Analysis of Financial Distress Using the Modified Altman, Ohlson, and Grover Models in Property and Real Estate Companies Listed on the Indonesia Stock Exchange (IDX) During the 2022–2024 Period

Abstract

This study analyzes financial distress in property and real estate companies listed on the Indonesia Stock Exchange (IDX) during 2022-2024 using the Modified Altman Z-Score, Ohlson O-Score, and Grover G-Score models, and evaluates each model's predictive accuracy against firms' actual financial condition. Despite its strategic economic role, this cyclical sector faces post-pandemic pressures, including rising interest rates, inflation, and declining purchasing power, increasing financial distress risk and the need for early detection. This research used a quantitative descriptive approach with secondary data from audited financial statements of IDX-listed companies. Purposive sampling yielded 42 qualifying companies over the three-year observation period. Distress scores were calculated using the three models, compared through non-parametric Kruskal-Wallis and Friedman tests, and validated against actual financial condition, defined by consecutive net losses and non-dividend distribution. Results reveal significant differences among the three models' predictions, confirmed by both the Kruskal-Wallis and Friedman tests (Asymp. Sig. < 0.001). The Modified Altman Z-Score and Ohlson O-Score classified all sample companies as non-distressed, while the Grover G-Score identified five companies as distressed. Accuracy rates reached 71.43% for both the Altman Z-Score and Ohlson O-Score, and 83.33% for the Grover G-Score, making it the most effective predictor. These findings offer practical implications for management, investors, and policymakers: guiding early-warning systems for risk management and strategic decisions, informing investment decisions by flagging at-risk companies, and contributing evidence on model accuracy in Indonesia's post-pandemic property sector. Future research should extend the study period, cover other sectors, and add models for more comprehensive results.