# RECORD 3fff47f2ad1427c42ba357bc9b2363011924b07a
# TITLE: Short-term rentals and the housing market: evidence from Portuguese metropolitan areas
# YEAR: 2026.0  VENUE: Portuguese Economic Journal  DOI: 10.1007/s10258-026-00295-2
# AUTHORS: Francisco Nobre; Diogo Jardim Gonçalves; Ronize Cruz
# PAGES: 35  CANDIDATE TABLE PAGES (1-based): [11, 17, 20, 21, 22, 27, 31, 32]
# ABSTRACT: 
 In this paper, we study the impact of short-term rentals (STR) on housing markets in Portugal, exploiting a 2014 policy reform that eased registration requirements. Using a novel dataset of property transactions from 2010 to 2017 for the Lisbon and Porto metropolitan areas, we estimate the effect of STR expansion using a two-way fixed effects model and a difference-in-differences design that leverages staggered treatment across postcodes. The analysis controls for detailed property characteristics, location and time fixed effects. We find that each additional STR per quarter and postcode raises transaction values by 2.3–6.0%, representing an average increase of €104–€329 per transaction. Effects are larger in higher-end market segments and in parishes with lower unemployment and population density. Event-study estimates show that effects emerge gradually and become significant around ten quarters after the first STR listing. We also find positive spillovers to commercial property values, particularly for retail stores, and in suggesting short-term rentals drive demand for local consumption amenities.


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ORIGINAL ARTICLE
Portuguese Economic Journal
https://doi.org/10.1007/s10258-026-00295-2
	 Francisco Nobre
f.nobre@kingston.ac.uk
Diogo Jardim Gonçalves
diogo.jardimgoncalves@bristol.ac.uk
Ronize Cruz
ronize.cruz@fe.uc.pt
1	
Kingston Business School, Kingston University, London, United Kingdom
2	
School of Economics, University of Bristol, 12 Priory Road, Clifton, Bristol  
BS8 1TU, United Kingdom
3	
Centre for Business and Economics Research, Faculty of Economics, University of 
Coimbra, Av. Dr. Dias da Silva 165, Coimbra 3004-512, Portugal
Received: 22 January 2024 / Accepted: 26 July 2026
© The Author(s) 2026
Short-term rentals and the housing market: evidence from 
Portuguese metropolitan areas
Francisco Nobre1
 · Diogo Jardim Gonçalves2 · Ronize Cruz3
Abstract
In this paper, we study the impact of short-term rentals (STR) on housing markets 
in Portugal, exploiting a 2014 policy reform that eased registration requirements. 
Using a novel dataset of property transactions from 2010 to 2017 for the Lisbon 
and Porto metropolitan areas, we estimate the effect of STR expansion using a 
two-way fixed effects model and a difference-in-differences design that leverages 
staggered treatment across postcodes. The analysis controls for detailed property 
characteristics, location and time fixed effects. We find that each additional STR 
per quarter and postcode raises transaction values by 2.3–6.0%, representing an 
average increase of €104–€329 per transaction. Effects are larger in higher-end 
market segments and in parishes with lower unemployment and population density. 
Event-study estimates show that effects emerge gradually and become significant 
around ten quarters after the first STR listing. We also find positive spillovers to 
commercial property values, particularly for retail stores, and in suggesting short-
term rentals drive demand for local consumption amenities.
Keywords  Policy analysis · Housing market · Portugal · Short-term 
accommodation


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Short-term rentals and the housing market: evidence from Portuguese…
or more of its attributes. Hence, the dataset contains information not only on property 
attributes but also on the specific reasons prompting each evaluation. Lastly, we link 
each transaction to its most recent evaluation through a unique property identifier 
(Evangelista et al. 2020).
Our sample consists of properties with either a single transaction, more than one 
transaction on the same day or with multiple transactions on different days. In total, 
over the time period considered, we have a sample of 134,079 properties, corre­
sponding to 185,654 transactions. Moreover, we have 41,526 properties in the met­
ropolitan area of Porto and 92,553 in Lisbon. Table 1 presents descriptive statistics 
of the properties and number of transactions, for those that we were able to match 
Number of 
transactions (per 
property)
Mean
Std. Dev.
Min
Max
1.385
0.697
1
9
Number of 
transactions 
(per quarter and 
postcode)
2.015
3.786
1
151
Number of 
transactions (per 
quarter and civil 
parish)
40.732
48.199
1
328
Number of trans­
actions (per year 
and postcode)
3.280
8.881
1
407
Number of trans­
actions (per year 
and civil parish)
134.046
179.999
1
1160
Number of 
transactions (per 
postcode)
9.348
31.657
1
1511
Number of trans­
actions (per civil 
parish)
851.624
1,011.053
1
4610
Number of 
transactions (per 
quarter)
5,801.688
3,737.254
1,541
13,011
Number of 
transactions (per 
year)
23,206.75
15,336.26
8,650
47,909
Value of 
transactions
209,655
569,802.4
2,002.49
7,883,690
Number of 
bedrooms
2.907
1.203
0
12
Number of floors
1.090
0.385
1
13
Area (square 
meters)
101.254
43.301
3.6
762.73
Age of the build­
ing (years)
23.813
21.422
0
298
Table 1  Summary statistics of 
properties transacted
Total number of transactions: 
185,654. This table reports 
the mean, standard deviation, 
minimum, and maximum 
values for key summary 
statistics of the transaction 
data. Transaction counts are 
presented across several time 
and geographical dimensions, 
including per property, quarter, 
year, postcode, and civil parish. 
The table also reports the value 
of transactions and property-
specific characteristics such 
as the number of bedrooms, 
floors, area (in square meters), 
and the age of the building. 
Source: IMT and IMI tax 
registries, Statistics Portugal
 


