French Riviera Short Term Accommodation: Analysis and Comparison of Price Determinants Using Airbnb and Booking.Com Data

Bachelor Thesis

French Riviera short term accommodation: analysis and comparison of price determinants using Airbnb and Booking.com data

Author: Augustine LE DU Supervisor: Thomas GIEBE Examiner: Mats HAMMARSTEDT Level: Bachelor Course code: 2NA12E:3

Abstract

This essay looks at the price determinants of short-term accommodations in the French Riviera. The paper approach is to compare the private accommodations market using Airbnb and hotel market using booking.com, to see if those two accommodations types have similar price determinants. Also, using different cities of sun and beaches tourism in the French Riviera, we can compare the results to see if there is a real difference in price determinants between them. Finally, the results can give an idea of the competition between the two-platforms comparing the willingness to pay for the different attributes in the two markets. Using the hedonic prices model, Nice, Cannes and a sample of French Riviera accommodations are studied for a double one-night stay in the 22 August 2020. The results are mixed between the cities and between the two types of accommodations. While amenities seem to have a real impact on prices for the different accommodations, location variable and reputation impact are very limited. Also, Nice and the French Riviera sample seems to have similar results while Cannes results are different. This paper also discusses some limitations due to the data collection. Some variables were not included because of the difficulties in the data collection due to the web scraping technique. Also, the number of accommodations is lower than expected for the hotels. Finally, the variables included in the two models are different, making complicated the comparison of the results. With those limitations, it is harder to make an analysis of the competition between the two platforms.

Key words Short term accommodations, price determinant, Hedonic model, French Riviera, hotel, private accommodation

Table of content 1 Introduction ...... 1 2 Literature Review ...... 3 3 Theory ...... 6 3.1 Hedonic Prices Model ...... 6 3.2 Application to the hospitality sector ...... 7 4 Data ...... 8 4.1 Platform choice ...... 8 4.2 Data collection ...... 9 5 Methodology ...... 13 5.1 General approach ...... 13 5.2 Airbnb application...... 14 5.3 Hotel application ...... 14 6 Results and discussion ...... 15 6.1 Results...... 15 6.2 Discussion ...... 20 7 Conclusion ...... 21 8 References ...... 23

1 Introduction

Tourism is “a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes.” (World Tourism Organization). According to the estimation of the United Nations World Tourism Organization the world tourist arrivals increased from 25 million in 1950 after World War 2 to 1.4 billion per year in 2018.

In this essay two types of short-term accommodation used for tourism are analysed: the private sector with data collected on Airbnb and the hotel industry using booking.com website. This paper proposes an analysis of both of those two types of accommodation and tries to identify the similarities and differences of room prices determinants.

France is the top worldwide touristic destination with 89 million visitors in 2018 followed by Spain with 83 million and United-States with 80 million (World Tourism Organization). In France, tourism plays a major role with a tourism-related consumption representing 7.5% of the GDP in 2017 (OECD, 2018). In France, excluding Paris, regional tourism volume is highly correlated with the presence of sea, sun and beaches (Barros et al., 2011). The top tourist destination is Île-de-France with 32% of overnight stays, followed by Auvergne Rhône-Alpes and Provence Alpes Côte d’Azur both representing 11% of the overnight stay (INSEE, 2017). The French Riviera is part of Provence Alpes Côte d’Azur and is located in the south of France on the Mediterranean coast. It includes some famous touristic cities as Nice know for the “promenade des Anglais”, Cannes with its famous annual and international “festival de Cannes” or St-Tropez with the private beaches and the luxury yacht. Between 2010 and 2018, the overnight stays at hotels in this region increased by 8%. The case of the French Riviera is particularly interesting to study because it is historically known for the ‘jet-set’ customers and the development of peer-to-peer platforms as Airbnb proposing lower prices than hotels can be a real opportunity to attract new client from the middle class.

Simultaneously the sharing model faces a big growth in multiple areas like cars, accommodations or other goods and services as money, electronics or education since 2000 (Hruška et al., 2018). According to the Oxford dictionary, a sharing economy is “an economic system in which people can share possessions, services, etc., usually by means of the internet”. The impact of the development of the sharing economy seems to be mixed. In one side the sharing economy has a positive impact, as the access to goods and services for a lower

price, more choices for guests, complement income for host and others (Skjelvik et al., 2017 ). It is also a good opportunity to diversify tourism, disperse tourists in different areas while keeping a small environmental footprint (World Bank report, 2018). But on the other side there are some critics about the unfair competition between the individuals providing sharing goods or services and the standard services that have more regulations, taxes and sanitary rules (Hruška et al., 2018; Zervas et al., 2017). There is an unequal competition between hotels and sharing-accommodation. Another problem is the contribution to over-tourism in some areas. (World Bank report, 2018)

The impact of Sharing-accommodation on hotels is unclear. Some papers found that the development of Sharing-accommodation as Airbnb does not impact hotels because the travellers using Airbnb are a new demand that wasn’t able to travel before (Heoa et al., 2018). But others found a negative effect of the Airbnb implantation on some of the hotel demand, more particularly the low-cost one (Zervas et al., 2017).

