*2.3. Seville and Porto Vacation Rental Overview*

The number of registered holiday rentals has grown significantly in Seville and Porto (see Figure 1). Starting in January 2017, the public was given access to data from official HR records in Seville<sup>8</sup> . Both cities present quite similar trends in holiday rentals' growth, with a more pronounced increase as of January 2018.

Porto has significantly more holiday rental facilities than Seville does. According to Portugal's National Institute of Statistics (INE-PT)<sup>9</sup> , the latest official data on Porto's total population set the total of residents at 222,252 in 2013 (RNT (Registo Nacional de Turismo) 2019). In contrast, Spain's National Institute of Statistics (INE–ES10) reported that Seville had a total population of 688,711 in January 2018 (INE-ES (Instituto Nacional de Estadística) 2018). Thus, by March 2019, Porto would have had a ratio of approximately one HR for every 35 inhabitants as compared to 167 inhabitants for each HR in Seville.

*2.3. Seville and Porto Vacation Rental Overview* 

*2.3. Seville and Porto Vacation Rental Overview* 

with a more pronounced increase as of January 2018.

<sup>3737</sup> <sup>4098</sup>

with a more pronounced increase as of January 2018.

*Economies* **2021**, *9*, x FOR PEER REVIEW 7 of 17

**Figure 1.** Evolution of number of HRs in Seville and Porto. Source: Authors, based on data from RTA (2016, 2017, 2018) and RNT9 (RNT 2019). **Figure 1.** Evolution of number of HRs in Seville and Porto. Source: Authors, based on data from RTA. (Registro de Turismo de Andalucía) (2016, 2017, 2018) and RNT<sup>11</sup> (RNT (Registo Nacional de Turismo) 2019). National Institute of Statistics (INE–ES11) reported that Seville had a total population of 688,711 in January 2018 (INE-ES 2018). Thus, by March 2019, Porto would have had a ratio of approximately one HR for every 35 inhabitants as compared to 167 inhabitants for each

population set the total of residents at 222,252 in 2013 (RNT 2019). In contrast, Spain's

The number of registered holiday rentals has grown significantly in Seville and Porto (see Figure 1). Starting in January 2017, the public was given access to data from official HR records in Seville8. Both cities present quite similar trends in holiday rentals' growth,

5843

<sup>3750</sup> <sup>4119</sup>

6400

Seville

The number of registered holiday rentals has grown significantly in Seville and Porto (see Figure 1). Starting in January 2017, the public was given access to data from official HR records in Seville8. Both cities present quite similar trends in holiday rentals' growth,

Porto has significantly more holiday rental facilities than Seville does. According to Portugal's National Institute of Statistics (INE-PT)10, the latest official data on Porto's total population set the total of residents at 222,252 in 2013 (RNT 2019). In contrast, Spain's National Institute of Statistics (INE–ES11) reported that Seville had a total population of 688,711 in January 2018 (INE-ES 2018). Thus, by March 2019, Porto would have had a ratio of approximately one HR for every 35 inhabitants as compared to 167 inhabitants for each An analysis was carried out of the relative number of beds for each city in March 2019. In Seville (see Figure 2a), the beds for vacation rentals and hotel establishments were equal—both around 45%. However, in Porto (see Figure 2b), the number of tourism-related beds was much higher since almost two out of every three accommodations in the city were vacation rentals (i.e., HR). HR in Seville. An analysis was carried out of the relative number of beds for each city in March 2019. In Seville (see Figure 2a), the beds for vacation rentals and hotel establishments were equal—both around 45%. However, in Porto (see Figure 2b), the number of tourism-related beds was much higher since almost two out of every three accommodations in the city were vacation rentals (i.e., HR).

**Figure 2.** Number of tourism accommodation beds in Seville (**a**) and Porto (**b**) in March 2019. Source: Authors, based on data from RTA (2018) and RNT (2019). **Figure 2.** Number of tourism accommodation beds in Seville (**a**) and Porto (**b**) in March 2019. Source: Authors, based on data from RTA (Registro de Turismo de Andalucía) (2018) and RNT (Registo Nacional de Turismo) (2019).

