Design and Evaluation of Smart Land Registry Systems for Urban Property Management

Thesis Abstract

The increasing demand for efficient, transparent, and secure land management systems in urban environments necessitates the development and evaluation of smart land registry platforms that leverage contemporary digital technologies. Traditional land registry systems have faced persistent challenges such as data inaccuracies, inefficient manual processes, susceptibility to fraud, and limited accessibility, which hinder effective urban property management and impede sustainable urban development. This study aims to design a novel smart land registry system tailored for urban property management, assess its functional performance, and evaluate its impact on data integrity, operational efficiency, and user satisfaction. The specific objectives include identifying core technological components required for an intelligent land registration platform, developing a prototype system grounded in blockchain and geospatial technologies, and evaluating its effectiveness through empirical testing. Employing a mixed-methods research design, the study combines qualitative and quantitative approaches to ensure comprehensive analysis. The qualitative component involves thematic analysis of interviews and focus group discussions with key stakeholders—including land registry officials, surveyors, property owners, and urban planners—to identify system requirements and user expectations. Quantitative data were collected via structured questionnaires administered to a purposively selected sample of 150 land registry officers and 200 property owners from a metropolitan city with a population of approximately 5 million residents. These data facilitate the assessment of user perceptions, technological readiness, and perceived benefits of the proposed system. The prototype system, developed using blockchain technology for immutable record-keeping and integrated with Geographic Information System (GIS) for spatial data management, was subjected to usability testing, performance benchmarking, and security analysis. The analytical framework employed includes descriptive statistics for initial data summaries, inferential analysis via regression modeling to identify factors influencing user acceptance, and ANOVA tests to compare perceptions among different stakeholder groups. The blockchain system's security and data integrity were evaluated through vulnerability assessments and transaction throughput testing, while user satisfaction was gauged through Likert-scale surveys. Findings are expected to reveal significant improvements in data accuracy, enhanced transparency, streamlined registration processes, and increased stakeholder trust. The study anticipates demonstrating that integrating blockchain and GIS technologies substantially reduces processing time by 40-50%, minimizes fraudulent record creation, and enhances data accessibility, thereby contributing to more effective urban property management. This research contributes to knowledge by providing a comprehensive framework for designing secure, scalable, and user-centric smart land registry systems, especially tailored for urban contexts with rapidly growing populations. It extends existing theoretical models such as the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory by incorporating blockchain-specific constructs like trust and transaction security. The methodological approach offers a replicable blueprint for urban land management institutions seeking to modernize their registration systems through digital transformation. The study concludes that the implementation of a smart land registry system leveraging blockchain and GIS technologies offers significant advantages over conventional methods, including enhanced data integrity, operational efficiency, and stakeholder confidence. It recommends policymakers and urban land authorities prioritize ICT infrastructure development, invest in capacity-building for staff, and adopt integrated digital platforms aligned with smart city initiatives. Future research should explore longitudinal evaluations of system deployment in diverse urban settings, scalability issues, and the integration of emerging technologies such as artificial intelligence for automated property valuation. Overall, this research provides a strategic foundation for advancing sustainable, transparent, and technologically driven urban land management systems in the digital era.