Article 11: Can London Speak for England and Wales? Extrapolating Stock-Level Energy Insights

A series mining the PhD thesis "London and UK Office Buildings: Investigating Energy Use and Landlord-Tenant Influences" (Azhari, 2025).

Key takeaway. Greater London holds 30 per cent of office floor area and 24 per cent of office premises in England and Wales. That is a tempting basis for treating London-derived findings as a proxy for the whole country. The thesis tests how far the analogy holds and where it breaks down.

Figure

Greater London's share of the England and Wales office stock

London holds about a third of office floor area but under a quarter of premises, which makes it a tempting yet uneven proxy for the whole country.

30%

Of office floor area

Floor area concentrates more heavily in London than premise counts, reflecting its denser stock of large, high-rise offices.

• – Greater London share of England and Wales office floor area
• – Remaining 70 per cent sits in the rest of England and Wales

24%

Of office premises

By premise count London is a smaller slice, so the same threshold applied to a Birmingham or Leeds market would produce a flatter distribution.

• – Greater London share of England and Wales office premises
• – Remaining 76 per cent sits in the rest of England and Wales

London is a useful proxy for some questions, a misleading proxy for others, and a context-dependent proxy for many.

A tempting shortcut

This is the integration article. If you have time for only one piece from the series, read this one. It revisits every empirical and qualitative finding from Articles 1 through 10 and asks the single question that every external reader will ask: does any of this generalise beyond Greater London?

Almost every analytical finding in this series rests on a Greater London dataset. The 30/85/89 concentration result. The 18 per cent regression ceiling. The 2,654-office EPC comparison. The four-model bake-off. All of them are computed on the 6,038-office Greater London sample.

The temptation, particularly for policymakers, is to read those findings as a national story. Greater London is, after all, a third of the UK office floor area. It is the largest single office market in Europe by some measures. It is the seat of regulation, real-estate investment and most of the headquarters of the organisations interviewed in the thesis. If London is not a proxy for the rest of the country, it is hard to see what would be.

This article puts that temptation under the microscope. The short answer is that London is a useful proxy for some questions, a misleading proxy for others, and a context-dependent proxy for many.

Where London is representative

Several London findings extrapolate cleanly to the rest of England and Wales because the underlying mechanism is not location-specific.

The split-incentive problem (Article 6) operates on commercial leases, not on geography. A Manchester multi-let office faces the same RICS service-charge constraints as a Holborn one. The legal plumbing of green leases (Article 7) is the same across the country. The four operational levers behind 38 to 73 per cent portfolio energy reductions (Article 8) are levers that work in any landlord-tenant context, in any city.

The empirical relationship between EPC band and operational energy use (Article 2) is governed by the standardisation embedded in the SBEM methodology, which is national. A regional sample would show the same gap. The headline finding that building characteristics explain only 18 per cent of electricity EUI variation (Article 3) reflects the limits of the variables in the dataset rather than anything specific to London. The same model on the same variables in any region would land at a similar ceiling.

For these questions, the Greater London evidence is a reasonable national proxy.

Where London is not representative

Other findings are unmistakably London-specific.

The 30/85/89 concentration result is more extreme in London than elsewhere. Greater London has a higher density of large offices, a higher concentration of high-rise stock, and a more concentrated pattern of ownership than the rest of England and Wales. Outside the central boroughs, the same threshold applied to a Birmingham or Leeds market would produce a flatter distribution. The 30 per cent, 85 per cent, 89 per cent ratio is genuinely a London number.

The borough variation in Article 5 is, by definition, London-specific. The City of London and Westminster have median electricity EUIs that reflect their tenant mix and building type. No regional equivalent exists.

The interview sample in Articles 6 to 8 is drawn from major UK landlords, several of whom hold substantial portfolios outside London. Their accounts therefore range beyond London. Their decisions, however, are made in headquarters that are nearly all in London. The qualitative findings travel with caveats.

Ownership concentration is also more extreme in London. Offices larger than 1,000 square metres in Greater London are held by a small number of major institutional and REIT owners. In regional markets, ownership is more fragmented. Policy designed around the London concentration may not bite on the more fragmented regional stock the same way.

Where the answer is "it depends"

A third category of findings is sensitive to specific assumptions. The 18 per cent ceiling for electricity EUI prediction is national in mechanism but the size of the ceiling will shift with feature availability. A study in a region where more granular operational data is available might be able to push the R-squared higher.

The benchmark refresh case in Article 10 needs to combine Greater London evidence with national stratification, which is exactly the kind of work that the BBP REEB dataset and similar industry collections support. Neither London alone nor a national average is ideal. A weighted combination would be.

The operational rating policy case in Article 9 is national in scope. The 1,000 square metre threshold case rests partly on the London concentration finding, which is exactly where it is most extreme. A national-scope policy would need to verify the threshold against a national distribution.

The 2026 National Buildings Database (DESNZ), which the author contributed to, provides the first systematic answer at national scale for the office sector. London holds 23 per cent of office premises in Great Britain, 23 per cent of office floorspace and 29 per cent of national office energy demand. London is more energy-intensive than the rest of the country on a per-premise basis. The concentration argument from Article 1 holds and is in fact sharper than the Greater London-only data implied. For the do-and-do-not table that follows, this means: when extrapolating, expect London to be the upper bound on intensity rather than the average.

