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lirik lagu aruna & rameses b – data mining on airbnb rental listings

data mining on airbnb rental listings

since its inception in 2008, airbnb has experienced meteoric growth, with the number of rentals listed on its website increasing exponentially each year. airbnb has effectively disrupted the conventional hospitality industry as more and more travellers, not only those looking for the best deal, but also business travellers, turn to airbnb as their primary lodging provider
with over 52,000 listings as of november 2018, new york city has been one of the hottest markets for airbnb. this means that over 40 homes are rented out on airbnb per square kilometre in new york city! the popularity of airbnb in new york city can be attributed to the high rates paid by hotels, which are largely motivated by the city’s exorbitant rental costs

in this article, i will conduct an exploratory review of the airbnb dataset obtained from the inside airbnb website in order to better understand the rental landscape in new york city via various static and interactive visualisations

the research was carried out in r. the source code is available on github: https://github.com/saranggupta94/airbnb

data description

the dataset is divided into three major tables:

listings ~ extensive listings data with 96 attributes for each listing. price (continuous), longitude (continuous), latitude (continuous), listing type (categorical), is superhost (categorical), neighbourhood (categorical), and ratings (continuous) are some of the attributes used in the study

reviews ~ detailed reviews provided by visitors, each with six attributes. date (datetime), listing id (discrete), reviewer id (discrete), and comment are all important attributes (textual)

calendar ~ provides information about bookings for the next year by listing. there are four attributes in total: listing id (discrete), date (datetime), accessible (categorical), and price (continuous)

a quick look at the data reveals that there are:

in total, there are 50,968 unique listings in nyc. the first rental in new york city opened in harlem, manhattan, in april 2008

since then, visitors have written over 1 million reviews

a listing will cost anything from $10 per night to $10,000 (!) per night. listings with a $10,000 price tag can be found in greenpoint, brooklyn, astoria, queens, and manhattan’s upper west side
the meteoric rise of airbnb in new york

airbnb’s popularity is dependent on its large host network as well as the number of guests who use its services to find vacation rentals. both the number of unique listings and the number of travellers booking their accommodations on airbnb have increased exponentially in new york city

the animation below depicts the development of airbnb’s host network in the city from 2008 to 2018. as the cluttered blue dots show, there is a rapid increase in the number of listings in all boroughs of the city

the number of airbnb listings has increased over time

airbnb’s first new york listing was in harlem in 2008, and the company’s growth has been exponential since then. in the first few years, approximately 600 properties were added, the majority of which were in manhattan and brooklyn. downtown manhattan and surrounding brooklyn neighbourhoods have always had a high airbnb presence. since then, the number of listings has approximately doubled per year. every neighbourhood in manhattan had multiple listings by 2015. interestingly, the bronx has so few listings, so few that the animation shows the manhattan~bronx boundary by the sudden decline in listings. airbnb listings have expanded to parts of staten island since 2016. a estimate of 70k properties by 2020 should not be out of the question

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i was unable to collect data on the amount of airbnb bookings made over the years. instead, i used the ‘number of reviews’ as a proxy for airbnb rental demand. according to the business, approximately 50% of the guests review the hosts/listings, so studying the amount of reviews would provide us with a reasonable estimate of the market

the number of unique listings earning feedback, like the number of hosts, has gradually increased over the years, showing an exponential growth in the demand for airbnb rentals

check out the interactive rshiny app below to get a granular view of all the listings. it allows users to sort the listings based on different criteria. a screenshot of the rshiny app is shown below. you can check out the app by going to https://ankitpeshin.shinyapps.io/listings/. (sorry for the delay, it takes a few seconds to load)

rshiny app for airbnb property finder
understanding nyc’s real estate scene: location, location, location!

airbnb users rate their stay based on a variety of factors such as location, cleanliness, and a variety of other factors. i’m working with the position score data here. the average position scores for each neighbourhood will be fascinating to see. the position scores must be a reliable measure of the neighborhood’s attractiveness. highly ranked neighbourhoods would have greater connectivity (subway stations) and will be closer to city hotspots (times square, empire state, wall street)

- lirik lagu aruna rameses b

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