THE EFFECTIVENESS OF MANAGEMENT CORPORATION AND ITS IMPACT ON DEVELOPMENT VALUE: A MACHINE LEARNING APPROACH
CHIAM WEI XIANG CHRISTOPHER
CHIAM WEI XIANG CHRISTOPHER
Citations
Altmetric:
Alternative Title
Abstract
A strong positive relation exists between corporate governance and a firm’s value, hence, suggest for the need of good corporate governance. Good corporate governance had been characterised by both quantitative and qualitative measures such as board size, independence and increasingly diversity. Similarities amongst the Management Corporations (MC) and a company suggest similar performance in real estate, driving residential property prices.
This research is the first of its kind to evaluate the effectiveness of MC by applying tenets of good corporate governance as observed in corporate finance, before evaluating its impact on residential prices. While the determinants of private residential prices had been well studied, literature on estate/property management is scarce. Findings from this study will bridge the gap and empower subsidiary proprietors (SP) to be stewards of their property value through active participation in the MC.
This study will construct a predictive model using machine learning – Random Forest Regression – which predicts the endogenous determinant of private residential using historical prices and data collected from a questionnaire.
A total of 192 MCs that was constituted when the Building Maintenance and Strata Management Act (BMSMA) was in force in July 2005 till Dec 2020 took part in the questionnaire. Feature selection was performed before constructing the preliminary model. The model was then validated and tuned to ensure its robustness.
The model predicts that (1) Managing Agent’s (MA) tenure, (2) Chairman’s Tenure and (3) Age Spread as the top three important determinants of private residential property prices. The turnover of MA and Chairman safeguards against familiarity threat and draws fresh perspective to the MC. The introduction of diversity enables a holistic consideration of issues and managerial decisions that may preserve and/or increase the property prices.
Keywords
2020/2021, Real Estate, Bachelor's, BACHELOR OF SCIENCE (REAL ESTATE), Yu Shi Ming, BMSMA, corporate governance, data science, diversity, governance, machine learning, management, management corporation, property management, Research Subject Categories::INTERDISCIPLINARY RESEARCH AREAS, strata title
Source Title
Publisher
Series/Report No.
Collections
Rights
Date
2021-04-15
DOI
Type
Dissertation