Core Course
REC603 – Research Methods
Course Unit Code: REC603
Type Of Unit: Core Course
Level of Course Unit: Postgraduate (Level 7)
Year of Study: First year
Semester: First Semester
Number of ECTS Credits: 7.5
Mode of Delivery
Face to Face
Prerequisites
None
Course Objectives
The course’s objective is to provide students with the necessary skills and knowledge to conduct rigorous research in real estate. Through a combination of theoretical concepts and practical applications, students will develop critical thinking and analytical skills essential for making informed decisions in the real estate industry. Particularly, the objectives of the course are: 1) to introduce students to various research methodologies commonly used in real estate research; 2) to equip students with practical skills in collecting, analyzing, and interpreting real estate data; and 3) To foster critical thinking and problem-solving abilities in the context of real estate research.
Learning Outcomes
- Define the principles and applications of qualitative and quantitative research methods.
- Organize, describe, and present data using frequency tables, distributions, and graphic presentation techniques.
- Calculate and interpret numerical measures such as mean, median, variance, and standard deviation.
- Conduct hypothesis testing, interpret p-values, and make decisions based on statistical evidence.
- Perform correlation and regression analysis to identify and quantify relationships between variables and predict outcomes.
- Develop proficiency in using Excel and STATA for data manipulation, statistical calculations, and creating visualizations.
- Apply multiple regression analysis to model and predict outcomes based on multiple predictors and present findings effectively.
Course Content
1) Introduction to Qualitative and Quantitative Research Methods
2) Qualitative Research: Interviews
3) Qualitative Research: Questionnaires
4) Ethical and Legal Considerations in Research: Focus on GDPR
5) Describing Data: Frequency Tables, Distributions, and Graphic Presentation
6) Describing Data: Numerical Measures
7) Normal Probability Distribution
8) Estimation and Confidence Intervals
9) Hypothesis testing
10) Correlation and Linear regression
11) Multiple regression analysis
12) Data Analysis using Software packages
Course Features
Planned learning activities and teaching methods
Lectures; in-class discussion; in-class exercises; interactive online learning via Moodle (quizzes, assignments, forums)
Assessment methods and criteria
Midterm Exam: 20%
Research Project: 50%
Final Examination: 30%
Readings
Main Textbooks:
“Basic Statistics for Business & Economics”, 10th Edition, Lind, D., Marchal, W., and Wathen, S. (2022) McGraw Hill.
Optional textbook:
Business Research Methods ISE, 14th Edition, By Pamela S.Schindler, 2022, 1264704658, McGraw Hill
Business Research Methods, 4th Edition, By Boris Blumberg, Donald R. Cooper, Pamela S. Schindler, 2014, McGraw Hill
Applied Statistics in Business and Economics ISE, 7th Edition, By David Doane, Lori . Seward, 2022, McGraw Hill
Essentials of Modern Business Statistics with Microsoft Office Excel. Anderson, D.R, Sweeney D.J., Williams T.A., Camm J.D., and Cochran J.J. (2018). Cengage Learning.
Introduction to Econometrics, Global Edition, 4th Edition. Stock, J., and Watson, M. (2020). Pearson Education
Groebner, D.F., Shannon, P.W., and Fry, P.C. (2017). Business Statistics: A Decision Making Approach. Pearson Education
Articles:
“Modelling the drivers of data science techniques for real estate professionals in the fourth industrial revolution era” by Osunsanmi Dayo, Ayodeji E Oke, Dr. Timothy O Olawumi, 2023
English (UK) 
Ελληνικά