https://github.com/Arun-Kumar-Venugopal/Housing-Market-Analysis-PowerBI-BigQuery
This project delivers an end-to-end housing market analytics solution using Google BigQuery as the cloud data source and Power BI for visualization.
It demonstrates:
- Cloud data ingestion using BigQuery
- SQL-based data transformation
- Advanced DAX time intelligence
- Multi-page, interactive Power BI dashboards
- Deployment to Power BI Service
This dashboard enables real estate analysts and investors to understand housing market trends. It provides insights into sales growth, pricing behavior, regional performance, and property type analysis to support data-driven investment and market strategy decisions.
Stakeholders need visibility into housing market performance, pricing trends, and regional variations to support real estate investment and strategic planning.
- Year-over-Year Sales Growth
- Median Sales Price Change
- Units Sold (Latest Year & Quarter)
- Last 12-Month Sales
- Average Price per SQM
- Offer-to-Purchase Price Ratio
- Housing data is a sample dataset and may not reflect real market conditions
- Inflation and interest rates are treated as static per record
- Analysis focuses on historical trends only
Google BigQuery
→ Power BI Desktop (SQL, Power Query, DAX)
→ Power BI Service (Publish & Sharing)
This analysis focuses on:
- Housing price trends over time
- Regional and property-type performance
- Pricing behavior across sales types
- Time-based growth metrics (YOY, YTD, rolling periods)
- Platform: Google BigQuery
- Dataset: Housing
- Records: ~100,000
- Schema: Auto-detected
- Created test tables for analysis
- Applied conditional updates
- Aggregated sales metrics by sales type and region
- Column profiling on full dataset
- Null value handling and replacements
- Data type validation and cleanup
- Year-over-Year Sales Growth
- Median Sales Price Change by Region
- Units Sold (Latest Year & Quarter)
- Offer Price vs Purchase Price (Scatter Plot)
- Sales by Region
- Key Influencers analysis
- Offer to SQM Ratio by Sales Type
- Total YTD Sales (Table)
- Avg Offer vs Purchase Price by House Type
- Inflation, Interest & Yield comparison
- SQM vs SQM Price analysis
- Interactive slicers for Area, City, Region & Sales Type
- Offer Price (derived using purchase price and offer variance)
- Property Age (calculated from build year and transaction date)
- Year-over-Year Sales Growth
- Median Sales Price Change
- Rolling 12-Month Sales
- Total YTD Sales
- Units Sold (Latest Year & Quarter)
- Offer to SQM Ratio
These calculations enable time intelligence, pricing comparisons, and regional performance analysis.
CALCULATE,TOTALYTDMEDIANX,DATESINPERIOD- Time intelligence (YOY, rolling 12 months)
- Advanced ratio and KPI calculations
- Connecting Power BI with Google BigQuery
- Performing SQL transformations in cloud data warehouses
- Implementing advanced DAX time intelligence
- Designing multi-page analytical dashboards
- Publishing and managing reports in Power BI Service
🔗 View-only report published to Power BI Service
(Microsoft login required)
- Implement incremental refresh for large datasets
- Add forecasting models for housing prices
- Introduce row-level security for regional analysis
- Optimize DAX for very large datasets
Arun Kumar Venugopal
Power BI | SQL | Cloud Analytics