ASSESSMENT GUIDE
COMM5000
Data Literacy

Milestone 2
Housing Market Trends &
Affordability: A Data-Driven
Business & Policy Analysis

Milestone 1 Information Term 1, 2025










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Table of Contents
Assessment Summary .................................................................................................................................................................................. 2
Assessment Administrative Details (Check Course Outline/Moodle) ...................................................................................................... 3
Turnitin .................................................................................................................................................................................................................................................. 3
Late Submissions ................................................................................................................................................................................................................................. 3
Extensions ............................................................................................................................................................................................................................................ 3
Special Consideration ........................................................................................................................................................................................................................... 3
CASE STUDY INFORMATION-- Housing Market Trends & Affordability Project Statement .................................................................. 4
Business and Economic Context .................................................................................................................................................................................. 4
The Dataset: Australian Housing Market Overview .................................................................................................................................................... 5
MILESTONE 2: Case Study Project Insight Analysis ................................................................................................................................. 6
Report details .................................................................................................................................................................................................................. 6
Milestone 2: Advanced Statistical Analysis of Housing Market Trends ................................................................................................................... 6
Question 1. Housing Affordability – Conflicting Perspectives from Policymakers and Financial Institutions ........................................................................................ 6
Tasks and expectations ........................................................................................................................................................................................................................ 7
Key Considerations ............................................................................................................................................................................................................................... 8
Question 2. Property Type and Market Valuation Study ....................................................................................................................................................................... 8
Tasks and Key Insights to Uncover ...................................................................................................................................................................................................... 8
Report Submission Expectation ................................................................................................................................................................................... 8
Student Guidelines for Writing the Report ............................................................................................................................................................................................ 9

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Assessment Summary
Assessment Task Weighting Due Date* Course Learning
Outcomes
Milestone 1: Case Study Preliminary Insight Development (due in
Week 4 20%)
Online Quiz in Week 4 during seminar time (TBA in Moodle)
15%

5%
Week 4 (Friday 5PM) 1, 2
Milestone 2: Case study project proposal
Online Quiz (will open in Week 6)
15%
5%
Week 7 (Sunday 11:59PM)
Week 7 (Sunday 11:59PM)
1, 2, 3, 4
Case Study business report
Online Quiz in Week 10 in Seminar time (TBA in Moodle)
40%
20%
Week 11 (Friday 11:59 PM) 2, 3, 4, 5
* Due dates are set at Australian Eastern Standard/Daylight Time (AEST/AEDT). If you are located in a different time-zone, you can use the time and date converter.

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Assessment Administrative Details (Check Course Outline/Moodle)
Turnitin
Turnitin is an originality checking and plagiarism prevention tool that enables checking of submitted written work for
improper citation or misappropriated content. Each Turnitin assignment is checked against other students' work, the
Internet and key resources selected by your Course Coordinator.
If you are instructed to submit your assessment via Turnitin, you will find the link to the Turnitin submission in your
Moodle course site. You can submit your assessment well before the deadline and use the Similarity Report to
improve your academic writing skills before submitting your final version.
You can find out more information on the Turnitin information site for students.
Late Submissions
The parameters for late submissions are outlined in the UNSW Assessment Implementation Procedure. For
COMM5000, if you submit your assessments after the due date, you will incur penalties for late submission unless you
have Special Consideration (see below). Late submission is 5% per day (including weekends), calculated from the
marks allocated to that assessment (not your grade). Assessments will not be accepted more than 5 days late.
Extensions
You are expected to manage your time to meet assessment due dates. If you do require an extension to your
assessment, please make a request as early as possible before the due date via the special consideration portal on
myUNSW (My Student profile > Special Consideration). You can find more information on Special Consideration and
the application process below. Lecturers and tutors do not have the ability to grant extensions.
Special Consideration
Special consideration is the process for assessing the impact of short-term events beyond your control (exceptional
circumstances), on your performance in a specific assessment task.
What are circumstances beyond my control?
These are exceptional circumstances or situations that may:
• Prevent you from completing a course requirement,
• Keep you from attending an assessment,
• Stop you from submitting an assessment,
• Significantly affect your assessment performance.

