Leveraging artificial intelligence for optimising land value capture in urban regeneration projects in Malaysia
Sabariah Eni et al.
What the paper says
Purpose This study explores the integration of artificial intelligence (AI) into land value capture (LVC) mechanisms within Malaysia's urban regeneration initiatives. While LVC plays a crucial role in financing urban redevelopment, traditional methods face challenges such as inconsistent property valuation, inefficiencies in revenue collection, and governance limitations. This research investigates how AI-driven tools can enhance valuation accuracy, improve taxation frameworks and optimise decision-making to support equitable and sustainable urban development in Malaysia. Design/methodology/approach A quantitative research approach was employed, incorporating surveys, empirical case studies, and statistical modelling. The study analyses AI-driven applications such as, automated valuation models, and predictive analytics to assess their impact on LVC efficiency and financial sustainability in cities such as Kuala Lumpur and Johor Bahru. Findings The findings indicate that AI integration significantly enhances property valuation accuracy, optimises municipal revenue generation, and strengthens investor confidence in Malaysia's urban development sector. AI-driven predictive models help streamline land valuation, reducing discrepancies and improving transparency in taxation and urban financial planning. However, challenges such as regulatory gaps, data privacy concerns, and limitations in technical expertise hinder widespread AI adoption in Malaysian urban governance. Originality/value This study is among the first to examine AI's application in LVC within Malaysia's urban regeneration landscape, bridging the gap between smart urban governance and financial sustainability. By leveraging AI-driven methodologies, it provides a data-driven framework for optimising LVC strategies, offering valuable insights for Malaysian policymakers, urban planners and real estate developers in creating more efficient and equitable urban regeneration policies.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.