When in Rome…

“Every one soon or late comes round by Rome.” - Robert Browning

Rome has beckoned travelers from afar for quite a few decades now. If Italy represents romance, Rome stands for intimacy. Intimacy between its glorious past and urban present. Intimacy between its spellbinding art and inspiring culture. There is always more to Rome, and no matter how many trips you take, there will always be more to Rome. Needless to say that Rome receives millions of tourists each year. Rome is the 11th most visited city in the world and 3rd most visited in Europe.

   

   

Introduction

Problem Statement

The purpose of this project is to perform exploratory analysis on the AirBnB rental data for Rome and understand the following:

• What are the most affordable neighborhoods for a traveler - family or single; based on room type, property type, bed type etc
• Among the most affrdable neighbourhoods, which ones have th most listings baed on accommodation type

After all, who doesn’t want to travel the world with minimum cost, if not for free?

       

Brief Overview

Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Inside Airbnb initiative, this dataset describes the listing activity of homestays in Rome, Italy and is sourced from publicly available information from the Airbnb site.

Compiled till 8thMay, 2017, the following Airbnb activity is included in this Rome dataset:

• Listings - Detailed Listings Data for Rome, including full descriptions and average review score.

       

Approach

R will be used to perform data analysis and visualization to explore and identify the most affordable neighbourhoods to stay for a solo or family trip. This will be done by categorizing them based on the variables.

       

What’s in it for you?

If you are a traveler or planning your next vacation to this exotic city, it will help you find the best neighborhoods to stay. If not, it might just inspire you to pack your bags!

       

Packages required

To perform the analysis to the best of my abilities, I will be using the following R packages:

• tidyverse - For easy installation of packages and for data manipulation.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• stringr - Simple, Consistent Wrappers for Common String Operations.
• knitr - Provides a general-purpose tool for dynamic report generation in R.
• ggmap - A collection of functions to visualize spatial data and models on top of static maps from various online sources
• DT - Data objects in R can be rendered as HTML tables plotly - Easily translate ‘ggplot2’ graphs to an interactive web-based version gridExtra - Provides a number of user-level functions to work with “grid” graphics, notably to arrange multiple grid-based plots on a page, and draw tables.

 

library(tidyverse)
library(dplyr)
library(stringr)
library(knitr)
library(ggmap)
library(DT)
library(plotly)
library(gridExtra)

       

Data Cleaning and Preparation

Background of data

Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world.

By analyzing publicly available information about a city’s Airbnb’s listings, Inside Airbnb provides filters and key metrics so you can see how Airbnb is being used to compete with the residential housing market.

The data behind the Inside Airbnb site is sourced from publicly available information from the Airbnb site.

Link to the data is available here

Loading the datasets

listings <- read.csv("data/listings.csv", na.strings=c(""," ","NA"))

       

First look of the data

Dimensions of the datasets

listings_dim <- dim(listings)

• Listings has 25275 rows and 95 columns.

 

Column names of the datasets

names(listings)

##  [1] "id"                               "listing_url"                     
##  [3] "scrape_id"                        "last_scraped"                    
##  [5] "name"                             "summary"                         
##  [7] "space"                            "description"                     
##  [9] "experiences_offered"              "neighborhood_overview"           
## [11] "notes"                            "transit"                         
## [13] "access"                           "interaction"                     
## [15] "house_rules"                      "thumbnail_url"                   
## [17] "medium_url"                       "picture_url"                     
## [19] "xl_picture_url"                   "host_id"                         
## [21] "host_url"                         "host_name"                       
## [23] "host_since"                       "host_location"                   
## [25] "host_about"                       "host_response_time"              
## [27] "host_response_rate"               "host_acceptance_rate"            
## [29] "host_is_superhost"                "host_thumbnail_url"              
## [31] "host_picture_url"                 "host_neighbourhood"              
## [33] "host_listings_count"              "host_total_listings_count"       
## [35] "host_verifications"               "host_has_profile_pic"            
## [37] "host_identity_verified"           "street"                          
## [39] "neighbourhood"                    "neighbourhood_cleansed"          
## [41] "neighbourhood_group_cleansed"     "city"                            
## [43] "state"                            "zipcode"                         
## [45] "market"                           "smart_location"                  
## [47] "country_code"                     "country"                         
## [49] "latitude"                         "longitude"                       
## [51] "is_location_exact"                "property_type"                   
## [53] "room_type"                        "accommodates"                    
## [55] "bathrooms"                        "bedrooms"                        
## [57] "beds"                             "bed_type"                        
## [59] "amenities"                        "square_feet"                     
## [61] "price"                            "weekly_price"                    
## [63] "monthly_price"                    "security_deposit"                
## [65] "cleaning_fee"                     "guests_included"                 
## [67] "extra_people"                     "minimum_nights"                  
## [69] "maximum_nights"                   "calendar_updated"                
## [71] "has_availability"                 "availability_30"                 
## [73] "availability_60"                  "availability_90"                 
## [75] "availability_365"                 "calendar_last_scraped"           
## [77] "number_of_reviews"                "first_review"                    
## [79] "last_review"                      "review_scores_rating"            
## [81] "review_scores_accuracy"           "review_scores_cleanliness"       
## [83] "review_scores_checkin"            "review_scores_communication"     
## [85] "review_scores_location"           "review_scores_value"             
## [87] "requires_license"                 "license"                         
## [89] "jurisdiction_names"               "instant_bookable"                
## [91] "cancellation_policy"              "require_guest_profile_picture"   
## [93] "require_guest_phone_verification" "calculated_host_listings_count"  
## [95] "reviews_per_month"

