Executive Cars
United States
(Core country: data based on in-depth analysis)
Vehicle Sales by make
in the Executive Cars market in thousand vehicles
Reading Support Executive Cars unit sales are expected to reach 556.0t in 2023.
Connected Cars Share
in the Executive Cars market in percent
Reading Support 91.72% of Executive Cars sold in 2019 are Connected.
Analyst Opinion
In the last decades, German brands, like Audi, BMW and Mercedes-Benz, have consistently dominated the Executive Cars segment. Although there are new players in the market today, this dominance would continue for the next few years. Sales will continue to be propelled by consumers in the upper-middle class: mid-senior and senior managers, as well as other successful professionals. Though they might cost a bit more than your average car, executive cars offer a touch of class and comfort to your driving experience.
Revenue by make
in the Executive Cars market in million US$
Reading Support Revenue in the Executive Cars market segment amounts to US$22,209m in 2019.
Average Price
in the Executive Cars market in US$
Reading Support The volume weighted average price of Executive Cars in 2019 is US$41,534.
Key Market Indicators
The following Key Market Indicators give an overview of the demographic, economic and technological development of the selected region on the basis of general KPIs. The calculation of Statista’s Market Outlook is based on a complex market-driver logic including over 400 region-specific data sets.
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Population in m Number of individuals (all ages) living in the selected region, the data reflect the United Nations' medium variant of World Population Prospects | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
0-14 years in m Number of individuals (age 0-14) living in the selected region, the data reflect the United Nations' medium variant of World Population Prospects | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
15-24 years in m Number of individuals (age 15-24) living in the selected region, the data reflect the United Nations' medium variant of World Population Prospects | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
25-34 years in m Number of individuals (age 25-34) living in the selected region, the data reflect the United Nations' medium variant of World Population Prospects | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
35-44 years in m Number of individuals (age 35-44) living in the selected region, the data reflect the United Nations' medium variant of World Population Prospects | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
45-54 years in m Number of individuals (age 45-54) living in the selected region, the data reflect the United Nations' medium variant of World Population Prospects | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
55+ years in m Number of individuals (age 55 and older) living in the selected region, the data reflect the United Nations' medium variant of World Population Prospects | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
Consumer spending (current) in US$ Average consumer spending per capita of private households in the selected region (in current prices, constant exchange rate) | Source: Statista, based on IMF, UN, World Bank, Eurostat and national statistical offices | ||||||||||||||||||||||
Transport in US$ Consumer spending per capita for transport (according to the Classification of Individual Consumption Purposes, COICOP) in the selected region (in current prices, constant exchange rate). This group inlcudes the purchase of vehicles, maintenenace of vehicles as well as transportation services. | Source: Statista, based on IMF, UN, World Bank, Eurostat and national statistical offices | ||||||||||||||||||||||
Consumer price index (CPI) Consumer price index (CPI) for a weighted basket of goods and services. The weights of the components vary by country according to local consumption patterns. The base year (100) has been set to 2017 for all countries, the base year of the input data may vary. | Source: Statista, based on IMF WEOD | ||||||||||||||||||||||
Price level (U.S.=100) Estimated price level index for all goods and services for individual private consumption (U.S.=100). Data has been extrapolated from the last iteration of the World Bank's International Comparison Program (ICP) using inflation data (GDP deflator). The indicator is valued at the current exchange rate of the respective year versus the US$. Numbers above 100 signal that prices are on average higher in this region than in the U.S., numbers below would mean prices in this region are on average lower than in the U.S.. | Source: Statista, based on World Bank, IMF, Eurostat and national statistical offices | ||||||||||||||||||||||
Transport Estimated price level index for transport (U.S.=100). This group inlcudes the purchase of vehicles, maintenenace of vehicles as well as transportation services. Data has been extrapolated from the last iteration of the World Bank's International Comparison Program (ICP) using inflation data (GDP deflator). The indicator is valued at the current exchange rate of the respective year versus the US$. | Source: Statista, based on World Bank, IMF, Eurostat and national statistical offices | ||||||||||||||||||||||