Available here is a list of circumstances that may be beyond your control. This is only a list of examples, and your
exact circumstances may not be listed.
You can find more detail and the application form on the Special Consideration site, or in the UNSW Special
Consideration Application and Assessment Information for Students.
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CASE STUDY INFORMATION-- Housing Market Trends & Affordability Project Statement
Business and Economic Context
The housing market is a critical sector influencing government policies, financial institutions, real estate investors, and urban planners. Property prices,
affordability, and market trends impact economic stability, investment risks, and infrastructure development.
Government housing agencies need to assess affordability trends to develop policies for first-time homebuyers and low-income families. Real estate investors
and developers require insights into high-growth suburbs to determine where to build or invest. Financial institutions and banks analyse property data to evaluate
mortgage risks and loan eligibility. Urban planners and infrastructure authorities depend on market insights to plan future housing projects based on demand
and population growth. These stakeholders rely on data-driven analysis to make informed decisions about housing policies, market investments, and economic
development.
In major cities like Sydney, Melbourne, and Brisbane, housing affordability is a growing concern. With property prices outpacing wage growth, many struggle to
enter the market, increasing the need for government intervention and affordable housing initiatives. Monitoring housing trends helps policymakers craft
effective policies to improve homeownership access and ensure fair housing opportunities.
Beyond affordability, real estate is a key driver of employment in construction, finance, and property services. The Reserve Bank of Australia (RBA) adjusts
interest rates in response to market shifts, influencing mortgage holders and consumer spending. Rapid price surges may require regulatory adjustments to
maintain economic stability. Analysing property data enables decision-makers to anticipate market changes and implement necessary financial measures.
For many Australians, property is both a home and a long-term investment. Housing prices impact wealth accumulation, retirement planning, and
intergenerational wealth transfer. Investors and financial institutions rely on market trends to assess risks and identify opportunities. Disparities between urban
and regional property markets also shape internal migration as people and businesses seek affordability and economic prospects.
The Australian housing market is also shaped by global factors such as economic trends, immigration policies, and foreign investment. Economic downturns,
trade shifts, and crises like COVID-19 have all impacted supply and demand. Tracking housing prices allows businesses and governments to anticipate risks
and develop strategies for market resilience.
Studying housing prices is more than tracking property values—it is a fundamental part of economic planning, investment strategies, and urban development.
By analysing real estate trends, decision-makers can shape policies that drive economic growth and improve the quality of life for Australians.
https://www.youtube.com/watch?v=P2chYfJ4cRs
https://www.youtube.com/watch?v=LBKIloe1Zuc

Your role as Data Scientist
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As a Housing Market Analyst, your role begins with an Exploratory Data Analysis (EDA) using descriptive statistics and visualization techniques to uncover
patterns, variations, and key trends in housing prices. This is the foundation of data-driven decision-making, where you will summarize distributions, identify
outliers, and assess relationships within the data.
Once a clear understanding of the dataset has been established, the focus will shift toward formulating key hypotheses, allowing us to test theories and claims
about the factors driving property prices. This step will help in identifying potential causal relationships, which will later be examined using statistical modelling
and inferential techniques. Ultimately, this process will enable us to move beyond simple observations and establish evidence-based insights that support
strategic decision-making in the housing market.

The Dataset: Australian Housing Market Overview
You will be working with a dataset containing real estate property records. The dataset includes information on property characteristics, pricing, and location
details. The key categories of information in the dataset are:
Location Data → State, suburb, street name, postcode.
Market Information → Market price of the property.
Property Characteristics → Building type, number of bedrooms, number of bathrooms.
Structural Features → Living area size, car area, outdoor area.







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MILESTONE 2: Case Study Project Insight Analysis
Report details

Week 7, Friday 11:59PM

15%

Report: This is individual work. Reports will be checked for plagiarism.

