# RECORD dde22a8458111f4d97568d09ce7efc13ea6e96aa
# TITLE: Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw
# YEAR: 2020.0  VENUE: European Journal of Transport and Infrastructure Research  DOI: 10.18757/ejtir.2020.20.2.3979
# AUTHORS: Marcin Torzewski
# PAGES: 19  CANDIDATE TABLE PAGES (1-based): [6, 9, 11, 15, 16, 17, 18]
# ABSTRACT: This paper identifies the relation between metro proximity and the prices of residential properties in Warsaw. The analysis is based on 2006-2013 AMRON-SARFiN data. The results show that metro has a positive impact on property prices if it is located at most 1.5 km from the property. The greatest increase in price of 8-13 per cent is observed when metro is located 400-800 meters from the property. The results indicate that when the metro transportation is poor, it causes a large increase in property prices, which is not comparable to the impact of other transports, such as bus or tram.


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Issue 20(2), 2020, pp. 41-59 
https://doi.org/10.18757/ejtir.2020.20.2.3979 
 
 
 
EJTIR 
 
 ISSN: 1567-7141 
http://ejtir.tudelft.nl/ 
 
Public transport accessibility and the prices of nearby 
properties: the case of the first metro line in Warsaw 
 
Marcin Torzewski1 
Faculty of Management, University of Warsaw, Poland 
This paper identifies the relation between metro proximity and the prices of residential properties 
in Warsaw. The analysis is based on 2006-2013 AMRON-SARFiN data. The results show that metro 
has a positive impact on property prices if it is located at most 1.5 km from the property. The 
greatest increase in price of 13 per cent is observed when metro is located up to 800 meters from 
the property. The results indicate that when the metro transportation is poor, it causes a large 
increase in property prices, which is not comparable to the impact of other transports, such as bus 
or tram.  
 
Keywords: hedonic pricing model, metro proximity, property prices, public transport, spatial models, 
Warsaw housing market. 
1. Introduction 
The subway / metro transportation plays a crucial role in the urbanization and urban development 
processes because it depends on its own specific infrastructure that is mostly located underground. 
This distinct feature of subway gives it a relative advantage over other means of transport, such as 
bus, trolleybus or tram. By its underground location metro allows its users to reach downtown city 
areas that struggle with high traffic limiting the number of vehicles and reducing the travel time. 
Due to the high number of users it also attracts the location of businesses and services, which seek 
for potential clients.  
Given the characteristics of the metro transportation network, its accessibility should be capitalized 
by the value of the surrounding properties. There exists an extensive number of studies that look 
at the relation between metro accessibility and the prices of nearby properties. Most of these studies 
focus on the cities with a highly developed railway network infrastructure, such as cities in the 
USA (Damm et al., 1980, Armstrong and Rodríguez, 2006), South Korea (Bae et al., 2003), Taiwan 
(Lin and Hwang, 2004), the UK (Du and Mulley, 2007), Portugal (Martínez and Viegas, 2009), Italy 
(Pagliara and Papa, 2011 and Gallo, 2018), Greece (Efthymiou and Antoniou, 2013) or China (Zhang 
                                                        
1 A: Szturmowa 1/3, 02-678 Warsaw, Poland. T: +48 791 676 646 E: m.torzewski@hotmail.com  


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EJTIR 20(2), 2020, pp.41-59 
 
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Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
Table 1. 
Summary statistics of property prices per square meter and distance related 
variables 
Variable 
Mean 
Std. Deviation 
Variable 
Mean Std. Deviation 
Price per m2 (PLN) 
7968.957 
2815.194 
Distance (km): Tram 1.727 
2.065 
Distance (km): Center 6.442 
3.532 
Tram: 0 - 200 m 
0.176 
0.380 
Centrum: 0 - 3 km 
0.187 
0.390 
Tram: 200 - 400 m 
0.183 
0.386 
Centrum: 3 - 5 km 
0.209 
0.407 
Tram: 400 - 800 m 
0.179 
0.383 
Centrum: 5- 7 km 
0.188 
0.391 
Tram: > 800 m 
0.463 
0.499 
Centrum: 7 - 10 km 
0.234 
0.423 
Distance (km): Bus 
0.179 
0.124 
Centrum: > 10 km 
0.182 
0.386 
Bus: 0 -100 m 
0.276 
0.447 
Distance (km): Metro 
3.934 
3.413 
Bus: 100 - 250 m 
0.514 
0.500 
Metro: 0 - 400 m 
0.075 
0.263 
Bus: 250 - 400 m 
0.156 
0.363 
Metro: 400 - 800 m 
0.133 
0.339 
Bus: > 400 m 
0.054 
0.225 
Metro: 800 - 1500 m 
0.103 
0.304 
Number of 
observations 
5576 
Metro: > 1500 m 
0.689 
0.463 
 
Source: Own calculations based on AMRON-SARFiN data and geographical coordinates of the properties 
recoded from their address and geographical coordinates of metro, bus and tram stops in Warsaw. 
 
