Content
The Content is worth 60% of the Group Assessment. You will be writing for a non-technical audience: imagine that you are writing an outline proposal to undertake a piece of research for the Mayor of London and they’ve asked you to answer some questions about how you’re going to ensure the quality of your analysis. Through set questions the Content will establish the suitability of data taken from the Inside Airbnb web site for London for policy-making purposes and, specifically, for the regulation of Short-Term Lets (STL) in London.
The responses to the set questions may be written without substantially new modelling or coding through the judicious use of descriptive statistics (see, for instance, Housing and Inequality in London and The suburbanisation of poverty in British cities, 2004-16: extent, processes and nature).
Students may use data from more than one time period if they wish, but this is not required. You can see the available data sets on Orca.
Style
This is not an essay, and students who submit answers using a traditional essay style will see their overall mark impacted as a result. You must preserve the question/response format, and your responses should stress intelligibility to an intelligent, but non-technical audience. This doesn’t mean that you don’t need citations, but you should not employ an academic writing style. See the models provided below for insights into how to write for a less technical audience.
There will also be opportunities to discuss the submission during the second half of term.
What makes writing a good briefing hard—and not just about writing good code—is finding the right balance of technical detail and high-level explanation: you can’t just say ‘here are the five types of accommodation we found…’, but you also can’t say ‘we tested clustering solutions in the range 3–50 and found the optimal result at k=12…’ You should have a look at the examples.
Word Counts & Figures
Word Counts
Each figure or table counts for 150 words, and so students should give careful consideration to the trade-offs involved: more figures may serve to illustrate your points but leave you with much less space to synthesise and present and argument.
The overall word limit for this assessment is 2,500 words.
Figures & Tables
Unless you are presenting (and citing) a figure from another source as part of your framing, all figures and tables used must be generated by Python code cells included in the markdown file. You may not modify or create figures in another application since this undermines the reproducibility of the analysis.
A/B Figures
A figure with A/B/C elements will count as one figure, but only where the parts parts are conceptually related (e.g. before/after; non-spatial/spatial distribution; type 1 and type 2; etc.). The output from PySAL’s LISA analysis library, for instance, is pre-formatted as 3 figures. Seaborn’s jointplot will only be considered to be one plot even though it is technically three because the distribution plots in the margin are related to the scatter plot that is the focus of the plot.
In principle, a briefing with 16 figures would have no space for any text or interpretation; this choice is deliberate because its purpose is to focus your attention on which charts and tables best-communicate your findings. In practice, using A/B/C figure layouts then you are looking at up to 48 separate figures before hitting the limit, though you would at this point be producing an infographic and not a briefing.
Referencing
You will need to reference various documents as part of this submission. It is possible to write these ‘by hand’, but we will award higher reproducibility marks to submissions making use of BibTeX and Markdown referencing in Quarto.
If you want to make use of BibTeX to automate referencing you’ll need BibDesk (Mac) or JabRef (Mac/Windows) or similar (Zotero shuould also work) to edit the BibTeX file outside of Docker. You will add/edit/remove works using one of these tools outside of Docker, but Quarto can still use the BibTeX file when rendering to crete the references.
Models
Although the following examples are all much longer than permitted under the assessment format, they are exemplary in their communication of the data and key findings in a manner that is clear, straightforward, and well-illustrated:
- Smith, D.A. (2010), Valuing housing and green spaces: Understanding local amenities, the built environment and house prices in London, GLA Economics; URL.
- Travers, T. Sims, S. and Bosetti, N. (2016), Housing and Inequality in London, Centre for London; URL.
- Bivens, J. (2019), The economic costs and benefits of Airbnb, Economic Policy Institute; URL.
- Wachsmuth, D., Chaney, D., Kerrigan, D. Shillolo, A. and Basalaev-Binder, R. (2018), The High Cost of Short-Term Rentals in New York City, Urban Politics and Governance research group, McGill University; URL.
- Chapple, K. (2009), Mapping Susceptibility to Gentrification: The Early Warning Toolkit, Centre for Community Innovation; URL
The last of these is bit more ‘academic’ in tone but still intended to be very accessible to a lay-reader (i.e. non-expert).
Notice how the format of the models below is broadly similar but differs from a traditional essay format. So instead of Introduction, Literature, etc. you will see two or more sections/chapters in which the evidence is developed in parallel with the background material. This format provides for more flexibility in style and presentation, though you will note that they all refer to a mix of academic and grey literature as well!
Possible Topics
The exact nature of the group’s response to the final questions in the assessment is up to you, but you should reference existing policies, where relevant, and feel free to make recommendations based on the analysis undertaken.
Below are some indicative topics and you should feel free to approach the lecturers if you wish to strike out because some other aspect of the question and data interest you:
- Impact of Airbnb on local area rental markets — this would require some assumptions about listings and lettings based on available data but as long as these are clearly stated this would be a strong approach; there are good examples of models used in other cities that it may be possible to draw on, or adapt to, London. You may want to consider things like the type of listing and the issues around the Short- and Long-Term Rental markets.
- Impact of Airbnb on London’s Tourism Economy — this would look at the distribution of London’s tourism venues and, possibly, hotels alongside Airbnb listings in order to evaluate the extent to which tourism ‘dollars’ might be spent in ways that positively impact less tourist-oriented areas if we assume (again, detail the assumptions) that some percentage of a tourist’s dollars are spent locally in an area. Again, there may be models developed elsewhere that could be adapted for the London context.
- Opportunities and Risks arising from Covid-19 — it should/may be possible to assess the impact of Covid-19 on London’s short- and long-term rental markets by looking at entry to/exit from the Airbnb marketplace by comparing more than one snapshot of London data. Again, this will require some reasonable assumptions to be drawn (are all flats withdrawn from Airbnb going back on to the Long-Term Rental Sector?) but these can be documented and justified.
- Opportunities for Place- or Listing-Branding — identifying key terms and features/amenities used to market listings by area and using these to identify opportunities for investment or branding. This would benefit from the use of NLP approaches and, potentially, word embeddings to identify distinctive patterns of word use as well as, potentially, One-Hot encoding to identify specific amenities that appear associated in some way with particular areas.
- The Challenge of Ghost Hotels — evaluating ways to automatically identify ghost hotels from the InsideAirbnb data and then, potentially, assessing their extent and impact on local areas where they dominate either ‘proper’ hotel provision or other types of listings. You will need to consider the way that Airbnb randomly shuffles listings to prevent exactly this type of application and textual similarity via NLP is an obvious application.
- The Professionalisation of Airbnb — this could be treated either as a regulatory challenge (is Airbnb not benefiting locals) or an investment opportunity (is this a way to ‘scale’ or develop new service offers for small hosts) depending on your interests. You will need to consider the different types of hosts and evaluate ways of distinguishing between them (e.g. number of listings, spatial extent, etc.).
- Impact Profiles — a geodemographic classification of London neighbourhoods based on how they have, or have not, been impacted by Airbnb. This would require you to think about how to develop a classification/clustering of London neighbourhoods and use data to develop ‘pen portraits’ of each so that policy-makers could better-understand the range of environments in which Airbnb operates and why a 1-size-fits-all regulatory approach may be insufficient. Again, this could be argued from either standpoint or even both simultaneously: these areas are already so heavily impacted that regulation is too little, too late, while these other areas are ‘at risk’.
Partial Bibliography
You will also want to expand on the partial bibliography shown in the Templates section. This is by means complete and you will likely find other relevant work ‘out there’, but this gives you a good starting point.
You might also want to have a look at Short-Term Lettings guidance for London: