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People Boxes, Inc. is a consortium of real estate owners who seek out growing real estate demand among young, affluent, highly-educated workers entering the workforce or relocating to new cities.

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EOPLE BOXES, INC.
APARTMENT COMPLEX CONSORTIUM PLANNING
EXPANSION BASED ON DEMOGRAPHIC CHANGES
BRIEF
People Boxes, Inc. is a consortium of real estate owners who seek out
growing real estate demand among young, affluent, highly-educated
workers entering the workforce or relocating to new cities. They
specialize in condominium-style new construction, saving on costs by
reusing blueprints in different cities and working with national
contracting firms. Their revenue growth depends on identifying new
markets to expand into, filling a niche in high-demand, high-income
cities.
They have collected data on demographics and income in a number of
metropolitan statistical areas (MSAs) in the United States and would like
assistance in analyzing the data to provide some background
information and some conclusions on the underlying relationships of
income and age, education levels, and rental prevalence, as well as the
determinants and effects of the supply of housing stock. 1. Youth
– Is income different between young cities and old cities? For
this, define “young” as “between 14 and 24”, and find the
proportion of each MSA that is young. Then split the data into
2 groups. Test to see if there’s a difference in average
household income between the youngest MSAs and the
oldest MSAs. Test to see if there’s a difference in the variance
across each of these two sub-groups.
– Use regression methods to address each of the following
models:
a. Household income depends on the percentage of the
population that is young. – b. Household income depends on the percentage of the
population that is young and the housing stock per
capita.
c. The housing stock per capita depends on the
percentage of the population is young.
Which model is better between a and b? What variables are
significant? What is your interpretation? Consider the
residual plots. Are there any problems with these
regressions? Draw any conclusions and make any
recommendations you’d like to offer your client. 2. Retirement
– Does income vary with retirement? For this, define “retirees”
as “people age 65 and up”, and find the proportion of each
city that is retirees. Then split the data into 2 groups. Test to
see if there’s a difference in household income between the

 
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