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Habitat International 30 (2006) 305–326
www.elsevier.com/locate/habitatint
Socio-economic differentials and stated housing
preferences in Guangzhou, China
Donggen Wang, Si-ming Li
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
Abstract
Households in Chinese cities today have to increasingly rely on the market to satisfy their housing needs.
The growing freedom in choosing one’s own residence implies increased variations in all aspects of housing
consumption. Examination of individuals’ housing preferences is crucial in understanding these variations.
This paper studies the housing preference of Guangzhou people through choice experiments framed in
state-of-the-art experimental design methods. Joint logit models comprising both neighbourhood and
dwelling attributes are estimated for all subjects and for various sub-samples classified by family income,
age, education, nature of employment organization, district of current residence, etc. The models are then
used to compute utilities for different attribute levels, the impacts of these attributes on choice probabilities,
and the relative prices that the subjects are willing to pay for buying a home in different districts, with
different accessibilities, of different types, etc. Neighbourhood and location-related attributes are found to
be more important than dwelling-related attributes in home purchase decisions. Further, factors such as
family income, age, education, nature of employment organization, etc. are found, to various degrees, have
affected housing preference. Based on the preference structures revealed, we envision a new urban
morphology to take shape in Chinese cities which is not too dissimilar from the ones in cities in the West,
with the inner core dominated by the aged and the urban poor and the outskirts occupied by younger
people and the rich and well-educated class.
r 2004 Elsevier Ltd. All rights reserved.
Keywords: Housing studies; Stated preference approach; Housing market; China
E-mail addresses: dgwang@hkbu.edu.hk (D. Wang), lisming@hkbu.edu.hk (S.-m. Li).
0197-3975/$ - see front matter r 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.habitatint.2004.02.009
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Introduction
Two and half decades have passed since China first launched its housing reform. For many
years under China’s planned economy, construction and provision of urban housing rested mainly
upon the state work units or simply work units or danwei1and were subsumed under capital
construction investment allocated to the work units under the annual budgetary exercise (Wu,
1996). Efforts were made during the early reform periods to disengage the work units from being
directly involved in housing construction. Development companies were established to build
‘‘commodity housing’’ for sale, presumably according to market principles. Initially, though, the
great majority of such commodity housing was sold to the work units for subsequent allocation to
their workers (Li, 2000). Individual households purchasing homes directly in the market were
rare. Also there were restrictions as to who could buy this ‘‘commodity housing’’. In general,
foreigners (including Hong Kong and Taiwan ‘‘compatriots’’) were precluded from doing so. In
cities with sizeable numbers of expatriates such as Beijing, Shanghai and Guangzhou, commodity
housing for foreigners or waixiao shangpifang, which was usually of higher quality, was also built
to accommodate the needs of and perhaps also to better monitor the foreign population. Urban
development tied to specific capital construction investment projects tended to be associated with
a high degree of haphazardness and uncertainty, especially when funding was subject to the
outcome of the annual budgetary exercise. A major objective of the supply side reform was to
bring in more orderly urban growth. Under the reform, the municipal government rather than the
work units became the most important player orchestrating urban development. Often the
municipal government would allocate a large tract of land to a development company, usually at a
price. The development company would then undertake housing and other real estate
development projects. It would also be responsible for roads, sewage, landscaping and other
infrastructure provision. By the early 1990s the bulk of new housing in urban China was
commodity housing built by the development companies. Invariably the new housing estates were
located outside the former work unit compounds, resulting not only in increased commuting but
also in new dimensions of differentiation of the urban residential space.
Comparatively speaking, demand-side reforms were carried out with much greater caution. In
China the term ‘‘public housing’’ refers to the housing provided by the state work units and also
the municipal housing bureau. Until mid-1990s the reform was restricted to gradually raising
public housing rents and selectively selling public housing to workers of state work units at
discounted prices (Li, 2000). Housing allocation remained largely the prerogative of the work
units. In fact it has been revealed that state work units had played an even greater role in housing
provision under the reform, despite all the rhetoric of marketization and commodification (Wu,
1996; Li, 2003). In recent years, though, the pace of reform has quickened considerably. The
pronouncement of cessation of welfare allocation of housing by the former premier, Zhu Rongji,
in 1998 perhaps marked a watershed in China’s urban housing reform history. Since then, there
have been massive disposals of the stock of public housing. In Shanghai and a few other cities full
property rights have been given to owners of ‘‘reform housing’’, i.e., former public housing that
1
This include state-owned enterprises as well as government departments and party and quasi-government
organizations.
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had been sold to workers of state work units and others at highly subsidized prices. The domestic
housing market and the market for foreigners have also been merged.
A major obstacle hindering home purchase by individual households in the market was the
problem of affordability. Under the traditional work unit system, in-kind payment including
virtually free housing constituted a major part of the reward the work unit paid to its workers.
Monetary income was of relatively minor importance. This resulted in exceedingly high price-toincome ratios for commodity housing. In Beijing, for example, in 1992 the average price of
commodity housing stood at RMB21613 per m2. A 60 m2 apartment would cost RMB96780. The
average household income, on the other hand, stood at RMB 8300. The price-to-income ratio was
therefore 11.65 (see Lau, 2003, for details of these and the computation on price-to-income ratios
reported below). That is, the average household had to save all its income for more than 11 years
in order to purchase a flat in the open market! Obviously, commodity housing was beyond the
means of most except the very rich. Five years later, in 1997, the average household income
increased markedly to RMB24056. Yet the average price for a 60 m2 apartment rose by an even
larger margin, to RMB320000. The price-to-income ratio increased further to 13.31. In more
recent years, there has been an apparent revamp of the remuneration system. There have been
further and quite drastic increases in wage, and many work units now grant cash subsidies to their
workers for home purchase in the open market. Also, home price appears to have stabilized. In
fact, the average price for a 60 m2 apartment in Beijing dropped slightly to RMB283000 in 2001
while the average household income continued to increase to RMB34980. Hence, the price-toincome ratio declined to 8.09. Commodity housing is now more affordable. The rate of urban
homeownership has showed corresponding sharp increases. For the first time in the history of the
People’s Republic of China, a large portion of urban households can now exercise choice in
housing consumption. The market finally begins to reign.
