A premium multi-page Power BI dashboard project focused on Airbnb marketplace growth, customer engagement, ratings intelligence, review behavior, and host trust analytics using Power BI, DAX, and Power Query.
⭐ If you found this project valuable, consider starring the repository!
The Global Airbnb Performance Dashboard is an executive-style business intelligence solution designed to analyze Airbnb’s global marketplace performance across listings, ratings, reviews, customer behavior, and host trust dynamics.
The project combines:
- advanced analytics
- modern dashboard UI/UX
- business storytelling
- semantic modeling
- interactive navigation
to transform raw Airbnb marketplace data into actionable insights.
The dashboard focuses not only on reporting KPIs, but also on uncovering:
- marketplace growth patterns
- customer review behavior
- city-level performance
- seasonal travel trends
- host verification insights
- room type evolution
- marketplace maturity trends
Global marketplace platforms like Airbnb generate large volumes of listing, host, and review data.
Without centralized analytics dashboards, it becomes difficult to:
- monitor marketplace growth
- identify high-performing markets
- evaluate customer satisfaction
- understand traveler behavior
- analyze review engagement
- assess trust & verification dynamics
This project addresses these challenges through an interactive end-to-end Power BI analytics solution.
The project was designed to answer the following analytical questions:
| Business Question | Objective |
|---|---|
| How has Airbnb grown over time? | Marketplace growth analysis |
| Which cities dominate platform activity? | Market concentration analysis |
| Which room types drive Airbnb inventory? | Listing composition analysis |
| Which cities deliver the best guest experience? | Ratings intelligence |
| How engaged are Airbnb customers? | Review frequency analysis |
| What seasonal travel patterns emerge globally? | Seasonality analysis |
| How trustworthy is the host ecosystem? | Host verification analysis |
The dataset used in this project was sourced from Maven Analytics Data Playground.
🔗 Dataset Link:
https://mavenanalytics.io/data-playground/airbnb-listings-reviews
The project uses Airbnb listing and review datasets containing:
- listing information
- pricing
- room types
- host details
- customer reviews
- review scores
- city-level marketplace data
| Table | Type | Description |
|---|---|---|
| Listings | Fact/Dimension | Core Airbnb listing, pricing, host, and rating data |
| Reviews | Fact | Customer review activity and reviewer behavior |
| _Measures | Measure Table | Centralized DAX calculations and KPIs |
| Airbnb Data | Staging | Hidden source/staging table |
| Tool | Purpose |
|---|---|
| Power BI Desktop | Dashboard development & visualization |
| DAX | KPI engineering & analytical calculations |
| Power Query | Data cleaning & transformation |
| Data Modeling | Semantic model & relationships |
| GitHub | Version control & project hosting |
Raw Airbnb Dataset
│
▼
Power Query Data Cleaning & Transformation
│
▼
Semantic Data Modeling
│
▼
Relationship Engineering
│
▼
Advanced DAX Calculations
│
▼
Interactive Multi-Page Power BI Dashboard
Global_Airbnb_Performance_Dashboard/
│
├── Docs/
│ └── Images/
│ ├── Home Page.png
│ ├── Overview Page.png
│ ├── Ratings Page - 1.png
│ ├── Ratings Page - 2.png
│ └── Reviews Page.png
│
├── README.md
│
└── LICENSEPremium landing page with custom navigation experience and Airbnb-inspired dashboard branding.
Analyzes Airbnb marketplace growth, listing trends, and platform evolution.
- Marketplace growth lifecycle
- Listings by year
- Room type trends
- Host growth
- Platform maturity analysis
Explores customer satisfaction, city performance, and pricing intelligence.
- Market concentration analysis
- Room pricing comparison
- City-level ratings
- Customer experience benchmarking
- Review score heatmaps
Analyzes customer review behavior, trust indicators, and travel seasonality.
- Review frequency analysis
- Customer engagement behavior
- Host trust matrix
- Seasonal travel patterns
- Verification analysis
The project includes advanced analytical workflows across multiple dimensions.
