Business Mafia Proposal
Contents
Project Motivation
A significant proportion of Airbnb hosts rent out portions of their own homes to generate additional side income. Instead of relying on a robust approach when setting prices, they tend to do so intuitively, relying on gut feeling. Our group hopes to offer these homeowners an alternative way to price their listings - through an amalgamation of factors such as their listing's geographical location and its relationship with Downtown Seattle.
However, the primary challenge here is simplifying and summarising the technical, complex analytics techniques into layman terms; it would require breaking down the technical jargon associated with it. In order to carry this out effectively, we created an RShiny Application which would guide owners systematically through the thought process. This would allow owners to not only derive the final proposed listing price, but also better understand our thought process and methodology behind the derivation of the price.
Project Objective
Through our project, we aim to:
- Derive individual walking distance between various key attractions and Airbnb listings in Downtown Seattle
- Analyse the spatial relationships between various key locations and Airbnb listings in Downtown Seattle to determine if the listing's location to key places affect its listing price
- Through the use of Local Geographical Weighted Regression (GWR) Model, we hope to help Airbnb owner(s) determine the better pricing for their listing(s).
Our Datasets
| Data | Source | Data Description | Source URL | Data Type |
|---|---|---|---|---|
Literature Review
Literature Sources:
- https://towardsdatascience.com/airbnb-rental-listings-dataset-mining-f972ed08ddec
- https://www.airbnbcitizen.com/the-airbnb-community-in-seattle/
- https://wiki.smu.edu.sg/18191isss608g1/ISSS608_Group07_Proposal
- https://wiki.smu.edu.sg/18191isss608g1/Group01_Proposal
Literature Review 1: Airbnb Rental Listings Dataset Mining
Literature's Objective: An exploratory analysis of Airbnb's Data to understand the rental landscape in New York City
- NYC's data was also obtained from Inside Airbnb and it contains the same three tables as ours, except that it was for New York City
- Listings.csv - contains 96 detailed attributes for each listing. Some of the attributes are continuous (i.e. Price, Longitude, Latitude, ratings) and others are categorical (Neighbourhoods, Listing_type, is_superhost) which is used for the analysis
- Reviews.csv - Detailed reviews given by guests with six attributes. The key attributes include date (datetime), listing_id (discrete), reviewer_id (discrete), comments (textual)
- Calendar.csv - Provides details about booking for the next year for each listing. There are four attributes in total, they are: listing_id (discrete), date (datetime), available (categorical) and price (continuous).
This is a screenshot of a paragraph taken off the literature. The context behind this paragraph was that the authors were trying to find out the number of days in a year each listing is made available for booking. Our group ran into a similar problem when analysing our Seattle Airbnb dataset.
Unfortunately, the number of days available for booking by each listing is not made publicly available by Airbnb. We found the method proposed by the authors useful as this was a simple solution that made a good enough estimation for us to gauge the number of days available for booking, and conclude if the listing was highly sought after or if it was one of those listings where it opened it's doors only few times each year.
Furthering on the earlier introduced idea that demand can be gauged from the number of reviews left by guests on their home owners, the authors investigated on how demand changes across three years - 2016 (left most graph), 2017 and 2018 (right most graph). All three graphs showed identical trends that demand across the year picks up. The period of peak demand across all three periods happens during the month of October. After which, demand tends to fall. This is mainly attributed to seasonality factors, as the seasons gradually shifts from Fall to Winter. From this, we can conclude that demand and seasonality in New York City are likely to be related to one another. This is a similar idea that can be looked into when exploring Seattle's Airbnb Dataset.
This graph shows the average listing prices per night for each month across all NYC Airbnbs. The average prices tend to increases as one progresses along in the year and Spikes in December. This pattern is similar to that of demand graph above, except in the months of November and December, where the demand starts falling since end October. Hence, one hypothesis that our group came up with when analysing listing prices and demand rates across months of the year(s) is that Airbnb owners do not take into account demand when setting prices. We attribute this to the lack of holistic pricing model(s) or analysis tool(s) that Airbnb owners do not use to understand demand patterns before determining prices. As most Airbnb owners set out to rent their apartments/rooms out to earn additional income, we believe that most owners set prices based on their intuition. This might likely be the case for Airbnb owners in Seattle as well.
This is the basic interactive graph with all the listing in New York City appearing in a clustered fashion. The user can click on the clusters to see the listing it comprises of. This gives a zoom-in view, more minute view of the landscape. A user can further click on each listing to see details of the listing such as the Listing Name, Host Name, Price of Listing, Property Type and Room Type. This visualisation helps to explore the listing geographically. It gives the overall sense of how the listings are distributed across neighborhood. We can see from the map that maximum listing are clustered around Manhattan and Brooklyn region, followed by Queens, Bronx and the least number of listing are in Staten Island. Click on the thumbnail and it will bring you to the animation!
When it comes to dealing with missing values in the dataset, the authors chose to construct a Visna plot to analyse the missing values for the variables they would be using for in their exploratory analysis. To preserve all the information, they imputed or dropped all the rows and columns containing null values when conducting their exploratory data analysis. The variables that they used in their visna plot were:
- host_is_superhost
- neighbourhood_group_cleansed
- host_response_time
- review_scores_rating
- name
- host_since
- zipcode
Few observations were made from this exercise:
- Most rows have no missing values
- reviews_score_rating variable have close to 30% missing fields across all rows
- zipcode, name, host_response_time, host_since and host_is_superhost have only few missing values across all rows and hence are not reflected in the visna plot.
From the plot above, it can be concluded that the Airbnb dataset only contains few missing values which further argues that the analysis done on the data can be performed without much loss of information.
The aim of this analysis is to see if there is any relationship between the different categorical variables in the dataset. The variables under examination are instant_booking, cancellation_policy and room_type. The goal of this study is to see if regular hosts and super hosts have different policies and to examine if there is a correlation among the variables.
Key takeaways from the above chart
- Majority of the listings are not available for instant bookings. Majority of the ones available tend to have strict cancellation policy.
- Properties that are entire homes or apartments tend to have a stricter cancellation policy compared to shared and private rooms. This makes sense as the hosts would want to prevent incurring a huge loss by last-minute cancellations on entire homes/apartments type bookings.
- As the number of shared rooms listings are very few, the line in the graph is very thin. From the graph, all the lines suggest that the shared rooms are available for instant booking and have slightly more flexible cancellation policy.
- Owners of shared rooms are generally regular hosts.
- Based on the features here under inspection, there are no major discrepancies in behaviour between regular hosts and super hosts as they seem to display similar behavior types.
Literature Review 2:
Literature's Objective:
Our Methodology
- Data wrangling/Data Cleaning
- Extracting only listings in Downtown Seattle
- Extracting out top 15 key attractions in Downtown Seattle
- Computing the street distance from each listing to the 15 different key attractions (Using Open Street Map Server)
- Geographically Weighted Regression Model
- Running correlation test obtain correlation of coefficient (R value) - choosing only variables that are significant at 95% confidence level
- Transformation of Variables
- Kernel Density Function Used
- Spatial Point Pattern Analysis
- First Order
- Second Order
R-Packages Used:
- Tmap
- SF
- SP
- Tidyverse
- Spatstat
Project Storyboard
Application Overview
Our Findings
Reflecting on our project
Project Timeline
