Pre-Experience PG Assessment Brief
\nModule Code and Title\tREMF37 Quantitative Techniques
\nModule Convenor\tProf Yiquan Gu (Convenor)
\nType of Assessment\t\u2610 Essay
\n\u2610 Report
\n\u2612 Project
\n\u2610 Practical Skills Exercise
\n\u2610 In-Class Test
\n\u2610 Group Report
\n\u2610 Group Presentation
\n\u2610 Portfolio<\/p>\n
Other: Click or tap here to enter text.<\/p>\n
Weighting of Assessment\t40% of Module Assessment
\nSubmission Deadline\t2.00 pm on Wednesday, 14th December 2022
\nSubmission Point (Blackboard\/Turnitin\/Other)
\nAll coursework submissions should be completed electronically on Blackboard via submission point on the relevant module site. No hard copy submissions will be accepted. Submission links will be available under \u2018Assignments\u2019 or \u2018Assessments\u2019 in the Blackboard Course menu for each module. You will be able to check the version uploaded during the process of submission.<\/p>\n
Items to be Submitted\tOne project ( help with nursing paper writing from experts with MSN & DNP degrees) in PDF format
\nIndividual or Group Assessment\t\u2612 Individual
\n\u2610 Group<\/p>\n
Module Convenor Office Hours\/Opportunities for advice and feedback\tProf Yiquan Gu
\nEmail: yiquan.gu@henley.ac.uk<\/p>\n
1. What is the Purpose of This Assessment?<\/p>\n
The following table shows which of the module learning outcomes are being assessed in this assignment. Use this table to help you see the connection between this assessment and your learning on the module.
\nModule Learning Outcomes Being Assessed<\/p>\n
\u2022\tApply a range of applied statistical techniques.
\n\u2022\tObtain a critical understanding of the concepts relating to econometric modelling.
\n\u2022\tApplication and use of quantitative methods with real estate data.
\n\u2022\tUse of software packages to perform analysis.
\n\u2022\tInterpret and evaluate published results of empirical research in real estate.<\/p>\n
2. What is the Task for this Assessment?<\/p>\n
Task (attach a separate briefing document if required)\tEconometric models are routinely applied to generate rental forecasts for commercial real estate markets. The purpose of this project ( help with nursing paper writing from experts with MSN & DNP degrees) is to analyse the current situation of the Hong Kong office market and provide an informed and analytically rigorous view of the market dynamics with a view to forecasting rental growth for the next few years.<\/p>\n
The main task is to produce a technical report on the outlook for Hong Kong office market. You aim to model rental growth looking at the possible determinants or drivers of the market. While the data for the dependent variable (i.e., the office rental index) and some independent variables are provided, you can collect additional independent variables that you deem appropriate in the modelling. The maximum number of independent variables you can use in the report is \u201c6\u201d.<\/p>\n
It is also important to state the source of your collected explanatory variables and why it is relevant for the intended purpose. Furthermore, you should provide an assessment\/in-sample forecast Assessment for rental growth in the last five years i.e. 2017 \u2013 2021 both years inclusive.<\/p>\n
3. What is Required of me in this Assessment?<\/p>\n
Guidelines\/Details of How to Prepare Your Submission\tAspects that you should take into consideration include the following:
\n1.\tAn overview and historic profile of trends in Hong Kong office market.
\n2.\tIdentify the drivers and provide an account of what has driven rent.
\n3.\tFormulate a multivariate regression model of explaining rental growth. Based on your expectations about the coefficient signs, interpret the results and comment on the quality of the model including the descriptive statistics.
\n4.\tEstimate an in-sample forecast Assessment on the outlook of office market rental growth.
\n5.\tDiscuss the limitations of any models, and the forecasts, that you have estimated.<\/p>\n
A good assignment will:
\n\u2022\tProvide a brief overview of the market segment;
\n\u2022\tDemonstrate an understanding of the underlying concepts;
\n\u2022\tDemonstrate an understanding of relevant data for empirical analysis;
\n\u2022\tDemonstrate an ability to carry out empirical analyses;
\n\u2022\tProduce a coherent report communicating your arguments and findings in a clear structure, explaining the steps and approach;
\n\u2022\tGenerally, show evidence of research, reading and effort.
\nThe assessment criteria to be used for marking this piece of work
\nRefer to the marking criteria rubric at the end of this document.
\nSelf-Regulation: Make sure That You\u2026\t\u2022\tRead the brief thoroughly and carefully.
\n\u2022\tRead the assessment criteria to make sure you tick all the boxes.
\n\u2022\tYou are likely to compute several combinations of models, you are ONLY expected to present two (2) or at most three (3) of the best models you compute, of which you will choose THE BEST MODEL to discuss.<\/p>\n
Three Key Pieces of Advice Based on the Feedback Given to the Previous Cohort who Completed This Assignment\t\u2022\tEnsure you provide enough motivation for the choice and selection of your variables.
\n\u2022\tEnsure you run stationarity tests for your series as well as apply and discuss diagnostic test.
