ЕКОНОМІКО-МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ ЦІНОУТВОРЕННЯ НА ВТОРИННОМУ РИНКУ АВТОМОБІЛЕЙ

Abstract

Today, in an era where mobility is highly sought after, owning a car has become a necessity to avoid reliance on alternative modes of transportation. However, the cost of purchasing a new car and the expenses associated with its maintenance are considerably high. That`s why a significant number of individuals and companies have shifted their attention towards the secondary car market. Consequently, it has become imperative to conduct a comprehensive analysis of the used car market, develop effective pricing models, and formulate strategic pricing approaches for car dealerships and other key participants in the market. In order to address these challenges, the authors of this study have implemented advanced statistical methods to meticulously examine the numerical data at hand. The technique of multi-factor linear regression has been employed to forecast the price formation trends in the secondary market. Additionally, econometric methods have been utilized to assess the precision of the price indicators. Moreover, a meticulously devised model has been extensively analyzed and formulated to accurately calculate the optimal price for used cars in the secondary market. The economic advantage of developing such a formula lies in the ability to effectively compare the approximate value of a used car obtained from the secondary market with that of a brand-new vehicle for the company. The findings of this study aim to guide individuals and automotive dealerships in making well-informed decisions by selecting the most suitable options based on their cost and quality. The developed pricing formula offers a distinct economic advantage by facilitating a direct comparison between the approximate value of used cars in the secondary market and that of new vehicles available to companies. In summary, this comprehensive study provides invaluable insights into the intricate dynamics of pricing within the secondary vehicle market. By shedding light on these dynamics, it significantly contributes to the informed decision-making process for various participants in the market, ultimately leading to better outcomes for all stakeholders involved.