course-details-portlet

BBAN4025 - Big Data in Real Estate Finance

About

New from the academic year 2023/2024

Examination arrangement

Examination arrangement: Group Assignment
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Group Assignment 100/100

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

Further on evaluation

The students must submit a mandatory assignment. The assignment can be completed with up to 3 group members.

Required previous knowledge

None

Course materials

The syllabus will be given at the start of the semester.

Credit reductions

Course code Reduction From To
BFIN4025 7.5 AUTUMN 2023
More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2023

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
  • Economics and Administration
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
NTNU Business School

Timetable

List view
Calendar view

Examination

Examination arrangement: Group Assignment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Group Assignment 100/100

Release
2023-12-15

Submission
2023-12-22


12:00


12:00

INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU