A company intends to build a racetrack for exotic cars in the USA. This racetrack is designed to target high-net worth individuals who are passionate about sports cars and car racing. The racetrack, after building, will partner with local exotic car dealerships and car clubs to host racing shows. Also, this racetrack will allow customers to become members so they can use this track to race too. This company will also build peripheral infrastructures that would provide both a luxury/convenient place to have fun, and enough support for car racing. This company wants to decide the potentially good regions to build this project. At this point this company only needs to know how big the market can be and where the market is, so factors like building cost, opportunity cost, regulations, and etc. are not of its concern
In this paper, I will find the regions that have large high net worth population, high sports car parc, and good weather for car racing that provide big enough market. Then I will use demographical data including income and age to find out how many potential customers will this racetrack have
This dataset contains more than 500,000 records of used car sales on craigslist, containing all used car transactions ever happened on craigslist, collected in January 2020.
This dataset features 25 variables including craigslist region, entry price, entry year, model of vehicle, condition of vehicle, generic type of vehicle, state of listing, and etc. I will mainly use type and price to filter exotic sports cars out, and sort them by state and county to find which state has the most exotic cars sales, and therefore find the ideal region to start the car club. Car condition might be a useful factor, reflecting if the car is heavily used for racing and fast driving, or if it is carefully maintained as a collection or commute tool.
1.2 2017 Demographical Data by Counties
This data is collected from the 2017 census, it contains demographical information of 3220 counties. It features variables including population, ethnic population, employment status, professions, income per capita, and means of transportation. These are all useful information to help find out the number of potential customers in each county. It is obvious that the counties with high income per capita and high population drive to work are likely to have more potential customers, but whether other factors including ethnic, professions, and gender would have a relationship with tendency to become customers require further research. Unfortunately, I could not find any data about age group, but I will keep looking for it. The age group will be very useful in defining the number of potential customers.
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