Group coursework

2 - Written assignment (including essay) - 25

Exam

1 - Written exam - 75

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The module focuses on statistical techniques for all aspects of real estate research, occupational, investment, development and portfolio. The techniques learnt in this module will be applied and expanded on in other modules.  
<ul><li>Characteristics, descriptive statistics and presentation of real estate data.</li><li>Probability assessment of market outcomes and use of confidence intervals in real estate investment.</li><li>Application of hypothesis testing in real estate investment analysis.</li><li>Regression analysis and its applications to real estate. .</li><li>Model building and diagnostics checking.</li><li>Quantification of the impact of shocks and major disruptors on real estate markets.</li><li>Random walks, unit roots and mean reversion and their relevance to real estate modelling and investment decisions.</li><li>Empirical applications of statistical techniques to building level data for valuation purposes.</li><li>Production of real estate market forecasts and market scenarios.</li><li>Linking module content to professional practice through senior guest speakers from the industry.</li><li>Practical applications throughout the module using Excel and a statistical software.</li></ul>

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

Directed Asynchronous - 

Self-directed - 

In-Person - 

Real Estate Modelling and Forecasting.

Applied Quantitative Analysis for Real Estate

London ; New York, Ny: Macmillan Education Palgrave.

 Econometrics by example.

SMM196 - Applied Statistics for Real Estate Analysis and Investment 

SMM196-PRD1-PER-ALV1

This module will be taught primarily through lectures and computer workshops and will also make use of practical examples and class discussions.

Contact hours are designed to be flexible and are delivered using a variety of methods to help you learn in the most effective way. A proportion of the core content may be delivered online and additional support materials and content will also be made available through Moodle.

Applied Statistics for Real Estate Analysis and Investment 

Communicate complex ideas and carry out independent research. 

Skills

Use appropriate quantitative analysis to study empirical relationships such as the impact of green certification on building pricing 

Knowledge

Apply learnt techniques to empirical investigations in other modules. 

Assess the practical purpose and pragmatic application of quantitative analysis 

Values and attitudes

Appreciate the key features of real estate data quality and their impact on research. 

Apply and utilise quantitative research methods relevant to real estate. 

Competent use of EViews econometric software and advanced use of Excel. 

Demonstrate knowledge and ability to apply key statistical methodologies to real estate market analysis in practice.  

Interpret results from quantitative analysis.  

Comprehend various statistical techniques used in real estate in practice. 

Apply statistical tools in a practical and relevant to the industry manner. 

Investigating market situations with real-world data. 

Build strong research and analytical skills. 

Critically evaluate relationships between the real estate market, the economy and capital markets. 

Write an industry report with clear interpretations after conducting statistical analysis. 