’s cluster effects on 55170 transaction records. Due to thelimitation of the database, we lose the information of FD_INV before 2007, which narrows down the valid
sample size into 25330. Meanwhile, due to the limitation of the database, we lose the information of FD_WK
in some real estate firms, which narrows down the valid sample size into 55049. Standard errors are inparentheses: *p<0.10, **p<0.05, *** p<0.01.

51

Table C2 Developer’s Financial Dependence and Project Design (Robustness Check)

Value-based weighting
lpro_floor

(1)

(2)

(3)

(4)

lunit_area

lpro_floor

lunit_area

lpro_floor-0.178***(0.0127)

lunit_area
FD_INV
FD_INV-i-0.0000344***(0.00000549)
0.0000331**(0.0000137)-0.257***(0.00822)-2.757***(0.147)-0.000101***(0.0000160)
0.000103***(0.0000383)

FD_WK
FD_WK-igeography
macro
micro
region effectyear effectproject’s cluster
Obs
Transaction-based weighting
lpro_floor

0.0161***(0.00266)-0.00662*(0.00365)
0.0302***(0.00895)
controlledcontrolledcontrolled
25330-0.178***(0.0127)

lunit_area
FD_INV
FD_INV-i-0.000101***(0.0000160)
0.000103***(0.0000383)
0.0786***(0.00680)
controlledcontrolledcontrolled
25330-0.0000344***(0.00000549)
0.0000331**(0.0000137)-2.757***(0.147)-0.000101***(0.0000160)
0.000103***(0.0000383)

FD_WK-i

macro
micro
region effectyear effectproject’s cluster
Obs-0.000636***(0.000102)-0.00181***(0.000123)
0.0423***(0.00196)-0.0112***(0.00239)-0.0137**(0.00616)
controlledcontrolledcontrolled
55049-0.00269***(0.000472)-0.00749***(0.000622)
0.171***(0.00901)-0.0476***(0.0112)-0.0375(0.0268)
controlledcontrolledcontrolled
55049-0.257***(0.00822)

FD_WK

geography-4.227***(0.265)0.0161***(0.00266)-0.00662*(0.00365)
0.0302***(0.00895)
controlledcontrolledcontrolled
25330

0.0786***(0.00680)-0.00939(0.0107)-0.0123(0.0265)
controlledcontrolledcontrolled
25330-4.227***(0.265)-0.000636***(0.000102)-0.00181***(0.000123)
0.0423***(0.00196)-0.0112***(0.00239)-0.0137**(0.00616)
controlledcontrolledcontrolled
55049-0.00269***(0.000472)-0.00749***(0.000622)
0.171***(0.00901)-0.0476***(0.0112)-0.0375(0.0268)
controlledcontrolledcontrolled
55049

Note: This table presents the impact of firm’s financial dependence index on the project’s design (buildingheight and unit area), controlled for region fixed effect, year fixed effect and project’s cluster effects on
55170 transaction records. Due to the limitation of the database, we lose the information of FD_INV before
2007, which narrows down the valid sample size into 25330. Meanwhile, due to the limitation of the database,
we lose the information of FD_WK in some real estate firms, which narrows down the valid sample size into
55049. We use 3 Stage Least Squares to estimate the above results. Standard errors are in parentheses:*p<0.10, **p<0.05, *** p<0.01.
52

Table C3 Developer’s Financial Dependence and Pricing-Timing Mechanism (Robustness Check)lunit_price
Value-based weighting
TOM

FD_INV
FD_INV-i
Transaction-based weighting
TOM

FD_INV
FD_INV-i
TOM
Value-based weighting
lunit_price

FD_INV
FD_INV-i
Transaction-based weighting
lunit_price

FD_INV(1)
3 Stage Least
Squares(2)
TOM Quintile(≤0.25)(3)
TOM Quintile(0.25 to 0.5)

(4)
TOM Quintile(0.5 to 0.75)(5)TOM Quintile(>0.75)(6)
Price Quintile(≤0.25)(7)
Price Quintile(0.25 to 0.5)

(8)Price Quintile(0.5 to 0.75)(9)Price Quintile(>0.75)0.0377***(0.00367)-0.342***(0.0134)0.00000121(0.00000443)-0.00000978(0.0000110)-0.119***(0.0202)-0.387***(0.0158)
0.000155***(0.0000201)-0.00126***(0.0000332)-0.504***(0.0402)-0.418***(0.0154)0.0000810***(0.00000849)-0.000144***(0.0000212)

0.329***(0.0701)-0.307***(0.0445)0.0000227**(0.00000949)-0.0000353(0.0000228)

