Justice pitch: interrogate the measure
Ākonga ask what the housing statistic COUNTS and what it hides — who is left out of a definition of homelessness or affordability, and how that changes the conclusion.
- Years 9–10 move: The measure itself put under scrutiny
- Evidence it produces: A stated limitation of the measure that changes the policy reading
Before you plan from this: the justice pitch below is now authored into the lesson body. The other route's body still overlaps strongly with this one but differs in emphasis — choose either route on topic and year band.
Data Detective: Housing in Aotearoa
Understanding the crisis through numbers
Ako | Learning Intentions
- Know: The difference between median and mean house prices and why we use percentages to compare data over time.
- Do: Create a time-series graph using the illustrative housing dataset to show housing affordability trends.
- Understand: How mathematical data reflects real-world social issues (Inequality).
He Kōrero Tīmatanga - Introduction
Everyone talks about how expensive houses are in New Zealand. But what does the data actually say? As mathematicians, our job is to look past the headlines and find the evidence.
Discussion Starter
"If your parents bought a house for $200,000 in 2000, and it's worth $1,000,000 today, did they make an $800,000 profit?"
Consider: Inflation, interest payments, maintenance, and the buying power of money.
Part 1: The Context (Whakawhanaungatanga)
Before looking at numbers, we need to understand the human side.
- Discuss: the history of NZ housing (e.g., State Housing in the 1940s vs today) — your kaiako may share photos or clips here.
- Brainstorm: What factors affect house prices? (Location, Size, Supply, Interest Rates).
Part 2: Data Exploration
We will use an illustrative dataset for the exercise — not Stats NZ figures — containing:
- Median House Price (National)
- Median Household Income
- Years: 2000 - 2024
📊 Data Visualisation Exercise
Students plot "Median House Price" vs "Median Income" on the same timeline.
Guiding Question: The lines are moving apart. Calculate the ratio of House Price to Income for 2000 vs 2024.
Ratio (2024) = $900,000 / $110,000 = 8.2
Illustrative figures for the exercise — not real housing-market data; real figures are kaiako-supplied.
Conclusion: In this dataset, the price-to-income ratio doubles — houses are twice as hard to buy relative to income. Check whether the real figures your kaiako supplies tell the same story.
Part 3: Telling the Story
Doing the calculation isn't enough. You must interpret it in context.
Task: Write a paragraph summarising your findings. Use the phrase "The data suggests that..." and refer to your calculated ratios.
🎬 Media Anchor
This clip is a global documentary on how wealth becomes power — it is not about Aotearoa. Use it for the general mechanism, then bring the discussion home with the housing figures below.
- Pause and discuss: Which claim from the video can you test directly with your dataset?
- Transfer task: Add one evidence sentence linking your ratio calculation to a social impact.
Kaiako Notes
Real figures are kaiako-supplied — the worked ratios above are illustrative only. Be sensitive to students' housing situations. Focus on the systemic data rather than individual family circumstances. This is an opportunity to discuss equity vs equality.
📋 Teacher Planning Snapshot
Ngā Whāinga Ako — Learning Intentions
Students use the statistical enquiry cycle to interrogate Aotearoa housing data — defining what each measure counts and who it leaves out, then collecting, displaying, and describing the distribution a measure produces. The justice pitch runs throughout: every figure is read as an argument that can be contested.
Ngā Paearu Angitū — Success Criteria
- ✅ Can define a housing measure precisely — what it counts, who it leaves out — before using it
- ✅ Can collect, display, and interpret Aotearoa housing data using appropriate statistical representations
- ✅ Can state one limitation of the measure that changes the policy reading, grounded in their own display
Differentiation & Inclusion
Scaffold support: Provide pre-structured data tables and sentence starters for the definition critique as an entry point. Extension tasks include recomputing the measure under a fairer definition and comparing the two distributions.
ELL / ESOL: Pre-teach statistical vocabulary alongside contextual terms (kāinga, papakainga, whanaungatanga); use annotated displays and real Aotearoa housing headlines to ground abstract definitions.
Inclusion: Offer manipulatives and digital tools alongside written tasks; neurodiverse learners benefit from step-by-step data investigation guides and reduced open-ended prompts.
Mātauranga Māori lens: Data has a whakapapa — where each housing and income figure comes from, who collected it, and what it leaves out. Kāinga and papakainga ground the statistics in real households; tūhuratanga (inquiry) is the same discipline this lesson’s data detective work practices.
Prior knowledge: Mean and median; reading time-series graphs.
Curriculum alignment
- Te Mātaiaho (2025) · Mathematics and Statistics · Phase 4 (Years 9–10) · Statistics (Practices): “- Critically considering data visualisations, including those from contemporary media, to see if they support or misrepresent the data”
- NZC (2007) · Mathematics and Statistics · Level 5: “Plan and conduct surveys and experiments using the statistical enquiry cycle: – determining appropriate variables and measures; – considering sources of variation; – gathering and cleaning data; – using multiple displays, and re-categorising data to find patterns, variations, relationships, and trends in multivariate data sets; – comparing sample distributions visually, using measures of centre, spread, and proportion; – presenting a report of findings.”