# RECORD 0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89
# TITLE: Willingness-to-pay for energy efficient homes: New insights from the 2022 energy crisis in the United Kingdom
# YEAR: 2026.0  VENUE: Energy Research & Social Science  DOI: 10.1016/j.erss.2026.104700
# AUTHORS: Yunbei Ou; Nick Bailey; D. P. McArthur; Qunshan Zhao
# PAGES: 13  CANDIDATE TABLE PAGES (1-based): [8, 10, 11]
# ABSTRACT: Improving the energy efficiency of the housing stock is essential for decarbonising the housing sector. However, achieving large-scale improvements requires substantial private investment, underscoring the importance of housing market incentives. An extensive literature shows a positive willingness-to-pay (WTP) for housing energy efficiency. However, the methodological issue of omitted variable bias (OVB) has weakened claims of a causal effect. It is also unclear whether any WTP reflects a concern with energy costs or a more symbolic “ green ” consumption. The recent exogenous shock of the 2022 global energy crisis offers a novel opportunity to examine both. If WTP changes significantly after the energy crisis, it effectively removes OVB issues and indicates that WTP for energy efficiency is driven (at least in part) by energy cost savings. Using over 170,000 listings for second-hand house sales in Greater Manchester, UK (2017 – 24), we model WTP for energy efficiency before and after the energy crisis using multilevel hedonic regressions. We find that WTP for more energy efficient homes increased after the energy crisis, largely because the penalty for the most inefficient homes increased. These results confirm the causal interpretation of the relationship between house prices and energy efficiency and indicate that consumers are motivated at least in part by energy cost savings. These findings suggest that policymakers could strengthen the WTP by providing related regulations or incentives, promote housing energy efficiency by focussing on its financial benefits, as well as support financially vulnerable households to ensure an equal transition to sustainable housing.


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Original research article
Willingness-to-pay for energy efficient homes: New insights from the 2022 
energy crisis in the United Kingdom
Yunbei Ou a,*, Nick Bailey a, David Philip McArthur a, Qunshan Zhao a
a Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, G12 8RZ, UK
A R T I C L E  I N F O
Keywords:
Housing energy efficiency
Willingness-to-pay
Energy crisis
Causal interpretation
Mechanism/driver
Omitted variable bias
Energy performance certificates (EPC)
A B S T R A C T
Improving the energy efficiency of the housing stock is essential for decarbonising the housing sector. However, 
achieving large-scale improvements requires substantial private investment, underscoring the importance of 
housing market incentives. An extensive literature shows a positive willingness-to-pay (WTP) for housing energy 
efficiency. However, the methodological issue of omitted variable bias (OVB) has weakened claims of a causal 
effect. It is also unclear whether any WTP reflects a concern with energy costs or a more symbolic “green” 
consumption. The recent exogenous shock of the 2022 global energy crisis offers a novel opportunity to examine 
both. If WTP changes significantly after the energy crisis, it effectively removes OVB issues and indicates that 
WTP for energy efficiency is driven (at least in part) by energy cost savings. Using over 170,000 listings for 
second-hand house sales in Greater Manchester, UK (2017–24), we model WTP for energy efficiency before and 
after the energy crisis using multilevel hedonic regressions. We find that WTP for more energy efficient homes 
increased after the energy crisis, largely because the penalty for the most inefficient homes increased. These 
results confirm the causal interpretation of the relationship between house prices and energy efficiency and 
indicate that consumers are motivated at least in part by energy cost savings. These findings suggest that poli­
cymakers could strengthen the WTP by providing related regulations or incentives, promote housing energy 
efficiency by focussing on its financial benefits, as well as support financially vulnerable households to ensure an 
equal transition to sustainable housing.
1. Introduction
The improvement of housing energy efficiency is vital for mitigating 
global environmental, economic and social challenges. Residential 
buildings account for around 17% of CO2 emissions globally [1], rep­
resenting a substantial and detrimental driver of the worsening climate 
crisis. They also account for 21% of global energy demand [1], making 
housing one of the most energy intensive sectors [2] with significant 
pressure on energy markets. Also, high energy costs disproportionately 
burden poorer households in energy inefficient homes, posing socio­
economic challenges of fuel poverty [3]. Moreover, the increasing fre­
quency of unforeseen challenges including extreme climate events and 
energy price volatility highlights the need for a more resilient housing 
stock.
