**1. Introduction**

Tourist activity is one of the main sources of wealth in many areas. However, it also a ffects the environment, cultural resources, and the hosting population. Due to this, the World Tourism Organization (UNWTO) is urging di fferent governments to consider sustainability as a global goal.

The emergence of new 2.0 net collaborative economies has brought about an increase in the number of travelers and the intensification of mass tourism, induced by a change in paradigm on the tourist-housing sector in major cities around the world, due to the proliferation of tourist housing.

There does not seem to be consensus on the definition of the collaborative-economy concept [1]; neither the European legal system nor that of each of the member states seems to be able to solve the problems that could arise from these new forms of business [2]. Hence, the European Commission decided to publish the "European Agenda for the collaborative economy" in which recommendations were directed to national legislators to adapt the regulations of the member states to the new needs of the emerging market for a collaborative economy. The European Commission [3] defines a collaborative economy as "Business models in which activities are facilitated through collaborative platforms

that create an open market for the temporary use of goods or services often o ffered by individuals. In general, collaborative-economy transactions do not imply a change in ownership and can be done with or without profit". Within a collaborative economy, the services that have experienced the fastest growth have been those related to transport and accommodation, both being closely related to tourism. Regarding the accommodation sector, one can find modalities in which there is no compensation, such as "couch-surfing" or "warm showers" [4]; in others, such as "home-swapping" or "night-swapping", there is reciprocity between participants [5]. On the other hand, we find modalities in which monetary consideration is paid, which is the case with our study. This sector has already accounted for more than 50% of the total number of operations carried out in Europe in 2015 within the scope of the collaborative economy [6].

According to information provided by DataHippo [7], over 238,000 adverts on Airbnb, one of the most globally important collaborative-economy sites, colonizes cities and tourist areas around Spain. Madrid and Barcelona are at the top of the list, followed by accommodation adverted on the Mediterranean coastline and the Canary and Balearic archipelagos. This is a specialized market, where only 5% of property owners are professionals, and individuals with more than one property represent one-third of tourist-housing o ffers.

However, not all tourist increase has been positive in its entirety; there are critical movements of the recent tourist development and growth, which shows that this is a globally shared phenomenon. Some of these negative e ffects can be seen in issues such as Touristification and gentrification processes in Berlin [8,9], tensions due to socio-spatial transformations and touristification processes in the slums in Rio de Janeiro [10]; social unrest because of housing dispossession and the urban revalorization and touristification processes in Palma de Mallorca historical center [11]; the rising unrest and annoyance regarding the overcrowding and socio-spatial transformations in the center of Amsterdam [12,13]; the emergen<sup>t</sup> mobilization related to the impact of tourism on Paris, especially regarding the proliferation of tourism housing [14]; the so-called Airbnb syndrome in Reykjavik [15]; the riots against cruises because of the increase in cruise passengers [16] and the consultative referendum held in Venice; the protests carried out by Hong Kong citizens against Chinese tourists [17]; and the emergence of people resisting the use of the land and local resources in Goa, India [18].

In many tourist destinations, the debate has focused on wider analysis of urban and political processes, and existing forces favor a growing "politicization from the grassroots" [19]. It should be noted that, in the tourist landscape, it is not only a matter of draining resources but also the rupture of necessary conditions for tourist activity to be satisfactory for all involved agents. Thus, every destination, depending on its particularities, products, and services, has to be assessed considering their capacity to bear tourist pressure [20].

One of the most significant cases is how tourist housing is a ffecting the prices of the real-estate market. In Spain, the average housing-rent price has increased by 18.6% in the last five years, between 2013 and 2018, Barcelona being the city with the highest increase (47.5%), followed by Madrid (38%), according to real-estate agency Fotocasa [21]. Moreover, five provinces, Baleares, Las Palmas, Salamanca, Barcelona, and Madrid, have already reached their historical maximum in 2018, exceeding the figures in 2007. Henceforth, although there are barely surveys to confirm it, many sectors relate this increase in rent prices to the proliferation of tourist housing, which is also said to be accelerating urban-gentrification processes.

As can be observed, the tourist-housing phenomenon is not free from controversy. There is confrontation between social and economic agents in the cities, since there is no global legal regulation regarding this phenomenon; in the case of Spain, autonomous communities and city councils are the responsible institutions for launching various regulatory initiatives.

