# RECORD 52cbefc12720084fe56091430bf37e9fbeba96b9
# TITLE: Do 5G cell phone towers decrease house prices? Evidence from Warsaw
# YEAR: 2024.0  VENUE: Journal of Housing and the Built Environment  DOI: 10.1007/s10901-024-10144-3
# AUTHORS: Bartłomiej Marona; R. Gaca; Michał Głuszak
# PAGES: 22  CANDIDATE TABLE PAGES (1-based): [12, 14, 16]
# ABSTRACT: This study investigates the impact of telecommunication infrastructure on residential property prices in Poland. This study contributes to the discussion of economic externalities related to new public infrastructure within the urban landscape. We use hedonic regression, matching techniques, and a difference-in-differences estimator to assess the impact of base transceiver stations on apartment prices in Warsaw, the capital of Poland, which covered market data on the sales of residential premises located in Miasteczko Wilanów estate in Warsaw from 2016 to 2021. In the analysed period, 1,825 residential sales were recorded and used for econometric modelling. The results do not confirm the influence of the vicinity of the wireless communication technology infrastructure on residential real estate prices in the studied local market. In particular, we did not observe a detrimental effect of cell phone towers on housing prices. Additionally, we investigate whether residential sale prices in proximity to BTS changed significantly after the introduction of the 5G standard. This particular issue has not been addressed in the economic literature. We found that the sale prices of apartments located in Warsaw were not statistically affected by the introduction of a fifth-generation technology standard for broadband cellular networks. Our research contributes to a better understanding of stigmatisation effects related to telecommunication infrastructure, and in particular, the links between the presence of cell phone towers and residential property values in the neighbourhood. The results may be of interest to all potential agents involved in neighbourhood conflicts arising from investments in cell phone towers and the development of new communication infrastructure in urban landscapes. The proximity of the base transceiver stations does not significantly affect the transaction prices. The introduction of 5G technology did not have a significant effect on residential property prices near cell phone towers. These findings contribute to a better understanding of the complex interplay between urban development, community conflicts, and housing prices.


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ARTICLE
Received: 24 January 2024 / Accepted: 28 June 2024 / Published online: 1 August 2024
© The Author(s) 2024
Extended author information available on the last page of the article
Do 5G cell phone towers decrease house prices? Evidence 
from Warsaw
Bartłomiej Marona1
 · Radosław Gaca1
 · Michał Głuszak1
Journal of Housing and the Built Environment (2024) 39:1821–1842
https://doi.org/10.1007/s10901-024-10144-3
Abstract
This study investigates the impact of telecommunication infrastructure on residential prop­
erty prices in Poland. This study contributes to the discussion of economic externalities 
related to new public infrastructure within the urban landscape. We use hedonic regres­
sion, matching techniques, and a difference-in-differences estimator to assess the impact 
of base transceiver stations on apartment prices in Warsaw, the capital of Poland, which 
covered market data on the sales of residential premises located in Miasteczko Wilanów 
estate in Warsaw from 2016 to 2021. In the analysed period, 1,825 residential sales were 
recorded and used for econometric modelling. The results do not confirm the influence 
of the vicinity of the wireless communication technology infrastructure on residential real 
estate prices in the studied local market. In particular, we did not observe a detrimental 
effect of cell phone towers on housing prices. Additionally, we investigate whether resi­
dential sale prices in proximity to BTS changed significantly after the introduction of the 
5G standard. This particular issue has not been addressed in the economic literature. We 
found that the sale prices of apartments located in Warsaw were not statistically affected 
by the introduction of a fifth-generation technology standard for broadband cellular net­
works. Our research contributes to a better understanding of stigmatisation effects related 
to telecommunication infrastructure, and in particular, the links between the presence of 
cell phone towers and residential property values in the neighbourhood. The results may 
be of interest to all potential agents involved in neighbourhood conflicts arising from in­
vestments in cell phone towers and the development of new communication infrastructure 
in urban landscapes.
Highlights
	
●
The proximity of the base transceiver stations does not significantly affect the transac­
tion prices.
	
●
The introduction of 5G technology did not have a significant effect on residential prop­
erty prices near cell phone towers.
	
●
These findings contribute to a better understanding of the complex interplay between 
urban development, community conflicts, and housing prices.
