AP 344 - Statistical and Computer Applications in Valuation

1. Collect, organize, and characterize data by identifying and classifying variable types, grouping and summarizing data, and applying descriptive statistical methods and graphical techniques to analyze trends and relationships.
2. Apply statistical measures and exploratory data analysis techniques to calculate and interpret measures of central tendency, dispersion, correlation, and data transformations, and to evaluate data quality, uncertainty, and model adequacy.
3. Utilize statistical software and database tools to manage datasets, generate descriptive and graphical analyses, perform cross-tabulations, and produce analytical reports relevant to real estate applications.
4. Develop, calibrate, and evaluate regression models - including simple and multiple linear regression - to predict property values, interpret key statistical parameters, detect and address multicollinearity, and assess model validity and predictive performance.
5. Integrate statistical and analytical methods into real estate valuation processes by identifying appropriate market segments, constructing sufficient and relevant datasets, and employing comparative and regression techniques to support property characterization and appraisal.
6. Interpret and communicate statistical analyses and results with precision and clarity, translating technical findings into accessible language for non-specialist audiences and critically assessing the accuracy, relevance, and limitations of statistical approaches in applied real estate contexts.
7. Critically analyze complex real estate scenarios to determine the suitability and limitations of statistical or computational approaches and formulate evidence-based solutions that address client objectives and market conditions.
8. Apply principles of research design and experimental methods - including control, replication, randomization, and blocking - to improve data reliability, support robust valuation analyses, and enhance model development.
9. Demonstrate professional and ethical responsibility in data analysis and reporting by adhering to best practices in data management, ensuring transparency and accuracy in statistical interpretations, and considering the broader implications of analytical outcomes in real estate decision-making.

workbook. (BUSI 344). Real Estate Division, Sauder School of Business, University of

British Columbia.

SPSS and MS Excel required software

Optional Resource Materials:
None

Conduct of Course:
AP 344 is an applied statistical lecture and computer lab course in which students use statistical software called SPSS and work on their own laptops. The course evaluation consists of chapter multiple choice assignments, two projects that apply the basics of computer mass appraisal to the appraisal and assessment industries and a final exam.

The course consists of lectures, labs, and/or fieldwork. Classroom instruction is delivered in a lecture format supported by visual aids. Questions and discussions are encouraged throughout to ensure understanding of the material. Assessment activities are completed during class time and cannot be rescheduled, as some materials are only available during scheduled sessions.

Regular attendance is essential for success in this course. Absence for any reason does not relieve a student of the responsibility to complete coursework and assignments to the satisfaction of the instructor. Poor attendance (more than 20%) may result in withdrawal from the course.

The instructor will recommend that the Registrar withdraw any student who does not meet the established attendance requirements. A failing grade of RW (Required to Withdraw) will appear on the student’s transcript.

In cases of repeated absences due to illness, students may be required to submit a medical certificate. Instructors also have the authority to require students to attend classes.

Content of Course:

1. Statistical Foundations for Real Estate Analysis
2. Statistical Software Applications for Real Estate Analysis
3. Exploratory Data Analysis
4. Market Identification and Characterization of Model Building
5. Valuation Case Studies 
6. Basics of Model Building
7. Model Building using Multiple Regression Analysis
8. Comprehensive Model Building - Data Screening and Testing
9. Automated Valuation Models (AVM’s)
10. Geographic Information Systems (GIS)

• Official final grades will be available on My Lakeland. Grades posted in D2L should be considered interim grades.  
• “Lakeland College is committed to the highest academic standards. Students are expected to be familiar with Lakeland College policies and to abide by these policies. Violations of these policies are considered to be serious and may result in suspension or expulsion from the College.”  

Students must maintain a cumulative grade of C (GPA - Grade Point Average of 2.00) in order to qualify to graduate.

Every effort has been made to ensure that information in this course outline is accurate at the time of publication. Lakeland College reserves the right to change courses if it becomes necessary so that course content remains relevant.

In such cases, the instructor will give students clear and timely notice of changes.

No part of this course outline may be reproduced in any form or resold without written permission from Lakeland College.

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