0.0213**(0.00909)-0.178***(0.0271)-0.0000954***(0.0000112)
0.000158***(0.0000327)-0.0644***(0.00356)
0.135***(0.0516)-0.0000257***(0.00000715)
0.0000650***(0.0000176)-0.00213***(0.000622)-0.0363***(0.00358)
0.00000404***(0.00000146)-0.00000559(0.00000356)

0.00656***(0.00127)-0.0970***(0.00646)
0.0000295***(0.00000857)-0.0000825***(0.00000890)-0.000353(0.00190)-0.108***(0.0113)-0.00000386(0.00000786)-0.000138***(0.0000210)

0.0376***(0.00366)-0.342***(0.0134)0.00000125(0.00000442)-0.00000982(0.0000110)-0.119***(0.0202)-0.388***(0.0158)
0.000156***(0.0000201)-0.00126***(0.0000332)-0.504***(0.0402)-0.418***(0.0154)0.0000810***(0.00000849)-0.000144***(0.0000212)

0.329***(0.0700)-0.307***(0.0444)0.0000227**(0.00000947)-0.0000353(0.0000228)

0.0213**(0.00907)-0.178***(0.0271)-0.0000951***(0.0000112)
0.000157***(0.0000326)-0.0644***(0.00356)
0.135***(0.0515)-0.0000257***(0.00000715)
0.0000649***(0.0000176)-0.00213***(0.000622)-0.0363***(0.00358)
0.00000404***(0.00000146)-0.00000560(0.00000356)

0.00654***(0.00127)-0.0969***(0.00646)
0.0000294***(0.00000857)-0.0000824***(0.00000890)-0.000402(0.00190)-0.107***(0.0113)-0.00000338(0.00000786)-0.000139***(0.0000211)

1.207***(0.409)1.276***(0.0948)0.0000380(0.0000716)
0.00000484(0.000178)-0.00597(0.0634)
0.124***(0.0146)0.000471***(0.0000401)-0.000365***(0.000111)

0.534***(0.0535)
0.0177(0.0112)0.00000392(0.0000125)
0.0000334(0.0000313)-0.876***(0.313)0.254***(0.0655)-0.0000485*(0.0000260)
0.0000971(0.0000648)

1.526(1.295)-0.155(0.284)0.000403*(0.000238)-0.00223***(0.000508)

12.63***(4.284)3.005***(0.730)-0.000466***(0.000138)
0.000760**(0.000348)

62.23***(10.31)1.795***(0.180)0.00118***(0.000126)-0.00307***(0.000300)

10.28**(4.042)2.707***(0.186)-0.00348***(0.000310)
0.00411***(0.000395)

1.477(1.533)-1.132***(0.302)-0.000726**(0.000298)-0.0000905(0.000852)

1.204***(0.409)1.276***(0.0948)0.0000390(0.0000716)-0.00589(0.0634)
0.124***(0.0146)0.000471***(0.0000401)

0.534***(0.0535)
0.0177(0.0112)0.00000392(0.0000124)-0.879***(0.314)0.254***(0.0655)-0.0000485*(0.0000260)

1.550(1.300)-0.155(0.284)0.000405*(0.000238)

12.64***(4.293)3.008***(0.729)-0.000465***(0.000138)

62.22***(10.31)1.795***(0.180)0.00118***(0.000126)

10.23**(4.040)2.704***(0.186)-0.00348***(0.000310)

1.476(1.535)-1.131***(0.302)-0.000720**(0.000298)

53

FD_INV-i

0.00000274(0.000178)-0.000365***(0.000111)

0.0000334(0.0000313)

0.0000972(0.0000648)-0.00223***(0.000508)

0.000758**(0.000348)-0.00307***(0.000300)

0.00410***(0.000394)-0.000106(0.000853)

Note: This table presents the impact of firm’s financial dependence index (FD_INV) on the pricing-timing mechanism estimated by 3 stage least squares. The dependent variable in
the upper section is lunit_price, while the dependent variable in the upper section is TOM controlled for region fixed effect, year fixed effect and project’s cluster effects on 55170
transaction records. Due to the limitation of the database, we lose the information of FD_INV before 2007, which narrows down the valid sample size into 25330. Column (1) exhibits
the simultaneous regression results of whole sample. Column (2) exhibits the simultaneous regression results in the cohort of the shortest duration of sales (lowest 25% TOM).
Column (3) exhibits the simultaneous regression results in the cohort of the sub-shortest duration of sales (TOM located in the interval 25%-50%). Column (4) exhibits thesimultaneous regression results in the cohort of the sub-longest duration of sales (TOM located in the interval 50%-75%). Column (5) exhibits the simultaneous regression results inthe cohort of the longest duration of sales (highest 25% TOM). Column (6) exhibits the simultaneous regression results in the cohort of the lowest transaction price (lowest 25%
unit_price). Column (7) exhibits the simultaneous regression results in the cohort of the sub-lowest transaction price (unit_price located in the interval 25%-50%). Column (8)exhibits the simultaneous regression results in the cohort of the sub-highest transaction price (unit_price located in the interval 50%-75%). Column (9) exhibits the simultaneous
regression results in the cohort of the highest transaction price (highest 25% unit_price). Standard errors are in parentheses: *p<0.10, **p<0.05, *** p<0.01.

