INTRODUCTION
Airbnb has transformed the way people travel, offering a unique platform where
hosts can rent out their homes and travellers can find accommodations that fit their
needs and budget.
In Munich, Airbnb has become a popular choice for tourists seeking a more
personalized and local experience. The city offers a wide range of accommodations
on Airbnb, from single rooms to entire homes.
The types of accommodations offered on Airbnb in Munich are diverse, including
Entire homes/apartments, Private rooms, and Shared rooms. Each type of
accommodation offers a different experience and price point, catering to a wide
range of traveller needs.
However, the growth of Airbnb in Munich also raises important questions about its
impact on the local housing market, neighbourhood dynamics, and city regulations.
In this presentation, we will analyse these issues, using data from Inside Airbnb to
analyze the Airbnb landscape in Munich. We will explore the average cost per night,
the distribution of listings across the city, the influence of reviews on price, and more.
The insights gained from this analysis will help inform policies and interventions to
ensure the sustainable development of the city and social justice for local residents.
DATA CLEANING AND PRE-PROCESSING
The first step in our analysis was to clean and pre-process the data. This is a crucial
step as it ensures the quality and reliability of our findings.
We started by importing the data from the listings.csv file, which contains detailed
information about each Airbnb listing in Munich.
We then performed an initial exploration of the data to understand its structure and
identify any potential issues. This included checking for missing values, duplicate
entries, and inconsistent data types.
We found that some columns, such as 'price', contained non-numeric characters, which
required cleaning. We removed these characters and converted the column to a
numeric data type.
We also found that some columns had missing values. For example, the 'reviews_per_month' column had missing values for
listings with no reviews. We filled these missing values with zero, assuming that no reviews mean no reviews per month.
We faced a few challenges during this process. For instance, the 'neighbourhood' column had many unique values, making it
difficult to analyze at a granular level. To address this, we decided to focus our analysis on the 'neighbourhood_cleansed'
column, which provides a more general view of the neighbourhoods in Munich.
Despite these challenges, we were able to clean and pre-process the data effectively, setting a solid foundation for our
analysis.
AVERAGE COST AND TYPES OF ACCOMMODATION
One of the key aspects we analysed was the average cost per night for
each type of room offered on Airbnb in Munich.
Our analysis revealed that the average cost per night varies significantly
depending on the type of room. Entire homes/apartments tend to be the
most expensive, followed by private rooms and then shared rooms.
We also calculated a 95% confidence interval for these estimates to
provide a range in which we are confident the true average cost lies. This
helps account for variability in the data and provides a more robust
estimate.
In terms of residential (i.e., home-based) accommodation, we conducted
one-way ANOVA test and our analysis showed that these tend to be
cheaper than hotel rooms. This is likely due to the fact that residential
accommodations often do not offer the same level of amenities or services
as commercial accommodations. (Appendix 1)
However, it's important to note that there is still a wide range of prices within each type of accommodation. Factors such as
location, size, and quality of the listing can all influence the price.
Out insights can help inform policies aimed at regulating short-term rentals and ensuring affordable accommodation options
are available for visitors to Munich.
DISTRIBUTION OF LISTINGS AND COST DIFFERENCE
Our analysis also explored the distribution of Airbnb
listings across different neighbourhoods in Munich.
We found that the neighbourhoods with the most listings
are Ludwigsvorstadt-Isarvorstadt, Maxvorstadt, and
Au-Haidhausen. These areas are popular due to their
central location and proximity to tourist attractions.
In terms of cost, the most expensive neighbourhoods to
stay in are Altstadt-Lehel, Ludwigsvorstadt-Isarvorstadt,
and Schwanthalerhöhe. These areas are known for their
high-end accommodations and prime locations.
When looking at residential listings, we found that they are most prevalent in
Ludwigsvorstadt-Isarvorstadt, Maxvorstadt, and Au-Haidhausen. This suggests that
these areas are popular for home-based accommodations.
We also analysed the cost difference between staying in the city centre districts and
further afield. Our analysis showed that accommodations in the city centre tend to be
more expensive than those in outlying areas. This is likely due to the higher demand for
accommodations in central locations. (Appendix 2)
These insights can help inform policies aimed at managing the distribution of short-term
rentals across the city and ensuring a balance between tourist accommodations and
residential housing.
INFLUENCE OF REVIEWS ON PRICE
We also investigated the relationship between review scores and the
price charged per night.
