Giter Club home page Giter Club logo

airbnb-listings-analysis-5-asian-cities's Introduction

[Power BI] Airbnb Listings Analysis - 5 Asian Cities

I. Introduction

1. Context

Airbnb Inc. is an American company operating an online marketplace for short- and long-term homestays and experiences. Started in 2007 with an air mattress in an extra room, Airbnb today has a market capitalization of over $90 billion with 7M+ listings, 4M+ hosts covering 100K cities and towns in 220+ countries and regions worldwide (1).

However, recently, many countries around the world, including in Asia, have started to place restrictions on Airbnb, especially short-term rentals in order to counteract its negative effects on local residences (it has been accused of inflating house prices, pushing out locals, straining resources and fuelling overtourism) (2). These regulations have resulted in dramatic changes on listings and how it runs in different local markets.

For example, Japan enforced new rules in 2018 requiring Airbnbs be registered with the government and limited short-term rentals to 180 days per year. A few months after that, nearly 80% of Japan's Airbnbs (unregistered properties) were removed (originally at more than 62,000 has dropped to 13,800 after 3 months accross the country) (3). Each property that is showing up today is playing by the rules. In Singapore, there are currently no licensing requirements for Airbnb properties, however, property rentals must be at least 3 months long (for private properties) or 6 months long (for public properties - HDB). HDB flats also cannot be rented out to tourists (4).

Within this context, this project aims to provide broad audiences with the latest big pictures of Airbnb listings (Data dated Jun 2023) in some famous tourist-attracted cities in Asia including Bangkok, Tokyo, Taipei, Hongkong and Singapore. The project will anwser some questions such as:

  1. How many listings and hosts are there in each city? Single-listings or multi-listings? How many listings does each host have?
  2. What types of room are listed? Which one is the most popular?
  3. Which rental term is more popular? Short-term or Long-term?
  4. How do the prices differ between room types and cities?

2. Dataset

The dataset was provided by Inside Airbnb with Data Policies and Disclaimers.

The data utilizes public information compiled from the Airbnb web-site. Data is verified, cleansed, analyzed and aggregated. No "private" information is being used.

This project will use 5 CSV files (city-listings.csv) of 5 Asian cities downloaded from Inside Airbnb website (dated June 2023), with the same structure and format.

Data Dictionary as follows:

Field Type Description
id integer Airbnb's unique identifier for the listing
name string
host_id integer
host_name string
neighbourhood_group text The neighbourhood group as geocoded using the latitude and longitude against neighborhoods as defined by open or public digital shapefiles.
neighbourhood text The neighbourhood as geocoded using the latitude and longitude against neighborhoods as defined by open or public digital shapefiles.
latitude numeric Uses the World Geodetic System (WGS84) projection for latitude and longitude.
longitude Uses the World Geodetic System (WGS84) projection for latitude and longitude.
room_type string
price currency daily price in local currency. Note, $ sign may be used despite locale
minimum_nights integer minimum number of night stay for the listing (calendar rules may be different)
number_of_reviews integer The number of reviews the listing has
last_review date The date of the last/newest review
calculated_host_listings_count integer The number of listings the host has in the current scrape, in the city/region geography.
availability_365 integer avaliability_x. The availability of the listing x days in the future as determined by the calendar. Note a listing may be not available because it has been booked by a guest or blocked by the host.
number_of_reviews_ltm integer The number of reviews the listing has (in the last 12 months)
license string

3. Project Goal

The goal of the Airbnb Listings Analysis of 5 Asian Cities project is to conduct a comprehensive analysis of lodging properties listed on Airbnb in Bangkok, Tokyo, Taipei, Hongkong and Singapore to derive meaningful insights into listings in these markets.

The primary tool used for the analysis is Microsoft Power BI, which enables the connection to the dataset, data transformations and the creation of a meaningful and insightful report.

4. Project Roadmap

  1. Data Acquisition: The initial step involved downloading the listings dataset from Inside Airbnb website.
  2. Data Connection: Import 5 csv files into MS Power BI Desktop, append all queries as a new query, display source table name column as the city name.
  3. Exploratory Data Analysis: Use Query Editor to gain insights into the data quality, structure and distributions.
  4. Data Cleaning: Due to the high quality of the dataset, no significant data cleaning was necessary.
  5. Data Modeling: There is only one large table appended from 5 csv files (of 5 different cities) with the same format and structure. There are some new tables created for easier and clearer calculation of measures. New relationships are established respondingly.
  6. Comprehensive Analysis: Utilized calculated columns and measures to generate new data insights and enhance analysis capabilities.
  7. Detailed Report: One-page report featuring valuable insights and practical information about Airbnb Listings in 5 Asian Famous Tourism Cities: Bangkok, Tokyo, Taipei, Hongkong and Singapore.

II. Final Report

MS PowerBI Link

airbnb-listings-analysis

III. Key findings

  • Listings Count: Among 5 cities, Bangkok has by far the most number of listings. Singapore has the least listings but the highest percentage of recently active listings (>80%). On the contrary, nearly 80% of listings in Tokyo have no reviews in the last 6 months.
  • Listings per Host: Almost 2/3 hosts in Bangkok have only one listing. In all cities, nearly a half of all hosts own 10+ listings. More than 1/3 hosts own 10-99 listings. In Hongkong, nearly 1/3 of hosts have 100+ listings, while in Taipei and Tokyo, no hosts have more than 100 listings. Top 10 Hosts own total 2.9K listings, equivalent to 6.7% of all listings, mainly distributed in Hongkong.
  • Room type: Entire home/apartment is the most popular room type in almost all cities, except for Hongkong, where Private room is predominant. Hotel room and Shared room are less common in all cities, account for only 5-10% of all listings.
  • Terms: Nearly 70% of listings in Singapore and more than 50% of listings in Hongkong require long-term rentals, while almost all listings in Tokyo accept short-term rentals.
  • Prices: Airbnb is not always cheaper than a hotel! Entire home/apartment is 35-45% more expensive than hotel room in Singapore and Hongkong. Singapore is most expensive with prices of 3.3 -2.5 times higher than the figures of Bangkok and Hongkong respectively. Prices in Singapore also fluctuate within the widest range, followed by Taipei. In all cities, mean prices are much higher than median, which means the majority of prices are lower, but have unusually high values.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.