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F. Nobre et al.
between the two tax registries.9 On average we have approximately 1.4 transactions 
per property, with the maximum being 9. We have around 2 transactions per quarter 
and postcode, with a sample mean value of e 209,655. On average, the properties 
contain, 3 bedrooms, 1 floor, and around 100 square meters and are in 23-year-old 
buildings.
3.3  Imovirtual
We employ a novel dataset from Imovirtual, one of the largest online real estate 
platforms in Portugal, which started to operate in 2011. The data provided by the 
website refers to listings of commercial properties, containing information on the 
type of property (store, office and storage unit), asking price, geographical location, 
square footage, operation (rental or sale) and the exact date the advertisement of the 
property became available online. Although we possess data from 2011, we choose 
as period of analysis the years 2015–2017, as we have a more balanced number of 
online advertisements during this period, as depicted in Figure A3.
In total, we possess 39,205 observations, with stores for sale comprising approxi­
mately 51% of our sample. Around 60% of our listed properties are located in the 
Metropolitan area of Lisbon. Table 8 reports the summary statistics for the (asking) 
price and number of advertisements (per quarter and postcode) for the different types 
of properties in our sample, with offices for sale having a higher average value and 
stores for sale being the most advertised.
4  Identification strategy
When estimating the effects of the development of local lodging establishments on 
surrounding house prices several factors could confound our estimations. For exam­
ple, it could be the case that in a particular neighborhood a considerable number of 
properties are being transacted to then be transformed into local lodging establish­
ments. We attempt to shed some light on this issue, by taking advantage of the unique 
features of our dataset and geolocating the properties in transaction dataset to the 
closest local lodging, through ArcGIS.10 As a result, for each property transacted we 
obtain all the short-term rentals that are within 35 m. Moreover, since we also have 
information pertaining to the characteristics of these local lodgings, we drop transac­
tion for properties that have the same number of bedrooms of a nearby short-term 
rental. In Table A6 we present some descriptive statistics for properties that were 
dropped due to being near to a short-term rental and having the same number of 
bedrooms. In total, we identify 7,901 properties that are within 35 m of the nearest 
short-term rental and have the same number of bedrooms. These properties have on 
9  For some properties, a match between the two tax registries could not be established, resulting in the 
inability to obtain their characteristics.
10  The geolocation described was provided by Statistics Portugal, as the local Safe center do not possess 
geospatial software.


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Table 2  Baseline model
Variables
Prices
Log(prices)
Log(prices)
Log(prices)
Log(prices)
Log(prices)
Log(prices)
Log(prices)
Residualized
Number of Local Lodging
0.0394**
0.0394*
0.0394**
0.0605**
0.0431**
0.0233*
0.0280**
0.0492***
(0.0166)
(0.0201)
(0.0196)
(0.0258)
(0.0196)
(0.0125)
(0.0128)
(0.0159)
Number of bedrooms
✓
✓
✓
✓
✓
✓
✓
✓
Number of floors
✓
✓
✓
✓
✓
✓
✓
✓
Area (square meters)
✓
✓
✓
✓
✓
✓
✓
✓
Age of building
✓
✓
✓
✓
✓
✓
✓
✓
Coefficients of location and quality
✓
✓
✓
✓
✓
✓
✓
✓
7-digit postcode FE
✓
✓
✓
No
No
✓
No
No
Quarter FE
✓
✓
✓
✓
✓
✓
✓
✓
4-digit postcode FE
No
No
No
✓
No
No
No
No
Civil parish FE
No
No
No
No
✓
No
✓
✓
Civil parish-time trends
No
No
No
No
No
✓
✓
✓
Number of clusters
15,833
157
217
158
165
157
165
165
Observations
181,626
181,626
181,626
185,647
185,653
181,626
185,653
185,653
R2
0.510
0.510
0.510
0.323
0.328
0.517
0.342
0.178
 Results for the baseline model at the quarterly level, where we regress the logarithm of transactions prices on the number of local lodging establishments opening in a 
quarter and postcode. Columns (1)–(3) present the results including full postcode and quarter fixed effects, while clustering standard errors at the full postcode, postcode 
sector (first 4 digits) and civil parish, respectively. In columns (4)–(5), employ postcode sector, civil parish fixed effects, respectively, while in columns (6)-(7) we use full 
postcode and civil parish fixed effects, respectively, and civil parish time trends. In columns (4)-(7), standard errors are clustered at the postcode sector level. In column 
(8) we use residualized prices as outcome variables. All regressions include a constant and property-specific controls. Standard errors are clustered at postcode sector. *** 
1%, ** 5%, * 10%. Data sources: IMT and IMI tax registries (Statistics Portugal); RNAL (Turismo de Portugal)


