Airbnb Business Analytics with Orange Data Mining

Discover how to use Orange Data Mining for Airbnb market analysis, including K-Means clustering, regression, and pricing strategies for NYC listings.

#data-mining#business-analytics#orange-data-mining#airbnb-dataset#market-analysis#k-means-clustering#nyc-data

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Business Analytics Using Orange Data Mining Tools on the Airbnb Dataset

Global Short-Term Accommodation Marketplace for Tourist Services

Data Mining

Business Analytics

Airbnb Dataset

Academic Research Project | Orange Data Mining | NYC Airbnb Open Data

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Introduction

The purpose of this project is to apply business analytics methods using a real life data set and to create business intelligence by using Orange Data Mining tool.

To carry out the analysis I used the New York City Airbnb Open Data, this data includes all the listings available on the platform along with prices, locations, types of accommodation and how engaged the listings are.

In order to maximize returns on investment of Airbnb listings in Boston, it is valuable to perform analysis on existing listings. To carry out such an analysis, we employed clustering, regression, and association rule mining techniques to gain better insight into the large number of listings.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Dataset Understanding

This dataset contains millions of listings on Airbnb across the entire city of New York. It provides information about pricing, availability, and buyer behavior.

Millions of Listings

NYC Coverage

Pricing & Availability Data

Business Analytics | Orange Data Mining | Airbnb Dataset

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Key Variables

Core dataset attributes used in the analysis

Price

Cost per night

Neighbourhood Group

Location category

Room Type

Entire home/Apartment, Private room, Shared room

Ava (Cap)

Abbreviation for Availability and Capacity

Number of Reviews

Customer engagement

Reviews per Month

Listing popularity

Minimum Nights

Booking requirement

Business Analytics | Orange Data Mining | Airbnb Dataset

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Business Relevance

"This data is useful for anyone wishing to see the demand and price levels for different areas to help Airbnb hosts, property investors and any hospitality business set their prices appropriately."

Airbnb Hosts

Optimize daily pricing to maximize occupancy rates

Property Investors

Identify lucrative neighborhoods for future investment

Hospitality Businesses

Analyze local market demand to refine market strategies

Business Analytics | Orange Data Mining | Airbnb Dataset

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Objectives

Analyze pricing patterns

Identify key influencing factors

Segment listings into meaningful groups

Generate actionable business insights

Business Analytics | Orange Data Mining | Airbnb Dataset

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Data Cleaning & Preprocessing

Removed unnecessary variables (ID, listing name, host name)

Replaced missing values in reviews_per_month with 0

Removed duplicate records & extreme values (>1000)

Removed outliers from the dataset

Formatted room type and neighbourhood group fields

Normalised numerical information

Created new price category feature (low/medium/high)

Data was preprocessed using Orange Data Mining suite.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Data Analysis & Descriptive Statistics

Manhattan Pricing

Listings in Manhattan are significantly more expensive than those in other neighborhoods.

Room Type Distribution

The majority of listings are for private rooms in order to afford the cost of housing.

Review vs Price

Very little correlation between the number of customer reviews and the price of a product.

Results show a connection between location/property type and both listing value and market image.

Affordable + Luxury

Room for affordable options and luxury pricing at the same time

Low Correlation

Reviews and pricing show very little direct relationship

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Clustering (K-Means)

We applied K-Means clustering to segment the listings by price, availability and number of reviews.

Cluster 1: Premium

High Prices

Low availability for booking. Premium class listings.

Cluster 2: Moderate

Moderate Prices

Moderate availability. The second largest segment.

Cluster 3: Budget

Low Prices

High availability. Highly competitive segment.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Regression Analysis

A regression analysis was performed to examine the variables that affect the price.

Top Predictor #1

Manhattan Location

Whether a listing is located in Manhattan is the strongest predictor of its price.

Top Predictor #2

Entire Home

Whether a listing is an entire home far exceeds the impact of any other variable.

According to an analysis of nearly 500,000 listings on Airbnb, prices for these two factors far exceed the impact of any other variable.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Association Rule Mining

In this study, association rule mining was applied to discover co-occurrences of attributes.

Entire Home Listings

Higher Prices

Entire home listings commanded higher prices

Higher Availability

Lower Prices

Listings with higher availability commanded lower prices

We will investigate these factors further to gain more insight into customer preferences and supply and demand.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Data Visualization

We provided graphical representations to support the analysis.

Bar Chart

Bars indicate average price per neighborhood, with Manhattan the priciest.

Pie Chart

Distribution of listings categorized as private, shared, or other.

Scatter Plot

Very weak correlation between price and reviews with many outliers.

Box Plot

Highlights variation in price within many of the neighborhoods.

It is often helpful to present the results of a regression in graphical form.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Business Recommendations

After reviewing your numbers, a few things can be done to improve this.

Dynamic Pricing Strategy

Incorporate dynamic pricing based on demand, location and property type.

Whole Home Listings

List the whole house as one booking and place it in areas with higher demand.

Listing Optimization

Optimize the listing for better descriptions, images, and customer service to gain more visibility.

Flexible Pricing

Offer a lower nightly rate for longer stays and pricing down during off-peak to encourage more bookings.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Limitations

Geographic Scope

The calculations are based on figures for one city only.

External Factors

No allowance has been made for factors such as time of year, market conditions and regulations.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Assumptions

Customer Engagement

Customer reviews are a good indicator for engagement.

Pricing Trends

Pricing trends are assumed to be constant.

Business Analytics | Orange Data Mining | Airbnb Dataset

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Conclusion

Business Analytics Applied

This project applied business analytics methods using Orange Data Mining to extract insights from real-world data.

Key Finding

The analysis shows that pricing is mainly affected by location and property type.

Impact

Business analytics helps in making better decisions.

Thank You

Academic Research Project | Orange Data Mining | NYC Airbnb Open Data

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