Type of Credit: Elective
Credit(s)
Number of Students
This course applies statistical methods and economic theory to the challenge of identifying, estimating, and testing 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, dependent variables that are discrete or categorical, and longitudinal and panel data analysis. In the lectures, there will be numerous empirical examples utilizing a diverse range of data sets. 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. To solve the empirical exercises and assignments, we will use the SAS software program.
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 provide students with the confidence they require to estimate and interpret their own 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 |
Quantitative Approach, Model Building in Real Estate Analysis |
Lecture |
|
|
3 |
Data Processing for Real Estate Analysis (1) |
Introduction of SAS software, Read and Write Data in SAS |
Lecture & Exercise |
|
|
4 |
Data Processing for Real Estate Analysis (2) |
Commonly Used Essentials in SAS(1): Basic Commands |
Lecture & Exercise |
|
|
5 |
Data Processing for Real Estate Analysis (3) |
Commonly Used Essentials in SAS(2): Conditional and Iterative Processing |
Lecture & Exercise |
|
|
6 |
Data Processing for Real Estate Analysis (4) |
Commonly Used Essentials in SAS(3): Combining Datasets |
Lecture & Exercise |
|
|
7 |
Basic Statistics for Real Estate Analysis (1) |
Basic Statistical Procedures in SAS(1): UNIVARIATE, FREQ |
Lecture & Exercise |
|
|
8 |
Basic Statistics for Real Estate Analysis (2) |
Basic Statistical Procedures in SAS(2): CORR, TTEST, GLM |
Lecture & Exercise |
|
|
9 |
Regression Analysis (1) |
Overview of Regression Analysis |
Lecture & Exercise |
|
|
10 |
Regression Analysis (2) |
Regression Analysis Using PROC REG in SAS |
Lecture & Exercise |
|
|
11 |
Regression Analysis (3) |
Further Issues in Regression Analysis: Endogeneity (1) |
Lecture & Exercise |
|
|
12 |
Regression Analysis (4) |
Further Issues in Regression Analysis: Endogeneity (2) |
Lecture & Exercise |
|
|
13 |
Discrete Dependent Variable Models |
The Logit and Multinomial Logit Models |
Lecture & Exercise |
|
|
14 |
Panel Data Analysis |
Basic Panel Data Models |
Lecture & Exercise |
|
|
15 |
Time Series Analysis (1) |
Overview of Time Series Analysis |
Lecture & Exercise |
|
|
16 |
Time Series Analysis (2) |
Regression Analysis with Autocorrelated Errors |
Lecture & Exercise |
|
|
17 |
Time Series Analysis (3) |
Modeling the Dynamics of Multiple Time Series |
Lecture & Exercise |
|
|
18 |
Final Exam |
|
Exam |
|
1. Class Participation: 10%
2. In-Class Exercises & Homework : 20%
3. Empirical Research Project: 40%
4. Final Exam: 30%
Reading materials will include books:
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. Brocklebank, John C., Dickey, David A., and Bong S. Choi (2018), SAS for Forecasting Time Series, Third Edition. SAS Institute.
5. Anders Milhøj (2016), Multiple Time Series Modeling Using the SAS® VARMAX Procedure. SAS Institute.
6. Wei, William W. S. (2018), Multivariate Time Series Analysis and Applications (Wiley Series in Probability and Statistics). Wiley.
7. SAS User's Guide, Programmer’s Guide, Procedures Guide, etc.
None