\n\u2022\tEnsure you discuss your results to the full extent.
\nFor Group Work Only:\tElements of Group Working:<\/p>\n
\u2610 Classroom briefing by the Module Convenor
\n\u2610 Regular meetings of all team members
\n\u2610 Record and keep evidence of meetings (agenda\/minutes)
\n\u2610 Record attendance and member contributions
\n\u2610 Team reflection document
\n\u2610 Submit Peer Assessment required
\n\u2610 Summary and review of progress with module staff<\/p>\n
Formatting Guidelines
\n1.\tAbstract
\n2.\tIntroduction\/Background\/Historical Trends
\na.\tSome literature (e.g., academic articles and market reports).
\nb.\tHistorical trends based on the data provided.
\n3.\tModel Specification and Variable Description
\na.\tDiscussion of variables chosen \u2013 why? i.e. theoretical expectation.
\nb.\tSummary statistics of the variables.
\nc.\tChoice and rationale for an econometric model, e.g. regression equation.
\nd.\tData diagnostics; issues with the variables; any transformation.
\n4.\tResults and Analysis
\na.\tStep-by-step description\/explanation of all regression model specification.
\nb.\tModel diagnostic tests across alternative specifications.
\nc.\tChoice of a specific model \u2013 statement and rationale for the choice.
\nd.\tInterpretation of regression output.
\ne.\tExplanation and discussion of the forecast.
\n5. Conclusion<\/p>\n
Word Limit\/Guidance and Penalty Applied\tThe report should be rigorous and consistent with tools and techniques in the module. The word guide of the assignment is 2,500 words, excluding the abstract, content page, table\/figure, footnote, reference and appendix. 4% of the total marks available for the piece of work will be deducted if the project ( help with nursing paper writing from experts with MSN & DNP degrees) is over a maximum of 2,750 words.<\/p>\n
Referencing Style
\nHarvard Referencing
\nGuidance on Academic Misconduct (including using Turnitin practice area)\tThe work you produce must be your own or that of members of your group if it is a group assessment.<\/p>\n
You should ensure that the work you produce adheres to the University\u2019s statement on academic integrity and to the regulations regarding academic misconduct (such as plagiarism and cheating). You can find information about this at: http:\/\/www.reading.ac.uk\/internal\/exams\/Policies\/exa-misconduct.aspx<\/p>\n
You are encouraged to put a draft of your work through the Turnitin practice area to satisfy yourself that the work is your own original work. You can find this in your module area on Blackboard. To do this, click on the Academic Misconduct Assessment course in your Blackboard course drop down list and find the \u2018Turnitin Practice Area\u2019 in the left side column. Further information and support for how to use Turnitin and interpret originality reports is available under \u2018Support for Students\u2019 tab on Blackboard.<\/p>\n
4. What Resources Might I Use to Prepare My Work?<\/p>\n
\u2022\tLectures and Tutorials.
\n\u2022\tRecommended Readings:
\n1.\tPapastamos, D., Matysiak, G., & Stevenson, S. (2015). Assessing the Accuracy and Dispersion of Real Estate Investment Forecasts. International Review of Financial Analysis, 42, 141 \u2013 152.
\n2.\tBrounen, D., & Jennen, M. (2009). Local Office Rent Dynamics: A Tale of Ten Cities. Journal of Real Estate Finance and Economics, 39, 385 \u2013 402.
\n3.\tKarakozova, O. (2004). Modelling and forecasting office returns in the Helsinki area, Journal of Property Research, 21, 51\u2013 73.
\n\u2022\tTextbooks:
\n1.\tBrooks, C. and Sotiris T. (2010). Real Estate Modelling and Forecasting. Cambridge University Press.
\n2.\tBrooks, C. (2019). Introductory Econometrics for Finance (4th Edition). Cambridge University Press.<\/p>\n
While the data for the dependent variable (i.e., the office rental index) and some independent variables are provided, you can collect additional independent variables that you deem appropriate in the modelling. The maximum number of independent variables you can use in the report is \u201c6\u201d.<\/p>\n
NOTE: PLEASE DO NOT USE THE DATASET FOR ANY OTHER PURPOSES THAN THIS ASSIGNMENT.<\/p>\n
5. Submission and Late Submission Arrangements<\/p>\n
Submission point located in Assessment folder on Module Blackboard site<\/p>\n
Coursework submission procedure:
\nAll coursework should be submitted electronically via Blackboard. Submission links will be available under \u2018Assessment\u2019 or \u2018Assignment\u2019 in the Blackboard Course menu for the module. To submit, click on \u2018Assessment\u2019 or \u2018Assignment\u2019 in the Blackboard course menu, find the relevant link, and follow the instructions to submit your coursework.<\/p>\n
Please note that you will have the opportunity to submit more than once, in case of mistakes or errors; previous submissions of the assignment will be overwritten up until the due date and time. The final submission is the one that will be marked.<\/p>\n
\u2022\tYou will be able to check the version uploaded during the process of submission. If you do not receive a digital receipt acknowledging successful submission please email pg-rep-admin@reading.ac.uk.
