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on Tourism Economics |
| By: | Elham Ghabouli; Carlos Marmolejo Duarte |
| Abstract: | Second home tourism has been a primary cause of real estate development of the Spanish coasts since the 1970s. Historically, Northern Europeans have been the driving force influencing the "second home residential tourism" market in Spain, which attracts thousands of foreign customers per year. Spain’s coastal region has undergone dramatic development due to this demand and in recent has become famous for being a prime destination for international retirees who tend to have their own specific demands and requirements when looking to purchase property abroad. Taking into account that the "baby-boomer" generation is poised for retirement within the next decade, Europe and its planners will need to develop proper responses through guidelines and planning of its territory, housing, and development, particularly those along the Mediterranean Sea. As a result, this research seeks to evaluate the territorial, cultural and natural attributes that underpin the decisions of the target group in case they search for a second home. The study area selected is situated along the Costa Brava on the Mediterranean Sea in the northeastern part of Spain, meanwhile the methodology consists of a 2016 self-administered questionnaire involving 191 European tourists, of which most were over the age of 50. A set of attributes were provided and categorized into the three categories mentioned previously, with a Likert Scale related to levels of importance. At first, the result’s rank was based on the attributes´ average weighted score. The top three considerations influencing the participant’s selection were cultural offerings including friendly people, security and cultural diversity, following by two of built environment attributes of proximity to shopping and well-maintained streets and sidewalks, and the natural environment attributes. Having evaluated the participants stated choices, further statistical differences were analyzed using a Pearson Chi-Square test which highlights the degree to which the specific stated characteristics of the participants influence their choices; for instance, the presence of a mild winter is more important for this group than others, which is further supported by previous studies. The final step is the use of a logistic regression model to ascertain the effects of the territorial, natural and cultural factors on the likelihood that participants choose a second home on the Costa Brava. |
| Keywords: | Costa Brava; Regional Evaluation; Retirement Migration; Second Home; Spanish Mediterranean Coasts |
| JEL: | R3 |
| Date: | 2017–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2017_80&r=tur |
| By: | Dănilă, Daniela Ileana |
| Abstract: | This paper aims to analyze the data regarding the cooperation programs supporting the development of agro-tourism of Romania. The data were taken from the Ministry of Agriculture and Rural Development and were processed according to the objectives of the paper, namely the allocation of European funds for the period 2007-2013 for Measure 3.1.3. - "Encouraging tourism activities. To achieve this it was necessary to analyze the projects submitted, the projects selected and contracted. The aim of the paper is to develop tourism activities in rural areas that will help to increase the number of jobs and alternative incomes, as well as to increase the attractiveness of the rural area. This measure aimed at investing in rural areas, namely: investing in infrastructure in areas with tourism potential, investing in recreational activities, investing in infrastructure, investing in information centers, investing in tourist marking development, development and marketing of tourist services as a part an integral part of rural tourism. |
| Keywords: | agritourism, cooperation programs, investments |
| JEL: | O11 Q13 |
| Date: | 2017–11–16 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:85372&r=tur |
| By: | Ciobanu (Rădoi), Eugenia - Dorina; Draghici, Manea |
| Abstract: | In this article we wanted to identify, analyze and present the evolution in the last years of the tourism and agrotourism on an European and national level. In order to do that, the following indicators have been analyzed: the evolution and structure of international arrivals of tourists around the globe, the evolution and structure of international tourism encash, the evolution and share of tourist accommodation in Romania, the evolution and share of the accomodation capacity in Romania, the evolution of arrivals in accomodation units from Romania and the evolution of overnight stays in accomodation units from Romania. Following this analysis that is presented in the article in the rows below, we come to the conclusion that the tourism and agrotourism, on an European and national level, has had a constant growth in the last 5 years, thus representing an important branch with possibilities to develop economy, generating profit. |
| Keywords: | tourism, agrotourism, boarding |
| JEL: | O52 Q11 |
| Date: | 2017–11–16 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:85370&r=tur |
| By: | Oscar Claveria (AQR-IREA, University of Barcelona); Enric Monte (Polytechnic University of Catalunya (UPC)); Salvador Torra (Riskcenter-IREA, University of Barcelona) |
| Abstract: | In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon. |
| Keywords: | STL decomposition; non-parametric regression; time series features; forecast accuracy; machine learning; tourism demand; regional analysis JEL classification: C45; C51; C53; C63; E27; L83 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:aqr:wpaper:201802&r=tur |