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Robin Morphet: Abstract and background reading

Title image Robin Morphet

SHORT ABSTACT: 

Airbnb is a disruptive internet platform coordinating buyers and sellers of available bed spaces in residential properties.  This may make the utilisation of property more efficient if it was otherwise unused.  It may, however, lead to changing long term accommodation into short term rentals to the detriment of local housing supply. It is therefore an activity of interest to policy makers. This analysis seeks to relate Airbnb locations to measures of accessibility to give some insight into which areas as yet unaffected, may become vulnerable and which areas may be able to accept Airbnb. 

READ MORE: 

Accessibility is a concept which to a member of the public may seem simple in their experience but which is measured in a variety of relatively complex ways (Geurs and van Wee, 2004).  In this study we compare the performance of two gravity model measures, the Lakshmanan-Hansen measure and a more direct measure based on the balancing factors of the doubly constrained gravity model. The use of the balancing factors in measuring accessibility has long been recognised (Williams and Senior, 1978) The original Hansen (Hansen, 1959) measure reflects this and incorporates a measure of attraction determined by size. The later Lakshmanen_Hansen measure of attractiveness is a power function of size and we use this index of accessibility in our first model. Size may be measured in trip ends, floor space, etc. The doubly constrained method reverts to the earlier Hansen approach but calculates both origin and destination accessibility simultaneously.  The accessibility factors developed by Martinez (1995) for the doubly constrained model equate to the von Thunen rents (Morphet 2013) and are used in this analysis.

In neither model do we have a measure of mean trip length which means non standard methods of calibration are required.  The first model calibration is carried out by finding the parameter values that maximally correlate Airbnb sites with the modified Hansen accessibility.  In the second, doubly constrained model where we would normally calibrate the β parameter against mean trip cost. Instead we calibrate the rents derived from the model’s balancing factors against the rents in the Airbnb properties. We use the criterion of minimum J-divergence (Rohde, 2016) to match the two. This gives a calibrated model for those zones which contain Airbnb properties. The other zones are estimated by interpolation.  This allows the identification of potential Airbnb locations and the use of Zoopla data on all rents check the validity of the generated rent surface.

BACKGROUND READING

Initial Reading

Shabrina, Z., E. Arcaute, and M. Batty (2019). Airbnb’s disruption of the housing structure in London.arXivpreprint arXiv:1903.11205

Morphet, R., & Shabrina, Z. (2020). Gravity Model Calibration by Rent. CASA Working Paper 223 UCL Centre for Advanced Spatial Analysis, University College London, London, ISSN, 1467-298. https://www.ucl.ac.uk/bartlett/casa/publications/2020/jun/casa-working-paper-223-gravity-model-calibration-rent

Shabrina, Z., Zhang, Y., Arcaute, E., & Batty12, M. (2017). Beyond informality: The rise of peer-to-peer (P2P) renting. Working Paper 209 UCL Centre for Advanced Spatial Analysis, University College London, London, ISSN, 1467-1298.                                                                          https://www.ucl.ac.uk/bartlett/casa/case-studies/2017/mar/casa-working-paper-209

Airbnb

Belk, R. (2014). Sharing versus pseudo-sharing in web 2.0. The Anthropologist, 18(1):7–23.

McLaren, D. and Agyeman, J. (2015).Sharing Cities: A Case for Truly Smart and Sustainable Cities. MIT Press

Richardson, L. (2015). Performing the sharing economy. Geoforum, 67:121–129.

Rifkin, J. (2001). The age of access: The new culture of hypercapitalism. Penguin.

Schor, J. et al. (2016). Debating the sharing economy.Journal of Self-Governance and Management Economics 4(3),7-22.

Accessibility – Balancing Factors – Rent

Hansen, W. G. (1959). How accessibility shapes land use.Journal of the American Institute of Planners 25(2),73–76.

Dieter, K. H. (1962, August). Distribution of Work Trips in Toronto.Journal of the City Planning Division 88(1),9 – 28

Lakshmanan, J. and W. G. Hansen (1965). A retail market potential model.Journal of the American Institute ofPlanners 31(2), 134–143

Williams, H. C. W. L. and M. L. Senior (1978). Accessibility, Spatial Interaction and the Evaluation of LandUse-Transportation Plans. In A. Karlqvist, L. Lundqvist, F. Snickars, and J. W. Weibull (Eds.),Spatial InteractionTheory and Planning Models, pp. 253 – 287. Amsterdam: North Holland.

Martinez, F. (1995). Access: The Transport-Land Use Link.Transportation Research B 29(6), 457 – 470

Geurs, K. T. and B. Van Wee (2004). Accessibility evaluation of land-use and transport strategies: review andresearch directions.Journal of Transport geography 12(2), 127–140

Morphet, R. (2013, July). Von Thünen’s Legendre Transform. Technical Report 193, Bartlett Centre for AdvancedSpatial Analysis, UCL

Entropy/Information Theory

Cowan, B. (2005).Topics in Statistical Mechanics, Volume 3 of Imperial College Press Advanced Physics Texts.Imperial College Press.

Dover, Y. (2004). A short account of a connection of power laws to the information entropy.Physica A: StatisticalMechanics and its Applications 334(3-4), 591–599

Wilson, A. G. and R. J. Bennett (1985).Mathematical Methods in Human Geography and Planning, Volume 7.Chichester: John Wiley & Sons.

Rohde, N. (2016). J-divergence measurements of economic inequality.Journal of the Royal Statistical Society:Series A (Statistics in Society) 179(3), 847–870