Reference no: EM131720999
Galen Development Company Case
The decision models to be used is Decision Tree in Excel
The Bill Galen Development Company (BGD) is considering the purchase and commercial development of a piece of property.
The property is now zoned residential; BGD needs a variance from the city in
order to do commercial development on a property.
The asking price for the property is a firm $300,000. BGD estimates it can construct a shopping center for an additional $500,000 and sell the completed center for approximately $950,000.
A variance application costs $30,000 in fees and expenses, and there is only a 40% chance that the variance will be approved.
Regardless of the outcome, the variance process takes two months.
If BGD purchases the property and the variance is denied, the company will sell the property and receive net proceeds of $260,000.
BGD can also purchase a three-month option on the property for $20,000, which would allow time for it to apply for the variance.
Finally, for $5,000 an urban planning consultant can be hired to study the situation and render an opinion as to whether the variance will be approved or denied.
The city council has been reluctant to grant variances, so there is a 60% probability the consultant will predict denial of the variance.
A friend of Bill, a professor at the local university, estimates the following conditional probabilities for the consultant’s opinion:
P(consultant predicts approval | approval granted) = .70
P(consultant predicts denial | approval denied) = .80
BGD has contracted with you to determine / recommend the optimal strategy regarding this parcel of property.
Use decision trees to solve problems, prepare a report that will help BGD make their decision in this situation.
Utilize the quantitative analysis approach in your report as follows:
1. Defining the problem: Describe what the problem entails.
2. Developing a model: Develop a decision tree for the problem identified in Step 1, using the Excel TreePlan add-in
3. Acquiring the data: Describe the data used in the decision tree and how you acquired the data.
4. Solve the problem using the decision tree developed in Step 2.
a. What are the expected returns for each of the possible strategies?
b. What are the maximum potential losses for the possible strategies?
c. What strategy do you recommend and why?
5. Testing the solution: Does the decision tree produce results that are accurate and complete.
6. Analyzing the results: What does the information you prepared in Steps 1-5 tell you about the decision situation?
7. Implementing the results: How would you implement the results?