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REMF37 - Quantitative Techniques

REMF37-Quantitative Techniques

Module Provider: Real Estate and Planning
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2018/9

Module Convenor: Dr Chen Zheng

Email: chen.zheng@henley.reading.ac.uk

Type of module:

Summary module description:

The module covers quantitative techniques and methods and their use in the context of real estate.


Aims:
The module aims to provide students with a comprehensive introduction to quantitative techniques and methods and their use and application in the context of real estate investment. The course will cover a wide range of core econometric topics. Case studies from the academic literature are employed to demonstrate the potential uses of each approach in both a general finance and specific real estate context. Extensive use is also made of econometric software to demonstrate the application of the techniques in practice.

Assessable learning outcomes:

The module introduces contemporary statistical and econometric techniques. The importance of the unique characteristics of property markets will be emphasised and its impact upon the nature of applied quantitative analysis. Upon successful completion of the module students will:



- be familiar with a range of applied statistical techniques;

- be introduced to the concepts relating to econometric modelling;

- understand the application and the use of quantitative methods with real estate data;

- understand the core issues relating to investment based quantitative techniques concerning risk and return and portfolio analysis;

- be able to evaluate the outcome of empirical research.


Additional outcomes:

The module will aid students in developing their technical and quantitative skills. It will also enhance students ability to analyse the economic and investment environments of real estate markets.


Outline content:


  • Classical Ordinary Least Squares Estimation (OLS)

  • Hypothesis Testing

  • Multivariate OLS and Diagnostic Testing

  • Vector autoregressive Models (VAR)

  • Cointegration and Error Correction Models

  • Forecasting in Real Estate Markets

  • Modelling markets volatility

  • Panel data analysis


Brief description of teaching and learning methods:
The module will comprise lectures and practical computer workshops. These workshops will complement the formal lectures and provide students with the opportunity to apply the methods taught.

Contact hours:
  Autumn Spring Summer
Lectures 20
Practicals classes and workshops 10
Guided independent study 170
       
Total hours by term 200.00
       
Total hours for module 200.00

Summative Assessment Methods:
Method Percentage
Written exam 60
Written assignment including essay 40

Summative assessment- Examinations:

Two hour examination


Summative assessment- Coursework and in-class tests:

Individual written assignment comprising of approximately 2000 words to be submitted in timetabling week 15 (week 11) of the autumn term.


Formative assessment methods:

For the computer workshops a range of datasets will be used to explain concepts, methods and tests presented in the lectures.


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: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:
The pass-mark for this module is 50%.

Reassessment arrangements:

Re-assessment will be by 3 hour written exam.


Additional Costs (specified where applicable):

1) Required text books: Brooks, C. and Tsolacos, S. (2010), Real Estate Modelling and Forecasting, Cambridge University Press (£50). Further references will be provided, however, there are no further costs.



 


Last updated: 5 December 2018

THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.

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