#### **3. Materials and Methods 3. Materials and Methods**

**Figure 2.** Number of tourism accommodation beds in Seville (**a**) and Porto (**b**) in March 2019. Source: Authors, based on data from RTA (2018) and RNT (2019). **3. Materials and Methods**  HPM models can take a variety of functional forms. The present study used a linear function as a reference point because it is the most commonly used function in HPM models, similar to the one proposed for this research. In addition, when other functional forms HPM models can take a variety of functional forms. The present study used a linear function as a reference point because it is the most commonly used function in HPM models, similar to the one proposed for this research. In addition, when other functional forms were tested, the results showed that the linear function produces the best outcomes. This type of function is expressed as Equation (1). Following this formula, the subsequent *Xs* HPM models can take a variety of functional forms. The present study used a linear function as a reference point because it is the most commonly used function in HPM models, similar to the one proposed for this research. In addition, when other functional forms were tested, the results showed that the linear function produces the best outcomes. This type of function is expressed as Equation (1). Following this formula, the subsequent *Xs* (1,2, . . . , *n*) correspond to the relevant variables that determine the daily rate of the accommodation (*Y*). The model estimations (*β*0, *β*1, . . . , *βn*) are the parameters that assess the direct influence in price that each variable (*X*1, *X*2, . . . , *Xn*) has.

$$Y = \beta\_0 + \beta\_1 X\_1 + \beta\_2 X\_2 + \beta\_3 X\_3 + \dots + \beta\_n X\_n + \varepsilon \tag{1}$$

To develop the HPM model, data had to be obtained for a sample of registered holiday rentals based on the cities' total units. The research population was defined as the number of HR facilities officially existing at the time of data collection. For HRs in Porto, only the 'apartment' category was selected. For HRs in Seville, the modality 'by rooms' was excluded from the sample due to the distortion that could occur in the model if different services (i.e., spare room or complete apartment) were compared. This study thus only focused on complete apartments, especially because spare rooms are an insignificant percentage of vacation rentals in Seville and Porto.

The final sample (see Table 2) comprised the total number of holiday rentals for which complete data could be obtained. It is checked that both sample sizes guarantee a confidence level of 95%. The HPM model was developed using IBM SPSS Statistics 25 and EViews 10 software. The number of cases included was higher than that of the initial sample because identical accommodations offered with different numbers of beds were quantified separately.


**Table 2.** Number of cases, sample and total population of vacation rentals in Seville and Porto.

Note: HRs = holiday rentals. Source: RTA (Registro de Turismo de Andalucía) (2018) and RNT (Registo Nacional de Turismo) (2019).

The variables to be analysed (see Table 1 above) were selected based on the literature review's findings. The information incorporated into the HPM models was extracted from searches of Booking.com (Booking.com 2018, 2019). The exceptions to this rule were the MIN variable (i.e., minutes needed to walk from accommodations to the city's main tourist attractions), which were taken from Google Google Maps (2018, 2019), and IDIS (i.e., district index according to the price per square metre (m<sup>2</sup> )), which was drawn from Tinsa (2018) for INE-PT (Instituto Nacional de Estatística) (2019) for Porto. In addition, VSAT (i.e., visual appeal according to photos) was evaluated by the authors.

Table 1 above presents the main similarities and differences between the holiday rentals in both cities. Major similarities include the average number of minutes needed to walk to the city's main tourist attractions and the availability of a TV, balcony, views, parking and pet admission. Other parallel features are the average rating given by previous guests on Booking.com and the number of photos in accommodations' profiles on that website, as well as the images' visual appeal. Notable differences appear in the average price (i.e., significantly lower in Porto) and accommodations' size—both in m<sup>2</sup> and in the number of beds offered. The most important contrasts in amenities are that washing machines are much less often available in Porto's HR facilities compared to the HRs analysed in Seville, and notable differences were found in whether a courtyard, patio and bathtub were available.