Keywords  Housing prices · BTS · Cell phone towers · 5G · Warsaw · Externalities
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B. Marona et al.
Table 2  Descriptive statistics (N = 1825)
Abbreviation
Category
Measurement
Mean
SD
Min
Max
Price
DV
Apartment’s transaction price 
in PLN (Natural logarithm)
717,799.3
(13.39)
341,466.5 
(0.41)
215,000 
(12.28)
2,900,000 
(14.88)
BTS_Prox
IV (N)
Proximity to BTS. 1 if the 
apartment being sold was 
located within a 200 m radius 
from BTS and 0 otherwise
0.48
0.50
0
1
BTS_Dist
IV (N)
Linear distance to the nearest 
elementary school (in 100 m)
2.13
0.87
0.29
4.98
BTS_Num
IV (N)
Number of BTSs in proximity 
(within a 200 m radius from 
the apartment being sold)
0.69
0.92
0
5
BTS_5G
IV (N)
Proximity to 5G BTS. 1 if 
the apartment being sold was 
located within a 200 m radius 
from 5G BTS and 0 otherwise
0.28
0.45
0
1
School
IV (N)
Linear distance to the nearest 
elementary school (in 100 m)
5.94
2.50
0.59
10.78
Bus
IV (N)
Linear distance to the nearest 
public transport stop (in 100 m)
2.25
1.18
0.22
5.01
Noise
IV (E)
Road noise measured in inter­
val brackets (less than 55 dB 
− 1, 55–60 dB 2, 60–65 dB – 3, 
65–70 dB – 4)
3.39
0.86
1
4
Area
IV (S)
Apartment’s usable area in m2
(Natural logarithm)
71.13 
(4.19)
29.43
(0.38)
26.09
(3.26)
287.94
(5.66)
Rooms
IV (S)
Number of rooms in the 
apartment
2.91
1.06
1
7
Floori
IV (S)
Location on floor. Dummy 
variables that take a value of 1 
if the apartment was located at 
a given floor and 0 otherwise 
(Floor1 -ground floor, base 
category omitted in regression 
models, Floor2 – other floors; 
Floor3 – first and second floor).
Floor1 (318 obs.), Floor2 (874), Floor3 (633)
Trans_C
IV (c)
The transaction involves a 
corporate participant. 1 when 
either buyer or seller is not a 
private person (household). 0 
otherwise
0.07
0.26
0
1
Timek
IV (t)
Time dummy variables taking 
a value of 1 if a transaction oc­
curred in a given quarter within 
the study period (from 2016Q1 
to 2021Q2)
2016q1 (40 obs.); 2016q2 (58); 2016q3 (74); 
2016q4 (42); 2017q1 (50); 2017q2 (74); 
2017q3 (87); 2017q4; (77); 2018q1 (74); 
2018q2 (88); 2018q3 (95); 2018q4 (110); 
2019q1 (91); 2019q2 (114); 2019q3 (124); 
2019q4 (114); 2020q1 (101); 2020q2 (64); 
2020q3 (104); 2020q4 (111); 2021q1 (107); 
2021q2 (26)
Note DV = dependent variable; IV = independent variable; N = neighbourhood attribute; S = structural 
attribute; E = environmental attribute; c = contract-related control variable; t = time-related control variable
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Do 5G cell phone towers decrease house prices? Evidence from Warsaw
prices of residential properties. However, we acknowledge the risk that we cannot account 
for all salient characteristics that may affect housing prices. The unobserved heterogene­
ity in housing quality is a major problem in hedonic price modelling (Bajari et al., 2012; 
Francke & Van de Minne, 2021; Osland, 2013), but to our defence, we were restricted by 
data availability. We did not have access to information on apartment interior design, techni­
cal condition, aspect, or view from the window. The housing stock in Miasteczko Wilanow 
is relatively homogenous in terms of building height, architectural design, building con­
struction technology, overall quality, and age (construction started in 2002). We believe 
that despite not directly controlling for other neighbourhood quality features, there are no 
substantial unobserved differences within the study area.
4  Results and discussion
4.1  Hedonic regression and matching estimators
To perform a comprehensive inference, calculations were performed for six models in the 
two groups (Table 3). The first three (Mod. 1 - Mod. 3) are baseline hedonic regression 
models based on all transactions within the sample (N = 1825). The remaining models (Mod. 