Table 2. 
Summary statistics of physical characteristics of properties 
Variable 
Mean Std. Deviation 
Variable 
Mean Std. Deviation 
Square meters 
62.514 31.000 
Floor 0 
0.158 
0.365 
Square meters: 0-35 
0.128 
0.334 
Floor 1 
0.166 
0.372 
Square meters: 35-60 
0.467 
0.499 
Floor 2 
0.200 
0.400 
Square meters: 60-90 
0.262 
0.440 
Floor 3 
0.178 
0.383 
Square meters: >90 
0.144 
0.351 
Floor 4 
0.125 
0.331 
Parking 
0.232 
0.422 
Floor 5-6 
0.105 
0.307 
Primary market 
0.256 
0.436 
Floor 7-9 
0.047 
0.211 
Secondary market 
0.744 
0.436 
Floor >=10 
0.020 
0.140 
Age of the building 
27.606 29.685 
Private ownership 
0.448 
0.497 
Age of the building: 0-2 
0.287 
0.453 
Perpetual ownership 
0.548 
0.498 
Age of the building: 2-10 
0.191 
0.393 
Other type of ownership 0.004 
0.064 
Age of the building: 10-50 
0.304 
0.460 
Number of 
observations 
5576 
Age of the building: >50 
0.218 
0.413 
Source: Own calculations based on AMRON-SARFiN data 
Table 3. 
Summary statistics of neighborhood related variables 
Variable 
Mean 
Std. Dev. 
Living condition index 
0.494 
0.155 
Population density (thousands/m2) 
4.390 
2.762 
Share of green areas 
12.633 
16.642 
Share of recreational areas 
6.128 
6.821 
Crimes per 1000 inhabitants 
32.867 
15.822 
Children per 1000 children in kindergartens 
757.333 
138.412 
Number of districts 
18 
Source: Own calculations based on GUS (2012) and MOBP (2013) reports. 
The next set of control variable denoted as neighborhoodm includes characteristics of the surrounding 
measured at the district level. More specifically these measures account for population density, 
share of green areas, share of recreation areas, number of crimes per 1000 inhabitants, number of 
children attending kindergarten per 1000 children, living condition index. The data are extracted 
from GUS (2012) and MOBP (2013) reports. The summary statistics for the neighborhood related 
variables are shown in Table 3. The estimated equation additionally controls for time trend, 
measured by year and quarters fixed effects (denoted by time).  


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EJTIR 20(2), 2020, pp.41-59 
 
47 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
In the analysis, we also test the hypothesis concerning the interaction effect between various public 
transportation modes. We thus ask whether the importance of the metro accessibility weakens 
when other means of transportation are located closer than metro. To do that we define a dummy 
variable denoted as MOT which takes the value of 1 if other means of transport (bus or tram) are 
closer to the property than the nearest metro station and the relative distance between them is more 
than 250 meters; in other case the variable takes a value of 0. Put it differently, the variable equals 
to 1 if there is bus or tram available that is closer than metro and the distance to metro compared 
to bus or tram is quite far (i.e. more than 250 meters). This variable is then interacted with variables 
measuring the distance to the nearest metro station. The estimated coefficients on the interactions 
reveal whether the effect of metro proximity is affected by the relative distance of metro to other 
means of transportation. It is expected that when the other means of transportation are located 
closer than metro (i.e. the variable MOT takes the value of 1), the role of metro in shaping the 
property price will be lower. In other words, it is expected that the estimated coefficients on the 
interaction terms will prove to be negative.  
3.2 Alternative methods: SEM and SAR models 
Hedonic pricing models, though widely used in applied research on property prices, suffer from 
several important limitations. These mostly relate to heteroscedasticity, collinearity between the 
variables, and the spatial dependence/autocorrelation.  
While the first problem – heteroscedasticity – is a common problem embedded in ordinary least 
squares estimation, it can be overcame with the use of logarithmic form and robust standard errors. 
Similarly, in order to avoid collinearity between variables, the VIF test can be performed and one 
of the linearly dependent variables can be dropped from the model. The third issue – the spatial 
dependence – is, however, more serious and therefore requires more attention.  
More specifically, the problem of spatial dependence relates to the correlation of values within a 
given group of close objects, which may lead to the misspecification and estimation bias (Anselin 
and Arribas-Bel, 2012). It is worth noting that despite its significance, this issue has not been 
properly addressed in the number of previous studies on the topic (Damm et al., 1980; Bae et al., 
2003, Li et al., 2017). Spatial autocorrelation is closely linked to the so-called first law of geography 
(Tobler, 1970), which says that objects which are near to each other are more similar (correlated) 
than those that are distant. In the context of real estate, this means that observation units 
(properties) in close proximity should exhibit similar effects (prices) to neighboring units. In this 
case, the dependence can be viewed as error dependence that violates the crucial assumption of 
OLS of the uncorrelation of the error terms (LeSage and Pace, 2009). 
To deal with this issue, we re-estimate the models by applying: 1) spatial error model (SEM), and 2) 
spatial lag model (SAR); (Anselin and Arribas-Bel, 2012). 
𝐴 spatial error model (SEM) introduces the spatial autocorrelation into the error term, as: 
 
ln(𝑝𝑟𝑖𝑐𝑒)𝑖= 𝛼0 + 𝛼1𝑚𝑒𝑡𝑟𝑜𝑖+ 𝛼2𝑏𝑢𝑠𝑖+ 𝛼3𝑡𝑟𝑎𝑚𝑖+ 𝛼4𝑐𝑒𝑛𝑡𝑒𝑟𝑖+
                         ∑
𝛼𝑘(𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙)𝑖,𝑘
𝑙
𝑘=5
+ ∑
𝛼𝑚(𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑)𝑖,𝑚
𝑛
𝑚=𝑙+1
+ 𝛼𝑛+1(𝑡𝑖𝑚𝑒)𝑖+ 𝜀𝑖  
(2) 
 
