Course Description and Outline

CPD 142: Mass Appraisal: Regression Analysis and Real Estate

Pre-requisite: this course assumes students have a working knowledge of statistics and computer applications to generate them; students must have completed BUSI 121 / CPD 140 or equivalent. As well, students will benefit from a working knowledge of appraisal theory and practices; recommend prior completion of BUSI 330 or CPD 130 or equivalent (e.g., Trading Services / real estate sales course).

The goal of this course is to help real estate professionals learn the foundations of Linear Multiple Regression Analysis (LMRA or MRA) and to provide the foundation for an understanding of how MRA can be used to assist in the analysis of real estate markets. The course starts out in Lesson 1 with a very simple regression and ends with a reasonably complex regression model being built in Lesson 3.

While much information on multiple linear regression analysis is presented in this course, it is not intended to teach all of the intricate technical aspects of MRA, but rather to provide you with enough information to ensure that you feel comfortable understanding how MRA functions and how its application can help you in your professional life. It will provide you with some hands-on experience with a statistical software package (SPSS) so that you can generate MRA models.

COURSE OUTLINE

FOREWORD: Introduction to Professional Development Courses

LESSON 1: Basics of Model Building

  • Instructor's Comments
  • Introduction to Model Building: What is Regression All About?
  • Building a Simple Linear Regression
  • Model Building using Multiple Regression Analysis – Simple MRA
  • Gathering Data
  • Multicollinearity
  • Summary
  • Review and Discussion Questions

LESSON 2: Model Building Using Multiple Regression Analysis

  • Instructor's Comments
  • Steps for Building MRA Models
  • Summary
  • Review and Discussion Questions

LESSON 3: Comprehensive Model Building – Data Screening and Testing

  • Instructor's Comments
  • Preliminary Data Screening
  • Summary: Comprehensive Model Building
  • Appendix 3.1: Additional Kruskal-Wallis Tests
  • Review and Discussion Questions

AFTERWORD: The Final Quiz

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