The lack of a model regulating the tourist-housing phenomenon might involve serious risks. Before such a situation, deciding agents need to be provided with a tool that enables them to diagnose the situation, so that they can sugges<sup>t</sup> initiatives to move towards a sustainable tourist model. It is necessary for them to analyze the concept of reception capacity that theoretically refers to the optimal

use of land pursuant to its sustainability. Gómez and Gómez [22] defines it as "an area's degree of adequacy or capacity for a certain activity, bearing in mind both how the environment meets its locational requirements and the e ffects of that activity on the environment," outlining the contribution by Canter [23–25], Clark and Bisset [26], Rau and Wooten [27], Hollick [28], and Lee [29,30], among others. To study reception capacity, di fferent authors have o ffered a scientific basis to techniques and procedures: Voogd [31], Janssens [32], Eastman et al. [33], Jankowski [34], Triantaphyllou [35], Roy [36], and Munda [37], and, in Spain, Romero [38], Barredo [39], Barba and Pomerol [40], Santos [41], Moreno [42,43], and Galacho and Arrebola [44]. In this sense, the bibliography highlighting multicriteria assessment techniques, combined with geographical information systems to evaluate an area's reception capacity on various topics, is extensive: Barredo and Bosque [45], Ocaña and Galacho [46], Bosque and Moreno [47], Gómez y Barredo [48], Molero et al. [49], Moreno and Buzai [50], and Galacho and Arrebola [44].

To face the issue of the development of tourist housing, the present work's objective is to o ffer a methodology supported by multicriteria decision methods in the field of geographical information systems, that enables us to assess tourist-housing reception capacity in Cordoba (Spain) based on tourist-sustainability criteria. Cordoba is a city with four UNESCO World Heritage Sites, with a grea<sup>t</sup> tourist claim, and with important threats and weaknesses regarding tourist housing according to a study carried out by the Council of Cordoba [51].

According to Galacho and Ocaña [46], "the advantage of the combined use of multicriteria decision methods and geographical information systems is the possibility of rigorously solving the interrelation between the di fferent variables of the area". As a result, we obtained an information layer about the city's central district that classifies every neighborhood based on an assigned rating according to value judgments. These judgments were defined following the guidelines set by the World Tourism Organization regarding issues that must be considered when planning a destination under sustainability goals.

#### **2. Materials and Methodology**

To analyze the tourist-housing reception capacity of Cordoba, we used the analytic hierarchical process (AHP), developed by Tomas L. Saaty [52]. This is a tool to address the discrete multicriteria decision problems, consisting of di fferent criteria and a certain number of alternatives, considering the opinions of all the agents that intervene in the decision. The problem is displayed on a hierarchical structure that indicates the objective, criteria, subcriteria, and corresponding alternatives to then calculate the influence of every factor that is part of the problem. The resulting choice is then justified since it is based on the obtained numerical results, favoring the transparency and objectivity of the process.

The chart below represents the phases of the analytic hierarchical process (see Figure 1).

