# RECORD ede799d5a89d38a16239a4734a2cb00510a8c501
# TITLE: The COVID-19 Pandemic, Airbnb and Housing Market Dynamics in Warsaw
# YEAR: 2021.0  VENUE: Critical Housing Analysis  DOI: 10.13060/23362839.2021.8.1.524
# AUTHORS: R. Trojanek; Michał Głuszak; Michał Hebdzyński; Justyna Tanas
# PAGES: 13  CANDIDATE TABLE PAGES (1-based): [7, 10]
# ABSTRACT: In this study, we analyse the impact of COVID-19 on house rents and prices in Warsaw, the capital of Poland. Hedonic indexes indicate a slight increase in prices (ca. 1.2%) and a substantial drop in long-term rents (ca. -7.7%) between March 2020 and December 2020. The largest decline in rents occurred in centrally located neighbourhoods, which was largely due to the inflow of new housing supply from the short-term rental market (the Airbnb Warsaw market shrank by almost 30% in December 2020 y/y). Using hedonic methods, we show the effect of the shrinking Airbnb market on the drop in long-term rents. The study indicates the elasticity of rents with respect to Airbnb supply, with a 1% change in Airbnb listings leading to a 0.031% change in rents.


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Volume 8 | Issue 1 | 2021 | 72-84 
Available online at www.housing-critical.com 
https://dx.doi.org/10.13060/23362839.2021.8.1.524 
 
72 
 
The COVID-19 Pandemic, Airbnb and Housing Market 
Dynamics in Warsaw 
 
Radoslaw Trojanek (corresponding author) 
 
Poznań Univeristy of Economics and Business, Poland 
radoslaw.trojanek@ue.poznan.pl 
 
 
Michal Gluszak 
 
Cracow University of Economics, Poland 
gluszakm@uek.krakow.pl 
 
 
Michal Hebdzynski 
 
Poznań Univeristy of Economics and Business, Poland 
michal.hebdzynski@phd.ue.poznan.pl 
 
 
Justyna Tanas 
 
WSB University in Poznan, Poland 
justyna.tanas@wsb.poznan.pl 
 
 
Abstract: In this study, we analyse the impact of COVID-19 on house rents and prices in Warsaw, the capital of 
Poland. Hedonic indexes indicate a slight increase in prices (ca. 1.2%) and a substantial drop in long-term rents 
(ca. -7.7%) between March 2020 and December 2020. The largest decline in rents occurred in centrally located 
neighbourhoods, which was largely due to the inflow of new housing supply from the short-term rental market (the 
Airbnb Warsaw market shrank by almost 30% in December 2020 y/y). Using hedonic methods, we show the effect 
of the shrinking Airbnb market on the drop in long-term rents. The study indicates the elasticity of rents with 
respect to Airbnb supply, with a 1% change in Airbnb listings leading to a 0.031% change in rents.  
 
Keywords: housing market; Airbnb; COVID-19. 
 
 
 
 
 
 
 


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Volume 8 | Issue 1 | 2021 | 72-84 
Available online at www.housing-critical.com 
https://dx.doi.org/10.13060/23362839.2021.8.1.524 
 
78 
 
Table 1: Partial estimation results 
 
 
January 2017—December 2020 
January 2017—March 2020 
April 2020—December 2020 
0.25Q 
0.50Q 
0.75Q 
OLS 
0.25Q 
0.50Q 
0.75Q 
OLS 
0.25Q 
0.50Q 
0.75Q 
OLS 
lnairbnb 
0.0303 
0.0310 
0.0244 
0.0297 
0.0308 
0.0322 
0.0247 
0.0306 
0.0183 
0.0219 
0.0233 
0.0228 
(0.0015) (0.0014) (0.0016) (0.0014) (0.0016) (0.0015) (0.0018) (0.0015) (0.0046) (0.0044) (0.0046) (0.0040) 
characteristics 
controls 
yes 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
area effects 
yes 
yes 
yes 
yes 
yes 
yes 
yes 
yes 
yes 
yes 
yes 
yes 
time effects 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
yes 
 
N 
41246 
41246 
41246 
41246 
35824 
35824 
35824 
35824 
5422 
5422 
5422 
5422 
R2 
0.8184 
0.8170 
0.8156 
Source: Authors. 
 


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Volume 8 | Issue 1 | 2021 | 72-84 
Available online at www.housing-critical.com 
https://dx.doi.org/10.13060/23362839.2021.8.1.524 
 
79 
 
Conclusion 
 
There are few economic studies on the impact of pandemics on the housing market, and none 
has addressed the role of the short-term rental market. This paper helps fill this gap by 
investigating the dynamics of housing prices and rents in Warsaw before and during the Covid-
19 pandemic. Using hedonic indexes, we found a slight increase in prices and a substantial drop 
in long-term rents between March 2020 and December 2020. The shrinking Airbnb market 
increased the supply in the long-term rental market, which at least partially explains the rent 
decrease. This is indicated by the spatial variation in rent declines and by the elasticity 
identified, which indicates a positive relationship between rents and Airbnb supply. 
 