 

** Taking a subset of the data for easier analysis**

At the first glance of the dataset, we’ve seen that it contains many irrelevant and redundant columns that we won’t want to use in our analysis. Undoubtedly columns such as “host picture url” and “host name” will not help us in our analysis. Thus, subsetting the data set with select columns for ease of analysis.

listings_sub <- listings %>% 
  select(latitude, longitude,neighbourhood_cleansed, price,accommodates,room_type, property_type, bed_type)

** A look at the first few rows of the data set**

datatable(head(listings_sub, n=10))

Data Cleaning of variables

Checking for null values

sum(is.na(listings_sub))

## [1] 0

We see that there are no null values present for any variable

 

Price being an important variable in our analysis, performing intial analysis and manipulation on the variable

Changing the type of the variable

First we need to fix up the price variable, which is given to us as a string containing dollar signs, dots, and commas.

listings_sub$price[1:5]

## [1] $58.00  $50.00  $150.00 $96.00  $89.00 
## 410 Levels: $1,000.00 $1,050.00 $1,400.00 $1,500.00 $1,540.00 ... $999.00

Changing the price variable to type numeric

listings_sub$price <- as.numeric(listings_sub$price)

Summary of the variable

summary(listings_sub$price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     1.0   151.0   304.0   249.5   354.0   410.0

       

Exploratory Data Analysis

By Listings

Listing the top 10 neighbourhoods with the maximum listings

listings_sub %>% 
   group_by(neighbourhood_cleansed) %>% 
   select(neighbourhood_cleansed) %>% 
   summarise(count = n())%>%
   arrange(desc(count)) %>%
   top_n(10)%>% 
   ggplot()+
  geom_bar(mapping = aes(x=reorder(neighbourhood_cleansed, count),
                          y=count),
            stat="identity", fill = "light blue")  +
   coord_flip() +
   labs(title="Top  10 neighbourhoods with maximum listings",
        x="Neighbourhoods", y="Number of listings") +
   theme_minimal()

It can be seen that the neighbourhood of I Centro Storico has the most number of listings with close to 12000 listings up for rent. It is interesting to note that Centro Storico is the historical center of his Italian city. Here’s where most visitors would want to spend their time!

Maximum number of listings lie in which price range?

listings_sub %>% 
   count(cut_width(price, 10)) %>% 
   arrange(desc(n)) %>% 
  top_n(1)

## # A tibble: 1 x 2
##   `cut_width(price, 10)`     n
##                   <fctr> <int>
## 1              (385,395]  1757

Most of the listings 1757 lie in the price range of 385 - 395 euros.

       

By Price

Finding the most costliest rental in Rome

listings_sub %>% 
  filter(price == max(listings_sub$price)) %>% 
  select(neighbourhood_cleansed ,property_type,price)

##   neighbourhood_cleansed property_type price
## 1        XII Monte Verde     Apartment   410

Interestingly this location has a pool and is located close to the vatican. To check the url of this location, click here

 

Listing out the neighbourhoods which lie within the 25 and 75 percentile of the price range

listings_sub %>% 
  filter(price >= quantile(listings_sub$price)[2] & price <= quantile(listings_sub$price)[3]) %>% 
  distinct(neighbourhood_cleansed)

