An Analysis of Quality Attributes of Housing Environment in Guangzhou China, Using Expert Judgments...
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Transcript of An Analysis of Quality Attributes of Housing Environment in Guangzhou China, Using Expert Judgments...
An Analysis of Quality Attributes of Housing Environment in Guangzhou
China, Using Expert Judgments
Fan WuPhD Candidate
Dept. of Real Estate and Construction, the University of Hong Kong
Supervisor: Dr. L. H. Li
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.
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
Housing Environment
schools
services
neighbors
neighborhood environment
accessibility to employment
retails
clinics
sense of belonging
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)
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).
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.
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.
Research Methods:
Hedonic pricing model Geographic Information Systems (GIS) Analytic Hierarchy Process (AHP) Objective and subjective
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).
Table 1 The studied factors and sub-factors
Mobility Public traffic networkPrivate traffic networkProximity to urban centerProximity to workplace
Community Facilities
Education FacilityMedical and Health FacilityRetail ServiceSports FacilitiesGreen Space and View
Community Social Capital
Sense of safetySense of belongingNeighborlinessDensity
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.
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
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.
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.
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
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 WorkplacePublic Traffic Network
Distance to Urban CenterRetail Service
Medical and Health FacilityEducation FacilitySense of security
Green Space and ViewSports Facilities
Privacy Traffic NetworkSense of belonging
NeighborlinessDensity
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
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 CenterPublic Traffic Network
Distance to WorkplaceNeighborlinessRetail Service
Green Space and ViewSense of security
Sense of belongingEducation Facility
Sports FacilitiesPrivacy Traffic Network
Medical and HealthDensity
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.
References
Adriaanse, C. C. M. (2007). Measuring residential satisfaction: a residential environmental satisfaction scale (RESS). Journal of Housing and the Built Environment, 22(3), 287.
Association, A. P. H., & Housing, C. o. t. H. o. (1945). An appraisal method for measuring the quality of housing : a yardstick for health officers, housing officials and planners. New York: American Public Health Association.
Bender, A., Din, A., Hoesli, M., & Brocher, S. (2000). Environmental preferences of homeowners Further evidence using the AHP method. Journal of Property Investment & Finance, 18(4), 445.
Bontje, M. (2004). From suburbia to post-suburbia in the Netherlands: Potentials and threats for sustainable regional development. Journal of Housing and the Built Environment, 19(1), 25.
Brown, L. A., & Moore, E. G. (1970). The Intra-Urban Migration Process: A Perspective. Geografiska Annaler, 52(1), 13. Buzzelli, M. (2000). After the sprawl? Suburban pasts & futures in the Greater Toronto Area. City of Toronto Archives. Toronto. Urban History Review, 28(2),
47. Cervero, R. (2002). Induced travel demand: Research design, empirical evidence, and normative policies. Journal of Planning Literature, 17(1), 3. Cervero, R., & Wu, K.-L. (1998). Sub-centring and commuting: Evidence from the San Francisco Bay Area, 1980-90. Urban Studies, 35(7), 1059. Chapple, K. (2006). Overcoming Mismatch: Beyond Dispersal, Mobility, and Development Strategies. American Planning Association. Journal of the
American Planning Association, 72(3), 322. Chen, H. (2006). Neighbourhood compactness and residential built environmental performance : a study of contemporary housing in Guangzhou, China
University of Hong Kong, Hong Kong. Emmanuel, D. F., Jack, E. H., & Deborah, D. G. (2001). Estimating housing quality for poverty and development policy analysis: CWIQ in Ghana. Social
Indicators Research, 53(2), 137. Fang, Y. (2006). Residential Satisfaction, Moving Intention and Moving Behaviours: A Study of Redeveloped Neighbourhoods in Inner-City Beijing. Housing
Studies, 21(5), 671. Gober, P., McHugh, K. E., & Leclerc, D. (1993). Job-rich but housing poor: The dilemma of a Western amenity town. Professional Geographer, 45(1), 12. . Guangzhou housing construction plan 2006-2010 (2006). Ha, M., & Weber, M. J. (1994). Residential quality and satisfaction: Toward developing residential quality indexes. Home Economics Research Journal, 22(3),
296. Hays, R. A., & Alexandra, M. K. (2007). Neighborhood attachment, social capital building, and political participation : A case study of low-and moderate-
income residents of Waterloo, Iowa. Journal of Urban Affairs, 29(2), 181. Ho, C. D. (2000). An analysis of property-specific quality attributes for office buildings. University of Hong Kong, Hong Kong. Ho, D., Newell, G., & Walker, A. (2005). The importance of property-specific attributes in assessing CBD office building quality. Journal of Property
Investment & Finance, 23(5), 424.
