SemesterFall Semester, 2025
DepartmentMA Program of Land Economics, First Year PhD Program of Land Economics, First Year MA Program of Land Economics, Second Year PhD Program of Land Economics, Second Year
Course NameApplied Econometric Analysis of Real Estate Market
InstructorCHU FANG-NI
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule










































































































Week

Topic

Content and Reading Assignment

Teaching Activities and Homework

1

Course Introduction

Course requirements, Class overview, and Review of Basic Statistics

Lecture

2

Overview of Real Estate Analysis (1)

Quantitative Approach, Model Building in Real Estate Analysis (1)

Lecture

3

Overview of Real Estate Analysis (2)

Quantitative Approach, Model Building in Real Estate Analysis (2)

Lecture

4

Data Processing for Real Estate Analysis (1)

Introduction of SAS software, Read and Write Data in SAS

Lecture & Exercise

5

Data Processing for Real Estate Analysis (2)

Commonly Used Essentials in SAS (1): Basic Commands

Lecture & Exercise

6

Data Processing for Real Estate Analysis (3)

Commonly Used Essentials in SAS (2): Conditional and Iterative Processing

Lecture & Exercise

7

Data Processing for Real Estate Analysis (4)

Commonly Used Essentials in SAS (3): Combining Datasets

Lecture & Exercise

8

Descriptive Statistics and Hypothesis Testing for Real Estate Analysis (1)

Basic Statistical Procedures in SAS (1): UNIVARIATE, FREQ

Lecture & Exercise

9

Descriptive Statistics and Hypothesis Testing for Real Estate Analysis (2)

Basic Statistical Procedures in SAS (2): CORR, TTEST

Lecture & Exercise

10

Descriptive Statistics and Hypothesis Testing for Real Estate Analysis (3)

Basic Statistical Procedures in SAS (3): GLM

Lecture & Exercise

11

Regression Analysis (1)

Overview of Regression Analysis

Lecture & Exercise

12

Regression Analysis (2)

Regression Analysis Using PROC REG in SAS (1)

Lecture & Exercise

13

Regression Analysis (3)

Regression Analysis Using PROC REG in SAS (2)

Lecture & Exercise

14

Regression Analysis (4)

Further Issues in Regression Analysis: Endogeneity (1)

Lecture & Exercise

15

Regression Analysis (5)

Further Issues in Regression Analysis: Endogeneity (2)

Lecture & Exercise

16

Discrete Dependent Variable Models

The Logit and Multinomial Logit Models

Lecture & Exercise

Teaching Methods
Teaching Assistant

None


Requirement/Grading

1.     Class Participation: 20%



2.     In-Class Exercises & Homework: 30%



3.     Empirical Research Project (Group Report): 50%



 



Course Policies on the Use of Generative AI Tools:  Conditional Permitted to Use





Generative AI tools can only be used as auxiliary tools for data collection. The information provided by the tool should be verified for authenticity and debugged. During use, academic ethics must not be violated (for example, the information provided by the tool cannot be directly copied and pasted into reports). Students must have personal ideas, creativity, and the ability to think and judge independently.

 



 



 


Textbook & Reference

Reading materials will include:

1.    Brooks, Chris and Sotiris Tsolacos (2010), Real Estate Modelling and Forecasting. Cambridge University Press.

2.    Ajmani, Vivek B. (2011), Applied Econometrics Using the SAS System. John Wiley & Sons.

3.    Cody, Ron(2018), Learning SAS by Example: A Programmer‘s Guide, Second Edition. SAS Institute.

4.    SAS User's Guide, Programmer’s Guide, Procedures Guide, etc.

5.    Academic journal articles or seminar papers

 


Urls about Course
None
Attachment