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, repeated sales, multilevel analysis 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, guest lectures and group exercises and supervision.

The students must submit a mandatory project assignment.

Compulsory assignments

• Group Assignment

Specific conditions

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