Ulrich Gunter

Forecasting tourism demand with Google Trends for a major European city destination.

The purpose of this study is to investigate whether using Google Trends indices for web and image search improves tourism demand forecast accuracy relative to a purely autoregressive baseline model. To this end, Vienna—one of the top-10 European city destinations—is chosen as a case example for which the predictive power of Google Trends is evaluated at the total demand and at the source market levels. The effect of the search query language on predictability of arrivals is considered, and differences between seasonal and seasonally adjusted data are investigated. The results confirm that the forecast accuracy is improved when Google Trends data are included across source markets and forecast horizons for seasonal and seasonally adjusted data, leaning toward native language searches. This outperformance not only holds relative to purely autoregressive baseline specifications but also relative to time-series models such as Holt–Winters and naive benchmarks, in which the latter are significantly outperformed on a regular basis.

The decline of tourism income elasticities in a global context

Based on the standard tourism demand model for quarterly tourism exports of six different world regions and their total, the authors applied a panel econometric approach to measure potential differences in income elasticities due to the medium-term speed of growth of the world economy. The evidence demonstrated that, related to the identified different growth periods, income elasticities showed significant variations. For 1977–1992, it was possible to measure the highest income elasticities of all periods. For 1994–2003 and 2004–2013, the income elasticities decreased from period to period. For the last decade, the values of the income elasticities were lower than 1. The reasons for the decline in the income elasticities from the first to the second period were the ongoing saturation process and the slowing down of economic growth resulting in a change in consumer behaviour. The decline in income elasticities from the second to the third period was mainly due to the dramatic deterioration in the economic environment contributing to higher uncertainty about the future, with the result that precautionary saving increased, liquidity constraints limited expenditures on luxuries in favour of necessities and tourists preferred domestic destinations instead of going abroad.

Determinants of Airbnb demand in Vienna and their implications for the traditional accommodation industry

This study identifies key determinants of Airbnb demand and quantifies their marginal contributions in terms of demand elasticities. A comprehensive cross-sectional data set of all Viennese Airbnb listings that were active between July 2015 and June 2016 is examined. Estimation results, which are obtained by cluster-robust ordinary least squares, show that Airbnb demand in Vienna is price-inelastic. Significant positive drivers include listing size, number of photos, and responsiveness of the host. Significant negative drivers include listing price, distance from the city center, and response time of the host. Implications for the traditional accommodation industry are that, on the one hand, it should better communicate its sought-after advantages (e.g. lower average minimum duration of stay). On the other hand, it should increase its offer of bigger and better equipped hotel rooms since hosting more than two guests at a time is one of the major benefits of Airbnb.

International Ecotourism and Economic Development in Central America and the Caribbean

Using annual data for the period 1995-2012 for seven Central American and Caribbean countries, six different open-economy growth models that allow for international (eco-) tourism are estimated using panel-data estimation techniques. The main result of the investigation is that not only international tourist arrivals per capita have a highly significant impact on real GDP per capita but also that five different sustainability indicators interacted with international tourism have a positive impact on economic development in addition to international tourism. Furthermore, quantile regression shows that lower and medium income deciles in particular benefit most from international (eco-) tourism. The results are complemented by very similar estimation results for a set of 12 Central American and Caribbean countries using two sustainability indicators only, thus corroborating the validity of the specification. In addition, control variables are also generally significant and feature the algebraic signs as expected from economic theory.

Contribution of international ecotourism to comprehensive economic development and convergence in the Central American and Caribbean region

ABSTRACT Drawing on the positive experience from Costa Rica, the study examines whether international ecotourism makes a significant contribution to comprehensive economic development for the Central American and Caribbean region and contributes to comprehensive economic convergence. Following a standard empirical growth model, a dynamic panel regression model is estimated using time-series data from 1995 until 2012 for a cross section of seven countries. The interaction of international tourism and various established sustainability indicators is employed allowing ecotourism to be consistently quantified across countries, while numerous country-specific structural characteristics are controlled for. The estimation results show that international ecotourism has a statistically significant positive effect on both traditional economic development (real GDP per capita) and comprehensive economic development (adjusted net savings; ANS per capita), which is a measure of a society’s potential future well-being, thus providing evidence in support of the tourism-led growth hypothesis and pointing towards an important role for ecotourism in driving comprehensive economic convergence.

Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria

Using data for the period 2010M06–2017M02, this study investigates the possibility of predicting total tourist arrivals to four Austrian cities (Graz, Innsbruck, Salzburg, and Vienna) from LIKES of posts on the Facebook pages of the destination management organizations of these cities. Google Trends data are also incorporated in investigating whether forecast models with LIKES and/or with Google Trends deliver more accurate forecasts. To capture the dynamics in the data, the autoregressive distributed lag (ADL) model class is employed. Taking into account the daily frequency of the original LIKES data, the mixed data sampling (MIDAS) model class is employed as well. While time-series benchmarks from the naive, error–trend–seasonal, and autoregressive moving average model classes perform best for Graz and Innsbruck across forecast horizons and forecast accuracy measures, ADL models incorporating only LIKES or both LIKES and Google Trends generally outperform their competitors for Salzburg. For Vienna, the MIDAS model including both LIKES and Google Trends produces the smallest forecast accuracy measure values for most forecast horizons.