The increasing role played by the market and the growing freedom in choosing one’s own
residence imply increased variations in all aspects of housing consumption in China: where the
residence is located; what kind of neighbourhood and location attributes the dwelling is associated
with; in what tenure mode housing is consumed and how much; etc. Probably because of this,
recently more attention has been given to the individuals and individual households in China
housing research. One major area of concern is housing tenure. Li (2000a), employing data
derived from a sample survey in Guangzhou, studies how different types of households are
channelled to different types of housing under a semi-marketized regime. Huang and Clark (2002)
and Ho and Kwong (2002) study tenure composition. They conclude that both market
mechanisms and institutional forces are of importance in structuring the mode of housing tenure.
A related area of research is residential mobility. Li and Siu (2001a, b) examine mobility
behaviours in Beijing and Guangzhou and reveal the continual dominance of danwei in
determining residential location, especially the movement to the suburbs. Li (2003) further reveals
that while the direction of movement is related to current housing tenure, it is unrelated to
previous tenure. Based on retrospective residential histories, Li (forthcoming) finds that in Beijing
the rate of residential mobility has exhibited a fluctuating but slightly downward trend since 1985,
despite the marketization drive.
2
RMB stands for Renminbi, the Chinese currency. At current rate of exchange, RMB 1=US $0.12 approximately.
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The above-cited works often purport to study tenure and housing choice, with the view of
unravelling residential preference of individuals and households. Both Ho and Kwong (2002) and
Huang and Clark (2002) claim to have estimated a choice model of the McFadden (1973) variety
(see also Quigley, 1976; Friedman, 1981). Yet their studies were based on the data collected at a
time when the state work units and other institutional forces still dominated the housing provision
scene. Even today, the housing market in urban China is still in its infancy. Work units and the
municipal housing bureau remain instrumental in deciding who get what, where, and how much.
The lack of a market clearing process means that the usual revealed preference approach is
inadequate, if not entirely inappropriate, in eliciting housing preferences in urban China. Thus,
the results of the aforementioned studies provide no more than anecdotal evidence on the
preference structure.
To date, very little is known about how individuals and households in urban China make
housing decisions in a market context. Are accessibility considerations important? What about
neighbourhood safety and availability of social capital in the neighbourhood, especially in light of
the gradual dismantling of work unit compounds? How do people in Chinese cities view dwelling
attributes, such as type of dwelling, size and layout of the dwelling, etc.? Are they willing to trade
one attribute for another, and if so, by how much? Previous studies conducted in western
countries have revealed systematic variations in the preferences for housing attributes and hence
the choice of housing according to position in the family life cycle and socio-economic status
(Quigley, 1976; Friedman, 1981; Clark & Dieleman, 1996). How and to what extent do housing
preferences vary across socio-economic classes and demographic groups in the Chinese case?
Education, for instance, broadens a person’s horizon and helps inculcate a certain worldview. A
person’s preference structure, therefore, varies with his/her education attainment. In a
redistributive economy, which until very recently has characterized China, education attainment
is particularly instrumental in determining a person’s position in the employment hierarchy and
hence the kind of resources at his/her disposal (Szelenyi, 1983). Does education have the same
effect on housing preference for Chinese people as it has for people in the West? In this
connection, it may be argued that both employment and previous housing experience will have an
effect on housing preference formation. In the case of China, work unit compounds used to
dominate the landscape. Do people with different housing experiences, in particular do people
living in work unit compounds and people living elsewhere, differ systematically in terms of their
views on location and dwelling attributes? The answer to all these questions have direct relevance
for the understanding of how housing decisions are made in China in an increasingly marketdriven setting. To address these questions in the absence of a well functioning housing market, an
alternative approach, more specifically the stated preference approach, is needed.
Recently, Wang and Li (forthcoming) conducted a stated preference experiment in Beijing. To
our knowledge, this is the first serious attempt to study housing preference in China. As the
capital of China, Beijing is perhaps the last stronghold of the planned economy. Government and
Party organizations and state enterprises dominate the employment scene. The Beijing sample
comprises a rather homogeneous group and may not be representative enough to reveal the extent
of variability in housing preferences that are emerging in other Chinese cities. The present study
builds upon and extends the Beijing study. A companion experiment was conducted in
Guangzhou in summer 2001. Guangzhou has served as China’s southern gateway and in many
occasions the only trading port to the outside world for more than two millenniums. Its rich
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commercial history and geographic proximity to Hong Kong accorded Guangzhou special
positions in the reform period. In comparison with that of Beijing, the Guangzhou economy was
much more complex and open. Market elements and foreign investments began to infiltrate
Guangzhou in the early reform period. Inflow of foreign capital already made an imprint in the
mid 1980s (Li & Chu, 1987). Economic growth accelerated in the 1990s. Per capita GDP reached
RMB 38007 in 2001 (SBGP, 2002, p. 75), ranking second amongst all cities in the country. In the
realm of housing, Guangzhou was among the first cities in China to introduce the housing
provident fund. It also took lead in terminating ‘‘welfare allocation of housing’’ (Guangzhou &
Bianzuan, 2001, p. 223). In fact, real estate developments mimic those in Hong Kong already
mushroomed in the early 1990s. Li (2000) and Li and Siu (2001a,b) reported that open market
housing made up nearly 30% of their sample, which was collected in 1996. In short, the
Guangzhou population exhibits high degrees of heterogeneity and the people in Guangzhou have
long and rich experience in dealing with the market.
In this study, we first depict the general structure of housing preference in Guangzhou, based on
choices expressed by subjects of the stated preference experiment. In addition to computing the
utilities derived from various location and dwelling attributes and their effects on the choice
probabilities, we also examine in more explicit terms the trade-offs between attributes. Attempts
will be made to compare our findings with those of Wang and Li (forthcoming) for Beijing to
assess to what extent urbanites in China share common housing preferences. The much more
heterogeneous Guangzhou sample allows us to explore such effects as previous residential
experience and employment on housing decisions, which was not feasible for the Beijing study. In
this sense the research findings to be detailed below provide a much richer description of housing
preference structures in Chinese cities than hitherto available.