54

Table C4 Developer’s Financial Dependence and Pricing-Timing Mechanism (Robustness Check)lunit_price
Value-based weighting
TOM

FD_WK
FD_WK -i
Transaction-based
weighting
TOM

FD_WK
FD_WK -i
TOM
Value-based weighting
lunit_price

FD_WK
FD_WK -i
Transaction-based
weightinglunit_price

FD_WK

(1)
3 Stage Least
Squares(2)
TOM Quintile(≤0.25)(3)
TOM Quintile(0.25 to 0.5)

(4)
TOM Quintile(0.5 to 0.75)(5)TOM Quintile(>0.75)(6)
Price Quintile(≤0.25)(7)
Price Quintile(0.25 to 0.5)

(8)Price Quintile(0.5 to 0.75)(9)Price Quintile(>0.75)0.00830***(0.00173)-0.223***(0.00646)-0.000404***(0.0000688)-0.0000695(0.0000823)-0.173***(0.0514)-0.615***(0.0156)-0.00126***(0.000147)
0.00140***(0.000179)-0.420***(0.0457)-0.383***(0.0111)-0.00175***(0.000254)
0.00209***(0.000268)-0.195***(0.0230)-0.108***(0.0157)-0.00301***(0.000256)
0.00148***(0.000264)

0.0556***(0.00532)
0.00608(0.0181)0.000261*(0.000148)-0.00339***(0.000265)

0.0437***(0.00397)
0.274***(0.0219)0.00158***(0.000201)-0.00523***(0.000463)-0.00466***(0.000919)-0.0304***(0.00247)-0.000201***(0.0000383)
0.000392***(0.0000466)

0.000299(0.00159)-0.0621***(0.00408)-0.000397***(0.0000601)
0.000370***(0.0000640)-0.00529***(0.00171)-0.102***(0.00826)
0.0000489(0.000103)-0.000665***(0.0000895)

0.00845***(0.00173)-0.224***(0.00647)-0.000403***(0.0000689)-0.0000749(0.0000824)-0.173***(0.0515)-0.616***(0.0156)-0.00126***(0.000147)
0.00140***(0.000179)-0.420***(0.0457)-0.383***(0.0111)-0.00175***(0.000254)
0.00209***(0.000268)-0.195***(0.0230)-0.108***(0.0157)-0.00302***(0.000257)
0.00148***(0.000265)

0.0561***(0.00535)
0.00625(0.0182)0.000265*(0.000148)-0.00342***(0.000266)

0.0439***(0.00398)
0.275***(0.0220)0.00159***(0.000201)-0.00524***(0.000464)-0.00467***(0.000920)-0.0304***(0.00247)-0.000201***(0.0000384)
0.000392***(0.0000466)

0.000281(0.00159)-0.0621***(0.00409)-0.000397***(0.0000602)
0.000368***(0.0000641)-0.00519***(0.00171)-0.102***(0.00826)
0.0000442(0.000103)-0.000670***(0.0000895)

3.806***(0.252)
1.074***(0.0626)-0.00259**(0.00124)
0.0112***(0.00148)

0.153***(0.0338)-0.0392***(0.0107)-0.000222(0.000236)
0.000886***(0.000269)

0.342***(0.0396)-0.0158*(0.00823)-0.000672*(0.000383)
0.0000242(0.000405)

0.404**(0.180)0.331***(0.0380)-0.00370***(0.000960)-0.00355***(0.00107)4.870***(0.662)-0.925***(0.221)-0.00509**(0.00198)
0.0459***(0.00297)

59.85***(2.371)-0.960***(0.315)-0.0573***(0.00537)
0.113***(0.00668)

36.60***(3.969)1.307***(0.103)-0.0104***(0.00272)-0.00821**(0.00366)

51.55***(7.246)2.417***(0.202)0.000677(0.00442)-0.0319***(0.00415)

3.364***(0.938)-0.209(0.176)
0.0412***(0.00241)0.0191***(0.00301)