Spatial price dependencies between the traditional accommodation sector and the sharing economy

The emergence of peer-to-peer (P2P) accommodation (e.g. Airbnb) has steadily increased the pressure on the traditional accommodation sector. Although Airbnb listings are perceived as being more affordable than hotels, this has not yet been conclusively demonstrated. Therefore, the aim of this study is to investigate whether significant price dependencies exist between the Airbnb and traditional accommodation sectors and to analyze the underlying pricing strategies. For this purpose, the Estonian capital city of Tallinn is used as a case example. Airbnb data, prices and locations of hotels in Tallinn, as well as spatial information such as distance to points of interest (POIs), and so on, are used in hedonic price regression models. The results show that Airbnb pricing positively depends on characteristics of the listing and the number of POIs within an optimal 650 m radius, which is obtained from a simulation study. Also, prices of hotels and of other Airbnb listings within the same radius positively impact Airbnb listing prices. Finally, Airbnb accommodations are shown to indeed be the more affordable alternative.

Conditional forecasts of tourism exports and tourism export prices of the EU-15 within a global vector autoregression framework

Purpose The purpose of this paper is to analyze the ex ante projected future trajectories of real tourism exports and relative tourism export prices of the EU-15, conditional on expert real gross domestic product growth forecasts for the global economy provided by the Organisation for Economic Co-operation and Development for the years 2013-2017. Design/methodology/approach To this end, the global vector autoregression (GVAR) framework is applied to a comprehensive panel data set ranging from 1994Q1 to 2013Q3 for a cross-section of 45 countries. This approach allows for interdependencies between countries that are assumed to be equally affected by common global developments. Findings In line with economic theory, growing global tourist income combined with decreasing relative destination price ensures, in general, increasing tourism demand for the politically and macroeconomically distressed EU-15. However, the conditional forecast increases in tourism demand are under-proportional for some EU-15 member countries. Practical implications Rather than simply relying on increases in tourist income, the low price competitiveness of the EU-15 member countries should also be addressed by tourism planners and developers in order to counter the rising competition for global market shares and ensure future tourism export earnings. Originality/value One major contribution of this research is that it applies the novel GVAR framework to a research question in tourism demand analysis and forecasting. Furthermore, the analysis of the ex ante conditionally projected future trajectories of real tourism exports and relative tourism export prices of the EU-15 is a novel aspect in the tourism literature since conditional forecasting has rarely been performed in this discipline to date, in particular, in combination with ex ante forecasting.

How Do Macroeconomic and Bank-specific Variables Influence Profitability in the Austrian Banking Sector? Evidence from a Panel Vector Autoregression Analysis

We examine the determinants of the net interest margin (NIM) and the net fee and commission income ratio (NFCIR) of Austrian banks as well as their interrelationship and whether portfolio separation between loan and deposit categories holds. We describe a conceptual framework for the profit optimization problem faced by banks as a Bertrand game with differentiated products and intrafirm product interactions. We contribute to the literature by factoring in banks’ business models in terms of their balance sheet structure. We empirically assess the implications of our conceptual framework using a unique supervisory data set of around 48,000 observations between 1998 and 2014. We estimate two panel vector autoregression models with a novel panel vector autoregression code. Apart from quantifying the contributions of the determinants (e.g., risk weighted assets, leverage ratio, loan loss provision ratio) to NIM and NFCIR, the empirical results show that interest income and fee and commission income should be regarded as strategic complements within a bank. We further conclude that portfolio separation between different loan and deposit categories does not hold.

International travel to and from Brazil – Overseas tourism as a luxury good and a status symbol

Using data for the period 1995Q1–2012Q4, single-equation error correction models for real tourism import demand to Brazil and real tourism export demand from Brazil are derived. According to breakpoint tests, two periods (pre-2003 and post-2003) have to be distinguished. While international travel to and from Brazil can be seen as a luxury good for the whole period, low economic growth rates at the global level and the real appreciation of the Brazilian currency “real” from 2003 onward have led to stagnating real tourism exports and decreases in long-term income and price elasticities. However, high economic growth rates in Brazil and the real appreciation from 2003 onward have led to strongly growing real tourism imports and increases in long-term income and price elasticities. In line with international travel being regarded as a status symbol in Brazil, a Veblen effect of conspicuous consumption can be confirmed for the post-2003 period.