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A Tool to Nowcast Tourist Overnight Stays with Payment Data and Complementary Indicators

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Abstract

This paper proposes a strategy for nowcasting tourist overnight stays in Italy by exploiting payment card data and Google Search indices. The strategy is applied to national and regional overnight stays at a time of a significant and unanticipated shock to tourism flows and payment habits (the COVID-19 pandemic). Our results show that indicators based on payment data are very informative for predicting tourist volumes, both at the national and at the regional level. Instead, the predictive power of Google Search data is more limited.

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Notes

  1. Other estimates produced by Istat (2020) indicate that the direct and indirect contribution to the value added are around 6 and 15 per cent, respectively.

  2. Examples include publicly financed advertisement campaigns or local transport organizations. More in general, following the constitutional reform of 2001, the regional administrations have seen an extension of their powers on many tourism issues.

  3. This means that until June of a given year, the regional data are only available until December of two years before.

  4. According to the psychological literature, people travel because they are “pushed” into making travel decisions by internal, psychological forces, and “pulled” by the external forces of the destination attributes (Yoon and Uysal 2005).

  5. GT series have been used in many other fields. Examples are: prediction of unemployment (D’Amuri and Marcucci 2017; Askitas et al. 2009, for USA and Germany respectively), trends in the housing market (Wu and Brynjolfsson 2015; Webb 2009); consumer confidence (Della Penna and Huang 2009).

  6. Overnight stays are the product of the number of the arrivals and the number of nights spent per each tourist arrived. In the estimations, we do not consider arrivals, but we focus exclusively on overnight stays. Over the short run, the dynamic of the two statistics is very similar; over the last ten years, arrivals have grown more because the average number of nights spent per tourist has declined sharply.

  7. Regulated by the “Testo unico di Pubblica Sicurezza” (art.109).

  8. Due according to the Regulation EU 692/2011.

  9. The product categories are: (1) clothing, (2) hotels and restaurants, (3) food, (4) home, (5) cash advance, (6) work, (7) retail, (8) services, (9) telephony, (10) travel and transport, (11) not defined.

  10. In 2021, Google Trends had around 92 per cent of the worldwide search engine marked share (Statcounter 2017).

  11. More precisely, the counts, available on a monthly basis, are reported only if exceeding an unknown threshold based on the geographical location, and are measured with a 0–100 index (normalized on the chosen time window). Importantly, the series generated by GT does not provide absolute numbers of searches, but a relative frequency of them. They represent the popularity of the searches for a keyword with respect to the total searches in the geographical area and time period selected, measured in relative terms.

  12. The classification into categories is done by an algorithm set-up by Google about which Google does not release details.

  13. For instance, for Veneto, we consider the top-10 destinations specified in the following webpage: https://www.tripadvisor.it/Tourism-g187866-Veneto-Vacations.html.

  14. In each case we use the scree plot to select the number of PCs, which, as a consequence, may differ according to the area of interest.

  15. Provisional data are delivered from Istat to Eurostat within 56 days from the end of the previous month. Eurostat publishes the data some days after, but they are then revised within four months: at that date, the statistics are published by Istat, too.

  16. Unfortunately, as already pointed out, data on payments are not available before 2014.

  17. Note that, by definition, \(Ly_t=y_{t-1}\), and \(L^my_t=y_{t-m}\).

  18. It assumes that future values of the target linearly depend on its past values and on the values of past (stochastic) shocks.

  19. The reason why step (A) considers as target the time series of the shares, in place of the more natural one in levels, is that we do not want to use \(y_{t,IT}\) as exogenous variable in the procedure, because it is too correlated with the \(y_{t,r}\). As a consequence, the impact of the other covariates would be artificially smaller. With this model instead, we are able to capture both the correlation between \(y_{t,r}\) and \(y_{t,IT}\), and the residual correlation between \(y_{t,r}\) and the other regressors.

  20. The number of principal components to include in the model is selected for each different year and region, visually, by detecting an elbow in the scree plot. Contrary to the other variables, the principal components of the GT series are included in levels, because GT series are provided by Google as indices and can not be recalculated in regional over national shares.

  21. The algorithm combines unit root tests, minimization of the Akaike Information Criterion (AIC) and MLE to obtain an ARIMA model.

  22. Recall that in the regional exercise we consider the months from June to September included. In the national case we exclude June, because official data for that month are already available in October of the same year.

  23. This monthly index corresponds to the total amount of acquiring transactions (made on a Pago Italian POS) in the tourist industry categories (hotel and restaurant plus travel and transport) made by cards issued by Italian banks.

  24. This corresponds to the total amount of acquiring transactions (made on a Pago Italian POS) in the tourist industry categories (hotel and restaurant plus travel and transport) made by cards issued by any national or international bank.

  25. This model is equivalent to including both Pago Ita and Pago foreigners = Pago all-Pago Ita.

  26. In practice, this happens because the total number of payments by cards issued by foreign banks is more stable in the years considered, while the payments by Italian-issued cards show a positive trend (data not shown for confidentiality reasons).

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Authors and Affiliations

  1. Bank of Italy, Via Nazionale 91, 00184, Rome, Italy

    Marta Crispino

  2. Bank of Italy, Corso Cavour, 5, 70121, Bari, Italy

    Vincenzo Mariani

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  1. Marta Crispino
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Correspondence to Marta Crispino.

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The views expressed in this paper are those of the authors and do not involve the responsibility of the Bank of Italy and/or the Eurosystem. We thank Matteo Alpino, Valentina Aprigliano, Laura Bartiloro, Andrea Carboni, Costanza Catalano, Andrea Doria, Simone Emiliozzi, Silvia Fabiani, Sara Lamboglia, Michele Loberto, Juri Marcucci, Alessandro Moro and Alfonso Rosolia for fruitful discussions and suggestions. We would also like to thank Marco Langiulli and Luca Bastianelli for providing useful information about the payment card data.

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Crispino, M., Mariani, V. A Tool to Nowcast Tourist Overnight Stays with Payment Data and Complementary Indicators. Ital Econ J (2024). https://doi.org/10.1007/s40797-024-00266-6

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