# RECORD 87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f
# TITLE: Spatial Variability of the 'Airbnb Effect': A Spatially Explicit Analysis of Airbnb's Impact on Housing Prices in Sydney
# YEAR: 2021.0  VENUE: ISPRS Int. J. Geo Inf.  DOI: 10.31235/osf.io/cw2a9
# AUTHORS: William Thackway; M. Ng; C. Lee; Vivien Shi; C. Pettit
# PAGES: 17  CANDIDATE TABLE PAGES (1-based): [10, 11, 12]
# ABSTRACT: Over the last decade, the emergence and significant growth of home sharing platforms such as Airbnb has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney - one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognising that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.


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Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit
Analysis of Airbnb’s Impact on Housing Prices in Sydney
Author:
Thackway, WT; Ng, MKM; Lee, CL; Shi, V; Pettit, CJ
Publication details:
ISPRS International Journal of Geo Information
v. 11
Chapter No. 1
2220-9964 (ISSN)
Publication Date:
2022-01-01
Publisher DOI:
https://doi.org/10.3390/ijgi11010065
License:
https://creativecommons.org/licenses/by/4.0/
Link to license to see what you are allowed to do with this resource.
Downloaded from http://hdl.handle.net/1959.4/unsworks_78888 in https://
unsworks.unsw.edu.au on 2026-10-20


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Table 2. Hedonic Modelling Results.
(1)
(2)
(3)
(4)
Model
Baseline
Baseline OLS
Control OLS
IV
Dep. Variable
log(sales)
Airbnb Density
0.0844 ***
0.0301 ***
0.0201 ***
(Standard Error)
(0.0020)
(0.0019)
(0.0024)
Observations
36,264
36,264
36,264
36,264
(Intercept)
12.749
12.700
12.94
13
Bedrooms
0.085
0.106
0.113
0.112
Baths
0.112
0.096
0.079
0.080
Parking
0.036
0.046
0.040
0.040
Dist. to City Centre
−0.000
−0.000
−0.000
−0.000
Dist. to Coast
−0.000
−0.000
−0.000
−0.000
Dist. to Commercial Zones
−0.000
−0.000
−0.000
−0.000
Dist. to Swim Place
−0.000
−0.000
−0.000
−0.000
Dist. to Industrial Zones
−0.000
−0.000
−0.000
−0.000
Within 400 m of Railway Station
0.144
0.082
0.030
0.030
Within 800 m of Railway Station
0.126
0.077
0.241
0.025
Within 1600 m of Railway Station
0.073
0.044
0.004
0.037
Crime Rate
−0.227
−0.200
−0.015
−0.010
Median Family Income
0.000
0.000
0.000
0.000
Within 100 m of Main Road
0.006
−0.016
−0.023
−0.022
Within 100 m of Railway Line
−0.065
−0.047
−0.061
−0.064
Adjusted R2
0.746
0.796
0.868
0.871
RSS
2593.372
2028.709
1312.713
1288.169
AICc
7287.198
−1615.774
−17,289.324
−17,973.784
Notes: Robust Standard Errors used. GOF statistics evaluated are: Adjusted R2 (adjusted coefficient of deter-
mination), RSS (Residual Sum of Squares), AICc (Corrected Akaike Information Criterion). Significance levels:
*** p < 0.01.
Upon implementing the TSLS regression with an IV specification, a positive and statis-
tically significant coefficient for Airbnb density is evident. This confirms the robustness
of our baseline results in which Airbnb activities do have a positive impact on housing
prices. Specifically, the IV model implies a 1% increase in Airbnb density is associated with
a 2.01% (column (4)) increase in house prices. The magnitude of Airbnb density in Model
(3) declines slightly compared with Model (2). This is in line with expectations, as the
purpose of the IV specification was to remove any correlation of the instrumental variable
with the error term and capture the effects of Airbnb more accurately. Ref. [22] provides
a detailed discussion of the validity of the IV term constructed through the ‘shift-share’
approach, concluding that it can be assumed exogenous from time-varying, municipality
level shocks. The exogeneity of the IV term with unobserved shocks contained in the error
term is expected to provide the most accurate estimate of the Airbnb density variable.
Therefore, the IV Airbnb density coefficient is the preferred measure of Airbnb’s impact on
housing prices in Sydney.
For the average postcode in terms of Airbnb activity (3.074), the IV Airbnb density coef-
ficient implies a house price increase of 2.26%, lower than the result of 3.7% in Barcelona [23].
However, with 50% more tourists annually in a city with roughly a third of Sydney’s pop-
ulation, it must be noted that Barcelona’s tourist market is far more substantial than the
Sydney market. Similarly, our estimate of a 2.01% increase in housing prices associated
with a 1% increase in Airbnb density is lower than the estimate of 3.5% in Lisbon and
Porto [24], however, there is a similar disparity in Sydney and Portugal’s tourist markets. It
must also be observed that these figures are not directly comparable, given the different
spatial granularity used in other studies performed at an aggregate level. Nonetheless, our
results are roughly aligned when considering the relative tourist markets of comparable
studies.


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In each stage of hedonic modelling the explanatory variable is statistically significant
and presents a clear associational impact of Airbnb activity increasing property prices.
Through the use of individual property sales data, the magnitude of Airbnb’s impact is able
to be evaluated for the first time in the context of residential property pricing. In general,
this does show that Airbnb activity leads to a higher level of housing prices. However, the
application of an OLS model to estimate Airbnb’s effect on the housing market ignores
both the spatial variation of Airbnb activity and housing submarkets within Sydney. A
GWR is therefore employed to capture the spatial nuances of Airbnb’s relationship with
the Sydney housing market.
4.2. GWR Results
Table 3 compares the goodness of fit (GOF) of the spatially explicit model against the
global models, and by all metrics, the GWR outperforms the OLS models. The adjusted
R2, which measures the proportion of total variation captured by the model, is 0.911
for the GWR compared to 0.871 for the best performing OLS model. Furthermore, the
GWR reduces the residual sum of squares of the IV model by a third from 1288.169 to
886.834, indicating that there is less unexplained variance in the spatially explicit model.
Finally, the corrected Akaike Information Loss (AICc) reduces from −17,973.784 with the IV
model to −26,824.740 for the GWR model, demonstrating that the GWR model minimises
information loss in the model.
Table 3. Model Performance Comparison.
Model
Adj R2
RSS
AICc
Baseline OLS
0.796
2028.709
−1615.774
Control OLS
0.868
1312.713
−17,289.324
IV
0.871
1288.169
−17,973.784
GWR
0.911
886.834
−26,824.737
Table 4 summarises the distribution of coefficient parameter estimates for the GWR.
It must be noted that GWR is foremost a descriptive spatial tool rather than a detailed
method for causal inference [29]. Therefore, the magnitudes of coefficient estimates are not
the primary concern of the GWR analysis, rather the variation of the directional association
(i.e., positive/negative) of the Airbnb density term and property prices. The median Airbnb
Density coefficient of 0.0127 corroborates the hedonic modelling results that Airbnb has
the predominant impact of increasing property prices. However, the 1st quantile estimate
of −0.0432 implies that for a significant portion of Sydney properties, Airbnb has a negative
influence on prices. Therefore, while Airbnb’s overall effect on the property market is
positive, the GWR results demonstrate that this impact is not homogenous, and in some
cases, the negative externalities produced by Airbnb may also reduce property prices.
Figure 2 visualises the Airbnb Density parameter estimates throughout Sydney. The
volatility of coefficient estimates may be related to bandwidth selection issues, due to
the application of a single bandwidth for all variables. This is a known limitation of
the GWR method, that could be potentially overcome in future work by applying an
MGWR [42]. It must also be noted that the magnitude of coefficient estimates may not
be directly comparable across Sydney areas, due to significant differences in underlying
Airbnb densities. Nevertheless, examining the direction of Airbnb density coefficients can
provide valuable insights into the geographical variation of Airbnb’s relationship with
property prices throughout Sydney.


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Table 4. GWR Model Results.
(1)
(2)
(3)
(4)
(5)
Min
Q1
Median
Q3
Max
Airbnb Density
−0.1217
−0.0432
0.0127
0.031
0.1456
Observations
36,264
36,264
36,264
36,264
36,264
(Intercept)
10.707
12.635
13.278
13.728
19.971
Bedrooms
0.003
0.082
0.102
0.132
0.213
Baths
−0.015
0.049
0.077
0.102
0.177
Parking
0.001
0.029
0.035
0.048
0.1
Dist. to City Centre
0
0
0
0
0
Dist. to Coast
0
0
0
0
0
Dist. to Commercial Zones
0
0
0
0
0
Dist. to Swim Place
0
0
0
0
0
Dist. to Industrial Zones
0
0
0
0
0
Within 400 m of Railway Station
−0.487
−0.026
0.036
0.121
1.081
Within 800 m of Railway Station
−0.485
−0.006
0.042
0.1
1.293
Within 1600 m of Railway Station
−0.591
−0.022
0.004
0.027
0.529
Crime Rate
−2.75
−0.291
−0.093
0.042
2.479
Median Family Income
0
0
0
0
0
Within 100 m of Main Road
−0.107
−0.045
−0.022
−0.005
0.051
Within 100 m of Railway Line
−0.173
−0.098
−0.047
0.016
2.366
 
