Lesson 1 of 3

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 Stats NZ data to show housing affordability trends.
Understand: How mathematical data reflects real-world social issues (Inequality).

He Kōrero Timatanga - 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.

Watch: Short video on the history of NZ housing (e.g., State Housing in the 1940s vs today).
Brainstorm: What factors affect house prices? (Location, Size, Supply, Interest Rates).

Part 2: Data Exploration

We will use a simplified dataset from Stats NZ containing:

Median House Price (National)
Median Household Income
Years: 2000 - 2024

📊 Data Visualization 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 (2000) = $180,000 / $45,000 = 4.0

                        Ratio (2024) = $900,000 / $110,000 = 8.1

Conclusion: Houses are mathematically twice as hard to buy today relative to income.

Part 3: Telling the Story

For NCEA Level 1 (AS 91945), simply doing the calculation isn't enough. You must interpret it in context.

Task: Write a paragraph summarizing your findings. Use the phrase "The data suggests that..." and refer to your calculated ratios.

🎬 Media Anchor

Use this clip to connect housing statistics with real inequality dynamics in Aotearoa.

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

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 apply mathematical skills (statistics, geometry, data analysis) to real Aotearoa housing and sustainability contexts — connecting mātauranga Māori principles of kāinga, papakainga, and whanaungatanga to contemporary housing challenges and design.

Ngā Paearu Angitū — Success Criteria

✅ Can collect, display, and interpret data about Aotearoa housing using appropriate statistical representations
✅ Applies geometric reasoning to evaluate sustainable design principles in whare design
✅ Connects mathematical findings to social justice questions about housing equity and Māori land rights

Differentiation & Inclusion

Scaffold support: Provide pre-structured data tables as an entry point for statistical analysis; use visual floor-plan templates for geometry tasks. Extension tasks include calculating comparative housing density statistics or modelling papakainga land-use scenarios.

ELL / ESOL: Pre-teach mathematical vocabulary alongside contextual terms (papakainga, whanaungatanga, toitū); use diagrams and real photographs of Aotearoa housing to ground abstract data.

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: Kāinga and papakainga as living mathematical contexts — whare design embodies geometric knowledge. Whanaungatanga shapes community housing decisions. Kaitiakitanga frames sustainability calculations. Māori land statistics connect tūhuratanga (inquiry) to tino rangatiratanga.

Prior knowledge: Basic statistics (mean, median, graphs); introductory geometry (area, perimeter, scale).

Curriculum alignment

Mathematics — Level 4 Statistics: Plan and conduct investigations using the statistical enquiry cycle; interpret findings in context.
Mathematics — Level 4 Geometry: Use side or edge lengths to find the perimeters and areas of rectangles, parallelograms, and triangles.