Assessing Low-Cost Housing Demand in Melaka: PLS-SEM Approach

Noor Yasmin Zainun; Nadzirah Roslan; Aftab Hameed Memon

doi:10.4028/www.scientific.net/AMR.838-841.3156

Paper Titles

Study on Business Integration Strategy for Real Estate Development
p.3135

Government Supervision Game for Infrastructure PPP
p.3142

Analysis of Economic Benefit of Tiexi Works Village Steel Structure Move Back Houses in Shenyang
p.3147

Based on the AHP Method Analysis the Factors about the Quality of Construction Project
p.3151

Assessing Low-Cost Housing Demand in Melaka: PLS-SEM Approach
p.3156

Building Information Modeling (BIM) should be Promoted in Construction Firm
p.3163

Accident Compensation of Occupational Fatalities in the Construction Industry
p.3167

Research on Regionalization of Project Management Based on the Construction Enterprise
p.3171

Higher Education Program Development for Structural Building Information Modeling(S-BIM)
p.3176

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 838-841Assessing Low-Cost Housing Demand in Melaka:...

Assessing Low-Cost Housing Demand in Melaka: PLS-SEM Approach

Abstract:

Housing is one of the basic needs of human. Population in Malaysia is increasing and expected to reach up to 35 million in year 2020. This phenomenon creates high demand for housing. To tackle the squatter problems, the government introduces low-cost housing. Low cost house is known as the government house, where the price is cheaper but still comfortable. Although there are many of low-cost housing projects have been completed to cope with the need of the citizen especially for low-income group. However, census report reveled that these is huge demand of low-cost housing. This demand might be because of various factors which are very essential to identify in order to meet the required demand of low cost houses. Hence, this study is carried out to assesse the demand of low cost housing in Melaka, determine the significant factors affecting demand of low-cost housing, and establish PLS-SEM model for assessing factors affecting low-cost housing demand. In this study, data are collected by distributing questionnaire in Melaka state. The collected data from survey was analyzed using statistical software SPSS and presented in graphs and chart. Further, factors affecting low cost housing demand in Melaka were modeled with the SmartPLS v2.0. The model shows the relationship between low cost housing demand and its indicators. The finding of the study showed that most significant indicators affecting the demand of low-cost housing in Melaka are the economic factors which include housing stock, inflation rate and Gross Domestic Products (GDP). The Goodness of Fit showed that the model has substantial explaining power for the assessing factors affecting low cost housing demand in Melaka which the values is 0.481. This means that the economic factor has a great influence on the low-cost housing demand in Melaka.

You might also be interested in these eBooks

Civil, Structural and Environmental Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 838-841)

Pages:

3156-3162

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.838-841.3156

Citation:

Cite this paper

Online since:

November 2013

Authors:

Noor Yasmin Zainun*, Nadzirah Roslan, Aftab Hameed Memon

Keywords:

Housing Stock, Low-Cost Housing, Partial Least Square

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Zarina Mohd Zain (2010).

Google Scholar

[2] Utusan Malaysia (2nd March 2011). Kelantan perlukan 14, 000 rumah untuk rakyat miskin. Utusan Malaysia online. Retrieved from http: /www. utusan. com. my.

Google Scholar

[3] Yahya, K. and Abd. Majid, M.Z. (2002).

Google Scholar

[4] Department of Statistics, Malaysia (2007). Population and Housing Census, Malaysia 2007 (2007 CENSUS). Putrajaya: Department of Statistics, Malaysia.

Google Scholar

[5] Sirat, M., Che Mamat, A.F., Abd Aziz, A. R., Rahim, A., Salleh, H., and Hj. Yaakob, U., (1999). Low-cost Housing in Urban Industrial Centres of Malaysia: Issue and Challengers., Penerbit Universiti Sains Malaysia, Kedah.

Google Scholar

[6] Gail Makinen (2003). Inflation: Causes, Costs and Current Status. United States: Economy Policy Consultant, Government and Finance Division.

Google Scholar

[7] Department of Human Services (2013). Income. Paragraph 1. Retrieved from http: /www. humanservices. gov. au.

Google Scholar

[8] Molenaar, K., Washington, S., & Diekmann, J. (2000). Structural equation model of construction contract dispute potential. Journal of Construction Engineering and Management 126(4), 268–277.

DOI: 10.1061/(asce)0733-9364(2000)126:4(268)

Google Scholar

[9] Haenlein, Michael and Andreas M. Kaplan (2004), A beginner's guide to partial least squares analysis, Understanding Statistics, 3 (4), 283 – 97.

DOI: 10.1207/s15328031us0304_4

Google Scholar

[10] Rigdon EE (1998). Structural Equation Modeling., In GA Marcoulides (ed. ), Modern Methods for Business Research, p.251–294. Lawrence Erlbaum Association, London.

Google Scholar

[11] Chin, W. W., &Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed. ), Statistical strategies for small sample research, p.307–341.

Google Scholar

[12] MacCallum, R.C., & Austin, J.T. (2000). Applications of Structural Equation Modeling in Psychological Research. Annu. Rev. Psychol 51, 201–226.

DOI: 10.1146/annurev.psych.51.1.201

Google Scholar

[13] Hair, J.F., Anderson, R.E., Tatham, R.L., & Black, W.C. (1998). Multivariate data analysis. 5th ed. Prentice-Hall, Upper Saddle River, N. J.

Google Scholar

[14] Ringle, C.M., Sarstedt, M., & Mooi, E.A. (2010).

Google Scholar

[15] Ringle, C., Wende, S., Will, A., (2005) SmartPLS. Retrieved from http: /smartpls. de/forum.

Google Scholar