0 / SPK / specialcollections@tulane.edu

This dissertation innovatively extends the hedonic value model by crafting a comprehensive and novel econometric framework to analyze the mediating role of financial performance in the valuation of vacation rental properties. The study not only conducts an empirical analysis of the fundamental hedonic determinants of vacation rentals’ performance and value but also examines the influence of vacation rental performance on their valuation by leveraging the capitalization rate theory, a prevalent metric for evaluating commercial real estate value, to ascertain its applicability in forecasting the worth of vacation rentals. The core contribution of this study is the discovery of how short-term rentals are assessed in Central Florida, the key factors contributing to their performance and value, and whether the current valuation methods are suited to the current market needs. The methodology is grounded in robust panel data analysis, encompassing annual performance metrics from the Central Florida market between 2014 and 2023. This approach integrates short-term rentals' extended property-level information and monthly performance databases from AirDNA with the comprehensive real estate database offered by Stellar-MLS, which includes property listing history, market sales, and property details available exclusively to licensed realtors in Central Florida. To integrate these two datasets, a location-based tool was developed using Python and Google API resources to match property addresses and calculate the total distance and driving time to main attractions in the area. Results confirmed the influence of performance on vacation rental valuation and the mediation effect of performance on the relationship between property attributes and value. The findings revealed that the valuation of vacation rentals in Central Florida is better suited to commercial-oriented real estate assessments rather than traditional residential appraisals. These transformative insights provide a robust econometric tool for better predicting the value and performance of vacation rentals, benefiting a range of stakeholders, including vacation rental owners, investors, and policymakers.

The hospitality industry confronts continuous challenges from external environments, such as the COVID pandemic, the proliferation of short-term rentals, and the disruptive innovations of Generative AI. For businesses, understanding these external conditions and adapting strategies accordingly is crucial yet challenging, especially considering environmental uncertainties. Therefore, this dissertation investigates the effectiveness of different strategies in navigating market, competitive, and technological uncertainties, through a big-data analytical approach. It incorporates three studies, each focusing on one specific strategy and its varying outcomes under environmental changes. These studies employ machine learning algorithms to quantify strategies and utilize econometric models to infer the causal relationships between strategies and their outcomes. The first study examines how standardization affects short-term rental unit survival across two market conditions: pre-COVID growth and during-COVID decline. The results indicate that the risks arising from standardization are heightened under market decline. In addition, the effectiveness of standardization varies with design attributes to which the strategy is applied. Standardizing functional design boosts unit survival in the growing market but leads to a higher failure rate during the decline. Aesthetic standardization, on the other hand, negatively impacts survival in both conditions, with a stronger effect in the declining market. The second study identifies the impacts of differentiation on unit performance in the short-term rental context in two competitive environments: local versus city-level. The findings suggest that the effectiveness of differentiation increases with competitive pressure. At the local level where firms face localized competition, differentiation enhances unit performance. Conversely, in city-level environments where direct competition diminishes, it yields negative outcomes. Moreover, competition intensity, as reflected by the number of competitors and the degree of market concentration, is found to amplify the benefits of and mitigate the drawbacks of differentiation. The third study explores if adopting Generative AI to hotel online review response can improve customer feedback, under varying technological settings. It finds that simulated AI adoption improves customer perceptions when Generative AI models operate at high temperatures, while models with low temperatures lead to negative outcomes. The findings further underscore the importance of task-technology fit, revealing that Generative AI's effectiveness varies with review valence. Specifically, high-temperature settings for positive reviews generate significant benefits, whereas low-temperature settings lead to adverse effects. Conversely, for negative reviews, AI adoption demonstrates more stable outcomes across temperature settings, indicating balanced benefits of both low and high temperatures. In short, this dissertation identifies that the effectiveness of standardization, differentiation, and AI adoption strategies is contingent on environmental conditions. It underscores the importance of strategic adaptation in navigating contemporary challenges. / Doctor of Philosophy / It is difficult to operate hospitality businesses because this industry faces constant challenges from ever-changing external conditions, including the COVID pandemic, the rise of short-term rental platforms, and the breakthroughs in technology like Generative AI. It is important but challenging for hotels and short-term rentals to understand these conditions and plan their operations accordingly. Thus, this dissertation aims to help business operators to understand how to deal with different external changes. It carries on a series of studies based on big data, using various analytical tools. This dissertation is composed of three studies. The first one finds that, generally, it is risker for short-term rental hosts to make one property similar to his/her other properties when the whole market declines. There are differences identified between functionality and aesthetics. Keeping the functionalities, such as WIFI and coffeemaker, consistent among multiple properties will make the property more likely to survive when the market grows but it increases the likelihood of failure when the market demand decreases. When deciding property aesthetics, like color or layout, it is risky to have properties similar to each other, no matter if the market demand grows or drops. The second study concludes that short-term rental hosts should decide the product design relative to their competitors from different scopes of areas. They are suggested to make their properties' interior design style different from their nearby competitors to gain high revenues, especially when there are more neighboring supplies managed by a large number of hosts. On the contrary, it is more beneficial to follow the general trend of properties located in the same city when deciding one property's aesthetic style. The third study guides hotels to apply Generative AI like ChatGPT to generate response to customer online reviews. It found that, to reply to online reviews with four- or five-star ratings, hotels should not use the default GPT model to increase the quality of customer communication. Instead, they need to use the professional OpenAI API and set the parameter called temperature to 2. However, when hotels reply to online reviews with lower star ratings, like one or two, there is no big difference between low and high temperatures (0 to 2). They can simply use the default model. In general, there are no one-size-for-all solutions to deal with external challenges. Hospitality operators are highly recommended to adjust their operations to fit different conditions.