Concluding remarks and policy implications

Despite the fact that the role of tourism on local economic growth is widely investigated in the current tourism literature, the effect of tourism on the housing market has been understudied. The majority of existing research is based on US evidence and performs cross-section analysis neglecting the possible dynamics of the tourism–house price relationship. Contrariwise, knowing the average effect of tourism on the housing market at the destination sites is crucial for urban policies and requires careful monitoring. On the one hand, a positive linkage between tourism and house prices can be considered a supplementary way to boost local economies; however, it can generate socio-economic problems of affordability and displacement of the resident population. On the other hand, a negative relationship can be considered as an indication that the presence of tourism activity generates some sort of negative externalities.

The purpose of the present analysis is to analyze whether and to what extent tourism activity (the tourism market) affects urban house prices in 103 Italian cities. It is used a System GMM approach for the time span of 1996–2007. After controlling for characteristics of the local housing markets, amenities, geographical variables and urban size, tests for the effect of tourism are performed by employing a composite index that captures the tourism specialization of each area under analysis.

Results are robust and confirm that overall and for the case of Italy, tourism has a positive and significant effect on house price levels. By comparing the city center, suburban and peripheral locations no great variations of these effects are found. The positive link between tourism and house prices in Italy needs to be interpreted cautiously because cities in Italy are very different. Further

investigation on this direction has given several hints on the existence of potential different tourism-house price relationships for group/types of cities.

Present findings induce the possibility for further research on the form of these effects. A possible extension of the present work is to see whether and to what extent this relationship is positive, negative or even not significant for the cities under investigation. This specific analysis requires the use of other types of estimators such as, for instance, the mixture models that search for different regimes in the relationships under analysis. Further development of the present work is to investigate whether and to what extent this relationship holds also for other tourism countries.

In terms of the policy implications, on the one side, results confirm that on average tourism is important for local economic growth of Italian cities; however, on the other side, there is a delicate environmental and social equilibrium in tourist destinations, which can easily be upset. In other words, from a strict economic point of view, the higher value of housing in tourism destinations can be observed as a positive signal of tourism–related local growth and the presence of natural, cultural and man-made amenities. However, to correctly evaluate the net overall benefits of the resource allocation in the tourism sector, it is essential to determine who benefits and who pays (Pearce 1989) for local tourism development (Butler 1980). Problems may arise when the pressure on house prices is such that it creates serious social effects in terms of affordability, displacement, and gentrification.

APPENDIX

Tourist indexes

In the present analysis, the choice of the Van der Waerden ranking score (see subsection 4.4.2) is based on the previous works by Biagi et al. (2012) and Biagi and Faggian (2004). Others tourist indexes exist, which have been used in economic literature for descriptive purposes. In order to provide a detailed view of these indexes, it is produced a list with a brief description and the source of these tools available for tourism analysis.

 Defert’s Tf (tourist function) by Defert (1967). It is a measure of the importance of tourism in a regional economy. The index is computed as a ratio between the number of tourist beds (N) and the resident population (P):

𝑇𝑓 = 100(𝑁) 𝑃⁄ (A.4.1)

According to Vaccaro (2007), the higher the index the higher the connection between resident population and tourists. High value of Tf

implies the dependence of resident population by the tourism economy. For Tf>100 tourists can be more than resident population in the area. But, as emphasized by Smith (1995), this index could be used with caution, because very large cities such as Paris or Mexico City will have a small Tf with respect to small resort towns. Nevertheless tourism sector in Paris is not unimportant.

Source: P. Defert (1967), Le taux de fonction touristique: mise au point et critique. Les cahiers du tourisme. Centre des Hautes Etudes Touristiques, Aix-en-Provence, C-13.

 Tourist function for hotels by ISTAT. It is computed as the Tf (A.4.1), but only for beds in the hotels.

Source: P. Innocenti (2007), Geografia del turismo, Carocci.

