Bureau of Transportation Statistics Finance
Bureau of Transportation Statistics Finance
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In this report, we compare three major United States airports: Newark Liberty International Airport (EWR), John F. Kennedy International Airport (JFK), and LaGuardia Airport (LGA). We assess patterns in airport finances and budgets that change across time. These airports are all located in the New York metropolitan area, which is one of the largest and most densely populated areas in the United States. With a population of over 20 million people, these airports handle a great deal of passenger and cargo air traffic. Because these airports are in such close proximity, they influence and compete with each other for similar passenger traffic and domestic and international route opportunities. Since the airports serve such an extensive clientele for many travel purposes, these airports are ideal for examining how airports with similar traits (shared geographic location and similar surrounding densities) experience financial and budgetary patterns that emerge over time. According to 2023 data from the FAA, these three airports were among the top 12 in the United States for total operating expenses. Additionally, they were in the top 4 for passenger airline landing fees. These statistics further demonstrate the airports’ similarity regarding air traffic volumes, complex operations, and strategic location within the New York metropolitan area. In this study, we analyze data from 2013, 2018, and 2023 to provide a perspective on the financial and budgeting changes that occur over a ten-year period.
Revenue Composition Over Time
Revenue Composition Over Time
#Extract JFK, EWR and LGA from datasets
#JFK
JFK_revenue <- bind_rows(
data_2013 %>% filter(LOC_ID == "JFK"),
data_2018 %>% filter(LOC_ID == "JFK"),
data_2023 %>% filter(LOC_ID == "JFK")
)
#EWR
EWR_revenue <- bind_rows(
data_2013 %>% filter(LOC_ID == "EWR"),
data_2018 %>% filter(LOC_ID == "EWR"),
data_2023 %>% filter(LOC_ID == "EWR"),
)
#LGA
LGA_revenue <- bind_rows(
data_2013 %>% filter(LOC_ID == "LGA"),
data_2018 %>% filter(LOC_ID == "LGA"),
data_2023 %>% filter(LOC_ID == "LGA"),
)
# Combine airports into one data frame
all_airports <- bind_rows(JFK_revenue, EWR_revenue, LGA_revenue)
# Extract Year from FYE
all_airports <- all_airports %>%
mutate(Year = substr(FYE, nchar(FYE) - 3, nchar(FYE)))
all_airports$Year <- as.numeric(all_airports$Year)
# Revenue
revenue_data <- all_airports %>%
select(Year, LOC_ID,
`Total Passenger Airline Aeronautical Revenue`,
`Total Non-Passenger Aeronautical Revenue`,
`Total Non-Aeronautical Revenue`) %>%
pivot_longer(cols = -c(Year, LOC_ID),
names_to = "Revenue_Type",
values_to = "Amount")
ggplot(revenue_data, aes(x = factor(Year), y = Amount, fill = Revenue_Type)) +
geom_bar(stat = "identity") +
labs(
title = "Revenue Composition Over Time for JFK, EWR and LGA",
x = "Year",
y = "Revenue",
fill = "Revenue Type"
) +
scale_y_continuous(labels = scales::comma) +
scale_fill_manual(values = c("Total Passenger Airline Aeronautical Revenue" = "skyblue",
"Total Non-Passenger Aeronautical Revenue" = "lightgreen",
"Total Non-Aeronautical Revenue" = "lightpink")) +
facet_wrap(~LOC_ID) +
theme_minimal()
Figure 1: Revenue composition over time
Non-aeronautical revenue, non-passenger aeronautical revenue, and passenger airline aeronautical revenue are the three main revenue categories used to analyze the airport's revenue composition over time. Revenue reports from EWR, JFK, and LGA (figure 1) all show steady growth between 2013 and 2023, with JFK leading in annual passengers and also revenue generated (62 million annual passengers vs. 49 million at EWR and 32 million at LGA). Despite LGA having two-thirds the number of annual passengers compared to JFK, it brought in less than half of JFK’s revenue in 2013, 2018, and 2023. Revenue per passenger was closer between EWR and JFK than it was between LGA and either EWR or JFK, and the reason for this is that LGA serves a different market, primarily domestic flights.
Total Revenue from Terminal Food and Beverage Sales Over Time
Total Revenue from Terminal Food and Beverage Sales Over Time
# Plot Terminal Food and Beverage Revenue
ggplot(all_airports, aes(x = Year, y = `Terminal-food and beverage`, color = LOC_ID, group = LOC_ID)) +
geom_line(size = 1.5) + #
geom_point(size = 3) +
labs(
title = "Total Revenue from Terminal Food and Beverage Sales Over Time for JFK, EWR, and LGA",
x = "Year",
y = "Revenue ($)",
color = "Airport"
) +
scale_y_continuous(labels = scales::comma) +
scale_x_continuous(breaks = c(2013, 2018, 2023)) +
theme_minimal()
Figure 2: Airport revenue from terminal food and beverage sales
Newark International Airport (EWR) stood out among the NYC area airports for a major increase in revenue from food and beverage sales in 2023 (figure 2). LaGuardia also saw more moderate but still a noteworthy increase between 2018 and 2023 in this category. These airports saw increases in the food and beverage sales category due to the opening of new terminals at each (Terminal A at EWR in 2023 and Terminal B at LGA in 2022). These new terminals prioritize food and retail space over direct access to gates after security. JFK’s aging infrastructure led to smaller food and beverage sales, seeing a decline from 2018 to 2023. This shows the importance of upgrading amenities and infrastructure in airport terminals to the revenue generated from food and beverage sales.
