Analysis of New York Airbnb Dataset

📘 Project Overview

This project analyzes the Airbnb dataset for New York City to understand factors affecting pricing, identify trends across neighborhoods, and find the most cost-effective accommodations. Using statistical and geospatial methods, this project provides actionable insights for Airbnb hosts, travelers, and policymakers.

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📂 Repository Structure

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🔍 Key Objectives

1. Pricing by Neighborhood: Understand average pricing trends in neighborhoods, focusing on Manhattan.
2. Distribution Analysis: Compare price distributions of specific neighborhoods to overall NYC distribution.
3. Cost-Effectiveness Analysis: Identify the top 1,000 cost-effective listings in NYC using custom scoring.
4. Statistical Confidence: Use bootstrap methods to estimate average prices with confidence intervals.
5. Variable Correlations: Examine the relationship between price and other factors, such as reviews and availability.

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📊 Methods and Approach

Data Source

• The dataset: Airbnb_Open_Data.csv
• Geospatial data: NY.geojson

Steps in Analysis

1. Data Cleaning:

   • Converted pricing and service fee columns to numeric.
   • Filtered outliers and irrelevant listings.
   • Focused on homes with sufficient reviews (≥10).

2. Geospatial Analysis:

   • Used NY.geojson with leaflet in R to visualize neighborhood-level price trends.
   • Created an interactive heatmap showing average prices by neighborhood.

3. Statistical Insights:

   • Bootstrapped average price estimates with 95% confidence intervals.
   • Analyzed linear correlations between price and variables like number of reviews and availability.

4. Cost-Effectiveness Scoring:

   • Designed a custom scoring algorithm based on price, reviews, and features like cancellation policy and host verification.

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📈 Visualizations and Findings

Heatmap of Pricing by Neighborhood

• A heatmap was created using leaflet in R to display average prices across NYC neighborhoods.
• Key finding: Central NYC neighborhoods had similar pricing trends, likely due to high competition and demand.

Distribution Comparison

• Neighborhood-specific price distributions were compared to overall NYC distribution.
• Key insight: Price distributions varied significantly, except for Midtown, which closely matched NYC's overall pattern.

Cost-Effective Listings

• Identified the top 1,000 cost-effective listings using a scoring algorithm.
• Observation: Listings with high scores often shared attributes like flexible cancellation policies and verified hosts.

Bootstrap Analysis

• Estimated the average price across NYC listings with a 95% confidence interval.
• Result: Average price ~524.27 (±1.94 standard error).

Variable Correlation

• Analyzed the linear correlation between price and variables like reviews, availability, and rating.
• Finding: Correlations were weak, indicating complex pricing dynamics.

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🛠 Tools and Technologies

• Programming: R
• Geospatial Analysis: leaflet, sf
• Data Cleaning: tidyverse
• Statistical Analysis: Bootstrapping, correlation analysis
• Visualization: Heatmaps, ggplot

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🚀 How to Run the Analysis

1. Clone this repository:

   git clone https://github.com/your-username/NY-Airbnb-Analysis.git
   

install.packages(c("leaflet", "sf", "tidyverse", "ggplot2"))

source("r (1).R")

📧 Contact For questions or collaboration opportunities, please contact:

Segev Cohen: [segev777701@gmail.com]