# RECORD 41a24683a4b966679145f8c822178e61a9a339a4
# TITLE: A Comparative Hedonic Analysis of Condominium Asking Rents in Bangkok, Phuket, and Pattaya
# YEAR: 2026.0  VENUE: International Journal of Building, Urban, Interior and Landscape Technology  DOI: 10.56261/built.v24.266592
# AUTHORS: Than Dendoung
# PAGES: 16  CANDIDATE TABLE PAGES (1-based): [8, 11]
# ABSTRACT: This study estimates condominium rental price differentials from online listings across Thailand’s three major tourist cities, using Bangkok as the benchmark for Phuket and Pattaya while controlling for observable unit characteristics. Intercity differentials are measured with a hedonic regression in which the dependent variable is the natural logarithm of monthly rent (in Thai baht). Model 1 controls for the number of bedrooms, while Model 2 adds unit size through log(size_sqm), allowing the effect of unit composition on estimated city effects to be assessed. Confidence intervals at 95% are based on heteroskedasticity-robust (HC1) standard errors. The main finding is the magnitude and persistence of city-level effects relative to Bangkok. In Model 1, Pattaya has a monthly rent 11.5% lower (95% CI -13.3% to -9.6%) and Phuket 23.8% higher (95% CI 21.4% to 26.3%). When apartment size is included in Model 2, the monthly rent in Pattaya is 16.8% lower (95% CI -18.3% to -15.2%) and in Phuket 14.3% higher (95% CI 12.5% to 16.2%), and the model fit improves as the adjusted R-squared rises from 0.491 to 0.663. These results indicate that factors beyond apartment size drive rental differences across cities, since the gaps remain statistically significant after adjusting for size. The study offers robust, transparent estimates of rental levels in Thailand’s major tourist cities that can support comparison and benchmarking.


===== PAGE 1 (score 16, numeric-paren 0, table-words 3, var-words 10) =====

Dendoung, T. 1
Research Article
A Comparative Hedonic Analysis 
of Condominium Asking Rents 
in Bangkok, Phuket, and Pattaya
Than Dendoung
Faculty of Architecture and Planning, Thammasat University, Phahonyothin Road, Khlong Nueng Subdistrict, 
Khlong Luang District, Pathum Thani Province 12121, Thailand
Corresponding author e-mail:  dendoung.than@gmail.com
Received: 1 Jul 2026; Revised from: 31 Aug 2026 ; Accepted: 8 Sep 2026
Print-ISSN: 2228-9135, Electronic-ISSN: 2258-9194, https://doi.org/10.56261/built.v24.266592
Abstract
	
In Thailand, rent comparisons between cities are based on unadjusted averages 
of listings, which confuses the mix of units advertised with the price a city sets on 
property. I ask how much of the rent difference between Bangkok, Phuket, and Pattaya is 
composition and how much survives it. I use a cross-section of 14,849 active condominium 
rental listings from FazWaz in January 2026 to estimate nested hedonic models. The 
dependent variable is the natural log of monthly asking rent in Thai baht; all inference 
uses heteroskedasticity-robust (HC1) standard errors. Model 1 controls bedroom count, 
Model 2 adds ln(size), Model 3 adds bathrooms and view type. The raw picture misleads: 
Model 1 places Phuket 22.8% above Bangkok (95% CI 20.3% to 25.4%) and Pattaya 
11.5% below (95% CI -13.4% to -9.6%). Controlling size moves both, Phuket to 14.4% and 
Pattaya to 16.8% below, and lifts adjusted R-squared from 0.492 to 0.662; about a third 
of the apparent Phuket premium is size composition, not location. The central result is 
asymmetric. Adding bathrooms and view type leaves Phuket essentially unchanged, 0.130 
to 0.134 log points, but widens the Pattaya discount from 15.6% to 22.4%: The listed 
stock in Pattaya leans toward sea-view and pool-view product and the city indicator had 
been absorbing that mix. City coefficients based on listing data are not location premiums 
or willingness to pay, but conditional associations. A third of the variation in rent is 
unexplained and the attributes that would close the gap are named.
Keywords: hedonic price model; condominium rental market; asking rents; online listings; 
Bangkok; Phuket; Pattaya
 
https://doi.org/10.56261/built.v24.266592


===== PAGE 8 (score 17, numeric-paren 0, table-words 4, var-words 7) =====

BUILT  24(2). 2026, 266592
8
4. Results
4.1 Overview of analytic sample
	
The analytical sample consists of 14,849 
condominium listings: 9,774 in Bangkok, 
2,835 in Phuket, and 2,240 in Pattaya. 
Bedroom counts are derived from the 
title-based extraction rule in Section 3.2, 
which reduces missingness and enhances 
comparability between cities by classifying 
studios as zero and parsing stated counts 
as integers. Table 5 lists the number of 
bedrooms, unit size, and monthly asking 
rent by city. The log-linear specification 
is motivated by the widely dispersed and 
strongly right-skewed nature of both rent 
and size.
4.2 Distributional diagnostics
	
All three cities’ asking rents have a long 
upper tail and a significant right skew. 
The rent distributions are plotted on a 
logarithmic horizontal axis in Figure 5, 
which allows the cross-city differences to 
be seen without distorting the majority 
of the observations. Because the log 
transformation stabilizes the variance and 
enables the coefficients to be interpreted 
as semi-elasticities and elasticities, the 
pattern supports modeling the dependent 
variable as ln(rent).
 
