The system of statistical indicators, which is necessary for the construction of mathematical and statistical models that reflect modern domestic trends in the development of the residential real estate market is explained. The official data from the Federal State Statistics Service (Rosstat), the Unified Interdepartmental Information and Statistical System (EMISS), the Central Bank of the Russian Federation (CBR), and the Unified Housing Construction Information System (UIIS) served as information sources for the empirical component of the study.Based on quarterly data for 2010–2021 using ARIMA and SARIMA models, a time series of residential real estate commissions in the Russian Federation was modeled and predicted for 2022. Both models make it possible to account for the influence of the seasonal component. Based on results of the time series regression analysis, the authors selected a mathematical and statistical model with the best approximating characteristics. To model the volume of commissioning of residential real estate in the Russian market, with due regard to the influence of macroeconomic factors, the ARMAX model was used, which has significant explanatory power.The results of the study presented in the article may be of interest to analytical agencies, developers, banking professionals, financiers, economists, analysts of the real estate market or related areas, as well as authorities for strategic planning of the development of the real estate market.

The research paper is devoted to developing a mathematical approach for dealing with time-varying parameters in rolling window logit models for credit risk assessment. Forecasting coefficients yields a better model accuracy than a trivial approach of using computed past statistics parameters for the next time period. In this paper, a new method of dealing with time-varying parameters of scoring models is proposed, which is aimed at computing the default probability of a borrower. It was empirically shown that in a continuously changing economic environment factors’ influence on a target variable is also changing. Therefore, forecasting coefficients yields a better financial result than simply applying parameters obtained by accumulated statistics over past time periods. The paper develops a new theoretical approach, incorporating a combination of the ARIMA class model, the DCC-GARCH model and the state–space model, which is more accurate, than using only the ARIMA model. Rigorous simulation testing is provided to confirm the efficiency of the proposed method.