**Figure 1.** Phases of analytic hierarchical process. Source: Casañ [53].

#### *2.1. Determining Criteria, Subcriteria, and Alternatives*

According to the World Tourism Organization (UNTWO) [54], sustainable tourism is defined as the one that "meets the needs of present tourists and host regions while protecting and enhancing opportunities for the future. It is envisaged as leading to the managemen<sup>t</sup> of all resources in such a way that economic, social, and aesthetic needs can be fulfilled while maintaining cultural integrity, essential ecological processes, biological diversity, and life-support systems". To measure the degree of sustainability, the OECD [55] distinguishes two approaches, the accounting and the analytical; in our study, we opted for the analytical since it provides adequate multidimensional evaluation as a local planning tool according to the objective of our research. This instrument, according to this approach, is given by "a set of indicators of sustainable tourism, understanding as such the measures that provide the necessary information to better understand the links and impact of tourism with respect to the cultural and natural environment in which it develops activity and on which it is widely dependent" [56]. Therefore, to obtain an analytical measure of sustainability, it is necessary to disaggregate the sustainable-tourism objective by identifying the aspects that constitute each dimension, and identifying the indicators that allow measuring each of the above aspects. To ensure that their values show progress towards a more sustainable state, indicators must meet the criteria of scientific validity, representativeness, relevance, reliability, sensitivity, predictive nature, understandability, comparability, quantification, cost e fficiency, transparency, and geographical coverage [57]. Once the system was defined, we assigned the variables taking as reference specialized works that define sustainability indicators at the local level. Attending to the objective of our research and our area under study being the city of Cordoba (Spain), we took works as reference that defined a set of synthetic indicators of sustainable tourism for the tourist destinations of Andalusia (Spain): Blancas et al. [58]; Ávila et al. [59]; Dachary and Arnáiz [60]; Fullana and Ayuso [61]. For this, we developed a hierarchical structure with three levels (Figure 2). On the first level, the three main criteria (social, economic, and environmental dimension) are shown, each one defined based on new subcriteria corresponding to the second (13 subcriteria) and a third level (10 subcriteria), respectively. In the social dimension, issues related to the socio-cultural impact that tourist housing has on the environment, the resident population, and cultural heritage were collected; in the economic dimension, aspects related to tourism activity as economic activity and its viability are represented in the long term; finally, in the environmental-dimension criterion, aspects related to the protection and preservation of the environment, as well as the future viability of tourism, were considered.

**Figure 2.** Chart of criteria, subcriteria, and alternative hierarchies. Source: Information compiled from Blancas et al. [58]; Gallego and Moniche [62]; Sancho and García [63]; Bowen and Valenzuela [64].

The criteria and subcriteria obtained from the three previously mentioned dimensions were used to value the alternatives in the different neighborhoods in the central district of Cordoba (Figure 2). These are the possible approaches to the problem, although the choice does not imply that the chosen alternative is optimal to solve it, but the best among all available possibilities to reach the goal [53].

## *2.2. Determining Preferences*

To establish priorities, we needed to compare criteria, subcriteria, and alternatives in pairs. To do so, we made value judgments expressed numerically using Saaty's AHP fundamental scale [52]. This scale gives punctuations from 1 to 9, 1 being the same importance between two elements and 9 extreme importance of an element over the other. These value judgments were issued by a representation of different groups that are affected by the tourist-housing phenomenon, such as the public sector (public managers) and private sector (restaurant managers, taverns, souvenir shops, traditional commerce, resident residents, tourists, and neighborhood associations); through a total of 148 conducted interviews, nonprobabilistic sampling was carried out for convenience in the case of public officials, the private sector, and neighborhood associations, while for residents and tourists residents, simple random probabilistic sampling was followed. Subsequently, comparisons are represented through the paired-comparison matrix (Figure 3) that shows the dominant and dominated values. It is a square matrix *n x n*, in which *aij,* numerically expresses the preference of an element in the *i* row when compared with an element of the *j* column, for *i*= 1, 2, 3, ... *n* and *j*= 1, 2, 3, ... *n;* therefore, when *i* = *j*, the value of *aij* = 1, since the element is being compared to itself.

$$\mathbf{A} = \begin{pmatrix} 1 & a\_{12} & \cdots & a\_{1n} \\ a\_{21} & 1 & \cdots & a\_{2n} \\ \vdots & \vdots & \vdots & \vdots \\ a\_{n1} & a\_{n2} & \vdots & 1 \end{pmatrix}$$

**Figure 3.** Paired-comparison matrix.

This matrix is based on four axioms [65]: reciprocity: *aij* = 1/*aji*; homogeneity, since all compared elements must belong to the same hierarchical level; dependence, which means that there must be hierarchical dependence between elements from two consecutive levels; and consistency, meaning that, when the paired-comparison matrix is perfectly consistent, the following is fulfilled: *aij* = *aik*/*ajk* for *i, j* and *k* = 1, 2, 3 ... *n.*

Hereafter, we used an approximation method to obtain priorities from judgments given in the comparison matrix *n* × *n*. The first step was to procure the normalized matrix: we summed the values on every column and divided every box of the column by its summation:

$$C\_j = \sum\_{i=1}^{n} a\_{ij} \, j = 1, 2, 3 \dots n. \tag{1}$$

The normalized paired-comparison matrix is

$$\mathbf{N} = \left\| n\_{\mathbf{i}\mathbf{j}} = \mathbb{a}\_{\mathbf{i}}/\mathbb{c}\_{\mathbf{i}} \right\| \mathbf{i}\mathbf{j} = 1, 2, 3 \dots \text{n.} \tag{2}$$