3.816***(0.251)1.073***(0.0626)-0.00258**(0.00124)

0.153***(0.0338)-0.0392***(0.0107)-0.000223(0.000236)

0.342***(0.0396)-0.0158*(0.00823)-0.000672*(0.000383)

0.404**(0.180)0.331***(0.0380)-0.00370***(0.000960)

4.840***(0.658)-0.926***(0.221)-0.00510**(0.00198)

59.49***(2.352)-0.991***(0.315)-0.0573***(0.00535)

36.62***(3.968)1.307***(0.103)-0.0104***(0.00272)

51.79***(7.274)2.423***(0.203)0.000763(0.00443)

3.376***(0.937)-0.210(0.176)
0.0412***(0.00241)55

FD_WK -i

0.0112***(0.00148)

0.000887***(0.000269)

0.0000244(0.000405)-0.00355***(0.00107)

0.0460***(0.00297)

0.112***(0.00666)-0.00820**(0.00366)-0.0319***(0.00416)

0.0192***(0.00301)

Note: This table presents the impact of a firm’s financial dependence index (FD_WK) on the pricing-timing mechanism estimated by 3 stage least squares. The dependent variable in
the upper section is lunit_price, while the dependent variable in the upper section is TOM controlled for region fixed effect, year fixed effect and project’s cluster effects on 55170
transaction records. Due to the limitation of the database, we lose the information of FD_WK in some real estate firms, which narrows down the valid sample size into 55049. Column(1) exhibits the simultaneous regression results of whole sample. Column (2) exhibits the simultaneous regression results in the cohort of the shortest duration of sales (lowest 25%
TOM). Column (3) exhibits the simultaneous regression results in the cohort of the sub-shortest duration of sales (TOM located in the interval 25%-50%). Column (4) exhibits thesimultaneous regression results in the cohort of the sub-longest duration of sales (TOM located in the interval 50%-75%). Column (5) exhibits the simultaneous regression results inthe cohort of the longest duration of sales (highest 25% TOM). Column (6) exhibits the simultaneous regression results in the cohort of the lowest transaction price (lowest 25%
unit_price). Column (7) exhibits the simultaneous regression results in the cohort of the sub-lowest transaction price (unit_price located in the interval 25%-50%). Column (8)exhibits the simultaneous regression results in the cohort of the sub-highest transaction price (unit_price located in the interval 50%-75%). Column (9) exhibits the simultaneous
regression results in the cohort of the highest transaction price (highest 25% unit_price). Standard errors are in parentheses: *p<0.10, **p<0.05, *** p<0.01.

56

(1)
Value-based weighting
lunit_price
TOM_

FD_INV

FD_INV -i

Table C5 Spillover Effect and Pricing-Timing Mechanism (Robustness Test)
Value-based weighting
Transaction-based weighting
(2)
(3)
(4)

0.0382***(0.00341)
0.149***

0.0161***(0.00174)
0.136***

0.0380***(0.00340)
0.150***

0.0159***(0.00174)
0.140***(0.00568)(0.00265)(0.00568)(0.00266)-0.0502***(0.00346)-0.314***(0.0134)-0.0216***(0.00167)-0.217***(0.00663)-0.0500***(0.00345)-0.314***(0.0134)-0.0213***(0.00167)-0.216***(0.00662)

0.00000725

0.00000728(0.00000445)(0.00000445)-0.0000189*-0.0000189*(0.0000103)(0.0000103)

FD_WK-0.00110***(0.0000698)
0.000635***(0.0000857)

FD_WK -i

-0.00111***(0.0000696)
0.000642***(0.0000855)

TOM
lunit_price_

FD_INV

1.271***

4.283***

1.267***

4.274***(0.435)-1.485***(0.0759)
1.023***(0.00998)
1.240***(0.0964)0.0000856(0.260)-1.504***(0.0492)
0.970***(0.00558)
0.946***(0.0629)(0.436)-1.485***(0.0770)
1.024***(0.00998)
1.239***(0.0963)0.0000865(0.261)-1.506***(0.0505)
0.971***(0.00558)
0.947***(0.0629)57

FD_INV -i(0.0000699)-0.0000769(0.000162)

FD_WK
FD_WK -iproject featuresmacro
region effectyear effectproject’s cluster
Obs
R-Squarecontrolledcontrolledcontrolledcontrolledcontrolled
25330(0.0000699)-0.0000784(0.000162)-0.00368***(0.00123)
0.00974***(0.00147)
controlledcontrolledcontrolledcontrolledcontrolled
55049

controlledcontrolledcontrolledcontrolledcontrolled
25330-0.00370***(0.00123)
0.00971***(0.00147)
controlledcontrolledcontrolledcontrolledcontrolled
55049