Modelling housing preference in a semi-marketized housing system: methodology and data
The stated preference approach
Many housing studies have adopted the hedonic approach to analyse how the marginal value of
housing attributes is priced (Rosen, 1974; Palmquist, 1984; Megbolugbe, 1991). Quigley’s (1976)
seminal work, which introduces the discrete choice analysis approach, has stimulated many
similar applications (Friedman, 1981; Fischer & Aufhauser, 1988; Timmermans & van Noortwijk,
1995). While most discrete choice-based housing studies use revealed preference data (i.e., housing
choice data from the real market), an increasing number of applications adopt the Stated
Preference (SP) method and use experimental data (Timmermans & van Noortwijk, 1995; Tu &
Goldfinch, 1996; Earnhart, 2002; Walker, Marsh, Wardman, & Niner, 2002; Wang & Li,
forthcoming). The stated preference method and its advantages and disadvantages in comparison
with the revealed preference approach are well documented in the literature. See Earnhart (2002)
and Walker et al. (2002). The stated preference method has proved to be particularly useful where
there is an absence of actual market information from which preferences can be revealed (Walker
et al., 2002). The fact that a full-fledged housing market is yet to be established in Guangzhou
implies that no reliable market information is available to derive housing preference and the
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stated preference method is probably the only choice for modelling housing preference in a semimarketized housing system like that of Guangzhou.
Housing choice is a multi-dimensional exercise, involving the choice of tenure, housing type,
neighbourhood, location, etc. Most studies examine only one or maximally two choice
dimensions. In particular, the stated preference method, almost as a rule, is applied to model a
single choice dimension. The first attempt to model two housing choice dimensions was made by
Wang and Li (forthcoming). The same modelling technique is adopted here. We analyse two
choice dimensions: the choice of dwelling and the choice of neighbourhood.
Selection of attributes, experiment design and sampling
The set of housing attributes employed here is similar to that employed in the Beijing study.
Table 1 presents details of the selected attributes and their levels. Four attributes are used to
define neighbourhood, namely, accessibility, living convenience, security and district. Accessibility
is usually defined in terms of distance to work in the literature (for example, Tu & Goldfinch,
1996). This may be appropriate for western cities where the private car is the major mode of
transport and people have a good sense of distance. It may not, however, be the best way to define
in Chinese cities like Guangzhou where public transport (bus, subway, etc.) is the choice for the
majority. Instead, accessibility is defined in this study in terms of access to public transport: (i)
highly accessible, with public transport connections to all districts in the city; (ii) reasonably
accessible, with public transport connections to major business centres; and (iii) limited
accessibility, with very few public transport links with the rest of the city. Living convenience
refers to the convenience of daily goods shopping. Urban Chinese families typically undertake
Table 1
Attributes and levels
Attributes
Neighbourhood
District
Accessibility to public
transport
Living convenience
(shopping)
Security
Dwellings
Price
Orientation
Type
Layout
Property management
Levels
1. Yuexiu; 2. Liwan; 3. Dongshan; 4. Haizhu; 5. Tianhe; 6.Baiyun; 7. Fangcun; 8. Huangpu
1. Highly accessible, public transport connections to all districts; 2. Reasonably accessible,
public transport connections to major city centres; 3. Limited accessibility, very few public
transport connections with other districts
1. Fresh and daily goods markets available within 500 m; 2. Fresh and daily goods markets
available within 1000 m; 3. Fresh and daily goods markets available beyond 1000 m.
1. Good public order; 2. Poor public order
1. 4000 yuan/m2 or below; 2. 4000–5000 yuan/m2; 3. 5000–6000 yuan/m2; 4. 6000 yuan/m2
or above
1. East; 2. South; 3. West; 4. North
1. Detached house; 2. Apartment building of 4 floors or fewer; 3. Apartment building of 5
floors or more without lift; 4. Apartment building of 5 floors or more with lift
1. Small living room but large bedrooms; 2. Large living room but small bedrooms
1. Pay for property management; 2. No property management
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daily shopping for fresh vegetables, meats, etc. Thus, availability of daily and fresh markets in the
vicinity of home is believed to be an important consideration for housing choice. Three levels of
living convenience are specified: fresh and daily goods markets available within 500 m, within
1000 m, and beyond 1000 m.
The majority of urban Chinese used to live in work unit compounds where most residents were
working in the same work unit and security was not a major concern. The new commodity
housing estates, however, have a greater mixture of people, which causes concern for security
problems. This concern is further aggravated by the rising crime rates in conjunction with much
loosened control over migration flows and presence of large numbers of laid-off workers in cities
in recent years. It is not surprised to find that in many Chinese cities neighbourhood security has
become an important consideration for housing purchase. To gauge this influence, we define two
security levels: ‘good public order’ and ‘poor public order’.
The district dummy is included to capture the unmeasured components of neighbourhood
features such as social class composition and district reputation. The eight urban and inner
suburban districts of Guangzhou form the eight levels of the attribute. To help comprehend the
modelling result regarding people’s district preference, we briefly introduce the eight districts, the
spatial configuration of which is depicted in Fig. 1. Dongshan, Liwan and Yuexiu comprise the
pre-Reform (and indeed pre-1949) urban core. While all three suffer from severe crowdedness,
Fig. 1. Spatial configuration of districts in Guangzhou.
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with the population density ranging from 34,800 per km2 for Dongshan to 48,600 per km2 for
Yuexiu (SBGZ, 2001, p. 57), these core districts nevertheless contain the bulk of urban amenities
in Guangzhou. Planned as a district for government and Party offices, Dongshan is somewhat less
crowded and has better provision of parks and other greenery. In many respects Dongshan is the
most reputed district in the city. The population of Dongshan comprises a disproportionate
number of cadres and intellectuals (Xu, Hu, & Yeh, 1989). Haizhu is a rather peculiar district. A
small strip on the waterfront facing Liwan and Yuexiu across the Pearl River, known as Henan in
the past, used to constitute part of the inner core. Yet until recently much of Haizhu has
remained rural. Also, shipyards and heavy industries dot the landscape. Located to the east of
Dongshan, Tianhe has been Guangzhou’s main district for urban expansion from the late
1980s onwards. A new central business district, with the Guangzhou East Station and
Tianhe Stadium being the focal points, is fast emerging in the district. Completion of
Stage I of Guangzhou Metro in 1998 has substantially improved Tianhe’s accessibility.
The other three districts, namely, Baiyun, Fangcun and Huangpu, are Guangzhou’s suburbs.
Baiyun Mountain, the city’s largest nature reserve, occupies much of Baiyun District. Despite its
rather congenial natural environment, the district’s hilly landscape limits accessibility and
acts as a major developmental constraint. In addition, the Guangzhou International
Airport, which is located in this district, poses an environmental nuisance. Fangcun, separated
from Liwan by the main channel of the Pearl, was formerly known as Huadi or ‘‘flower place’’.