Despite the importance of housing energy efficiency, investment in 
improvements fall far short of the requirements of net-zero goals [4]. A 
substantial share of the housing stock is still highly inefficient, as is the 
case in the UK [5], making public spending insufficient to renovate all 
inefficient buildings [6]. Therefore, private investment must grow, with 
market incentives playing a key role in fostering such investment. If 
energy efficiency is capitalised into housing prices, homeowners, in­
vestors and other stakeholders would have stronger financial incentives 
to undertake energy efficiency retrofits. A positive willingness-to-pay 
(WTP) for more energy-efficient dwellings can therefore help foster 
private investment, offering a cost-effective pathway to accelerate im­
provements across the housing stock. In Europe, the market incentive 
mechanism has been strengthened by the introduction of Energy Per­
formance Certificates (EPCs), which are mandated to appear in housing 
advertisements throughout the EU and the UK as a former EU member 
state (Directive 2010/31/EU, p.11). EPC ratings provide information on 
building energy performance and its impact on potential running costs 
using EPC bands or scores (more details provided in Section 3.2.2).
Extensive studies have examined the price premium of housing en­
ergy efficiency as indicated by EPC ratings, generally finding a positive 
* Corresponding author at: 7 Lilybank Gardens, University of Glasgow, G12 8RZ, Glasgow, UK.
E-mail addresses: y.ou.1@research.gla.ac.uk (Y. Ou), Nick.Bailey@glasgow.ac.uk (N. Bailey), David.Mcarthur@glasgow.ac.uk (D.P. McArthur), Qunshan.Zhao@ 
glasgow.ac.uk (Q. Zhao). 
Contents lists available at ScienceDirect
Energy Research & Social Science
journal homepage: www.elsevier.com/locate/erss
https://doi.org/10.1016/j.erss.2026.104700
Received 17 November 2025; Received in revised form 30 March 2026; Accepted 6 April 2026  
Energy Research & Social Science 135 (2026) 104700 
Available online 13 April 2026 
2214-6296/Crown Copyright © 2026 Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). 


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term. The price premium began to have a clear rising trend after 2022 
especially for the “green premium”. In terms of the “brown discount”, 
the rising trend seems to start a bit earlier from 2019 to 2020. For band 
F/G, a clear and consistent increase can be found from 2022 onwards 
where 2022 experienced the largest rise, while there is also a step in­
crease in 2020. These confirm the WTP started to increase extensively 
from around 2022 i.e., the energy crisis, which continue to rise in the 
following years. More discussion around the temporal trend is presented 
in the following section.
5.3. Robustness check
In our modelling, we use all Zoopla listings which we can link to an 
EPC record. In our work to validate the Zoopla data, we used a subset of 
listings (55.5% of all listings) which we could link to official registered 
transaction data (LR PPD) to check that the listing price was indeed a 
strong indicator of the agreed sales price. The unmatched Zoopla listings 
might indicate unsold properties where the listing price is not a good 
indicator of market value, but they may also indicate simply a failure in 
the address-based matching process. To check the robustness of results, 
the main regression models (Models 1 and 2) are repeated using only 
listings that can be matched to official transactions. Results show that 
estimations of WTP for energy efficiency remain consistent with those 
using all listings in the same period (Table 5).
6. Discussion
This study exploits a period with a dramatic increase in domestic 
energy costs to strengthen the evidence that energy efficiency has a 
causal impact on house prices. Previous studies relying on cross- 
sectional designs find a positive association but have the common 
problem limiting a causal interpretation that this may be (in part or in 
whole) the result of omitted variables associated with both energy ef­
ficiency and price [8]. Some have examined the impact of short-term 
and relatively modest variations in energy prices, yielding mixed con­
clusions regarding their role in shaping WTP [11,27,37]. In contrast, the 
2022 energy crisis represents a largely exogenous energy price shock of 
unprecedented scale, providing a unique opportunity to estimate the 
causal impact of energy efficiency on house prices since it is highly 
unlikely that the valuation of unobserved confounders would change at 
the same time. It also sheds some light on the underlying mechanism or 
driver.
Across the whole period, our models confirm the significant positive 
association between energy efficiency and housing prices which is 
already well-documented in the wider literature [8]. This implies 
around a 1% price premium for each band above D, with a slightly larger 
discount for those below that level, especially those in Bands F/G. 
