Pricing Strategy for a Smart-Tourist Area: Does Location Matters?

. This study aims to investigate the effect of hotel characteristics, particularly their locations, on room pricing strategy for hotels in the Bogor Area, Indonesia. The author applies the quantile hedonic regression model on a dataset of Bogor Hotels collected through a travel agent's website. A total of 194 hotels were collected and used as data samples. Our findings suggest that locations near tourist attractions are a significant factor in explaining hotel room prices in the Bogor area, while in contrast, the city center location is not. In addition, we also find that hotel room size and stars-awarded have significant positive effects on room rates. The outcomes of this study advocate for the continuation of fine-tuning the existing pricing strategy adopted by hoteliers to optimize revenue.


Introduction
In recent years, the tourism industry has undergone significant changes driven by technological advancements and smart tourists who utilize technology and information to optimize their travel experiences.
In a smart-tourist area, the location of a hotel can significantly impact its competitiveness and revenue potential.Numerous studies have confirmed this notion and investigated the influence of location on hotel pricing decisions.For instance, Chen and Wei argued that hotels in prime locations with easy access to popular tourist attractions charge higher room rates than those in less desirable areas [1].This finding suggests that location plays a significant role in determining hotel pricing.Similarly, O'Neill and Mattila showed that hotels near transportation hubs and prominent landmarks tend to have higher average daily rates due to their strategic locations [2].In a smart-tourist area like Bogor, where location is vital in attracting visitors, it is therefore crucial to examine how different locations could impact room pricing strategies.
Bogor, a vibrant city in Indonesia's picturesque West Java province, has become a popular smart-tourist area due to its rich cultural heritage, natural beauty, and proximity to Jakarta, the capital city.As a result, the hospitality sector in Bogor has experienced significant growth, with numerous hotels and accommodations catering to a diverse range of tourists.
The hotel industry recognizes the importance of strategic pricing in maximizing revenue and enhancing customer satisfaction.Traditionally, hotel room pricing has been based on factors such as room type, amenities, seasonality, and market demand.However, in the context of a smart-tourist area like Bogor, where location plays a crucial role in attracting tourists, it is imperative to investigate the impact of location on hotel room pricing strategies.
Understanding the relationship between location and pricing can provide valuable insights for hoteliers, destination managers, and policymakers to make informed decisions regarding pricing strategies in smart-tourist areas.By examining the specific case of Bogor, this study aims to shed light on how location affects hotel room pricing and identify the key factors that influence pricing decisions in the context of a smarttourist area.
In this study, an analysis of hotel room pricing data from various locations within the Bogor area is conducted.The study will also consider proximity to tourist attractions, transportation hubs, and other amenities to assess their impact on pricing decisions.Furthermore, the study explores the influence of other factors, such as hotel stars' ratings, on hotel pricing strategies in Bogor.

Objectives
The primary objective of this study is to examine the relationship between hotel room pricing and location in the smart-tourist area of Bogor.The following specific purposes are addressed: Investigate the impact of location on hotel room pricing: This objective aims to assess the extent to which the location of hotels in the Bogor area influences their pricing strategies.By analyzing historical pricing data, the study identifies any patterns or trends indicating the influence of location on room rates.It explores whether hotels in prime locations, such as near tourist attractions or transportation hubs, command higher prices than those in less desirable locations.Identify the key factors influencing hotel room pricing decisions in Bogor: This objective aims to identify the primary determinants of hotel room pricing in the Bogor area.Factors such as proximity to popular tourist attractions, accessibility to transportation networks, availability of amenities, and competition are considered.By understanding these factors, the study aims to provide insights into the specific elements that hoteliers consider when determining room rates in a smart-tourist area like Bogor.
Provide practical implications for hoteliers and destination managers in Bogor.Based on the study's findings, this objective offers practical recommendations for hoteliers and destination managers in Bogor.Moreover, the study provides insights into effective pricing strategies based on location and other relevant factors, enabling hoteliers to optimize their revenue and maximize customer satisfaction.Destination managers can utilize the findings to develop policies that promote sustainable tourism and enhance the overall competitiveness of the Bogor area as a smart-tourist destination.By accomplishing these objectives, this study aims to contribute to the existing literature on hotel pricing strategies in smart-tourist areas.The findings will provide valuable insights for hoteliers, destination managers, and policymakers, helping them make informed decisions to enhance the competitiveness and sustainability of the hospitality industry in the Bogor area.

