Module Objectives

The main objective of this module is to introduce students to the use of new technologies and geoinformatics (such as Geographic Information Systems and geospatial data on the web) in property management in the private sector (such as Property Management Companies) and public sector (such as National Cadastre & Mapping Agency S.A.).

An additional aim is the introduction to property valuation based on the European Valuation Standards as they are used in the real estate market in Greece. Moreover, a thorough presentation of the Geographically Weighted Regression (GWR) method to estimate property values ​​based Hedonic Price Modelling theory takes place. It is a requirement for this module that students have a good knowledge of statistics, spatial analysis and GIS.

By the end of this module the student should:

– Understand how new technologies and geoinformatics can help in better property management
– Know what it is and what the National Cadastre & Mapping Agency S.A. does
– Know what are the values ​​of a property in the Greek real estate market
– Know which property evaluation methods are used in the Greek real estate market
– Be able to collect the appropriate data and apply methods of spatial analysis to estimate property values ​​based on hedonic price modelling theory .
– Has knowledge about contemporary spatial analysis methods, such as the Geographically Weighted Regression, and be able to apply them to real estate data and to draw conclusions from the results of the analysis

Course Content

Indicative lectures:

– Introduction to Property Management
– Geoinformatics and Real Estate
– Geographic Information Systems in Property Management
– Introduction to property valuation
– Property Valuation Standards and Methods
– Hedonic Price Models for Property Valuation
– Geographically Weighted Regression (GWR)
– The Greek real estate market and Property Management Companies

Teaching and Assessment

The teaching of this module includes real estate theory and a computer practical in Geographically Weighted Regression. Usually the course consists of three hours of theory on a weekly basis and in one or two weeks teaching in the computer lab.

Students are encouraged to participate in conferences or workshops relating to real estate (property valuation, real estate investments and management).

Assessment:

The assessment  includes a written examination at the end of the semester (3 hours ) or alternatively an essay in the form of scientific articles (2000 to 3000 words), which should include the application of methods of spatial analysis taught in the module into analysing real estate data.

Teaching material:

The teaching material includes lectures and notes for the computer practical in Geographic Weighted Regression.