\n\u2022\tFurther information and support can be found on the \u2018Blackboard Help (student)\u2019 tab on Blackboard where you will find a detailed student guide on how to submit Turnitin assignments along with other guides.<\/p>\n
Late Submission Penalty: \u2612 The University standard penalties apply (see below):<\/p>\n
\u2022\tWhere 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;
\n\u2022\tWhere 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.<\/p>\n
If you are having difficulties meeting the deadline due to illness or other extenuating circumstances, please see the ECF Guidance Pages for the steps that you need to take. If you have further queries regarding this process, please contact the Henley School Office.<\/p>\n
If you are experiencing technical difficulties at the time of submission, please email pg-rep-admin@reading.ac.ukor visit the HBS Admin deck.<\/p>\n
\u2610 Other: Click or tap here to enter text.<\/p>\n
Plagiarism:
\n\u2612 The University\u2019s standard policy on Academic Misconduct applies
\n\u2610 Other: Click or tap here to enter text.<\/p>\n
6. Feedback Arrangements<\/p>\n
Timing of feedback:
\n\u2612 Within 15 working days of submission deadline<\/p>\n
\u2610 When examinations marks are released<\/p>\n
\u2610 Other Click or tap here to enter text.<\/p>\n
Type of feedback:
\n\u2612 Mark
\n\u2612 Individual Feedback
\n\u2610 Audio Feedback
\n\u2610 Breakdown of Mark<\/p>\n
\u2610 Generic Feedback
\n\u2610 Comments written on the assessment
\n\u2610 Video Feedback
\n\u2610 Other: Click or tap here to enter text.<\/p>\n
Location of Feedback:<\/p>\n
\u2610 Blackboard
\n\u2610 RISIS<\/p>\n
\u2612 Turnitin
\n\u2610 Other: Click or tap here to enter text.<\/p>\n
Assessment Criteria Rubric – Postgraduate<\/p>\n
Module Code: REMF37\tModule Title: Quantitative Techniques\tAssessment Task: Project\tAcademic Year: MSc 2022\/2023<\/p>\n
GRADE BANDS
\nDistinction\tMerit\tPass\tFail
\nASSESSMENT CRITERIA
\n(Indicative Weighting)
\n(70+)\t(60-69)\t(50-59)\t(0-49)
\nCriterion 1 (20%):
\nVisual and qualitative overview of the market segment, demonstrating an understanding of key economic events in relation to the property market
\nAlmost complete appropriate qualitative discussion on the market overview with support of visual representations using graphs and charts.
\nLargely appropriate qualitative discussion on the market overview with some support of visual representations using a few graphs and charts.\tSound qualitative discussion on the market overview with little support of visual representations using few graphs and charts.\tLimited qualitative discussion on the market overview with no support of visual representations.
\nCriterion 2 (30%):
\nSpecification of a model and rationale for choosing variables whilst demonstrating underlying concepts and robustness checks<\/p>\n
Great effort of data collection, along with excellent understanding of the data based on statistics derived from the data which informs the derivation of models with strong evidence of their robustness.\tGood effort of data collection, along with good understanding of the data based on statistics derived from the data which informs the derivation of models with good evidence of their robustness.\tSome effort of data collection, along with
\nsound understanding of the data based on statistics derived from the data which informs the derivation of models with little evidence of their robustness.\tLittle effort of data collection, along with limited understanding of the data, no statistics derived, major mistakes in the derivation of models with no evidence of their robustness.
\nCriterion 3 (30%):
\nCommunication and presentation of key arguments based on empirical findings\tAlmost complete scrutiny of the evidence, ability to make logical and coherent arguments based on empirical findings.\tGenerally good scrutiny of the evidence, ability to make logical and coherent arguments based on empirical findings\tSound scrutiny of the evidence, little ability to make logical and coherent arguments based on empirical findings.\tLimited scrutiny of the evidence, little ability to make logical and coherent arguments based on empirical findings.
\nCriterion 4 (10%):
\nEvidence of research and reading relevant to the project ( help with nursing paper writing from experts with MSN & DNP degrees)
\nExcellent use of literature review, which extends beyond material introduced in the module.\tGood use of literature review, with little evidence of going much beyond material introduced in the module.\tSound use of literature review, with little evidence of going much beyond material introduced in the module.\tLimited evidence of the literature review.
\nCriterion 5 (10%):
\nClarity of organisation and structure
\nCompetent execution of structure and organisation of report and appropriate use of language, fluency, grammar and complete references.\tLargely good presentation of structure and organisation of report and generally correct use of language, grammar and references.\tSound presentation of structure, and organisation of report and reasonable use of language, grammar and incorrect references.\tWeak structure and organisation of report and limited use of appropriate language, grammar and limited or incorrect references.<\/p>\n
Note: Student coursework must adhere to the University of Reading policy on Academic Misconduct.
\nFurther information is available at: https:\/\/www.reading.ac.uk\/exa-misconduct.aspx<\/p>\n","protected":false},"excerpt":{"rendered":"
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