Until the early 1990s horticulture was Fangcun’s main economic activity. Completion of a crossriver tunnel in 1996 provided the district with a ready link to the inner core. Accessibility
improved further with the extension of Guangzhou Metro to Fangcun. But the river still acts a
major impediment to communication. To the mind of Guangzhou people, Fangcun remains an
inaccessible and backward rural region. Huangpu, located some 30 km down the Pearl River from
the old urban core, is Guangzhou’s deep-water port and heavy industrial base. To many
Guangzhou residents, Huangpu is inaccessible and a place with rather undesirable living
environments.
Five attributes, namely, price, orientation, layout, dwelling type, and whether management
fee is needed, are selected to define dwelling. Though price is considered in this study as
a factor differentiating dwellings, it should be noted that it is a variable related not only to
dwelling but also to neighbourhood and location. The average selling price of commodity
housing in Guangzhou was about RMB 3,700 per m2 in 2000 (see SBGZ, 2001, p. 37). Given the
fact that housing price had increased during the years proceeding 2001 and we should
provide a full range of prices for respondents to consider, the following four price levels are
defined (in RMB per m2): 4000 or below; between 4000 and 5000; between 5000 and 6000; and
6000 or above.
The direction that the dwelling faces affects sunlight penetration and ventilation. It is an
important housing consideration in China. To evaluate the impact of this factor on housing
choice, the direction variable is included and the four direction descriptions, namely, ‘North’,
‘South’, ‘East’ and ‘West’, are used as the levels to differentiate people’s preference towards the
four different housing directions.
The attribute of dwelling type is selected to capture the importance of dwelling type on housing
choice and the preference towards different types. Based on the availability of housing types in the
market, five levels are identified: ‘detached house’; ‘apartment building of 4 storeys or fewer’;
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‘apartment building of 5 storeys or more without lift’; and ‘apartment building of 5 storeys or
more with lift’.
Dwellings in China used to have large bedrooms but small living rooms. This is because the
functions of living room were not fully recognized and people liked to have many wardrobes in
their bedrooms. Nowadays, people, in particular the younger generation, seem to favour the
layout of large living room but small bedrooms. To capture this preference change, this variable is
selected. Two major layouts of dwelling are identified: ‘small living room but large bedrooms’ and
‘large living room but small bedrooms’.
Finally, to see whether property management will affect housing choice, we include presence/
absence of management fee as another attribute in the model. People in Chinese cities have
become aware of the importance of estate management, but this requires payment of management
fee. It is thus important to see if respondents are willing to pay for property management.
There are, of course, other attributes that may be used to define neighbourhood and dwelling.
The problem is that respondents are not able to evaluate too many attributes in the experiments
and if too many attributes are involved, the problem becomes not tractable. In any case we believe
that the above are among the most salient variables that structure the housing preference of
urban Chinese.
Statistical methods are used to combine the selected attributes into hypothetical choice
alternatives of neighbourhood and dwelling. The uniform design method introduced by Wang
and Li (2002) and the choice experiment design technique for modelling multi-dimensional
choices proposed by Wang, Borgers, Oppewal, and Timmermans (2000) are employed to
derive the hypothetical housing choice experiments. Specifically, a total of 72 choice sets are
generated. Each choice set consists of three choice alternatives for neighbourhood and three
for dwelling.
The data were collected in conjunction with an interview survey of 1500 Guangzhou residents in
summer 2001. A multi-level probability proportion to size (PPS) sampling strategy was adopted to
select the subjects. The subjects were to be distributed in proportion to the population of the
original eight urban districts of Guangzhou (Fig. 1). The heads of households, identified as such
by members of the family interviewed, were invited to do the choice experiments. To limit the
burden on the respondents and ensure data quality, each respondent was presented with only four
choice experiments.
Perhaps, because the sample comprised only heads of households, male respondents
outnumbered females by a factor of 2:1. In terms of age, the sample approximated the
distribution of the Guangzhou population aged 20 and over, as given by the 2000 Population
Census. As for the current home ownership, 67.8% of the respondents had the ownership of their
house either from the housing market (40.4%), or work units (28.8%), or other sources (such as
inherited from parents, 30.8%). The rest either rented their house or did not provide this
information. With respect to household income, the sample exhibited substantial variability, in
line with Guangzhou’s relatively open and complex economy. While 23.3% had annual incomes
below RMB 20000, 11.3% earned incomes in excess of RMB 60000. Regarding employment
organization, 51.7% of the sampled subjects worked in state work units, and 48.3% in non-state
work units. In comparison, the Guangzhou Statistical Yearbook gave corresponding figures of
52.1% and 47.9% (SBGZ, 2001, p. 73). All in all, the sample approximated the Guangzhou
population quite well.
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Developing econometric models from stated preference data
Discrete choice models
As there are two choice dimensions involved in this study, one may estimate either joint logit
models or nested logit models (Ben-Akiva & Lerman, 1985). The specification of the joint as well
as nested logit model for the choice of neighbourhood and the choice of dwelling is given in Wang
and Li (forthcoming).
Once the choice model is estimated, one may assess the impact of attributes on choice
probability by calculating the marginal choice probability in relation to the marginal utility of
attributes. For example, to assess the impact of one unit change of attribute X on choice
probability, we may construct two choice alternatives A and B, which are different only on
attribute X by one unit: the value of attribute X of A is x, while that of B is x þ Dx. Based on the
utility function estimated, we may calculate the choice probability of the two alternatives:
PðAÞ ¼
expðV þ bx xÞ
;
expðV þ bx xÞ þ exp½V þ bx ðx þ DxÞ
ð1Þ
PðBÞ ¼
exp½V þ bx ðx þ DxÞ
;
expðV þ bx xÞ þ exp½V þ bx ðx þ DxÞ
ð2Þ
where P(A) and P(B) are the choice probability of A and B respectively, V is the systematic
component of other attributes and bx is the coefficient of attribute X. The impact of one unit
change of attribute X on choice probability can then be calculated by:
PðBÞ PðAÞ
¼ expðbx DxÞ 1;
PðAÞ
ð3Þ
which is the relative choice probability change.
