This paper examines whether short-term rental listings in the sharing accommodation market take account of market risk in their pricing. To do so, we estimate time-varying market risks, and forecast price changes using daily supply-price time series. The empirical analysis was conducted using daily data for the Canary Islands sharing accommodation market for the period January 2016 to September 2021. The following main results were obtained. First, individual listings face systematic risks that are lower than the average market listing return, but multi-unit hosts are more sensitive to market index variations than single-unit hosts. Second, there is a time-varying but long-range dependence on market risk, indicating a slow reversion to the mean level of volatility. Price changes also reflect negative long-range dependence or anti-persistence. Third, volatility does not affect price adjustments in the market (no evidence of risk-return trade-off) for types of hosts and lodgings. Fourth, models can be used to perform risk management using value-at-risk approaches, and market risks are greater for houses and single-unit hosts in the GBP market. Finally, prices can be predicted in different horizons using long-range dependence models.
AbrateGSainaghiRMauriAG (2022) Dynamic pricing in Airbnb: individual versus professional hosts. Journal of Business Research141: 191–199.
2.
AğazadeSTükenmezEGUzunM (2023) Is the volatility of international tourism revenues affected by tourism source market structure? An empirical analysis of Turkey. Tourism Economics29(2): 291–304.
3.
Al ShehhiMKarathanasopoulosK (2018) Forecasting hotel prices in selected Middle East and North Africa Region (MENA) cities with new forecasting tools. Theoretical Economics Letters8(9): 1623–1638.
4.
Al ShehhiMKarathanasopoulosK (2020) Forecasting hotel room prices in selected GCC cities using deep learning. Journal of Hospitality and Tourism Management42: 40–50.
5.
AmpountolasA (2021) Modeling and forecasting daily hotel demand: a comparison based on SARIMAX, neural networks, and GARCH Models. Forecasting3: 580–595.
6.
ApergisNMervarAPayneJE (2017) Forecasting disaggregated tourist arrivals in Croatia: evidence from seasonal univariate time series models. Tourism Economics23(1): 78–98.
7.
ArbulúIRazumovaRey-MaquieiraMJSastreF (2021) Measuring risks and vulnerability of tourism to the COVID-19 crisis in the context of extreme uncertainty: the case of the Balearic Islands. Tourism Management Perspectives39: 100857.
BalliHOTsuiWHKBalliF (2019) Modelling the volatility of international visitor arrivals to New Zealand. Journal of Air Transport Management75(C): 204–214.
10.
BengioYSimardPFrasconiP (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks5(2): 157–166.
BiJWLiuYLiH (2020) Daily tourism volume forecasting for tourist attractions. Annals of Tourism Research83: 102923.
13.
BodeaTFergusonM (2014) Segmentation, Revenue Management and Pricing Analytics, New York, NY: Routledge.
14.
BiJ-WLiCXuH, et al. (2022) Forecasting daily tourism demand for tourist attractions with Big Data: an ensemble deep learning method. Journal of Travel Research61(8): 1719–1737.
15.
Boto-GarcíaDMayorMDe la VegaP (2021) Spatial price mimicking on Airbnb : multi-host vs single-host. Tourism Management87: 104365.
16.
CaiYZhouYMaJ, et al. (2019) Price determinants of Airbnb listings: evidence from Hong Kong. Tourism Analysis24(2): 227–242.
17.
ChanFLimCMcAleerM (2005) Modelling multivariate international tourism demand and volatility. Tourism Management26(3): 459–471.
18.
ChangCLKhamkaewTTansuchatR, et al. (2011) Interdependence of international tourism demand and volatility in leading ASEAN destinations. Tourism Economics17(3): 481–507.
19.
ChattopadhyayMMitraSK (2020) What Airbnb host listings influence peer-to-peer tourist accommodation price?Journal Jof Hospitality and Tourism Research44(4): 597–623. DOI: 10.1177/1096348020910211
20.
ChenYXieKL (2017) Consumer valuation of Airbnb listings: a hedonic pricing approach. International Journal of Contemporary Hospitality Management29(9): 2405–2424.
21.
ChuFL (2008) A fractionally integrated autoregressive moving average approach to forecasting tourism demand. Tourism Management29: 79–88.
ChuFL (2011) A piecewise linear approach to modeling and forecasting demand for Macau tourism. Tourism Management32: 1414–1420.
24.
CoshallJT (2009) Combining volatility and smoothing forecasts of UK demand for international tourism. Tourism Management30: 495–511.
25.
DamodaranA (2012) Investment Valuation - Tools and Techniques for Determining the Value of Any Asset. 3rd edition. New York: WILEY - John Wiley and Sons, Inc.
26.
DavidsonJ (2004) Moment and memory properties of linear conditional heteroscedasticity models, and a new model. Journal of Business and Economic Statistics22: 16–29.
27.
DogruTModyMLineN, et al. (2021) The Effect of Airbnb on Hotel Performance: Comparing Single- and Multi-Unit Host Listings in the United States. New York: Cornell Hospitality Quarterly.
28.