𝜀𝑖= 𝜆𝑾𝜀𝑖+ 𝑢𝑖 
 
where 𝜆 is an autoregressive coefficient, 𝑾 is a spatial weight matrix and 𝑢 is an iid. error term.  
A spatial lag model (SAR), on the other hand, regresses the dependent variable (property price) on 
the set of independent variables, as well as on the property price reweighted by the spatial matrix. 
Spatial dependence is included here as an additional regressor, that is a spatially lagged dependent 
variable: 
 
ln(𝑝𝑟𝑖𝑐𝑒)𝑖= 𝛼0 +  𝜌𝑾∗ln(𝑝𝑟𝑖𝑐𝑒)𝑖+ 𝛼1𝑚𝑒𝑡𝑟𝑜𝑖+ 𝛼2𝑏𝑢𝑠𝑖+ 𝛼3𝑡𝑟𝑎𝑚𝑖+ 𝛼4𝑐𝑒𝑛𝑡𝑒𝑟𝑖+
                         ∑
𝛼𝑘(𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙)𝑖,𝑘
𝑙
𝑘=5
+ ∑
𝛼𝑚(𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑)𝑖,𝑚+ 𝛼𝑛+1(𝑡𝑖𝑚𝑒)𝑖+ 𝜀𝑖
𝑛
𝑚=𝑙+1
  
(3) 
 


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EJTIR 20(2), 2020, pp.41-59 
 
49 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
3-5 per cent in case it is located within 200 meters. The negative impact of a close proximity of bus 
and tram stops on the property selling price might be related to the noise caused by both bus and 
tram, which as opposed to metro (which in Warsaw is entirely located underground) operate on 
regular roads. As expected, close proximity to the city center of up to 3 km is associated with the 
highest property selling prices; such properties are even by 26 per cent more expensive than similar 
properties located at the city’s peripheries (more than 10 km away from the city center).  
Table 4. 
Estimation results using OLS, SEM and SAR methods 
Method: 
OLS  
SEM  
SAR  
Variable: 
coef/se 
coef/se 
coef/se 
Metro: 0 - 400 m 
0.131*** 
0.131*** 
0.084*** 
  
(0.025) 
(0.020) 
(0.014) 
Metro: 400 - 800 m 
0.132*** 
0.133*** 
0.088*** 
  
(0.026) 
(0.017) 
(0.012) 
Metro: 800 - 1500 m 
0.108*** 
0.110*** 
0.069*** 
  
(0.021) 
(0.017) 
(0.013) 
Metro: >1500 m 
(base) 
(base) 
(base) 
Bus: 0 -100 m 
-0.044** 
-0.027 
-0.031** 
  
(0.023) 
(0.020) 
(0.016) 
Bus: 100 - 250 m 
-0.019 
-0.001 
-0.007 
  
(0.020) 
(0.019) 
(0.015) 
Bus: 250 - 400 m 
-0.006 
0.007 
0.003 
  
(0.018) 
(0.019) 
(0.016) 
Bus: >400 m 
(base) 
 (base) 
 (base) 
Tram: 0 - 200 m 
-0.049*** 
-0.032* 
-0.030*** 
  
(0.018) 
(0.016) 
(0.012) 
Tram: 200 - 400 m 
-0.006 
0.003 
0.002 
  
(0.018) 
(0.015) 
(0.011) 
Tram: 400 - 800 m 
0.045** 
0.044*** 
0.035*** 
  
(0.019) 
(0.014) 
(0.010) 
Tram: >800 m 
(base) 
(base) 
(base) 
Centrum: 0 - 3 km 
0.250*** 
0.251*** 
0.173*** 
  
(0.051) 
(0.028) 
(0.021) 
Centrum: 3 - 5 km 
0.212*** 
0.209*** 
0.152*** 
  
(0.047) 
(0.023) 
(0.017) 
Centrum: 5- 7 km 
0.126*** 
0.118*** 
0.092*** 
  
(0.033) 
(0.020) 
(0.014) 
Centrum: 7 - 10 km 
0.103*** 
0.104*** 
0.078*** 
  
(0.034) 
(0.016) 
(0.011) 
Centrum: >10 km 
(base) 
(base) 
(base) 
Constant 
8.348*** 
8.346*** 
5.431*** 
  
(0.129) 
(0.050) 
(0.142) 
No. of observations 
5 576 
Notes: *** p<0.01, ** p<0.05, * p<0.1. The models control for: usable area, floor location, ownership, age of the 
building, parking place, type of the market, district characteristics (living conditions index, population density, 
share of green and recreational areas, crime rate and childcare availability), and year fixed effects. 
Table 5. 
Direct, indirect and total effects derived for SAR model 
  
Direct 
Indirect 
Total 
Metro: 0 - 400 m 
0.086 
0.041 
0.127 
Metro: 400 - 800 m 
0.090 
0.043 
0.133 
Metro: 800 - 1500 m 
0.071 
0.033 
0.104 
Bus: 0 -100 m 
-0.032 
-0.015 
-0.047 
Bus: 100 - 250 m 
-0.007 
-0.003 
-0.011 
Bus: 250 - 400 m 
0.003 
0.001 
0.004 
Tram: 0 - 200 m 
-0.031 
-0.015 
-0.046 
Tram: 200 - 400 m 
0.002 
0.001 
0.003 
Tram: 400 - 800 m 
0.036 
0.017 
0.052 


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EJTIR 20(2), 2020, pp.41-59 
 
50 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
Centrum: 0 - 3 km 
0.176 
0.083 
0.260 
Centrum: 3 - 5 km 
0.155 
0.074 
0.229 
Centrum: 5- 7 km 
0.094 
0.044 
0.138 
Centrum: 7 - 10 km 
0.079 
0.038 
0.117 
 