Total Operating Expenses Over Time
Total Operating Expenses Over Time
cost_data <- all_airports %>%
select(
LOC_ID,
Year, # Place Year after LOC_ID for consistency
`Personnel compensation and benefits`,
`Communications and utilities`,
`Supplies and materials`,
`Contractual services`,
`Insurance claims and settlements`,
`Other Operating Expenses`
) %>%
pivot_longer(
cols = -c(LOC_ID, Year),
names_to = "Cost_Type",
values_to = "Amount"
)
ggplot(all_airports, aes(x = Year, y = `Total Operating Expenses`, color = LOC_ID, group = LOC_ID)) +
geom_line(size = 1.5) + #
geom_point(size = 3) +
labs(
title = "Total Operating Expenses Over Time for JFK, EWR, and LGA",
x = "Year",
y = "Total Operating Expenses",
color = "Airport"
) +
scale_y_continuous(labels = scales::comma) +
scale_x_continuous(breaks = c(2013, 2018, 2023)) +
theme_minimal()
Figure 3: Total operating expenses over time
All three airports show an upward trend in operating costs between 2013 and 2023 (figure 3), with JFK consistently having the highest total operating costs compared to EWR and LGA. EWR saw the biggest increase in annual operating costs, rising by nearly 400 million USD, a 67% jump from 2013 to 2023. JFK saw a more moderate rise of 24% over the same period. Notably, LGA had the most dramatic change, with operating costs more than doubling, reflecting the highest percentage increase among the three airports. These increased operating costs for LGA and EWR are likely driven by the major terminal redevelopment projects, while the high operating costs of JFK are likely due to the high maintenance costs of aging infrastructure. The most significant change in operational costs across all three airports was in ‘Insurance Claims and Settlements’.
Cost Composition Over Time & Total Expenses from Insurance Claims and Settlements
Cost Composition Over Time & Total Expenses from Insurance Claims and Settlements
#Total Composition Over Time
gplot(cost_data, aes(x = factor(Year), y = Amount, fill = Cost_Type)) +
geom_bar(stat = "identity") +
labs(
title = "Cost Composition Over Time for EWR, JFK and LGA",
x = "Year",
y = "Cost (USD)",
fill = "Cost Type"
) +
scale_y_continuous(labels = scales::comma) +
scale_fill_manual(values = c(
"Personnel compensation and benefits" = "lightblue",
"Contractual services" = "#E6E6FA",
"Communications and utilities" = "#FFDAB9",
"Insurance claims and settlements" = "#FFE4E1",
"Supplies and materials" = "lightgreen",
"Other Operating Expenses" = "lightpink"
)) +
facet_wrap(~ LOC_ID) +
theme_minimal()
#insurance claims
ggplot(all_airports, aes(x = Year, y = `Insurance claims and settlements`, color = LOC_ID, group = LOC_ID)) +
geom_line(size = 1.5) + #
geom_point(size = 3) +
labs(
title = "Total Expenses from Insurance Claims and Settlements",
x = "Year",
y = "Costs ($)",
color = "Airport"
) +
scale_y_continuous(labels = scales::comma) +
scale_x_continuous(breaks = c(2013, 2018, 2023)) +
theme_minimal()
Figure 4: Cost composition over time
Figure 5: Total expenses from insurance claims and settlements
Between 2013 and 2023, this cost category saw a substantial increase at JFK, EWR, and LGA, likely driven by post-COVID-19 insurance coverage spikes. In this category, JFK saw the largest increase in expenses from insurance claims and settlements, jumping from less than $15 million in 2013 to nearly $100 million in 2023. JFK and EWR saw larger increases in expenses from insurance claims and settlements than LGA, and this might be due to the larger international flight catalog at these airports compared to the broadly domestic portfolio of LGA.
From 2013 to 2023, all three airports experienced steady revenue growth in addition to rising operating expenses, reflecting increases in passenger volumes, redevelopment projects, and pressure due to rising costs after the COVID-19 pandemic. JFK consistently led in both passenger traffic and revenue, maintaining its position as the region’s primary international hub. Newark demonstrated a significant growth in both total revenue and operating costs, largely driven by recent terminal upgrades and expanded amenities, particularly in food and beverage. LaGuardia had the largest percentage increase in operating costs, reflecting major airport modernisation projects and the reconstruction of terminals. Insurance claims and settlements saw the most dramatic cost increases between 2013 and 2023 for all three airports, likely linked to post-COVID-19 insurance renewal costs. Overall, the data revealed that infrastructure investment and passenger volume are the strongest drivers of financial performance among New York’s major airports, and continued modernization is essential to sustain growth, control costs, and enhance non-aeronautical revenue.
Group project by Chloe Robinson, Ryan Swett and Joshua Grossman
Sources:
Form 127 – Airport Financial Reports. Accessed via: https://cats.airports.faa.gov/Reports/reports.cfm
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