DolnicarS (2018) Unique features of peer-to-peer accommodation networks. In: DolnicarS (ed), Peer-to-Peer Accommodation Networks: Pushing the Boundaries. Oxford: Goodfellow Publishers, pp. 1–14.
29.
DoornikJA (2009) An Introduction to OxMetrics 6: A Software System for Data Analysis and Forecasting. London: Timberlake Consultants Ltd.
30.
EngleRFLilienDMRobinsRP (1987) Estimating time varying risk premia in the term structure: the Arch-M model. Econometrica55(2): 391–407.
31.
ErtEFleischerAMagenN (2016) Trust and reputation in the sharing economy: the role of personal photos in Airbnb. Tourism Management55: 62–73.
32.
GarlapatiAGarlapatiKMalisettyN, et al. (2021) Price Listing Predictions and Forthcoming Analysis of AirBnb. 12th International Conference on Computing Communication and Networking Technologies. ICCCNT), pp. 1–7. DOI: 10.1109/ICCCNT51525.2021.9579773
33.
GiacominiRWhiteH (2006) Tests of conditional predictive ability. Econometrica74(6): 1545–1578.
34.
GibbsCGuttentagDGretzelU, et al. (2018a) Goodwill pricing in the sharing economy: a hedonic pricing model applied to Airbnb listings. Journal of Travel & Tourism Marketing35(1): 46–56.
35.
GibbsCGuttentagDGretzelU, et al. (2018b) Use of dynamic pricing strategies by Airbnb hosts. International Journal of Contemporary Hospitality Management30(1): 2–20.
36.
Gil-AlanaLAMervarAPayneJE (2015) Measuring persistence in Croatian tourism: evidence from the Adriatic regions. Applied Economics47(46): 4901–4917.
37.
GiotPLaurentS (2003) Value-at-Risk for long and short positions. Journal of Applied Econometrics18: 641–664.
38.
GreffKSrivastavaRKSteunebrinkJBR, et al. (2017) LSTM: a search space odyssey,” IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222-2232.
39.
GunterUÖnderIZekanB (2020) Modeling Airbnb demand to New York City while employing spatial panel data at the listing level. Tourism Management77: 104000.
40.
HassaniHSilvaEAntonakakisN, et al. (2017) Forecasting accuracy evaluation of tourist arrivals. Annals of Tourism Research63: 112–127.
41.
HeKJiLWuCWD, et al. (2021) Using SARIMA–CNN–LSTM approach to forecast daily tourism demand. Journal of Hospitality and Tourism Management49: 25–33.
42.
HernándezJMBulchand-GidumalJSuárez-VegaR (2021) Using accommodation price determinants to segment tourist areas. Journal of Destination Marketing and Management21: 100622.
43.
HochreiterSSchmidhuberJ (1997) Long short-term memory. Neural Computation9(8): 1735–1780. DOI: 10.1162/neco.1997.9.8.1735
44.
HotiSMcAleerMShareefR (2005) Modelling country risk and uncertainty in small island tourism economies. Tourism Economics11: 159–183.
45.
HotiSMcAleerMShareefR (2007) Modelling international tourism and country risk spillovers for cyprus and malta. Tourism Management28: 1472–1484.
46.
JiménezJLOrtuñoAPérez-RodríguezJV (2023) How does Airbnb affect local Spanish tourism markets?Empirical Economics62: 2515–2545.
47.
KimSKimH (2016) A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting32(3): 669–679.
48.
KimSSWongKKF (2006) Effects of news shock on inbound tourist demand volatility in Korea. Journal of Travel Research44(4): 457–466.
49.
KingmaDBaJ (2014) Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.
50.
KourtitKNijkampPÖsthJ, et al. (2022) Airbnb and COVID-19: SPACE-TIME vulnerability effects in six world-cities. Tourism Management93: 104569.
51.
KupiecPH (1995) Techniques for verifying the accuracy of risk measurement models. Journal of Derivatives3: 73–84.
52.
KwokLXieKL (2019) Pricing strategies on Airbnb: are multi-unit hosts revenue pros?International Journal of Hospitality Management82: 252–259.
53.
LaurentS (2009) Estimating and Forecasting ARCH Models Using G@RCH 6. London: Timberlake Consultants.
LeoniVNilssonW (2021) Dynamic pricing and revenues of Airbnb listings: estimating heterogeneous causal effects. International Journal of Hospitality Management95: 102914.
56.
LiuYChengPOuYangZ (2019) Disaster risk, risk management, and tourism competitiveness: a cross-nation analysis. International Journal of Tourism Research21: 855–867.
57.
MacVicarARodgerJ (1996) Computerized yield management systems: a comparative analysis of the human resource management implications. International Journal of Hospitality Management15(4): 325–332.
58.
MagnoFCassiaFUgoliniMM (2018) Accommodation prices on Airbnb: effects of host experience and market demand. TQM Journal30(5): 608–620.
59.
McAleerMRiazSda VeigaB (2005) Risk management of daily tourist tax revenues for the Maldives. Nota di Lavoro, Viggiano, ItalyFondazione Eni Enrico Mattei, vol. 137.2005.
60.
McGuireKA (2015) Hotel Pricing in a Social World: Driving Value in the Digital Economy. Haboken, NJ: John Wiley & Sons.