Regarding the data extraction process, the price (PRCE) was estimated per holiday rental facility and day for Seville based on a stay of two days, which is the average for that city according to the Seville Tourism Data Centre<sup>12</sup> (Centro de Datos Turísticos del Ayuntamiento de Sevilla 2017). The average stay is, however, only 1.73 nights for tourism accommodations in Portugal's northern region and thus for Porto, according to the INE-PT (Instituto Nacional de Estatística) (2019). Taxes, tourist fees and other added expenses (e.g., cleaning) were included.

In the case of a property that offered different types of lodgings at the same price, the one that provided the greatest added value was chosen to reflect how any rational consumer would act. Priority was given to the option of cancellation within a specific period and/or a partial refund. Finally, the no refund option was selected only when no other possible alternative was given.

The minutes to walk from accommodations to the city's main tourist attraction (MIN) were determined for Seville using the Plaza del Triunfo. This square is located between the Cathedral of Seville and the Real Alcázar, which are the two most visited monuments according to (Centro de Datos Turísticos del Ayuntamiento de Sevilla 2017). For Porto, Praça da Liberdade was taken as the reference point, as it begins at Avenida dos Aliados, which is considered the city's centre. This square's proximity to the São Bento train station

also played a fundamental role in the choice of the Praça as Porto's main tourist attraction. This variable should negatively influence the price since the less time spent reaching major points of interest from the accommodations means the more expensive they will be.

The district index (IDIS) was quantified as the average price per m<sup>2</sup> according to the Seville district or Porto parish in which the vacation rentals were located (see Table 3). The predefined hypothesis posited that a higher value per m<sup>2</sup> in a district or parish implies a higher property value, which will be reflected on accommodations' price. This index was composed by giving the highest-priced district or parish a value of one, after which the rest of the cities' zones were given a proportional value. Tests were also carried out on the model in which each district or parish served as a dummy variable, except the one zone that served as a basis, because the inclusion of all districts or parishes would increase the chances of an exact multicollinearity problem appearing in the model.


**Table 3.** District index.

Note: EUR/m<sup>2</sup> = euros per square metre; UF = União de Freguesias (Joint Parishes). Source: Tinsa (2018) and INE-PT (Instituto Nacional de Estatística) (2019).

Regarding the accommodations' amenities, the model specified that only views (VIEW) of the city and/or emblematic monuments would be considered rather than views of patios, courtyards and/or interior gardens. For the parking variable (PARK), both parking in the establishment itself and private parking near it were quantified. Finally, Table 4 reflects the different dates on which the price of a stay was based. For Seville, May–June and January were selected as the high and low seasons, respectively, thereby avoiding holidays that could cause specific price increases. In addition, special events in the city such as Holy Week (i.e., the week leading up to Easter) and the April Fair were highlighted. For Porto, August was set as the high season and November as the low season to exclude holidays again, and São João was selected as the city's most characteristic celebration.

To determine the seasons' weight (see Table 1 above), the accommodations' price was divided into approximately two halves to take into account both high and low seasons (i.e., from April to September and from October to March, respectively). Greater weight was given to the high season due to the associated increase in overnight stays. Weekends account for just over two-sevenths of all cases in comparison to weekdays due to a significant increase in overnight stays on weekends. The special events of Holy Week (HW) and April Fair (FAIR) are approximately one week each, so those dates were assumed to quadruple and double, respectively, the 2% that an average week takes up of the total year, due to the increase in overnight stays in these two periods. São João (SJ) was given a slightly lower proportion than the April Fair because Porto's festivities take up fewer days.


**Table 4.** Dates when prices were taken.

Note: Var. = variable; NA = not applicable. Source: Booking.com (2018, 2019).

Andalusian (RTA) and Portuguese (RNT) Tourism Registry were the key sources used to develop the database with which the model was constructed. However, other sources were also consulted, such as Booking.com and Google Maps. The information was processed with IBM SPSS Statistics version 25 and EViews version 10 software.
