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BUSI 444 Advanced Computer Assisted Mass Appraisal
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The Advanced Computer Assisted Mass Appraisal course BUSI 444 is intended to give the real estate practitioner student a working knowledge of computer‐assisted mass appraisal principles and practices. This course is unique in that it presents a hands‐on approach to computer assisted mass appraisal techniques. Students will use a personal computer and statistical software to develop models designed to improve the consistency and quality of real property assessments. The material is intended to be introductory in nature; it is important to keep in mind that study of this course by itself does not certify the reader as a qualified mass appraisal model builder. However, the course should give the student a solid foundation in mass appraisal which can be further enriched by real‐world practice. Note that this course has been adopted by several jurisdictions as meeting their entry level employment requirements and it also meets the educational course requirements for professional accreditation in several appraisal and assessment organizations. After reading the text and proceeding through the course workbook, the student should have a basic understanding of computer‐assisted mass appraisal and the techniques involved in designing effective mass appraisal models. Listed below are general objectives for what a student should learn from this course. After completing the course, the student should be able to: Explain the importance of mass appraisal in real property tax assessment and discuss the practical application of computer‐assisted modeling techniques. List and apply the statistical measures and techniques which form the basis for mass appraisal. Explain the general structure of mass appraisal models and be able to discuss the strengths and weaknesses of the various model types available. Describe methods for appraising land value using statistical modeling. Discuss the theory underlying the cost approach to value in terms of how this method is employed in property tax assessment. Develop a mass appraisal model based on the cost approach to value. Discuss the theory underlying the sales comparison approach to value in terms of how this method is employed in property tax assessment. Develop a mass appraisal model based on the sales comparison approach to value. Specify and calibrate additive multiple regression analysis (AMRA) models and multiplicative multiple regression analysis (MMRA) models. Develop a simple mass appraisal model based on non‐linear regression techniques. Discuss the theory underlying the income approach to value and know how to develop a mass appraisal model based on the income approach. Test the performance of mass appraisal models in terms of accuracy and equity, by applying ratio studies. Explain how statistical testing procedures can be used in mass appraisal performance evaluation. UBC Real Estate Division 247 ‐ 2053 Main Mall Vancouver, BC Canada V6T 1Z2 1.877.775.7733 www.realestate.ubc.ca Discuss geographic information systems (GIS) applications related to computer‐assisted mass appraisal (CAMA) and explain the evolution towards CAMA/GIS integration. Discuss how neural networks can serve as an alternative to regression modeling, and develop a simple neural networks model. LESSON 1 – Review of Statistical Software and Valuation Modeling Basics 1. Discuss the basis for mass appraisal and the capabilities, advantages, and disadvantages of various modeling methods; 2. Describe the steps in developing a mass appraisal model application; 3. Explain and interpret the statistics and graphics used in developing mass appraisal models; and 4. Use statistical software as a tool for creating and analysing mass appraisal models. LESSON 2 – Data Screening – Preparing Data for Modeling 1. Use graphic analysis to determine relationships between the given variables and the adjusted sale price; 2. Use graphic analysis to determine relationships among potential independent variables; 3. Use correlation analysis to determine relationships among potential independent variables; 4. Analyze a database to determine the need for a time adjustment and apply techniques to make any necessary time adjustments; 5. Analyze variables to determine the existence of outlier observations and determine the need to remove these; 6. Determine if there are enough observations of specific characteristics to allow the use of these in further regression modeling; and 7. Determine the need to transform variables for use in the modeling process.
