Determination of corporate real estate composition and performance for selected Malaysian Public Lised Companies

• Main Author: Sulaiman, Nurul Nabila
• Format: Thesis
• Language: English; English; English
• Published: 2017
• Subjects: HD Industries. Land use. Labor; HD101-1395.5 Land use Land tenure
• Online Access: http://eprints.uthm.edu.my/7841/2/24p%20NURUL%20NABILA%20SULAIMAN.pdf; http://eprints.uthm.edu.my/7841/1/NURUL%20NABILA%20SULAIMAN%20COPYRIGHT%20DECLARATION.pdf; http://eprints.uthm.edu.my/7841/3/NURUL%20NABILA%20SULAIMAN%20WATERMARK.pdf; http://eprints.uthm.edu.my/7841/
• Institution: Universiti Tun Hussein Onn Malaysia

Corporate world in Malaysia has few of CREM knowledge and CRE performance measurement. However, these public listed companies owned a lot of CRE as one of their asset. The way of this company managing their CRE could not been identified. CRE in the company is not fully utilised as CRE is recognised as a cost contribution to a company and not as a profit generator. However, it is difficult to convince the management without a proper fact and figure. Furthermore, the management in a company makes a decision based on the financial measure performance and justification. Therefore, this research's aim is to examine the composition of CRE ownership in selected public listed company and to evaluate the relationship of CRE performance with the company performance. Descriptive analysis was used in this research to examine the composition of CRE in 39 public listed companies. Industrial product shows the highest composition of CRE ownership for plant and machinery, building and land. Panel data regression was used to evaluate the relationship of CRE performance with company performance. Results from Hausman test and Breusch-Pagan Langrage Multiplier has indicated that only CRER and PP/BV have significant impact to company's ROE. Results from this research can be an evidence to prove that there is a significant relationship between CRE performance and company performance. In future, this research can be improved by using a bigger sample and longer time frame data.