The goal of this paper is to identify what are the price determinants of two different kinds of short term accommodations, the sharing accommodation market and the traditional hotels market looking at a specific area. Those results can allow doing a comparison between the two different accommodation component prices. In that way, this paper plans to evaluate the potential competition between the two kinds of touristic accommodations by price component using the Hedonic price method.

As far as my knowledge goes, this study is the first to compare sharing-accommodations and hotels data, in the French Riviera area, with the Hedonic price method. The hedonic prices model is used to determine the added value of each of the characteristics of the accommodation in the final prices of the accommodations. Using this model, one of the advantages is to have results easy to interpret and to compare.

The interest of the paper is twofold. First, it benefits both the accommodation host and hotel manager providing information about the willingness to pay of the consumers for the different goods and services they provide. In that respect, they can improve their offer adding component clients search for, and at the same time allow them to increase their price or be more attractive. Subsequently, it allows to knowing if hotel and sharing accommodation clients have the same profile in term of expectation and requirement. Knowing if customers are similar or different, can contribute to understanding if the growth of sharing accommodation can have an

impact on hotel demand and prices or if there are two different markets with different customers.

This paper is divided into 6 sections. Section 1 contextualize and motivate the research question. Section 2 presents an overview of previous research related to the topic and Section 3 of the theoretical framework. Section 4 explains the data collection. Section 5 concerns the methodology used. Section 6 present and discuss the results. Finally, section 7 concludes the paper.

2 Literature Review

Previous research has been made to identify the determinants of the prices of the accommodations for both Hotels and Sharing accommodation and while some attributes seem to always influence the prices, e.g., the free parking, others have little or no impact.

Amenities

First, most of the studies about price determinants in both sharing accommodation and hotels analyse at the amenities component. More specifically, they look at which amenities have an impact on the prices. Studies among different locations and different types of accommodations have very mixed results. Some found that most of the amenities proposed are positively correlated with the price (Lorde et al., 2019; Wang and Nicolau, 2017). However, others found that only some of the amenities have a positive impact on the price. Portoland (2013) found that on the Croatian sharing accommodation market, only the amenities parking, garden and balcony have a significant impact on the accommodation price. Chen and Rothschild (2010) studying hotel room prices determinant in Taipei. They found out that the amenities prices component differentiates between those two types of customers. One amenity seems to be always significant: the presence of free parking (Espinet et al. 2003; Monty and Skidmore, 2003; Portolan, 2013; Thrane, 2007).

Location

The second component that is determinant of the price in the previous research is the location. Portolan (2013) found that in Croatia prices are negatively correlated with the distance to the beaches, a result corresponding to the one Espinet et al. (2003) found for the Spanish sun and beaches tourism. Juaneda et al. (2011) study the prices of hotels and private apartments on

different municipalities on the Mediterranean Coast and found differences in prices for the same kind of hotel and apartment proposed in different municipalities. Looking at big cities location seems also important, Thrane (2007) and Guenter (2012) found than in Oslo and Bolzano, Italy, accommodation in the centre are more expensive, and prices decrease with city-centre distancing. Hung et al. (2010) found an insignificant correlation between city centre distancing and accommodation prices in Taiwan, but the explanation must be that in Taiwan most of the hotels are in the city-centre. So, analysing the location, it’s important to be sure that there is a sufficient geographic dispersion to have a significant and meaningful result. But globally among the different papers the quality of the destination influences the price of the accommodation. A complementary research has been made by Yang et al. (2016) to determine the correlation between hotel accessibility and the prices of the accommodations in Caribbean Island. Their results show a positive correlation between accessibility and prices, more difficult is it to access the island; lower is the room rate.

Reputation

The third most important component identified by many studies is the reputation of the accommodation, which is important for a host of private accommodation but also for the hotels.

Concerning the sharing-accommodation, reputation appears to be a very important factor in the price determination because the guest tries to identify if the accommodation is conformed with its description. Lorde et al. (2019), as well as Liang et al. (2017), found out that prices are positively correlated with the “superhost” badge on the Airbnb platform. The “superhost” badge can be achieved with a rating higher than 4.8 (with a maximum score of 5), ten stays minimum on the previous year, less than 1% cancellation rate and 90% of response in less than 24 hours. In exchange, they have higher visibility on the platform. The badge is a quality label deliver by the platform that allows the host to set higher prices (Airbnb.com).

Also, for Sharing-accommodation, the host’s profile seems to be important for the price of the accommodation. Teubner et al. (2016), Teubner et al. (2017) and Wang and Nicolau (2017) found that host characteristics highly contribute to change in prices. For example, hosts having a verified profile or a profile picture can propose a higher price because they are more trustworthy. However, the profile could also create discrimination as Marchenko (2019) evokes in his paper. He found that Black and Asian profiles tend to have lower prices and fewer bookings than white men for the same type of accommodation in cities of the US. More

generally research that includes the reputation as a price determinant highlights the importance of customer review and star-rate they give.