Once we had the normalized matrix, we calculated the relative priority of each of the compared elements. We obtained an average value for every row in the normalized matrix, these values being

$$p\_i = \frac{1}{n} \sum\_{j=-1}^{n} n\_{ij}.\tag{3}$$

Since the hierarchy (Figure 2) is made of criteria and subcriteria, the three criteria's priorities were calculated according to the objective. Then, comparison matrices were made for each subcriterion, resulting in the relative priorities for each subcriterion on the second level. Those were multiplied by the corresponding criterion's priority to determine how it affects the objective. The process for the third-level subcriteria was the same. Afterward, to determine each alternative's priority, 20 relative comparisons matrices were made (corresponding to the 20 not-itemized subcriteria). Subsequently, aspects taken into account and data sources used for the pertinent survey are indicated (Table 1), and all of them properly georeferenced:



**Table 1.** Database used to evaluate each subcriterion.

> Source: Own elaboration.

QGIS software was used for treating georeferenced information. It was necessary to apply a spatial-disaggregation technique for the following layers of information: population retention, young population, aging population, social burden, and generated employment. Those layers have a 250 × 250 m square polygon vector format, so when assigning data to the territory subject of study, some polygons were divided. To do so, the areal-interpolation technique was used: information about the distribution values of a variable from an origin layer for a certain territory (in this analysis, demographic

spatial data in statistical enmeshes) was transferred to another layer of destiny information (territory subject of study) through their intersection. Then, the superficial proportion that each polygon on the origin layer had on the destiny layer was calculated to obtain the distribution of each variable in the new spatial units.

Afterward, we obtained each alternative's relative priority regarding the corresponding criterion or subcriterion; then, each alternative's general priority regarding the corresponding criterion or subcriterion was calculated by multiplying the relative priority by the compared criterion or subcriterion's general priority. Then, all priorities for each alternative were summed to obtain its priority regarding the objective [73]. Finally, the AHP allowed measuring the inconsistence of judgments through the consistency ratio, and they had to be revised and corrected. For 3 by 3 matrices, the value of the consistency ratio had to not be higher than 5%; in the case of 4 by 4 matrices, it would not exceed 9%; for all the other matrixes, it would be 10% or less [73]. The software used to carry out the analytic hierarchical process was Total Decision.

The result of the process is summarized in a layer of information that shows zoning of the studied area with a valuation assigned to every part of the territory depending on its capacity to accept the evaluated uses.

#### *2.3. Implementation on Urban Area*

The territory subject of study was Cordoba (Spain), a city whose four UNESCO World Heritage Sites have had increased mass tourism in the last few years, besides an unregulated increase in tourist accommodation. Out of the 10 total territorial districts that conform to the city of Cordoba, we chose the central district since it hosts the highest concentration of tourist housing, with 1456 tourist housing over a total of 24,457 built houses, that is, 5.95% [51]. Here (Figure 4), the distribution of tourist housing for each neighborhood in the central district is shown:

**Figure 4.** Tourist housing per neighborhood in the central-district map. Source: Own elaboration.

There are eight neighborhoods over the tourist-housing average (6.02%), such as the neighborhoods of La Catedral, San Francisco-Ribera, El Salvador y la Compañia, and San Pedro, which exceed 10% of tourist housing. There are also ten neighborhoods under the average, such as Cerro de la Golondrina, Ollerías, and El Carmen, which do not reach 1%.

According to a recent study carried out by the Council of Cordoba [51] on the effects that tourist housing has on the city of Córdoba, the city has the following threats and weaknesses: Regarding threats, there is a gradual loss of population and the substitution of residential use for other uses, weakening of traditional commerce, saturation of public spaces, and coexistence deterioration, detraction of housing from the rental market, and price increase, and deterioration of cultural tourism. With respect to weaknesses, there is a lack of knowledge about existing tourist homes and clandestinity in the activity of some caused due to the autonomous regulatory framework, the absence of municipal regulation of housing for tourism purposes, the existence of empty buildings, and dizzying growth in the supply of housing for tourism purposes.
