New Orleans Airbnb Market Data Analytics Project

This project highlights how I analyzed various New Orleans' Airbnb market trends to help with selecting the perfect Airbnb to add to my fictional rental portfolio.

Introduction

Traveling has always been one of my passions, and I have always relished acquiring new experiences through traveling. New Orleans was one of my favorite cities to travel to because of the amazing food, southern hospitality, vibrant culture, and rich history. I was deeply captivated by the Big Easy and began to imagine how I could visit more frequently and enjoy the city while minimizing traveling costs. The concept of purchasing an Airbnb immediately came to mind.

I always wanted to dive into real estate investing and owning a short term rental property in New Orleans would offer such an exciting opportunity to become a real estate investor. However, I realize that research needs to be conducted before embarking on this journey. This exploratory analysis was conducted to facilitate the purchase of a short-term rental Airbnb in New Orleans, with the goal of renting it out the majority of the year and occasionally enjoying it during less lucrative months. Owning a short-term rental?property would also grant me the opportunity to receive monthly passive income.

Dataset

The dataset contains various elements such as ratings, geographic location, host information, property details, financial metrics and various other data points. The dataset contains all the active New Orleans Airbnb listings as of March 2024 and can be found in the link provided below.

?Dataset Link: https://insideairbnb.com/get-the-data/

Tableau Dashboard?

I created a Tableau dashboard to provide a comprehensive overview of the New Orleans Airbnb market. This dashboard will be utilized to gain insights about the New Orleans Airbnb market. The summary below explains the elements of the dashboard.?

Tableau Dashboard Visualizations

1) Monthly Projected Revenue: Shows the top 10 neighborhoods that have the highest total monthly projected revenue.

2) Map View of Listings: This map visualization displays the average nightly price for Airbnbs in each New Orleans neighborhood.

3) Price per Night By Bedroom: This chart illustrates the average price for Airbnb listings that have one to five bedrooms and studio apartments/houses.

4) Host Listing Count: This visualization provides competitor intel. Each bubble shows the name of the host and total number of Airbnbs they own/manage.

5) Total Listings vs Average Price Per Night: The combo chart compares the total number of listings to the average price per night for the top 10 neighborhoods with the highest average price per night.

Data Exploration Utilizing MySQL:

My GitHub repository contains the SQL queries utilized to create the Tableau visualizations and the additional queries that can be used for diving deeper into the dataset. The SQL queries can be accessed in the my GitHub repository link below.

GitHub Link: https://github.com/Trevanti93/New-Orleans-Airbnb-Project/blob/main/NOLA%20Airbnb-Final%20Project.sql

Exploratory Analysis

As a prospective Airbnb owner, I know that leveraging Airbnb data to make a major real estate acquisition is very imperative. When acquiring a rental property, investors usually focus on location, price, quality of the homes, and quality of the neighborhoods. The downtown area of a city is oftentimes the heart of the city. I quickly realized that areas near downtown were so illustrious and vibrant. The French Quarter and Central Business District are highly desirable due to their proximity to museums, restaurants, nightlife, shopping centers, and other attractions. While owning an Airbnb in these areas is advantageous, competition and acquisition costs are high. Although I'm open to purchasing in these areas, I want to consider other nearby neighborhoods.

I decided to start my analysis by creating a SQL query to capture the average price per night for each area of New Orleans. The data from the SQL query was used to create the map visualization in the Tableau dashboard. Creating this visualization helped me to pinpoint the average price per night across all the neighborhoods in New Orleans.

Tableau Dashboard Screenshot: Map View Listings

Understanding the average nightly rates by neighborhood was insightful for identifying high-revenue areas. However, I realized that I needed to analyze the nightly rates by bedroom type to determine the optimal price point for my future rental. As expected, the number of bedrooms influences the average nightly price throughout the New Orleans Airbnb market.