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F. Nobre et al.
is lower between 2.3 and 2.8%, but still statistically significant. In column (8), we 
show the results for our detrending procedure outlined in the previous section, where 
we first regress our outcome variable on quarter, parish fixed effects and parish-spe­
cific time trends, capture the residuals, and then regress the residuals on the number 
of local lodgings, property characteristics and time dummies. The point estimate is 
larger, around 5%, and highly significant. Overall, our main effects range from 2% to 
6%, showing robustness to different specifications.
For the remainder of the section, we use one of our most demanding specifications 
with property attributes, civil parish time trends, while also employing civil parish 
fixed effect, to control for neighborhood unobservables, instead of full postcode fixed 
effect, which could be too strict. In the next exercise, we provide suggestive evidence 
of whether short-term rentals affect the number of transactions and transactions of 
new built properties, which can be seen in Table 9. In the top panel we use as outcome 
the number of transactions in a given postcode and quarter, focusing on those with at 
least one transaction. We find a positive and significant impact for the entire sample 
and both metropolitan areas, with the coefficient being of larger magnitude in Porto. 
In the bottom panel, we use as outcome variable a binary variable that takes the value 
of 1 if in a postcode and quarter there is a transaction occurring after an evaluation 
from the fiscal authority was made due to a property being new.
Although our outcome variable of interest constitutes an imperfect measure of 
housing supply, it is the closest we have to know whether a transaction has happened 
in a new building. We observe a negative, although, non-significant, effect of local 
lodgings on the transactions of newly built properties for the overall sample, with 
this relationship being negative and statistically significant in the metropolitan area 
of Lisbon. However, for the metropolitan area of Porto, the coefficient is positive 
and marginally significant. Overall, although the impact on the transaction of new 
housing is inconclusive, there is some indication that in certain areas, it can lead to a 
reduction in housing supply.
Overall, our main results indicate that a rise in the quantity of local lodging estab­
lishments corresponds to an increase in transaction prices. Given that, on average, 
0.026 new venues open per quarter and postcode with transaction, this implies that 
over the duration of our study period, there was a quarterly increment of between 
0.05 and 0.16% in transaction values, which is a significant and noteworthy change.
It is important to place our findings within the context of the broader literature on 
the effects of short-term rentals on house prices. However, this can be a challenging 
endeavor, as other studies have looked at different types of policies, used alternative 
specifications and data and even employed other measures to assess local lodging 
activity. For example, Gonçalves et al. (2022); Barron et al. (2021); Koster et al. 
(2021), have looked at the effect of placing restrictions on short-term rentals in Lis­
bon, Chicago, San Francisco and Los Angeles, respectively. Overall, they find that 
it led to a decrease in house prices ranging from 2% to 10%. Even though, we also 
document a positive relationship between new short-term rental listings and housing 
prices, we analyze a period of continuous growth in short-term rental activity, provid­
ing it a very different context that those in the studies mentioned.
To the best of our knowledge, the two papers more closely related to our research 
are Franco and Santos (2021) and Garcia-López et al. (2020), who investigate the 


