Nowadays in many large industrial enterprises, one of the motivational incentives for hiring employees, as well as for encouraging them, is the provision of temporary or permanent use of residential real estate. This raises the question of choosing the best option of the property from a variety of proposals on the market. This article addresses the issue of estimating the value of residential property in the city of Krasnoyarsk. The descriptive signs of the apartment were not only its internal parameters, such as the area or number of rooms, but also the external characteristics that describe the environment of the apartment house. The data on the apartments were taken from the website of the apartment sales announcements and from various open data sources. The number of organizations of each type considered within a radius of 1000 m serves as a quantitative measure of the house environment. The model built using a random forest showed good results and solved the problem. The relative error of the forecast was 8%. In addition, it was shown the positive impact of the apartment external characteristics on the quality of the constructed model. As a result, an easily scalable model was built that can be applied to other cities.

The problems of complementarity of the subordinate elements of the system of modeling and improving the mathematical accuracy of assessing the effectiveness of capital-intensive and long-term investment projects in the agricultural sector are presented. The solution to the problem of strict interdependence of model blocks is to create a rearrangeable and complemented cognitive optimization system for the target base center. The model consists of eight blocks, four of which are resource modeling blocks, and four are organizational and economic modeling blocks. The basis of the target base center is a cognitive system based on the work of the stochastic and deterministic method, which allows smoothing out complementarity and at the same time not going beyond the boundaries of established efficiency. In an unsteady economy, investment analysis must take into account the factors that change the value of money over time, uncertainty and risk. This requires a comprehensive and systematic analysis based on the logical and mathematical concept of the time value of money and discounting when forming the appraisal apparatus of investment projects. The principle of a logical and mathematical concept is proposed, which consists in the fact that only the accumulated amount can be discounted, not the capital gain. Arguments are presented in favor of the fact that it is mathematically incorrect to compensate for the effect of inflation, risk and the alternative cost of capital on cash flows of a real investment project by discounting cash inflows.

Climate change is adversely affecting smallholder farming households in Eritrea mainly due to the dependence their livelihood to the climate-regulated activity. This study examines the degree of vulnerability of smallholder farming households in Eritrea using a Vulnerability Livelihood Index (VLI). Major components of vulnerability to climate change were identified as Exposure, Sensitivity and Adaptive Capacity. More than 88% of the farming households were found to be vulnerable or highly vulnerable to climate change as a result of the combined effect of their exposure to external factors, sensitivity to internal factors, and lower adaptive capacity. Female-headed households and those belonging to disadvantaged low-income groups were more vulnerable and in need of being preferentially targeted by policy measures. Improving human resource development by focusing on education and health, and enhancing adaptive capacity by focusing on access to food and water can develop the resilience of the farming households.

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The article considers the issue of pricing for the secondary real estate market regarding local causes (physical properties of housing). The aim of the study was to verify the following hypotheses: the influence of the pricing factors of residential real estate on its value is determined by its price segment and the influence of infrastructure on the value of apartments in different cities is the same. In the hypothesis test, data were used on the secondary housing market of the cities of Novosibirsk and Krasnoyarsk, taken from the site of «CIAN» apartment sale announcements and from various open data sources. During the study, non-parametric methods of machine learning, model-agnostic methods for the interpretation of predictive models, hierarchical clustering are involved. As a result of the work, the first hypothesis was confirmed and the second hypothesis was refuted, the high accuracy of forecasting the cost of an apartment was achieved, and the peculiarities of price formation for secondary housing objects were revealed and described.