Nowadays the leading trend in the construction industry is the creation of building information models of construction objects (BIM-models), which are able to accumulate information about all stages of design, construction of the object and its operation. Today the structure of BIM models includes several blocks that take into account architectural and planning decisions, the constructive part, the part of construction technology and organization, cost estimates and operation of objects. The article proposes to supplement the block “construction organization” with models for forecasting seasonality, since the organizational and technological parameters of the same type of processes can differ significantly when they are performed at different times of the year. The article suggests an algorithm for forecasting the effect of seasonality on construction processes, a database structure, which should accumulate data on deviations during construction, namely: deviations in cost, labor intensity and construction terms. The authors propose a methodical approach to identifying the effect of seasonal fluctuations on construction parameters and an approach to creating models that could be used for adjusting construction schedules, supplies of materials and technical resources, optimization of labor resources and planning of financial flows of construction projects. The proposed system will allow you to accumulate data on deviations of resources and financial flows of construction while constructing various objects and store them in the format of BIM models. The resulting database is the basis for creating dependencies that will allow taking into account the impact of seasonality on the construction parameters of similar objects or technologically similar works. Each season, the relevant database must be adjusted and supplemented. In addition, the digital model, if necessary, can provide information and practical experience on organizational, technological, financial, legal ways and methods of eliminating construction deviations and preventing risks