Willingness-To-Pay
When a cost variable (such as price) is included in the utility function of the choice model, it is
possible to value the other house attributes in dollars, thus providing information similar to that
calculated by the hedonic price approach. This is referred to Willingness-To-Pay (WTP), a
concept and measurement that are widely used to value travel time savings (Evans, 1971; Hensher
& Truong, 1984; McFadden, 1998; Louviere, Hensher, & Swait, 2000, p. 61) and environmental
goods and services (Goodman, 1989; Freeman, 1991; Huang, Haab, & Whitehead, 1997). WTP
indicates the amount of money that an individual is willing to pay to obtain some benefit and
avoid some cost from a specific action so that the level of utility attained remains unchanged
(Louviere et al., 2000, p. 61). It is defined as the ratio between the coefficient of other attributes in
the utility function and that of the price attribute:
W¼
ba
;
bc
ð4Þ
where ba represents coefficients of other attributes and bc coefficient of the price attribute. The
WTP measurement is applied in this study to estimate the differential amount that respondents
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are willing to pay for housing in different districts, of different types, in neighbourhoods
characterized by different conveniences and accessibilities, etc.
Modelling impacts of individuals’ socio-economics on housing preference
There are two approaches to model the impacts of individuals’ socio-economic characteristics
on housing choice behaviour. Firstly, we may develop models for different socio-economic groups
and compare the results. We may then conduct sensitivity analysis to see how each group react to
change of key attribute in terms of choice probabilities. We may also calculate the WTP for some
key variables and to find out if there is any difference between groups. Alternatively, we may
develop a single model incorporating the socio-economic variables (Walker et al., 2002). In this
case, we need to construct choice sets for each individual. This turns out to be impractical to
operate for this study because the data set is too large. We therefore choose the first option and
develop models for different socio-economic groups.
Findings
The choices given by the respondents were aggregated for each choice set. To facilitate
estimation of the models, levels of the attributes were coded by an orthogonal coding scheme
(Kerlinger & Pedhazur, 1973). A k-level attribute is coded by k 1 vectors. For continuous
attributes such as price, these k 1 vectors represent different components of the attribute effect.
For example, a two-level attribute is assumed to have only linear effects on housing choice and
only one vector will be needed to code this attribute. On the other hand, a three-level attribute is
assumed to have a non-linear effect that can be decomposed into two components: linear and
quadratic. As a result, two vectors (representing the linear and quadratic effects, respectively) will
be needed to code this attribute. Similarly, a four-level attribute will need three vectors
(representing the linear, quadratic and cubic effects, respectively) to code. As for the case of
discrete attributes such as dwelling orientation, the same coding scheme is employed, but the
coding vectors are used to differentiate the utilities of different categories (similar to dummy
coding) and do not represent the different components of the non-linear effect.
The joint logit models were estimated for the entire sample and for different socio-economic
groups. The models were used to compute utilities, choice probability impacts of attributes, and
the WTP. Because of limitation of space, only the results for the entire sample are presented here.
Table 2 lists the choice model. Coefficient estimates of the coding vectors are presented. To help
readers understand the results, the utilities of attribute levels are computed from Table 2 and are
given in Table 3. To compare the relative importance of various attributes, the impact of these
attributes on choice probability are estimated using the choice model. The results are given in
Table 4. Table 5 provides another angle to evaluate the relative importance of the attributes: the
WTP estimates, which show in dollar terms how much the respondents would be willing to pay for
an improvement of a certain neighbourhood or dwelling attribute. The results for various socioeconomic groups, the full set of which is available upon request, are given in conjunction with our
discussion of specific neighbourhood and dwelling attributes and of specific socio-economic
factors.
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Table 2
The choice model for the entire sample
Dwelling variables
Coefficient (sig.)
Price
Linear
Quadratic
Cubic
0.133 (0.000)
0.035 (0.082)
0.022 (0.014)
Orientation
Linear
Quadratic
Cubic
0.068 (0.000)
0.038 (0.062)
0.094 (0.000)
Types
Linear
Quadratic
Cubic
0.018 (0.040)
0.070 (0.000)
0.001 (0.899)
Layout
Linear
Layout
0.125 (0.000)
Property management
Linear
Property management
0.001 (0.975)
Neighbourhood variables
Coefficient (sig.)
District
Yuexiu
Liwan
Dongshan
Haizhu
Tianhe
Baiyun
Fangcun
Huangpu
0.376
0.385
0.544
0.089
0.381
0.381
0.323
0.914
Accessibility
Linear
Quadratic
0.268 (0.000)
0.029 (0.037)
Living convenience
Linear
Quadratic
0.251 (0.000)
0.045 (0.001)
Security
Linear
0.340 (0.000)
Model fit
w2
Rho-square
(0.000)
(0.000)
(0.000)
(0.130)
(0.000)
(0.000)
(0.000)
(0.000)
1417.8
0.054
Note: see the first paragraph of Findings for the explanations of linear, quadratic and cubic.
All estimated choice models are highly significant, although the Rho-square (pseudo-R2)
obtained, in line with most choice equation estimates, is not high. Most of the coefficient estimates
are also highly significant. Below, we first discuss the housing preferences of the entire sample. We
then analyse the differences between socio-economic groups.
Overall housing preference
Restricting ourselves to the equation for the entire sample, we see from Table 2 that, with the
exception of property management, all neighbourhood (or location) and dwelling attributes under
consideration are highly significant: every variable has at least one attribute level significant at
po0:001. The coefficient estimates for the location attributes are generally much larger in
magnitude than those for the dwelling attributes; the difference is in the order of 10. Thus,
Guangzhou people tend to attach greater importance to neighbourhood attributes than dwelling
attributes in choosing a place to live. This finding echoes the one previously found for residents of
Beijing (Wang & Li, forthcoming).