The coefficients obtained on variables measuring physical characteristics of the properties are in 
line with the theoretical predictions and expectations (see Appendix Table A.1). First, the most 
expensive properties are the smallest ones (0-35 square meters). Second, the floor location matters 
for the price: compared to the price of the properties located on the ground floor, the price of 
properties located up to the 7th floor are higher. Perpetual ownership is also associated with a 
higher selling price of the property (of around 3 to 5 per cent). Compared to properties located in 
old buildings (more than 50 years old), the properties located in new buildings aged up to 2 years 
are by around 12-18 per cent more expensive, and properties located in buildings aged 2-10 by 
even 16-24 per cent more expensive. We also observe that the selling price per square meter is by 
around 7-10 per cent lower at the primary market than at the secondary one. Importantly, the 
coefficients obtained on spatial parameters in SEM and SAR models (see Appendix Table A.1) are 
significant and reveal an existence of the spatial dependency between the neighboring properties. 
Finally, we assess how the proximity to other means of transportation (bus or tram) affects the 
uncovered relationship between metro accessibility and the prices of nearby properties. To do that  
we interact variables reflecting metro proximity with the variable MOT measuring the relative 
distance between metro and other means of transportation. The main results on the interaction 
terms are presented in Table 6 (Appendix Table A.3 gives the full set of coefficients). The MOT 
variable is interacted with variables indicating that metro is within 0-400 and 400-800 meters but 
not with the variable indicating that it is within 800-1500 meters. This is because of the definition 
of MOT variable, which takes a value of 1 if other means of transport (bus or tram) are closer to the 
property than the nearest metro station and the relative distance between them is more than 250 
meters. If metro is located within 800-1500 meters from the property it is very unlikely that there 
are no other transports that are closer to the property; in other words in this case the MOT variable 
nearly always takes a value of 1. 
The results obtained from the model with the interactions show that if the nearest metro station is 
very close to the property – within 0-400 meters – the fact that other means of transport are closer 
relative to metro does not matter for the property price, as indicated by the insignificant and close 
to zero interaction term between metro 0-400 and MOT variables in all three models. Even if other 
means of transport are closer than metro, the fact that metro is within 0-400 meters from the 
property increases its selling price by 13 per cent. As for the metro proximity of 400-800 meters the 
results indicate that when the nearest metro is within 400-800 meters and other means of transport 
are even further away or are in a close proximity to metro (variable MOT takes a value of 0), the 
presence of metro has a high and significant impact on the price of the property of 14 per cent to 
even 18 per cent (depending on the model used). On the other hand, if the nearest metro is within 
400-800 meters and the bus or tram is closer than metro (and at the same time metro is quite far 
away compared to bus or tram, i.e. more than 250 meters), the importance of the presence of metro 
for the property price is lowered by around 1-5 per cent. This result is, however, significant at 5% 
significance level only in the case of OLS model; for other two models we also find negative 
coefficients but they are not statistically significant. The OLS results thus show that if the nearest 
metro is relatively far (400-800m) and there are other transports that are much closer, we may not 
expect as high increase in property prices caused by metro as in the case when there are no other 
transports that are closer than metro or other transports are nearly as far as the metro is (i.e. the 
distance between metro and tram/bus is less than 250 meters). 
 
 


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EJTIR 20(2), 2020, pp.41-59 
 
51 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
Table 6. 
Estimation results with the interactions: OLS, SEM and SAR methods 
Method: 
OLS 
SEM 
SAR 
Variable: 
coef/se 
coef/se 
coef/se 
Metro: 0 - 400 m 
0.130*** 
0.132*** 
0.084*** 
  
(0.026) 
(0.021) 
(0.015) 
Metro: 0 - 400 m x MOT 
0.001 
-0.006 
-0.002 
  
(0.025) 
(0.038) 
(0.036) 
Metro: 400 - 800 m 
0.181*** 
0.143*** 
0.115*** 
  
(0.034) 
(0.032) 
(0.028) 
Metro: 400 - 800 m x MOT 
-0.055** 
-0.011 
-0.030 
  
(0.025) 
(0.032) 
(0.013) 
Metro: 800 - 1500 m  
0.108*** 
0.110*** 
0.069*** 
  
(0.021) 
(0.017) 
(0.029) 
No. of observations 
5 576 
Notes: the same as in Table 4; Variable MOT is defined as a dummy variable equal to 1 if the nearest metro station 
is further away than 250 meters from other transport (bus or tram) and 0 if the nearest metro station is closer than 
250 meters from other transport (bus or tram). 
 