The conceptual price of a property
The conceptual price of a property is the estimated value or worth of a property based on various factors such as its features, condition, size, amenities, and market conditions.It is an abstract or hypothetical value assigned to a property, often used for valuation purposes or when discussing potential prices.
The impact of location on property prices is significant and plays a crucial role in determining property value [3] [4] & [5].Location is one of the key factors that buyers consider when purchasing real estate.Here are some ways in which location influences property prices: Desirability: A prime or desirable location, such as a neighborhood with good schools, and proximity to amenities like parks, shopping centers, restaurants, and transportation hubs, tends to increase the value of a property.Buyers are willing to pay more for properties in areas that offer convenience and high quality of life.
Accessibility: Easy access to major roads, highways, public transportation, and airports can positively impact property prices.Properties located in well-connected areas tend to have higher values as they offer convenience in commuting and traveling.
Neighborhood and Surroundings: The characteristics of the area and surroundings can affect property prices.Low crime rates, well-maintained infrastructure, attractive landscapes, and nearby recreational facilities or cultural attractions can contribute to higher property values.
Economic Growth and Development: Locations experiencing economic growth, development, and job opportunities could increase property prices.Areas with thriving industries, new businesses, and infrastructure projects tend to attract buyers and investors, leading to an appreciation in property values.
Views and Natural Features: Properties with scenic views, such as waterfront or mountain views, can command higher prices.Proximity to natural features like parks, beaches, or mountains can enhance the desirability of a location and increase property values.
Supply and Demand: A particular location's supply and demand dynamics can influence property prices.Prices tend to rise if there is a limited supply of properties in a high-demand area.Conversely, prices may be lower if properties are oversupplied in a less desirable location.
Based on the previous discussions, it could be concluded that the impact of location on property prices can vary based on local market conditions and specific factors unique to each area.Therefore, it is interesting to examine the impact of the location of smart-tourist properties such as hotels.

Hotel pricing strategies
Pricing strategies in the hotel industry are crucial for achieving profitability and maintaining competitiveness.Previous research has extensively examined various factors that influence hotel pricing decisions.These factors include room type, amenities, seasonality, and market demand.However, in the context of a smart-tourist area, the role of location becomes even more significant.Location-related factors, such as proximity to tourist attractions, transportation hubs, and other amenities, can substantially impact a hotel's pricing strategy [5].As such, this literature review aims to explore the relationship between hotel room pricing and location within the specific context of a smart-tourist area, focusing on the case of Bogor.

