Predicting Lodging Demand Trends in the U.S. Hotel Industry

O’Neill, John W.; Ouyang, Yuxia

doi:10.1177/1938965520916443

Cited by 8 publications

(7 citation statements)

References 37 publications

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“…There is extensive literature that discussed and empirically investigated how the economic status and financial market condition impact the tourism and hotel market demand and performance. In a recent study, O’Neill and Ouyang (2020) analyzed and ranked the predictive ability of various economic variables. They identified GDPI (Gross Domestic Private Investment, the portion of GDP generated by private business investment, also known as GPDI) as having the strongest predictive ability.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, we included GDPI to control for the state of the economy in our robust analysis. In addition, we used SP500 (S&P 500 index) to control for financial market conditions (SP500 was also an outperforming factor in the three-factor model documented in O’Neill & Ouyang, 2020). It showed that both GPDI and SP500 were significantly associated with the hotel cash flow risk.…”

Section: Resultsmentioning

confidence: 99%

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Do Brand-Affiliated Hotels Have Lower Cash-Flow Risk?

Liu

O’Neill

2022

Cornell Hospitality Quarterly

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Hotels are generally perceived as the riskiest type of commercial real estate (CRE) investment because hotel “leases” have relatively high turnover. Existing literature regarding CRE investment risk and return lacks investigation of hotels at the unit level—which is the level of analysis undertaken by existing and prospective hotel investors. Two major types of hotels are branded and independent ones. The purpose of this study is to investigate the variability (risk) of key performance indicators (KPIs) such as occupancy rate, and revenues and profit of branded versus independent hotels. Using a large sample of performance data regarding over 4,000 U.S. hotel properties from 2000 to 2019, we examine the extent to which branding affects the volatility of KPIs. We find that brand-affiliated hotels have lower cash flow risk measured as lower volatilities of KPIs compared with independent ones. Furthermore, the level of volatility reduction of branded hotels is greater for profit than for revenue, and profit may be the most important KPI for hotel investors. The magnitude of volatility reduction also increases as the measurement window length (number of years) increases. We also study the long-term returns of branded versus independent hotels. This study contributes to the understanding regarding the relationships between investment risk of branded versus independent hotels, extends the literature regarding hotel investment, and provides hotel investors and analysts information regarding risk to aid decisions such as developing, purchasing, holding, or disposing of hotel assets.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Do Brand-Affiliated Hotels Have Lower Cash-Flow Risk?

Liu

O’Neill

2022

Cornell Hospitality Quarterly

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“…Apart from developing new models, integrating other external factors that affect hotel demand into the model is also an effective way to improve forecasting accuracy. Existing external factors often considered in forecasting models include quarterly gross domestic product (GDP) (O’Neill and Ouyang, 2020), income levels (Song et al , 2011), location (Zheng et al , 2020), weather information (Pan and Yang, 2017), website traffic (Yang et al , 2014) and rare events (Kamola and Arabas, 2020). With the growth of online booking websites, advanced booking data play an important role in improving the accuracy of forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Daily hotel demand forecasting with spatiotemporal features

Huang

Zheng

2022

IJCHM

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Purpose Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region. Design/methodology/approach For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units. Findings The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period). Practical implications From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region. Originality/value The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

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“…Another avenue for future research involves further incorporating relevant 3 rd party-generated economic indices into forecasting models used to establish KPI goals. O'Neill and Ouyang (2020), for example, find that gross private domestic investment (GPDI) is a potentially effective predictor of lodging demand. GPDI is the portion of GDP generated by private business investment (O'Neill and Ouyang, 2020, p. 240).…”

Section: Conclusion and An Agenda For Future Researchmentioning

confidence: 99%

“…O'Neill and Ouyang (2020), for example, find that gross private domestic investment (GPDI) is a potentially effective predictor of lodging demand. GPDI is the portion of GDP generated by private business investment (O'Neill and Ouyang, 2020, p. 240). Within the context of this paper, if 3rd party-generated forecasts or prognostications of such economic indices gauge the likelihood of events such as the release of an effective COVID-19 vaccine, then such indices can help inform KPI goals.…”

Section: Conclusion and An Agenda For Future Researchmentioning

confidence: 99%

The increased importance of competitor benchmarking as a strategic management tool during COVID-19 recovery

Magnini

Crotts

Calvert

2020

IHR

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PurposeWhile all recoveries are good, some are better than others with regard to their speed and/or magnitude. Many revenue-related key performance indicators (KPIs), such as comparisons to budgets and forecasts that were designed pre-pandemic to assess a hotel's or destination's performance are no longer valid. Therefore, the primary purpose of this conceptual paper is to highlight the need to peg financial-related KPIs relative to competitors' performance during and following a radical market disruption. The secondary purpose of this paper is to summarize advances reported in the literature and in the industry related to competitor benchmarking and accurately identifying competitor sets.Design/methodology/approachThis conceptual paper synthesizes research from disparate sources to offer a series of recommendations to the industry regarding best practices for developing and monitoring revenue-related KPIs during pandemic recovery. Such KPIs will be different based upon hospitality or tourism sector but must be largely founded upon benchmarking off comparable operations.FindingsIndustry disruptions triggered by COVID-19 underscore the need (1) to increasingly utilize competitor-based revenue KPI benchmarks; (2) to have reliable competitor benchmarking data more readily available for use by hotels and destination marketing organizations (DMOs) and (3) for both hotels and DMOs to more accurately identify their competitive sets.Originality/valueThe recommendations offered in this paper are anchored with appropriate theories and empirical research; and as a consequence, offer guidance for the industry for KPI formulation during and following the pandemic.

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Predicting Lodging Demand Trends in the U.S. Hotel Industry

Cited by 8 publications

References 37 publications

Do Brand-Affiliated Hotels Have Lower Cash-Flow Risk?

Do Brand-Affiliated Hotels Have Lower Cash-Flow Risk?

Daily hotel demand forecasting with spatiotemporal features

The increased importance of competitor benchmarking as a strategic management tool during COVID-19 recovery

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