Table 7. 
Direct, indirect and total effects derived for SAR model with interactions 
 
Direct 
Indirect 
Total 
Metro: 0 - 400 m 
0.086 
0.041 
0.127 
Metro: 0 - 400 m x MOT 
-0.002 
-0.001 
-0.003 
Metro: 400 - 800 m 
0.117 
0.055 
0.172 
Metro: 400 - 800 m x MOT 
-0.030 
-0.014 
-0.045 
Metro: 800 - 1500 m  
0.071 
0.033 
0.104 
5. Conclusion 
This paper provides quantitative evidence on how the proximity to metro affects selling prices of 
properties in Warsaw. The relation was assessed using AMRON-SARFiN 2006-2013 data and three 
estimation methods: OLS regression with clustered standard errors, SEM and SAR models.  
We found that metro proximity matters for the property prices and properties that are located close 
to the metro stations are valued more by the market. The positive impact of metro on prices of 
nearby properties – that are located up to 800 meters from the metro - is estimated to be 13 per cent. 
The positive effect is revealed in all three models and the two spatial models – SEM and SAR – 
additionally show spatial dependency of the neighboring properties. The effect is considerably 
higher than the findings received from meta-analyses of existing studies on the topic, which report 
virtually zero effect. As opposed to previous studies that analyzed the situation in cities with well-
developed metro infrastructure, the current study shows how metro affects prices of nearby 
properties in Warsaw, in which the metro system is far less developed. The high positive effect of 
metro availability on property prices that has been uncovered suggests that the impact of metro is 
much larger when it is not easily accessible and is not considered as the main public transportation 
mode. Furthermore, the fact that housing affordability in Warsaw is low may also lead to a greater 
differentiation in property prices, with relatively more expensive properties having highly valued 
characteristics, such as easy access to metro system. 
At the same time, the obtained results are somehow lower than the ones provided for Warsaw by 
Bazyl (2009). This difference may stem from the fact that in the present study we use transaction 
prices, while the analysis in Bazyl (2009) is based on asking prices. In consequence, higher positive 
effects of metro found with the use of asking prices than actual transaction prices suggests that 
metro is perceived as highly valuable and it is expected to be capitalized in the prices of residential 
properties even by a higher value.  


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EJTIR 20(2), 2020, pp.41-59 
 
52 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
Finally, because Warsaw public transportation system may be characterized by poor metro 
infrastructure but rich system of other public transports, we also analyze how the availability of 
other means of transport affects the uncovered high positive impact of metro. The results obtained 
from all three models suggest that the fact that other transports are closer than metro is not 
important for the high increase in the property selling price associated with metro if metro is close 
enough to the property (within 0-400 meters). As for the metro proximity of 400-800 meters from 
the property, the results suggest that other means of transport that are closer than metro lower the 
high positive impact of metro. The significant finding is, however, found only for OLS model.  
Several comments concerning the data and the analysis are called for. Given that the available data 
covered property transactions during 2006-2013 period, the present analysis examined the impact 
of metro proximity on property prices in Warsaw focusing on the first metro line. Since 2015 
Warsaw metro network has been extended and a second metro line is now in operation. The results 
presented in this paper could be re-assessed by additionally including data from 2015 onwards to 
see whether the uncovered relations hold. An interesting future extension would be the 
comparison of the impacts of newly opened and the existing metro stations on property prices. 
Other possible extension could include the analysis of selected districts to reveal the regional 
heterogeneity in the estimated effect of metro proximity on property prices. 
Acknowledgments 
This work was supported by the grant No. 2016/21/N/HS4/00080 from the National Science Centre, 
Poland. The author would like to thank the participants of 2019 Spatial Statistics conference. 
References 
 
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Anselin, L., and D. Arribas-Bel (2012). Spatial fixed effects and spatial dependence in a single cross-
section. Papers in Regional Science, 92(1), 3-17.  
Armstrong, R.J., and Rodríguez, D.A. (2006). An Evaluation of the Accessibility Benefits of Commuter 
Rail in Eastern Massachusetts using Spatial Hedonic Price Functions. Transportation, 33, 21–43. 
Bae, C.C., Jun, M., and Park, H. (2003). The impact of Seoul’s subway Line 5 on residential property 
values Transport Policy, 10, 85–94. 
Bajic, V. (1983). The effects of a new subway line on housing prices in metropolitan Toronto. Urban 
Studies, 2, 147–158. 
Bazyl, M. (2009). Hedonic price model for Warsaw housing market. Working Papers, Department of 
Applied Econometrics, Warsaw School of Economics, 8, 1–16. 
Borkowska, M., Rozwadowska, M., Śleszyński, J., and T. Żylicz (2001). Environmental Amenities on the 
Housing Market in Warsaw, Hedonic Price Method Research. Ekonomia, 3, 70-82. 
Bowes, D. R., and Ihlanfeldt, K. R. (2001). Identifying the Impacts of Rail Transit Stations on Residential 
Property Values. Journal of Urban Economics, 50(1), 1–25.  
Cervero, R., and Kang, C. D. (2011). Bus rapid transit impacts on land uses and land values in Seoul, 
Korea, Transport Policy, 18(1), 102–116.  


===== PAGE 15 (score 164, numeric-paren 139, table-words 1, var-words 7) =====

EJTIR 20(2), 2020, pp.41-59 
 
55 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
Appendix 
Table A.1. 
 Full estimation output for the main model using OLS, SEM and SAR methods 
 Method: 
  
OLS  
SEM  
SAR 
 Variable: 
  
coef/se 
coef/se 
coef/se 
Distance variables Metro: 0 - 400 m 
0.131*** 
0.131*** 
0.084*** 
  
