Airbnb in Paris Analysis

Analysis of 8,877 Airbnb listings in Paris to identify pricing disparities and the key factors influencing nightly rates.

zrzut ekranu 2026 01 5 o 19.04.30

Link to PowerBI

Objective

This project analyzes Airbnb listings in Paris to understand how prices vary across neighborhoods and accommodation types. The goal is to identify the primary drivers of pricing differences and provide data-driven insights into how location, room type, and supply characteristics impact nightly rates. The analysis focuses on uncovering structural patterns in the Paris short-term rental market rather than short-term demand fluctuations.

Approach

  • Collected and cleaned Airbnb listing data for Paris (as of September 6, 2024)

  • Prepared the dataset using MS Excel Power Query (missing values, inconsistencies, column reduction)

  • Conducted Exploratory Data Analysis (EDA) and statistical analysis

  • Built interactive dashboards in Power BI using DAX measures

  • Applied decomposition-tree analysis to identify key price drivers

Key Insights

Overall Market Overview

  • 8,877 properties listed by 7,381 hosts

  • ~83,000 reviews in the past year (~7,860 per month)

  • Entire homes/apartments dominate the market (88%)

  • Average nightly price: 231 EUR, with strong price dispersion

Price Distribution

  • Median price: 151 EUR

  • 50% of listings are priced below the median

  • Over 28% of properties are classified as expensive (priced above the average)

  • Only 121 listings fall within the average price range (225–235 EUR), indicating significant inequality

Key Price Drivers

  • Room type and neighborhood are the strongest determinants of price

  • Decomposition-tree analysis shows that entire homes/apartments in Champs-Élysées contribute most to high prices

Full Analysis

Neighborhood Impact

Neighborhoods play a crucial role in determining Airbnb prices in Paris. The highest average prices are observed in Passy, Champs-Élysées, and Palais-Bourbon—central and prestigious areas with strong tourist appeal and proximity to major attractions. These neighborhoods consistently command premium prices, regardless of listing volume.

Room Type Analysis

Room type significantly influences pricing:

  • Hotel rooms have the highest average price (331 EUR/night), well above the market average

  • Prices for hotel rooms vary widely by neighborhood, ranging from 191 EUR to nearly 1,000 EUR per night

  • Shared rooms are the cheapest option, reflecting limited space, lower privacy, and fewer amenities

This variation highlights how accommodation type amplifies neighborhood-based pricing differences.

Supply vs Price

Contrary to basic supply-demand assumptions, there is no clear correlation between the number of listings in a neighborhood and average prices. High-density areas do not necessarily offer lower prices, suggesting that location desirability and accommodation type outweigh supply effects in the Paris market.

Summary

  1. Location is the dominant pricing factor
    Central and prestigious neighborhoods consistently command premium rates, regardless of listing density.

  2. Room type strongly amplifies price differences
    Hotel rooms and entire homes significantly outperform other accommodation types in terms of price.

  3. Supply alone does not reduce prices
    High availability does not lead to lower prices, indicating strong and stable demand in desirable areas.

Overall, the Paris Airbnb market is characterized by high price dispersion, driven primarily by neighborhood prestige and accommodation type, rather than supply volume. This insight is particularly relevant for pricing strategy, market entry decisions, and revenue optimization in short-term rentals.