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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Forecasting Housing Choices Selection In Penang, Malaysia

[Full Text]



Mohd Ali, N.H.S., Zainun, N.Y



Household, headship rate, choices of probabilities, prediction.



National Property Information Centre (NAPIC), Malaysia recorded that for the first half year 2018 there was a decline about 37.8% on the residential stock especially for new planned supply and an increment of 18.2% on the overhang units. However, in Penang, it was estimated the demand to reach a total of 46,740 units due to the growing number of populations, formation of new households and the replacement of existing houses. Crucial housing challenge in Malaysia, especially in Penang is majorly related to a mismatch in demand and supply for affordable housing. The goal of this research is to predict a housing demand in Penang based on 4 categories of housing which is consist of low-cost, low-medium cost, medium cost, and high-cost houses based on household formation. The research used Census Data 2010 from the Department of Statistic Malaysia to determine the headship rate and will use the Headship Rate Method to obtain household formation. A questionnaire was designed and approved by the expert before distributed to 400 households. The households were choose based on five districts in Penang and the respondents or household were divided according to 14 levels of age group which are; (1) 15-19 years old; (2) 20-24 years old; (3) 25-29 years old; (4) 30-34 years old; (5) 35-39 years old; (6) 40-44 years old; (7) 45-49 years old; (8) 50-54 years old; (9) 55-59 years old; (10) 60-64 years old; (11) 65-69 years old; (12) 70-74 years old; (13) 75-79 years old and (14) 80 and above. Multinomial Logit analysis was carried out to determine the choice of probability on house type selection and to produce a Choice Probabilities (CP), Model. The result analysis showed that Medium Cost housing was the most preferable type of house with the highest CP value of 0.4875. This prediction can assist local authorities, developers, consultants, contractors to plan which type of housing to construct based on demand in the future.



[1] Alias, A. R., Zainun, N. Y., & Rahman, I. A. (2018). Headship Rate Projections for Housing Demand in Headship Rate Projections for Housing Demand in Johor ,.
[2] Bayaga, A. (2001). Multinomial Logistic Regression: Usage and Application in Risk Analysis. Context, 5(2), 288–297.
[3] Chan, Y. H. (2011). Multinomial logistic regression. Singapore Medical Journal, 46(6), 259–269. https://doi.org/10.1016/j.cose.2005.05.003
[4] Economic Planning Unit. (2015). Eleventh Malaysian Plan (2016-2020). Eleventh Malaysian Plan. https://doi.org/10.1017/CBO9781107415324.004
[5] El-habil, A. M. (2012). An Application on Multinomial Logistic Regression Model. Journal of Statistics and Operation Research, 8(2), 271–291. https://doi.org/10.18187/pjsor.v8i2.234
[6] Furlong, F. (2016). Household Formation among Young Adults.
[7] Holmans. (2012). Household Projections in England : their history and uses.
[8] Hong, T. T. (2016). Affordable Housing for First-Time Homebuyers : Issues and Implications from the Malaysian Experience, 5921(March). https://doi.org/10.1080/14445921.2013.11104381
[9] Ibrahim, I. S., Zainun, N. Y., & Rawan, N. M. (2017). Head of Households in Terengganu, 3015, 1–6. https://doi.org/10.1051/matecconf/201710303015
[10] Iv, C. (1957). Headship Rate Method, 31–40.
[11] Kamal, E. M., Hassan, H., & Osmadi, A. (2016). Factors Influencing the Housing Price: Developers’ Perspective. World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 10(5), 1656–1662. https://doi.org/scholar.waset.org/1999.10/10004541
[12] Krejcie, R. V, & Morgan, D. (1970). Determining Sample Size for Research Activities, (Education and Psychological measurement), 607–610.
[13] Macdonald, S. (2011). Supply and demand in the Penang housing market : Assessing affordability October 2011, (October).
[14] Madhu, B., Ashok, N. C., & Balasubramanian, S. (2014). A Multinomial Logistic Regression Analysis to Study the Influence of Residence and Socio-Economic Status on Breast Cancer Incidences in Southern Karnataka. International Journal of Mathematics and Statistics Invention, 2(5), 1–8.
[15] Munir, S., Azlan, O., Fauziah, M. T., Ishak, I., Hasnah, H., Noor, H. A., & Wan Mohd Muhiyuddin, W. I. (2010). The State of Penang , Malaysia. The State of Penang, Malaysia: Self-Evaluation Report, (201 0), 107–120.
[16] NAPIC. (2018). Property Market Report: First Half 2018.
[17] Penang Institute. (2018). Penang Statistics Quarter 3, 2018. Retrieved from https://penanginstitute.org/resources/data-centre/97-quarterly-penang-statistics/
[18] Siniavskaia. (2018). Geography of Declining Young Adult Household Formations. Retrieved from http://eyeonhousing.org/2018/07/geography-of-declining-young-adult-household-formations/
[19] Willekens, F., & van Imhoff, E. (2015). Families and Households, Formal Demography of. International Encyclopedia of the Social & Behavioral Sciences: Second Edition (Second Edi, Vol. 8). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.31016-9
[20] Zeng, Yi, Lan Li, Zhenglian Wang, Helin Huang, and J. N. (2013). Effects of Changes in Household Structure on Future Housing Demand in Hebei Province , China, (2), 85–111.