Type of Credit: Elective
Credit(s)
Number of Students
This course applies statistical methods and economic theory to identify, estimate, and test economic models in the real estate market. This course covers concepts and methods relevant to the empirical analysis of the real estate market. The emphasis of the course is on various empirical applications. Topics covered in this course include the classical single-equation regression model, multiple regression models, and models with dependent variables that are discrete or categorical. In the lectures, numerous empirical examples will utilize diverse data sets and applications emphasizing actual data and models. During classes, theory and empirical exercises are combined and practiced. Students are expected to be active and read materials in advance. We will use the SAS software program to solve the empirical exercises and assignments.
能力項目說明
Students will learn how to conduct econometric analysis and critique empirical studies related to the real estate market. This will give students the confidence to estimate and interpret their models. Upon successful completion of the course, you will have the ability to:
1. Obtain and process real estate data.
2. Specify a suitable economic model for real estate.
3. Use econometric software to estimate the economic model for real estate.
4. Evaluate the model's estimated results.
5. Interpret estimation output and make inferences.
6. Generate forecasts using the estimated model.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
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 |
National Day (Holiday) |
October 10, 2024, is National Day, and classes will be closed |
Suspend |
|
6 |
Data Processing for Real Estate Analysis (2) |
Commonly Used Essentials in SAS (1): Basic Commands |
Lecture & Exercise |
|
7 |
Data Processing for Real Estate Analysis (3) |
Commonly Used Essentials in SAS (2): Conditional and Iterative Processing |
Lecture & Exercise |
|
8 |
Data Processing for Real Estate Analysis (4) |
Commonly Used Essentials in SAS (3): Combining Datasets |
Lecture & Exercise |
|
9 |
Descriptive Statistics and Hypothesis Testing for Real Estate Analysis (1) |
Basic Statistical Procedures in SAS (1): UNIVARIATE, FREQ |
Lecture & Exercise |
|
10 |
Descriptive Statistics and Hypothesis Testing for Real Estate Analysis (2) |
Basic Statistical Procedures in SAS (2): CORR, TTEST |
Lecture & Exercise |
|
11 |
Descriptive Statistics and Hypothesis Testing for Real Estate Analysis (3) |
Basic Statistical Procedures in SAS (3): GLM |
Lecture & Exercise |
|
12 |
Regression Analysis (1) |
Overview of Regression Analysis |
Lecture & Exercise |
|
13 |
Regression Analysis (2) |
Regression Analysis Using PROC REG in SAS (1) |
Lecture & Exercise |
|
14 |
Regression Analysis (3) |
Regression Analysis Using PROC REG in SAS (2) |
Lecture & Exercise |
|
15 |
Regression Analysis (4) |
Further Issues in Regression Analysis: Endogeneity (1) |
Lecture & Exercise |
|
16 |
Regression Analysis (5) |
Further Issues in Regression Analysis: Endogeneity (2) |
Lecture & Exercise |
|
17 |
Discrete Dependent Variable Models |
The Logit and Multinomial Logit Models |
Lecture & Exercise |
|
18 |
Flexible Supplemental Instruction Week |
Completion of designated after-course assignment (Completion of the designated report) |
Suspend |
1. Class Participation: 20%
2. In-Class Exercises & Homework: 30%
3. Empirical Research Project (Group Report): 50%
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.
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
| 書名 Book Title | 作者 Author | 出版年 Publish Year | 出版者 Publisher | ISBN | 館藏來源* | 備註 Note |
|---|
None