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Inside AirBnb: New York, Adding Data to Debate – Amit Borkar

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Detail analysis of Airbnb Homes in New York from September-2019 to September 2020

AirBnb has become a go-to place for people to lease or rent short-term lodging. Whether you are looking to rent a room in your house or find a cabin in the woods, you can use AirBnb. Airbnb has seen a meteoric growth since its inception in 2008 with the number of rentals listed on its website growing exponentially each year. Airbnb has successfully disrupted the traditional hospitality industry as more and more travelers, not just the ones who are looking for a bang for their buck but also business travelers resort to Airbnb as their premier accommodation provider.

New York City has been one of the hottest markets for Airbnb, with over 48,000 listings as of September 2020. This means there are over 40 homes being rented out per square km. in NYC on Airbnb!

In this post, we will take a closer look at the listings associated with New York. Specifically, there were two main datasets used in the analysis: listings and calendar.

After looking through the data I arrived at four questions aimed at various topics of interest — availability, cost, common streets, neighborhood. Specifically, I looked at:

What are the most common Airbnb property types in New York available for renting?What is the availability of the properties throughout the year?

What is the average pricing of the properties for renting?Are there any seasonal spiking in pricing of the properties?

What are the most common streets in New York for Airbnb properties? What are most costliest streets in New York? Does the neighborhood group affect the pricing of the properties?

Can we use some of the features in the listing data to predict property pricing?What are the most important factors affecting the cost of the properties?

1)What are the most common Airbnb property types in New York available for renting?What is the availability of the properties throughout the year?

For analyzing the most common property types in New York, listings dataset was used. The plot below shows the most common property types in New York for Airbnb homes.

Not surprisingly, Apartments and Houses take up an overwhelming majority of all listings followed by Townhouse and Condominium.There are total 36 types of properties available for renting in New York out of which, the property types displayed in the graph above have the maximum listings.

For analyzing the proportion of the properties available over time, contents i the calendar dataset were used. Following are the the results of the home availability analysis.

The highest availability of Airbnb homes will be on February 2020 with 40% of properties available for renting while lowest availability was observed on September-2019 with just 10–15% of homes available. The availability largely increased after September-2019. There will be quite a dip of 10% in the available after February-2020. There won’t be major ups and downs in the availability after February with the range of availability lying in 25–32%.

A detail analysis of availability over time can be seen the graph below.

Day-1 corresponds to 09/12/2019 and Day-360 corresponds to 09/11/2020.

2)What is the average pricing of the properties for renting?Are there any seasonal spiking in pricing of the properties?

I have analysed pricing of the homes in the listings dataset and plotted a graph below.Most of the properties has pricing below $300. In-fact 75% of the homes has pricing less than $175.

Most of the properties fall in the range of 0–200$. As the price increases you can see the dip in frequency of the homes.The average pricing of homes in New York around $152 while median and highest pricing is around $105 dollars and $10,000.

A plot of the pricing of properties over the course of a year is shown in the plot below. There are a few interesting takeaways from this plot.

Similar to availability, we can also see how listings prices change over time. The highest average property price will be on the December-2019 ranging just above $162. The lowest would observe on February-2020, due to the highest availability of the homes in that period. A huge downfall of the pricing will be observed after December lasting till February-2020. After which price tends to rise till April-2020. There won’t be a major fluctuation in the pricing from April-2020 to September-2020.

Detail analysis of pricing over time is provided in plot below.

Day-1 corresponds to 09/12/2019 and Day-360 corresponds to 09/11/2020.

3)What are the most common streets in New York for Airbnb properties? What are most costliest streets in New York? Does the neighborhood group affect the pricing of the properties?

Below you can see the most common streets in New York for Airbnb homes.

New York and Brooklyn street has the highest number of homes for renting followed by Queens and Bronx.

Streets having the highest average price are shown in the table below.

Forest Hills and Chelsea dominate the pricing of the properties in New York with average pricing for them ranging 1005 dollars and 800 dollars respectively.

Analyzing the neighborhood group with the pricing, I have plotted the graph below

Manhattan is the costliest neighborhood for Airbnb properties with average pricing ranging at $200 . Bronx seems to be cheapest with the average pricing at approximately 80 dollars. Brooklyn, Queens and State Island are lying at the affordable pricing of $100–125 dollars.

4)Can we use some of the features in the listing data to predict property pricing?What are the most important factors affecting the cost of the properties?

After analyzing the first three questions with appropriate graphs and tables, predicting the price of the property based on the data we have is the most challenging part because of the number of columns present.

There are over 90 columns present in the listings data, and selecting the appropriate columns for predicting the prices was really a challenging part.

I developed a Machine Learning model to predict prices which provided a median absolute score of just 20.Median absolute error metric was used here for calculating the error instead of mean because of the outliers present in the dataset.

According to the model, the most important factors that affect the pricing of the properties are a follows:

Longitude is the most important factor affecting the pricing which suggests that there is a huge difference in the pricing of the properties as we go from west to east in the New York. Latitude is also one the most important factors which explains the difference in the pricing from north to south in New York.

Here is a little insight in to the price distribution over Latitude and Longitude

Room types rank among the second most important factor for pricing.There are four types of room available in New York Airbnb: Private, Hotel, Shared and Entire home.

Here is a little insight into the average pricing of different room types.

The pricing of the homes tends to go up with increasing accommodates which is quite obvious. Number of bedrooms also heavily affects the pricing followed by the availability of the homes throughout the year.

In this Article we took a look at the pricing, availability, common property types, common streets and most important factors affecting the pricing of the properties in New York for Airbnb.

  1. We looked at the most common property types which turned out to be apartments and houses. We analysed availability of the homes over time.
  2. We then looked the average pricing of the properties which is around $152. We dug through to find out how the pricing changes over seasons or over time.
  3. We calculated the most common and costliest streets to rent in New York. Forest Hills seemed to be the most costliest while New York street is the most common listed street for Airbnb homes.
  4. Finally, we predicted the price of the homes based on the listings data and got the most important factors affecting the pricing of homes.

According to you, what do you think are the most important factors affecting the pricing of Airbnb properties?

To see more about this analysis, see the link to my Github available here.



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