Modul Real Estate Economics and Econometrics
Module No. M-REM4-009
The Elective Real Estate module provides students with the basic understanding of the microeconomic and macroeconomic forces that drive real estate markets. In addition students will learn basic and advanced econometric methods which are used in modeling and forecasting real estate data. Thus, students will not only understand the economic theories that explain the relationship among relevant variables such as house prices, rents, construction costs, location, and the vacancy rate, they will also be able to test and verify these theories by applying the appropriate econometric techique on real world data. In this context students will learn the userfriendly and powerful econometric software EViews in computer exercise classes.
1st Course: Real Estate Economics and Econometrics
Course No. L-REM4-016
The real estate economics and econometrics course provides a basic understanding of theoretical modeling of real estate markets. The goal of this course is to understand the economic forces which affect real estate markets. Basic economic models are combined with real world data to convey a sophisticated understanding of real estate markets. It also familiarizes students with the most important econometric methods in the field of real estate economics. Thereafter, theoretical knowledge is applied using the econometric software tool EViews. The aim is to provide students with both the theoretical perspective as well as the ability to predict and forecast the mechanism of real estate markets (e.g., forecasting models for offce space and rents).
- 1. Introduction to Real Estate Markets
- 2. Microeconomic Analysis of Property Markets
- 3. Macroeconomic Analysis of Property Markets
- 4. Real Estate Econometrics
- 5. Application with EViews
2nd Course: Advanced Real Estate Econometrics
Course No. L-REM4-017
This course is intended to provide graduate students with a working knowledge with key estimation methods of current use in real estate and urban economics. In addition, the course will review and deepen students' understanding of how to interpret regression results and how to deal with common problems of data preparation. The course is applications oriented rather than focused on estimation theory.