4 - Mod. 6) use the matching transaction data. We applied the Coarsened Exact Matching 
(CEM) algorithm(Iacus et al., 2011) to match residential units in proximity to BTS (treated 
group) to relatively similar apartments that were not directly affected by the presence of 
BTS (control group). The sample size in these models was small (N = 834). Within each 
group, we used different measures of exposure to BTS, as described in detail in the previous 
section. In particular, we control for the presence of BTS using a binary proximity measure 
(BTS_Prox), linear distance (BTS_Dist), and number of BTS in proximity to the property 
(BTS_Num).
We use a log-linear specification; thus, the natural logarithm of the sale price is a depen­
dent variable (Price). Table 3 presents the estimation results.
The R2 coefficients obtained from the hedonic regression models range from 0.883 for 
the baseline models (Mod. 1- Mod. 3) to 0.875 for the CEM matching. The models exhibited 
a relatively good fit with the empirical data. All models show price growth in the subsequent 
years of the analysis, which we controlled for using quarterly fixed effects time dummies.
Generally, the coefficients estimated for the statistically significant variables have intui­
tive (logical) signs. Along with expectations, the area is positively linked with sale prices; 
bigger apartments are more expensive, and others remain equal. The estimated price elas­
ticities (0.88–0.92) have an intuitive economic interpretation that is also consistent with the 
law of diminishing marginal utility derived from an increase in apartment size. The number 
of rooms did not have a statistically significant effect on apartment sale prices after control­
ling for the usable area.
A similar interpretation applies to the Floori variables (the story where the apartment was 
located). There was a significant discount related to the location on the ground floor (base­
line category) compared with the other floors. Other floors remained equal, whereas the sale 
prices were significantly higher in the case of apartments located on the first or second floor 
(Floor3). Street noise (Noise) did not have a significant impact on property prices within 
our sample, potentially because it was not seen as a crucial factor differentiating properties. 
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B. Marona et al.
Distance from the closest school has negative impacts on apartment prices. The result is 
also intuitive; in general, the farther from the school, the lower the apartment price, and the 
other stay equal. However, we report that the positive coefficient for the bus variable – the 
increase in distance from the bus stop–is associated with an increase in apartment prices. 
This finding is somewhat surprising; however, the marginal increase in accessibility may be 
due to disturbances caused by the traffic associated with the operation of buses in this par­
ticular district. Additionally, in all models, we controlled for parties involved in apartment 
sales (Trans_C). We observed that transaction prices, where either a buyer or seller was a 
business organisation, were significantly lower than in the case of scale between private 
market participants, while others remained equal. One potential explanation is that, on the 
one hand, companies are less likely to be anchored by unrealistic offer prices compared to 
private market participants when selling real estate. However, when buying apartments, 
Table 3  Hedonic regression models. The dependent variable is the natural logarithm of the sales price
Variables
Mod. 1
Mod. 2
Mod. 3
Mod. 4
Mod. 5
Mod. 6
BTS_Prox
0.014*
-0.001
(0.007)
(0.008)
BTS_Dist
0.006
0.008
(0.004)
(0.005)
BTS_Num
0.006
0.003
(0.004)
(0.005)
Ln Area
0.913***
0.919***
0.912***
0.882***
0.886***
0.880***
(0.017)
(0.017)
(0.017)
(0.023)
(0.023)
(0.023)
Rooms
0.008
0.006
0.008
0.011
0.010
0.011
(0.006)
(0.006)
(0.006)
(0.008)
(0.008)
(0.008)
Floor2
0.014
0.015
0.014
-0.007
-0.007
-0.007
(0.009)
(0.009)
(0.009)
(0.013)
(0.013)
(0.013)
Floor3
0.076***
0.077***
0.076***
0.037**
0.037**
0.037**
(0.010)
(0.010)
(0.010)
(0.013)
(0.013)
(0.013)
Noise
0.000
-0.001
-0.000
-0.006
-0.006
-0.006
(0.004)
(0.004)
(0.004)
(0.005)
(0.005)
(0.005)
School
-0.006***
-0.005***
-0.006***
-0.006***
-0.006**
-0.006**
(0.001)
(0.001)
(0.001)
(0.002)
(0.002)
(0.002)
Bus
0.010***
0.009**
0.011***
0.013***
0.011**
0.013***
(0.003)
(0.003)
(0.003)
(0.004)
(0.004)
(0.004)
Trans_C
-0.097***
-0.097***
