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Rationalizing the Dynamic Hotel Room Pricing in ASEAN Region
Using Panel Regression Analysis: Foundation for Revenue

Management
Christian C. Aguado

Colegio de San Juan de Letran, Philippines

DOI: https://doi.org/10.51583/IJLTEMAS.2025.1408000201

Received: 10 Sep 2025; Accepted: 20 Sep 2025; Published: 25 September

Abstract— This study investigates the determinants of hotel room pricing in ASEAN member states using a dataset of 18,215
observations. The research extends the traditional hedonic pricing model by retaining only statistically significant predictors,
thereby improving both explanatory accuracy and practical application. The findings highlight five critical determinants of hotel
room pricing: hotel class, occupancy rate, room revenue, resort-town or beachfront location, and independence from major chains.
Revenue per Available Room (RevPAR) demonstrates the strongest positive association, while independent hotels consistently
price lower than chain-affiliated properties, underscoring the influence of brand strength. External geographic factors further
enhance pricing power, confirming prior evidence on location-based premiums. In contrast, variables such as hotel size, franchise
ownership, and generalized location categories were found to be statistically insignificant, streamlining the model and improving
interpretability. Beyond methodological refinement, this research signals a shift in pricing strategy attitudes within the ASEAN
hospitality sector. By aligning econometric rigor with industry relevance, the study advances both theory and practice. It
demonstrates how robust statistical models can guide managerial decision-making, inform policy, and enrich classroom instruction.
As a first step in the integration of econometric approaches into regional hospitality pricing, the framework provides a foundation
for more precise, evidence-based strategies across both academic and professional domains.

Keywords— Hotel Room Price, Characteristics, KPI, ASEAN Region, Regression Model

I. Introduction

Tourism is an important part of a country’s economy, and it is also a growing industry for many countries. The tourism industry is
made up of many sectors, one of which is hospitality. Hotels have also been growing rapidly, especially in ASEAN where
competition is starting to get intense. The World Travel & Tourism Council claims that tourism is a significant driver of economic
recovery, and Statista’s Market Forecast for 2024 claims that the hotel industry in Southern Asia is projected to reach $13.28 billion
in 2025, and is expected to reach $16.41 billion in 2029, growing at 5.43% CAGR annually. This means that hotel room pricing,
which is critical to the hotel revenue, must be strategically developed.

Hotel rooms are considered the most important assets a hotel has. However, empty rooms, which are not sold, depreciate and are
considered a loss (Calinao et al, 2022). As such, pricing strategies should be employed in such a way that maximizes profit and
occupancy. Several hotel attribute determinants influence pricing, such as hotel brand, location, tier and size (Jiang & Taylor, 2020;
Calinao et al, 2022). The occupancy and revenue per available room (RevPAR) are some the metrics that are used to gauge the
profitability and financial performance of a company (Hua et al, 2018). However, there is limited accessible literature that has
addressed the pricing of hotel rooms across Southeast Asia (HVS Research, 2019).

This study fills in the gap in the literature by utilizing panel regression techniques on data covering the years 2014 to 2024. The
availability of dataset facilitated building robust econometric models for hotel pricing in ASEAN. Previous works on hotel pricing
have focused on customer satisfaction or sustainable development, while very few have analyzed hotel pricing using econometric
techniques (Chica-Olmo & Zafra-Gomez, 2022). In contrast to the Philippine context where economists Morales & Villanueva
(2021) and the Department of Tourism (2023) describe the use of panel data to study a single hotel over time, the method in question
offers a more sophisticated approach by allowing the simultaneous study of multiple hotels over time.

The study is guided by the two objectives of: (1) determining the critical relationships between hotel characteristics, associated
KPIs, and pricing, and (2) developing a rational pricing strategy model. The study uses secondary longitudinal data to analyze
ASEAN hotels and focuses on critical pricing metrics such as RevPAR and occupancy. The data analysis, though guided solely by
confidentiality policies and room revenue, contributes to an improved understanding of pricing determinants.

Throughout this research, and in both theory and practice, the researcher had identified and documented pricing strategies for hotels
in ASEAN countries based on secondary data and regression analysis. This demonstrates the use of regression analysis in hospitality
management, as well as its application to several other fields beyond tourism and hospitality.

