A GIS-based decision support system for hotel room rate estimation and temporal price prediction: The hotel brokers' context

Abstract

The vastly increasing number of online hotel room bookings are not only intensifying the competition in the travel industry as a whole, but also prompt travel intermediates (i.e. e-companies that aggregate information about different travel products from different travel suppliers) into a fierce competition for the best prices of travel products, i.e. hotel rooms. An important factor that affects revenues is the ability to conclude profitable deals with different travel suppliers. However, the profitability of a contract not only depends on the communication skills of a contract manager. It significantly depends on the objective information obtained about a specific travel supplier and his/her products. While the contract manager usually has a broad knowledge of the travel business in general, collecting and processing specific information about travel suppliers is usually a time and cost expensive task. Our goal is to develop a tool that assists the travel intermediate to acquire the missing strategic information about individual hotels in order to leverage profitable deals. We present a GIS-based decision-support system that can both, estimate objective hotel room rates using essential hotel and locational characteristics and predict temporal room rate prices. Information about objective hotel room rates allow for an objective comparison and provide the basis for a realistic computation of the contract's profitability. The temporal prediction of room rates can be used for monitoring past hotel room rates and for adjusting the price of the future contract. This paper makes three major contributions. First, we present a GIS-based decision support system, the first of its kinds, for hotel brokers. Second, the DSS can be applied to virtually any part of the world, which makes it a very attractive business tool in real-life situations. Third, it integrates a widely used data mining framework that provides access to dozens of ready to run algorithms to be used by a domain expert and it offers the possibility of adding new algorithms once they are developed. The system has been designed and evaluated in close cooperation with a company that develops travel technology solutions, in particular inventory management and pricing solutions for many well-known websites and travel agencies around the world. This company has also provided us with real, large datasets to evaluate the system. We demonstrate the functionality of the DSS using the hotel data in the area of Barcelona, Spain. The results indicate the potential usefulness of the proposed system.

Intelligent Decision Support Systems

“…The temporal prediction of room rates can be used for monitoring past hotel room rates and for adjusting the price of the future contract. This research makes three major contributions (Kisilevich et al 2013). First, Kisilevich et al (2013) present a GIS-based decision support system, the first of its kind, for hotel brokers.…”

“…This research makes three major contributions (Kisilevich et al 2013). First, Kisilevich et al (2013) present a GIS-based decision support system, the first of its kind, for hotel brokers. Second, the DSS can be applied to virtually any part of the world, which makes it a very attractive business tool in real-life situations.…”

“…This framework is expected to improve the quality of healthcare, reducing medical errors and guaranteeing the safety of patients by helping clinicians to make correct, accurate, knowledgeable and timely decisions (El-Sappagh and El-Masri 2014). Kisilevich et al (2013) present a GIS-based decision support system that can both, estimate objective hotel room rates using essential hotel and locational characteristics and predict temporal room rate prices. Information about objective hotel room rates allows for an objective comparison and provides the basis for a realistic computation of the contract's profitability.…”

Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia

“…This is because the location determines the prevailing land price [16]. In addition, the location also determines the ease of access to public facilities, such as schools, campus, hospitals and health centers, as well as family recreation facilities such as malls, culinary tours, or even offer a beautiful scenery [17], [18]. In general, the factors affecting the house prices will be presented in Table 1.…”

A GIS-Based Web Approach for Serving Land Price Information

“…For example, the real estate information systems employed by local governments in many countries have been developed mainly to enhance information gathering and management [21][22][23]. Scholars in Germany have developed a GIS-based system that offers brokers information to support hotel room pricing [24]. A web page with GIS tools in Spain serves as an information platform for leasing rural land plots [25].…”