For hotels, the reputation looks to be also very important in price determination. The difference is that hotels don’t have a host profile, but some got chain affiliation. Chain affiliation impact on the price is mixed. Thrane (2007) found a small correlation between hotel affiliation and prices in Bolzano, Hung et al. (2010) found a clear positive correlation in Taiwan and Israeli (2002) found no significant correlation in Israel. The different results could be explained by the particularities of the destinations themselves in terms of culture and touristic profile. So, the correlation between hotel chain affiliation and prices should be studied case-by- case independently.

Something that also differs from country to country is the price differences between different star rating hotels. Comparison between hotel star ratings of different countries, as Wang and Nicolau did, seems to have limitations because the value of the star-rating system differs from country to country. Guenter (2012) found that the most significant variable on price determination is the star-rating of the hotel, particularly for leisure clients. Israeli (2002) found also a positive correlation between star- rating system and prices.

Advance booking

Additionally, another factor that can be taken into account on the price determination is booking in advance. Bezzubtsevand and Ignatov (2013) confirms the hypothesis that booking in advance reduces accommodation prices by looking at 11 of the biggest European cities. They couldn’t identify, however, the optimal time to book to have the lowest price. Guizzardi et al. (2017) by analysing Rome and Milan found different results on the advanced booking. Advance booking does not have a strong impact on hotel prices in Rome. However, for Milan, there is a tendency of decreasing prices overtime to attract clients mostly in the low- price hotels tier. The impact of booking in advance has an inverse effect comparing to the previous paper. Again, advance booking determinant seems to be a strategy that has different results among different locations. Concerning the sharing accommodation, the advance booking influence on the price is unclear. As the accommodation owners are not professional, the price strategy is very irregular.

Global view of price determinant

Results are difficult to compare because most of the studies have a small sample and focus on a specific location, which shows different results. In that respect, it is difficult to generalize the results to other locations because tourist profiles and accommodation market are different. The few studies that have tried to include multiple countries faced some limitations because they do not take into account the local differences. Wang and Nicolau (2017), for example, compare different cities around the world without considering the differences between cities’ price level or specificities. Lorde et al. (2019) choose to analyze price determinants of different Caribbean countries and they highlight the heterogeneity of the results between countries but also between different areas of the same country. That confirms the limitation of Wang and Nicolau’s study (2017).

The impact of sharing accommodation on hotels

The impact of the development of sharing accommodation on the hotel industry is a controversial issue. Zervas et al. (2017), using a difference in difference approach, found that local-hotel rooms’ revenue in Texas is negatively correlated with the entry of Airbnb in the local market. They also point out that Airbnb is not a substitute for all kinds of hotels, and low- cost hotels are the most affected by the development of the sharing-accommodation platform.

However, in terms of competition, sharing-accommodation presents various advantages compared to hotels. It is more flexible to changes in demand, it faces less regulation, and put a wide range of different accommodations available in the market: Bungalows, apartments, villas, yurts or even castles. Zervas et al. (2017) analysed the peak of accommodation demand during the “South by Southwest” festival in Austin, Texas. They found out that Airbnb supply was very reactive to the increase in accommodation demand. It costs almost nothing to hosts to add or remove their offer from the platform, so they can easily adapt the offer to the peak demand.

3 Theory

3.1 Hedonic Prices Model

Lancaster (1966) and Rosen (1974) discussed a new approach in consumer theory considering that consumer utility is not directly given by the goods but by the sum of their characteristics components. The model has been used in different informal studies before being properly developed by those authors. The Hedonic price model has been used in different

sectors to analyze prices component. Griliches (1961) applied it to the car industry in the US comparing the different indices prices to the evolution of the car component. Goodman (1978) applies this model to the housing market comparing the housing prices in different areas. They found that city-centre houses cost more than suburbs one, comparing houses with same characteristics.

3.2 Application to the hospitality sector

The hedonic price model has been used in different studies in the hospitality sector because it allows to find out what variables impact the room rates. Different studies about hotel or sharing-accommodation room rate have been made with this model for different locations and using different price component.

The Hedonic model has been used by Lorde et al. (2019) to compare the price determinants of Caribbean countries sharing-accommodation. They enhance the important heterogeneity of the results across countries and areas while studying a very specific area. Others papers used the Hedonic model on different locations, Portolan (2013) look at Croatia, Monty and Skidmore (2003) study South East Wisconsin and results differ.

It seems that the Hedonic model has been more developed for hotel than sharing accommodation and number of papers already used the model to analyse room rates. As sharing-accommodation, prices appear to have different components which vary across locations. Guizzardi et al. (2017) analyse Rome and Milan, Andersson (2008) analyse Singapore and Thrane (2007) focus on Oslo. Espinet et al. (2003) investigate tour operator prices on the sun beaches tourism in Spain, a very similar market to the one we look at in this paper. This paper can help to know if the result of Espinet et al. (2003) can be generalised to a very similar market.