(0.025) 
(0.020) 
(0.014) 
Metro: 400 - 800 m 
0.132*** 
0.133*** 
0.088*** 
  
(0.026) 
(0.017) 
(0.012) 
Metro: 800 - 1500 m 
0.108*** 
0.110*** 
0.069*** 
  
(0.021) 
(0.017) 
(0.013) 
Centrum: 0 - 3 km 
0.250*** 
0.251*** 
0.173*** 
  
(0.051) 
(0.028) 
(0.021) 
Centrum: 3 - 5 km 
0.212*** 
0.209*** 
0.152*** 
  
(0.047) 
(0.023) 
(0.017) 
Centrum: 5- 7 km 
0.126*** 
0.118*** 
0.092*** 
  
(0.033) 
(0.020) 
(0.014) 
Centrum: 7 - 10 km 
0.103*** 
0.104*** 
0.078*** 
  
(0.034) 
(0.016) 
(0.011) 
Tram: 0 - 200 m 
-0.049*** 
-0.032* 
-0.030*** 
  
(0.018) 
(0.016) 
(0.012) 
Tram: 200 - 400 m 
-0.006 
0.003 
0.002 
  
(0.018) 
(0.015) 
(0.011) 
Tram: 400 - 800 m 
0.045** 
0.044*** 
0.035*** 
  
(0.019) 
(0.014) 
(0.010) 
Bus: 0 -100 m 
-0.044** 
-0.027 
-0.031** 
  
(0.023) 
(0.020) 
(0.016) 
Bus: 100 - 250 m 
-0.019 
-0.001 
-0.007 
  
(0.020) 
(0.019) 
(0.015) 
Bus: 250 - 400 m 
-0.006 
0.007 
0.003 
  
(0.018) 
(0.019) 
(0.016) 
Physical 
characteristics 
Square meters: 0-35 
0.054*** 
0.055*** 
0.052*** 
  
(0.011) 
(0.010) 
(0.010) 
Square meters: 60-90 
0.000 
-0.009 
-0.005 
  
(0.010) 
(0.008) 
(0.008) 
Square meters: >90 
0.011 
-0.002 
0.003 
  
(0.043) 
(0.011) 
(0.010) 
Floor 1 
0.047*** 
0.038*** 
0.038*** 
  
(0.018) 
(0.011) 
(0.011) 
Floor 2 
0.036* 
0.020* 
0.025** 
  
(0.022) 
(0.011) 
(0.011) 
Floor 3 
0.034* 
0.019* 
0.024** 
  
(0.020) 
(0.011) 
(0.011) 
Floor 4 
0.041* 
0.033*** 
0.034*** 
  
(0.024) 
(0.012) 
(0.012) 
Floor 5-6 
0.044** 
0.034*** 
0.035*** 
  
(0.022) 
(0.013) 
(0.013) 
Floor 7-9 
0.024 
0.024 
0.021 
  
(0.024) 
(0.017) 
(0.017) 
Floor >=10 
0.053 
0.068*** 
0.056** 
  
(0.033) 
(0.024) 
(0.024) 
Perpetual ownership 
0.033*** 
0.033*** 
0.032*** 
  
(0.012) 
(0.009) 
(0.008) 
Other type of ownership 
-0.001 
0.019 
0.018 
  
(0.044) 
(0.052) 
(0.050) 
Parking 
0.001 
0.007 
0.005 
  
(0.011) 
(0.008) 
(0.008) 
Age of the building: 0-2 
0.129*** 
0.135*** 
0.119*** 
  
(0.020) 
(0.014) 
(0.013) 
Age of the building: 2-10 
0.177*** 
0.171*** 
0.157*** 


===== PAGE 16 (score 85, numeric-paren 52, table-words 2, var-words 14) =====

EJTIR 20(2), 2020, pp.41-59 
 
56 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
  
(0.020) 
(0.013) 
(0.013) 
Age of the building: 10-50 
-0.014 
0.002 
-0.005 
  
(0.016) 
(0.010) 
(0.010) 
Primary market 
-0.082*** 
-0.069*** 
-0.065*** 
  
(0.019) 
(0.011) 
(0.011) 
Neighborhood 
characteristics 
Living condition index 
0.169 
0.219*** 
0.122*** 
  
(0.110) 
(0.065) 
(0.047) 
Population density (thousands/m2) 
-0.009 
-0.013*** 
-0.008*** 
  
(0.006) 
(0.004) 
(0.003) 
Share of green areas 
-0.001 
-0.001*** 
-0.001** 
  
(0.001) 
(0.001) 
(0.000) 
Share of recreational areas 
0.006*** 
0.006*** 
0.005*** 
  
(0.002) 
(0.001) 
(0.001) 
Crimes per 1000 inhabitants 
-0.000 
-0.001** 
-0.001** 
  
(0.001) 
(0.001) 
(0.000) 
Children per 1000 children in kindergartens 
0.000 
0.000 
0.000* 
  
(0.000) 
(0.000) 
(0.000) 
Constant 
8.348*** 
8.346*** 
5.431*** 
  
(0.129) 
(0.050) 
(0.142) 
Lambda 
  
0.366*** 
  
  
  
(0.017) 
  
Rho 
  
  
0.335*** 
  
  
  
(0.016) 
Number of observations 
5576 
 
Table A.2. 
 Direct, indirect and total effects derived for full estimation output for SAR 
model   
 