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In column (4), we drop observations in the 95th percentile in terms of number of 
local lodging establishments by civil parish and quarter, with estimates around higher 
than the baseline model and still significant at the 10% level. In column (5), we use 
the years before the policy change as a placebo test, finding no significant results 
and showing that it was the exponential increment in short-term rentals after 2014 
that led to an increase in house prices. In column (6), we use a measure of price per 
square meter as outcome variable, which provides again stronger and highly signifi­
cant results than our baseline model. Given an average of 0.026 local lodgings per 
quarter and postcode, our point estimates suggest that it led to a marginal increase 
Table 3  Robustness checks
Variables
Drop be­
fore 2014
Only 1 
transaction 
per day
No new 
built
Dropping 
95pct LL
Drop after 
2013
SUTVA
Log(prices) Log(prices) Log(prices) Log(prices) Log(prices) Log(prices/
m2)
Log(prices)
Number 
of LL
0.0233*
0.0314**
0.0342***
0.0306*
−0.00316
0.0342***
(0.0128)
(0.0128)
(0.0128)
(0.0178)
(0.0519)
(0.0120)
LL 
neigh­
bourhood 
postcodes
0.00775
(0.0544)
Property 
attributes
✓
✓
✓
✓
✓
✓
✓
Civil par­
ish FE
✓
✓
✓
✓
✓
✓
✓
Quarter 
FE
✓
✓
✓
✓
✓
✓
✓
Civil 
parish-
time 
trends
✓
✓
✓
✓
✓
✓
✓
Num­
ber of 
clusters
164
165
165
163
139
165
165
Observa­
tions
144,579
177,182
167,032
178,327
41,067
185,653
185,653
R2
0.372
0.341
0.304
0.340
0.293
0.226
0.341
This table reports the estimates for a set of robustness checks. In column (1) we consider only transactions 
from 2014 onwards, in column (2) only properties with one transaction per day, in column (3) we drop 
the first transaction after an evaluation by fiscal authorities occurred due to the building being new, in 
column (4) we drop areas in the 95th percentile in terms of number of local lodging establishments per 
quarter and civil parish, in column (5) we only consider the period before 2014, as a placebo test. In 
columns (1)–(5), the outcome variable is the log value of transactions in the present period for a given 
postcode. In column (6), the outcome variable is a measure of price per square meter. In column (7), 
we test for the SUTVA condition, with the outcome variable being the log value of transactions for 
a given postcode and quarter and the main explanatory variable being a quarterly average of Local 
Lodgings of all postcodes with the same 6-digits. All regressions include a constant, property-specific 
controls, quarter and civil parish fixed effects, civil parish specific time trends. We exclude properties 
that are within 35 m from a Local Lodging and have the same number of bedrooms. Standard errors are 
clustered at postcode sector. *** 1%, ** 5%, * 10%. Data sources: IMT and IMI tax registries (Statistics 
Portugal); RNAL (Turismo de Portugal)


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of around €186 of transactions prices per square meter, still lower than the results 
reported in Franco and Santos (2021).
Lastly, in column (7) we test for whether the SUTVA condition holds in our set­
ting. Formally, this assumption states that treatment in one unit should not be affected 
by treatment in other units. To present some suggestive evidence that this condition 
holds, we run a regression where the outcome refers to the log of transactions value 
for a given postcode and quarter and as main explanatory variable, we employ an 
average per quarter of local lodgings opening in the postcodes with the same first 
six digits, which represent nearby areas. If the assumption holds, presumably the 
coefficient would be non-significant. Indeed, this is the case, as seen in column (7), 
supporting the validity of the SUTVA condition in our context.
Then, we analyze the timing and persistence of our effects and perform a falsifica­
tion exercise, displayed in Table 4. In column (1), we use as variable of interest the 
number of local lodging establishments opening one quarter before, respectively. The 
effects exhibit a considerable level of persistence, with the number of new venues 
opening 1 quarter before having a positive and significant impact on the value of 
transactions in the current period, being larger in magnitude than the baseline model.
The persistence in these effects could be ascribed to the renovation and revamping 
necessary to turn the establishments into temporary accommodation services, or local 
lodging. This transformation process might increase the attractiveness of a particular 
area, potentially leading to a higher level of tourism, raising the value of the prop­
erties located there. These effects can persist over subsequent periods, influencing 
property values in the long run.
Table 4  Falsification exercise and timing of the effects
Variables
Log(prices) 
Log(prices) 
Log(prices)
Number of Local Lodgingt−1
0.0734***
(0.0237)
Number of Local Lodgingt
0.0280**
(0.0128)
Number of Local Lodgingt+1
0.0280
(0.0172)
Property attributes
✓
✓
✓
Civil parish FE
✓
✓
✓
Quarter FE
✓
✓
✓
Civil parish time trends
✓
✓
✓
Number of clusters
165
165
165
Observations
183,316
185,653
172,640
R-squared
0.343
0.342
0.337
Results for the falsification and timing of the effects exercises, at the quarterly level. Column (1) presents 
the results when using the number of local lodging establishments opening the quarter before. In column 
(2) is the baseline model. Column (3) displays the estimates when using the number of establishments 
opening the quarter after. In all specifications, the outcome variable is the log value of transactions in 
the present period and include a constant, the full set of property-specific controls, quarter and civil 
parish fixed effects, civil parish specific time trends. We exclude properties that are within 35 m from a 
Local Lodging and have the same number of bedrooms. Standard errors are clustered at postcode sector. 
*** 1%, ** 5%, * 10%. Data source: IMT and IMI tax registries (Statistics Portugal); RNAL (Turismo 
de Portugal)