 
Funding 
 
This study is based on research project financed by the National Science Centre of Poland (grant 
no. 2017/27/B/HS4/01848). 
 
 


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Volume 8 | Issue 1 | 2021 | 72-84 
Available online at www.housing-critical.com 
https://dx.doi.org/10.13060/23362839.2021.8.1.524 
 
81 
 
Appendix B 
 
Table B1: Regression estimation results 
 
 
January 2017 - December 2020 
January 2017- March 2020 
April 2020 – December 2020 
0.25Q 
0.50Q 
0.75Q 
OLS 
0.25Q 
0.50Q 
0.75Q 
OLS 
0.25Q 
0.50Q 
0.75Q 
OLS 
Const 
7.0818 
7.1488 
7.3370 
7.1012 
7.0969 
7.1538 
7.3570 
7.1158 
7.0173 
7.0874 
7.1793 
7.0122 
(0.0183) 
(0.0163) 
(0.0191) 
(0.0161) 
(0.0194) 
(0.0172) 
(0.0206) 
(0.0172) 
(0.0555) 
(0.0510) 
(0.0530) 
(0.0468) 
age 
-0.0068 
-0.0074 
-0.0076 
-0.0049 
-0.0070 
-0.0073 
-0.0076 
-0.0050 
-0.0056 
-0.0074 
-0.0074 
-0.0046 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0003) 
(0.0002) 
(0.0002) 
(0.0002) 
age2 
0.0001 
0.0001 
0.0001 
0.0001 
0.0001 
0.0001 
0.0001 
0.0001 
0.0000 
0.0001 
0.0001 
0.0001 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
(0.0000) 
area 
0.0153 
0.0167 
0.0181 
0.0172 
0.0150 
0.0165 
0.0179 
0.0169 
0.0167 
0.0180 
0.0200 
0.0188 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0001) 
(0.0003) 
(0.0003) 
(0.0003) 
(0.0003) 
lnairbnb 
0.0303 
0.0310 
0.0244 
0.0297 
0.0308 
0.0322 
0.0247 
0.0306 
0.0183 
0.0219 
0.0233 
0.0228 
(0.0015) 
(0.0014) 
(0.0016) 
(0.0014) 
(0.0016) 
(0.0015) 
(0.0018) 
(0.0015) 
(0.0046) 
(0.0044) 
(0.0046) 
(0.0040) 
lncc 
-0.0800 
-0.0960 
-0.1305 
-0.1125 
-0.0799 
-0.0944 
-0.1302 
-0.1111 
-0.0894 
-0.1088 
-0.1359 
-0.1232 
(0.0024) 
(0.0021) 
(0.0025) 
(0.0021) 
(0.0026) 
(0.0023) 
(0.0027) 
(0.0023) 
(0.0065) 
(0.0060) 
(0.0063) 
(0.0055) 
lnsubway 
-0.0179 
-0.0168 
-0.0179 
-0.0123 
-0.0191 
-0.0181 
-0.0191 
-0.0137 
-0.0066 
-0.0060 
-0.0082 
-0.0024 
 
(0.0014) 
(0.0012) 
(0.0012) 
(0.0012) 
(0.0015) 
(0.0012) 
(0.0013) 
(0.0012) 
(0.0043) 
(0.0038) 
(0.0036) 
(0.0035) 
lnuga 
-0.0043 
-0.0069 
-0.0130 
-0.0093 
-0.0037 
-0.0070 
-0.0139 
-0.0091 
-0.0100 
-0.0066 
-0.0065 
-0.0115 
(0.0012) 
(0.0011) 
(0.0012) 
(0.0011) 
(0.0013) 
(0.0011) 
(0.0013) 
(0.0011) 
(0.0035) 
(0.0033) 
(0.0033) 
(0.0030) 
quality 
0.0456 
0.0462 
0.0417 
0.0520 
0.0458 
0.0473 
0.0427 
0.0521 
0.0469 
0.0418 
0.0340 
0.0504 
(0.0015) 
(0.0013) 
(0.0016) 
(0.0013) 
(0.0016) 
(0.0014) 
(0.0018) 
(0.0014) 
(0.0041) 
(0.0038) 
(0.0038) 
(0.0035) 
q2 
-0.0173 
-0.0131 
-0.0079 
-0.0068 
-0.0162 
-0.0121 
-0.0070 
-0.0071 
(0.0045) 
(0.0041) 
(0.0049) 
(0.0040) 
(0.0044) 
(0.0040) 
(0.0049) 
(0.0040) 
q3 
0.0417 
0.0393 
0.0488 
0.0389 
0.0412 
0.0398 
0.0505 
0.0390 
(0.0054) 
(0.0049) 
(0.0059) 
(0.0049) 
(0.0053) 
(0.0048) 
(0.0059) 
(0.0048) 
q4 
0.0408 
0.0347 
0.0344 
0.0394 
0.0402 
0.0343 
0.0356 
0.0394 
(0.0040) 
(0.0037) 
(0.0044) 
(0.0036) 
(0.0040) 
(0.0036) 
(0.0044) 
(0.0036) 
q5 
0.0275 
0.0216 
0.0269 
0.0273 
0.0264 
0.0217 
0.0264 
0.0270 
(0.0043) 
(0.0040) 
(0.0047) 
(0.0039) 
(0.0043) 
(0.0039) 
(0.0048) 
(0.0039) 
q6 
0.0102 
-0.0021 
0.0058 
0.0079 
0.0105 
-0.0014 
0.0051 
0.0071 
(0.0056) 
(0.0051) 
(0.0061) 
(0.0051) 
(0.0056) 
(0.0051) 
(0.0062) 
(0.0050) 
q7 
0.0346 
0.0206 
0.0212 
0.0309 
0.0345 
0.0201 
0.0210 
0.0298 
(0.0058) 
(0.0053) 
(0.0063) 
(0.0052) 
(0.0057) 
(0.0052) 
(0.0064) 
(0.0052) 
q8 
0.0390 
0.0145 
0.0062 
0.0225 
0.0389 
0.0136 
0.0056 
0.0217 
 
