Assessing the housing market incentives from residential building energy efficiency regulations

Ou, Yunbei (2026) Assessing the housing market incentives from residential building energy efficiency regulations. PhD thesis, University of Glasgow.

Abstract

If buyers are willing to pay a price premium for more energy efficient homes, this can act as a market incentive for investment in improvement, supporting net-zero goals in the housing sector. While the effectiveness of energy labels as signals of energy efficiency and hence the basis for house price premiums has been extensively examined, the mixed evidence and methodological issues (including omitted variables bias (OVB), undetermined mechanisms, price premium heterogeneity) have undermined the robustness of findings and their ability to inform decision-making. The overall aim of this thesis is to advance understanding and research practice of the price premiums of housing energy efficiency as represented by Energy Performance Certificates (EPCs). The thesis carries out three progressive steps from knowledge synthesis to application of innovative methodology to address the above research gaps.
Beginning with a systematic scoping review summarising 68 European studies of the price premium from EPCs covering published research to May 2024, it outlines research gaps and synthesises the evidence to guide the empirical analyses. It finds that studies are largely limited to countries where EPC data are openly available, and that OVB is a major methodological issue among studies. Findings show that each additional EPC band contributes to 1-3% increase in house prices, offering a solid benchmark for the following empirical analyses.
Second, focussing on the second-hand house sales market of Greater Manchester, UK, between 2017 and 2024, a multilevel hedonic model (MLM) is developed at the property scale using Zoopla listings, EPC data, and several neighbourhood-level datasets. The MLM uses a natural experiment, modelling the change in the price premium after the 2022 energy crisis. It offers novel insights into both omitted variables issues and the mechanisms underpinning willingness-to-pay (WTP). It confirms the causal interpretation of the relationship between housing energy efficiency and house prices, and suggests that a desire for energy cost saving forms at least part of the mechanism underlying WTP.
In the final analysis, causal machine learning approaches are integrated into the hedonic framework to offer data-driven insights into heterogeneity of price premiums, again focussing on the same housing market scope of Greater Manchester. Based on the individualised price premiums estimated from the meta-learner framework (X-/DR-/R-learner), post hoc heterogeneity analysis is applied.
The price premiums are found to be heterogeneous across housing submarkets within Greater Manchester, which is consistently found between meta-learners suggesting robustness of results in offering policy guidance.
Overall, this study confirms the effectiveness of EPCs in providing positive market incentives and the increase in premiums following the energy crisis but also its heterogeneity in housing submarkets. Importantly, the findings offer valuable insights for housing decarbonisation policy design in terms of regulatory, informational, subsidies, and financial market policies to support an efficient achievement of the net-zero goals.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported in part by SGSSS Supervisor-led Steers Studentship [ES/P000681/1], awarded by the Scottish Graduate School of Social Science under the Economic and Social Research Council and ESRC’s funding for the Urban Big Data Centre (UBDC) at the University of Glasgow [ES/L011921/1 and ES/S007105/1].
• Subjects: H Social Sciences > H Social Sciences (General)
• Colleges/Schools: College of Social Sciences > School of Social and Political Sciences > Urban Studies & Social Policy
• Funder's Name: Economic and Social Research Council (ESRC), Economic and Social Research Council (ESRC), Economic and Social Research Council (ESRC)
• Supervisor's Name: Bailey, Professor Nick, McArthur, Dr. David Philip and Zhao, Professor Qunshan
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-86042
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 22 Jun 2026 09:16
• Last Modified: 23 Jun 2026 10:05
• Thesis DOI: 10.5525/gla.thesis.86042
• URI: https://theses.gla.ac.uk/id/eprint/86042

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