Smart Asset Lifecycle Platform for Real Estate Portfolio Optimization

Thesis Abstract

This study investigates how a Smart Asset Lifecycle Platform (SALP) can enhance real estate portfolio optimization by integrating asset management processes with real-time data analytics, predictive maintenance, and scenario-based investment planning to address inefficiencies in capital allocation, risk management, and performance measurement. The problem addressed is the misalignment between asset-level operational data and portfolio-level strategic decisions, which often leads to suboptimal investment timing, higher carrying costs, and diminished asset value in volatile markets. The aim is to develop and empirically validate a technology-driven platform that automates data integration from multiple sources, applies asset-level and portfolio-level optimization techniques, and supports decision-makers in achieving risk-adjusted returns. Specific objectives include (1) designing an architecture for SALP that harmonizes property-level information systems with portfolio management dashboards, (2) evaluating the impact of SALP on capital expenditure prioritization and lifecycle cost control, (3) assessing how predictive maintenance and condition-based monitoring influence occupancy performance and net operating income, (4) developing a multi-criteria optimization model that balances liquidity, yield, and risk under different market scenarios, and (5) validating the platform through a mixed-methods study in a real estate investment firm. Methodologically, the study adopts a sequential explanatory mixed-methods design. The population comprises 15 real estate portfolios managed by a regional property investment firm and 120 portfolio properties across office, retail, and mixed-use categories. A purposive sample of 12 portfolio managers and 8 senior asset managers provides qualitative insights, while quantitative data are collected from 5 years of historical asset-level records, including maintenance logs, CAPEX approvals, occupancy rates, rental income, and market risk indicators. Data collection instruments include structured surveys for managers, semi-structured interviews guided by SALP components, asset performance databases, and system-generated logs from a prototype SALP implementation. Validity and reliability are ensured through triangulation, pilot testing of instruments with 2 portfolios, and Cronbach’s alpha analysis for survey scales (targeting ? ? 0.80). The SALP prototype integrates data from building management systems, property accounting, leasing databases, and external market data feeds, enabling real-time dashboards and a modular optimization engine. Analytical techniques comprise descriptive statistics for baseline characterization, regression analysis to quantify relationships between predictive maintenance schedules, occupancy performance, and net operating income, and time-series forecasting (ARIMA and Prophet) to project cash flows under multiple scenarios. A multi-objective stochastic optimization model using a Pareto frontier approach is employed to optimize lifecycle costs, capital allocation, and risk-adjusted returns, incorporating constraints such as liquidity requirements, debt covenants, and regulatory compliance. The theoretical frameworks guiding the study include the Resource-Based View (RBV) for capabilities in data-driven asset management and the Real Options theory to capture managerial flexibility under market uncertainty. Thematic analysis of interview data informs platform design preferences and organizational barriers to adoption, ensuring practitioner relevance. Expected findings indicate that SALP reduces unnecessary CAPEX by 12–18% and lowers annual maintenance costs by 8–15% through predictive maintenance and condition monitoring. The platform is anticipated to improve portfolio internal rate of return (IRR) by 3–6%, increase occupancy stability, and strengthen risk-adjusted performance through scenario-based capital budgeting and liquidity-aware optimization. The study contributes to knowledge by operationalizing an integrated SALP that connects asset lifecycle data to portfolio decision-making, advancing theory at the intersection of information systems in real estate asset management and financial optimization under uncertainty. It provides a transferable methodological blueprint for implementing technology-enabled asset lifecycle platforms in diversified real estate portfolios. In conclusion, SALP demonstrates significant potential to transform real estate portfolio optimization by delivering data-driven, agile, and transparent decision support. Recommendations include scaling SALP architecture to multi-regional portfolios, investing in data governance and interoperability standards, expanding scenario libraries to reflect climate-related risks, and pursuing longitudinal studies to assess long-term performance impacts and organizational learning effects.