-0.097***
-0.039*
-0.039*
-0.040*
(0.013)
(0.013)
(0.013)
(0.018)
(0.018)
(0.018)
Constant
9.302***
9.278***
9.307***
9.525***
9.496***
9.525***
(0.062)
(0.064)
(0.062)
(0.084)
(0.086)
(0.084)
Time fixed-effects (Quarterly)
yes
yes
yes
yes
yes
yes
Matching (CEM)
no
no
no
yes
yes
yes
R-squared
0.883
0.883
0.883
0.875
0.875
0.875
Adj R-squared
0.881
0.881
0.881
0.870
0.870
0.870
AIC
-1935.767
-1933.516
-1933.813
-1153.1
-1155.791
-1153.508
BIC
-1764.977
-1762.727
-1763.024
-1006.586
-1009.277
-1006.995
N
1825
1825
1825
834
834
834
Note Standard errors are in parentheses. Statistical significance * p < 0.05, ** p < 0.01, *** p < 0.001
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Do 5G cell phone towers decrease house prices? Evidence from Warsaw
corporate buyers may be more rational and not guided by emotions. The results were statis­
tically significant for all specifications.
In summary, taking into account the economic interpretation of the results obtained for 
the control variables, both the coefficients’ signs and values are fairly consistent with the 
knowledge of the particular factors’ impact on real estate prices. Generally, it should also 
be noted that the results obtained for the control variables are consistent and stable for all 
specifications used, as they are not always statistically significant.
The most relevant estimates were related to the BTS (BTS_Prox, BTS_Dist, and BTS_
Num). The signs of the coefficient do not necessarily align with a priori expectations based 
on results from previous studies and widespread public beliefs. Nonetheless, both of these 
variables proved to be statistically significant. In the case of proximity to BTS (BTS_Prox, 
which was used in Mods 1 and 4), the coefficient was positive (0.004), which is counterin­
tuitive. If statistically significant, the results would suggest that apartments located within 
a 200 m radius from the BTS were generally more expensive. Paradoxically, and more 
in line with expectations, the coefficient is also positive in the case of distance from BTS 
(BTS_Dist) (Mod 2 and Mod 5), and the number of BTS within the 200 m radius from the 
apartment (BTS_Num) variables that were used in alternative specifications. The results are 
mixed, but due to the lack of statistical significance, they are not conclusive.
4.2  Difference in differences estimator
We used differences-in-differences regression (DID) to assess the causal effect of the intro­
duction of 5G technology in May 2020 and compared the transaction prices of residential 
properties in proximity to BTS with 5G antennas (treatment group) to the sales prices of 
comparable housing units not affected by the event (control group). In line with the empiri­
cal specifications used in hedonic regression models, we control for the presence of BTS 
using a binary proximity measure (BTS_Prox), linear distance (BTS_Dist), and the number 
of BTS in proximity to the property (BTS_Num). The estimation results of the three DiD 
models (Mod. 7 - Mod. 9) are listed in Table 4.
DiD is reasonably well suited to empirical data. Additionally, we tested the parallel-
trend assumption. Based on the test results (Prob > F = 0.073), we could not reject the null 
hypothesis that the linear trends were parallel in the pretreatment period. Additionally, 
based on the Granger causality test, we note that anticipation of treatment had no effect 
(Prob > F = 0.1425).
We used the Average Treatment Effect on the Treated (ATET) to assess the causal effect. 
ATET coefficients (ranging from − 0.022 to -0.021) were not statistically significant in any 
specification. We conclude that the introduction of 5G technology did not have a significant 
effect on residential property prices in the study area. We note that there is no solid support 
for the claim that sale prices dropped significantly in the vicinity of those BTS that offered 
5G signals. This effect is presented graphically in Fig. 2.
Telecommunication infrastructure cannot create in my back yard (NIMBY) opposition 
of residents living in proximity. Wireless communication infrastructure, similar to other 
similar developments related to conflicting land uses, may generate neighbourhood conflicts 
due to the potential stigmatisation of the area or subjective assessment of environmental and 
health risks by the local community (Bennett & Davies, 2015; Llurdés et al., 2003; Mei et 
al., 2021). These concerns often manifest as decreases in house values in neighbourhoods.