The advantage of the Hedonic prices model is that it is useful for both customers, accommodation suppliers and policymaker. It gives customers the price they have to pay to access different attributes in both sharing accommodation and hotels. In that respect, it could help them to choose between those two types of accommodation looking at the prices and the attributes available. Similarly, the Hedonic prices help the supply side with their price strategy, but also witch amenities could allow them to increase the price or reinforce their attractiveness. Finally, it gives information to policymaker about price determinant and competitiveness on the market to better regulate the market.

There is also some drawback for using the Hedonic prices model. First, the customer needs to have perfect information about the characteristics of the good. This paper assumes that the website chosen includes all the most important information including the previous customer comment that complete the missing information. Also, it does not include taxes or interest rate, although it is influencing the prices of the short-term accommodation rental. Mis-specification of the function can happen if some variables are missing, and as a consequence, the estimator can be biased (Chin and Chau, 2003). Finally, multicollinearity can happen with some characteristics, a test for multicollinearity is included in this paper to detect it.

4 Data When individuals search for short-time accommodations, the search is made in terms of some attributes. With the search engine nowadays available and the development of multiple platforms to book short term accommodations, customers can compare easily the different accommodation. They can also use filters on their research to find the different type of accommodation and prices depending on their requirement.

4.1 Platform choice

Short story of the platforms

In 2007 a big design conference happened in San Fransisco, hotels where consequently full and three men got the idea of renting an air mattress in their room. Some month later Airbnb (AirBed and breakfast) was officially created by Brian Chesky, Joe Gebbia, and Nate Blecharczyk (Gallagher, 2017). “Today, Airbnb Hosts offer millions of listings in more than 220 countries and regions and over 100,000 cities” (Airbnb, 2020). Airbnb has in 2020 more than 5 million global listings (Airbnb, 2020).

Booking.com was created in 1996 in Amsterdam, Netherland. This platform goal was to match place travellers want to be and accommodation offer on this place by a simple online platform. Booking.com became in 2005 the European leaders in online booking. In 2018, booking.com reported more than 27 million global listings in 227 countries (Booking.com, 2018).

Very similar websites

Airbnb website is made to facilitate the research of the guest in term of their criteria. Entering the website, the customer first adds his destination, travel dates and number of guests.

In a second time, he can add criteria (price, location, any specific facilities, services…) and the website display a list of accommodation that corresponds as much as possible to the criteria add. The customer just has to choose between the available accommodations. To help his choice the guest has access to photos of the accommodation, the guest profile, previous guest review, star rating… Booking.com website is very similar to Airbnb one, customers first choose dates, location and number of guest and can after select the attributes they want.

The choice of those two websites has been made first because they have both a very large listing in France so the sample we are going to take will be bigger. The second reason is the websites similarities, consumers can easily compare accommodation between those two websites entering in both his requirement and comparing prices, review, photos...

4.2 Data collection

Main research

The paper analyses a one-night stay on the weekend, the Saturday 22 of August 2020 because the touristic activity is usually higher on the weekend and the high season. The choice of August month is because it is the month of the highest touristic activity in the French Riviera. Also, the date has been chosen because the hotel availability was the highest this weekend among the month. Concerning the main research, the paper focuses on a double room because it is the most common and available one.

Location

This paper selects two cities that are very attractive for tourism in the French Riviera, but also that had a sufficient number of accommodations available to make a relevant Hedonic analysis. The first is Cannes with on 97 available hotels on Booking.com and more than 300 on Airbnb. The second one is Nice with 130 available hotels on Booking.com and more than 300 on Airbnb. Then the paper analyses the prices on the French Riviera globally to see if the results of those two cities correspond to the average results of the French Riviera. The first idea was to include Antibes and Saint-Tropez but at this period only 20 hotels are available for Antibes and 15 for Saint-Tropez, so the Hedonic Prices model must be unreliable in a sample that small. Only Nice and Cannes have enough hotels available on Booking.com in the French Riviera. The last category is the French Riviera in general including a selection of cities located on the French Riviera to see if the result of the independent cities is similar to the prices of the accommodations in the global French Riviera.

Variable choice

To choose the variables, this paper considers the results of previous studies and also the information available on the platform. Internet access is not included because almost all the hotels give internet access so the impact on the price must be insignificant as previous paper found out. Some variables that must be included have not been because of difficulties with the data collection. Including the distance from the beaches and the centre of Airbnb was not possible because Airbnb does not show the exact location of the accommodation. The parking couldn’t be included for hotels because they are for almost all the hotel considered as an extra cost. The Chain affiliation was not included either as it was planned because of the difficulties to collect the data.

Airbnb Variable Expected effect Hotel variable Expected effect Verified profile + Distance beaches - In Airbnb since - In Booking.com since - Entire lodging + Sea view + TV + TV + Air conditioner + Fitness + Swimming pool + Swimming pool + Parking + Distance from centre - Superhost + Recommended + Number comments + Number review + Star rate + Star rate + Kitchen + Number travellers +

Data extraction using web scraping

One of the main difficulties of this paper was to collect the data needed to do the regression. For the regression, the data have to appear clearly in a table, where they are originally information on the website page. The web scraping allows selecting on a page the information wanted and create with them a database.

