1. An Analysis of Quality Attributes of
Housing Environment in Guangzhou
China,
Using Expert Judgments
Fan Wu
PhD Candidate
Dept. of Real Estate and Construction,
the University of Hong Kong
Supervisor: Dr. L. H. Li
2. Introduction
Housing environment is not only about residential surroundings, but
also presents the attitudes towards lifestyle.
With the rapid development of economy and higher expectation on
quality of life, people devote themselves in pursuing high quality of
housing environment.
The rapid progress of urbanization and suburbanization brings a large
number of urban problems which might reduce living quality and
housing environment of urban residents.
3. Housing environment
When buying a house,
consumers purchase a variety of
environmental attributes as well
as services at a particular
location, rather than a concrete
box (Kain & Quigley 1970).
Three typical levels of housing
communities in Guangzhou, China
5. Research Background:
House quality(Association & Housing 1945; Fiadzo, Houston & Godwin 2001;
Rindfuss et al. 2007)
Residential satisfaction(Adriaanse 2007; Fang 2006; Kellekci & Berkoz 2006)
Housing environmental quality (Ha & Weber 1994)
Attractiveness of residence (Kauko 2006; Linneman 1981)
Neighbourhood attachment(Hays & Alexandra 2007; Karien 2007; Li 2008)
Social capital (Kevin 2003; Kleinhans, Priemus & Engbersen 2007; Middleton,
Murie & Groves 2005)
6. In the context of Guangzhou, China
Guangzhou is probably
the earliest city to
experience the onslaught
of global market forces,
largely because of its
proximity to Hong Kong
(Li & Li 2006).
The city area now covers
7,434 square kilometers
with an official population
of around 7.7 million
(statistics in 2007).
7. Objective
This research try to find out the preference of
housing environment for housing consumers and
experts by analyzing three major issues related to
housing, namely, mobility, community facilities,
and community social capital.
8. Hypothesis
The housing environmental performances are
influenced by the correlative issues, namely:
mobility, community facilities, community social
capital.
The preference of housing environment by
housing consumers and industry experts are the
same.
9. Research Methods:
Hedonic pricing model
Geographic Information Systems (GIS)
Analytic Hierarchy Process (AHP)
Objective and subjective
10. Analytical Hierarchy Process
Multi-attribute modeling is a suitable method for evaluation of other
than monetary values.
The AHP is a specific technique within this approach. Bender et al.
2000) (see for example (Chen 2006; Ho, CD 2000; Ho, D, Newell &
Walker 2005).
AHP has been used extensively in research on built environment,
house selection, and housing quality, see (Ball & Srinivasan 1994;
Bender et al. 2000; Ho, D, Newell & Walker 2005; Kauko 2003, 2006;
Schniederjans, Hoffman & Sirmans 1995).
Full mathematical details of the AHP methodology are given in Bender
et al. (1999) and (Saaty 1994).
11. Table 1 The studied factors and sub-factors
Mobility Public traffic network
Private traffic network
Proximity to urban center
Proximity to workplace
Community
Facilities
Education Facility
Medical and Health Facility
Retail Service
Sports Facilities
Green Space and View
Community
Social Capital
Sense of safety
Sense of belonging
Neighborliness
Density
12. Table 2 Definition of the attributes
Public traffic network
(PUT)
The public traffic network refers to the level of public transport system connected to the
neighbourhood
Private traffic network
(PIT)
The private traffic network means the level of private transport system of the neighbourhood,
like the convenience of private car parking and close to expressway exits.
Proximity to urban
center (PUC)
Proximity to urban center is the proximity to urban center where concentrates the commerce
and service trade of a city.
Proximity to workplace
(PTW)
Proximity to workplace refers to the proximity to employment for residents.
Education Facility (EDF) A high level of education facility refers to high quality of kindergartens, primary schools, high
schools and libraries near neighbourhood.
Medical and Health
Facility (MHF)
A high level of medical and health facility relates to the quantity and quality of clinics and
hospital near neighbourhood and the neighbourhood hygiene.
Retail Service (RES) The retail service degree relates to the presence of adequate number of shops, stores,
markets, and supermarkets.