In the ASEAN region, attentively analyzing each hotel’s room pricing, considering bath amenities, service levels, location, and
other relevant features within the hedonic pricing framework, contributes to the value assigning attributes of and pricing within
hospitality. For instance, hedonic pricing and related assumptions were aptly employed to forecast the rates of beach resorts in
Thailand by Tochwat and Likitanupak (2019) and they conclude that the rating of a resort, its position and other environment,

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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together with other geographical and physical attributes of the area play a dominant role in the revenue earned by the resort. In the
same manner, Soler et al. (2022) has established evidence of the corresponding relationship among hotel category and location
within the Algarve region and room rental rates. These works cited above testify to the ability of hedonic pricing theor to the pricing
anomalies and the ASEAN hotel market as well.

On the other hand, hedonic pricing theory suffers from a lack of theoretical advancement. Within real estate, the developments in
big data and big neural networks' predictive modeling remain predictive and do not advance theory (Zhang et al., 2021). Hospitality
as a field of study has a greater reliance upon dynamic assumptions and, more such, fails to integrate proactive assumptions of
relational time (Abrate et al., 2023). Developing hedonic pricing by integrating dynamic frameworks of architecture will address
such gaps and enhance the theory application.

This analysis utilizes quantitative methods with a descriptive and longitudinal approach while obtaining secondary data. This dataset
facilitates the study of the hotel’s key characteristics, performance indicators, and pricing. The goals of the study are: (1) to analyze
the relationships between hotel attributes and performance indicators with room prices and (2) to develop a more accurate pricing
model. The data collected minimizes the time-consuming techniques of surveys and interviews while still providing strong evidence
for econometric research.

All ASEAN member countries are included within the study region, with the Philippines, Singapore, Indonesia, Brunei, Thailand,
Malaysia, Vietnam, Cambodia, Myanmar, and Laos. The use of panel regression in this case studies is justified as it accounts for
both the cross-section at a particular point in time (spatial) and the changes in the same entity over time (temporal) (Beck & Katz,
1995). The econometric models used in this study are pooled OLS, fixed effects, and random effects (Cottrell & Lucchetti, 2023).
Given the characteristics of the variables in the study, such as location and type of accommodation offered, fixed effects are more
appropriate. The application of random effects estimation, as opposed to pooled OLS, is justified given the strong differences
between the hotels in the sample and is proven through a Breusch-Pagan Lagrange Multiplier test. The dependent variable of the
model is hotel room pricing, which is explained by the following six independent variables: hotel class, accommodation type,
location, size, occupancy rate and RevPAR. Data preparation involved cleaning, recoding, and transformation of highly skewed
data to log scale prior to analysis in SPSS and Gretl.

This research applies panel regression techniques, thereby extending the reach of hedonic pricing in the hospitality industry and
providing pricing insights for hotels in ASEAN countries. This will assist hotel operational managers in formulating more
reasonable pricing policies, while also contributing to the scholarly discourse on econometric work in tourism and hospitality. As
Guillet and Mohammed (2023) argue, these methods improve the accuracy of revenue forecasting, which in turn improves
profitability and revenue management in the context of a growing regional economy.

II. Results and Discussion

The regression analysis reveals a consistent, statistically significant relationship between the class of a hotel and the pricing of its
rooms within the ASEAN region, suggesting class determination borders the apex of performance revenue. Upgrading a property's
class increases room prices, and the impact is non-negligible. This is like the market, which has seen the rapid resurgence of the
midscale to upper midscale and upper-tier hotel segments after the pandemic, due to enhanced guest trust and brand perception.
Since the model empirically documents a higher price floor for each class, it re-affirms the old contention that segmentation based
on classification is a price elastic durable barrier and shields a property over the price turbulence or broader economic downturn.
Geographically, the ASEAN region is relatively simple, which is that the class and the resulting positioning a hotel decides to
pursue will determine its room pricing in all countries aligned to the ASEAN principles. This is why, bolstered by strong regressions,
the research transforms the traditional belief of the pricing power of hotel class as a segmentation variable into quantifiable
hypotheses and forecasts as to the benefits attached to ascending the classification grade system.