Crucially, our results indicate that this WTP increased following the 
onset of the energy crisis. While the price premium for properties rated 
Table 2 
Descriptive statistics of variables.
Variable
Value of category
Full sample
Before energy crisis
After energy crisis
Mean/%
Std
Mean/%
Std
Mean/%
Std
Dependent variable
Listing Price (log)
12.179
0.497
12.075
0.490
12.360
0.453
Property-level variables
EPC band
A/B
0.58%
0.56%
0.61%
C
27.70%
24.14%
33.91%
D
54.61%
56.01%
52.18%
E
14.68%
16.42%
11.65%
F/G
2.42%
2.87%
1.64%
EPC score
62.344
9.903
61.559
10.156
63.711
9.291
Tenure
Freehold
54.96%
54.10%
56.44%
Leasehold
45.04%
45.90%
43.56%
Property type
House
91.94%
91.74%
92.29%
Bungalow
8.06%
8.26%
7.71%
Total floor area (log)
4.457
0.278
4.452
0.276
4.465
0.280
Habitable rooms
1–3
9.35%
9.61%
8.89%
4
33.89%
34.51%
32.81%
5
35.35%
34.92%
36.11%
6–7
18.79%
18.42%
19.45%
8–10
2.62%
2.55%
2.74%
Built form
Detached
16.59%
16.37%
16.96%
Semi-detached
40.84%
40.35%
41.70%
Terraced
42.57%
43.28%
41.33%
Building age
Before 1900
8.74%
9.18%
7.97%
1900–1929
22.59%
23.10%
21.70%
1930–1949
21.22%
21.02%
21.57%
1950–1966
15.87%
15.73%
16.11%
1967–1982
14.32%
14.05%
14.77%
1983–2002
12.28%
12.09%
12.60%
2003–2011
4.85%
4.80%
4.93%
2012–2024
0.13%
0.01%
0.34%
Modern kitchen
True
23.57%
23.74%
23.27%
Garage
True
33.57%
33.80%
33.18%
Basement
True
1.20%
1.16%
1.25%
Garden
True
87.29%
87.05%
87.72%
School quality
2.873
0.414
2.874
0.412
2.870
0.417
Listing end time
Energy crisis
True
36.49%
Neighbourhood-level variables
Population density (natural log)
3.463
0.831
3.470
0.832
3.451
0.830
Accessibility PC1
−1.273
4.834
−1.233
4.868
−1.343
4.772
Accessibility PC2
−0.009
1.585
0.003
1.593
−0.029
1.572
Income Deprivation
0.146
0.095
0.147
0.096
0.144
0.095
Observations
170,137
108,051
62,086
Y. Ou et al.                                                                                                                                                                                                                                       
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above Band D remains broadly stable (non-significant increases), the 
discount applied to less efficient homes increases significantly in the 
post-crisis period. Using EPC scores rather than bands, we also see a 
significant increase in the premium for energy efficiency overall. This 
result is consistent with a recent UK study based on a more limited 
follow-up period (Jan 2021 to Aug 2022) using transaction price and a 
different research design [39], also finding modest price increases for 
high-rated properties alongside substantially larger price discounts for 
low-rated homes. We find this response emerges gradually rather than 
immediately, suggesting an adaptive response by market participants 
with beliefs about future energy prices evolving over time.
Over the same period as the energy crisis, house prices will have been 
impacted by a variety of economic factors, but we do not believe these 
would have had the same differential impact in relation to energy effi­
ciency. In particular, the dramatic rise in interest rates in late 2022 led to 
higher mortgage costs, and likely resulted in reduced housing demand 
and downward pressure on prices overall. However, the rising mortgage 
rates would be expected to affect all properties similarly. More relevant 
perhaps was the introduction of a Minimum Energy Efficiency Standards 
(MEES) for the private rental market, prohibiting landlords from letting 
properties with EPC bands below E which came into force in April 2020. 
This policy is likely to have had some impact on energy efficiency price 
premiums at the bottom end and may help explain the increases in the 
“brown discount” observed for EPC F/G properties in 2020. However, it 
is unlikely to account for the much steeper rise in the “brown discount” 
seen from 2022 onwards.
