Normal (typical) project duration is estimated at the initial stage of a renovation project and is an important reference for project control. However, its estimation has not been researched extensively owing to the complexity and uncertainties of renovation. Thus, a model was developed for predicting the duration of sustainable apartment renovation. Experts were asked to estimate a baseline schedule for extension-type renovation projects, factors that influence critical path activities, and the range of project durations considering these factors. An equation for estimating the duration of a renovation project was developed, and the range of project durations was derived using a MCS to reflect uncertainty. The proposed model was validated by applying it to actual cases. The case study shows that the model would be more suitable for complex renovation construction (i.e., more than two buildings or vertical extension). The model can be applied to various renovation projects and used as a reference for determining contract time. It can fill the knowledge gap of construction duration forecasting by adapting the concept of control activities to simplify the assessment of uncertainties in renovation of apartments, and can be applied for forecasting sustainable renovation time for other project types or in other locations.

The low productivity of the construction industry has been identified as a chronic problem, and this has been exacerbated by recent environmental factors, including COVID-19 and climate change. As a response to these environmental changes, policies related to recovery after COVID-19, digital transformation, and the Green New Deal have been established worldwide, and related projects have been launched. These three issues may seem to have different goals but they are interrelated, and guidelines for the direction of the construction industry should be outlined. In this study, key initiatives that need to be prioritized to successfully achieve the goals of the three issues are identified. Based on these, strategic directions and mid- to long-term measures to address environmental changes are suggested. This study surveys the perceptions of construction experts to establish the key initiatives via the analytic hierarchy process method, and their expected utility was evaluated. This study is significant in that it analyzes how construction experts perceive environmental changes and contributes to the development of the construction industry by identifying key initiatives that should be undertaken.

Operating profit is one of the most important measures in financial statements to evaluate a organizational performance. In the construction industry, however, the profit has a possibility to be misestimated as a loss and can be included in Unbilled Accounts Receivable (UAR) and shown as a profit; this is due to the uncertainty of predicting a total construction cost and project progress on which the calculation of profit is based. UAR results from the different perceptions regarding project progress between clients and contractors and can include costs related to loss that cannot be acknowledged as a progress. Therefore, UAR can be a significant clue to understanding estimation errors of a contractor’s financial performance data. This study investigated the possibility of estimation error of contractors’ operating profit by analyzing the relationship between UAR and other relevant financial performance measures. The accounting data of 41 Korean major contractors was collected and analyzed based on the correlation analysis. The results of this study implies that the profit of construction companies has the possibility to contain estimation errors, causing a significant variance in the process of adjusting the evaluation errors at the end of projects, which can cause unexpected losses to investors. In addition, this study found that the UAR containing estimation errors could be different depending on market in which contractors operate; therefore, when dealing with contractors’ financial performance data, it is necessary to discern whether their profit data contains distortion and, in the case that errors are included, appropriate data preprocessing should be conducted for more reliable and sustainable construction investment and project management.