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Property Data Engineering Sprint - Part 4

Multi-page Power BI dashboard with interactive analysis of property, rental, crime, school, and transport data built over a dimensional model.

Project Overview

  • Objective: Build an interactive Power BI dashboard over the star schema to explore state-wide housing and public service data by location.
  • Tools Used: Power BI, SQL Server (DW connection), DAX, Drillthrough, Custom Groups/Buckets, Map Visualizations


Dashboard Pages

  • House Value: Distribution and median trends by suburb, city, and state with grouped value buckets.
  • Rental Value: Analysis by property type, region, and grouped rent brackets.
  • School Data: Breakdown by type, location, and number of schools with maps and filters.
  • Transport: Count of stations by city and suburb, mapped by transport mode.
  • Crime Summary: Incidents grouped by category and subcategory with slicers by region and date.
  • Summary Page: High-level stats and KPIs with clickable drillthrough to detailed pages.
  • Home Page (Optional): Navigation tiles with image buttons linking to individual reports.


Key Features

  • Dynamic Bucketing: Created DAX columns to group house and rent values into ranges (e.g. “$400K–$600K”, “Above $600”).
  • Drillthrough Enabled: Clickable summary page metrics link directly to filtered report views.
  • Custom Tooltips: Hover over charts to see more details like suburb count and average metrics.
  • Fully Connected Data Model: Connected fact tables to dimensions through appropriate surrogate keys with proper cardinality and cross-filtering.


Insights Discovered

  • House prices above $800K were heavily concentrated in capital city suburbs.
  • Rental affordability was lowest in high-transport-density suburbs with good schools.
  • Crime reporting showed higher variation by suburb than by state, supporting location-based policy recommendations.


Key Skills Demonstrated

  • Interactive report building using Power BI's visual and modeling tools.
  • Custom DAX measures and calculated columns for grouping and conditional analysis.
  • Map visualizations using latitude/longitude from dimension tables.
  • User-friendly dashboard layout with navigation and drillthrough support.


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