0.722
0.722
0.678
0.684
Note: This table presents the impact of the spillover effect on the pricing-timing mechanism. Financial independence index is controlled in the regression. We apply three stage leastsquare to estimate the simultaneous regression. The dependent variable in column (1) and (3) is lunit_price while the dependent variable in column (2) and (4) is TOM, controlled
for region fixed effect, year fixed effect and project’s cluster effects on 55170 transaction records. Due to the limitation of the database, we lose the information of FD_INV before
2007, which narrows down the valid sample size into 25330. Column (1) exhibits the simultaneous regression results of whole sample. Due to the limitation of the database, we lose
the information of FD_WK in some real estate firms, which narrows down the valid sample size into 55049. Standard errors are in parentheses: *p<0.10, **p<0.05, *** p<0.01.

58Appendix D. Decomposition of developer decisions into internal
factors, external factors and unexplained components
The procedure here follows Gyourko (2009). It is based on pseudo R-square calculated in three-stage leastsquares (3sls) regressions.
1) Run the regression including internal, external and control variables on the RHS. Calculate the pseudo
R square (R0).
2) Calculate the unexplained share (unexp%):
unexp%=1-R0
3) Run the regression including internal and control variables on the RHS. Calculate the pseudo R square(R1).
4) Run the regression including external and control variables on the RHS. Calculate the pseudo R square(R2).
5) Calculate internal share (int%):
int%=R0
6) Calculate external share (ext%):
ext%=R0
In this way, unexp%+ int%+ ext%≡1
Notice that this procedure naturally removes the explanatory share of “other control variables” such as areaof the housing units, height of housing units, year of completion, etc.

59Appendix E. More details of the Beijing housing market.
This appendix provides more details about the Beijing housing market. As we explain in
the text, this study employs data from different sources. Our macroeconomic data comes fromthe Beijing Statistical Yearbook (2007-2009) and includes permanent and external
population,21 land area, population density, disposable income per capita, fixed assets and realestate investment. They are deflated by the annual consumer price index (CPI). Table E1
provides descriptive statistics.
Table E1 Descriptive Statistics of Macroeconomic and Urban Variables
Variable

Definition

Meann_subway
perma_pop

Macroeconomic Attributes
number of subway line
permanent population

24.0083
205.5841

19.71746
105.459

63.72992
127.9793
14334.71
647797.3
10787.6
1.13E+07624.8064
5.552781
21655.75
15274.05
4.59112935.225461.84703
12260.54367426.7
6116.678
7631436446.7557
1.038193
2631.232
2221.829
1.164765

71249.45
16.12491
123.2227
8.476661
1510306
5.759752

47230.78
7.719355
60.60121
6.113568
1532998
6.64339

15679.81
5498.367
7809.088

10037.39
6716.824
7692.845

8022.387
8093.282

6979.566
7621.499

exeter_pop
registered_pop
move_in_out
no_employed
no_unemployedgdp
fix_investment
real_estate_investment
personal_disposable_income
personal_consumption
personal_income_employed
pro_area
pro_floor
unit_area
unit_floor
tprice
TOM
d_cbd
d_sub
d_h1
d_pri
d_middle

external population
registered population
census register population to move in
number of labor
registered urban unemployed people
Gross Domestic Product (RMB)
fixed assets investment(10,000 RMB)
real estate investment(10,000 RMB)
disposable income per capita (RMB)
consumption per capita (RMB)
labor compensation per capita(10,000 RMB)
Project and Unit Attributes
project area(m2)total floors of the building
housing area(m2)floors of the housing (m2)total sold revenue
time difference between listing time andselling time
distance to CBD(m)distance to the nearest subway station(m)distance to the nearest Third Level 1st Class
hospital(m)distance to the nearest primary school(m)distance to the nearest middle school(m)Std. Dev.

Our second category of data is housing-related. The micro-level housing data are drawn
from the City Housing Index Database, which was established by the Ministry of Construction(MOC) and the Department of Housing and Urban and Rural Construction Committee in
Beijing. We focus on development projects by listed firms, as the corresponding financial
information is readily available. After the usual data-cleansing procedures,22 we obtain 59,451
residential unit transaction records from 2006 to 2008. The sample contains information on the
project name, unit-selling price, selling time and issue date of the pre-sale license, in addition
to the physical attributes of the project and unit, such as project area, the total number of floors
in the building, unit area, and the height of the unit. We used GIS Mapping to acquire thelocation and neighborhood attributes, including the distance to CBD, the nearest subway station,
21 “Permanent population” refers to the population with the registered citizenship (hokou).