Impact of location and stars-awarded
The geographical location of a tourist destination has long been recognized as a critical factor in influencing tourist behavior and pricing effectiveness.Studies have shown that the proximity of a destination to major attractions, transportation hubs, and urban centers significantly affects tourists' decision-making processes and willingness to pay.Destination attractiveness, accessibility, and competition within the vicinity can also impact pricing strategies.Moreover, the location within a destination itself can play a role in pricing effectiveness.Different areas within a tourist destination may have varying levels of popularity, amenities, and smart services, which can influence the perceived value of pricing strategies.Understanding the relationship between location and pricing strategies is crucial for destination managers to tailor their pricing decisions based on the unique characteristics and competitive advantages of different areas within the destination.
Another essential aspect to consider when studying hotel pricing strategies is the influence of customer segments and market dynamics.Different customer segments, such as business travelers, leisure tourists, and international visitors, may have distinct preferences and varying willingness to pay for accommodations [6].Therefore, hotels often adopt differentiated pricing strategies based on the target market segment.Market conditions, including seasonal fluctuations and supply and demand dynamics, can significantly impact hotel pricing decisions [7].Understanding these factors is crucial for hoteliers to develop effective pricing strategies that align with customer preferences and market trends.In the context of Bogor as a smart-tourist area, it is essential to explore how customer segments and market dynamics influence hotel pricing decisions.
With the rise of smart cities and technology integration in tourism, smart-tourist areas have gained attention as destinations that provide innovative and sustainable experiences for visitors [8,11].These areas leverage technology to enhance the visitor experience, optimize resource management, and promote sustainability.Pricing strategies in smart-tourist areas must align with these destinations' unique characteristics and objectives.However, limited research focuses explicitly on hotel room pricing strategies within smart-tourist areas.Thus, this paper aims to contribute to the literature by examining the hotel room pricing strategy in the smart-tourist place of Bogor, shedding light on the role of location in determining room rates.
Meanwhile, the relation between stars awarded and hotel pricing is generally based on the hotel's quality and level of service.Stars awarded to hotels are a standard rating system that indicates the overall quality and amenities a property offers.The specific criteria for awarding stars may vary by country or organization, but in general, a higher star rating is associated with higher pricing due to factors such as: Quality and Amenities: Higher-starred hotels typically offer higher quality, luxury, and service.They often provide more amenities such as upscale dining options, fitness centers, spas, concierge services, and other premium facilities.The cost of providing these additional amenities and maintaining a higher standard of service is reflected in the pricing.
Facilities and Infrastructure: Hotels with more stars often have superior infrastructure and facilities, including larger rooms, more spacious public areas, business centers, conference rooms, swimming pools, and other recreational facilities.The cost of building, maintaining, and operating these facilities contributes to the overall pricing of the hotel.
Brand Reputation: Certain hotel brands or chains are renowned for their high-quality service and luxury offerings.These brands have established a reputation associated with a higher price point.The stars awarded to hotels under these brands may reflect the brand's overall standard, further influencing the pricing.
Market Demand and Competition: Market demand and competition in a specific area can also impact hotel pricing.In popular tourist destinations or during peak travel seasons, higher demand for accommodations can drive prices up, regardless of star ratings.Similarly, intense competition among hotels in the same category may lead to more competitive pricing strategies.
From the former discussions, despite the growing importance of pricing strategies in smart tourism, limited research has specifically examined the relationship between hotel room pricing strategies, location, and smart tourism in Bogor, Indonesia.Bogor is a diverse tourist destination with various attractions, including natural landscapes, cultural heritage sites, and recreational facilities.The location of Bogor, close to Jakarta, the capital city of Indonesia, further adds to its appeal and potential for tourism growth.However, there is a need to investigate how pricing strategies can be optimized to attract tourists to different locations within the Bogor area.This study aims to fill this research gap by conducting a comprehensive case study that explores the effectiveness of pricing strategies in a smart-tourist area, considering the unique geographical location of Bogor.In this study, the following hypotheses are adopted: H1: A significant positive association exists between hotel geographical location and hotel room price.H2: A significant positive association exists between the number of stars awarded and the hotel room price.

Data
Hotels operating in the Bogor area are listed on multiple Internet travel agent platforms.However, due to the industry's monopolistic competition, price differentials between these platforms for the same hotel are typically insignificant.
Therefore, to ensure data consistency, this study exclusively gathers data from one internet travel agent, Traveloka.com.The data collection was conducted in November 2022, focusing on a single day and month to mitigate any issues related to seasonality.By leveraging the data collected through the Traveloka platform, the authors could search and compare hotel prices, services, and amenities to gain insights into smart tourists' behavior, preferences, and trends.Although information about all hotels in Indonesia is accessible online, this research solely concentrates on hotels located in Bogor.This decision is made to avoid potential heterogeneity caused by regional variations.
The study primarily analyzes hotels' pricing, occupancy, and amenities within the Bogor region.The sample consists of hotels in various categories, including luxury, budget, business, and resort, as classified on the Traveloka.comwebsite.Detailed information about each hotel, such as location, facilities, room types, the number of rooms, and prices for different room amenities, can be found online.The attributes considered in this research encompass both quantitative and qualitative components.The quantitative feature used to determine room price is the size of the room, measured in square meters.The qualitative attributes include hotel location, amenities, and other relevant features.These features encompass the hotel's proximity to the city center, whether it holds an international star rating (ranging from 1 to 5 stars), and whether it is part of a hotel chain.Through these methods, 194 hotels were collected and used as data samples.

