## RE3SQT-Statistics and Quantitative Techniques

Module Provider: Real Estate and Planning
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Yiquan Gu

Type of module:

Summary module description:

This module covers core statistical concepts and basic econometric modelling with a focus on applications in the real estate environment. The main goal is to develop an understanding for applied quantitative data analysis.

Since the module focuses on applications, students learn how to use statistical software, enabling them to implement theoretical concepts independently. Focusing on the real estate context, students will learn how to describe, modify and visualise data. Students will use econometric modelling to test research hypotheses and build forecasting models.

Aims:

The module will cover statistical concepts and basic econometrics. It will introduce research methods commonly used in research or investment decisions. Lastly, the module aims to introduce students to statistical software.

Assessable learning outcomes:

The module introduces contemporary statistical, and econometric quantitative techniques. The importance of the unique characteristics of property markets will be emphasised, as well as their impact upon the nature of applied quantitative analysis.

Upon completion of the module, it is expected that the student will be able to:

• Apply a range of statistical techniques;

• Develop, implement and test simple econometric models;

• Have a critical understanding of the concepts relating to econometric modelling;

• Understand the application and the use of quantitative methods for real estate data analysis;

• Verbally describe data and analysis results.

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

Outline content:

Introduction to Statistics:

• Central tendency, and variability measures

• Sampling and Hypothesis Testing

• Probability Distributions

Using statistical inference:

• Hypothesis development

• Ordinary Least Squares (OLS) Estimation and Hypothesis Testing

• Multivariate OLS and Diagnostic Testing

• Introduction to Time-Series and forecasting

Brief description of teaching and learning methods:

The module will comprise lectures and practical 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 10 Practicals classes and workshops 7 Guided independent study: Wider reading (independent) 28 Preparation for seminars 10 Essay preparation 45 Total hours by term 100 0 0 Total hours for module 100

Summative Assessment Methods:
 Method Percentage Project output other than dissertation 100

Summative assessment- Examinations:
None

Summative assessment- Coursework and in-class tests:

One piece of project-based coursework that will involve statistical analysis, interpretation of results, and production of a report of approximately 2,500 words.

The submission deadline of the report is in week 7 of the Spring term (timetable week 26).

Formative assessment methods:

Penalties for late submission:

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

• 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 five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/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.

Assessment requirements for a pass:

A mark of at least 40%.

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.