1000-1500 words (not including tables, graphs, and references)

Via Moodle course site

Milestone 2: Advanced Statistical Analysis of Housing Market Trends
This milestone builds on your EDA from Milestone 1 by applying confidence intervals, hypothesis testing, to real estate data. You will analyse housing trends using statistical
inference techniques and interpret your findings in the context of investment decisions, and housing policy.
Data Used in This Milestone
The dataset you analysed in Milestone 1 represents housing market data for 2017-18, and you will continue using the same dataset for this milestone. Your goal is to
apply statistical inference techniques to test affordability trends and market risks based on the available data, comparing them with external benchmarks to inform key
stakeholders.
To enhance engagement, you will assume the role of a data scientist advising a Real Estate Market Advisory Board, composed of investors, policymakers, mortgage lenders,
and urban planners. Your task is to analyse available housing market data and provide evidence-based recommendations to stakeholders who rely on statistical insights for
decision-making.
Question 1. Housing Affordability – Conflicting Perspectives from Policymakers and Financial Institutions
Stakeholders Scenario Housing affordability remains a major policy issue in Australia, but the way it is measured influences the conclusions drawn and the
policies implemented. Government policymakers and housing advocates argue that affordability should be assessed based on the proportion of properties
accessible to median-income households. They focus on the percentage of properties affordable to a household earning the median income, using the (5 x income
rule). This measure reflects how many properties are within reach of middle-income buyers and is used to shape affordability programs, homebuyer incentives,
and zoning regulations to promote accessible housing.
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Financial institutions, mortgage lenders, and property investors take a different approach. Rather than focusing on the percentage of affordable properties, they
rely on the Price-to-Income Ratio (PIR), which compares median property prices to median household income. This measure provides a broader view of long-term
market sustainability. A high PIR suggests that house prices are increasing faster than incomes, raising concerns about potential overvaluation and financial
instability. Lenders may adjust mortgage approval criteria based on PIR trends, while investors assess housing market risk and potential price corrections.
This debate has gained urgency with recent reports suggesting a decline in affordability. PropTrack (2023) estimates that only 13% of homes are now affordable
for a median-income household, reinforcing concerns that affordability has worsened. The question remains whether affordability has significantly declined
since 2018 and what that means for future policy and financial decisions.
Policymakers will look at whether affordability has declined to justify homebuyer support programs, zoning changes, or subsidies. If the affordability proportion
is significantly lower than the 13% benchmark, they may advocate for stronger interventions. Lenders and investors will use PIR to assess whether housing
markets are overheating and whether tighter mortgage lending rules are necessary. If PIR is much higher than 5× income, financial institutions may impose
stricter borrowing requirements. Urban planners must consider how affordability trends impact future housing supply needs.
You task is to advise the board whether the two measures of affordability lead to different conclusions and discuss which measure is more relevant for specific
stakeholders. You must consider doing the analysis by state, ie., for each of NSW, VIC and QLD.
Median household income values from 2018 based on ABS data for each state:
• NSW: $52,800
• VIC: $51,300
• QLD: $49,500
Tasks and expectations
• Compute the sample affordability measures for each state.
• Perform hypothesis tests for each state to determine if the affordability percentage in 2017-18 is significantly higher than the 2023 benchmark (13%):
o Clearly state the null and alternative hypotheses.
o Compute the test statistic and p-value.
o Interpret the results to determine whether affordability has significantly declined.
• Compare and interpret the results for different stakeholders:
o For policymakers: If affordability has declined significantly, what interventions should be considered?
o For financial institutions and investors: Does the PIR suggest housing markets are overheating, and should lending policies be adjusted?
o For urban planners: How should the findings guide housing supply strategies and zoning policy
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Key Considerations
• For policymakers: Which measure better captures housing stress for lower-income households? Should affordability policies be designed around
income-adjusted affordability thresholds or household expenditure burdens?
• For investors and lenders: Does the 5× median income measure provide a more stable long-term view of affordability? How should mortgage lenders
adjust lending criteria if housing stress levels are rising?
• For urban planners: If different regions experience affordability stress differently based on the measure used, how should zoning regulations and housing
development strategies be adjusted?
Question 2. Property Type and Market Valuation Study
Stakeholders Scenario Real estate investors seek to maximize returns by identifying property types with the best potential for appreciation. They need to
determine whether differences in pricing trends reflect genuine market advantages or are merely due to sample variability. Mortgage lenders assess property
type risks to adjust financing terms based on investment stability, ensuring they are not overexposed to volatile segments. Urban planners analyse price trends
to guide future zoning policies and housing supply allocations, ensuring that development aligns with market demand. Meanwhile, government policy analysts
evaluate property valuation trends to shape tax policies and housing incentives, aiming to balance affordability with sustainable market growth.
Tasks and Key Insights to Uncover
Your objective is to assess differences in prices across property types, applying statistical inference techniques to derive meaningful insights. Key areas of
focus include evaluating price trends across property types, identifying which segments exhibit higher median and average prices, and examining price variability
by constructing sampling distributions and estimating the statistical significance of observed differences. Your objective is to conduct a statistical analysis to
determine whether differences in property prices across houses, apartments, and townhouses are statistically significant or due to random variability. You will
complete the following tasks:
• Conduct Hypothesis Testing: Use pairwise t-tests to determine whether differences in property prices are statistically significant.
• Interpret Results for Investment Decision-Making: Explain whether certain property types consistently offer better returns or if price differences are
driven by sample variability.
Report Submission Expectation
Your role as a Housing Market Analyst is to provide clear, data-driven insights, not lengthy descriptions. Focus on answering the questions concisely and
meaningfully while ensuring your findings are useful to stakeholders. Your report should be concise and structured, focusing on clear numerical summaries,
visualizations, and insightful interpretations.
Word Limit: 1,500 words (excluding tables & figures)
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Submission Format: ONE file in PDF or Word Document
Key Focus: Be concise, structured, and insightful—avoid unnecessary descriptions.
Student Guidelines for Writing the Report
You are responsible in allocating word count depending on your analysis. Total word count MUST be at maximum 1500 words
1. Executive Summary (10%)
- Clearly summarize key findings from both affordability and property valuation analyses.
- Highlight main trends in affordability measures, price distributions, and statistical significance of differences.
- Provide one or two key insights relevant to stakeholders.

