Introduction
The short term rental market is constantly shifting and platforms like Airbnb need more than descriptive statistics to stay ahead.For this project, i took a deep dive into Boston’s Airbnb listings to answer three high-impact business questions:
- What makes some hosts incredibly successful while others struggle?
- Which Boston neighborhoods show strong demand and which ones are oversaturated?
- What drives exceptional guest experiences, and how can Airbnb detect quality risks early?
My goal wasn’t just to analyze data, but to translate it into real market intelligence that can guide growth strategy, host enablement, and guest experience improvement.
The End-to-End Data Pipeline
1. Data Preparation in Python
Loaded the raw Airbnb Boston dataset
Checked data structure, completeness, and quality
Cleaned missing values, removed duplicates, and treated outliers
Performed exploratory data analysis (EDA)
Analyzed pricing patterns, room types, correlations, and neighborhood distribution
2. Data Modeling & Analysis in PostgreSQL
Loaded the cleaned dataset into PostgreSQL using psycopg2
Designed relational schemas for listings, hosts, and neighborhood data
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Used SQL and CTEs to calculate:
- Host success metrics
- Neighborhood market health
- Oversaturation indicators
- Guest engagement via reviews-per-month
3. Dashboard Creation in Power BI
Connected PostgreSQL to Power BI
Built DAX-based KPIs for host performance, market health, and guest experience
Designed interactive dashboards with slicers and buttons
Visualized insights for hosts, neighborhoods, and reviews
Key Insights
1. High performing hosts share common behaviors
They set competitive prices, maintain high-quality listings, and benefit strongly from central neighborhoods like Back Bay, Beacon Hill, and the West End.
2. Not all neighborhoods are equal
Areas like Longwood Medical Area, Bay Village, and Back Bay show strong demand.Others Dorchester, Roxbury, Jamaica Plain, Fenway show signs of oversaturation.
3. Guest experience can be predicted
Listings with high review velocity (more reviews per month) consistently reflect better guest satisfaction and listing quality.
Recommendations
1. Help hosts succeed
Provide better pricing guidance to match market demand
Support new hosts with onboarding and listing improvement tips
Boost visibility for small or new hosts
2. Manage neighborhood growth strategically
Focus expansion in high-demand areas
Be cautious adding new listings in saturated neighborhoods
Encourage improvements (quality, pricing) for hosts in slow demand regions
3. Protect guest experience
Track listings with declining reviews-per-month
Benchmark each listing against similar ones in the same neighborhood
Flag risk listings early and intervene before quality drops affect platform reputation
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