**6. Conclusions and Implications**

The comparison of the models developed produced especially interesting results on similarities and differences between the two cities. Strong conditioning factors in both models include accommodations' size in m<sup>2</sup> , location, walking distance to the centre and visual appeal, as well as the influence of high and low seasons and, in particular, local festivities. The main differences are more secondary issues such as holiday rentals'

amenities, district or parish and number of photos in Booking.com profiles. The large number of variables that proved to be insignificant for the model is also noteworthy primarily specific amenities including, among others, the availability of a TV, washing machine, views, soundproofing or parking. The district index also was irrelevant to the configuration of vacation rentals' final stay price for both models.

The most interesting conclusion drawn from this research is that conclusive results can be obtained by applying the same methodology when developing a model for estimating holiday rentals' prices for two different cities. In summary, the literature review and findings confirm that the strongest price determinants to consider in pricing models for cultural destination holiday rentals are distance to the city centre, number of beds, m<sup>2</sup> , seasonality factors and special events. These results also underline the convenience of using Booking.com and Google Maps as a source of data on all these variables. The methodology used in this study will likely produce different results for other cultural tourism cities as researchers accept or discard variables according to each city's realities. However, this study detected the same similarities as Tong and Gunter (2020) and Gyódi and Nawaro (2021) did, except for seasonality, which was not included in the latter investigation. Thus, the proposed methodology appears to be applicable to multiple cultural city destinations. The application of this methodology to the comparison of daily rate estimation of cultural city destinations using data from Booking.com is the main theoretical contribution of this study.

The model's main practical implication is related to estimating accommodations' daily rate under previously defined conditions (i.e., variables) since the model is easy for the relevant practitioners to customise. This research's contribution consists of presenting two models of price estimation whose application entails the obtention of a certain price through easily modifiable variables. Thus, a collection of predetermined variables will assess a confident daily rate estimation under those circumstances. This tool can help holiday rentals' managers or consumers determine in advance if a price is in line with what the market normally offers under specific circumstances. These estimations can also be useful for municipal councils' tax agencies to calculate reasonable tax bases, especially in a sector in which the informal economy is prominent.

The study's limitations include, first, the impossibility of creating larger datasets due to the difficulty of obtaining complete data for all cases and variables and, second, the data collected reflecting a pre-coronavirus disease-19 (COVID-19) period. Finally, future lines of research could involve replicating the above methodology for holiday rentals in other cultural city destinations of great importance to tourists such as Paris, Barcelona, Rome, Venice or Amsterdam. These studies need to analyse the new models' main similarities to and differences from—with a special focus on COVID-19's effects—the two models developed in this research or to adapt the methodology to fit other types of tourism accommodations.

**Author Contributions:** M.Á.S.-S. contributed to the investigation, methodology, validation and formal analysis; J.A.C.S., conceptualization, investigation, writing—original draft preparation and funding acquisition; M.C.S., conceptualization and validation; M.Á.F.-G., formal analysis, validation, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This paper is financed by National Funds provided by FCT—Foundation for Science and Technology—through project UIDB/04020/2020.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data concerning holiday rentals' daily rate pricing for the city of Seville, presented in this study, are openly available in *Data in Brief* at [https://doi.org/10.1016/j.dib. 2019.104697], reference number [104697].

**Conflicts of Interest:** The authors declare no conflict of interest.