Empirical model
The research employs the heteroskedasticity-robust standard error estimator and quantile regression method to estimate the effect of hotel characteristics on room price.Using a quantile regression approach is particularly useful in two situations: first, when dealing with the non-normal distribution of the dependent variables, such as in this research, where we use hotel room prices as the dependent variable.Second, quantile regression is practical when the effect of independent variables varies across the level of the dependent variable.
Therefore, the quantile regression method is ideal for investigating the impact of different characteristics on hotels' room prices.For example, consider the variable room size of a hotel.The room size may affect the price of a hotel with a relative price in the 25th percentile of the entire hotel distribution compared to the hotels with a relative probability in the 90th percentile of the entire bank profitability distribution.Another advantage of quantile regression is that it is robust to outliers [9,10].
The following variables are considered to affect the decision on hotel rooms pricing: In Equation ( 1), RoomSize represents the total room size in square meters (m²), star denotes the star rating awarded to the hotel (ranging from 1 to 5), and Chain is a dummy variable indicating whether the hotel is part of a chain (with non-chain hotels as the base group).CityCentre is another dummy variable that indicates whether the hotel is located within the city center (Yes = 1).Pool and Bath are dummy variables representing the availability of a swimming pool and bathroom, respectively (Yes =1).Similarly, Conference and Shuttle are dummy variables that consider the availability of the corresponding facilities in the hotel (Yes = 1).The for each of the four quantiles (q = 20 th , 50 th , 70 th , and 90 th ) using the same model from Equation (1).Mathematically, this can be expressed as: where ( | ) represents the qth conditional quantile hotel room price Y, X denotes the set of independent variables described previously, subscripts i = 1,2,3…, N represents individual hotels.In the quantile regression, the distribution of error terms, is left unspecified due to the non-parametric nature of the model [10].

Summary and descriptive
Table 1 shows the dependent and independent variables summary statistics.Based on the data in Table 1, hotel room prices in Bogor range from a minimum of Rp79 thousand to a maximum of Rp3,726 thousand.Meanwhile, room size ranges from 5m2 to 90m2, and hotels could have 0 to 5-star ratings.It is also evident that hotels with prices higher than Rp1 million only comprise less than 10% of the data.The mean of the price and the room size, as well as the hotel's stars, are Rp497.91thousand, 21.80 m2, and 2.1 stars, respectively.The standard deviation of the price is Rp541.5074.The typical room size is 21.80m2 with a standard deviation of 10.50.At the same time, the average hotel's star is 2.09, with a standard deviation of 1.33.Table 1 also shows that most of the hotels in Bogor have swimming pool facilities, as data indicates that the mean of the pool variable is higher than 0.5 (0.515).
In contrast, most hotels in Bogor do not have conference facilities (mean 0.21) or shuttle services (mean 0.11).Likewise, most hotels in Bogor are not part of a group or are located within the city center (mean 0.18 and mean 0.19).Most hotels, too, do not have bathroom facilities (mean 0.15).
In addition, Table 2 provides summary statistics for price at the 0-25th, 25th-50th, 50th-75th, and 75th-100th quantiles.Breaking down the data into four quantiles provides a more meaningful summary per Table 2, where the data have been divided into four different quantiles (the 25th, 50th, 75th, and 100 th ) based on the price.As seen in Table 2, the prices range from Rp at the lowest quantiles (0-25 th ) reserved for budget-type hotels, Rp79 thousand, to Rp242 thousand.Meanwhile, the medium-price hotels (the 25 th -50 th ) have a price range of Rp243 thousand to Rp350 thousand, and the medium-high price range of Rp357 thousand to Rp500 thousand.Finally, for the high-price hotels (the 75 th -100 th ), the price ranges from Rp506 thousand to Rp3.73 million.

