Case Study: Business Operations, Forecasting: Dallas Real Estate

The year is 2015 and you are the marketing manager for Acme Realty, a real estate company specializing in listings in Dallas, Texas. You have been asked to create a quick forecast for the coming year. You have collected a data set with the average house sale price over the past few years and assembled it into the table shown below.

Yearly Average House Sale PriceTime (Year)

Price

2010

$80,000

2011

$90,000

2012

$95,000

2013

$100,000

2014

$115,000

2015

$110,000

2016

???

Tips for Solving Problems 1 and 2:

• We suggest conducting a regression analysis to solve problems 1 and 2. In your regression analysis, be sure to use the years as values in your calculations.
• If you choose to solve by plotting the points in Excel, we have found that Scatter Plots and their associated trend lines give reliable results, but line graphs (including the one that some versions of Excel recommend) may not.
• With regards to the y-intercept, it will be the point in your model that represents what the sales price would have been in the year 0 (i.e., more than 2000 years ago). Many students have instead been coming up with an answer that represents the sales price from the year 2009. We believe this error is due to the way some versions of Excel are depicting a line graph of this data. When you calculate the y-intercept, if your answer seems more appropriate for 2009 than for 0, please reconsider your calculations before selecting an answer.

Notes:

• US-English conventions are used for numbers. Periods (.) are used to separate whole numbers from decimals and commas (,) are used to separate thousands.
• Be sure to use the data exactly as it is presented in the table.Use the years as values in your calculations/regression analysis.
• Several of these questions ask for estimates, not exact calculations. This means you should select the answer that is closest to the results of your calculations.
• Use standard confidence level of 95%.
• You only have one opportunity to answer each question.

Problem 1

Estimate the forecasted sales price value for the year 2016.

$100000

$110000

$120000

$130000

Problem 2

Estimate the y-intercept for the sales history trend line.

$45,000

$60,000

$95,000

Other

Problem 3

Calculate the value of the coefficient for the Time (Year) variable, where we express the relationship between Sales Price and Time(Year) in the standard form of a linear equation.

$7,000/year

$12,500/year

$20,500/year

$50,500/year

Problem 4

Identify the direction of the trend represented by the sales price data.

Increasing sales price over the time period measured

Decreasing sales price over the time period measured

No change in sales price over the time period measured

No relevant data to identify sales price trend