A short do-and-do-not table

Do use London findings for: the mechanisms of EPC standardisation, the split-incentive problem, the operational levers behind portfolio energy reductions, the broad statement that building characteristics explain a minority of energy variation, the case for an operational rating scheme.

Do not use London findings as proxies for: regional concentration ratios, ownership distributions outside the major REITs, regional benchmark calibration, the specific design of geographically targeted regulation.

Use with care when extrapolating: the size of the regression ceiling (mechanism transfers, magnitude varies), the absolute level of EUI medians (London is on the higher side), the four-borough concentration finding.

The thesis tests representativeness statistically in Chapter 3.2. The headline figure (30 per cent of national office floor area in Greater London) is the start of the answer, not the end. The detailed distribution tests give a more nuanced picture of where the analogy holds.

What the data does not see

Greater London has structural features (ownership concentration, large-asset prevalence, tenancy mix, climate) that the rest of England and Wales does not share. The representativeness tests are conducted on aggregate floor-area and premise-count distributions, not on operational variables. Cross-regional comparisons rely on VOA classifications that are administrative and not consistent across all local authorities. Regional variation in energy prices and policy enforcement is not captured by the dataset used for extrapolation. The piece offers heuristic guidance for using London-derived insights elsewhere, not a formal statistical reweighting.

The penultimate article in the series turns to a question every reader has asked of their own building at least once in the past five years. Article 12 looks at what COVID-19 and hybrid work have done to office energy use.

Limitations

Greater London has structural features (ownership concentration, large-asset prevalence, tenancy mix, climate) that the rest of England and Wales does not share. The representativeness tests are conducted on aggregate floor-area and premise-count distributions, not on operational variables. Cross-regional comparisons rely on VOA classifications that are administrative and not consistent across all local authorities. Regional variation in energy prices and policy enforcement is not captured by the dataset used for extrapolation. The piece offers heuristic guidance for using London-derived insights elsewhere, not a formal statistical reweighting.

References

• Azhari, R. (2025) London and UK Office Buildings Investigating energy use and landlord/tenant influences. Doctoral thesis (Ph.D), UCL (University College London). URL: https://discovery.ucl.ac.uk/id/eprint/10204821/

• Valuation Office Agency (2012). Non-Domestic Rating List statistics for England and Wales. Available at: https://www.gov.uk/government/collections/non-domestic-rating

• Steadman, P., Evans, S., Liddiard, R., Godoy-Shimizu, D., Ruyssevelt, P., and Humphrey, D. (2020). Building stock energy modelling in the UK: the 3DStock method and the London Building Stock Model. Available at: https://doi.org/10.5334/bc.52

• Evans, S., Liddiard, R., and Steadman, P. (2017). 3DStock: a new kind of three-dimensional model of the building stock. Available at: https://doi.org/10.1177/2399808317725161

• Bruhns, H. (2008). Identifying determinants of energy use in the UK non-domestic stock.

• BEIS (2019). Energy consumption in the UK. Available at: https://www.gov.uk/government/collections/energy-consumption-in-the-uk

• Evans, S., Fennell, P., Humphrey, D., Liddiard, R., Oraiopoulos, A., Palmer, J., Ruyssevelt, P., Shamsi, H., Amrith, S., and Steadman, P. (2026). National Buildings Database Phase 2. Department for Energy Security and Net Zero. Available at: https://www.gov.uk/government/publications/national-buildings-database-phase-2-understanding-great-britains-buildings

Read next

• Article 1: The 30/85/89 Problem.

• Article 4: Mapping the Stock.

About this series

This article is part of a fifteen-piece series adapting the 2025 PhD thesis "London and UK Office Buildings: Investigating Energy Use and Landlord-Tenant Influences" (Azhari, 2025) for a mixed academic and industry readership. The empirical findings draw on the 3DStock model of 6,038 office Self-Contained Units in Greater London with metered energy data for 2017, supplied by BEIS under a data-sharing agreement, alongside the Better Buildings Partnership Real Estate Environmental Benchmark. The qualitative findings draw on semi-structured interviews with seven major UK property organisations, conducted during the 2021 lockdown. Interviewees and their organisations are anonymised by role and organisation type. Please cite the original thesis for academic use.

Author. Rayan Azhari completed his PhD at the UCL Bartlett School of Environment, Energy and Resources in 2025, supervised by Paul Ruyssevelt and Kathryn Janda. The research was supported by the EPSRC Centre for Doctoral Training in Energy Demand (LoLo) and UK Research and Innovation through the Centre for Research into Energy Demand Solutions.

Other articles in the series. Article 1 The 30/85/89 Problem; Article 2 Why EPCs Do Not Tell You How Much Energy a Building Uses; Article 3 Eighteen Per Cent; Article 4 Mapping the Stock; Article 5 Height, Age and the Fuel Question; Article 6 The Split-Incentive Problem; Article 7 Green Leases and Service Charges; Article 8 From 38 to 73 Per Cent Energy Savings; Article 9 NABERS for Britain; Article 10 Time to Retire ECG-19; Article 11 Can London Speak for England and Wales; Article 12 The Hybrid-Work Footprint; Article 13 Why I Used Linear Regression Over Random Forest; Article 14 Vertical Postcodes; Article 15 What Is a Building?