✅ Marketplace growth analysis
✅ Pareto contribution analysis
✅ Customer ratings intelligence
✅ Behavioral review analytics
✅ Host trust analysis
✅ Seasonal travel analysis
✅ Dynamic KPI storytelling
✅ Comparative city benchmarking
✅ Review frequency analysis
✅ Executive dashboard storytelling
✅ Multi-page interactive navigation
Cumulative Reviewers =
VAR CurentReviews =
MAXX( Reviews, Reviews[Reviews per Reviewer] )
RETURN
CALCULATE(
DISTINCTCOUNT( Reviews[reviewer_id] ),
FILTER(
ALL( Reviews[Reviews per Reviewer] ),
Reviews[Reviews per Reviewer] <= CurentReviews
)
)
Calculates the running cumulative count of unique reviewers ordered by review frequency, enabling behavioral Pareto analysis.
Cumulative % Rerview Frequency =
DIVIDE(
[Cumulative Reviewers],
CALCULATE(
[Total Reviewers],
ALL( Reviews[Reviews per Reviewer] )
)
)
Measures the cumulative percentage contribution of reviewers based on review frequency to identify customer engagement concentration.
Total Reviews =
DISTINCTCOUNT( Reviews[review_id] )
% of Monthy Reviews =
DIVIDE(
[Total Reviews],
CALCULATE(
[Total Reviews],
ALLSELECTED( Listings[city] )
)
)
Calculates each city's share of total monthly reviews relative to selected cities, supporting seasonal and comparative review analysis.
- Airbnb experienced rapid listing expansion between 2011–2015.
- Growth slowed during regulatory tightening in 2016–2017.
- Platform activity rebounded before declining sharply during the COVID-19 period.
- Paris, New York, and Sydney contribute a disproportionate share of listings and reviews.
- Paris remains the platform’s largest and most engaged marketplace.
- Mexico City and Rio de Janeiro recorded the highest overall guest satisfaction.
- Cleanliness and value-for-money consistently scored lower than communication and location.
- Most customers leave only a small number of reviews.
- Nearly all review activity is concentrated among low-frequency reviewers.
- Fully verified hosts dominate the platform ecosystem.
- Anonymous and unverified host profiles represent only a minimal share of the marketplace.
The project involved extensive preprocessing and transformation:
- Standardized city-level marketplace data
- Engineered review frequency calculations
- Built trust verification matrix
- Created cumulative contribution measures
- Developed review seasonality logic
- Optimized semantic relationships
- Structured centralized DAX measure table
- Built analytical calculated columns
- Designed executive-focused KPI measures
- Implemented interactive storytelling visuals
- Advanced Power BI dashboard engineering
- Semantic model design
- DAX KPI development
- Power Query transformation workflows
- Executive dashboard storytelling
- UI/UX dashboard optimization
- Analytical visualization techniques
- Behavioral analytics implementation
- Airbnb marketplace activity is highly concentrated in a few major cities
- Customer review behavior is dominated by low-frequency reviewers
- Entire-home listings increasingly dominate the platform
- Trust verification plays a major role in host ecosystem quality
- Seasonal travel behavior differs significantly across regions
Planned enhancements for the project:
- Drill-through city analysis
- Advanced tooltip pages
- Predictive trend forecasting
- Mobile-responsive dashboard layout
- Dynamic filter panel
- Geospatial city analysis
- Sentiment analysis integration
Hi there! I'm Kaustubh Sutar, a data enthusiast and aspiring Data Analyst & Data Engineer skilled in Power BI, SQL, Python, Excel, PySpark, and Databricks. I enjoy building scalable data pipelines, analyzing datasets, and creating dashboards that transform raw data into actionable business insights.
I also have growing interests in Data Engineering, Machine Learning, and AI, continuously exploring modern technologies to expand my analytical and engineering capabilities.
Let's stay connected!
If you found this project insightful:
- ⭐ Star the repository
- 🍴 Fork the project
- 📢 Share it with others
- 💼 Connect for analytics collaborations
This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.