22 We removed observations with incomplete information on one of the following categories: location, project design, andtransaction details. We also removed projects that are not developed by listed (A-share) real estate firms in China.
60hospitals, and schools.
Tables E2 describes the distribution of residential structure design in Beijing from 2006 to
2008. We find mid-rise (>6 floors, ≤30 floors) and middle units (>60 m2, ≤120 m2) appear
with the highest frequency. The distribution of housing stock is influenced by both time-varying
market demand, urban planning requirement, as well as the so-called “90/70 policy” enacted
by the joint declaration of Chinese MOC and other eight ministries and commissions.23
Variable

pro_floor
12
3
unit_area
1
2
3

Table E2 Statistics of project floor and unit area
Definition
Freq.
Percent

Cumulative
percentage

low-rise (≤6 floors)
mid-rise (>6 floors, ≤30 floors)
super high-rise (>30 floors)

7,655
49,122
2,674

12.88
82.63
4.50

12.88
95.50
100.00small units (≤60 m2)
middle units (>60 m2, ≤120 m2)
large units (>120 m2)

5,263
28,04926,139

8.85
47.18
43.97

8.85
56.03
100.00Since this paper focuses on Beijing, it may be instructive to provide more backgroundinformation of the city. Geographically, Beijing is divided into different “rings.” Roughly
speaking, the higher the number of the ring, the further the ring is located from the central
business district (CBD). We therefore divide our sample into four sub-samples, namely, inside
the second ring (the “core function zone’’), inside the fifth ring (the “expanded function zone”),
inside the sixth ring (the “development zone”) and outside the sixth ring (the “ecological zone”).
Figure E1 provides a visualization of the geographical distribution of the fifteen districts. We
first aggregate them into four Functional Zones or Regions. Table E3 compares themacroeconomic status of the four regions and shows the percentage of units sold in each area
in Beijing. During our sampling period, most of the new-developed projects are located inside
the fifth ring, and few projects are located inside the 2nd ring or outside the 6th ring. It is
probably due to the following facts: (1) the core function zone is already well developed, (2)the infrastructure and amenities in the ecological zone is under-supplied, and (3) the landleasing policy imposed by the government that may not be driven by purely economic reasons.

23 In May 2006, Chinese MOC and other eight ministries and commissions issued an opinion about the adjustment ofresidential supply structure and price, which mandatory require that the new residential project whose construction area isbelow 90 square meters, has to account for more than 70 percent of the whole project. Officially, the intention is to promote
housing supply and stabilize housing price, although there may be un-intended consequences, as usual for government policies.
For more analysis of the 90/70 policy, see Leung and Wang (2007), among others.
61

Figure E1 Distribution of Four Functional Zones in Beijing

62

Table E3 Definition and Statistics of the Four Functional Zones in Beijing
RegionRegion 1
Region 2
Region 3
Region 4
Definition of Functional Zones
inside 2nd ring inside 5th ring inside 6th ring outside 6th ring(ecological(development(core function (expandedzone)
function zone) zone)
zone)
Huairou, Miyun,
Changping,
Chaoyang,
Corresponding Districts
Dongcheng,
Yanqing,
Daxing,Fengtai,
Xicheng,
PingguFangshan,Haidian,Chongwen,
Shunyi,
Shijingshan
XuanwuTongzhou,Mentougou
Panel A: Projects Distribution in Four Functional Zones
Freq.
4294
37333
17128
696
Percent
7.22
62.8
28.81
1.17
Cumulative percentage
7.22
70.2
98.83
100
Panel B: Mean Value of Macroeconomic Variables in Four Functional Zones
personal_disposable_income
22163.82
22661.88
19492.32
17793.34(RMB)
personal_consumption (RMB)
16527.92
16441.54
12562.55
11643.07
pop_density (10 thousand/km2)
2.05
0.65
0.08
0.02
working_ratio (%)
38.05
52.44
39.83
25.06
dependency_ratio (%)
39.07
71.55
61.46
46.85
Panel C: Mean Difference of Macroeconomic Variables in Four Functional Zones (Compared with
Region 1, t-test)
personal_disposable_income-498.066***
2671.503***
4370.477***(RMB)
personal_consumption (RMB)
86.385***3965.376***4884.854***
pop_density (10 thousand/km2)
1.408***
1.974***
2.038***
working_ratio (%)-0.144***-0.018***0.130***
dependency_ratio (%)-32.479***-22.385***-7.778***The oligopolistic nature of the Beijing housing market is well illustrated in its distributionof sales volume. Figure E2 shows that the top 10 developers account for about one half of thetotal sales volume from 2006 to 2008. We naturally turn to the industrial organization literatureand measure market concentration. We first calculate the Herfindahl–Hirschman Index (HHI),
which is one of the most popular measures in this regard.24 During this period, HHI reaches
0.26, which indicates that the market is highly concentrated. A concrete example to illustrate
the idea: if the ratio of market volume share of the biggest developer (Beijing Urban
Construction) to the second-biggest developer (Beijing Capital Development) is 1.5, then theratio of market volume share of the biggest developer (Beijing Urban Construction) to the tenthbiggest developer (Oceanwide Construction Group Co., Ltd) quickly increases to 2.6. As analternative index, we also calculate the market share of different developers in the four “zones”
mentioned above using absolute market concentration (CRn) as a metric25 and discover a Ushaped, non-linear relationship for the market share of the top 10 developers (CR10 equals to
24 HHI is a widely used measure of the concentration of industry. It is beyond the scope of this paper to review the related