Sports Facility (SPF) Sports facility refers to the presence of arena and gymnasiums near the neighbourhood.
Green Space and View
(GSV)
Green space and view refers to the closeness to garden, open areas, or lake and General
unobstructed view to surroundings.
Sense of safety (SES) Sense of safety is the degree of safety residents feel. a low degree of victimization
corresponds to a high degree of safety.
Sense of belonging
(SEB)
Sense of belonging to the community indicates the degree to which residents identify
themselves as part of the immediate larger housing community
Neighborliness (NBL) A friendly neighborliness means that residents are in good relation with their neighbors.
Density (DEN) Density refers to the satisfaction of residents to the density of the neighbourhood.
13. RESIDENCIAL ENVIRONMENT AND THE HOUING IN SUBURBAN CHINA
Community Facilities
Mobility
Community Social
Capital
Housing
ENVIRONMENT
Stage 1
Stage 2
Stage 3
METHOD
Literature
Review
Sub criterions AHP
(Export
Choice)
Questionnaire
Housing Environment
Satisfaction
Index of Importance of the
major housing issues
Relationship
CONCLUTION
SPSS
Relationship
Analysis
Figure 1 Conceptual framework of the study
14. Data Collection and Analysis
Questionnaire survey and interview are the main approaches for data
collection.
30 questionnaires will be delivered to housing experts and 150
questionnaires will be delivered to housing consumers.
Questionnaire surveys will be conducted primarily at face-to-face
basis.
Computer package ECproTM version 13 by Expert ChoiceTM Inc. will
be used for weighting value manipulation.
15. Pilot Test
32 questionnaires were sent and answered.
27 of them are for housing consumers (HC) in
Guangzhou, 5 of them are for experts (EP) in
housing industry.
72% of the responders have consistency radio of
0.1 or less, which is considered very well.
16. Table 3 The weights of factors on the housing environment by consumers
Factors Weight
Distance to Workplace 0.1390
Public Traffic Network 0.1195
Distance to Urban Center 0.1029
Retail Service 0.0911
Medical and Health Facility 0.0868
Education Facility 0.0862
Sense of security 0.0784
Green Space and View 0.0716
Sports Facilities 0.0589
Privacy Traffic Network 0.0569
Sense of belonging 0.0432
Neighborliness 0.0361
Density 0.0299
Total 1.0000
17. Figure 2 The weights of factors on the housing environment by consumers
The weights of factors on the housing environment by consumers
0.0000 0.0200 0.0400 0.0600 0.0800 0.1000 0.1200 0.1400 0.1600
Distance to Workplace
Public Traffic Network
Distance to Urban Center
Retail Service
Medical and Health Facility
Education Facility
Sense of security
Green Space and View
Sports Facilities
Privacy Traffic Network
Sense of belonging
Neighborliness
Density
18. Table 4 The weights of factors on the housing environment by experts
Factors Weight
Distance to Urban Center 0.1240
Public Traffic Network 0.1228
Distance to Workplace 0.1204
Neighborliness 0.1074
Retail Service 0.0846
Green Space and View 0.0840
Sense of security 0.0814
Sense of belonging 0.0572
Education Facility 0.0546
Sports Facilities 0.0482
Privacy Traffic Network 0.0444
Medical and Health Facility 0.0392
Density 0.0318
Total 1.0000
19. Figure 3 The weights of factors on the housing environment by experts
The weights of factors on the housing environment by experts
0.0000 0.0200 0.0400 0.0600 0.0800 0.1000 0.1200 0.1400
Distance to Urban Center
Public Traffic Network
Distance to Workplace
Neighborliness
Retail Service
Green Space and View
Sense of security
Sense of belonging
Education Facility
Sports Facilities
Privacy Traffic Network
Medical and Health
Density
20. Expected Outputs of the Research
Instead of measuring the monetary value of different
attributes in the market, the findings of this proposal is
hoped to understand the general demand pattern and
preferences of consumers in the housing market based on
multidimensional values and benefits.
It is hoped that the findings will offer more information for
urban planners and housing developers from a social and
cultural perspective.
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