- Housing Affordability Analysis (40%) Compute affordability measures for NSW, VIC, and QLD.
- Conduct hypothesis testing to determine if affordability in 2017-18 is significantly higher than the 2023 benchmark
- Compare results across the two affordability measures and discuss their implications for different stakeholders.
- Interpret findings in the context of policy interventions, mortgage lending, and housing supply strategies.

2. Property Type and Market Valuation Study (40%)
- Compute summary statistics (mean, median, standard deviation) for property prices by type.
- Construct sampling distributions and confidence intervals for mean prices.
- Perform hypothesis testing to assess whether observed price differences are statistically significant.
- Discuss the significance of price differences and whether certain property types consistently offer better returns.
- Evaluate risks for investors and implications for mortgage lending.
3. Conclusion & Recommendations (10%) Provide a concise summary of key findings from affordability and property valuation analyses, focusing on major
trends and statistical results.
- Offer high-level recommendations for policymakers, investors, and urban planners based on statistical insights, ensuring they are actionable and
relevant.
- How should affordability policies be designed based on statistical findings?
- Should mortgage lending policies be adjusted based on PIR trends and affordability stress?
- What zoning or development policies should be implemented to balance affordability and market growth?
- Briefly mention limitations and suggest directions for future analysis, keeping the focus on practical implications.

Report Structure & Writing Style
• Use clear section headings and a logical flow.
• Keep writing concise—avoid unnecessary details.
• Use bullet points sparingly, only when summarizing key insights.
• Use professional, neutral language suitable for a business audience.



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