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In column (3) we present a falsification exercise. As previously stated, if our speci­
fication is correct, the number of establishments opening periods ahead should not 
affect house prices at the current period. This is confirmed by the non-significant, and 
even negative, coefficient obtained when using the number of new local lodgings in 
a civil parish in the quarter ahead. In sum, the robustness checks performed reinforce 
the conclusions of the main analysis in Table 2, while validating the appropriateness 
of the identification strategy.
5.1.3  Heterogeneous effects
We proceed by leveraging the extensive amount of information in our dataset to 
perform a series of heterogeneous exercises, using our specification at the quarterly 
level and including quarter and civil parish fixed effects and civil parish specific time 
trends. Our aim is to understand how the effects differ by geographical locations and 
by types of property, which addresses a gap in the literature.
We start by dividing our sample by metropolitan areas, with specific emphasis on 
the municipalities of Lisbon and Porto. For the Portuguese case, we believe this focus 
is particularly relevant to understand how the effects differ in the two biggest cities, 
addressing a crucial gap in the existing literature. The results for this comparative 
analysis are displayed in Table 5. In columns (1) and (4), we look at the metropolitan 
areas of Lisbon and Porto, respectively. The effects are stronger for the metropolitan 
area of Porto, both in terms of magnitude and significance. In fact, for the metropoli­
tan area of Porto the coefficient estimated is even slightly higher than the baseline 
model. In column (2) and (5), we narrow down the geographical scope of our analy­
sis and focus on the municipalities of Lisbon and Porto, respectively. We now find 
Table 5  Heterogeneous effects: by metropolitan area
Variables
Lisbon
Porto
Metropolitan area
Log(prices)
Municipality
Log(prices)
Historical center
Log(prices)
Metropolitan area
Log(prices)
Municipality
Log(prices)
Historical 
center
Log(prices)
Number of LL
0.0201
0.0303*
0.0576*
0.0394*
0.0134
0.0185*
(0.0149)
(0.0165)
(0.0298)
(0.0230)
(0.0122)
(0.00966)
Property attributes
✓
✓
✓
✓
✓
✓
Quarter FE
✓
✓
✓
✓
✓
✓
Civil parish FE
✓
✓
✓
✓
✓
✓
Civil parish time trends
✓
✓
✓
✓
✓
✓
Number of clusters
118
35
13
47
12
11
Observations
127,347
33,999
6,973
58,306
14,176
5,087
R2
0.356
0.333
0.370
0.309
0.372
0.410
Results for heterogeneous exercises, by metropolitan area, at the quarterly level. Columns (1) and (3) present the results when 
considering separately the metropolitan areas of Lisbon and Porto, respectively. Columns (2) and (4) display the results when 
considering separately the municipalities of Lisbon and Porto, respectively. Columns (3) and (6) show the estimates when 
considering separately the historical centers of Lisbon and Porto, respectively. In all specifications, the outcome variable is 
the log value of transactions in the present period and include a constant, property-specific controls, quarter and civil parish 
fixed effects and civil parish time trends. We exclude properties that are within 35 m from a Local Lodging and have the same 
number of bedrooms. Standard errors are clustered by postcode sector. *** 1%, ** 5%, * 10%. Data sources: IMT and IMI tax 
registries (Statistics Portugal); RNAL (Turismo de Portugal)