Quantile regression results
The estimated vector of coefficients for various quantiles (0.20, 0.50, 0.70, 0.90) is presented in Table 3.
In this table, the column "q50" represents the estimation results for the 50th quantile, corresponding to the median regression.The columns q20, q70, and q90 display vectors of estimated coefficients for the regression at the corresponding quantiles of the hotel room prices distribution.
Analyzing Table 3, we observe that the variables RoomSize (hotel room size) and star (stars awarded) have significant positive coefficients.These findings confirm that larger room sizes and higher star ratings positively impact prices.However, it is essential to note that the magnitude of these effects varies across the quantiles.Overall, the estimated coefficients for RoomSize and Star are higher in volume for the higher quantiles, indicating that larger room sizes and more stars awarded are associated with higher prices.
Additionally, being part of a hotel chain or group is found to be associated with the ability to charge higher prices.This association is particularly evident in the uppermost quantile (q=90), where hotels affiliated with a chain/group can charge approximately one million rupiahs more than those not associated.The variable CityCenter (city center location) does not significantly affect room prices, except for high-end luxury hotels located in the city center, which hurts the price.Interestingly, most high-end luxury hotels are situated in the city outskirts near tourist attraction areas.
Furthermore, specific amenities such as Baths, facilities, pools (swimming pools), and Shuttle services positively affect the price, although not all coefficients are statistically significant.Similarly, the availability of conference and bath facilities does not significantly affect the price, although the coefficients are positive for most price quantiles.However, there are exceptions for the availability of conference facilities in the highquantile hotels (0.70, 0.90), where the coefficients are negative but not statistically significant.
These findings should be considered with the significant negative effect of city center location on luxury hotel prices, especially considering that most hotels are located on the outskirts near tourist attraction centers.These findings suggest that most visitors to Bogor are likely not there for business purposes but rather for leisure or recreation, primarily staying in the tourist attractions area.

Conclusion and recommendations
In conclusion, our analysis of the estimated coefficients for various quantiles of hotel room prices has provided valuable insights into the factors influencing pricing in the Bogor hotel market.The findings highlight the significance of certain variables in shaping hotel room prices while revealing variations in their effects across different quantiles.
Our results confirm that hotel room size and star ratings significantly impact prices, supporting our initial hypotheses.Larger room sizes and higher star ratings are associated with higher prices, with the effects becoming more pronounced as we move towards higher quantiles.This result emphasizes the importance of these factors in determining pricing strategies, especially for hotels aiming to target the upper segments of the market.
Moreover, the association between hotel chain/group affiliation and pricing is particularly noteworthy.Our findings indicate that hotels affiliated with a chain or group can charge higher prices than their non-affiliated counterparts.This effect is most prominent in the uppermost quantile, where affiliated hotels can command a premium of approximately one million rupiahs.This finding suggests that the reputation and resources of a hotel chain/group contribute significantly to the perceived value and subsequent pricing decisions.
Interestingly, we observe that the impact of city center location on room prices is not statistically significant for most hotels.However, this relationship changes for high-end luxury hotels, where being situated in the city center negatively influences prices.It is worth noting that most luxury hotels are positioned on the outskirts near popular tourist attractions.This finding suggests that the primary motivation of visitors to Bogor is leisure or recreation rather than business, leading to a preference for accommodation outside the bustling city center.
Furthermore, our analysis reveals that specific amenities, such as "Bath" facilities, swimming pools, and shuttle services, positively affect prices, albeit with varying levels of statistical significance.Additionally, the availability of conference and bath facilities does not significantly impact prices, despite positive coefficients across most quantiles.However, an exception arises for the availability of conference facilities in high-quantile hotels (0.70, 0.90), where the coefficients display a negative, albeit statistically insignificant, relationship.
All in all, these findings offer practical implications for hotel operators and stakeholders in the Bogor market.Hoteliers can leverage the positive influence of room size, star ratings, and specific amenities to enhance their pricing strategies, mainly when targeting higherend customers.Additionally, the negative impact of city center location on luxury hotel prices suggests the importance of locating luxury establishments near tourist attractions on the outskirts.By aligning their offerings with the preferences and motivations of Bogor visitors, hotels can optimize their pricing decisions and attract the desired customer segments.
It is essential to acknowledge that our analysis is based on the specific context of the Bogor hotel market and may not be directly applicable to other regions or markets.Further research is recommended to explore additional factors and refine our understanding of the dynamics shaping hotel pricing in different contexts.

Table 1
Summary Statistics

Table 2
Summary by Quantiles