The first step consists in creating a listing of all the accommodations available on the 22 of August 2020 for 2 people, one night stay. Six different listings have been created one for each location (Cannes, Nice or French Riviera) and type of accommodation (Airbnb or Booking.com). For the web scraping, Web Scraper offered by webscraper.io, version 0.4.2 was used.

When all the accommodation available are listed the second step is to select the information to extract for the regression. On the following screen capture, there is in red an example of the information that can be extracted. Bellow, there is the different selectors chosen for the regression.

To extract the data, the variables needed are selected after the creation of the listing. For example, for the price, the price is selected for one of the accommodations, and because all the accommodations have been listed before we need to select the price only one time to have all the prices. This operation is repeated for all the selectors needed.

One of the difficulties faced was the pagination different for all the listing for some pieces of information. For example, the neighbourhood of Airbnb was appearing in different places as the two following photos have shown. The first have the exact address of the accommodation on top of the map when the other has only the neighbourhood and appear on the bottom of the map. As a consequence, some data were not collected because no solution was found to adapt the data collection to each pagination.

Finally, the data have to be cleaned before being used for the regression. After the data collection, the data appears as a list of words that have to be transformed into numbers to be able to be used. For this step, Dataprep by Trifacta in Google Cloud was used. Dataprep allows creating a data cleaning recipe that can be reused for the other locations. Two recipes where created, one for Airbnb and one for Booking.com.

4.3 Limitations in Data collection

The first difficulty faced was data collection. Concerning Booking.com only 2 destinations, Nice and Cannes, had enough data to be included in the study. One of the reasons is that more than 80% of the hotels were not available on the 22 of August 2020. All the other dates tested had more than 80% hotel unavailable, even in low season, the 22 of August was finally kept. A solution was not found to obtain more hotels. For the next study about hotel pricing in the French Riviera, it will be important to find a solution to have access to more hotel to have a more representative result of the global offer.

Concerning Airbnb, the problem with data collection was different, when research is made on Airbnb only 300 results appear but we don’t know the real number of accommodations available. Another difficulty was to compare the location because the exact address is hidden and there is only an area rounded on the map to evaluate more or less the location of the accommodation. One of the solutions for that problem can be to take the average latitude and longitude on the map and integrate it on the regression but is it only an average and it takes time to collect the data. Accommodation size is not included in the regression because some hosts do not include the information on their announce and it was very difficult to scrape, but for the next paper, it could be interesting to include it.

5 Methodology

5.1 General approach

The Hedonic price method decomposes rooms rates into n different characteristics. The choice of using the log-linear model and not the linear model is because it allows valuing different characteristics proportionally to others.

The hedonic price function of accommodation is given as: PA=f(B,C,D)

PA : the price of the accommodation B : represent the amenities component of the accommodation (swimming pool, TV…). C : the location of the accommodation (nearest beaches, distance from the centre ) D : the reputation of the accommodation (star rate, number of reviews, “superhost”…)

The model used is the following one : Ln(Pi)=α+∑βi wi +εi

Pi : Accomodation price α : intercept

βi : regression coefficient

wi : characteristic

εi :Error term

5.2 Airbnb application

The OLS model used for Airbnb is the following:

LnPRICE= α + β0VERIFPROFILE + β1SINCE + β2ENTIRELODG + β3TV +β4AIRCOND +

β5POOL + β6PARKING + β7KITCHEN + β8NBCOMMENTS + β9STARRATE +

β10SUPERHOST + β11NBTRAVELLER + εi

5.3 Hotel application

The OLS model used for Booking.com is the following:

LnPRICE= α + β0STARRATE + β1NBREVIEW + β2DISTBEACHE + β3FITNESS + β4POOL

+ β5TV + β6SEAVIEW + β7SINCE + β8DISTCENTRE + β9RECOMMANDED + εi

6 Results and discussion

6.1 Results

Table 1 : Descriptive statisctics Airbnb Cannes (N=299) Nice (N=293) French Riviera (N=320) Mean S.D Mean S.D Mean S.D Price 190.88 201.2961 93.38225 54.07653 106.5719 48.89796 Verified profileb .8628763 .3445546 .8737201 .3327328 .9 .3004699 On airbnb since 2015.679 2.127778 2015.928 2.27516 2016.063 1.943359 (year) Entire lodgingb .7190635 .45021 .7372014 .440907 .596875 .4912937 TVb .6020067 .6595677 .5085324 .605988 .51875 .5539471 Air conditionnerb .7123746 .4534144 .5904437 .4925932 .6625 .4735976 Swimming pool b .1103679 .3138731 .003413 .0584206 .175 .3805622 Parkingb .3377926 .5012834 .0921502 .2897327 .49375 .5485447 Kitchenb .8160535 .3880901 .8395904 .3676133 .771875 .4202808 Superhostb .1705686 .3767621 .2559727 .4371532 .253125 .4354829 Number of 25.96596 34.86431 77.67188 83.51692 102.8406 109.9671 comments Star rate 4.615567 .5898133 4.655643 .2146967 4.725073 .1762148 Number of 3.16388 1.312012 2.976109 1.455778 3.19375 1.254444 travellers b : binary variable, as a consequence the Mean is the proportion of the accommodation that include this attribute.