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Table 3
Utilities of attribute levels (for the entire sample)
Dwelling variables
Utilities
Price
4000o
4000–5000
5000–6000
0.412
0.164
0.234
Orientation
East
South
West
North
Types
Detached house
4 storey
5+ storey without lift
5+ storey with lift
Layout
Small living, large bed rooms
Large living, small bedrooms
Property management
Yes
No
0.072
0.388
0.312
0.148
0.15
0.085
0.055
0.125
0.125
0.125
Neighbourhood variables
District
Yuexiu
Liwan
Dongshan
Haizhu
Tianhe
Baiyun
Fangcun
Huangpu
Accessibility
Highly accessible
Reasonable
Limited
Utilities
0.376
0.385
0.544
0.089
0.381
0.381
0.323
0.914
0.239
0.058
0.297
Living convenience
Very convenient
Reasonable
Not convenient
0.206
0.090
0.296
Security
Good public order
Poor public order
0.340
0.340
0.001
0.001
City districts
As Table 2 shows, the district dummies generally exhibit high degree of statistical significance,
and their coefficient estimates are large in magnitude. The utility figures in Table 3 show that
respondents attach significantly different preferences or utilities to different districts of the city:
the three inner core districts, namely Dongshan, Liwan and Yuexiu, have large utilities and are
strongly preferred. Among the three, Dongshan District is the most preferred. Table 5 shows that,
on average, Guangzhou residents are willing to pay an extra RMB 669 per m2 and RMB 633 per
m2 (or roughly 20% of the average selling price of commodity housing of about RMB 3700 per m2
for the city in 2000; see SBGZ, 2001, p. 37), respectively, for a dwelling in Dongshan, as compared
with buying one in Yuexiu and Liwan. Tianhe is the only district outside the inner core to
which respondents attach strong preference, with utility yield almost identical to those given by
Liwan and Yuexiu. In that sense Tianhe is now considered a constituent part of Guangzhou’s
urban core.
Haizhu is geographically and administratively considered an inner core district. Yet the utility
attached to it is much lower than those of the three inner core districts on the northern bank of
the Pearl River. Table 5 shows that a price premium of RMB 1143, 1179 and 1813 per m2,
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Table 4
Impacts of Key Attributes on Choice Probability
A
District
Price (1000/m2)
Accessibility
Living
convenience
Security
Layout
Orientation
Huangpu
Liwan
Yuexiu
4 or less
4–5
5–6
Limited
Reasonable
Not
convenient
Reasonably
convenient
Poor public
order
Small living,
large bed
rooms
East
South
West
B
Dongshan
Fangcun
Dongshan
4–5
5–6
6 or more
Reasonable
Highly
accessible
Reasonably
convenient
Very
convenient
Good public
order
Large living
room, small
bedrooms
South
West
North
Entire
sample
ðPB PA Þ=PA
(%)
Sub-samples by household
Sub-samples by
income
nature of work unit
Low
(%)
Medium
(%)
High
(%)
State
(%)
Nonstate
(%)
330
51
18
22
33
10
43
20
475
50
23
21
25
7
50
32
448
49
17
25
24
15
48
15
197
54
19
19
45
15
34
19
261
52
15
17
28
13
46
19
431
49
21
26
37
8
38
22
47
85
56
23
42
54
12
14
19
5
21
4
97
99
83
111
122
76
28
30
27
31
42
16
37
50
18
32
42
3
28
46
12
49
58
41
42
55
34
34
46
3
respectively, is needed for the average Guangzhou resident to become indifferent between living in
Yuexiu, Liwan and Dongshan, on the one hand, and in Haizhu, on the other.
The negative views of Guangzhou people on Baiyun and Fangcun are demonstrated by the
utility and WTP computations. As Table 5 shows, on average, dwellings in Dongshan commands
a premium of RMB 3685 per m2 and RMB 3454 per m2, respectively, over those in Baiyun and
Fangcun.
Inaccessibility and rather undesirable living environments render Huangpu the least desirable
district of Guangzhou. As Table 3 shows, the utility level obtained for this district (0.914) is
much lower than that for Baiyun (0.381), the second lowest district. For reference, Dongshan
commands a utlitity of 0.544. The probability impact figures given in Table 4 show that
Guangzhou people are 330% more likely to choose to live in Dongshan than in Huangpu, all
other things being equal. People are willing to pay an extra RMB 5809 per m2 to stay in
Dongshan, as compared to moving to Huangpu (see Table 5).
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Table 5
Willingness-to-pay (in terms of housing price RMB per square meters)
A. District
Yuexiu
Liwan
Dongshan
Haizhu
Tianhe
Baiyun
Fangcun
Huangpu
Yuexiu
Liwan
0
36
669
1143
20
3016
2785
5139
36
0
633
1179
16
3052
2821
5175
Dongshan
669*
633
0
1813
649
3685
3454
5809
Haizhu
Tianhe
Baiyun
Fangcun
Huangpu
1143
1179
1813
0
1163
1873
1641
3996
20
16
649
1163
0
3036
2805
5159
3016
3052
3685
1873
3036
0
231
2124
2785
2821
3454
1641
2805
231
0
2355
5139
5175
5809
3996
5159
2124
2355
0
B. Orientation of dwelling facing
East
South
West
North
East
South
West
North
1530
2789
0
653
876
2135
653
0
0
1259
1530
876
1259
0
2789
2135
Highly
accessible
Reasonably Limited
accessible
accessibility
C. Accessibility
Highly accessible
Reasonably
accessible
Limited
accessibility
0
721
721
0
2135
1414
2135
1414
0
D. Convenience
Very
convenient
Very convenient
Reasonably
convenient
Not convenient
Reasonably Not
convenient convenient
0
462
462
0
2000
1538
2000
1538
0
E. Type of dwelling
Detached
house
Detached house
4storey building
5storey building
no lift
5storey building
with lift
4 storey
building
5 storey
5 storey
building no building
with lift
lift
0
936
817
936
0
120
817
120
0
100
837
717
100
837
717
0
Note: *Potential home buyers would be willing to pay 669 RMB Yuan higher price for a house in Dongshan than in
Yuexiu.
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Computation of the WTP, if conducted at all points in space, generates the loci of the urban bid
price function for housing (Alonso, 1966; Arnott, 1987). Under competitive equilibrium, they also
give the market housing price function. The above discussion shows that in a fully developed
market, given the strong preference for the core districts, the housing price gradient (and hence the
urban land rent gradient) in Guangzhou would be quite steep. It falls off quite rapidly outside the
urban core. However, it is highly unlikely that the housing price and land rent contours would
consist of a series of concentric circle. Factors such as administration boundary, alignment of the
Pearl River, government policy especially the Tianhe development plan, and ingrained perception
of Guangzhou people (such as the perception of Fangcun as a backward agricultural region) all
have an impact on the preference structure and hence the shape of the bid price function. The end
product is likely to be a highly elongated oval-shape housing price and land rent contours,
probably with multiple maxima at the major business centres in the three inner core districts and
in Tianhe, and spurring towards the east downstream along the Pearl River.