(a) Sydney 
(b) Zoom into inner Sydney 
Figure 2. Spatial distribution of GWR parameter estimates for Airbnb density variable with 190 near-
est neighbours.
Spatial clustering of positive coefficient estimates is evident towards Manly and along
the northern beaches, where there is substantial Airbnb activity. This may be expected,
given the conceptual framework of Airbnb reducing the supply of long-term rentals and in
turn increasing housing prices. There is also a significant collection of positive coefficients,
ranging up to 0.146, towards the western centre of Paramatta. The emergence of Paramatta
as Sydney’s second CBD over the last decade may explain business motivated Airbnb usage
in these areas [2]. In these areas, where housing prices are lower and the tourist market is
less developed, an increase in Airbnb may be seen as a positive sign for homeowners in
signalling growth and greater potential rental revenue. Furthermore, ref. [43] documented
an increasing prominence of ‘investor landlords’ in Western Sydney, for whom Airbnb
presents a direct opportunity to increase rental yield. While Airbnb density here compared
to the more touristy central and coastal areas is lower and the impact on housing prices
will therefore be less, this nonetheless demonstrates that Airbnb can affect housing prices
outside of traditional tourist hotspots.


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Surprisingly, towards Sydney’s more traditional tourist areas of the CBD and eastern
suburbs, the Airbnb density coefficients are insignificant or negative. The insignificant
coefficients in the city centre may be explained by the significant pre-existing hotel and
holiday rentals in these areas, meaning the entrance of Airbnb has not disrupted the housing
market as significantly as non-traditional tourist areas. The negative coefficients evident
in the eastern suburbs and lower north shore raise the question of whether in high-price
areas, there exists a peak threshold of Airbnb activity, whereafter the over-saturation of the
tourist market is seen as a detractor for local residents. In these cases, negative externalities
associated with Airbnb activity such as noise, congestion, and access to amenities for locals,
may outweigh the increased rental revenue potential for homeowners [34]. Notably, it
has been shown in Sydney that high- and low-end housing submarkets follow different
pricing mechanisms [17,18]. As such, it is not surprising to document a dissimilarity effect
of Airbnb activities on housing prices, highlighting the importance of a disaggregated
study in gauging the effect of Airbnb.
It must be noted that upon applying this rationale, it might be expected that Manly
and the northern beaches would also experience negative Airbnb coefficients. While this
demonstrates the potential limitation of GWR in identifying specific Airbnb property
market hot/cold spots, it serves to highlight the spatial complexity of the Airbnb effect
across the city. Subsequently, one may assume that Airbnb’s impact on affordability is also
uneven due to heterogeneous effects. This is an important consideration in the context of
affordability policies and highlights the need for policymakers to consider local tourist and
housing market conditions when creating Airbnb regulations.
Clearly, Airbnb’s effect within a city is highly contextual to the city’s local tourist
and housing market dynamics. Therefore, understanding how Airbnb’s housing market
varies spatially in other global cities across different continents may help shed light on its
potential heterogeneous effect in different housing submarkets. Future work could also
examine the variable impacts of Airbnb using case studies of housing submarkets within a
city, as well as the temporal evolution of the Airbnb effect over the last decade. Finally, with
the global COVID-19 pandemic greatly altering Airbnb activity in major cities, analysing
market dependence on pandemic development may provide a new and updated lens into
Airbnb’s relationship with the housing market. Ultimately, through accounting for spatial
variation in modelling, our results demonstrate that the relationship between Airbnb and
housing prices is complex and varies significantly across and within cities such as Sydney.
5. Conclusions
The rapid expansion of home-sharing platforms, such as Airbnb, that has been border-
less and largely unregulated has led many to question what their effect on local communities
and housing has been. In large tourist cities such as Sydney, where the growth of Airbnb
has coincided with rising rental and housing prices, its relationship to the housing market
has been a subject of considerable attention for both academics and government regulators.
However, as policymakers and local governments have begun to apply a range of regula-
tory measures in response to these concerns, the lack of quality empirical evidence to gauge
the causal impact of platforms such as Airbnb on housing prices has become evident.
This study examines empirically the extent to which Airbnb impacts housing prices
in Sydney for the first time. Several stages of OLS modelling were performed to control
for external shocks and address potential collinearity concerns. The OLS modelling was
performed from a residential property valuation perspective, including property charac-
teristics, mobility variables, and proximity to amenity measures for the first time in an
Airbnb housing market study. Our results indicate that a 1% increase in Airbnb density is
associated with a 2.01% increase in property prices. This corroborates existing research on
the overall impact of Airbnb on the housing market.
Despite this, the existence of housing submarkets and documented spatial unevenness
of Airbnb activity throughout Sydney imply that a global model will be insufficient in
accounting for the spatial nuances of Airbnb’s property market impact. Therefore, a GWR