course-dprep/what-influences-airbnb-prices

Estimating the effect of Hawaiian AirBnB listing characteristics, the time until the booking starts (in days) and the season on the price per night.

How do Property Characteristics influence AirBnB prices and does booking early guarantee a Lower Price?

1.Purpose and Motivation

AirBnB offers many tourists and travelers a relatively affordable and convenient accommodation option and home owners an extra source of income. This project aims to estimate the effects of the property characteristics, the time left until the booking starts and the season on the price per night of Hawaiian AirBnB listing by using the publicly available data from Inside AirBnB.

Our findings are not only relevant for tourists by giving them insights into how prices fluctuate across time or which neighborhood is the cheapest, and thus help them save money on their trip, but also AirBnB hosts by helping them develop a suitable pricing strategy based on the characteristics of the properties they own.

2.Research Question

What are the effects of the property characteristics and the number of days left until the booking starts on the price per night of Hawaiian AirBnB listings and do prices differ per season?

Conceptual Model

Figure 1. Conceptual Model

3.Method

3.1. Included Variables

Overview of the variables included in this study:

Variable (group)	Description
room_type	Since there are 4 room types: "Entire home/apt", "Private room", "Shared room", "Hotel", three dummy variables have been coded (a hotel room being the baseline) for the purpose of including them in the regression analysis. Entire_home_apt_room_type (1 if listing is an entire home or apartment, 0 if the listing is of a different type); Private_room_room_type (1 if listing is a private room, 0 if the listing is of a different type). Shared_room_room_type (1 if listing is a shared room, 0 if the listing is of a different type)
neighbourhood_goup	Since in Hawaii there are 4 neighborhood groups: "Hawaii", "Kauai", "Maui", "Honolulu", three dummy variables have been coded (Honolulu being the baseline) for the purpose of including them in the regression analysis. Hawaii_neighborhood (1 if the listing is in the Hawaii neighbourhood, 0 if the listing is in a different neighbourhood. Kauai_neighborhood (1 if the listing is in the Kauai neighbourhood, 0 if the listing is in a different neighbourhood. Maui_neighborhood (1 if the listing is in the Maui neighborhood, 0 if the listing is in a different neighborhood.
accommodates	the maximum number of guest the property can accommodate
bathrooms	the number of bathrooms of the property
bathroom_type	dummy variable to indicate whether the bathroom(s) are private or shared
bedrooms	the number of bedrooms of the property
beds	the number of beds of the property
review_score_rating	the review score rating of the property (on a 5-point scale)
instant_bookable	dummy indicating whether the property can be booked automatically or the host's approval is needed
time_diff	the number of days left until the booking starts
winter	dummy variable indicating the season (Hawaii only has two seasons) taking the value 1 for winter (November-April) and 0 for summer (May-October)

Overview of other variables used to merge different datasets or aggregate the data:

Variable	Used for:
id	merging the listings and the calendar datasets
date	aggregating the dataset per season

Data dictionary of the raw datasets can be found here.

3.2.Research Method

In order to estimate the effect of several metric and non-metric variables (property characteristics, time until booking starts and season) on another metric variable (listing price of the Hawaiian Airbnb listing), we opt for a regression analysis. We are interested in which property characteristics drive the price up or down and which have the largest effects. All independent variables are outlined in the table above. The regression equation can be summarized as follows:

Y = β0 + β1 * entire_home_apt_room_type + β2 * private_room_room_type + β3 * shared_room_room_type + β4 * hawaii_neignborhood + β5 * kauai_neignborhoodbathroom_type + β6 * maui_neignborhood + β7 * accommodates + β8 * bathrooms + β9 * bathroom_type + β10 * bedrooms + β11 * beds + β12 * review_score_rating + β13 * instant_bookable + β14 * time_diff + β15 * winter + ε

4.Repository overview

├── README.md
├── data
├── gen
│   ├── analysis
│   ├── data-preparation
│   └── paper
└── src
|  ├── analysis
|  ├── data-preparation
|  └── paper
└── make file

5.Dependencies

Please follow this guide to install R.

Also, make sure you install the following packages:

install.packages("tidyverse")
install.packages("utils")
install.packages("stringr")

6.Running Instructions

Cloning Repository

1. Open Git Bash
2. Change working directory to preferred location
3. Type git clone https://github.com/course-dprep/what-influences-AirBnB-prices.git

Running makefile

1. Change working directory to what-influences-AirBnB-prices
2. Type make

7.Results, Vizualizations and Conclusion

7.1.Results

The regression output can be found below.