Table 2 : descriptive statistics Booking Cannes (N=97) Nice (N=124) French Riviera (N=491) Mean S.D Mean S.D Mean S.D Price 192.3723 147.3834 171.2823 68.73959 190.2658 153.7457 Star rate 3.227273 1.21037 2.830645 1.124175 2.898167 1.24778 Number review 340.9794 221.1781 433.2419 262.6908 367.3686 250.7047 Distance beaches 795.7143 2435.743 3409.569 5272.759 1456.951 3554.983 Fitnessb .185567 .3907764 .1048387 .3075883 .1568228 .3640045 Swimming poolb .3298969 .4726179 .1370968 .3453448 .3136456 .4644474 TVb .7628866 .4275218 .6129032 .4890621 .706721 .4557296 Sea view .1134021 .3187308 .1048387 .3075883 .1832994 .3873064 In booking since 2005.227 25.2564 2008.976 4.478428 2009.157 8.740117 Distance centre 7241.753 12639.02 4091.129 7225.008 Recomandedb .6391753 .4827346 .8790323 .3274127 .5824847 .4936523 b : binary variable, as a consequence the Mean is the proportion of the accommodation that include this attribute.

Table 3: Estimated result of the hedonic pricing model Airbnb Cannes Nice French Riviera Whole sample Whole sample Whole sample (N=194) (N=241) (N=272) Coefficient T-value % diff Coefficient T-value % diff Coefficient T-value % diff

Verified profile .0151686 0.10 .2184637 2.59** 24,41 -.1847597 -1.18 (=1) (.1503032) (.0843322) (.1570937) On airbnb since .039218 1.74 -.0109992 -1.03 -.0738532 -3.98*** -7,12 (year) (.0225568) (.0107135) (.018547) Entire lodging -.0165262 -0.14 .3697849 6.45*** 44,74 .3644762 4.06*** 43,97 (=1) (.1144495) (.0572955) (.0898445) TV (=nb) .0136532 0.18 -.0337502 -0.91 -.0319066 -0.70 (.077138) (.0370873) (.0457465) Air conditionner .1704023 1.84 .1441791 3.19** 15,51 .2122108 3.65*** 23,64 (=1) (.0927251) (.0451348) (.0582127) Swimming pool .1220159 0.91 .2306087 3.54*** 25,94 (=1) (.1341644) (.0652071) Parking -.1208659 -1.47 .0509189 0.72 -.2588451 -5.83*** 22,80 (=nb) (.0822803) (.0708661) (.0444129)

Kitchen .0644503 0.46 -.1552969 -1.97* -14,38 .185263 3.18** 20,35 (=1) (.1401102) (.0787621) (.0583131) Number of -.0015103 -1.24 -.0002677 -0.97 -.0007162 -2.74** 0 comments (=nb) (.001216) (.0002772) (.000261) Star rate -.3278473 - -27,95 .1518049 1.24 -.611489 -2.02* -45,74 (=nb) (.0715556) 4.58*** (.1224373) (.3029936) Superhost (=1) .0337736 0.32 .1470169 2.58* 15,84 .1283384 1.80 (.1042834) (.057022) (.0712009) Number of .2110556 6.06*** 23,49 .1081593 7.43*** 11,42 .0897861 3.04** 9,39 travellers (=nb) (.0348413) (.0145497) (.0294936 Constant -73.5795 -1.62 25.03621 1.17 155.8 4.11*** (45.51287) (21.47839) (37.89646) F-value 8.93*** 19.26*** 47.47***

Adj-R2 0.3301 0.4556 0.6730

Note : *p<0.05 ; **p<0.01 ; ***p<0.001***

Table 4: Estimated result of the hedonic pricing model booking.com

Cannes Nice French Riviera Whole sample Whole sample Whole sample (N=78) (N=116) (N=384) Coefficient T-value % diff Coefficient T-value % diff Coefficient T-value % diff Star rate (=nb) .210864 3.88*** 23,47 .1354578 5.43*** 14,51 .1721685 8.85*** 18,79 (.0542928) (.0249378) (.0194541) Number review -.0006443 -2.19* 0 .0000401 0.46 -.0001568 -1.91 (=nb) (.0002948) (.0000875) (.0000823) Distance from the -.0000344 -1.85 -.0000227 -4.63*** 0 -.0000174 -2.98** 0 beaches (=metre) (.0000186) (4.90e-06) (5.82e-06) Fitness (=1) .0627492 0.38 -.1672107 -1.24 .1289423 1.99* 13,76 (.1647554) (.1345997) (.0648055) Swimming pool .1589492 1.35 .5114441 5.16*** 66,77 .3698629 6.91*** 44,75 (=1) (.1174902) (.099209) (.0535057) TV (=1) -.1771184 -1.58 .0232247 0.48 -.0953381 -2.13* -9,09 (.1121407) (.04816) (.0447858) Sea view (=1) .5027065 2.16* .2646522 2.54* 30,29 .2632694 5.02*** 30,11 (.2330921) (.1041674) (.0524332) In booking since .003112 1.53 .0313454 5.54*** 3,18 .0047122 2.21* 4,72 (=year) (.0020362) (.0056548) (.002132) Distance from the -8.76e-06 -2.24* 0 -4.82e-06 -1.31 centre (=metre) (3.92e-06) (3.68e-06) Recommanded by .0505671 0.45 -.0783028 -0.97 -.075069 -1.61 booking (=1) (.1122323) (.0810668) (.0466247) constant -1.494705 -0.37 -58.20785 -5.11*** -4.792468 -1.12 (4.090224) (11.38382) (4.29131) F-value 8.42*** 14.77*** 35.17*** 2 Adj-R 0.4906 0.5449 0.4715