Crucially, our results help to relieve the long-standing concern of 
omitted variable bias in estimates of the WTP for housing energy effi­
ciency, which has weakened causal claims and limited the credibility of 
policy prescriptions. By utilising the natural experiment of the energy 
crisis, we conclude that coefficients on the EPC capture WTP for energy 
efficiency itself, at least to some extent. This confirmation provides a 
more credible message to the market that buyers do value more energy- 
efficient homes. The confirmation of the causal relationship allows more 
informed decision-making for policy (including government subsidy 
sizes, energy efficiency standards, green mortgage design, etc), busi­
nesses (in more effective investment decisions), and households (in 
choosing housing products).
Furthermore, this result also indicates that people are at least partly 
driven to pay for more efficient homes by energy cost savings. Previous 
research analysing relationship between energy prices and WTP for 
efficient homes have found little influence of energy prices [27,37], 
suggesting the price premium does not come from energy cost consid­
erations. This study, on the contrary, finds that energy costs do influence 
people's WTP for more energy-efficient homes. When energy prices are 
higher, the returns on investment in energy efficiency rise both through 
reduced operational costs and through increased house values. Other 
than expectations of high energy prices, one study found that people are 
highly uncertain about future trend of gas and electricity prices [63], 
which could be another reason for customers to choose more energy 
efficient homes in order to reduce risks. This finding might encourage a 
greater policy focus on the monetary benefits of energy efficiency 
measures instead of environmental ones. However, recognising the 
importance of energy cost savings highlights the inequality between 
high- and low-income households. While the former can afford energy 
efficient improvements, the latter cannot and thereby suffer from higher 
burdens from energy costs. It is therefore important for Governments to 
support low-income households to minimise the gap.
Our finding of about a 1% price premium per band translates into 
£1900 or £2200 in housing price capitalisation based on the median and 
mean housing price respectively for listings in this study. The average 
cost to improve dwellings with EPC band of D to an EPC band of C or 
above is estimated at £6018 per dwelling [64] albeit with a wide range. 
For those planning to sell a dwelling in the near future, there still ap­
pears to be little market incentive to make significant energy efficiency 
improvements. For those planning to remain for some time, however, 
the increase in capital value does increase the overall returns on such 
investments. One analysis [65] suggests that the average annual energy 
Fig. 3. Distribution of key attributes at LSOA11 level.
Y. Ou et al.                                                                                                                                                                                                                                       
Energy Research & Social Science 135 (2026) 104700 
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cost saving of improving from EPC D to C was around £680 per year at 
the peak of the energy crisis. The capital value gains from energy 
Table 3 
Main regression results.
Value of category
Model 1: EPC bands
Model 2: EPC score
Coef.
[95% CIs]
Coef.
[95% CIs]
Intercept
9.923
9.842
Property-level variables
EPC band
A/B
0.022***
[0.011, 0.033]
–
–
C
0.013***
[0.011, 0.015]
–
–
E
−0.015***
[−0.018, −0.013]
–
–
F/G
−0.041***
[−0.047, −0.035]
–
–
EPC score
–
–
0.001***
[0.001,0.001]
Tenure
Leasehold
−0.007***
[−0.009, −0.005]
−0.007***
[−0.009, −0.005]
Property type
Bungalow
0.094***
[0.090, 0.097]
0.094***
[0.090, 0.097]
Total floor area (log)
0.496***
[0.491, 0.501]
0.496***
[0.491, 0.501]
Habitable rooms
4
0.014***
[0.010, 0.017]
0.013***
[0.010, 0.017]
5
0.054***
[0.051, 0.058]
0.054***
[0.051, 0.058]
6–7
0.126***
[0.121, 0.130]
0.126***
[0.121, 0.130]
8–10
0.196***
[0.189, 0.203]
