Determinants of corporate real estate financing choices in emerging Gulf and mature Asian markets

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Type of the article: Research Article

Abstract
Corporate real estate financing is a channel through which macro-financial volatility, regulation, and strategic orientation affect firms’ balance sheets. This study explains how firms in the United Arab Emirates, Saudi Arabia, and Singapore choose between leasing, owning, and hybrid property-financing structures and how these choices perform under uncertainty. The empirical framework combines Generalized Structural Equation Modeling with Monte Carlo simulation using macroeconomic and real estate data, latent constructs for strategic orientation, financial constraints, regulatory pressure, and perceived risk, and an outcome indicating the dominant property-financing structure. Measurement reliability is acceptable (Cronbach’s alpha 0.77–0.82, composite reliability 0.83–0.87, average variance extracted 0.57–0.62). Structural estimates show that strategic orientation (β = 0.36) and financial constraints (β = 0.41) have significant effects on property-financing choices, and regulatory pressure also contributes (β = 0.27), and perceived risk reduces the likelihood of ownership (β = −0.38) while mediating strategic and regulatory influences (indirect β = −0.13 and β = −0.17). Country context significantly moderates the impact of financial constraints (β = 0.12) and perceived risk (β = −0.10). Simulation results indicate net present values of 3.75, 2.80, and 4.10 million USD for the United Arab Emirates, Saudi Arabia, and Singapore. The study concludes that property-financing structure is a strategic decision and that the combined structural-simulation framework is a useful tool for analyzing corporate decisions in heterogeneous markets.

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    • Figure 1. Conceptual framework
    • Table 1. Reliability and validity
    • Table 2. Fornell–Larcker test
    • Table 3. Sensitivity ranking
    • Table 4. Descriptive statistics
    • Table 5. GSEM results
    • Table 6. Mediation and moderation
    • Table 7. NPV simulation results
    • Conceptualization
      Salah Kayed, Mohammad Ahmad Alnaimat, Abdulhadi Ramadan, Hanadi A. Salhab
    • Data curation
      Salah Kayed, Abdulhadi Ramadan
    • Formal Analysis
      Salah Kayed, Mohammad Ahmad Alnaimat, Hanadi A. Salhab
    • Investigation
      Salah Kayed, Mohammad Ahmad Alnaimat
    • Methodology
      Salah Kayed, Mohammad Ahmad Alnaimat, Abdulhadi Ramadan, Hanadi A. Salhab
    • Project administration
      Salah Kayed
    • Resources
      Salah Kayed, Hanadi A. Salhab
    • Software
      Salah Kayed, Mohammad Ahmad Alnaimat, Abdulhadi Ramadan
    • Supervision
      Salah Kayed
    • Validation
      Salah Kayed, Mohammad Ahmad Alnaimat
    • Visualization
      Salah Kayed
    • Writing – original draft
      Salah Kayed, Hanadi A. Salhab
    • Writing – review & editing
      Salah Kayed, Mohammad Ahmad Alnaimat, Abdulhadi Ramadan
    • Funding acquisition
      Mohammad Ahmad Alnaimat, Hanadi A. Salhab