Note : *p<0.05 ; **p<0.01 ; ***p<0.001***

Global view of the results

The first part of the results concerns the descriptive statistics for the different variables of Airbnb and Booking.com. First looking at the prices, Cannes have almost the same average price for a sharing accommodation as for a hotel. For Nice, the result almost doubles from an average of 93 Euro per night on Airbnb to 171 Euro for a hotel. We also observe a very high standard deviation for Cannes and a quite low one for Nice. The star rate average given by previous customers on Airbnb is especially high with a mean for all three categories between 4,6 and 4,7 and a low standard deviation. As a consequence, the price must not be impacted by a change in the star rate. The average number of travellers on Airbnb and the Standard deviation is almost the same for the three categories, mean that we compare accommodations that can in average host three guests.

We can compare two types of equipment proposed by both sharing accommodations and hotels, the swimming pool and the presence of a TV. The proportion of both types of equipment is higher on hotels than on Airbnb for all the localisations. We couldn’t compare the parking equipment because most of the hotels do not have free parking at those locations. Equally Air conditioner is included in almost all the hotels so the regression does not include it.

There is also a large difference in the sample size, the number of available accommodations on Airbnb was very large when on Booking.com all the hotel available on the chosen date are included in the study. Besides, after eliminating duplicates and the one with omitted variables the number of observations is even lower.

To test the multicollinearity the VIF test was computed on Stata and all the result were below the usual level of acceptance (10) except one, the star rate for Airbnb in the French Riviera category where the VIF equal 10. It can be driven by multiple variables. Deleting the star rate on the regression all the VIF decrease and are below 5.14. It can be explained by client satisfaction that depends on the attributes of the house. The results of the regression analysis are presented in Table 3 for Airbnb and Table 4 for Booking.com. Airbnb and Booking.com hotels have been estimated separately because they do not have the same estimators.

Regarding the explanatory power the model show differences among the different locations. For Airbnb Cannes have a low Adjusted-R2 of 33% of the price explained by the

estimators when the French riviera has a high Adjusted-R2 of 67%. On Booking.com the three categories have an Adjusted-R2 ranging between 47% and 54% of the prices explained by the chosen estimators. The third column of the table (%diff) is calculated as (eβ-1) and the results correspond to the percentage increase of the price due to one unit increase in the variable. For example, in Table 4, for Cannes location, the %diff is 23. That means that an increase of one star for the hotel will increase the price by 23%.

Amenities The analysis of the result begins with the effect of the amenities component on the price of the accommodation. For Airbnb in Cannes, none of the amenities included on the model is significant. However, renting an entire lodging instead of a room increase by 44% the price of the night for both Nice and the French Riviera. For Airbnb in the French Riviera, all the amenities seem to increase the price between 20% and 25%. For Nice only Air conditioner and presence of a kitchen increase the price significantly by around 15% each. The effect of the amenities is quite different on hotels. Cannes hotels amenities are not as significant as for Airbnb. However, for both Nice and French Riviera the sea view from the hotel room does increase the price by 30%. Also, the swimming pool is associated with an increase in the price of 67% in Nice and 45% in the French Riviera. That difference can be explained by the low number of hotels proposing swimming pool in Nice, 14%, in comparison, 31% of the hotel includes a swimming pool in the French Riviera.

Location The second price determinant group analysed in the regression is the location. For the location, Airbnb was a problem because the location of the accommodations is not given precisely. The website only shows an area on a map to localise approximatively the accommodation. Consequently, information was not precise enough to be included in the model. In addition, scrapping a map would take a long time for non-precise information. As a consequence, no location variable is included in Airbnb regression model. For hotels, we could include two variables, distance from the beaches and distance from the centre. In contrast with previous research, the distance from the centre as well as the distance from the beaches appears to be or not significant or without any effect. The null coefficient of the distance from the beaches can be explained by the relatively low difference of distance. For example, in the

accommodation of the French Riviera 90% of the hotels are located at less than 1km of the beaches. Clients may not be willing to pay more for only some metres.

