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The Influence of Vacation Home Rentals on Neighborhood Crime and Disorder

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Abstract

This study examines the association between vacation home rentals (VHR) and residential burglary, disturbances, and substance crimes from social disorganization and routine activity perspectives. Airbnb and crime data for 2018 in Austin, Texas, were analyzed spatially and with multivariate count regression models. Census block groups were used as the unit of analysis (N = 602). Other variables considered include socio-demographic variables, a spatial lag, and bar and nightclub locations. Negative binomial regression analyses revealed VHR properties to be significantly and negatively associated with residential burglary, substance crimes, and disturbances when the rental was the entire structure; however, VHR properties were associated with all crime types in a significant and positive manner when the rental was only for an individual room in the property. This is the second study to find negative associations between entire structure VHRs and crime, though this study varies from others in several aspects. VHRs do not appear to contribute to crime in all circumstances, and listing type matters. However, this is a newer research topic and future studies would benefit from using longitudinal methods, addressing the temporal order of VHR properties and crime in neighborhoods, as well as considering property management characteristics.

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Data Availability Statement

Crime data, alcohol establishment data, and census data are publicly available. VHR data are proprietary and purchasable.

Notes

  1. “Vacation home rentals” (VHR) are sometimes referred to as “transient vacation rentals” (Jordan & Moore, 2018), “home sharing lodging” (Yang et al., 2019), or more generally as “short-term rentals” (DiNatale et al., 2018).

  2. Hotel and motel data are not available for the present study, though they may constitute a confounding effect when establishing the association between VHRs and crime. The presence of sporting venues or participation in sporting events may also be useful when data are available. When possible, future studies should consider these other types of lodgings and activites.

    The Texas Alcoholic Beverage Commission (TABC) has more than 50 kinds of alcohol related permits and licenses. Rather than using all of them, the one used for this study was the Mixed Beverage Permit. The Mixed Beverage permit “[a]llows the holder to sell mixed beverages from unsealed containers and wine, beer, ale and malt liquor in containers of any legal size for consumption on the premises” (TABC, 2020).

  3. LISREL Student Version 10.20 was used for the confirmatory factor analysis for both concentrated disadvantage and residential instability. Factor loadings and post-estimation statistics are available upon request.

  4. Four primary models were initially considered, negative binomial, Poisson, zero-inflated negative binomial, and zero-inflated Poisson. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values preferred negative binomial over all others, though the difference between negative binomial and zero-inflated negative binomial were inconsequentially similar (Hilbe, 2011); given this, the negative binomial model was selected.

  5. Two other coordinate pair distance calculations were initially used and all three equations produced results consistent within 99.7%. This consistency is less than 250’ of error when calculating a distance of coordinate pairs 18 miles apart (95,040’).

  6. The maximum value for bars, substance crimes, and disturbances were located in CBGs in the downtown area of the city. Alternative models were initially constructed to assess whether effects changed among all variables of interest if additional “downtown” and “not downtown” variables were constructed that considered VHRs in the downtown area versus outside of the downtown area. The variables were omitted from the presented models here as model fit statistics found the additional variables contributed little to the models and did not substantially alter VHR influence on outcome variables.

  7. The data classification method used for the symbology of each map was manual intervals; the counts and distributions of each variable varied such that manual intervals yielded more useful visualizations compared to using quartiles, equal intervals, or others.

  8. Also contrary to popular perception, low-income housing projects have been found to decrease neighborhood crime in Austin, Texas (Woo & Joh, 2015).

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Acknowledgements

A special thanks to Lucia Summers, Ashley Hewitt, and William Stone for their review of previous drafts of this paper.

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  1. School of Criminal Justice and Criminology, Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Daniel Reinhard

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Correspondence to Daniel Reinhard.

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Reinhard, D. The Influence of Vacation Home Rentals on Neighborhood Crime and Disorder. Am J Crim Just 48, 233–249 (2023). https://doi.org/10.1007/s12103-021-09635-8

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