Brisbane, AUS Airbnb Analysis
Project Overview
This project draws inspiration from my love of traveling and exploring new places. While browsing Airbnb's worldwide, I found myself particularly drawn to Brisbane due to my strong connection to the city. This led me to combine my passion for travel with data analytics in my first self-guided project. It was an exciting experience, as I could validate my findings by discussing them with friends currently living in Australia, gaining insights into why certain areas are priced higher than others.
Objective
Explore key questions related to market trends, host and property insights, and revenue optimization for Airbnb listings in Brisbane.
Features
SQL Techniques: Aggregations, window functions, joins, subqueries, and data type conversions.
Sentiment Analysis: Evaluation of customer comments to derive sentiment scores, providing numerical insights into customer satisfaction and feedback trends.
Data Analysis: Insights into average prices, listings by city, price rankings, and listings above average prices.
Advanced SQL Operations: Keyword search in listings, filtering based on amenities, and calculating monthly availability.
SQL Visualization Script
Market Trends and Performance
1.Neighbourhood Rate Summary
2.Neighbourhood-Room Occupancy Overview
3.Room Type Occupancy Comparison
4.Seasonal Demand Analysis
Host and Property Insights
5.AVG Price By Property type
6.AVG Price By Neighbourhood
7.Review & Occupancy Correlation Analysis
Revenue And Pricing Optimization
8.Revenue Estimates Across Neighborhoods
9.Neighbourhood-Based Price Status Evaluation
Results and Recommendations
Market Trends and Performance
The average daily rates of listings vary significantly by location, with listings in more affluent areas commanding higher rates.
There is a slight preference for private room or entire home listings over hotel rooms and shared rooms, resulting in higher occupancy rates for those types.
Autumn is the peak season for occupancy; lowering rates during Winter and Spring could increase overall occupancy and revenue.
Host and Property Insights
Number of reviews has little correlation with average rating, which typically ranges between 4.0 and 4.2 excluding outliers.
Airbnb prices have remained relatively stable over the past year, with some outliers possibly due to host changes.
Hotel rooms have the highest average cost ($246/night), and private rooms the lowest ($124/night); prices showed little change over the data period.
Revenue and Pricing Optimization
Prices did not vary much over time; adjusting prices seasonally may improve occupancy, though sample size limits conclusions.
Listings are mostly concentrated near the CBD; incorporating amenities and other factors could improve pricing analysis.
Complete Brisbane Analysis
This project analyzes Airbnb listings in Brisbane to uncover trends in pricing, occupancy, and revenue potential. By examining factors like location, room type, seasonal demand, and host behavior, the goal was to identify actionable insights for optimizing pricing strategies and understanding market performance across different property types.