<![CDATA[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.]]>
