MBA-FP6131-Statistics- Case Study - 'A Case Study on Determination of House Selling Price Model...RAR

MBA-FP6131-Statistics- Case Study - 'A Case Study on Determination of House Selling Price Model..


Before you begin, you may wish to read Zainodin and Khuneswari's 2009 article "A Case Study on Determination of House Selling Price Model Using Multiple Regression," which is linked in the Resources under the Internet Resources heading. The article discusses a case study that uses a predictive model based on multiple regression for home sales and may serve as an example for the analysis you will do in this assessment.

Consider the following case study:

Kanta Mantra, a small business, is considering a venture in high-end real estate. Management has engaged you to perform an analysis of the high-end realty market in Tsadobe County, a community known to be associated with the rich and famous in New State. They want you to develop a predictive model of housing prices in five areas.

The Founder and CEO of the company is also interested in purchasing one of the properties. Specifically, he is interested in the property at 17724 Auburn Village Dr. Your task is to develop a predictive model based on multiple regression analysis of the properties in the county and also make a recommendation to the CEO regarding this particular property.

Data describing several property features of homes in the Tsadobe County can be found in the Housing DataExcel file, linked in the Resources under the Capella Resources heading. This file contains data on 299 high-end homes in five prominent zip codes in the county. The Code Book for Housing Data document, also linked in the Resources under the Capella Resources heading, describes the variables in the file.


Use the following steps to guide you as you complete the analysis:

Step 1

Determine the comparables for 17724 Auburn Village Drive—that is, similar properties that would have comparable selling prices, with appropriate adjustments made.

  • Develop a descriptive housing profile for the five areas, summarizing the features of the homes you find significant.

  • Determine whether there is a significant difference in the average selling price of the homes among the five areas. Discuss your results.

Step 2

Use multiple regression analysis to build a predictive model for the selling price of the homes in Tsadobe County. The model should include all potential independent variables that make sense:

  • Develop and write the best predictive model.

  • Determine how well the model predicts home prices.

  • Determine the significant predictors of selling price. Discuss the predictability of your model.

  • Use the final model to predict a selling price for the property at 17724 Auburn Village Drive. Is the house reasonably priced? Explain.

Step 3

Complete a report of your analysis and, based on your findings, recommend a decision for the CEO. You may structure your report based on the Management Report Template (linked in the Resources under the Capella Resources heading) you have used throughout the course, but be sure to meet the following additional requirements:

  • Write the report in a professional style, with no grammatical or mechanical errors. It should include both a brief introduction recapping the situation and a brief conclusion summarizing the major findings and recommendations. It should be organized logically, using headings and subheadings as appropriate.

  • Include the following components:

  • An explanation of the process by which the comps were identified.

  • The descriptive profile of the group of comparables.

  • The analysis of the mean selling prices among the five areas.

  • A description of how the data were checked prior to running regression analysis to ensure that the assumptions required for linear regression were not violated. Also, describe if any changes were made to the data as a result (such as eliminating outliers).

  • The results of the final regression model. This should include the actual regression output (in an appendix) and a discussion of the model's characteristics including goodness of fit, statistical significance, and the actual regression equation.

  • The predicted selling price of 17724 Auburn Village Drive, based on the regression model.

For this assessment, present your report and recommendations clearly and concisely. It is not necessary to submit an additional Word document showing your work. However, the relevant information, based on your results, should be incorporated into the body of your report.

Remember that the CEO of Kanta Mantra, who has limited statistical knowledge, is your audience for the report. Focus on providing him with the information that he needs rather than on detailing the steps you undertook. Explain any statistics terminology with which someone new to statistics would not be familiar.

Additional Requirements

Use the following program standards for all assessment submissions in this course:
Written communication: Written communication should be free of errors that detract from the overall message.
APA formatting: Your writing should be formatted according to APA (6th edition) style.
Font and font size: Times New Roman, 12 point.
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