The attached dataset includes data on each county in the United States, as well as how
many Home Depot and Lowe’s locations there are in each county. The assignment (done
individually) is to assess how these two chains make decisions on where to locate stores,
whether they are different at all, and where they should consider opening new stores in the future.
For those who don’t know, both Home Depot and Lowe’s are large, warehouse-style home improvement stores. They carry tools, appliances, storage, lawn mowers, lumber, screws, lights, garden plants, and a large host of other supplies. They primarily serve three customer segments:
Contractors, who are hired by homeowners to build additions or do repairs, and who come to the store to buy supplies.
“DIY” or “Do It Yourself” consumers, which are typically homeowners or renters that want to take on home improvement tasks on their own (both stores offer classes in how to do many daunting tasks like install flooring).
Consumers looking for “finished” products, which could include appliances like refrigerators, but also lamps, area rugs, storage boxes, potted plants, etc.
The stores are typically free-standing and large – averaging nearly 120,000 square feet
with a 20,000 square foot garden center. While there are some differences, both store
chains are very similar – in number of units, in store size, in assortment, in pricing, and in
history (HD was founded in GA and Lowe’s in NC). Thus, they can be viewed as nearly –
though not perfectly – competitive imitators of each other.
The dataset contains the following information (in order in the data):
County identifiers, including name, an ID number, the state, and two region variables grouping states into generally accepted regions (one uses 9 regions and the other 4 regions).
The count of Lowe’s and Home Depot stores in each county. You may focus on how many stores the chain has in a given county, or on whether the chain has any stores in the county, or both. Interesting insights can be gleaned from either approach.
A set of demographic variables on each county for both 2000 and 2010, including:
The total population
Average household income
The percent of the county’s residents under the age of 18
The percent of the county’s residents with a college education
The percent of the residences in the county occupied by the homeowner
The population density (residents per square mile) The percent of the population that identifies as “Non-Latino White or Caucasian”, as well as the percent that identifies as “African-American or Black”
Be aware that some of these variables may be over dispersed (i.e., lots of observations at a very low level, with a small number of observations with VERY high levels). Taking the natural log (ln) of these variables may help them to be more normally distributed and make for better analysis, though this does mask the implications of any outlier counties.
Your assignment is to do the following three things in a single document with both text
and analytical output (you may use visuals and/or tables, as you prefer):
How do these two chains make their decisions about where to have store locations? What are the major criteria that drive this decision, and can you provide a very brief rationalization for each? Essentially, this question gets at how the two chains are similar in their decision making.
Are there ways in which the two chains are different in the types of locations they target? What are those differences, and why do you think that they may be apparent in the data? Characterize the targeting strategies for each of the two chains.
What counties appear underserved in the data, by one or both store chains? Where would you expect Home Depot to open its next 2-3 locations? Lowe’s?
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