Prediction Supply for High-Cost Multi-Storey House Toward The Development of Sustainable Cities

Authors

  • Fazilah Ramli
  • Rozlin Zainal
  • Rozilah Kasim

Keywords:

Housing overhang, housing oversupply , , high-cost multi-storey houses

Abstract

The absence of concerns about the problem of housing overhang in developer’s inventory can result in the growth rate of supply exceeding the growth rate of demand. The uncertainty of the economic sector at the international level has a direct impact on the economy in Malaysia, especially the real estate sector. This discussion paper applies literature review, analysis as well as regression approach to develop the supply prediction model for high-cost multi-storey houses. The published documents such as GDP per capita, inflation rate, unemployment rate and per centage of housing overhang for 10 years from beginning 2010 until 2020 are analyzed using secondary data analysis before regressed. Based on such findings, the GDP factor resulted in an increase in the percentage of house price supply for categories between RM 300,000 to RM 400,000, RM 600,000 to RM 700,000 and RM 800,000 to RM 900,000. Second, the factor of unemployment rate resulted in an increase in the percentage of house price supply for a category between RM 300,000 to RM400,000. Third, the factor of inflation rate results in an increase in the percentage of house price supply for categories between RM 400,000 to RM 500,000, RM 700,000 to RM 800,000 and above RM1,000,000. This model allows the developers to predict a better outcome for their housing development project besides to help them easily carry out the feasibility study phase without wasting time.

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Published

09-05-2022

How to Cite

Fazilah Ramli, Zainal, R. ., & Kasim, R. (2022). Prediction Supply for High-Cost Multi-Storey House Toward The Development of Sustainable Cities . International Journal of Sustainable Construction Engineering and Technology, 13(2), 100–109. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/IJSCET/article/view/11301

Issue

Section

Special Issue 2022: Knowledge Management

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