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Prepare an analysis of housing affordability in the area surrounding the company

 Real Estate Pricing Econ 3100

The Issue You are a statistical analyst for a company in the Puget Sound area that hires employees both locally and from outside the area. The Human Resources Director of the company believes that the high cost of housing is making it difficult for the company to attract and retain employees. The Director has asked you to prepare an analysis of housing affordability in the area surrounding the company. Your task is to research the relationship between home prices and home size and other characteristics in order to provide information about what employees might expect to pay for a single-family home. Data Your first task is to choose a location for your company (feel free to choose an actual company and base your choice on its location), then gather data on homes for sale in that zip code and three contiguous zip code areas. Please work with two or three other students to gather a common data set. You may use zillow.com or a similar real estate website to randomly select 15 single-family homes that have recently been sold in your chosen area and three adjoining zip-code areas (60 data points in total). Your data set will be unique to your group. For each home you select, please record the following information:

1. PRICE: the price for which the home was sold 2. BEDROOMS: the number of bedrooms in the home 3. BATHROOMS: the number of bathrooms in the home 4. HOUSE FT2: the square footage of the home 5. LOT FT2: the square footage of the lot 6. YR BUILT: the year the home was built 7. ZIP CODE: the zip code where the home is located.

If there are other variables you would like to collect, make sure that the information is available for each observation so the data set is consistent. Statistical Analysis Use the data provided to create the most accurate yet economical, model of home prices that you can. In building your model, please take into consideration the following:

• Your model should be as simple as possible without sacrificing predictive power. • If you include dummy variables in the model, think carefully about the choice of

the omitted variable. • You may want to consider eliminating outliers or influential variables in

developing your model (should you do so, explain this in the data section). • Not every variable in the model need be statistically significant at a particular

level, as long as it contributes to the predictive power of the model.

Writing Project Your written project will have two parts:

1. A detailed report of your findings (2000 word maximum, plus tables), to include: a. A brief introduction that motivates the analysis, providing the background and

goals or the project. b. A data section that carefully describes the data set, defining each variable and

providing summary statistics. You should also include the complete data set as an appendix to the report.

c. A results section. The results section should i. A regression results table with at least three models

ii. Explain how you arrived at the model you did, by showing how the results were affected by the inclusion/exclusion of particular variables

iii. Interpret the economic meaning of the final model you arrived at. That is, carefully explain what each coefficient in your final model tells you.

d. A conclusion and summary. The conclusion should briefly summarize the results and provide recommendations to the company based on the results.

The Dvorak paper, online in the Projects folder, is a good model for the detailed report and the formatting of the tables in the detailed report. However, your introduction does not need to include a literature review or discussion of previous findings, as the Dvorak paper does.

2. A 1-2 page memo (500 word maximum, plus tables) to the Human Resources director that summarizes your report, gives them the “bottom line” on area home affordability based on your findings, without the technical detail of the full report. In your memo you might:

a. Give an overview of the distribution of home prices in your chosen area. b. Explain the factors that have the greatest impact on price. c. Use your model to estimate what an employee might expect to pay for 2,000,

2,500, or 3,000 square foot homes in the neighborhoods included in the data set. d. Make a recommendation to the Housing Director: how affordable is housing in

the area, and what, if anything, should the company do to address housing affordability?

Deadline December 10, 11:59 pm, upload to Canvas


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