Application of Business Information Management in Cross-border Real Estate Project Management

Abstract

Cross-border real estate project management is inherently challenging due to its complexity and diversity. This study investigates the efficacy of business information management systems (BIMS) in managing such projects and employs machine learning models for performance prediction analysis. Utilizing data from 250 valid questionnaires and 15 in-depth interviews, multiple regression analysis, classification algorithms, and clustering analysis models were applied. The results indicate that system quality, information quality, and service quality significantly enhance project management efficiency and user satisfaction. Specifically, the adoption of BIMS reduces average project completion time and cost overrun rates, thereby improving management effectiveness. Commercial real estate projects reported the highest average investment at $70 million, mixed-use projects exhibited the longest average completion time of 25 months, and residential real estate projects achieved the highest management efficiency score, averaging 8.0. The regression model's coefficient of determination (R²) was 0.68, the classification model achieved an 85% accuracy in identifying risk factors, and clustering analysis categorized projects into high-efficiency management, risk-concentrated, and resource-intensive types. These findings underscore the substantial value of BIMS in cross-border real estate project management, providing robust management tools and decision support. However, the study's limitations include a small sample size and restricted data sources. Future research should aim to expand the sample size and incorporate more diverse data sources to enhance the findings' generalizability and accuracy.