REMB29-Applied Analytical Methods

Module Provider: Real Estate and Planning
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Spring term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Prof Anupam Nanda


Type of module:

Summary module description:

This module introduces quantitative techniques that can be applied to component modules across the MSc Flexi programme. 


This module introduces quantitative techniques that can be applied to component modules across the MSc Flexi programme. Topics will include: introductory data analysis and statistical inference, regression analysis, and modelling and forecasting relationships. At the end of the module students will have developed skills enabling them to substantially enhance their understanding and application of quantitative and modelling methods used in the property industry. The module will be a blend of lectures and computing workshops/group exercises using Excel software applied to real estate data. 

Assessable learning outcomes:

Upon completion of this module, students will be able to: 

  • examine real estate problems from a quantitative angle; 

  • describe and summarise data and variable relationships using basic statistics;? 

  • apply appropriate quantitative methods to perform computer-assisted analysis; 

  • interpret the quantitative results of analyses and communicate those results to a non-statistical audience; 

  • utilise spreadsheet tools to perform analysis. 

Additional outcomes:

  • enhance their oral and communication skills through discussions of the applied workshop exercises and results; 

  • develop their IT skills through the computer-based workshops; 

  • enhance their capacity to read complex published material in a critical fashion; 

  • develop their business report writing skills through the coursework.

Outline content:

  • Summarising data and descriptive statistics 

  • Statistical distributions 

  • Statistical measures for portfolio investment applications 

  • Hypothesis testing and confidence intervals 

  • Regression analysis and associated diagnostics 

  • Building, estimating and evaluating forecasting models 

  • Developing econometric and time-series real estate models 

Brief description of teaching and learning methods:

The unit comprises lecture-based and applied computing components. Additionally, students will be expected, on an independent basis, to read prescribed articles in order to prepare for the applied workshops. 

Contact hours:
  Autumn Spring Summer
Lectures 15
Seminars 10
Tutorials 5
Guided independent study:      
    Wider reading (independent) 120
    Essay preparation 50
Total hours by term 0 200 0
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Written exam 100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:


The module will be assessed through one written assignment. This will be a major practice-based assignment, which will involve data collection, statistical analysis, interpretation of results and the production of a report. The assignment will be provided at the end of the module. The guide length of the assignment is 5,000 words (or equivalent). All word counts are for guidance only. Unless the specific requirement of an individual assignment states that the word limit is strict, then there is some discretion with the guide length.? 

Assignment Submission Deadline: Monday 30th March 2020 (Out of term time) 

Formative assessment methods:

Penalties for late submission:

Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information:

Assessment requirements for a pass:

Reassessment arrangements:

Reassessment will be by the same method as for the module’s original assessment requirements, subject to variation by the Examination Board where appropriate. 

Additional Costs (specified where applicable):

No additional costs

Last updated: 29 April 2019


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