Estimate The Demand Equation For A-1 Car Rental

Estimate the demand equation for A-1 Car Rental including relevant variables

A-1 Car Rental operates as one of two regional car rental agencies at a small Midwest airport, serving both airline passengers and local residents. Understanding the demand for rental cars is fundamental for managing fleet resources, planning marketing strategies, and maximizing revenue. To obtain a comprehensive estimate of the demand function, it is crucial to identify and select variables that influence customer traffic and rental decisions. This analysis involves reviewing available data, choosing appropriate explanatory variables, explaining their significance, predicting the expected signs of their relationships with demand, and performing regression analysis to quantify these effects. The goal is to better forecast demand patterns and enhance operational efficiency.

Selection and justification of variables for the demand function

In developing the demand equation for A-1 Car Rental, I propose the inclusion of the following variables:

1. Number of airline passengers arriving at the airport

This variable directly captures the segment of customers who are flying into the airport and potentially need rental cars. Since 40% of A-1 Car Rental's clientele consists of airline passengers, fluctuations in airline traffic will likely influence rental demand. A higher volume of arriving airline passengers generally increases the need for rental vehicles, making this an essential determinant. The expected sign of this variable’s coefficient is positive, indicating that as airline passenger arrivals increase, so does rental demand.

2. Distance from the airport to the college campus and city center

The proximity to key locations influences the convenience for potential renters. The offered data mentions the airport is within two miles from the campus and about six miles from the city center. Variations or changes in accessibility or local infrastructure might impact demand. For example, better connectivity or improvements in transportation could increase or decrease the need for rental cars. Given the current static distances, this variable could serve as a proxy for accessibility; if included in dynamic models, a positive relationship might be expected with demand due to ease of access or increased local demand from the campus and city travelers.

3. Customer type indicator (Airline Passenger vs. Local Dwellers)

This categorical variable distinguishes between airline passengers and local residents who rent cars for leisure or business trips. Since their motivations and trip characteristics differ, their rental patterns also vary. Typically, airline passengers might have more predictable demand tied to flight schedules, while local customers might show demand fluctuations based on local events, seasons, or economic factors. Including this variable helps to separate these demand segments. The expected relationship is positive, especially for airline passengers, but it might vary if local demand decreases during certain periods.

4. Day of the week or seasonality indicators

Rental demand often exhibits weekly and seasonal patterns. For example, demand might be higher on weekends or during holiday seasons. Incorporating variables representing days or months helps capture these cyclical fluctuations. The expected sign for these variables is generally positive during peak periods, such as holidays or summer months.

5. Economic activity indicator (local employment rate or regional economic index)

Local economic conditions influence discretionary travel and car rental needs. A higher employment rate or regional economic growth typically leads to increased leisure and business travel, raising rental demand. The expected sign of this variable is positive because better economic conditions tend to boost customer traffic.

6. Competitive factors or presence of alternative transportation options

Though more complex to quantify, variables reflecting the availability and pricing of alternative transportation (such as taxis, rideshare services, or public transit) can influence demand. An increase in alternatives likely diminishes rental demand, implying a negative relationship for variables measuring these options.

Estimating the demand function via regression analysis

Utilizing the selected variables, I will estimate the demand function using multiple regression analysis. The model can be specified as follows:

Demand = β0 + β1  Airline_Passengers + β2  Distance_Center + β3  Customer_Type + β4  Seasonal_Factors + β5 * Economic_Indicators + ε

Where:

  • Demand: Number of rentals per period
  • Airline_Passengers: Number of airline travelers arriving
  • Distance_Center: Proximity to city center and campus
  • Customer_Type: Dummy variable indicating airline passenger vs. local dweller
  • Seasonal_Factors: Variables capturing seasonal variations
  • Economic_Indicators: Regional economic activity measures
  • ε: Error term capturing unobserved factors

Interpretation of estimated coefficients

Once the regression model is estimated, each coefficient (β) will provide insights into how the corresponding variable affects rental demand:

1. Intercept (β0)

This represents the baseline demand when all explanatory variables are zero or at their reference levels. It captures the inherent demand for rentals independent of factors like airline traffic or seasonality.

2. Airline_Passengers (β1)

Expected to be positive, indicating that an increase in airline travelers correlates with higher rental demand. For instance, a 1% rise in airline passengers may increase rentals proportionally, depending on the estimated coefficient.

3. Distance_Center (β2)

Likely to have a positive sign if increased proximity to the city center and campus area encourages rentals, or negative if increased distance discourages rentals. Given the fixed distances, this coefficient may reflect accessibility-related demand sensitivity.

4. Customer_Type (β3)

This coefficient captures the difference in demand between airline passengers and local customers. A positive coefficient suggests airline passengers rent more frequently than locals, or vice versa.

5. Seasonal_Factors (β4)

Expected to be positive during peak travel seasons such as summer holidays or winter holidays. These variables help tailor demand forecasts to seasonal trends.

6. Economic_Indicators (β5)

Anticipated to be positive, reflecting increased demand during periods of economic growth or high employment, which stimulate travel and leisure activities.

Conclusion and implications

The estimated demand function will enable A-1 Car Rental to forecast future customer traffic more accurately and optimize fleet management. Recognizing significant variables and their impact helps in strategic planning, marketing, and operational adjustments. For example, if airline passenger numbers are highly influential, the company might focus efforts on partnerships with airlines or marketing during peak flight seasons. Similarly, understanding seasonal trends can guide inventory adjustments and promotional campaigns.

References

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