Pricing in various areas of business industries depends on many dynamic internal and external factors, the correlation between which is not apparent at first glance. Nevertheless, the totality of external factors can often be explained by one that considers all of the above so that sellers can more quickly track the determinants of pricing, for example, for cars, and buyers more competently plan serious purchases. The pandemic has had a significant impact on economies worldwide, affecting every area of business and retail, especially international shipping. The population’s purchasing power has decreased due to the crisis, job cuts, and many other reasons. As a result, various restrictions were introduced at the federal levels in many countries, which indefinitely suspended the receipt of imported goods, completely closing the borders. Moreover, the automotive retail sector suffered significantly due to the above-stated reasons. Cars are often delivered to many countries using water transport (Gray, 2020). After the purchase, the buyer rarely receives his goods immediately: from the need to draw up documents, ending with the expectation of receiving a new car. Often, a new car is also sent by water transport. As a result, people can wait a very long time for their purchases due to pandemic restrictions.
Due to these difficulties, water transport, which guarantees the delivery of goods, begins to raise the price. Shipping costs are rising due to ships stuck in closed countries, causing shipping companies to suffer losses. In order to replenish the costs of new orders, prices are naturally increasing. Car dealers in the retail sector also suffer losses due to a decrease in the purchasing power of the population, and at the same time, have to pay more to transport companies (Kowalski et al., 2020). Consequently, the price of cars for the final consumer rises, even if the car’s production price remains the same. The topic of this work will be to find the relationship between the increasing terms of water transportation of goods and the retail price of classic non-electric vehicles. The dependent variable, in this case, will be the retail price of cars, and the independent variable is the time of delivery of goods by water, including vehicles. One should consider inflation, the political and economic situation in a particular country, and the development of personal electric transport. These factors will be considered and equated for different periods to obtain the model’s most excellent adequacy.
The problem is essential because of the potential for price predictions for buyers and car dealers. While the former can plan a long-term purchase and keep in mind the focus on prices and delivery times, the latter can better plan the business and adjust prices, coupled with all the complex reasons. Car dealers in the aggregate of a crisis may look for a way to maintain the prices of cars to attract more buyers with such offers. Nevertheless, for this work, regression analysis will be applied to find the correlation between the delivery time of cars to the final consumer and the price of cars. For the statistical adequacy of the application, several models of various foreign machine brands available in the United States will be analyzed. This method is well suited for this analysis due to its predictive function, the need for a small sample of information, and simplicity in calculations.
It turns out that the end consumer is forced to pay more and wait longer for their product. From this point of view, customer service suffers significantly, and against the background of a decrease in purchasing power, car dealers risk losing new customers. This situation requires a particular decision, which, possibly, will be partly explained by this study. First, car dealers will receive critical information about a possibly seemingly subtle external factor affecting pricing. Secondly, compromise solutions can also be reached, for example, in logistics, where the indicated correlation can be revealed.
Several foreign brands were taken for the assessment, namely Honda, Volvo, Toyota, and Fiat. Only imported brands have a sufficiently long delivery time and depend on water transport, prices, and terms in this area. For each brand, the latest innovations in models, which are no more than a year from the date of the announcement, were analyzed, and the prices of models for 2018-2019 before the pandemic. The selected cars differ in class, but each class will be considered separately. Pricing information will be taken from a single source; for example, a Volvo pricing page is shown in the references (CCP, 2021). If the delivery of cars by water transport to the United States from different European countries usually took from 14 to 28 days, then it increased by almost a month and a half during the pandemic (Ryu & Yeo, 2020). Information on the delivery of foreign brands of cars will also be taken from the official websites of dealers in the United States. For example, Volvo ships its cars to the US in 8-12 weeks; therefore, for this analysis, the average value for each car model will be taken (Volvo, 2021). The dynamics will be compared in the periods before the pandemic and now. It is worth noting that only new cars are taken into account, since the aftermarket of used cars, in part for the same reasons, is also experiencing an increase in car prices. There are many more determinants of prices in the secondary market, and therefore they are more challenging to track.
Below is Table 1, which shows the models selected for analysis. The columns show the prices for the cars, as well as the delivery time. Only those brands that include the delivery of cars by water are taken into account.
The R-squared value obtained in the course of the regression calculation is ultimately equal to 0.5396, which indicates the presence of this relationship. The Y-intersection in the coefficients is 9500, at which the number of delivery days should be zero. It is noteworthy that lower prices for cars, reaching such values, are much more common in the secondary market, where there is no need for delivery by water. Of course, it is worth bearing in mind the geographic remoteness of some car-making countries: Sweden, as a Volvo manufacturer, is farther from America, unlike Fiat, which is made in Italy. Distance, it can be seen, also affects the results obtained; however, it is one of several criteria for price formation. Solving the multicriteria problem requires additional research and the introduction of weight categories for each variable and is an idea for another study.
Otherwise, the price of cars in 2022 in the same classes as the selected models of 2018-2019 has naturally increased. It should be borne in mind that here too, additional factors were not only an increase in the cost of water transportation but also inflation, an increase in the cost of materials, and an improvement in the technical equipment of these models. Nevertheless, linear regression was seen both in a complete comparison of models of all brands and separately, where the R-squared values reached 0.98. Figure 1 below shows a scatter plot with a trend line showing the linear regression for this example.
As can be seen from the analysis, both mathematical and systemic, the delivery times for cars by water were increased due to the pandemic, which affected the prices of cars for the end consumer. As a result, other factors were identified that also impact pricing, but their cumulative effect did not prevent us from finding a correlation between the dependent and independent variables taken. For further research, it is necessary to increase the sample, carry out a more profound differentiation by class, technical characteristics of cars, the distance of the manufacturer, and the extension of the time frame of the selected models. Considering these factors in the tasks of a more multi-criteria analysis will require the determination of the weight categories for each selected variable. This tool can be handy for car dealers, allowing them to conduct a more precise analysis of the business area and the influence of surrounding external factors. Buyers, in turn, will be able to focus not only on the final prices on the websites of official dealers but also to correlate a set of external factors and news that may affect prices in the short or long term. As a result, consumers will be able to plan their car purchases more intelligently.
CCP. (2021). Volvo Car Prices USA, Volvo New Cars 2022.
Gray, R. S. (2020). Agriculture, transportation, and the COVID‐19 crisis. Canadian Journal of Agricultural Economics/Revue Canadienne D’agroeconomie, 68(2), 239-243.
Kowalski, D., Kowalska, B., Bławucki, T., Suchorab, P., & Gaska, K. (2019). Impact assessment of distribution network layout on the reliability of water delivery. Water, 11(3), 480.
Ryu, Y., & Yeo, G. T. (2020). A study on the evaluation of the functionality of shipping logistics platform using IPA analysis. Journal of Navigation and Port Research, 44(1), 32-43.
Volvo. (2021). Volvo Car USA Support.