The information brought forth in this document further sustains the idea of controlled dynamic pricing while providing the necessary
schedule to players in the hotel industry to determine when to optimize the price of hotel room supply, how to adjust the precision
of market segment targeting, and when to anticipate demand shifts throughout the ASEAN region.

The analysis demonstrates that the operations of a hotel greatly influence the prices displayed to guests, especially in the ASEAN
region. Overall, chain-managed properties command higher room prices relative to independent properties, who sit on average five
percent lower, a wide gap. Some of that gap stems from the structural advantages chains possess, which include industry-leading
brand recognition, centralized booking systems, shared marketing spend, and economies of scale cost reductions. Franchised hotels
behave almost like fully owned brands in terms of room rates, which is puzzling. Overall, loyalty programs, brand-driven
distribution, and in-house revenue management systems combined give chain properties a competitive advantage that independent
hotels often struggle to overcome. All these factors should lead revenue managers to focus on building, acquiring, or collaborating
with chains that have brand power. Independent properties also have a fighting chance. Those providing unique cultural offerings,
hyper-personalized service, or strong local affiliation can command higher prices. Without low marketing cost though, these hotels
usually operate from a structural disadvantage.

Managers may utilize this evidence when formulating focused pricing strategies and narrowing down on points of distribution and
tactics that fit their brand promise and offer the highest return. Ultimately, the findings indicate that still having a brand allows

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hotels within the ASEAN region to hold their prices, and sometimes increase them, even during periods of turbulence in the
marketplace.

Hotel location earns a resort markup of approximately 24% above urban hotels. With the spa, sandy beaches, and unbeatable
relaxation settings rarely found in urban hotels, the market for leisure travellers has been set. Residual demand hotel strategies allow
managers to access set costs, deploy the best forecasting methodologies, and set the costs when the hotel is a resort. Data proves
the higher priced leisure compared to the geography is salient, and the attributes are crucial to supporting pricing decisions. For
revenue managers, the location remains crucial in setting the hotel room price. Traditionally, resorts unbundle room rates with a set
of activities in a way that revenue peaks and valleys are predictable and manageable. Hotels, particularly those on the outskirts of
resort districts, must depend on the superior service offered at the lowest price, which is a standard-driven price in the busy city.
To summarize the main point, location does matter, but what determines the price of a room in any hotel across the ASEAN region
is the peculiar value attached to a hotel’s location.

In the case of ASEAN, it is evident that the value that hotel room averages are priced at is not significantly influenced by the value
of a hotel sized which contradicts the general assumption that bigger hotels have higher prices. On the contrary, the dominant belief
that large hotels can afford lower prices but do not do so is proven wrong if class, brand, and location are considered, and size is
not relevant. These results demonstrate the importance of regression analysis, which can efficiently identify what matters, and
assists in the avoidance of expensive misconceptions. Rather than that, hotel revenue managers are advised not to bother with the
number of available rooms but instead spend their time on the reputation of the brand, the quality of the service offered, and the
speed at which the hotel responds to demand when developing price setting strategies. In this regard, it means creating a dominant
position in the market by providing high level of customer service to guests, instead of adding inventory.

Both occupancy and revenue influence other key results for hotels in the ASEAN region. For each 1 percent change in occupancy,
revenue managers estimate a 1.1 percent change in revenue. All other changes, including seasonality in the revenue line, keep
occupancy as the dominant demand driver emerging out of the cloud of noise. Revenue managers do not like to refer to analytically
fueled conclusions as assumptions, but rather as reputable results, and equations do not lie. What the models show, and what other
properties are showing, is that no matter whether the type of property or the type of market, occupancy is a key driver of pricing.
In the end, improving pricing and revenue performance for hotels in this region is reasonable, at least in part, as a function of
enhancing the responsiveness of hotels to changes, or in this case, the changes in demand. In other words, the occupancy and pricing
changes merged at the right revenue management balance.

In the ASEAN market, analysis correlating revenue and room prices shows RevPAR has a strong association with a hotel's room
price. An increase of RevPAR by 1 dollar would mean an increase in the RevPAR room price by 0.2%. Though an increase in 0.2%
adjustment per room seems trivial, in terms of large inventories and months, RevPAR becomes a strong driver of revenue growth.
Evidence from CBRE indicates RevPAR gains brought increases in average daily rate or room prices, thus supporting the notion
that RevPAR should be utilized to guide price setting. In total, the analysis provides a concrete strategy for the revenue teams in
the entire region – increase room prices for high RevPAR and maximize profits, regardless of market conditions.