Figure 3. Rent per sqm density by city
Figure 4. Rent per sqm by bedrooms, faceted by city 
Table 5 | Descriptive Statistics by City | N = 14,849.
City
N
Minimum
Maximum
Mean
Std. Deviation
Bangkok
Monthly rent (THB)
9,774
3,500
1,390,000
40,403
44,184
Unit size (sqm)
9,774
20.00
772.49
66.82
59.46
Bedrooms
9,774
0.00
6.00
1.42
0.79
Phuket
Monthly rent (THB)
2,835
10,000
1,420,000
41,444
52,192
Unit size (sqm)
2,835
17.50
650.00
61.20
53.55
Bedrooms
2,835
0.00
5.00
1.12
0.80
Pattaya
Monthly rent (THB)
2,240
6,000
290,000
27,633
22,032
Unit size (sqm)
2,240
21.00
450.00
56.25
40.74
Bedrooms
2,240
0.00
5.00
1.10
0.80
Note. Summary statistics for the analytic sample (N = 14,849) reporting monthly asking rent (THB), unit size (sqm) and bedroom count by city. 


===== PAGE 9 (score 12, numeric-paren 0, table-words 4, var-words 3) =====

Dendoung, T. 9
Figure 5. Rent distributions by city, log scale
Table 6 | Coefficients, Heteroskedasticity-Robust (HC1) Standard Errors | N = 14,849.
Model
Unstandardized 
Coefficients
Standardized Coefficients
t
Sig.
95% Confidence Interval for B
B
Robust Std. Error
Beta
Lower Bound
Upper Bound
1
(Intercept)
9.491
.008
1128.881
.000
9.474
9.507
Phuket
.205
.011
.117
19.402
.000
.185
.226
Pattaya
-.122
.011
-.064
-11.309
.000
-.143
-.101
Bedrooms
.589
.005
.693
114.160
.000
.579
.599
2
(Intercept)
6.902
.038
181.536
.000
6.827
6.977
Phuket
.135
.009
.077
15.625
.000
.118
.151
Pattaya
-.184
.009
-.096
-19.629
.000
-.202
-.165
Bedrooms
.104
.009
.122
12.202
.000
.087
.120
ln(size)
.822
.012
.699
67.554
.000
.798
.846
Note. Estimated coefficients for Models 1 and 2 with heteroskedasticity-robust (HC1) standard errors and associated test statistics. Robust inference is 
emphasized given cross-sectional listing data and potentially heteroskedastic residuals. The dependent variable is ln(rent) and Bangkok is the reference 
city category. N = 14,849


===== PAGE 11 (score 33, numeric-paren 0, table-words 9, var-words 13) =====

Dendoung, T. 11
4.5 Richer specification: bathrooms and 
view type
	
The city indicators in Models 1 and 2 
may absorb unmeasured attributes that 
systematically differ between markets. As a 
result, Model 3 adds the two characteristics 
that the listing records report at high 
coverage: the number of bathrooms and 
the advertised view type. Figure 7 shows 
the city coefficients for each of the three 
nested specifications, and Table 7 presents 
the estimates with robust standard errors.
	
Both added attributes are priced 
as expected. Keeping size, number of 
bedrooms, and city constant, an extra 
bathroom is linked to an 8.0% increase in 
rent. View type is economically substantial. 
Sea views are about 17.2% and lake views 
are about 20.0% more expensive than city 
views, which are the reference category; 
pool, garden, mountain, and river views 
are between 7% and 15% less expensive. 
The latter should not be interpreted as 
distaste for those outlooks because the 
coefficient shows that these outlooks are 
more common in less expensive inland and 
secondary regions.
Table 7 | Model 3 with Heteroskedasticity-Robust (HC1) Standard Errors | N = 13,224.
Unstandardized 
Coefficients
Standardized 
Coefficients
t
Sig.
95% Confidence 
Interval for B
%Δ
B
Robust Std. Error
Beta
Lower Bound
Upper Bound
(Intercept)
7.097
.046
152.770
.000
7.006
7.188
Phuket
.134
.012
.077
10.974
.000
.110
.158
+14.4
Pattaya
-.254
.013
-.135
-20.278
.000
-.279
-.230
-22.4
View: Garden
-.074
.016
-.024
-4.728
.000
-.105
-.043
-7.1
View: Lake
.183
.033
.029
5.490
.000
.117
.248
+20.0
View: Mountain
-.079
.017
-.027
-4.519
.000
-.113
-.045
-7.6
View: Pool
-.136
.013
-.054
-10.314
.000
-.162
-.110
-12.7
View: River
-.158
.049
-.013
-3.205
.001
-.255
-.061
-14.6
View: Sea
.159
.015
.078
10.491
.000
.129
.188
+17.2
View: Skyline
.044
.014
.017
3.206
.001
.017
.071
+4.5
View: Unblocked 
Open
-.092
.015
-.031
-6.075
.000
-.121
-.062
-8.8
Bedrooms
.088
.009
.104
9.521
.000
.070
.107
+9.2
ln(size)
.755
.016
.633
47.358
.000
.723
.786
Bathrooms
.077
.013
.075
5.997
.000
.052
.102
+8.0
Note. Model 3 adds bathroom count and view type to Model 2. The dependent variable is ln(rent), Bangkok is the reference city category and a city 
view is the reference view category. N = 13,224. Adjusted R squared = 0.668. The %Δ column converts a coefficient to a percentage effect using Equation 
4 and is reported only where a one-unit change in the regressor is meaningful, that is, for the indicator and count variables. It is left blank for ln(size), 
whose coefficient is an elasticity and is read as the percentage change in rent associated with a one percent increase in floor area. 
	