Reputation The last category included in the regression is the reputation of the accommodation. For that category, we try to use variables as much similar as possible for sharing accommodation and hotel to compare their effect and results. The number of reviews is not significant neither for Booking.com nor for Airbnb. The hotel star rate is highly significant for the three locations, 15% for nice to 23% for Cannes. Airbnb not having directly Star rate system, the star ratting taken is the one given by the precedent guests but the coefficients are negative. Looking at the descriptive statistics this result may be explained by the very low standard deviation. Almost all the star rate given is between 4.2 and 5, so customers do not take the star rate as a signal of quality. Gibbs et al. (2017) found a similar result in Canada biggest agglomeration Airbnb accommodations. The superhost label and the verified profile of the host were also tested as a signal of quality for Airbnb but they appear to be significant only for Nice. The number of reviews is not significant for the price and the selection of Booking.com recommended accommodation is neither significant.

6.2 Discussion

The analysis presented in this paper has several limitations that need to be discussed.

First, the paper focuses on one-night stay on the high season on the weekend, so the researchers do not include either week differences, or seasonal differences. As Chen and Rothchild (2010) found for Taipei hotels, weekend and weekdays price determinant results can be different. Also, it could be interesting to see if the attributes add the same value to the accommodation price in high and low season. For example, we could expect to see a higher willingness to pay for a swimming pool in the high season than in the low season because in high season most the customers are tourists and the temperature is high, conditions that make the swimming pool valuable. In contrast, on the low season, it is cold and windy on the French Riviera and customers are more business customers. That makes the willingness to pay for a swimming pool lower.

The second limitation is the localisation, the paper results work for the French Riviera and as previous papers have shown, is it not possible to generalise results to other locations.

Some variables could be included to have maybe more reliable results. For hotels, the chain affiliation that Hung et al. (2010) found very significant on price determination should be interesting to test. Also, for both Airbnb and Booking.com, a practical solution is needed to include the location data. For Booking.com, the location is available but the formatting is different for each hotel page making the web scraping very difficult. Concerning Airbnb, the location is not available but an average location is available on a map. A solution can be to make an average of the latitude and longitude of the location in the map to approximate the location.

For a next paper on price determination, it could be interesting to combine quantitative analysis as done in this paper with the hedonic price model with a qualitative survey completed by customers. In that respect, a comparison could be made between customers survey results and regression results.

Finally, one of the goals of this paper was to compare the results of booth Booking.com and Airbnb to see if the willingness to pay for those two types of accommodations are the same. It is complicated to compare the results because the variable included in both models are not the same. For example, for the reputation influence on the price of the accommodation, we didn’t find a system on Airbnb comparable to the star rate on hotels. This way we cannot compare the importance of the reputation on those two types of accommodation.

7 Conclusion

The goal of this paper was to investigate the price determinants of short-term accommodations on the French Riviera market, comparing short-term accommodation with Airbnb and hotels with Booking.com. The results obtained are mixed and difficult to compare between the two short term accommodation sectors. The amenities as a swimming pool or a TV present on both do not have the same impact on the prices. But the two regression does not include the same amenities, so the comparison is difficult. Concerning the location, the paper couldn’t include it for Airbnb and the results for hotels are null or insignificant. The location inclusion on the model is important for future research because it has been shown to be an important determinant on the price in many previous studies on hotel and sharing accommodation price determination. Finally, concerning the reputation estimators, most of them seem to be not significant, particularly for Airbnb. More research has to be done to find out what are the reputation variables that influences the prices of sharing accommodation prices.

As a consequence of the differences in price determinants between Airbnb and Booking.com it is difficult to conclude about the competition between those two platforms. Not including the same variable on both model results are difficult to compare. We can see for the French Riviera and particularly for Nice that the mean price of Airbnb in lower than hotels one. Only Cannes has the same mean price but with very high variance for both Airbnb and Booking.com. Mean and variance are very different among the three locations and the two- platform making the comparison very difficult. Some observation can be made even if limited for example about the swimming pool. In the French Riviera, 17% of the private accommodations have a swimming pool and 30% of the hotels. Concerning the prices, adding a swimming pool on a private accommodation increase the price by 26% when it increases 45% for hotels. So, the willingness to pay for a swimming poll on hotels his much higher than for private accommodations. This difference of willingness to pay can explain that more hotels have a swimming pool than private accommodation because the return in investment in higher.

The second goal was to compare the different locations to see if is it possible to generalise the result of different cities in the French Riviera to the whole sample or if price determinants varies among cities. Cannes result was for both Airbnb and Booking.com limited because most of the included variables were not significant. For Airbnb, it seems that Cannes has a price much higher than both Nice and French Riviera so the target group for this location must be different. However, for hotel both Cannes, Nice and French Riviera had a very similar mean price. The non-significance of the result on booking could come from the sample size including 78 hotels when Nice had 116 and the French Riviera 384 hotels. It is worth pointing, however some similarities between Nice results and French Riviera for both Airbnb and Booking.com. The result can be influenced by the percentage of accommodation in Nice included on the French Riviera sample, around 30%.

The results of Nice and the French Riviera, globally, are very similar. This similitude can be explained by the similarity of the destination, Nice is one of the main touristic cities in the French Riviera and very representative of the global offer. Thus, in a future paper, it could be very interesting to compare sun and beaches tourism accommodation offers in different countries. For example, comparing the French Riviera with the Costa Brava in Spain.

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