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# Data Analysis and Decision Making - Imagine you are a real estate investor presented with a regression analysis of home sales in the neighborhood of one of your investment properties.

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Imagine you are a real estate investor presented with a regression analysis of home sales in the neighborhood of one of your investment properties. Unfortunately, the report stops short of making the decision for you. Given the data as presented in those three worksheets, you need to determine:

Which is the better predictor of selling price: appraised value, square footage, or number of bedrooms?
How much value is added per \$1,000 of appraised value?
How much value is added per 100 square feet?
How much value is added per bedroom?

At what price should you offer your four bedroom 2,050-square-foot investment home that has recently been appraised at \$135,200?
The data for this problem comes from problem P11.1 on page 584 of your textbook. Feel free to search your local property records, change as many X values as may be available and compute the market value of your own home. If you have ever wondered how to provide evidence for the contention that home size affects electric costs you may want to take a look at a similar problem, particularly Problem 11.7 in the text. It is another excellent example of how regression can be used to help form relationships between two variables.

What I need?

1). How do I perform a regression analysis on this information, while incorporating each of these areas?

2). The problem suggests that we change as many X values as may be available and compute the market value of your own home (\$135,200). How do I do this?