The main outcome of the extended 
specification is that the effect on the 
city coefficients is asymmetric. Since the 
Phuket premium, as estimated on the 
common sample, is essentially unchanged, 
changing from 0.130 to 0.134 log points, 
the conclusion that Phuket commands a 
genuine premium over Bangkok survives 
the addition of these variables. In contrast, 
the Pattaya discount increases from -0.170 
to -0.254 log points, or around 15.6% 
to 22.4% less than Bangkok. Pattaya’s 
listed stock is weighted toward sea-view 
and pool-view product, and once those 
attributes are priced individually the city’s 
underlying position turns out to be weaker 
than the size-adjusted comparison suggests. 
This is direct evidence for H3, and evidence 
that city indicators in parsimonious hedonic 
specifications can absorb compositional 
differences in amenity mix as well as in 
unit size. It is the principal reason all city 
coefficients reported here are described 
as conditional associations rather than as 
location premiums. 


===== PAGE 12 (score 43, numeric-paren 0, table-words 11, var-words 21) =====

BUILT  24(2). 2026, 266592
12
BUILT  24(2). 2026, 266592
12
Figure 7. City coefficients relative to Bangkok across nested specifications
4.6 Robustness checks
	
Two further attributes are reported for 
only part of the sample, so I examine them 
in subsamples rather than in the pooled 
specification. Table 8 reports both. 
For Bangkok, the listing records report 
distance to the nearest BTS or MRT station 
for 83% of listings. Added to the Model 3 
specification for Bangkok alone, it returns 
a coefficient of -0.082, so each additional 
kilometer from a station is associated 
with rent about 7.9% lower, holding unit 
attributes constant. This is the accessibility 
control that the pooled specification lacks, 
and its magnitude is consistent with the 
accessibility literature discussed in Section 
2.4. The comparable field is absent for 
Phuket and Pattaya, where urban rail does 
not operate. 
	
More than 95% of listings in Phuket 
and Pattaya provide the floor level, while 
only 4% of listings in Bangkok do so. I add 
floor level to the Model 3 specification for 
the two coastal cities and get a coefficient 
of 0.0017 per story that is not statistically 
significant (p = 0.18). Once view type 
is controlled, height per se does not 
command a measurable premium in these 
markets: it is the outlook that is priced, 
for which floor level is only a proxy. In this 
subsample a sea view is worth about 31.5% 
and the Pattaya discount relative to Phuket 
about 34.3%.
	
Additionally, I created and tested 
an index counting advertised amenities, 
covering furnishing, balcony, washing 
machine, wifi, pool access, gym, microwave, 
television and recent renovation, and 
excluded it from the reported specifications. 
It entered negative and significant, 
an implausible sign that diagnostic 
examination attributes to the source data’s 
structure rather than preferences. There 
are only a few attribute slots available in 
the listing design, which are filled with basic 
appliances in lower-priced ads and various 
descriptions in higher-priced ads.
Mean amenity count accordingly falls 
from 2.35 in the smallest size quartile 
to 1.82 in the largest, and the individual 
indicators price a television at -5.0% and 
Table 8 | Subsample Robustness Checks | N = 7,195 and 4,422.
Specification
B
Robust Std. 
Error
Sig.
%Δ
Bangkok 
(+ transit distance)
Distance to BTS/
MRT (km)
-.082
.004
.000
-7.9
ln(size)
.735
.024
.000
Bathrooms
.086
.015
.000
+9.0
Bedrooms
.070
.014
.000
+7.2
Phuket & Pattaya 
(+ floor level)
Floor level
.002
.001
.183
+0.2
ln(size)
.629
.021
.000
Bathrooms
.073
.026
.005
+7.6
Bedrooms
.157
.014
.000
+17.0
Pattaya
-.420
.016
.000
-34.3
Note. Both specifications add the stated attribute to the Model 3 covariate set. The 
Bangkok specification is estimated on 7,195 listings with adjusted R squared 0.674. The 
Phuket and Pattaya specification is estimated on 4,422 listings with adjusted R squared 
0.703, with Phuket as the reference city. The dependent variable is ln(rent) in both cases. As 
in Table 7, the %Δ column is left blank for ln(size), whose coefficient is an elasticity.