Figure 2. Regression Output

7.2.Interpretation

All else equal,

• An entire room or apartment listed on AirBnB is on average €65.51 cheaper compared to a hotel room, while a private room listed on AirBnB is on average €23.40 cheaper than a hotel room;
• AiBnB listings in the Hawaii neighborhood are on average €29.47 cheaper than listings in the Honolulu neighborhood and listings in the Maui neighborhood are on average €85.41 more expensive than listings in the Honolulu neighborhood;
• A property that can accommodate an additional guest will have an average price higher by €30.37;
• A property that has an additional bedroom will have an average price higher by €9.61;
• On average, AirBnB listings in Hawaii that have a review score higher by 1 unit will charge €22.92 more;
• A listing that can be booked instantly (i.e., doesn't require the host's approval) costs on average €21.24 more than listings for which the host's approval is required;
• When the time difference in days between the date of booking an accommodation and the start date the reservation increases by 1 day, the price will decrease by €0.36;
• On average, AirBnB listings in Hawaii are €13.19 more expensive during winter (November-April) than during summer (May-September).

7.3. Selected Vizualizations

Does Booking Early Guarantee a Lower Price?

Travelers looking to save money seem to be best off if they book their AirBnB approximately 90-100 days in advance (~ three months), with an average price for an entire home/apartment of around $310 and an average price for a private room of around $270. Interestingly, prices seem to spike around 200 days prior to the booking start date, with the average entire home or apartment reaching $355, making it very disadvantageous for travelers to book around six months in advance. Rather, it would be better to book a bit later.

Which Neighborhood should you Choose?

Regardless of the room type travelers are looking for, Hawaii and Honolulu seem to be the cheapest neighborhoods and Muai is the most expensive one. Interestingly, a private room in Maui is more expensive than an entire home or apartment.

7.4. Conclusion

Our findings are relevant for both tourists and travelers as well as AirBnB hosts.

For hosts:

• Consumers are willing to pay a premium of €22.92 for properties that have a review score higher by 1 unit;
• You can charge higher prices by €13.19 during winter season as opposed to summer;
• The Hawaii neighborhood has the cheapest listings, while the Maui neighborhood is the most expensive;
• Prices in general increase over time;
• You can charge higher prices by €21.24 by listing your property as instantly bookable;

For consumers:

• Book early! Prices on average increase by €0.36 per day.
• The Hawaii neighborhood has the cheapest AirBnB listings.
• If you're flexible, visit Hawaii during summer! AirBnB listings in Hawaii are on average €13.19 more expensive during winter (November-April) than during summer (May-September).

Team 10 Data Preparation & Workflow Management

• Alexandra Bock [[email protected]]
• Koray Kul [[email protected]]
• Ana Luca [[email protected]]
• Merel van Stekelenburg [[email protected]]

what-influences-airbnb-prices's Issues

clean and prepare neighbourhood group and room type

review and prepare review score variable

Editing make file/ adding makefiles to subdirectories

write code for data preparation

beds and bedrooms variable: document missing values as comments

write readme file

neighborhood and room type: document missing values as comments

choose dataset

Hawaii dataset chosen

price variable: document missing values as comments

Missing text in bathrooms_text column

I just discovered that there are 23 listings that do not have any text offering information about bathrooms. What should we do about this? We could replace them with NAs and have them ignored when we perform the analysis

write descriptions of datasets

write code for data analysis

Assign values to NA in bedrooms variable

Bedrooms variable with a blank need to be changed into a value.

write code for downloading and saving data

data analysis march df

transforming bathrooms variable

write makefile

Review and prepare beds and bedrooms variable

aggregate merged datasets per season

download prototype data set

a script to get started downloading your first data set

download.file('http://data.insideairbnb.com/the-netherlands/north-holland/amsterdam/2022-06-05/visualisations/listings.csv', destfile = 'ams.csv')
library(tidyverse)
df <- read_csv('ams.csv')

Define Unit of Analysis

bathrooms variable: document missing values as comments

review score variable: document missing values as comments

create variable time_diff

variable for days difference between initial date and booking date

accommodates and instant bookable variables: document missing values as comments

rearrange files of Git to the right folder

update readme

make list of variables to include

IV s to include in regression from listings dataset (Hawaii)

• (optional) neighbourhood_group_cleansed (variable takes values: Hawaii, Honolulu, Kauai, Maui)
• room_type (variable takes values: Entire home/apt, Private room, Shared room, Hotel room)
• accommodates (numeric, number pdf people that can stay at the property)
• bathrooms_text (need to extract the number of baths and type of bath (?/shared/private)
• bedrooms (numeric)
• beds (numeric)
• instant_bookable (boolean)

Optional IVs (might be interesting)

• review_scores_rating
• review_scores_accuracy
• review_scores_cleanliness
• review_scores_checkin
• review_scores_communication
• review_scores_location
• review_scores_value

DV

• price (calendar datafile)

Group by:

• season (calendar datafile)

Merge by:

• id

make file

review and prepare accomodates and and instant_bookable variable

proposed rq's

1. How was the housing market in X* city affected by Covid-19?
2. What changes did Covid-19 bring into the consumer behavior in terms of housing preferences?
   2.1. Did the demand for long-term stays (30+ days) increase as a result of the Covid-19 pandemic?
3. Do the listings of "superhosts" get booked more?

data analysis june df

write code for exploring data

RMarkdown document with findings (incl. plots)

data analysis december df

prepare LaTeX document