Our analysis revealed a positive correlation between review scores
and price. This suggests that listings with higher review scores tend to
charge higher prices.
However, the correlation was not very strong, indicating that while
reviews may have some influence on price, they are not the only
factor. Other factors such as location, type of accommodation, and
amenities offered likely also play a significant role in determining
price.
The trend line appears to be significant with every 1 unit increase in review score leading to 9.96
euros increase in the price. (Appendix 3)
It's also worth noting that the relationship between reviews and price may not be causal. It could be
that higher-quality (and thus more expensive) listings are more likely to receive higher review
scores, rather than higher review scores leading to higher prices.
Despite this, reviews are still an important aspect to consider as they can impact a guest's decision
to book a particular listing. Listings with higher review scores may be more appealing to guests and
could potentially command higher prices as a result.
Another factor that might influence the price is number of reviews but it appears to have significant
relationship but increase in reviews appears to show a negative impact on pricing. (Appendix 4)
SUPERHOSTS VS. ORDINARY HOSTS
Another aspect we explored was the difference between superhosts and
ordinary hosts.
Superhosts are experienced hosts who provide a high level of service, and
they are recognized by Airbnb with a special status. Our analysis showed
that superhosts tend to have similar variations in the price as that of
ordinary hosts.
We also found that superhosts are more evenly distributed across the city,
while ordinary hosts are more concentrated in certain neighbourhoods. This
could suggest that superhosts are more likely to offer accommodations in a
wider range of locations. (Appendix 5)
In terms of price, there was not a significant difference
between superhosts and ordinary hosts. This suggests that
while superhosts may provide a higher level of service, they
do not necessarily charge higher prices.
Based on these findings, it could be beneficial to support
superhosts as they contribute to a high-quality, diverse range
of accommodations across the city. However, it's also
important to ensure that all hosts are meeting certain
standards to ensure a positive experience for guests.
MULTIPLE PROPERTY OWNERS
We also conducted an analysis to identify hosts who offer multiple
properties on Airbnb.
Our analysis revealed that a significant number of hosts in Munich offer
more than one property. These hosts tend to have a higher number of
total listings and higher review scores compared to hosts who only offer
one property.
We found that multiple property owners are more prevalent in certain
neighbourhoods, suggesting that these areas may be more affected by
the short-term rental market.
The presence of multiple property owners can have various effects on
local residential property markets. On one hand, it can increase the
supply of short-term rentals and potentially drive down prices. On the
other hand, it can also reduce the supply of long-term housing and
drive up rents, particularly in neighbourhoods where multiple property
ownership is prevalent.
These findings highlight the need for policies that balance the benefits
of short-term rentals with the need to maintain affordable housing
options for residents. This could include regulations on the number of
properties a single host can list or measures to encourage the listing of
unused properties.
Top 10 Super Hosts
host_id
308052561
180080792
249602552
493125906
54239071
1621313
377192563
16320926
52981690
163543823
host_name
Super Host
Euro Youth Hotel
14
Homely
9
Boardinghouse Amalienburg
9
Hanna
9
Julia & Igor
8
Regina
6
Schwan Locke
6
Peter
5
Lisa
5
LISA And AMAD
5
Top 10 Ordinary Hosts
host_id
376961462
205832270
395880389
51557252
164225004
434055750
2266917
175608026
7901771
176165882
host_name
Vonder
K Eins Mysecondhome De
Boutique Hotel Germania
One
Ramona
MyMINGA
Max
The FLAG
Thomas
Stoke
Ordinary Host
73
33
26
22
16
16
15
14
12
11
SUSTAINABLE URBAN DEVELOPMENT
Balancing economic benefits and social justice is key in sustainable urban
development. Airbnb can significantly boost Munich's economy by increasing tourism,
stimulating local businesses, and providing additional income for hosts (Mangi et al.,
2020).
However, Airbnb's growth can lead to social justice concerns such as increased
housing prices, reduced long-term rentals, and neighbourhood character changes, as
highlighted in studies like Leyk et al. (2020).
The impact of urban development on ecosystem services (Yu et al., 2022) and the
role of urban green spaces in maintaining habitat connectivity (Nguyen et al., 2020)
are additional factors to consider when assessing Airbnb's sustainability in Munich.