##         neighbourhood_cleansed
## 1             I Centro Storico
## 2              III Monte Sacro
## 3                 IV Tiburtina
## 4          VI Roma delle Torri
## 5  VII San Giovanni/Cinecittà
## 6            VIII Appia Antica
## 7                       IX Eur
## 8               X Ostia/Acilia
## 9         XI Arvalia/Portuense
## 10             XII Monte Verde
## 11                XIII Aurelia
## 12             XIV Monte Mario
## 13          XV Cassia/Flaminia
## 14        II Parioli/Nomentano
## 15     V Prenestino/Centocelle

 

Categorising the variable price for better understanding

• The listings within the 1st Quantile is categorised as affrodable
• The listings within the 1st and 3rd quantile is categorised as moderate
• The listings above the third quantile is categorised as expensive

 listings_sub$price_cat <- cut(listings_sub$price, c(0, 151, 354, 410), 
                           labels = c("affordable", "moderate", "expensive"))

Total count of each price category

datatable( listings_sub %>% 
   group_by(price_cat) %>% 
   summarise(count=n()))

       

A look into affrodable listings

Subsetting the litsings under the affordable category

 affordable.rentals <- listings_sub %>% 
   filter(price_cat == "affordable")

 

Categorising accommodation into 3 types

• Single - If accommodates 1 person
• Family - If accommodates 2-4 persons
• Group - If accommodates more han 4 persons

affordable.rentals$accommodation_cat <- cut(affordable.rentals$accommodates, c(0,1,4,20), 
                                   labels = c("single", "family", "group"))

 

Grouping the average prce by accommodation and room type

 plot1 <- affordable.rentals %>% 
   group_by(accommodation_cat) %>% 
   summarise(mean=mean(price)) %>% 
   ggplot() +
   geom_bar(mapping = aes(x=reorder(accommodation_cat, mean),
                          y=mean),
            stat="identity", fill = "light blue")  +
   labs(title="Average price by acccomodation",
        x="Accommodation type ", y="Average price of the listings") +
   theme_minimal()

 plot2 <- affordable.rentals %>% 
   group_by(room_type) %>% 
   summarise(mean=mean(price)) %>% 
   arrange(desc(mean)) %>%
   # top_n(5) %>% 
   ggplot() +
   geom_bar(mapping = aes(x=reorder(room_type, mean),
                          y=mean),
            stat="identity", fill = "light blue")  +
   labs(title="Average price by room type",
        x="Room type ", y="Average price of the listings") +
   theme_minimal() 



grid.arrange(plot1, plot2, ncol=2)

It can be seen that the most priced listings are for Single and Shared Room. These are mostly hostels and dormitories.

 

Grouping the average price by property type

 affordable.rentals %>% 
   group_by(property_type) %>% 
   summarise(mean=mean(price)) %>% 
   arrange(desc(mean)) %>%
   # top_n(5) %>% 
   ggplot() +
   geom_bar(mapping = aes(x=reorder(property_type, mean),
                          y=mean),
            stat="identity", fill = "light blue")  +
   coord_flip() +
   labs(title="Average Price property wise",
        x="Property type ", y="Average price of the listings") +
   theme_minimal()

It can be seen that Bungalows are the most priced proerty types in Rome!

 

Grouping the average price by bed type

 affordable.rentals %>% 
   group_by(bed_type) %>% 
   summarise(mean=mean(price)) %>% 
   arrange(desc(mean)) %>%
   # top_n(5) %>% 
   ggplot() +
   geom_bar(mapping = aes(x=reorder(bed_type, mean),
                          y=mean),
            stat="identity", fill = "light blue")  +
   # coord_flip() +
   labs(title="Average Price by bed type",
        x="Bed type ", y="Average price of the listings") +
   theme_minimal() 

       

Affordable listings for family

Further deep diving into our analysis, we can will now check the locations which are most affordable for a family trip.

Filtering our subsetted data for accommodation of family type

family.affordable <- affordable.rentals %>% 
  filter(accommodation_cat =="family")

 

Counting and mapping the locations which accommodate families according to room type

map <- get_map(location = 'Rome',maptype = "roadmap", zoom = "auto", scale = "auto")

locations <- ggmap(map) + geom_point(aes(x = longitude, y = latitude, color = room_type), data = family.affordable, alpha = 0.8)

locations

datatable(family.affordable %>% 
   group_by(room_type) %>% 
   select(room_type) %>% 
   summarise(count = n()))

As can be seen from the map and the count, most of the listing are type Entire home/apt and Private Rooms. Only 37 of the lisings are of type shared, this is expected as families would would prefer private rooms while travelling.