literature. Among others, see Tremblay and Tremblay (2012) and the reference therein. The formula of HHI is simple andstated as follows:swhere s is the market share of firm i in the market, and N is the number of firms. Conventionally, a value of HHI below 0.01
may indicate a highly competitive industry, below 0.15 may indicate an un-concentrated industry, between 0.15 to 0.25 may
indicate moderate concentration, and above 0.25 may indicate high concentration.
25 The concentration ratio is the percentage of the market share held by the largest n firms in an industry,
CR

S

where S is the market share and CR defines the n firm. High concentration ratio in an industry could indicate a relatively
low level of competition among firms in that industry (Kambhampati, 1996).
63

88.7%, 69.6%, 89.0%, and 100% respectively, as shown in Table E4). The market is moreconcentrated in the inner city as well as the outer sub-urban. This result is intuitive. The land
cost in the inner city is very high. And in the outer sub-urban, the market demand is highly
uncertain. It discourages financially weak developers from entering those markets. (We will
provide a more systematic analysis on the location choices of the developers). Furthermore,
the major developers in the four zones are all different, which seems to suggest that different
developers might have distinct "comparative advantages" in each zone. Notice that thecomparative advantage might be economically (such as local knowledge in a specific area), or
politically (such as the top executives being more politically connected to the local government
officials). 26 More discussion and analysis related to such market concentration will bepresented later.

26 Notice that the comparative advantage might be economically (such as local knowledge in a certain zone), or politically(such as the top executives being more politically connected to the local government officials).
64

Beijing Urban construction
Group Co. Ltd , 6730

Others, 38090

BBMG
Co.,Ltd ,6150

Poly Real Estate Group
Co.,Ltd, 5210
Beijing
Vantone RealEstateCo.,Ltd,
4840

Beijing Capital
Development Co.,ltd., 7140

Top 10, 53690

Jingtou Yintai
Co.,Ltd, 8380

Yeland Group
Co.,Ltd, 4690
Guangzhou Donghua
Enterprise Co.,Ltd. , 3860

Oceanwide Construction
Group Co.,Ltd, 3250
Thaihot Group Co.,Ltd,
3440

Figure E2 Sale Volume during 2006-2008

65

Table E4 Distribution of Sale Volume of Top 10 in Four Functional Zones in Beijing
Region 1
Region 2
Region 3
Citychamp Dartong Co.,Ltd.
Metro Land Co.,Ltd. (12.5%)
Poly Real Estate Group Co.,Ltd.
(27.5%)(27.4%)

Region 4
Beijing Vantone Real Estate
Co.,Ltd (63.6%)

2

Shanghai New World Co.,Ltd.
(20.4%)

Beijing Capital Development
Co.,Ltd (10.4%)

CAC Group (20.4%)

Beijing Homyear Capital Holdings
Co.,Ltd (35.6%)

3

Shenhua Group Co.,Ltd. (10.2%)

Beijing Urban Construction
Investment & Development
Co.,Ltd. (10.0%)

Lawton Development (8.5%)

Tsinghua Tongfang Co.,Ltd. (0.8%)

4China Sports Industry Group
Co.,Ltd (5.2%)
Metallurgical Corporation Of China
Co.,Ltd. (5.0%)
China Railway Construction
Corporation Co.,Ltd. (4.5%)
Metro Land Co.,Ltd. (4.1%)

Bbmg Co.,Ltd. (9.0%)

Dongzhao Changtai Group (6.8%)

Yeland Group Co.,Ltd (7.2%)

Pearl River Investment

Guangzhou Donghua Enterprise
Co.,Ltd. (5.9%)
Oceanwide Construction Group Co.,
Ltd. (5.0%)

Gemdale Co., Ltd.