(0.0073) 
(0.0066) 
(0.0079) 
(0.0066) 
(0.0072) 
(0.0066) 
(0.0080) 
(0.0065) 
 
 
 
 
q9 
0.0324 
0.0204 
0.0163 
0.0210 
0.0309 
0.0197 
0.0151 
0.0197 
 
(0.0087) 
(0.0080) 
(0.0095) 
(0.0079) 
(0.0087) 
(0.0079) 
(0.0096) 
(0.0078) 
 
 
 
 
q10 
0.0253 
0.0148 
0.0021 
0.0230 
0.0245 
0.0131 
0.0042 
0.0212 
(0.0079) 
(0.0073) 
(0.0087) 
(0.0072) 
(0.0079) 
(0.0072) 
(0.0088) 
(0.0072) 
q11 
0.0418 
0.0385 
0.0394 
0.0337 
0.0400 
0.0368 
0.0380 
0.0320 
(0.0079) 
(0.0072) 
(0.0086) 
(0.0071) 
(0.0078) 
(0.0071) 
(0.0087) 
(0.0071) 
q12 
0.0283 
0.0224 
0.0206 
0.0207 
0.0276 
0.0216 
0.0194 
0.0190 
(0.0093) 
(0.0085) 
(0.0101) 
(0.0084) 
(0.0092) 
(0.0083) 
(0.0102) 
(0.0083) 
q13 
0.0437 
0.0276 
0.0218 
0.0320 
0.0419 
0.0257 
0.0227 
0.0307 
(0.0086) 
(0.0078) 
(0.0093) 
(0.0077) 
(0.0085) 
(0.0077) 
(0.0094) 
(0.0077) 
q14 
-0.0214 
-0.0200 
-0.0286 
-0.0272 
(0.0075) 
(0.0068) 
(0.0081) 
(0.0068) 
q15 
-0.0249 
-0.0173 
-0.0078 
-0.0218 
-0.0012 
0.0072 
0.0124 
0.0055 
(0.0078) 
(0.0071) 
(0.0085) 
(0.0070) 
(0.0106) 
(0.0100) 
(0.0103) 
(0.0091) 
q16 
-0.0381 
-0.0208 
-0.0042 
-0.0185 
-0.0207 
0.0029 
0.0199 
0.0034 
(0.0050) 
(0.0046) 
(0.0055) 
(0.0046) 
(0.0094) 
(0.0087) 
(0.0088) 
(0.0080) 
N 
41246 
41246 
41246 
41246 
35824 
35824 
35824 
35824 
5422 
5422 
5422 
5422 
R2 
0.8184 
0.8170 
0.8156 
Source: Authors. 
 
 


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Volume 8 | Issue 1 | 2021 | 72-84 
Available online at www.housing-critical.com 
https://dx.doi.org/10.13060/23362839.2021.8.1.524 
 
82 
 
Table B2: A description of the variables 
 
Symbol 
Description 
q1, …., q16 
16-time dummy variables used in the global model. If the dwelling 
was sold in a given year-quarter, it takes the value 1; otherwise, it 
takes 0. 
area 
area of dwelling 
age 
age of the building in years 
uga 
distance to the nearest urban green area in metres 
subway 
distance to the nearest subway station 
cc 
distance to the city centre in metres 
airbnb 
monthly number of active apartments in  
the district in which the apartment was offered for long-term rent 
quality 
quality of the apartment (values 1-5)  
Source: Authors.