Evaluating the Effectiveness of Financial Distress Prediction Models in the Property and Real Estate Sector - Chandra Setiawan; Febriana Valentina Gultom (AIBPM Publisher, 2025)
Financial distress poses a serious threat to Indonesia’s property and real estate sector, requiring reliable prediction models to prevent bankruptcy and maintain economic stability. This study compares the predictive accuracy of four classical financial distress models (Altman Z-Score, Zmijewski X-Score, Springate S-Score, and Grover G-Score) using data from 30 IDX-listed firms (150 observations) from 2020 to 2024. A quantitative approach was applied through descriptive statistics, Kolmogorov-Smirnov normality tests, Kruskal-Wallis, and Mann-Whitney U tests, with cash flow patterns as the benchmark of financial distress. The results show significant differences among the models (p0.001), confirming H1, with the Zmijewski X-Score achieving the highest accuracy (79%), followed by Altman (76%), Grover (72%), and Springate (29%). The Zmijewski model’s logistic regression approach and emphasis on leverage make it the most effective predictor for firms in volatile market conditions, supporting H2. These findings highlight that model performance depends on economic context, emphasizing the need for continuous validation in emerging markets. The Zmijewski X-Score offers practical value for investors, managers, and policymakers in strengthening financial resilience and early distress detection.