Segmentation of the Primary Residential Real Estate Market of Russia by Competition Sectors

As part of this final qualifying work, a study of the primary residential real estate market will be carried out at the national level (housing market in Russia) and at the regional level (housing market in the Moscow region) in terms of types of apartments and developer companies. For the residential real estate market of the Russian Federation, the influence of macroeconomic factors on the volume of commissioning of primary housing in the country will be assessed. Also, the predicted values of the volume of commissioned primary residential real estate for several years ahead will be modeled. In the regional primary housing market, depending on the location of an apartment in a particular sector, the cost of real estate will be analyzed depending on factors characterizing the comfort of housing, the state of the environment in the area, remoteness from infrastructure facilities, etc. The chosen topic is relevant for several reasons. First, over half of Russian citizens under the age of 35 see mortgage lending as an opportunity to improve their living conditions, and the average borrower is a household consisting of 1 or 2 certified workers with children (the most economically active type of household). The behavior of this type of consumer has a significant impact on market demand. Secondly, the traditionally stable Russian real estate market during the pandemic and the economic crisis of 2020 practically did not reduce key indicators (building area, level of demand for housing), therefore, it seems necessary to determine the reasons for such stability of this sector of the domestic economy. The high rate of development and the level of demand for primary residential real estate in certain regions of the Russian Federation determines the need for statistical analysis of these subjects. One of these regions is the Moscow region, which is in first place among the regions of the Russian Federation in terms of the area of housing commissioned, taking into account residential buildings built on land plots for gardening. Therefore, in a regional context, the housing market of the specified entity will be investigated. The aim of the study is to determine the macroeconomic factors affecting the volume of commissioning of primary residential real estate in Russia and to predict this parameter for several years ahead, as well as to identify the main variables that determine the cost of 1 square meter of primary housing in the Moscow region in various market sectors. The main preliminary hypotheses put forward at the beginning of the study: - The main factors that determine the cost of primary housing at the regional level are the variables responsible for the environment and comfort of real estate; - The most significant effect on the volume of housing commissioning in Russia is the price of oil and the exchange rate of the national currency. This work consists of the 3 chapters. Chapter 1 will provide an overview of the main sources for writing this study, developed a system of indicators and described the structure of the Russian residential real estate market. In Chapter 2, the regional primary residential real estate market will be divided into clusters, each of which will analyze the cost of apartments depending on many independent variables. Chapter 3 will provide a statistical analysis of macroeconomic factors affecting the real estate market in the Russian Federation. For statistical analysis in Chapter 2 (the regional housing market), spatial data on apartments in the Moscow region will be used. For the analysis in Chapter 3, time series (quarterly data from 2010 to 2020) are used for the main macroeconomic variables that potentially have an impact on the state of the residential real estate market. Determination of the factors affecting the cost of housing in the regional context will be conducted using the construction of the OLS regression. To identify the determinants of the volume of residential property commissioned at the national level, ARIMAX models will be built. SARIMA models and ARIMA models with dummy seasonal variables will be built to predict the values of the specified time series.