Course - Big Data in Real Estate Finance - BBAN4025
Big Data in Real Estate Finance
About
About the course
Course content
- The course aims to be an advanced and very research-related subject on the master level, which aims to enable the students to analyze big data with high level of complexity.
- The course starts with an introduction to real estate finance, focusing on analysis, banking and valuation, as this will be the basis for the data analyzes.
- The course will introduce the students to analyze technics that can be applied to big data analyzes in real estate finance including, hedonic regressions, repeat sales, fixed effects and the use of artificial intelligence (AI) and machine learning techniques.
Learning outcome
Knowledge
- The students should have knowledge of valuation of property.
- The students should have knowledge of how automated valuation models for real estate work.
- The students should have knowledge of how big data can be used to solve practical decision-making problems in real estate finance.
- The students should have knowledge of how big data can be used to solve practical decision-making problems related to property-related banking issues.
Skills
- The students should be able to plan, facilitate and carry out data analyses within real estate finance.
- The students should be able to carry out valuation of property.
- The students should be able to use hedonic regressions and repeated sales to through real estate analyses.
General competence
- General knowledge of real estate finance and how the real estate market affects the banking industry.
- The course will also give the students general knowledge of how big data can be analyzed, including analyses that apply artificial intelligence and machine learning techniques.
Learning methods and activities
Lectures and group exercises.
Further on evaluation
A combined assessment will be made based on the results from a group project assignment and an individual written school exam, each accounting for 50%. The project assignment must be submitted in groups of 1-3 members.
A mandatory coursework requirement must be passed before the final submission of the project assignment. Further information will be provided at the start of the semester.
If you do not pass, it will be possible to retake the individual partial assessment.
If students want to improve their grade, it will be possible to retake the individual partial assessment.
The course is only available to students admitted to a study program under "special conditions."
Specific conditions
Admission to a programme of study is required:
Accounting and Auditing (MRR)
Economics and Business Administration (MSIVØK5)
Economics and Business Administration (ØAMSC)
Financial Economics (MFINØK)
Industrial Economics and Technology Management (MTIØT)
Recommended previous knowledge
We recommend that students who wish to take the course have basic knowledge related to quantitative empirical method and data processing.
Required previous knowledge
None
Course materials
The syllabus will be given at the start of the semester.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| BFIN4025 | 7.5 sp | Autumn 2023 |
Subject areas
- Economics and Administration