REMF37: Quantitative Techniques

Module code: REMF37

Module provider: Real Estate and Planning; Henley Business School

Credits: 20

Level: Postgraduate Masters

When you'll be taught: Semester 1

Module convenor: Dr Yiquan Gu, email: yiquan.gu@henley.reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: Yes

Last updated: 28 May 2024

Overview

Module aims and purpose

The module covers quantitative techniques and methods and their applications in the context of real estate. It aims to provide students with knowledge in statistics and econometric modelling with a focus on real estate data. This module will also introduce statistical software

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 module 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.

Module learning outcomes

By the end of the module, it is expected that students will be able to:  

• Demonstrate a systematic understanding of a range of applied statistical techniques.
• Comprehensively understand concepts relating to econometric modelling.
• Develop a practical understanding of the application and the use of quantitative methods with a focus on real estate markets.
• Develop a critical awareness of strengths and weaknesses of eceonometric modelling techniques.
• Evaluate outcomes of original empirical research.

Module content

• Introduction to econometrics and simple regression assumptions.
• Hypothesis testing.
• Multiple regression, t-ratios and testing multiple hypotheses.
• Goodness of fit statistics and hedonic models.
• Heteroscedasticity and autocorrelation.
• Multicollinearity, functional form, and normality.
• Model constancy and model specification.

Structure

Teaching and learning methods

The module is taught using a combination of lectures and seminars. Each week, one lecture will be devoted to presenting and discussing key concepts and methods. This will be followed by a seminar where concepts and methods are applied. Students are expected to complete all work sets before their seminars, and they may be called upon to answer questions.

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Formative assessment includes a series of seminar exercises where students attempt to solve problems. Participation is essential to acquire full knowledge. Seminars will provide feedback to students who want to improve their knowledge and they should be regarded as an effective feedback system to test abilities and ask for clarifications.

Weekly practice MCQ tests via Blackboard.

Reassessment

Additional costs