0.196***
[0.188, 0.203]
Built form
Detached
0.174***
[0.171, 0.177]
0.174***
[0.171, 0.177]
Terraced
−0.172***
[−0.175, −0.170]
−0.172***
[−0.175, −0.170]
Building age
Before 1900
0.001
[−0.003, 0.005]
0.002
[−0.002, 0.006]
1900–1929
−0.032***
[−0.035, −0.028]
−0.031***
[−0.034, −0.027]
1930–1949
0.014***
[0.011, 0.017]
0.015***
[0.012, 0.018]
1967–1982
0.026***
[0.023, 0.029]
0.025***
[0.022, 0.029]
1983–2002
0.099***
[0.095, 0.103]
0.098***
[0.094, 0.102]
2003–2011
0.132***
[0.127,0.137]
0.128***
[0.123,0.133]
2012–2024
0.139***
[0.115, 0.162]
0.132***
[0.109, 0.156]
Property facilities
Modern kitchen
0.043***
[0.041, 0.045]
0.043***
[0.041, 0.045]
Garage
0.057***
[0.055, 0.060]
0.058***
[0.055, 0.060]
Basement
0.051***
[0.043, 0.059]
0.052***
[0.044, 0.060]
Garden
0.073***
[0.070, 0.076]
0.073***
[0.070, 0.075]
School quality
0.008***
[0.003, 0.012]
0.008***
[0.003, 0.012]
Time
Yes
Yes
Neighbourhood-level variables
Population density (natural log)
−0.006
[−0.023, 0.010]
−0.006
[−0.023, 0.010]
Accessibility PC1
0.017***
[0.015, 0.019]
0.017***
[0.015, 0.019]
Accessibility PC2
−0.004
[−0.011, 0.003]
−0.004
[−0.011, 0.003]
Income Deprivation
−1.589***
[−1.694, −1.484]
−1.590***
[−1.694, −1.484]
σ2
ϵ
0.031
0.031
σ2
u
0.053
0.053
σ2
f
0.215
0.215
VPC
63.10%
63.10%
Pseudo R2
89.60%
89.60%
Observations
170,137
170,137
Note: The variance partitioning coefficient (VPC) measures the proportion of total variance in an outcome variable that is attributable to the grouping structure. It is the 
level 2 random effect variance divided by level 1 and level 2 random effect variance: VPC = σ2
u/
( σ2
u + σ2
ϵ
)
. The Pseudo R2 is calculated based on framework [61]: R2 =
σ2
f + σ2
u/
(
σ2
f + σ2
u + σ2
ϵ
)
where σ2
f is the variance explained by level 1 and level 2 predictors. *** p < .001. ** p < .01. * p < .05.
Table 4 
Price premium of energy efficiency with change after energy crisis.
Variable
Model 3: 
Interaction with EPC bands
Model 4: 
Interaction with EPC score
Coef.
[95% CIs]
Coef.
[95% CIs]
Variables of interest
EPC A/B
0.020*
[0.006, 0.034]
After energy crisis * EPC A/B
0.005
[−0.017, 0.028]
EPC C
0.012***
[0.009, 0.015]
After energy crisis * EPC C
0.001
[−0.003, 0.005]
EPC E
−0.013***
[−0.016, −0.010]
After energy crisis * EPC E
−0.008**
[−0.013, −0.003]
EPC F/G
−0.036***
[−0.043, −0.030]
After energy crisis * EPC F/G
−0.019**
[−0.032, −0.006]
EPC score
0.0012***
[0.0011, 0.0013]
After energy crisis * EPC score
0.0003***
[0.0001, 0.0005]
VPC
63.02%
63.02%
Pseudo R2
89.60%
89.60%
Observations
170,137
170,137
Note: Only variables of interest are presented. *** p < .001. ** p < .01. * p < .05.
Y. Ou et al.                                                                                                                                                                                                                                       
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Fig. 4. Price premium for EPC bands before and after the energy crisis. 
Note: The coefficient estimates of effects in the presence of interactions i.e., “coefficients after energy crisis” is the addition of coefficient estimate of EPC band and 
that of the corresponding interaction term. The confidence interval of “coefficients after energy crisis” is calculated based on [62]. The p-values indicate the statistical 
significance of interaction components EPC band*Energy crisis i.e., difference between coefficients before and after the crisis. *** p < .001. ** p < .01. * p < .05.
Fig. 5. Estimates of price premium after different years. 
Note: The coefficient estimates and confidence intervals are calculated using the same approach as specified in the note of Fig. 4.
Table 5 
Robustness check: main regression on matched and complete listings.
Model
Variable
Matched listings 
(2017.09–2023.10)
All listings 
(2017.09–2023.10)
EPC band
A/B
0.018* [0.001, 0.035]
0.019** [0.007,0.031]
C
0.012*** [0.009, 0.015]
0.012*** [0.010, 0.014]
E
−0.014*** [−0.018, −0.011]
−0.016*** [−0.018, −0.013]
F/G
−0.045*** [−0.053, −0.037]
−0.040*** [−0.046, −0.035]
EPC score
Score
0.001*** [0.001, 0.001]
0.001*** [0.001, 0.001]
Notes: N = 64,292 for matched listings and N = 149,526 for all listings.