Table I Regression results

The proposed framework incorporates some key elements in the theoretical approach to hotel room prices hedonic pricing model.
While the original model in hedonic pricing theory viewed price as simply the arithmetic average of features and services such as
ambiance, location, and cleanliness, the modified model retains only the traits that survive rigorous statistical scrutiny, enhancing
both explanatory power and practical relevance. Coupling the trimmed list of predictors panel regression with Beck-Katz error
correction provides stronger estimates by resolving the heteroskedastic and autocorrelated biases that are pervasive in longitudinal
datasets. The stronger model based on correction econometric evidence as opposed to abstract logic provides better, well-defined
directions to hotel managers attempting to set or decipher room prices.

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The most significant enhancement to the model is the integration of five key predictors: hotel class, occupancy rate, room revenue,
location of the resort, and whether the property is independently operated. Revenue per Available Room (RevPAR) is, of these, the
most positively correlated driver, indicating that the more revenue a hotel makes, the higher the room prices it can charge. Classes
of the hotel and occupancy rate also increase the prices, as customers apparently are willing to pay more for highly rated properties
that are almost always sold out. More recently, Vives and Ostrovskaya (2024) supported these notions, demonstrating that internal
service scores, as well as occupancy and classification features, influence hotel market value.

While the model focuses on how a hotel operates internally, it considers external factors as well. For example, a property next to a
beach or a hotel located in a resort town significantly boosts its value. Qiao, et.al. (2021) demonstrated the same thing using a
specialized map-based regression technique. They concluded that the local topography and its attractiveness largely determine the
rates of rooms situated in the countryside and in resort town areas. Furthermore, research indicates that the tendency of independent
hotels to charge lower rates than those associated with a prominent hotel chain is a strong phenomenon. This illustrates the power
of the brand, which offers the guests a reliable cover and consistent standard of quality and service, which is the same expectation
from chain operated premises.

No less important is what the augmented model leaves out. Hotel size, franchise ownership, and type of location (suburban, small
metro, interstate) proved to be statistically insignificant in the regression run. These variables, for earlier versions of the hedonic
pricing model, were on the list because they were considered reasonable. Removing them now gives the new version a cleaner,
more rational appearance. This shift aligns with emerging modelling preferences that emphasize sleek, easily verifiable frameworks
over overstuffed models with variables that fail to contribute. Sánchez-Lozano et al. (2021) make the point, adding useless or
densely overlapping predictors simply complicates the analysis and makes the results less useful.

Bottom line is, the new framework does not simply adjust the classic hedonic model, it moves the discussion forward by focusing
on measurement precision, robust methods, and situational realism. By distinguishing between pertinent and non-pertinent
variables, the model simplifies complex statistical analyses and provides managers and researchers with a ready reference. It is fully
aligned with hospitality pricing literature, and therefore easily applicable in classrooms, boardroom meetings, as well as in routine
pricing and marketing activities in the hotel industry.

Figure 1. Proposed Model / Framework

III. Conclusion

In the hotels of the ASEAN region, the overall room price is set based on the class of the hotel, the operational model, and most
importantly, the location of the property. Once those variables are kept constant, overall size matters more than any of the other
factors. Chain-managed hotels, higher rated hotels and seaside or tropical resorts can charge higher prices because of the
overwhelming and exclusive brand trust and exceptional experiences offered. Besides, the statement indicates that, to set room
prices in ASEAN region, occupancy percentage and RevPAR values are greatly important, and even a slight change in either value
can greatly change the prices over a certain period. If these values are monitored closely, then the hotel’s management can set the
prices, respond to the current situation in the hospitality market, and maximize profits no matter how the conditions are.

Future scholars from within and beyond the university as well as researchers working within the industry are advised to consider
additional qualitative variables such as guest satisfaction ratings, digital marketing efforts, and sustainability practices for more in-
depth studies, these attributes because certain other attributes related to pricing may come to light.

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www.ijltemas.in Page 1655

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