 Tourist function for other accommodation by ISTAT. It is computed as the Tf (A.4.1),but only for beds in other accommodation.

 Attractiveness of tourist consumption by ISTAT. It is computed as a ratio between tourist nights of stay (or overnights stay; O) in total tourist accommodation and resident population (P):

𝐴 = 𝑂/𝑃 (A.4.2)

This index could be also called tourist rate and represents the level of crowding in a tourist destination in the period under analysis.

Source: ISTAT.

 Gross occupancy rate of bed-places by ISTAT. It is obtained by dividing the total number of nights of stay (P) by the number of the bed places on offer and the number of days:

𝑈𝑙 = 𝑃 𝐿𝐺⁄ ∗ 100 (A.4.3)

where:

P=nights of stay in tourist official accommodation

L=beds in tourist official accommodation

G=number of days in the period under analysis

Source: ISTAT.

 Net occupancy rate of bed-places by ISTAT. It is obtained by dividing the total number of nights of stay by the number of the bed places on offer and the number of days when the bed places are actually available for use (net of seasonal closures and other temporary closures for decoration, by police order, etc.).

Source: ISTAT.

 Density of accommodation establishments by Italian Ministry of Tourism. Along with the Tf index, this is considered an important indicator able to evaluate the impact of tourism in the destinations. In addition, it allows one to compare different tourist destinations, such as coastal and mountain locations. The computation is based on ISTAT data at municipality level and it is the ratio between tourist nights of stay (O) and municipality surface:

𝐷 = 𝑂/𝐾𝑚2 _(A.4.4)

Source:Osservatorio nazionale del turismo.

 Composite rate of accommodation function by Vaccaro (2007). It is called Tr and is computed as a ratio between the number of beds (L) in

total accommodation establishments and the resident population (Pop) times the surface (Sur) in Km2:

𝑇𝑟 = (𝐿 𝑃𝑜𝑝 ∗ 𝑆𝑢𝑟)⁄ ∗ 100 ∗ 100 (A.4.5) This index represents the density of the tourism sector supply in respect of the area and the population. The higher the Tr, the higher the exploitation of resources in the area. Values too high of Tr could indicate the saturation level of the area.

Source: Vaccaro G. (2007), La statistica applicata al turismo, Hoepli Editore, Milano.

 Jan O. Lundgren rate by Lundgren (1966). It indicates the tourist attitude of an area as a ratio between resident population and number of tourist accommodations. As a result the area will be more tourist when the index has the lower value.

Source: Jan O. Lundgren (1966), Tourism en Quebec, in Rev. De Géogr. De Montreal, 20(2):59-73.

 Residential tourist function rate by Barbier (1965). It is the ratio between the number of residential houses and the total number of occupied house in an area.

Source: B. Barbier (1965), Méthodes d’études des résidences secondaires. L’exemple des Basses-Alpes, in “Mèditerranée” 2: 89-115.

 Second homes index by C. Commerçon (1973). It is the ratio between the number of non-occupied houses and used as holidays homes and the total number of houses in an area.

Source: C. Commerçon (1973), Le residances secondaires du Maconnais: essai d’etude quantitative, in “Revue de Geogr. E Lyon”, XLIII, 4: 332.

 Touristic affluence spatial index by J. P. Lozato-Giotart (1985). It is the spatial index of tourist flows and it computed as a ratio between the number of tourist accommodation and the area surface in Km2.

Source: Jean-Pierre Lozato-Giotart (1985), Géographie du tourisme. De l’éspace régardé à l’éspace consommé, Collection Géogr., Masson, Paris. The indexes described in this appendix are the most important tools used in descriptive tourism analysis, but they are not the unique. Among these do not cited in this work, there are the peaking index, the directional bias index and the

tourism attractiveness index, developed specifically for tourism and recreation. While others, are more famous in social sciences and geography.108

Maria Giovanna Brandano 114

CHAPTER V