Y. Ou et al.                                                                                                                                                                                                                                       
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efficiency improvement are therefore equivalent to around three years 
of running cost savings so forms a significant additional incentive.
There are some limitations to this study. One key limitation is the 
focus on the second-hand housing market in one UK metropolitan area, 
although we have no reason to doubt that results here would apply 
across the rest of the UK at least. Given the energy crisis is global in scale, 
generating different impacts across regions [66] and housing markets, 
future studies could analyse cross-regional or international markets to 
assess how the energy crisis influences the relationship between energy 
efficiency and housing prices more widely. Another limitation lies in 
potential heterogeneity in WTP for housing energy efficiency across 
different socio-demographic groups, property types, etc. In reality, 
preferences to invest in energy efficiency improvements may vary 
significantly [67,68]. Ignoring this heterogeneity may lead to over­
generalised conclusions and limit the applicability of the findings to 
granular policy or market interventions. Therefore, a priority for future 
research is to explore and examine the heterogeneous WTP for housing 
energy efficiency.
7. Conclusions
Taking the dramatic price increase from the global energy crisis as an 
opportunity, this study investigates whether residents' willingness-to- 
pay for more energy-efficient homes increased following the crisis. 
The findings show that energy-efficient properties command a positive 
price premium of above 1% per band, and that this has increased in the 
post-crisis period. By documenting an increase of WTP after the crisis, 
we argue that the causal interpretation of the relationship between 
housing energy efficiency and housing prices is strengthened. Further­
more, the results indicate that energy cost considerations play a sub­
stantive role in this WTP, rather than the effect reflecting only 
environmental concern or signalling. Overall, this study provides new 
insights on the price premium associated with energy-efficient homes in 
the context of the energy crisis, offering a valuable contribution to the 
existing literature.
The findings have important policy and practical implications. First, 
the strengthened evidence of WTP for more energy-efficient homes 
could increase investors' willingness to finance retrofit works, enabling 
more homeowners to upgrade. It should also encourage policy makers to 
consider introducing minimum energy efficiency standards for the 
housing sales markets. Second, the identification of the association be­
tween WTP and energy costs suggests that policymakers could place 
more emphasis on energy bill reduction when promoting energy effi­
ciency measures in policy design. Moreover, it is important for policy­
makers to recognise the disparity of the energy transition process among 
high- and low-income households, to provide targeted subsidies to 
ensure more equal outcomes. Finally, the increase in WTP following the 
recent energy crisis highlights its potential role as a catalyst for behav­
ioural change, offering policymakers an opportunity to leverage this 
opportunity to reach broader sustainability objectives.
CRediT authorship contribution statement
Yunbei Ou: Writing – original draft, Visualization, Validation, 
Software, Methodology, Investigation, Formal analysis, Data curation, 
Conceptualization. Nick Bailey: Writing – review & editing, Supervi­
sion, Methodology, Funding acquisition, Conceptualization. David 
Philip McArthur: Writing – review & editing, Supervision, Methodol­
ogy, Funding acquisition, Conceptualization. Qunshan Zhao: Writing – 
review & editing, Supervision, Methodology, Funding acquisition, 
Conceptualization.
Declaration of Generative AI and AI-assisted technologies in the 
writing process
During the preparation of this work, the authors used ChatGPT-5 in 
order to improve language and writing. After using this tool/service, the 
authors reviewed and edited the content as needed and take full re­
sponsibility for the content of the published article.
Declaration of competing interest
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper.
Acknowledgement
This work was made possible by the ESRC-funded Advanced Quan­
titative Methods (AQM) Doctoral Studentship [ES/P000681/1] and 
ESRC’s on-going support for the Urban Big Data Centre [ES/L011921/1 
and ES/S007105/1]. Dr Qunshan Zhao has received the support from 
the Royal Society International Exchange Scheme [IEC\NSFC\223042]. 
We would like to thank the reviewers and attendees at the Housing 
Studies Association Conference 2025 for providing useful feedback.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.erss.2026.104700.
Data availability
The Zoopla data with UPRN is not openly available due to privacy 
laws, links to other data sources are provided in text and the code is 
available at: https://github.com/YunbeiOu/WTP_EPC_EnergyCrisis.
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