APPLICATION OF TIME SERIES ANALYSIS METHODS FOR FORECASTING PRICING IN THE REAL ESTATE MARKET

Abstract

The paper presents theoretical and methodological approaches to forecasting pricing in the real estate market based on the study of stochastic models of time series, which are statistical samples of prices of real estate in certain segments of this market generated consistently over time. The main stages of the research of time series in the real estate market are given. Two main components of time series of real estate prices are distinguished - fundamental (regular) and random (irregular). The fundamental (regular) component of the time series contains trend, cyclical and seasonal trends. The random component is noise, which slightly deviates the value of the time series from the trend. The work analyzed the fundamental price-forming factors in the real estate market, which have the greatest influence on the formation of the real estate price and determine its trend. The results of the analysis of time series of prices on the secondary real estate market in the traditionally most active business cities of Ukraine (Kyiv, Kharkiv, Dnipro, Odesa, Lviv, Ivano-Frankivsk) in the period 2016-2023 are presented. It is shown that prices on regional real estate markets are formed under the influence of both general trends throughout the country and random crisis phenomena inherent in this region. In the regional section of the analyzed real estate markets, on average, a growing trend is observed, which is stimulated by the increasing solvent demand for residential square meters. At the same time, it is of particular interest to study the behavior of real estate market prices during periods of crises caused by extraordinary events and conditions (pandemic and war) observed in 2020, 2022 and in the current year 2023. If the coronavirus, as a crisis phenomenon, affected the dynamics of regional real estate markets more or less equally, then the impact of a full-scale war manifested itself differently in different regions of our country. In front-line cities that suffer quite a lot from shelling (for example, Kharkiv), the real estate market reacted by lowering prices and demand. At the same time, in the western regions (for example, Lviv, Ivano-Frankivsk), the cost of square meters has increased significantly, which is due to a sharp increase in demand due to temporarily displaced and evacuated persons. Further directions of time series research for forecasting pricing in the real estate market have been determined.