Course Title: Property Data Analysis

Part A: Course Overview

Course Title: Property Data Analysis

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

OMGT1117

City Campus

Undergraduate

325H Property, Construction & Project Management

Face-to-Face

 Sem 1 2006,
Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016,
Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022,
Sem 1 2023,
Sem 1 2024

Course Coordinator: Woon-Weng Wong

Course Coordinator Phone: +61 3 9925 1726

Course Coordinator Email: woon-weng.wong@rmit.edu.au

Course Coordinator Location: 8.8.58

Course Coordinator Availability: To be advised


Pre-requisite Courses and Assumed Knowledge and Capabilities

None


Course Description

This course provides you with  an introduction to property data analysis. The course is specifically designed to provide you  with a sound understanding of how elementary statistical tools may be sensibly employed to enhance decision-making and analysis in a real estate context. A complementary aim of the course is to expose you to computer software packages that facilitate statistical analysis. This course will develop your ability to:

  • progress from simple data and information collection towards more detailed property data presentation and analysis
  • utilise statistical software to facilitate data analysis and decision making
  • work effectively as part of a team and positively contribute to professional outcomes.
     


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes (PLOs)  

This course will develop the following program learning outcomes:

  • PLO1: Determine and apply knowledge of complex property and valuation theory to your professional practice and/or further study
  • PLO2: Professionally communicate to a range of audiences, demonstrating in depth knowledge of the discipline and of the needs of diverse property and valuation stakeholders
  • PLO3: Apply logical, critical and creative thinking to analyse, synthesise and apply theoretical knowledge, and technical skills, to formulate evidenced based solutions to industry problems or issues


Course Learning Outcomes (CLOs)

Upon successful completion of this course, you will be able to:

  1. CLO1: Estimate and employ statistically significant econometric relationships to predict the behaviour of variables of interest to property professionals
  2. CLO2: Determine the appropriate sample size in order to attain a given degree of precision or reliability associated with various inferential tasks
  3. CLO3: Select and communicate the most suitable form of data presentation to facilitate effective data description and analysis
  4. CLO4: Calculate and interpret various statistical measures designed to describe the nature of a property or construction related data set
  5. CLO5: Estimate appropriate time series models to forecast future movements in property and construction related variables
  6. CLO6: Construct as well as interpret index numbers that facilitate the analysis of movements over time of property and construction related prices, quantities/volumes and values.



 


Overview of Learning Activities

This course will engage you in a range of learning activities including lectures workshops, online quizzes, software demonstrations, analysis of readings and problem solving.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through our online systems.

You will be expected to utilise library and electronic resources (as well as other appropriate resources) to engage in professional reading of relevant literature in statistical methods. The learning resources used in this course include: an on-line prescribed text book and accompanying lecture overheads, solutions to problems in the prescribed textbook, workshop activity sheets, recommended texts, revision quizzes for each topic and an on-line class-room where these resources may be accessed. RMIT will provide you with the resources and tools for learning in this course through our online systems.


Overview of Assessment

You will be assessed on how well you meet the course’s learning outcomes and on your development against the program learning outcomes.

Task 1: Individual Quiz: Short quiz undertaken in class to assess understanding of basic data presentation, descriptive statistics, probability theory and its application to property markets. 20% weighting. CLO1, CLO2, CLO3, CLO4

Task 2: Individual Quiz: Short quiz undertaken in class to assess understanding of hypothesis testing, hedonic modelling and regression analysis with an emphasis on property valuation. 20% weighting. CLO1, CLO3, CLO6 

Task 3: Online activity: Interactive online assessment module to assess understanding of decision analysis, the normal distribution and confidence interval estimation. 20% weighting. CLO1, CLO3 

Task 4: Exam: Cumulative assessment incorporating elements of problem solving, complex analysis, critical thinking, data interpretation and making recommendations. 40% weighting. CLO1, CLO2, CLO3, CLO4, CLO5, CLO6

Feedback will be given on all assessment tasks.

Equitable Learning Services

Equitable Learning Services (ELS) provide support and equal opportunities for students with a disability, long-term illness and/or mental health condition and primary carers of individuals with a disability. You can contact the ELS if you would like to find out more: https://www.rmit.edu.au/students/support-and-facilities/student-support/equitable-learning-services. You can also contact the course coordinator or the program coordinator if you would like to find out more.

An assessment charter (http://mams.rmit.edu.au/kh6a3ly2wi2h1.pdf) summarises your responsibilities as an RMIT student as well as those of your teaching staff.

Your course assessment conforms to RMIT assessment principles, regulations, policies and procedures which are described and referenced in a single document: http://www.rmit.edu.au/browse;ID=ln1kd66y87rc.