Other neighbourhood attributes
The other neighbourhood attributes, including accessibility to public transport, shopping
convenience and neighbourhood security, are also significant and have relatively large coefficient
estimates. Table 5 shows that respondents are willing to pay, on average, RMB 721 per m2 and
RMB 2135 per m2 extra in order to trade a dwelling with only ‘‘limited accessibility’’ of public
transport, respectively, for one with ‘‘reasonable accessibility’’ and one with ‘‘high accessibility’’.
Similarly, they are willing to pay RMB 462 per m2 extra in order to trade a dwelling with ‘‘not
convenient’’ shopping to one with ‘‘reasonably convenient’’, and a further RMB 1538 per m2 for
one with ‘‘highly convenient’’ shopping. Neighbourhood security is of particular importance. A
price difference of RMB 2709 per m2 is needed in order to make the average Guangzhou resident
indifferent as to choosing a dwelling with ‘‘bad public order’’ and one with ‘‘good public order’’.
Price
Price influences the utility level attained through its effect on the budget constraint. Table 4
shows that the choice probability for Guangzhou people is quite price-sensitive. An increase in the
price of dwelling from RMB 4000 per m2 or less to RMB 4–5000 per m2 brings about a 22%
reduction in the probability of purchase; further increases from RMB 4–5000 per m2 to RMB
5–6000 per m2, and from RMB 5–6000 per m2 to over RMB 6000 per m2 result in additional
reductions of 33% and 10%, respectively.
Other dwelling attributes
Three physical dwelling attributes, namely dwelling orientation, type of dwelling, and dwelling
layout, are under examination. In line with the results previously obtained for Beijing, southfacing dwellings are the most favoured, given the prevailing wind directions, and given the relative
ease of sunlight penetration for south-facing dwellings. Because of the heat brought by the
afternoon sun, west-facing dwellings are the least preferred. Table 5 suggests that a price
reduction of RMB 2789 per m2 is needed in order to make a person indifferent between residing in
a south-facing dwelling and a west-facing residence. As for type of dwelling, ‘‘detached house’’
and ‘‘buildings of five-storeys or more with elevator’’ are favoured over other types of dwelling.
Respondents are willing to pay a price of RMB 837 per m2 higher to buy an apartment in a
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building of ‘‘five-storey plus with lift’’ than that of ‘‘four-storey or less’’ (Table 5). Table 4 shows
that a change of layout from ‘‘small living room and large bedrooms’’ to ‘‘large living room
and small bedrooms’’ will increase the choice probability by 28%. The willingness-to-pay data
indicate that the potential homebuyers were willing to pay an extra RMB 996 per m2 for the latter
(Table 5).
Impacts of selected socio-economic factors and residential experience
Household income
Income affects the utility derived from housing consumption mainly through the budget
constraint. But people with different incomes also have different life experiences. In Guangzhou
and other cities in China the bicycle and the bus are the major transport modes for low-income
people. The relative immobility of these people very much curtails their residential location
choice. The findings show that the low-income group in Guangzhou, in comparison with the
middle and high-income groups, is much more reluctant to choose the newly developed areas,
including the new core district, Tianhe. On the other hand, the high-income group, which can
readily afford the Metro or even the private car, are more willing to move to the suburbs. In fact,
their preference for Tianhe (utility=0.777) is almost identical to that for Dongshan
(utility=0.772), traditionally the most reputed district. The differential reliance on public
transport is demonstrated by the results on accessibility to public transport. For the low-income
group, an improvement from ‘‘limited’’ to ‘‘reasonable’’ accessibility increases the choice
probability by 50%; for the high-income group, the increase is only 34% (Table 4). Differential
mobility also affects the preference for shopping convenience. For the low-income group, an
improvement from ‘‘not convenient’’ to ‘‘reasonably convenient’’ increases the choice probability
by 85%. For the high-income group, this only increases the choice probability by 23%.
Income also affects the preference for dwelling attributes. In particular, while ‘‘property
management’’ is not significant for the overall equation and for the other equations, it is
significant at po:005 for the high-income equation. Perhaps this is because the high-income group
values more on the better residential environment and the enhanced value of the property that
property management may render. The high-income group also shows greater concern for
dwelling orientation: they especially dislike west-facing dwellings. The utility difference between
south and west facing is equal to 0.870. For the low-income group, the difference is only 0.534.
Education
Education is closely correlated with income. But education, which directly moulds a person’s
preferences, has an effect that goes beyond its effect through income. In a redistributive economy,
which still lingers in many sectors in China, a person’s position in the job hierarchy, and hence
housing experience under the danwei system, is particularly tied to his/her education background
(Szelenyi, 1987). The influence of education on residential preference is therefore strong. The
estimated choice equations show systematic variations across education groups. In terms of
district preference, people with junior secondary education or below have a strong preference for
the three inner core districts, Yuexiu, Liwan and Dongshan. They are willing to pay rather high
premiums to reside in the inner core. To them, the newly added core district Tianhe is not a
favoured choice; they view Tianhe more or less on par with Haizhu. On the other hand, for those
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with college or above education, Tianhe is the most favoured. This group is willing to pay
premiums of RMB 1112 per m2, RMB 1699 per m2, and RMB 762 per m2, respectively, to trade a
dwelling in Yuexiu, Liwan and Dongshan for one in Tianhe.
Age
Studies on residential decisions and mobility invariably point to the importance of age and
hence the personal and family life cycle as a factor governing the residential location and
relocation process (Clark, 1982). The lack of cases restricts us from adopting a more refined
classification. Only two age groups, namely, ‘‘40 or below’’ and ‘‘older than 40’’, are examined.
Probably because of the rather coarse classification employed, the findings generally fail to reveal
major age differences in the preference for districts. The only exception is Tianhe: the younger age
group has a relatively high preference for this district (utility=0.574, which is the second highest
among all districts), whereas the older age group has a relatively low preference for it
(utility=0.116, which ranks four among the eight districts). Again, this shows that the younger
age group is more prepared to trade more familiar surroundings for an environment endowed
with better amenities. On the other hand, the older group shows greater concern for
neighbourhood familiarity and hence security. They are willing to pay a premium of RMB
4550 per m2 to secure a building with ‘‘good public order’’ over one with ‘‘poor public order’’; in
comparison, the younger group is only willing to pay a premium of RMB 1886 per m2 for
this purpose.
