To Airbnb? Factors Impacting Short-Term Leasing Preference

Abstract

The growth of Airbnb and other short-term rental platforms have presented absentee owners of urban residential properties with a choice of leasing strategy: traditional long-term rental or a short-term approach, known as “Airbnb-ing.” In this paper we identify those situations—location, structure type, and property characteristics—that lead to the highest likelihood of favoring a short-term strategy over a long-term one. Additionally, we test the impacts of hosting policies the results of which suggest that even the right property may need the right owner(s) or strategy to make short-term rental the more profitable approach.

Acknowledgements

The authors would like to thank the Real Estate Research Institute (RERI) for funding this work and the Australian Property Monitors (APM) for providing the long-term rental data. We’d also like to thank an anonymous reviewer for suggesting the matched pairs approach as a check on our hedonic model, and another for helping to shape our research question.

Notes

1 Flipkey.com, VRBO.com, Homeaway.com, Tripping.com, Stayz.com.au are examples.

2 See Fane and Richardson (2004) for a complete explanation of negative gearing.

3 Note that such cost are considered here at the revenue stage because they are specific to the revenue generation process and not to the on-going maintenance of the tenancy or property.

4 White goods are usually not supplied in the Australian rental market.

5 Note that Airbnb’s rating policy may help protect hosts as bad guests can be flagged on the platform.

6 This fee could be considered analogous to the leasing fees, L, that long-term rentals pay.

7 In fact, our choice of study area was done so to specifically minimize the number of potential traditional vacation homes in our sample.

8 Our long-term rental dataset does not include rooming houses, purpose built student accommodations and other types of rentals which may offer private or shared room options

9 As there are no official host types, we could not run more reliable supervised models.

10 Suburbs in Melbourne are much smaller than their North American counterparts.

11 If we only look at actual revenue, ignoring blocked days and properties with less than a full 12 months of listing, the figure is 42.5%

12 Hosts set the total number of guests. We normalize this by bedroom to avoid co-linearity in our model with the bedroom variables.