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 analyze 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 craft trends helps policymakers
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 analyzing real estate trends, decision-makers can shape policies that drive economic growth and improve the quality of life for Australians.
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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 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 dataset examined using statistical modeling
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.
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 analyze 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 analyzed 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 analyze 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 earns 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, lenders mortgages, 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 mortgages lenders
adjust lending criteria if housing stress levels are rising?
• For urban planners: If different regions experience affordability stress differently based on the measure, how should zoning regulations used 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 analyze 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 relevant insights 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
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.