Counting the cost of gentrification?

Dr. Jon Reades

6 July
London: Aspects of Change
#RuthGlass60

Why so little quantitative work in the UK?

Perhaps context points away from extensive methods?

  • Stronger state/regeneration focus.
  • Richer conceptualisation of types of gentrification.
  • More significant data limitations (e.g. Census, though see Atkinson, 2000).

Why the change now?

Rapid developments in both methods and data:

  • Online real estate listings covering sales and rentals.
  • Other new forms of data including social media and planning applications.
  • Novel linked data which gives us access to household change and mobility.
  • Improvements in computation and techniques to support wide and long data sets.

Why it’s still not easy

An play in three parts:

  • Act 1: Defining (Re)Development
  • Act 2: Defining Displacement
  • Act 3: Measuring Change

All of these generate a lot of drama.

Act 1: Defining (Re)Development

Few domains throw up as many challenges as housing and demographic change:

  • Large changes are visible, and often complex (see, e.g, Hubbard et al., 2024).
  • Small changes are invisible, and often not tracked (see, e.g, Chng, Reades and Hubbard, 2024).
  • Very few ‘certainties’ in the data.
  • Underlying structural dimension is inaccessible.

Act 2: Defining Displacement

Understanding whether populations have ‘changed’ in very hard in a retrospective context:

  • Census is a snapshot with no ‘memory’ (see, e.g., Hamnett, 2003; Watt, 2008; Slater, 2010)
  • GDPR (rightly, but frustratingly) limits access to individual identifiers
  • No data on situation or motivation.

Act 3: Measuring Change

Measure what matters, and what we measure matters:

  • Sales and rental prices
  • Skill, education, and income levels
  • Household composition and churn
  • Other?

So what is
the quant contribution?

‘You can’t move in Hackney without bumping into an anthropologist’ (Neal et al., 2016)

‘… qualitative strategies for identifying gentrified neighbourhoods may overlook areas that expereienced similar changes…’ (Barton, 2016, p. 92)

Test/Control

Comparison of units to number of detected relocations (Reades et al., 2023)

Distance

Proportion of relocations within administrative area (Reades et al., 2023)

Time

Waltham Forest, 2010–2016 (Almeida, 2021)

Proportions of inward moves and outward moves 1997–2016 by the 2019 English IMD quintiles (van Dijk, Lansley and Longley, 2021)
Q1 Q2 Q3 Q4 Q5
Spitalfields and Banglatown In-movers 0.228 0.353 0.182 0.135 0.103
Out-movers 0.251 0.389 0.164 0.112 0.084
Whitechapel In-movers 0.230 0.338 0.185 0.014 0.106
Out-movers 0.255 0.360 0.172 0.124 0.089
Hoxton East and Shoreditch In-movers 0.209 0.324 0.200 0.146 0.120
Out-movers 0.232 0.355 0.186 0.130 0.096

Note: Quintile 1 is the most deprived quintile, quintile 5 is the least deprived quintile.

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Context and Confidence

Standard deviation of change in rank 2001–2011 (Reades, De Souza and Hubbard, 2019)

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Prediction

Predicted typologies of future gentrifying LSOAs (Yee and Dennett, 2022)

Wrap-Up

So what can quantitative research contribute?

And what can quantitative research learn?

One more thing…

Gentrification Bingo time!

  • Paper of record for global elite
  • Scene from a brewery
  • On land now owned by Blackrock
  • Which has successfully applied to redevelop
  • Photo by the new precariat
  • Who is resident (?) in local area
  • … and …
  • Academic just out of frame

Questions?

A partial bibliography

Almeida, A. (2021) Pushed to the Margins. Runnymede Trust. Available at: https://www.runnymedetrust.org/publications/pushed-to-the-margins.
Atkinson, R. (2000) “Measuring gentrification and displacement in Greater London,” Urban studies, 37(1), pp. 149–165.
Barton, M. (2016) “An exploration of the importance of the strategy used to identify gentrification,” Urban Studies, 53(1), pp. 92–111.
Chng, I., Reades, J. and Hubbard, P. (2024) “Planning deregulation as solution to the housing crisis: The affordability, amenity and adequacy of Permitted Development in London,” Environment and Planning A: Economy and Space, 56(3), pp. 961–978. doi: 10.1177/0308518X231209982.
Hamnett, C. (2003) “Gentrification and the Middle-class Remaking of Inner London, 1961-2001,” Urban Studies, 40(12), pp. 2401–2426. doi: 10.1080/0042098032000136138.
Hubbard, P. et al. (2024) “Shrinking homes? The geographies of small domestic properties in London, 2010–2021,” Environment and Planning B: Urban Analytics and City Science, 51(5), pp. 1137–1152. doi: 10.1177/23998083231208732.
Hubbard, P., Reades, J. and Walter, H. (2021) “Housing: Shrinking homes, COVID-19 and the challenge of homeworking,” Town Planning Review, 92(1), pp. 3–10. doi: 10.3828/tpr.2020.46.
Neal, S. et al. (2016) “‘You can’t move in Hackney without bumping into an anthropologist’: why certain places attract research attention,” Qualitative Research, 16(5), pp. 491–507.
Reades, J. et al. (2023) “Quantifying state-led gentrification in London: Using linked consumer and administrative records to trace displacement from council estates,” Environment and Planning A: Economy and Space, 55(4), pp. 810–827. doi: 10.1177/0308518X221135610.
Reades, J., De Souza, J. and Hubbard, P. (2019) “Understanding urban gentrification through machine learning,” Urban Studies, 56(5), pp. 922–942. doi: 10.1177/0042098018789054.
Slater, T. (2010) “Still missing Marcuse: Hamnett’s foggy analysis in London town,” City, 14(1-2), pp. 170–179. doi: 10.1080/13604811003633719.
van Dijk, J., Lansley, G. and Longley, P. (2021) “Using linked consumer and administrative data to model demographic changes in londons city fringe,” in Big data applications in geography and planning. Edward Elgar Publishing, pp. 43–51.
Watt, P. (2008) “The Only Class in Town? Gentrification and the Middle-Class Colonization of the City and the Urban Imagination,” International Journal of Urban and Regional Research, 32(1), pp. 206–211. doi: https://doi.org/10.1111/j.1468-2427.2008.00769.x.
Yee, J. and Dennett, A. (2022) “Stratifying and predicting patterns of neighbourhood change and gentrification: An urban analytics approach,” Transactions of the Institute of British Geographers, 47(3), pp. 770–790. doi: 10.1111/tran.12522.

Jon Reades (CASA @ UCL)