The mass withdrawal from the tourism market and the bankruptcy of operators with many years of history show that there have been radical changes in this type of business. The digital economy, the rapid development of online services, aggregator sites change the structure of demand for travel, recreation and entertainment. First of all, it is necessary to take into account that the generation of zets becomes the most active consumer for these services. Since the global network has become the main source of information and a way of interaction for this age group, it is necessary to change the concept of supply. It is necessary to diversify the assortment sales matrix in addition to traditional vouchers. The expansion of the range of tourism products should be based not only on a deep analysis of market demand, but also on modern algorithms for processing large sets of consumer preference data. It is beneficial to take advantage of digital technologies. It is necessary to take into account a combination of two circumstances: on the one hand Internet surfing potential customers of tourist services, on the other the possibility of tour operators purchasing from search portals and sites aggregators of data on leads (lead), landing pages (Landing Page), CTR (Clickthrough Rate) and other tools of Internet marketing statistics. This work is devoted to the development of an algorithm for processing sets of data on consumer preferences and the development of an optimal market strategy for the supply of tourism services in a competitive business environment. Methods of stochastic optimization, mathematical theory of search for solutions in conditions of uncertainty, computer modeling have been used to solve the problem.

Apartments have become a new trend in the market over the past few years. This type of real estate is becoming more and more popular every year, both among investors and tourists. The purpose of the article is to make a forecast of prices for apartments in St. Petersburg. Research methods: description, comparison, analogy, generalization and analysis. In the course of the study, the dynamics of changes in prices per square meter of apartments in St. Petersburg for 2014-2019 is considered; the main factors influencing the price index of apartments in St. Petersburg are identified. The influence of the identified factors on the price indicator is analysed on the basis of multiple linear regression. The study showed that almost all factors have a fairly strong relationship with the resulting indicator. The most significant factors are identified, on the basis of which the final model of the cost of apartments is built. On the basis of the exponential smoothing method, an assessment of changes in factors in the forecast period was made and a forecast of apartment prices was made based on the obtained values. The study shows that apartment prices will rise in the coming period. The forecast of the cost of service apartments developed by the authors can have a positive effect when conducting real estate transactions in St. Petersburg.

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Modern technologies have penetrated into all spheres of human life. At the same time, digital technologies continue to develop at a rapid pace, some areas of activity have achieved high rates in the introduction and application of modern technologies. Digital platforms can be used by all participants in the real estate market. Services are actively used and developed that simplify the interaction between the developer, the buyer and banks, constructors to strengthen developers’ websites, technologies for construction and real estate management, services for appraising an apartment, obtaining a mortgage, legal support, checking debts, encumbrances and history of an apartment, tools for remote demonstration of objects. The ability to use, compare, choose the most appropriate service for a specific request is relevant in modern conditions. The aim of the work is to compare digital services in the field of real estate and make a forecast for the development of the retail segment. The work used the method of comparing the characteristics of the objects of study, correlation and regression analysis. As a result of the study, a sales price forecast for the retail segment was determined and the most informative digital platform in the real estate industry was identified.