Ji, K. (2006). Suburbanization and motorcar industry. New motorcar. Karien, D. (2007). Social Capital, Neighbourhood Attachment and Participation in Distressed Urban Areas. A Case Study in The Hague and Utrecht, the
Netherlands. Housing Studies, 22(3), 355. Kauko, T. (2006). What makes a location attractive for the housing consumer? Preliminary findings from metropolitan Helsinki and Randstad Holland using
the analytical hierarchy process. Journal of Housing and the Built Environment, 21(2), 159. Kellekci, O. L., & Berkoz, L. (2006). Mass Housing: User Satisfaction in Housing and its Environment in Istanbul, Turkey. European Journal of Housing Policy,
6(1), 77-99. Kevin, M. L. (2003). Social capital and the built environment: The importance of walkable neighborhoods. American Journal of Public Health, 93(9), 1546. Kleinhans, R., Priemus, H., & Engbersen, G. (2007). Understanding Social Capital in Recently Restructured Urban Neighbourhoods: Two Case Studies in
Rotterdam. Urban Studies, 44(5/6), 1069. Leishman, C. (2001). House building and product differentiation: An hedonic price approach. Journal of Housing and the Built Environment, 16(2), 131. Levine, J. (1998). Rethinking accessibility and jobs-housing balance. American Planning Association. Journal of the American Planning Association, 64(2),
133. Li, L. H. (2008). The physical environment and a "sense of neighborhood" in residential communities in Hong Kong. 26, 7. Li, S.-M., & Li, L. (Writer) (2006). Life Course and Housing Tenure Change in Urban China: A Study of Guangzhou. Li, Z., & Wu, F. (2006). Socio-spatial Differentiation and Residential Inequalities in Shanghai: A Case Study of Three Neighbourhoods. Housing Studies,
21(5), 695. Linneman, P. (1981). The demand for residence site characteristics. Journal of Urban Economics, 9(2), 129-148. Ma, K.-R., & Banister, D. (2006). Extended Excess Commuting: A Measure of the Jobs-Housing Imbalance in Seoul. Urban Studies, 43(11), 2099. Middleton, A., Murie, A., & Groves, R. (2005). Social Capital and Neighbourhoods that Work. Urban Studies, 42(10), 1711. Ottensmann, J. R. (1977). Urban Sprawl, Land Values and the Density of Development. Land Economics, 53(4), 389. Peng, Z.-R. (1997). The jobs-housing balance and urban commuting. Urban Studies, 34(8), 1215. Rindfuss, R. R., Piotrowski, M., Thongthai, V., & Prasartkul, P. (2007). Measuring housing quality in the absence of a monetized real estate market.
Population Studies, 61(1), 35. Rouwendal, J., & Meijer, E. (2001). Preferences for housing, jobs, and commuting: A mixed logit analysis. Journal of Regional Science, 41(3), 475. Sirmans, G. S., Macpherson, D. A., & Zietz, E. N. (2005). The Composition of Hedonic Pricing Models. Journal of Real Estate Literature, 13(1), 3. Steel, R. G. D., Torrie, J. H., & Dickey, D. A. (1997). Principles and procedures of statistics : a biometrical approach. New York: McGraw-Hill. Temkin, K., & Rohe, W. M. (1998). Social Capital and Neighborhood Stability: An Empirical Investigation. Housing Policy Debate, 9(1), 28. Wachs, M., Taylor, B. D., Levine, N., & Ong, P. (1993). The changing commute: A case-study of the jobs-housing relationship over time. Urban Studies,
30(10), 1711.