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MSB205 Economics of Housing Markets and Applied Spatial Econometrics

Course description for academic year 2024/2025

Contents and structure

The course consists of two parts: Housing economics and applied spatial econometrics. These two parts are explained below:

Housing is important for the welfare of people. It constitutes a major proportion of overall wealth, and it is important for the overall national economy. This is particular so in countries like Norway where home ownership rates are high. Knowledge about how housing market works is therefore important. We will mainly use an applied microeconomic approach. The course starts to examine the basic market model of demand and supply in partial equilibrium solutions of housing markets. Housing markets are special in comparison to many other markets, and we will show that the traditional standard model of competitive markets has important limitations, because of the particular characteristics of housing. The most important are heterogeneity, spatial fixity and durability. We will study theories that relates inter alia to the spatial pattern of housing prices, and the hedonic model which focus on heterogeneity. Moreover, housing is both a consumption and a durable investment good. We will therefore study theory of user cost, tenure choices, and fundamental determinants of housing prices.

The use of spatial data in the social sciences has a long history and it is a field that has developed rapidly in later years. The essence of spatial analysis is that distance and space matters, and it is important that students in urban, regional and housing economics are acquainted with basic methods and software for analysing spatial data. The course covers how to use open source software (GIS-tools and R) to import and export data, data management and visualization (mapping and measures of correlation). Different types of spatial data commonly used will be presented, such as point patterns and lattice data. In the modelling part we will start with the traditional ordinary least squares estimator, and we will focus on the assumptions which frequently are violated when using spatially ordered data. Relevant test procedures and descriptive measures, which aids the researcher in choosing the appropriate spatial econometric model, will be presented. We will focus on estimating basic spatial econometric regression models, and on understanding the meaning of spatial effects, the related models and how to specify and estimate alternative spatial weight matrices.

Learning Outcome


Upon completion of the course, the student has:

  • knowledge of the main distinctive characteristics of housing as a good and the housing markets from an economic perspective.
  • knowledge about how housing markets interacts with the wider economy.
  • knowledge of central theories of housing economics, focusing on central characteristics of housing and housing markets
  • knowledge of impacts of selected important governmental interventions in the housing market.
  • knowledge of central characteristics of spatially ordered data
  • knowledge about basic descriptive statistics used to characterise spatial autocorrelation and spatial heterogeneity.
  • knowledge about basic spatial econometric models, how they should be interpreted and when they could be used
  • knowledge about tests for spatial effects and the resulting model selection
  • basic knowledge about maps (graphic representations of geographical information) in the form of vector representations (point, lines and polygons)


Upon completion of the course, the student should:

  • Be able to present and explain the main insights in the studied housing models
  • Be able to discuss the assumptions, applicability, and limitations of the studied housing models.
  • Be able to import and export digital vector-based maps
  • Be able to install the relevant open source software
  • Be able to manipulate maps using appropriate software
  • Be able to perform exploratory spatial data analysis
  • Be able to create neighbourhood information structures from maps
  • Be able to create suitable spatial-weights matrices from these neighbourhood structures
  • Be able to run basic spatial econometric models
  • Be able to perform statistical test to decide which estimator is the one most suitable for the given dataset and research questions
  • Be able to interpret the results from the estimated models

General Competence

Upon completion of the course, the student is:

  • able to read and understand scientific literature that make use of the concepts and models that are introduced in the course
  • able to apply the models of the course in their own academic work, for example in an empirical master’s thesis.
  • able to apply the software, estimators and methods we present in the course in their own academic work such as an empirical master´s thesis

Entry requirements


Recommended previous knowledge

Completed microeconomics course at undergraduate levels. Course in econometrics at the graduate level.

Teaching methods

The teaching format is a combination of lectures, computer and data-excercies and students' own work, which includes providing summary of scientific papers.

Compulsory learning activities

Students have to hand in one written group report during the course in order to qualify for the exam. One group consists of 2-3 persons.


One written school examination, 4 hrs.

Grading scale is A-F where F is fail.

Examination support material


More about examination support material