Nature of employment organization
China is a transitional economy. While the market and the private sector are playing an
increasing role, state work units or danwei remain to be important job providers. It may be
hypothesized that because of their rather different housing experiences, workers in state work
units and workers in other organizations have quite different housing preferences. However, the
findings show that the two groups generally exhibit rather similar preferences. But they do differ
in some respect to a number of attributes. People working in state work units, who are used to the
secluded environment of danwei compounds, are more concerned with neighbourhood security.
They are willing to pay a premium of RMB 3635 per m2 to trade a dwelling in a neighbourhood
with ‘‘poor public order’’ with one in a neighbourhood with ‘‘good public order’’. People working
in non-state sectors are willing to pay a much smaller premium—RMB 1993 per m2—for the same
trade off. Moreover, workers in state work units, probably tired of the dull and uniform danwei
housing blocks, are more concerned with the internal design of the building: they are willing to
pay an extra RMB 1616 per m2 to secure a dwelling with ‘‘large living room and small bedrooms’’
against one with ‘‘small living room and large bedrooms’’. In comparison, workers in other work
organizations are willing to pay a premium of only RMB 516 per m2 for this purpose. In addition
to the above, the lack of market experience for the state work unit group has resulted in their
somewhat insensitivity to price change. Table 4 shows that for workers in state work units, an
increase in price from RMB 4000 per m2 or less to RMB 4–5000 per m2 reduces the choice
probability by 17%; for workers in non-state work units, the reduction is 26%. Similarly, a price
increase from RMB 4–5000 per m2 to RMB 5–6000 per m2 yields a corresponding reduction of
28% and 37%, respectively for the state and non-state groups. Perhaps workers in state work
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units have to learn more about how the market operates before they can make rational choices in
a fully marketized environment.3
Current residential location
People learn from experience and acquire information about the environment. Thus, the
current residence would have an influence on future housing choice. We partition the sample
according to whether the current residence is located in one of the core districts, i.e., Yuexiu,
Liwan, Dongshan and Tianhe. The findings show that residents of the core districts generally have
a greater dislike of the non-core districts; conversely residents of the non-core districts show less
strong preference for the core districts. To illustrate, core districts residents are willing to pay
premiums of RMB 4926 per m2, RMB 4362 per m2 and RMB 6802 per m2, respectively, for a
dwelling in Dongshan over one in Baiyun, Fangcun and Huangpu. Residents of the non-core
districts are only willing to pay corresponding premiums of RMB 1951 per m2, 2081 per m2 and
4356 per m2.
Conclusions
We employed state-of-the-art experimental design methods to develop choice experiments for a
sample of 1500 household heads in Guangzhou, China. We estimated joint logit models for the
entire sample and for various sub-samples classified by family income, age, education, nature of
employment organizations, and district of current residence. We then used the models to compute
utilities for attribute levels, the impacts on the choice probability of attributes, and the relative
housing prices that respondents were willing to pay for buying a house in different districts, of
different accessibility, and of different types, etc.
We revealed that people in Guangzhou attach greater importance to neighbourhood- and
location-related attributes than to dwelling-related attributes when considering buying a home. A
similar finding was formerly obtained for people in Beijing (Wang & Li, forthcoming). Reputed
districts are strongly favoured over other districts. Potential homebuyers in Guangzhou are
willing to pay a price difference up to RMB 5000 per m2 to trade a dwelling in the least preferred
district for one in the most reputed one. People in Beijing, too, have similarly strong preference for
reputed districts. However, unlike the case of Guangzhou in which there is uniformly strong
preference for the inner core districts, in the case of Beijing not all inner core districts are preferred
(Wang & Li, forthcoming). In addition to preference for district, potential homebuyers in
Guangzhou, like those in Beijing, also place great emphasis on the quality of neighbourhood in
terms of security image, accessibility and convenience. These facts suggest that despite the
differences between Beijing and Guangzhou in terms of lifestyle, extent of marketization, nature
of employment organization (more people in Beijing work in state work units), etc. the structures
of housing preference in these two cities are quite similar. This is probably due to the strong
influence of the central government through its top-down approach in implementing policies,
3
In order to sharpen our analysis on danwei Vs non-danwei housing experience, we have also partitioned the sample
according to current residence in danwei Vs non-danwei housing. The findings are very similar to those reported in the
text pertaining to the state Vs non-state work unit partition.
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organizing the society and educating people. Consequently, people in different places of the
country develop quite similar tastes and preferences.
Socio-economic factors were found to be influential in housing preference formation for the
urban Chinese. In comparison with the high-income group, the low and medium income groups
show stronger preference towards the inner core districts and place more importance on living
convenience and accessibility to public transport. The high-income group, however, is more
willing to move to outlying district and pay more attention to the quality of dwelling such as
orientation. Similarly, people with less education attainment show stronger preference towards
the inner core districts. While the age effect is generally not strong, the findings showed that the
younger age group particularly favours Tianhe, the suburb that contains the new CBD. These
findings have an important implication regarding future urban morphology. Based on them, we
may envision a residential pattern not very dissimilar from the ones in the West to emerge in
Guangzhou, with the inner core dominated by the urban poor, while the outskirts especially
districts with good reputation and congenial natural environment gradually occupied by the rich
and well-educated people.
Somehow unique to the case of China, nature of people’s employment organization was found
to be an important factor structuring housing preference. People in state work units are less pricesensitive than those in non-state work units. This is probably because state work unit workers
usually do not have market experience as their housing is in most cases either provided or
subsidized by the work unit. Probably because of their previous residential experience, people
working in state work units are generally more concerned with neighbourhood security and the
internal design of the building. This finding suggests that institutional forces in China not only
affects housing choice behaviour through imposing constraints and direct intervention of the
housing allocation system, but also act as a structuring factor on the individuals’ housing
preference.
Acknowledgement
The authors would like to acknowledge the Centre for Urban and Regional Studies of
Zhongshan University for the assistance in the conduct of the choice experiment. They would also
like to thank Jiukun Li, Doris Fung and Fion Law for their assistance in preparing and processing
the data. This study is sponsored by two Hong Kong Research Grant Council (RGC) projects:
HKBU 2018/00 H and HKBU 2080/99 H.
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