مطالعه عوامل فیزیکی و جغرافیایی موثر بر قیمت‌گذاری هتل‌های ایران

هتل‌ها یکی از نیازهای اساسی گردشگران در هر سفر به‌شمار می‌روند و حدود نیمی از مخارج آنان صرف اقامت در مقصد می‌شود. هر هتل می‌تواند برای تعیین قیمت خود راهبردهای گوناگونی را به‌کار گیرد؛ به‌طور مثال یا محصول خود را با یک قیمت رقابتی پایین به بازار عرضه کند و یا با افزودن ویژگی‌ها و امکانات اضافی، میزان قیمت برای هر شب اقامت را بالا ببرد. هدف این مقاله مطالعه‌ی عوامل تاثیرگذار بر نرخ هتل و ارزش‌گذاری هتل‌ها براساس ویژگی‌های مازاد است. در این مقاله، برای شناخت واکنش گردشگران دربرابر ویژگی‌های مختلف هتل، از روش قیمت‌گذاری هدانیک استفاده شده است. فرضیه‌های این پژوهش ازطریق روش رگرسیونی حداقل مربعات معمولی و با استفاده از داده‌های جمع‌آوری‌شده از 265 هتل سنجیده شده‌اند. باتوجه به اطلاعات به‌دست‌آمده، وجود منابع فیزیکی مانند استخر، فضای سبز و پارکینگ هیچ مزیت رقابتی را برای هتل‌های ایران ایجاد نمی‌کند؛ درحالی که ساخت مجموعه‌ی ورزشی به متمایز شدن محصول عرضه‌شده‌ی هتل‌ها کمک می‌کند. طبق تابع تحقیق، 62 درصد از تغییرات متغیر وابسته، یعنی قیمت، توسط متغیرهای مستقل پژوهش تعیین می‌شود و قرارگیری در شهر تهران بیشترین ارزش رقابتی را درمقایسه با شهرهای موردبررسی دیگر ایجاد می‌کند.

Study of Effective Physical and Geographical Factors on Pricing in Iran Hotels

1. IntroductionThis study investigates the impact of a variety of attributes or lsquo;characteristics rsquo; on the rates charged for hotel rooms in Iran. The aim of this paper is to provide information for tourist destinations through an analysis of the valuation of the location implicit in the price of accommodation. Using OLS model (that is, taking into account that demand valuation can vary along the hotel price distribution), the authors find that huge price differences between 5star hotels and the rest, is coupled with practically of no difference between 1star and 2star hotels. Other attributes with a significant effect on price are towns. With regard to the valuation of location, a hotel in Tehran location is valued much more at higher percentiles.The study of hotelroom pricing is complex because of seasonality, different price regimes (fullboard, halfboard, bed breakfast), and discounts and supplements on various grounds (additional bed for children, single room, view of the sea, additional room equipment such as airconditioning, television, or minibar).The value of attributes and characteristics are unobserved, as they are not separately traded in any market. Only the overall prices of hotel rooms, including particular combinations of attributes are observed. Our analysis draws upon the hedonicprices tradition of fitting statistical models to estimate the effect of attributes on price (early theoretical developments in hedonic prices are those of Lancaster 1966; Rosen, 1974. Empirical applications in the tourist sector are found in Andersson, 2010; Chen and Rothschild, 2010; Castro and Ferreira, 2014; and Espinet, Coenders, and Fluvia 2003). The product a given hotel H is offering can be regarded as a set of attributes, which may consist of services (such as swimming pool, garden, television in the room), or characteristics (star category, town, year of first opening, number of rooms, etc):Hi = (qi1, qi2, qi3, hellip;, qik, hellip;, qim ) (1)Where i= 1 hellip; n represents the hotel and qik (k=1, hellip;, m) each of its attributes.Thus, the hedonic price function for each hotel is represented as:Pi = P(qi1, qi2, qi3, hellip;,qik, hellip;, qim ) (2)2. Methodology This regression model offers us estimates of the homogeneous parameters between individuals and its application is justified by hedonic price theory. In the context of tourism, it is also easy to appreciate that the valuations individuals make of the physical characteristics (destination and time) of their accommodation differ according to their price. That is, it would be interesting to know the behavior of the explanatory variables along the price distribution. For this, an estimator is required that allows heterogeneous responses: the estimator stemming from the linear regression ( beta;i). Furthermore, a medianbased estimator is also attractive because it is less sensitive to outliers than a meanbased estimator. Therefore, the bias from unobserved characteristics (quality, renovation) should be smaller.Dependent variable: PriceThe per night price of a room in the case of hotels and of an entire unit in the case of hotel.Explanatory variables: ResortEsfahan: a dummy variable that takes a value of one if the accommodation is located in Esfahan and zero otherwise.Tabriz: a dummy variable that takes a value of one if the accommodation is located in Tabriz and zero otherwise.Tehran: a dummy variable that takes a value of one if the accommodation is located in Tehran and zero otherwise.Mashhad: a dummy variable that takes a value of one if the accommodation is located in Mashhad and zero otherwise, and etc. CategoryOne star: a dummy variable that takes a value of one if the hotel is onestar and zero otherwise.Two stars: a dummy variable that takes a value of one if the hotel is twostar and zero otherwise, and etc. Type of room (Single, Double and Suite).Rooms: number of hotel/apartment rooms. Swimming pool: a dummy variable that takes a value of one if the hotel has a swimming pool and zero otherwise. Car park: a dummy variable that takes a value of one if the hotel has a car park and zero otherwise. Garden/terrace: a dummy variable that takes a value of one if the hotel has a garden/terrace and zero otherwise.3. Results and DiscussionOne of the most relevant characteristics ratios of a hotel to its price is star category. Figure 1 clearly shows that the greatest differences in price occur for 5star hotels, while those with 1 and 2 stars hardly vary. Given the marked differences among the towns under study, the town in which the hotel is situated is another potentially very relevant variable.4. ConclusionThis article has identified some variables that affect the price paid by tourists in Iran hotels. The attributes or characteristics that allow hotels to increase price can also be seen as attributes that contribute to the differentiation of their offers.The use of hedonic functions has allowed us to quantify the effects of each of the significant variables (town, star category, number of rooms, and availability of parking place) on price. Thus, hotel managers can make economic estimates of the impact of decisions concerning changes in these variables. This should make the results very useful to hotel managers, and to a lesser extent, to tour operators and public authorities