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stronger effects for the municipality of Lisbon, with the point estimate being again 
slightly larger than the corresponding specification in the baseline model. Lastly, in 
columns (3) and (6), we consider solely the Historical centers of Lisbon and Porto, 
respectively.11 Our findings suggest that the impact on house prices is stronger in 
the Historical center of Lisbon, with the coefficient being 3 times larger than for the 
Historical center of Porto, while being equally significant.
The results in Table 5 reveal some interesting features. Firstly, when controlling 
for the time, civil parish fixed effects and civil parish time trends, our findings sug­
gest that the effects in Lisbon are quite constricted to its municipality and historical 
center, while in Porto the impact of local lodgings is broader and felt across the entire 
metropolitan area. Secondly, and recalling that civil parish-specific linear time trends 
allow us to explore variations from the parishes’ own trend, our results indicate that 
the impact of local lodgings on these deviations from the trend are very similar across 
the municipality of Porto. In this regard, the point estimates are nearly identical when 
considering the entire municipality or just the historical center of Porto. On the other 
hand, for Lisbon, the coefficients estimated increase in magnitude and significance as 
we narrow down our scope of analysis. For the entire metropolitan area, the impact 
of local lodgings on deviations from the individual time trends of each parish trends 
appears to be non-significant, while for the historical center, the effect of short-term 
rentals is around 2 times higher than the baseline model.
Up to this point, our results seem to suggest that the impact of local lodging 
establishments is not homogeneous across regions. The absence of effects in the 
municipality of Porto might be considered surprising as it is at odds with the findings 
documented in Franco and Santos (2021), who evidence a positive and significant 
effect in both municipalities. We further scrutinize the impact of short-term rentals 
on house prices in the municipalities through quantile regressions, which allows us 
to estimate the difference in effects across our distribution. We follow the estimator 
proposed in Machado and Santos Silva (2019), which makes it possible to perform 
quantile regressions with two fixed effects. In order to allow to control for time trends 
as well, we adopt the price residualized approach, by first regressing the log of trans­
action values on civil parish, quarter fixed effect and parish time trends, capturing the 
residuals and using those as the outcome in our quantile analysis, while controlling 
for property characteristics and time dummies. We focus on the 0.10,0.25,0.5,0.75 
and 0.90 quantiles and our results are displayed graphically in Fig. 4.
The results reveal some interesting patterns. For the municipality of Lisbon, the 
coefficient estimated is insignificant at 0.10 percentile, while in the case of Porto, the 
point estimate is significant in all quantiles. Moreover, while in both municipalities 
the impact of short-term rentals is stronger at the upper quantiles, which represent the 
higher-end of the housing market, the difference in effects across the distribution is 
much smaller for Porto. In fact, while in Porto, the coefficients range from 0.0550 to 
0.0559, in Lisbon they range from 0.0363 to 0.0535. Nevertheless, the larger effects 
11  We consider part of the Historical center of Lisbon the civil parishes of Campo de Ourique, Estrela, 
Misericórdia, Penha de França, Santa Maria Maior and São Vicente, while the Historical center of Porto 
is composed by the parishes of Bonfim, União das freguesias de Cedofeita, Santo Ildefonso, Sé Miragaia, 
São Nicolau e Vitória and União das freguesias de Lordelo do Ouro e Massarelos.


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Short-term rentals and the housing market: evidence from Portuguese…
periods after treatment we see an upward trend, which becomes more evident after 
period 9. The point estimates become significant at period 10 after treatment, which 
corresponds to around two and a half years after the first short-term rental appears. 
Moreover, in periods 10 and above the ATT can reach up to 0.79, with the aggregate 
post-treatment ATT being around 0.20 and statistically significant at 1% level.
Table 6  Spillover effects: Residualized prices of commercial properties
 Panel A: Overall sample
Variables
Offices
Offices
Stores
Stores
for rent
for sale
for rent
for sale
Number of Local Lodging
-0.077
-0.002
0.052*
0.102***
(0.061)
(0.041)
(0.030)
(0.038)
Quarter FE
✓
✓
✓
✓
Civil parish FE
✓
✓
✓
✓
Civil parish time trends
✓
✓
✓
✓
Number of clusters
122
125
147
158
Observations
5,159
5,412
8,619
20,000
R2
0.112
0.042
0.171
0.076
Panel B: Metropolitan area of Lisbon
Offices
Offices
Stores
Stores
Variables
for rent
for sale
for rent
for sale
Number of Local Lodging
0.066
0.062
−0.013
0.120
(0.118)
(0.114)
(0.047)
(0.095)
Quarter FE
✓
✓
✓
✓
Civil parish FE
✓
✓
✓
✓
Civil parish time trends
✓
✓
✓
✓
Number of clusters
87
88
105
116
Observations
3,271
2,730
5,326
12,173
R2
0.179
0.059
0.235
0.114
Panel C: Metropolitan area of Porto
Variables
for rent
for sale
for rent
for sale
Number of Local Lodging
−0.008
−0.016
0.140***
0.101***
(0.039)
(0.014)
(0.032)
(0.033)
Quarter FE
✓
✓
✓
✓
Civil parish FE
✓
✓
✓
✓
Civil parish time trends
✓
✓
✓
✓
Number of clusters
37
39
46
50
Observations
1,888
2,682
3,293
7,827
R2
0.352
0.053
0.132
0.115
Results for spillover effects to commercial properties, at the quarterly level. In columns (1) and (2), we 
look at offices for rent and sale, respectively, while in columns (3) and (4) we analyze stores for rent and 
sale. Panel A uses the composite sample, containing both metropolitan areas. Panel B and C analyze 
separately the effects on the metropolitan area of Lisbon and Porto, respectively. We use as outcome 
variable the residualized asking prices, which are obtained by regressing the logarithm of prices on 
quarter FE, civil parish FE and civil parish time trends. The results displayed are of a regression of these 
residuals on the count of local lodging by quarter and civil parish, on the square footage of properties 
and time dummies. Standard errors are clustered at the postcode sector. *** 1 %, ** 5 %, * 10 %. Data 
sources: Imovirtual; RNAL (Turismo de Portugal).