  
Direct 
Indirect 
Total 
Distance variables 
Metro: 0 - 400 m 
0.086 
0.041 
0.127 
Metro: 400 - 800 m 
0.090 
0.043 
0.133 
Metro: 800 - 1500 m 
0.071 
0.033 
0.104 
Centrum: 0 - 3 km 
0.176 
0.083 
0.260 
Centrum: 3 - 5 km 
0.155 
0.074 
0.229 
Centrum: 5- 7 km 
0.094 
0.044 
0.138 
Centrum: 7 - 10 km 
0.079 
0.038 
0.117 
Tram: 0 - 200 m 
-0.031 
-0.015 
-0.046 
Tram: 200 - 400 m 
0.002 
0.001 
0.003 
Tram: 400 - 800 m 
0.036 
0.017 
0.052 
Bus: 0 -100 m 
-0.032 
-0.015 
-0.047 
Bus: 100 - 250 m 
-0.007 
-0.003 
-0.011 
Bus: 250 - 400 m 
0.003 
0.001 
0.004 
Physical 
characteristics 
Square meters: 0-35 
0.053 
0.025 
0.079 
Square meters: 60-90 
-0.005 
-0.002 
-0.007 
Square meters: >90 
0.003 
0.002 
0.005 
Floor 1 
0.039 
0.018 
0.057 
Floor 2 
0.026 
0.012 
0.038 
Floor 3 
0.025 
0.012 
0.037 
Floor 4 
0.035 
0.016 
0.051 
Floor 5-6 
0.036 
0.017 
0.053 
Floor 7-9 
0.021 
0.010 
0.032 
Floor >=10 
0.058 
0.027 
0.085 
Perpetual ownership 
0.033 
0.015 
0.048 
Other type of ownership 
0.018 
0.009 
0.027 
Parking 
0.005 
0.002 
0.008 
Age of the building: 0-2 
0.122 
0.058 
0.179 
Age of the building: 2-10 
0.161 
0.076 
0.237 
Age of the building: 10-50 
-0.005 
-0.002 
-0.008 
Primary market 
-0.066 
-0.031 
-0.098 


===== PAGE 17 (score 146, numeric-paren 117, table-words 1, var-words 12) =====

EJTIR 20(2), 2020, pp.41-59 
 
57 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
Neighborhood 
characteristics 
Living condition index 
0.125 
0.059 
0.184 
Population density (thousands/m2) 
-0.008 
-0.004 
-0.012 
Share of green areas 
-0.001 
0.000 
-0.001 
Share of recreational areas 
0.005 
0.002 
0.008 
Crimes per 1000 inhabitants 
-0.001 
0.000 
-0.001 
Children per 1000 children in kindergartens 0.000 
0.000 
0.000 
 
Table A.3.  
Full estimation output for the model with interaction terms using OLS, SEM and 
SAR methods 
 Method: 
  
OLS 
SEM 
SAR 
 Variable: 
  
coef/se 
coef/se 
coef/se 
Distance 
variables 
Metro: 0 - 400 m 
0.130*** 
0.132*** 
0.084*** 
  
(0.026) 
(0.021) 
(0.015) 
Metro: 0 - 400 m x MOT 
0.001 
-0.006 
-0.002 
  
(0.025) 
(0.038) 
(0.036) 
Metro: 400 - 800 m 
0.181*** 
0.143*** 
0.115*** 
  
(0.034) 
(0.032) 
(0.028) 
Metro: 400 - 800 m x MOT 
-0.055** 
-0.011 
-0.030 
  
(0.025) 
(0.032) 
(0.029) 
Metro: 800 - 1500 m  
0.108*** 
0.110*** 
0.069*** 
  
(0.021) 
(0.017) 
(0.013) 
Centrum: 0 - 3 km 
0.251*** 
0.251*** 
0.173*** 
  
(0.051) 
(0.028) 
(0.021) 
Centrum: 3 - 5 km 
0.213*** 
0.209*** 
0.153*** 
  
(0.046) 
(0.023) 
(0.017) 
Centrum: 5- 7 km 
0.126*** 
0.118*** 
0.092*** 
  
(0.033) 
(0.020) 
(0.014) 
Centrum: 7 - 10 km 
0.104*** 
0.104*** 
0.078*** 
  
(0.033) 
(0.016) 
(0.011) 
Tram: 0 - 200 m 
-0.048*** 
-0.032* 
-0.030** 
  
(0.018) 
(0.016) 
(0.012) 
Tram: 200 - 400 m 
-0.006 
0.003 
0.002 
  
(0.018) 
(0.015) 
(0.011) 
Tram: 400 - 800 m 
0.045** 
0.044*** 
0.035*** 
  
(0.019) 
(0.014) 
(0.010) 
Bus: 0 -100 m 
-0.042* 
-0.026 
-0.030* 
  
(0.021) 
(0.020) 
(0.016) 
Bus: 100 - 250 m 
-0.016 
-0.001 
-0.006 
  
(0.019) 
(0.019) 
(0.015) 
Bus: 250 - 400 m 
-0.006 
0.007 
0.003 
  
(0.018) 
(0.019) 
(0.016) 
Physical 
characteristics 
Square meters: 0-35 
0.054*** 
0.055*** 
0.052*** 
  
(0.011) 
(0.010) 
(0.010) 
Square meters: 60-90 
0.001 
-0.009 
-0.005 
  
(0.010) 
(0.008) 
(0.008) 
Square meters: >90 
0.011 
-0.002 
0.003 
  
(0.043) 
(0.011) 
(0.010) 
Floor 1 
0.047*** 
0.038*** 
0.038*** 
  
(0.018) 
(0.011) 
(0.011) 
Floor 2 
0.036* 
0.020* 
0.025** 
  
(0.022) 
(0.011) 
(0.011) 
Floor 3 
0.033* 
0.019* 
0.024** 
  
(0.020) 
(0.011) 
(0.011) 
Floor 4 
0.041* 
0.032*** 
0.034*** 
  
(0.024) 
(0.012) 
(0.012) 
Floor 5-6 
0.044** 
0.034*** 
0.035*** 
  
(0.022) 
(0.013) 
(0.013) 
Floor 7-9 
0.024 
0.024 
0.021 


===== PAGE 18 (score 103, numeric-paren 80, table-words 1, var-words 12) =====

EJTIR 20(2), 2020, pp.41-59 
 
58 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
  
(0.024) 
(0.017) 
(0.017) 
Floor >=10 
0.053 
0.068*** 
0.056** 
  
(0.033) 
(0.024) 
(0.024) 
Perpetual ownership 
0.033*** 
0.033*** 
0.032*** 
  
(0.012) 
(0.009) 
(0.008) 
Other type of ownership 
0.001 
0.019 
0.019 
  
(0.045) 
(0.052) 
(0.050) 
Parking 
0.002 
0.007 
0.005 
  
(0.011) 
(0.008) 
(0.008) 
Age of the building: 0-2 
0.129*** 
0.135*** 
0.119*** 
  