61.
Moreno-IzquierdoLEgorovaGPeretó-RoviraA, et al. (2018) Exploring the use of artificial intelligence in price maximisation in the tourism sector: its application in the case of Airbnb in the valencian community. Investigaciones Regionales – Journal of Regional Research42: 113–128.
62.
Moreno-IzquierdoLRamón-RodríguezABSuch-DevesaMJ, et al. (2019) Tourist environment and online reputation as a generator of added value in the sharing economy: the case of Airbnb in urban and sun-and-beach holiday destinations. Journal of Destination Marketing and Management11: 53–66.
63.
ParkSYJeiSY (2010) Determinants of volatility on international tourism demand for South Korea: an empirical note. Applied Economics Letters17(3): 217–223.
64.
Pérez-RodríguezJVHernándezJM (2022) The effect of type of lodging and professionalism on the efficiency of P2P accommodation. Tourism Economics. Published online: DOI: 10.1177/13548166221114228
65.
Pérez-RodríguezJVSantana-GallegoM (2020) Modelling tourism receipts and associated risks, using long-range dependence models. Tourism Economics26(1): 70–96.
66.
Pérez-RodríguezJVLedesma-RodríguezFJSantana-GallegoM (2015) Testing dependence between GDP and tourism's growth rates. Tourism Management48: 268–282.
67.
Perez-SanchezVRSerrano-EstradaLMartiP, et al. (2018) The what, where, and why of Airbnb price determinants. Sustainability10(12). 4596. DOI: 10.3390/su10124596
68.
PriambodoFSihabuddinA (2020) An extreme learning machine model approach on AirBnb base price prediction. International Journal of Advanced Computer Science and Applications11(11). DOI: 10.14569/IJACSA.2020.0111123
69.
SarkerIH (2021) Deep Learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science2(6): 420.
70.
SeoJHParkSYYuL (2009) The analysis of the relationships of Korean outbound tourism demand: jeju Island and three international destinations. Tourism Management30(4): 530–543.
71.
ShareefRMcAleerM (2005) Modelling international tourism demand and volatility in small island tourism economies. International Journal of Tourism Research7: 313–333.
72.
ShareefRMcAleerM (2007) Modelling the uncertainty in international tourist arrivals to the Maldives. Tourism Management28: 23–45.
73.
ShareefRMcAleerM (2008) Modelling international tourism demand and uncertainty in maldives and seychelles: a portfolio approach. Mathematics and Computers in Simulation78: 459–468.
74.
ShokoohyarSSobhaniASobhaniA (2020) Determinants of rental strategy: short-term vs long-term rental strategy. International Journal of Contemporary Hospitality Management32(12): 3873–3894.
75.
SongHQiuRTRParkJ (2019) A review of research on tourism demand forecasting: launching the annals of tourism research curated collection on tourism demand forecasting. Annals of Tourism Research75: 338–362.
76.
SrivastavaNHintonGKrizhevskyA, et al. (2014) Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
77.
Suárez-VegaRPérez-RodríguezJVHernándezJM (2022) Substitution among hotels and P2P accommodation in the COVID era: a spatial dynamic panel data model at the listing level. Current Issues in Tourism, 26(17), 2883-2899. DOI: 10.1080/13683500.2022.2111296
78.
TeubnerTHawlitschekFDannD (2017) Price determinants on Airbnb: how reputation pays off in the sharing economy. Journal of Self-Governance and Management Economics5(4): 53–80.
79.
TranX (2011) Price sensitivity of customers in luxurious hotels in U.S. e-Review of Tourism Research (eRTR).9(4): 122-133. https://ertr.tamu.edu/
80.
TseYK (2002) Residual-based diagnostic for conditional heteroscedasticity models. The Econometrics Journal5(2): 358–374.
81.
Voltes-DortaASánchez-MedinaA (2020) Drivers of Airbnb prices according to property/room type, season and location: a regression approach. Journal of Hospitality and Tourism Management45: 266–275.
82.
WangDNicolauJL (2017) Price determinants of sharing economy-based accommodation rental: a study of listings from 33 cities on Airbnb.com. International Journal of Hospitality Management62: 120–131.
83.
WittSWittCA (1995) Forecasting tourism demand: a review of empirical research. International Journal of Forecasting1(3): 447–475.
84.
XieKHeoCYMaoZE (2021) Do professional hosts matter? Evidence from multi-listing and full-time hosts in Airbnb. Journal of Hospitality and Tourism Management47: 413–421.
85.
YuYSiXHuC, et al. (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation31(7): 1235–1270.
86.
ZekanBGunterU (2022) Zooming into Airbnb listings of European cities: further investigation of the sector’s competitiveness. Tourism Economics28(3): 772–794.
87.
ZekanBÖnderIGunterU (2019) Benchmarking of Airbnb listings: how competitive is the sharing economy sector of European cities?Tourism Economics25(7): 1029–1046.
88.
ZhangZChenRJCHanLD, et al. (2017) Key factors affecting the price of Airbnb listings: a geographically weighted approach. Sustainability9(9): 1–13.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