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1 3
F. Nobre et al.
The lag in the reported effects is in line with other studies on housing markets. 
Gonçalves et al. (2022) show that the ban on short-term rentals (which is arguably 
a more salient policy than one that promoted a gradual growth of local lodgings), in 
Lisbon, took 3 quarters to have a negative impact on house prices. Cellini and Nobre 
(2026), Blanco and Neri (2025), Tang (2021) show that impact of urban regeneration 
phenomena on housing markets, in London, only started to be felt 3 to 5 years after 
treatment adoption.
In Panels (b) and (c) of Fig. 5, we perform the analysis for the Metropolitan areas 
of Lisbon and Porto, respectively. The figures show similar patterns to the overall 
sample. For Lisbon, the periods before treatment show a non-significant aggregate 
ATT, with only lead 9 being statistically significant. The post-treatment period exhib­
its a significant aggregate of around 0.19, with the effects starting to be statistically 
significant only in period 10. In Porto, we also see a non-significant aggregate ATT 
and a post-treatment ATT of the same size as Lisbon. The effects become statistically 
significant starting in period 7 after treatment but gain robustness and consistency 
only from period 10 onwards.
Overall, our results using the Callaway and Sant’Anna (2021) estimator show the 
absence of house prices trends in the periods before the arrival of local lodgings, 
Fig. 5  Event study: Callaway and Sant'Anna (2021). Data sources: IMT and IMI tax registries (Statis­
tics Portugal); RNAL (Turismo de Portugal)
 


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Short-term rentals and the housing market: evidence from Portuguese…
Appendix
Mean
Std. Dev.
Min
Max
(a) Asking Price
Offices for rent
1,483.079
2,262.307
70
21,400
Offices for sale
229,598.9
328,404.8
750
2,580,000
Stores for rent
1,287.835
2,598.468
150
35,000
Stores for sale
178,013.2
210,204
4,200
1,500,000
(b) Advertisements 
(per quarter and 
postcode)
Offices for rent
1.944
3.028
1
53
Offices for sale
2.007
2.96
1
74
Stores for rent
1.751
2.467
1
59
Stores for sale
2.237
3.356
1
100
Table 8  Summary statistics 
of commercial properties 
advertised
This table reports the mean, 
standard deviation, as well 
as minimum and maximum 
values for asking
price of the commercial 
properties advertised on 
the platform Imovirtual and 
number of advertisements per
civil parish and postcode. 
Source: Imovirtual
 
Mean
Std. Dev.
Min
Max
Total
Per quarter and 
postcodes
0.091
0.527
0
70
16,622
Per quarter and post­
codes (just those with 
residential houses 
transactions)
0.026
0.231
0
14
16,622
Per quarter and civil 
parish
2.498
15.452
0
457
16,622
Per quarter and civil 
(just those with 
residential houses 
transactions)
9.841
36.038
0
457
16,622
Per quarter
519.438
664.747
19
2642
16,622
Per year
2077.75
2604.526
116
7193
16,622
Number of bedrooms 
per local lodging
2.175
2.418
0
59
16,622
Table 7  Summary statistics: 
Number of new local lodging 
establishments
This table reports the number 
of observations, mean, standard 
deviation, as well as minimum 
and maximum values for new 
local lodging establishments 
in our sample. We present 
statistics based on various time 
dimensions, quarter and year 
and geographical determinants 
such as the civil parish and 
postcode. Source: RNAL 
(Turismo de Portugal)
 