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B. Marona et al.
The results indicate that this was not the case for BTS in Miasteczko Wilanów, as we did 
not find significant evidence that prices were lower in proximity. The lack of an economic 
impact of BTS on apartment prices requires further comment, as has been observed in many 
prior studies, especially when neighbourhood conflicts were intense and extensively cov­
ered by the media (Bond, 2007a). First, the turbulent introduction of BTS has become a 
more acceptable part of urban and rural environments. In recent years, more people have 
used wireless technology and are reluctant to express negative emotional arousal when 
exposed to the presence of BTS. Many recent studies suggest that the negative impact of 
BTS is currently smaller than that during the early 2000s and is often not statistically signifi­
cant (Witkowski & McLaughlin, 2021). Second, the technologies also changed significantly 
and blended more aesthetically into the city landscape. Currently, BTS are less visible and 
are often concealed. Other studies have suggested that the potential impact of BTS on house 
prices in the vicinity is significantly reduced when the disamenity is not directly visible 
(Affuso et al., 2018).
Table 4  Difference-in-differences regression models. The dependent variable is a natural logarithm of the 
sale price
Variables
Mod. 7
Mod. 8
Mod. 9
ATET
Treatment (BTS_5G*after)
-0.021
-0.022
-0.022
-0.021
(0.005)
(0.004)
(0.005)
(0.005)
BTS_Prox
0.026
(0.005)
BTS_Dist
0.010
(0.011)
BTS_Num
0.014
(0.012)
Ln Area
0.919*
0.916*
0.915*
(0.019)
(0.015)
(0.020)
Rooms
0.007
0.007
0.007
(0.009)
(0.008)
(0.009)
Floor2
0.015
0.015
0.014
(0.004)
(0.005)
(0.005)
Floor3
0.077
0.077*
0.077
(0.007)
(0.005)
(0.006)
Noise
-0.001
-0.001
-0.002
(0.003)
(0.003)
(0.003)
School
-0.006*
-0.005
-0.006
(0.000)
(0.001)
(0.001)
Bus
0.012
0.007
0.013
(0.006)
(0.006)
(0.005)
Trans_C
-0.098*
-0.095*
-0.098*
(0.006)
(0.003)
(0.005)
Constant
9.275**
9.278**
9.288**
(0.064)
(0.019)
(0.071)
Time fixed-effects (Quarterly)
yes
yes
yes
N
1825
1825
1825
Note ATET estimates are adjusted for covariates, group effects, and time effects. Note: Standard errors are 
in parenthesis. Statistical significance * p < 0.05, ** p < 0.01, *** p < 0.001
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Do 5G cell phone towers decrease house prices? Evidence from Warsaw
Our study had some limitations. First, it is unclear whether the results can be generalised 
to other rural and urban areas in Poland. Most likely, the results would be similar in major 
cities, where telecommunication infrastructure blends within urban landscapes and the 
majority of market participants are relatively unaware of their exact location, but different 
in rural and suburban areas where cell phone towers are less concealed. Extending empirical 
investigations beyond major cities to less urbanised areas seems to be a promising direc­
tion for future research. Second, despite our best efforts, we were unable to control for all 
relevant housing attributes. Unobserved heterogeneity may lead to biased estimation results, 
as suggested by econometric literature (Francke & Van de Minne, 2021). We attempted to 
reduce this risk by employing matching techniques and quasi-experimental settings. Finally, 
the fact that the proximity of BTS did not affect residential property prices, may be due to 
asymmetric information between buyers and sellers. Telecommunication infrastructure has 
blended into the urban landscape thus buyers are often not aware of the presence of BTS 
(Klaps et al., 2016). As a result, the information about the proximity of BTS may not be 
revealed in hedonic prices (Pope, 2008). It is worth extending the research to include stated 
preferences data, particularly in terms of buyers’ attitudes towards 5G networks but also 
awareness that this infrastructure exists in the location where they live.
5  Conclusion
This article engages in a discussion on the perception of telecommunication infrastructure 
within an urban landscape. This research provides new evidence on how base transceiver 
stations (BTS) influence housing prices. This study aimed to determine the impact of the 
Fig. 2  Causal effect of the introduction of 5G in Miasteczko Wilanów
 
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