 

Average price of the room types

(family.affordable %>% 
   group_by(room_type) %>%
   summarise(AveragePrice=round(mean(price), 2)) %>% 
   arrange(AveragePrice))

## # A tibble: 3 x 2
##         room_type AveragePrice
##            <fctr>        <dbl>
## 1 Entire home/apt        39.55
## 2    Private room        54.91
## 3     Shared room        81.27

 

Counting and mapping the locations which accommodate families according to bed type

locations <- ggmap(map) + geom_point(aes(x = longitude, y = latitude, color = bed_type), data = family.affordable, alpha = 0.8)

locations

datatable(family.affordable %>% 
   group_by(bed_type) %>% 
   select(bed_type) %>% 
   summarise(count = n()))

As expected, most listings that accommodate families have bed type of Real Bed. Nothing new here!

 

The affordable neighbourhoods which have the most listings for a family accommodation

datatable(family.affordable %>% 
  group_by(neighbourhood_cleansed) %>% 
  select(neighbourhood_cleansed) %>% 
   summarise(count = n())%>%
   arrange(desc(count)) %>%
   top_n(5))

 

The affordable neighbourhoods average price for a family accommodation

datatable(family.affordable %>% 
  group_by(neighbourhood_cleansed) %>% 
   summarise(AveragePrice=round(mean(price), 2)) %>% 
   arrange(AveragePrice))

       

Affordable listings for a single person

For more analysis, we can will now check the locations which are most affordable for a solo trip.

Filtering our subsetted data for accommodation of family type

single.affordable <- affordable.rentals %>% 
  filter(accommodation_cat == "single")

 

Counting and mapping the locations which accommodate singles according to room type

locations <- ggmap(map) + geom_point(aes(x = longitude, y = latitude, color = room_type), data = single.affordable, alpha = 0.8)

locations

datatable(single.affordable %>% 
   group_by(room_type) %>% 
   select(room_type) %>% 
   summarise(count = n()))

For singles also it seems that Private Rooms are the most listed!

 

Average price of the room types

datatable(single.affordable %>% 
   group_by(room_type) %>%
   summarise(AveragePrice=round(mean(price), 2)) %>% 
   arrange(AveragePrice))

 

Counting and mapping the locations which accommodate singles according to bed type

locations <- ggmap(map) + geom_point(aes(x = longitude, y = latitude, color = bed_type), data = single.affordable, alpha = 0.8)

locations

datatable(single.affordable %>% 
   group_by(bed_type) %>% 
   select(bed_type) %>% 
   summarise(count = n()))

 

The affordable neighbourhoods which have the most listings for a family accommodation

datatable(single.affordable %>% 
  group_by(neighbourhood_cleansed) %>% 
  select(neighbourhood_cleansed) %>% 
   summarise(count = n())%>%
   arrange(desc(count)) %>%
   top_n(5))

It can be seen that the neighbourhoods of VII San Giovanni/CinecittA and I Centro Storico are the most listed neighbourhoods!

 

The affordable neighbourhoods average price for a single accommodation

datatable(single.affordable %>% 
  group_by(neighbourhood_cleansed) %>% 
   summarise(AveragePrice=round(mean(price), 2)) %>% 
   arrange(AveragePrice))

       

Summary

This analysis is mainly to figure out the most affordable and listed neighbourhoods in the city of Rome. The following have been the main insights from this analysis:

• The most listed neighbourhood is I Centro Storico with more than 12000 listings while the next listed neighbourhood is VII San Giovanni/Cinecitt with less than 2500 listings. This can be attrbuted to the fact that I Centro Storico is the main center of the town.
• Looking at a price range of 10 euros. The price range of 385 - 395 has the most number of listings at 1757
• The highest priced listing is 410 euros per night.
• On analysing the average price of property types, it observed that Bungalow cost the most and a night spent in lawn costs the least.
• A night spent on bed type on Air bed (air mattress) costs the most and on a Futon costs the least.
• For a accommodations of 2-3 people, the average price for a single night would be 40 euros for an apartment and the cheapest neighbourhood to live would be X Ostia/Acilia with an average price for a single night being 34 euros.
• For single accommodations, the average price for a single night would range from 60 euros for an apartment to 99 euros for a private room and the cheapest neighbourhood to live in would be XIII Aurelia with an average price of 75 euros.
• For someone looking to share accommodations, a trip to Rome would be cheaper as compared to someone who wants a single private accommodation!

Futher analysis can be done by looking at the varibles which impact the proce of the listings and creating model to predict the price of locations on the basis of amenities offered.