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1 3
F. Nobre et al.
Table 10  Heterogeneous effects: by characteristics of the civil parish
Variables
Unemployment rate
Density
Coastal vs non-coastal
Below median
Log(prices)
Above median
Log(prices)
Below median
Log(prices)
Above median
Log(prices)
Non-coastal
Log(prices)
Coastal
Log(prices)
Number of Local 
Lodging
0.0344**
0.0309
0.0721*
0.0185
0.0106
0.0357*
(0.0155)
(0.0203)
(0.0423)
(0.0114)
(0.0123)
(0.0190)
Property attributes
✓
✓
✓
✓
✓
✓
Quarter FE
✓
✓
✓
✓
✓
✓
Civil parish FE
✓
✓
✓
✓
✓
✓
Civil parish time trends
✓
✓
✓
✓
✓
✓
Number of clusters
111
92
107
89
108
53
Observations
93,116
92,537
94,201
91,452
114,199
54,318
R2
0.359
0.271
0.328
0.360
0.336
0.388
Results for heterogeneous exercises, by characteristics of the civil parish, at the quarterly level. In column (1)-(2) we consider 
civil parishes that are below and above the median in terms of unemployment rate, respectively, while in column (3)-(4) we 
look at civil parishes that are below and above the median in terms of population density. In column (5) we look at non-coastal 
civil parishes, while analyzing coastal civil parishes in column (6). In all specifications, the outcome variable is the log value 
of transactions in the present period and include a constant, property-specific controls, quarter and civil parish fixed effects 
and civil parish time trends. We exclude properties that are within 35 meters from a Local Lodging and have the same number 
of bedrooms. Standard errors are clustered by postcode sector. *** 1 %, ** 5 %, * 10 %. Source: IMT and IMI tax registries 
(Statistics Portugal); RNAL (Turismo de Portugal).
Overall sample
Metropolitan area of Lisbon
Metropolitan area of Porto
Variables
Number of Transactions
Number of  Transactions
Number of Transactions
Number of Local Lodging
0.672***
0.620***
0.765***
(0.149)
(0.205)
(0.192)
Civil parish FE
✓
✓
✓
Quarter FE
✓
✓
✓
Civil parish-time trends
✓
✓
✓
Number of clusters
165
118
47
Observations
78,662
53,532
25,130
R2
0.025
0.022
0.034
Transactions
New built
Transactions
New built
Transactions
New built
Number of Local Lodging
−0.00193
−0.0102*
0.0124*
(0.00463)
(0.00605)
(0.00632)
Civil parish FE
✓
✓
✓
Quarter FE
✓
✓
✓
Civil parish-time trends
✓
✓
✓
Number of clusters
165
118
47
Observations
78,662
53,532
25,130
R2
0.104
0.127
0.059
 Effects on the number of transactions and transaction of new buildings, at the quarterly and postcode level. 
Column (1) presents the results for the entire sample, column (2) for the metropolitan area of Lisbon and column 
(3) for the metropolitan area of Porto. In the top panel the outcome variable is the number of transactions in the 
present period, while in the bottom panel the outcome variable is an indicator variable that takes the value of 1 
if in a given postcode quarter there was a transaction of a new building. All specification include quarter, civil 
parish and civil parish time trends. Standard errors are clustered at postcode sector. *** 1%, ** 5%, * 10%. 
Source: IMT and IMI tax registries (Statistics Portugal); RNAL (Turismo de Portugal)
Table 9  Outcome variable: number of total transactions and of new built properties
 


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Short-term rentals and the housing market: evidence from Portuguese…
Supplementary Information  The online version contains supplementary material available at ​h​t​t​p​s​:​/​/​d​o​i​.​
o​r​g​/​1​0​.​1​0​0​7​/​s​1​0​2​5​8​-​0​2​6​-​0​0​2​9​5​-​2​.​
Acknowledgments  We would like to thank Yanos Zylberberg, Arnaud Philippe, Martin Foureaux Koppen­
steiner, João Santos Silva, Stefano Cellini, Matthias Parey, João Pereira dos Santos, Sebastian Ellingsen, 
Rodrigo Martinez-Mazza for their useful comments. We would also like to thank the participants of the 
Surrey internal PhD workshop, 11th PhD-student WIPE, EEA-ESEM 2023 congress and 2nd Diversity 
and Human Capital Workshop for their helpful feedback. We are most grateful to the two anonymous 
reviewers for their insightful comments and suggestions. We are also appreciative to the Portuguese Sta­
tistical Office, Statistics Portugal, for help with accessing the data through their Safe Centre, especially to 
Idílio Freire, Álvaro Silva, Marcelo Pereira, Bartholomeus Schoenmakers and Alexandra Pessoa and to 
Diogo Lopes, from OLX group, for providing access to Imovirtual data. 
Author contributions  Francisco Nobre: Conceptualization, Data curation, Formal analysis, Methodology, 
Software, Writing – original draft, Writing – review & editing.
Ronize Cruz: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – origi­
nal draft, Writing – review & editing.
Diogo Jardim Gonçalves: Conceptualization, Data curation, Formal analysis, Methodology, Software, 
Writing – original draft, Writing – review & editing.
Funding  Diogo Jardim Gonçalves acknowledges funding from the Fundação para a Ciência e a Tecnolo­
gia (FCT) under the grant 2021.07555.BD.
Declarations
Ethical approval  The research complies with standard ethical guidelines for social science research.
Conflict of interest  The authors declare that they have no known competing financial interests or personal 
relationships that could have appeared to influence the work reported in this paper.
Open Access   This article is licensed under a Creative Commons Attribution 4.0 International License, 
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long 
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative 
Commons licence, and indicate if changes were made. The images or other third party material in this 
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line 
to the material. If material is not included in the article’s Creative Commons licence and your intended use 
is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission 
directly from the copyright holder. To view a copy of this licence, visit ​h​t​t​p​:​/​/​c​r​e​a​t​i​v​e​c​o​m​m​o​n​s​.​o​r​g​/​l​i​c​e​n​
s​e​s​/​b​y​/​4​.​0​/​.​
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