Recreation, culture Estimated price level index for recreation and culture (U.S.=100). Data has been extrapolated from the last iteration of the World Bank's International Comparison Program (ICP) using inflation data (GDP deflator). The indicator is valued at the current exchange rate of the respective year versus the US$. | Source: Statista, based on World Bank, IMF, Eurostat and national statistical offices | ||||||||||||||||||||||
Tax rates Click arrow to expand | ||||||||||||||||||||||
Corporate tax rate in % Displayed is the typical rate for corporate income. Due to local taxes, the overall tax rate may vary within the country. | Source: Statista, based on KPMG | ||||||||||||||||||||||
Personal income tax rate in % Displayed is the top marginal tax rate. Due to local taxes, the overall tax rate may vary within the country. | Source: Statista, based on KPMG | ||||||||||||||||||||||
Mobility Click arrow to expand | ||||||||||||||||||||||
Cars per 1,000 capita Number of passenger cars in the selected region in relation to the total population | Source: Statista, based on OICA | ||||||||||||||||||||||
Passenger km (road) in pkm Total movement of passengers on roads using inland transport on a given network. Data are expressed in passenger-kilometres per-capita, which represents the transport of each one passenger on a distance of one kilometre. | Source: Statista, based on OECD | ||||||||||||||||||||||
Rail lines in k km Rail lines are the length of railway route available for train service, irrespective of the number of parallel tracks. | Source: Statista, based on World Bank | ||||||||||||||||||||||
Passenger km (rail) in pkm Total movement of passengers by rail using inland transport on a given network, data are expressed in passenger-kilometres per capita, which represents the transport of each one passenger on a distance of one kilometre | Source: Statista, based on OECD | ||||||||||||||||||||||
Air passengers in m Number of passengers carried by air transport in the selected region | Source: Statista, based on ICAO, World Bank | ||||||||||||||||||||||
Pump price for gasoline / liter in US$ Fuel prices refer to the pump prices of the most widely sold grade of gasoline. Prices have been converted from the local currency using a constant exchange rate. | Source: Statista, based on World Bank, GIZ | ||||||||||||||||||||||
Pump price for diesel fuel / liter in US$ Fuel prices refer to the pump prices of the most widely sold grade of diesel. Prices have been converted from the local currency using a constant exchange rate. | Source: Statista, based on World Bank, GIZ | ||||||||||||||||||||||
Infrastructure Click arrow to expand | ||||||||||||||||||||||
Rail investment in % of GDP Infrastructure investment covers spending on new transport construction and the improvement of the existing rail network. | Source: Statista, based on OECD | ||||||||||||||||||||||
Road investment in % of GDP Infrastructure investment covers spending on new transport construction and the improvement of the existing road network | Source: Statista, based on OECD | ||||||||||||||||||||||
Airport investment in % of GDP Infrastructure investment covers spending on new transport construction and the improvement of the existing airport network. | Source: Statista, based on OECD | ||||||||||||||||||||||
Road maintenance in % of GDP Infrastructure maintenance covers spending on preservation of the existing road network | Source: Statista, based on OECD | ||||||||||||||||||||||
International trade Click arrow to expand | ||||||||||||||||||||||
Trade (% of GDP) in % Sum of exports and imports of goods and services in relation to total gross domestic product (GDP) | Source: Statista, based on World Bank, OECD | ||||||||||||||||||||||
Tariff rate in % Simple mean of applied tariff rates for all products in the selected region | Source: Statista, based on World Bank | ||||||||||||||||||||||
Households in m Total number of households in the selected region | Source: Statista | ||||||||||||||||||||||
Urban population share in % Estimated share of the total population in the selected region living in urban areas | Source: Statista, based on UN DESA and national statistical offices | ||||||||||||||||||||||
GDP (current) in US$ Gross domestic product (in current prices, constant exchange rate) of the selected region per capita | Source: Statista, based on IMF WEOD | ||||||||||||||||||||||
Source: Statista, November 2019 |
Methodology
We collect over 32 million data points from various sources, including but not limited to company reports and websites, vehicle registries, car dealers, and environment agencies. The data is modelled and forecasted using machine learning techniques in combination with the experience and knowledge of a team of international analysts, in order to produce the best market estimates and predictions.
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