20 Jun WHAT TYPE OF INVESTMENTS WOULD INSTEAD NOT BE A GOOD IDEA?
Problem #1 (10 points)
The manager of a shopping mall has noticed a continuous drop in overall mall sales in the last few years. Therefore he decided that he might need to attract some new tenant shops. His quick glance at the tenant mix indicated that shops which sell clothing and footwear appear to be underrepresented in his tenant clientele. He knows that the majority of shoppers who come to his mall are people between 25 and 34 years old. The manager would like to identify the best rent per square foot of leasable area that he should offer to new tenants in the clothing and footwear sector. For that, he hired you as a real estate market analyst and asked you to calculate the purchasing potential of consumers in this age bracket for 2018.
(a) On the Bureau of Labor Statistics (BLS) website go to “Data Tools”. There find database on consumer spending collected from BLS’s Consumer Expenditure (CE) Survey. Both “one-screen data search” (green button) and “multi-screen data search” (yellow button) should be able to help you find the data you need. Spending on clothing and footwear is labeled as “apparel and services”. What are the annual expenditures for 2006-2016? Show results in a table and in a time series graph.
(b) Forecast the expenditure for 2018 using the moving average (MA) method. From the
(1) 2-year simple MA,
(2) 3-year simple MA,
(3) 2-year weighted MA,
and (4) 3-year weighted MA
methods which one should you use for your 2018 forecast? Why? Show and explain all calculations, and show the numbers you plug in to do your calculations.
Problem #2 (10 points)
A real estate investment company in Southern California is applying for a construction loan for a proposed new gated community with 250 single-family detached houses. If the loan is approved, it can start the construction as early as next week and have all houses completed and available for sale in 2018. The company’s research department recently performed a market study and identified Orange County, CA to be the main trade area that will draw the majority of households interested in buying the houses.
For both parts (a) and (b), show and explain you calculations, and show the numbers you plug in to do your calculations.
For the source of data, go to the US Census website, on the main page scroll all the way down and click on American FactFinder, then go to Advanced Search, and select your search criteria.
(a) What does the company’s research team project for the total number of households in 2018? The team used the regression forecasting technique. It obtained information on the historical numbers of households in Orange County, CA for recent three years, 2014, 2015, 2016, from the US Census Bureau website, 1-year estimates, based on the American Community Survey (ACS).
(b) What is the required capture rate for this project, assuming the demand for new housing comes only from the increase in the number of households in the trade area?
Problem #3(10 points)
Parts (a) and (b) of this problem are looking at the Location Quotient technique that is commonly used by real estate market analysts to see how concentrated a particular occupation, industry, demographic group, etc. is in a specific region as compared to a larger geographic area, such as the entire nation.
For both parts (a) and (b), show and explain you calculations, and show the numbers you plug in to do your calculations.
For the source of data, go to the US Census website, on the main page scroll all the way down and click on American FactFinder, then go to Advanced Search, and select your search criteria.
(a) You are interested in the concentration of employees in different industry sectors – in a metropolitan statistical area of your choice as compared to the entire USA. The US Census website has information on the number of employees in your base area in different NAICS sectors. When you do your analysis pick the most recent available year. Which year is that? What do the results tell you about potential real estate investment opportunities in your picked analysis area? What type of investments would instead not be a good idea?
(b) Now, do similar Location Quotient calculations, except this time based on the population distribution by age rather than employment by industry. Compare any county of your choice with the entire state in which it is located – what are the relative concentrations of the population in different age groups? What does this tell you about potential real estate investment opportunities? For example, you may find that your chosen county has a high concentration of the population above 65 compared to the state statistic, which may suggest a good potential for senior housing developments; and so on. Again, show the numbers for the most recent year with available data (which year is it?).
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