(0.020) 
(0.014) 
(0.013) 
Age of the building: 2-10 
0.177*** 
0.171*** 
0.157*** 
  
(0.020) 
(0.013) 
(0.013) 
Age of the building: 10-50 
-0.014 
0.002 
-0.005 
  
(0.016) 
(0.010) 
(0.010) 
Primary market 
-0.082*** 
-0.069*** 
-0.065*** 
  
(0.019) 
(0.011) 
(0.011) 
Neighborhood 
characteristics 
Living condition index 
0.169 
0.219*** 
0.122*** 
  
(0.110) 
(0.065) 
(0.047) 
Population density (thousands/m2) 
-0.009 
-0.013*** 
-0.008*** 
  
(0.006) 
(0.004) 
(0.003) 
Share of green areas 
-0.001 
-0.001*** 
-0.001** 
  
(0.001) 
(0.001) 
(0.000) 
Share of recreational areas 
0.006*** 
0.006*** 
0.005*** 
  
(0.002) 
(0.001) 
(0.001) 
Crimes per 1000 inhabitants 
-0.000 
-0.001** 
-0.001** 
  
(0.001) 
(0.001) 
(0.000) 
Children per 1000 children in kindergartens 
0.000 
0.000 
0.000* 
  
(0.000) 
(0.000) 
(0.000) 
Constant 
8.344*** 
8.345 
5.435*** 
  
(0.129) 
(0.050) 
(0.143) 
Lambda 
  
0.366*** 
  
  
  
(0.017) 
  
Rho 
  
  
0.335*** 
  
  
  
(0.016) 
Number of observations 
5576 
 
 
 


===== PAGE 19 (score 27, numeric-paren 0, table-words 1, var-words 14) =====

EJTIR 20(2), 2020, pp.41-59 
 
59 
Torzewski 
Public transport accessibility and the prices of nearby properties: the case of the first metro line in Warsaw 
 
Table A.4.  
Direct, indirect and total effects derived for full estimation output for SAR model 
with interactions 
  
  
Direct 
Indirect 
Total 
Distance variables 
Metro: 0 - 400 m 
0.086 
0.041 
0.127 
Metro: 0 - 400 m x MOT 
-0.002 
-0.001 
-0.003 
Metro: 400 - 800 m 
0.117 
0.055 
0.172 
Metro: 400 - 800 m x MOT 
-0.030 
-0.014 
-0.045 
Metro: 800 - 1500 m  
0.071 
0.033 
0.104 
Centrum: 0 - 3 km 
0.177 
0.084 
0.261 
Centrum: 3 - 5 km 
0.156 
0.074 
0.230 
Centrum: 5- 7 km 
0.094 
0.044 
0.139 
Centrum: 7 - 10 km 
0.080 
0.038 
0.118 
Tram: 0 - 200 m 
-0.030 
-0.014 
-0.045 
Tram: 200 - 400 m 
0.002 
0.001 
0.003 
Tram: 400 - 800 m 
0.036 
0.017 
0.053 
Bus: 0 -100 m 
-0.030 
-0.014 
-0.045 
Bus: 100 - 250 m 
-0.006 
-0.003 
-0.009 
Bus: 250 - 400 m 
0.003 
0.001 
0.004 
Physical 
characteristics 
Square meters: 0-35 
0.053 
0.025 
0.079 
Square meters: 60-90 
-0.005 
-0.002 
-0.007 
Square meters: >90 
0.003 
0.002 
0.005 
Floor 1 
0.039 
0.018 
0.057 
Floor 2 
0.026 
0.012 
0.038 
Floor 3 
0.025 
0.012 
0.036 
Floor 4 
0.034 
0.016 
0.051 
Floor 5-6 
0.036 
0.017 
0.052 
Floor 7-9 
0.021 
0.010 
0.032 
Floor >=10 
0.058 
0.027 
0.085 
Perpetual ownership 
0.033 
0.016 
0.048 
Other type of ownership 
0.019 
0.009 
0.029 
Parking 
0.005 
0.003 
0.008 
Age of the building: 0-2 
0.122 
0.058 
0.179 
Age of the building: 2-10 
0.161 
0.076 
0.236 
Age of the building: 10-50 
-0.005 
-0.002 
-0.008 
Primary market 
-0.066 
-0.031 
-0.098 
Neighborhood 
characteristics 
Living condition index 
0.125 
0.059 
0.184 
Population density (thousands/m2) 
-0.008 
-0.004 
-0.012 
Share of green areas 
-0.001 
0.000 
-0.001 
Share of recreational areas 
0.005 
0.002 
0.007 
Crimes per 1000 inhabitants 
-0.001 
0.000 
-0.001 
Children per 1000 children in kindergartens 0.000 
0.000 
0.000