Tableau Dashboard Screenshot: Price Per Night By Bedrooms

Finding the top 10 areas that generate the most revenue was my next task. My initial speculation was that the French Quarter and the Central Business district were the highest revenue generating neighborhoods. However, the Central Business District generated the most revenue while the French Quarter ranked 8th amongst the top 10 revenue generating neighborhoods. The monthly revenue projection analysis definitely allowed me to see other profitable areas to invest in outside of the French Quarter and Central Business District.?

Tableau Dashboard Screenshot: Monthly Projected Revenue

After identifying the highly profitable neighborhoods, I conducted research in regards to the total listings and average price per night in the top 10 neighborhoods with the highest nightly rates. I wanted to determine whether a correlation existed between total listings and prices. My research indicated that there is no direct correlation between the amount of listings and the pricing of the rentals.

Tableau Dashboard Screenshot: Total Listings vs Average Price Per Night

Acquiring knowledge about the potential competitors provides me with the ability to observe if I am competing with large corporations or smaller?privately owned rentals. Analyzing this data will allow me to research their business models and determine how many properties they own. My analysis shows that most of the top hosts are corporations. The graph below shows the top 10 Airbnb hosts with the most listings.

Tableau Dashboard Screenshot: Host Listing Count

My objective is to maximize bookings for my short-term rental so I decided to develop a detailed query to analyze occupancy rates by neighborhood. The analysis indicates that areas with lower occupancy rates have higher projected revenues. My assumption based on my research is that high occupancy areas likely have more extended stay rentals. Investing in these areas is less ideal due to their appeal for long-term renters. Acquiring a property in a high-revenue area, setting competitive rental rates, and maintaining an 80% monthly occupancy rate can help me outperform my competitors, whose current occupancy rates are around 50%-60%.

Screenshot: Output of SQL Occupancy Rate Query

?My previous research has been focused on quantity. However, I wanted to conduct qualitative research to gain insights about the quality of Airbnbs in the New Orleans area. When booking an Airbnb I focus on cleanliness, location, and overall property value. I created a query to compute the overall ratings of properties across neighborhoods based on my personal focal points. The query also only included areas that had a total listing amount of 100 to 500 since I prefer to own an Airbnb in neighborhoods with a moderate amount of listings. Marigny, Milan, and Bywater emerged as the top rated areas based on my criteria.

Screenshot: Output of SQL Ratings Query

Conclusion/Takeaways

Based on my analysis, Marigny, Tulane-Gravier, and Treme Lafitte stand out as promising investment areas. The area summary section below explains the rationale behind selecting each neighborhood.

Area Summary

Marigny

• Relatively close to the French Quarter.
• Ranked 8th among 66 neighborhoods in average nightly price.
• Third highest monthly projected revenue amongst all neighborhoods.
• Highest overall rating out of neighborhoods with listings of more than 100.

Tulane-Gravier

• Relatively close to the French Quarter.
• Ranks 4th among 66 neighborhoods in average nightly price.
• Eighth highest monthly projected revenue by neighborhood.
• Fifth highest overall rating out of all neighborhoods with listings more than 100.

Trime-Lafitte

• Closest area to the French Quarter.
• Fourth highest monthly projected revenue amongst all neighborhoods.
• Tenth highest overall rating out of all neighborhoods with listings of more than 100.

Follow Up Analysis/Recommendations

Additional research is required to finalize the selection of the neighborhoods for my short-term rental purchase. I have included post-analysis recommendations to aid in this decision-making process.

Recommended Research Follow Up Questions

Which bedroom types are most popular among short term rentals in Marigny, Tulane-Gravier, and Trime-Lafitte?

What are the costs associated with owning a short term rental?

How safe are the neighborhoods?

Is the city trying to reduce the amount of Airbnbs?within the next couple of years?

Closing

I? enjoyed working on this project and found it both challenging and rewarding to analyze the New Orleans Airbnb market. The insights gained were invaluable and have sparked my enthusiasm for further exploration in short term real estate acquisitions. I look forward to sharing more data analytics projects relating to personal interests and passions.