8

Guoxing Rongda Real Estate
Co.,Ltd. (4.0%)

Beijing North Star Company
Limited (3.2%)

Financial Street Holdings Co.,Ltd.

9

Rongfeng Holding Group Co.,Ltd.
(3.9%)
Beijing Urban Construction
Investment & Development Co.,Ltd(3.8%)

Yangguang Co.,Ltd (3.0%)

Hubei Golden Ring Co.,Ltd. (2.8%)

China Sports Industry Group
Co.,Ltd (2.8%)

Beijing Sanyuan Foods Co.,Ltd.
(2.7%)

1

5
6
7

10

China Vanke Co., Ltd.

Our third source of data is financial. The RESSET Financial Database provides us the information of the listed developers, including the earnings per share,
current ratio, interest cover ratio, inventory turnover ratio, debt-to-asset ratio, PE ratio, and yearly market capitalization. These indices reflect profitability,66

competitiveness, and the debt-paying ability of the firm.27 Since the recent literature also demonstrates the potential benefits of political connectedness,28 whichis also referred as un-booked financial status or political economy variables in this paper, we also include additional information such as the firm’s corporate
governance information including the governance background of firm’s board members, supervisors and executive leaders, etc., as a proxy of the firm’s politicalconnections. Following Faccio (2006), we identify firm as politically connected if one of the firm’s board members, supervisors, or executives used to haveofficial governmental background including the National People’s Congress (NPC) deputy, Chinese People’s Political Consultative Conference (CPPCC)
member, and officials at national, provincial, city or county level. Thus, for each project, we can match the financial status and political connection status of
the mother company at the listing time. Table E5 provides a quick summary of the financial and political economy variables of the firms. Several remarks are
in order. Recall that the interest cover ratio is a company's earnings before interest and taxes (EBIT), divided by the company's interest expenses for the same
period. The top 10 developers have significantly higher interest cover ratio, which is about 140 on average, than the full sample, which is only about 40,
suggesting that the interest burden is in fact “lighter” for the big developers.
Moreover, the PE ratio of the top 10 developers is 76, compared to the full sample average, which is less than 6. We may say that the top 10 developers
have some advantages in terms of finance. On the other hand, it seems that the top 10 developers are less politically connected at the national and provisional
level, but more connected than the average at the city and county level.
Table E5 Descriptive Statistics of Financial and Political Economy Variables of Listed Developers
Variablecurrt
intcvrdbastrtperatio
ebitdanpc_cppcc
official_1_2
official_3_4

Definition
Financial Attributes (booked)
current ratio
interest cover ratio
debt-to-assets ratio
PE ratio
earnings per share
Political Economy Indicators
dummy variable equals to 1 if the corporate management level
used to have a NPC and CPPCC background 29
dummy variable equals to 1 if the corporate management level
used to have a national official backgrounddummy variable equals to 1 if the corporate management level
used to have a provincial or ministerial official background

Mean (FullSample)Std. Dev. (FullSample)Mean(Top 10 developers)

Std. Dev. (Top 10
developers)

1.600742
39.38115
57.51366
5.260944
2.37E+08

2.072399
260.5568
32.52184132.6877
4.76E+08

1.05806
144.4856.85389
76.25905
1.62E+08

0.520694
567.0533
20.84452
60.5248
8.87E+07

0.292370

0.454855

0.169015

0.374781

0.084841

0.278648

0.020798

0.142714

0.138616

0.345548

0.020798

0.142714

27 Our choice of variables is guided by the literature and constrained by data availability. For a survey of the literature, see Shleifer and Vishny (1997), among others.
28 For a review of that literature, see Lambert and Volpin (2017), among others. For the case of China, see Chan et al. (2012), Li et al. (2007, 2008), among others.
29 “Management level” denotes firm’s board members, supervisors and executive layers. NPC denotes National People’s Congress and CPPCC denotes Chinese People’s Political Consultative Conference (CPPCC).

67

official_5_6
official_7_8
official_9_12
official_otherdummy variable equals to 1 if the corporate management level
used to have a city-level or bureau-level official backgrounddummy variable equals to 1 if the corporate management level
used to have a county-level official backgrounddummy variable equals to 1 if the corporate management level
used to have a township official backgrounddummy variable equals to 1 if the corporate management level
used to have an other official background68

0.136059

0.342855

0.177759

0.382326

0.419111

0.493418

0.546689

0.497837

0.209261

0.406785

0.169015

0.374781

0.673249

0.469029

0.569864

0.495116