NYC Airbnb Market

Introduction

Welcome to my New York City Airbnb project. The following analysis and presentation was created as my final capstone project for the Springboard Data Analytics Certification. In a future update, I will provide my presentation notes for more in-depth understanding of the key takeaways. For now, I recommend viewing my analysis in Python to follow methdology and review explanations of decisions made: Deepnote workbook.

Phase 1: Ask

What is the problem I am trying to solve?

New York City is a popular travel destination with high potential for property investment. Enlisted by a fictional investment firm, my role is understand customer demand and preferences to provide focused solutions on neighborhoods to invest in for maximizing profits.

• The goal is to identify high demand NYC neighbourhoods and customer preferred housing types for network and revenue growth.

• The business task is to recommend a portfolio of room types and neighbourhoods to achieve potentially $2M in revenue.

Phase 2 & 3 : Prepare and Process

The data has been made publically available through Arian Azmoudeh on Kaggle. This dataset was made available through the Public Domain on Kaggle with an Open Data Commons Open Database license. It should be considered fictional and not relied on for current insights to the industry. For more information, please visit Kaggle directly.

Approximately 61,000 market listings were included with information on room type, nightly price, extra fees, neighbourhood, listing headline, and more. For this project, I chose to use Python for exploratory data analysis and Tableau for spatial analysis. Insights from both programs were compiled to the powerpoint at the bottom of this page.