Policy measures are needed to manage these challenges and ensure Airbnb's
benefits are equitably distributed. This could involve listing regulations, affordable
housing support, and initiatives to distribute tourism benefits city-wide.
CONCLUSION & RECOMMENDATIONS
Our analysis of Airbnb data for Munich has revealed key insights into the city's short-term
rental market. We found significant variations in average nightly costs based on room type
and location, with residential accommodations generally cheaper and city centre locations
more expensive (Mangi et al., 2020).
Review scores influence price, but other factors such as location, accommodation type, and
amenities also play a significant role (Leyk et al., 2020). Super hosts tend to have more listings
and higher review scores, but not necessarily higher prices.
The presence of hosts offering multiple properties, especially in certain neighbourhoods, could
impact local residential property markets, potentially increasing short-term rentals and driving
up rents (Nguyen et al., 2020).
Balancing Airbnb's economic benefits with social justice needs is crucial for sustainable urban
development. This may require listing regulations, affordable housing support, and initiatives
to distribute tourism benefits city-wide (Yu et al., 2022).
Based on these findings, we recommend further research on Airbnb's impact on Munich's
housing market and community, and consideration of regulations to manage short-term rentals
growth and ensure equitable benefit distribution.
REFERENCES
Leyk, S., Uhl, J. H., Connor, D., Braswell, A., Mietkiewicz, N. P., Balch, J., & Gutmann,
M. (2020). Two centuries of settlement and urban development in the United States.
Retrieved July 12, 2023, from https://dx.doi.org/10.1126/sciadv.aba2937
Mangi, M. Y., Yue, Z., Kalwar, S., & Lashari, Z. A. (2020). Comparative Analysis of
Urban Development Trends of Beijing and Karachi Metropolitan Areas. Retrieved July
12, 2023, from https://dx.doi.org/10.3390/su12020451
Nguyen, T., Barber, P., Harper, R., Linh, T. V. K., & Dell, B. (2020). Vegetation trends
associated with urban development: The role of golf courses. Retrieved July 12,
2023, from https://dx.doi.org/10.1371/journal.pone.0228090
Yu, H., Yang, J., Sun, D., Li, T., & Liu, Y. (2022). Spatial Responses of Ecosystem Service
Value during the Development of Urban Agglomerations. Retrieved July 12, 2023,
from https://dx.doi.org/10.3390/land11020165
APPENDIX 1
Anova: Single Factor
SUMMARY
Groups
Entire home/apt
Hotel room
Private room
Shared room
Count
3692
35
1882
47
ANOVA
Source of Variation
Between Groups
Within Groups
SS
5328977
1.96E+08
Total
2.01E+08
Sum
665513
7211
217412
6349
df
3
5652
5655
Average
180.2581
206.0286
115.5218
135.0851
Variance
41902.27
7503.382
20643.81
44216.51
MS
F
1776326 51.28064
34639.3
P-value
1.08E-32
F crit
2.60648
APPENDIX 2
t-Test: Two-Sample Assuming Unequal Variances
Mean
Variance
Observations
Hypothesized Mean Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
City Center
Afield
183.08
147.27
34864.13592 36116.416
1770
3887
0
3370
-6.608018763
2.255E-11
1.645305909
4.51001E-11
1.960668171
APPENDIX 3
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.030681676
R Square
0.000941365
Adjusted R Square 0.000721017
Standard Error
171.0582723
Observations
4536
ANOVA
df
Regression
Residual
Total
Intercept
Review Score
1
4534
4535
SS
MS
F
Sig. F
125007.7267 125007.7 4.272172 0.038798
132669068.1 29260.93
132794075.8
Coefficients
Standard Error
104.844
23.219
9.961
4.819
t Stat
4.515
2.067
P-value Lower 95% Upper 95%
0.000
59.323 150.364
0.039
0.513
19.408
APPENDIX 4
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.0331
R Square
0.0011
Adjusted R Square
0.0009
Standard Error
188.6189
Observations
5647
ANOVA
df
Regression
Residual
Total
Intercept
Number of Reviews
SS
1 220794.8
5645 2.01E+08
5646 2.01E+08
MS
F
220794.8 6.206093
35577.1
Coefficients Standard Error t Stat
161.055
2.675
60.217
-0.090
0.036
-2.491
P-value
0.000
0.013
Sig. F
0.0127596
Lower 95%
155.812
-0.161
Upper 95%
166.299
-0.019
APPENDIX 5