ASSESSMENT GUIDE
COMM5000
Data Literacy
Final Report
Housing Market Trends & Affordability: A Data-Driven Business & Policy Analysis
Term 1, 2025
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.
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Your role as Data Scientist
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.
Final Report: Case Study Business Report
Report details
Week 11, Sunday May 4th 11:59PM
40%
Report: This is individual work. Reports will be checked for plagiarism.
1400-1700 words (5-8 pages not including tables, graphs, and references)
Via Moodle course site
Case Study Business Report: Applied Housing Intelligence for Reform
You are a Housing Market Data Analyst working for a cross-sectoral advisory taskforce led by the Department of Housing and Urban Infrastructure. This taskforce was formed in response to mounting concerns around declining housing affordability, rising inequality, speculative bubbles in key suburbs, and uneven access to mortgage credit.
The purpose of this advisory group is to bring together diverse perspectives and expertise from across government, finance, planning, and development to build evidence-based housing policy reforms. Your role is to provide robust, ethically responsible, and actionable modeling insights that help resolve tensions between economic growth, market stability, and social inclusion in housing.
The final assessment simulates your culminating task as part of this taskforce. You are responding to a series of stakeholder briefs, each grounded in plausible economic or policy concerns. Your work builds on earlier insights (Milestone 1 and 2) and now requires you to synthesize those skills using more advanced techniques such as multiple regression, causal reasoning, predictive modeling, and theory- informed analysis.
Grounding Framework: The Hedonic Pricing Model
Before responding to the stakeholder briefs, you are required to ground your analysis in the hedonic pricing model — a core economic framework used to estimate the value of individual characteristics of a differentiated good such as housing.
In theory, the price of a property Pi reflects the sum of the implicit prices of its characteris- tics (X1i, X2i,..., Xki ), including structural (e.g., bedrooms, bathrooms), locational (e.g., proximity to CBD), and neighborhood features (e.g., school zone, crime rates).
Pi = f(X1i, X2i,..., Xki ) + εi
By applying multiple regression, we estimate the function f(·) using observed data. This yields:
log(Pi ) = β0 + β1 X1i + β2 X2i + ... + βkXki + εi
This log-linear specification allows us to interpret coefficients as percentage changes in price for one-unit changes in each attribute, holding others constant. You will be using this structure as a benchmark. In the briefs, Hedonic model is also referred to also as log model. In fact, the latter is one interpretation of the Hedonic price model.
Stakeholder Briefs
1. Government Housing Policy Unit
”Recent claims suggest that home prices in high-PIR suburbs have grown faster than income in the past five years. This might suggest a bubble. We’re considering a cap on developments in areas where affordability has deteriorated the most. But is there really a consistent pattern?”
Context: The government is under pressure to curb speculation and improve housing access. Your analysis should help determine whether PIR levels systematically predict higher price growth — and if so, under what conditions. Do prices behave differently in high-PIR suburbs once structural differences are controlled for? How might these patterns vary by state or property type?
2. Urban Planning Authority
”We’re updating zoning laws and want to know how much property features (e.g., number of bedrooms, car space) matter for price in different states. Some people say these things matter less in dense inner-city markets than in suburban ones. Is this true?”
Context: Planners want to know whether existing zoning restrictions align with how buyers actually value property features. Your task is to test the hedonic assumptions about structural attribute values and examine whether their implicit prices vary significantly by location or density. Are interaction effects (e.g., bedrooms × state) statistically and economically meaningful?
3. Mortgage Lender
”We’re rethinking lending criteria. There’s concern that applicants in high-density regions are over- leveraged despite lower property values. We want to know: do higher prices reflect true structural value, or are they inflated by speculative location factors?”
Context: Lenders need to assess collateral risk. If locational premiums drive price inflation without structural justification, loan portfolios may be vulnerable. Your model should help decompose price into structural and locational components and assess whether some areas pose more risk due to overvalua- tion. Consider using dummy variables for high-density postcodes or interaction terms to reveal hidden dynamics.
4. Private Developer
”We’re building a new mixed-use development. Our sales team claims that adding more car spaces or ensuite bathrooms can raise the price significantly. Can you help us forecast potential returns for different unit configurations?”
Context: Developers need evidence-based pricing for design choices. Your job is to simulate predicted prices (with CIs and PIs) for different hypothetical units using your hedonic model. Use model outputs to estimate returns on design changes, and discuss the risk of overcapitalising in certain suburbs or unit types.
5. Developer Planning Simulation
You are working with a mid-tier property developer planning a new residential project in the subur- ban fringe of Greater Melbourne. The project aims to deliver moderately priced, higher-density dwellings that balance affordability and lifestyle appeal for young families and downsisers.
The developer is evaluating whether to allocate more funding toward car parking, outdoor space, or higher-end finishes (like ensuite bathrooms and brick veneer structures). They’re particularly interested in understanding how these features are likely to impact expected sale prices under current market conditions, using historical data as a benchmark.
To assist in this investment decision, you’ve been asked to provide a price prediction for a proposed unit type:
“We’re targeting a middle-income couple looking to buy their first home. The property will be a 3-bedroom townhouse, built with modern detached design, offering two bathrooms, a car space of about 80 sqm, and a small garden area of 25 sqm. It’s located about 30 minutes from Melbourne CBD in a growing suburb with high family appeal.”
6. Ethics and Public Interest Watchdog
”There is public concern about algorithmic bias in housing data. Some developers claim their models are objective — but we worry that underrepresented suburbs or buyer segments may be systematically excluded. What are the dangers here?”
Context: Ethical scrutiny is increasing. Your model must be examined for bias, omitted variables, or inference risks. Reflect on whether your results reinforce inequities. Are assumptions (e.g., linearity, zero conditional mean) violated? What mitigation steps could improve transparency and fairness?