1# RECORD 0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b892# TITLE: Willingness-to-pay for energy efficient homes: New insights from the 2022 energy crisis in the United Kingdom3# YEAR: 2026.0 VENUE: Energy Research & Social Science DOI: 10.1016/j.erss.2026.1047004# AUTHORS: Yunbei Ou; Nick Bailey; D. P. McArthur; Qunshan Zhao5# PAGES: 13 CANDIDATE TABLE PAGES (1-based): [8, 10, 11]6# 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.789===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) =====1011Original research article12Willingness-to-pay for energy efficient homes: New insights from the 2022 13energy crisis in the United Kingdom14Yunbei Ou a,*, Nick Bailey a, David Philip McArthur a, Qunshan Zhao a15a Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, G12 8RZ, UK16A R T I C L E I N F O17Keywords:18Housing energy efficiency19Willingness-to-pay20Energy crisis21Causal interpretation22Mechanism/driver23Omitted variable bias24Energy performance certificates (EPC)25A B S T R A C T26Improving the energy efficiency of the housing stock is essential for decarbonising the housing sector. However, 27achieving large-scale improvements requires substantial private investment, underscoring the importance of 28housing market incentives. An extensive literature shows a positive willingness-to-pay (WTP) for housing energy 29efficiency. However, the methodological issue of omitted variable bias (OVB) has weakened claims of a causal 30effect. It is also unclear whether any WTP reflects a concern with energy costs or a more symbolic “green” 31consumption. The recent exogenous shock of the 2022 global energy crisis offers a novel opportunity to examine 32both. If WTP changes significantly after the energy crisis, it effectively removes OVB issues and indicates that 33WTP for energy efficiency is driven (at least in part) by energy cost savings. Using over 170,000 listings for 34second-hand house sales in Greater Manchester, UK (2017–24), we model WTP for energy efficiency before and 35after the energy crisis using multilevel hedonic regressions. We find that WTP for more energy efficient homes 36increased after the energy crisis, largely because the penalty for the most inefficient homes increased. These 37results confirm the causal interpretation of the relationship between house prices and energy efficiency and 38indicate that consumers are motivated at least in part by energy cost savings. These findings suggest that poli39cymakers could strengthen the WTP by providing related regulations or incentives, promote housing energy 40efficiency by focussing on its financial benefits, as well as support financially vulnerable households to ensure an 41equal transition to sustainable housing.421. Introduction43The improvement of housing energy efficiency is vital for mitigating 44global environmental, economic and social challenges. Residential 45buildings account for around 17% of CO2 emissions globally [1], rep46resenting a substantial and detrimental driver of the worsening climate 47crisis. They also account for 21% of global energy demand [1], making 48housing one of the most energy intensive sectors [2] with significant 49pressure on energy markets. Also, high energy costs disproportionately 50burden poorer households in energy inefficient homes, posing socio51economic challenges of fuel poverty [3]. Moreover, the increasing fre52quency of unforeseen challenges including extreme climate events and 53energy price volatility highlights the need for a more resilient housing 54stock.55Despite the importance of housing energy efficiency, investment in 56improvements fall far short of the requirements of net-zero goals [4]. A 57substantial share of the housing stock is still highly inefficient, as is the 58case in the UK [5], making public spending insufficient to renovate all 59inefficient buildings [6]. Therefore, private investment must grow, with 60market incentives playing a key role in fostering such investment. If 61energy efficiency is capitalised into housing prices, homeowners, in62vestors and other stakeholders would have stronger financial incentives 63to undertake energy efficiency retrofits. A positive willingness-to-pay 64(WTP) for more energy-efficient dwellings can therefore help foster 65private investment, offering a cost-effective pathway to accelerate im66provements across the housing stock. In Europe, the market incentive 67mechanism has been strengthened by the introduction of Energy Per68formance Certificates (EPCs), which are mandated to appear in housing 69advertisements throughout the EU and the UK as a former EU member 70state (Directive 2010/31/EU, p.11). EPC ratings provide information on 71building energy performance and its impact on potential running costs 72using EPC bands or scores (more details provided in Section 3.2.2).73Extensive studies have examined the price premium of housing en74ergy efficiency as indicated by EPC ratings, generally finding a positive 75* Corresponding author at: 7 Lilybank Gardens, University of Glasgow, G12 8RZ, Glasgow, UK.76E-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@ 77glasgow.ac.uk (Q. Zhao). 78Contents lists available at ScienceDirect79Energy Research & Social Science80journal homepage: www.elsevier.com/locate/erss81https://doi.org/10.1016/j.erss.2026.10470082Received 17 November 2025; Received in revised form 30 March 2026; Accepted 6 April 2026 83Energy Research & Social Science 135 (2026) 104700 84Available online 13 April 2026 852214-6296/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/ ). 868788===== PAGE 8 (score 41, numeric-paren 0, table-words 4, var-words 20) =====8990term. The price premium began to have a clear rising trend after 2022 91especially for the “green premium”. In terms of the “brown discount”, 92the rising trend seems to start a bit earlier from 2019 to 2020. For band 93F/G, a clear and consistent increase can be found from 2022 onwards 94where 2022 experienced the largest rise, while there is also a step in95crease in 2020. These confirm the WTP started to increase extensively 96from around 2022 i.e., the energy crisis, which continue to rise in the 97following years. More discussion around the temporal trend is presented 98in the following section.995.3. Robustness check100In our modelling, we use all Zoopla listings which we can link to an 101EPC record. In our work to validate the Zoopla data, we used a subset of 102listings (55.5% of all listings) which we could link to official registered 103transaction data (LR PPD) to check that the listing price was indeed a 104strong indicator of the agreed sales price. The unmatched Zoopla listings 105might indicate unsold properties where the listing price is not a good 106indicator of market value, but they may also indicate simply a failure in 107the address-based matching process. To check the robustness of results, 108the main regression models (Models 1 and 2) are repeated using only 109listings that can be matched to official transactions. Results show that 110estimations of WTP for energy efficiency remain consistent with those 111using all listings in the same period (Table 5).1126. Discussion113This study exploits a period with a dramatic increase in domestic 114energy costs to strengthen the evidence that energy efficiency has a 115causal impact on house prices. Previous studies relying on cross- 116sectional designs find a positive association but have the common 117problem limiting a causal interpretation that this may be (in part or in 118whole) the result of omitted variables associated with both energy ef119ficiency and price [8]. Some have examined the impact of short-term 120and relatively modest variations in energy prices, yielding mixed con121clusions regarding their role in shaping WTP [11,27,37]. In contrast, the 1222022 energy crisis represents a largely exogenous energy price shock of 123unprecedented scale, providing a unique opportunity to estimate the 124causal impact of energy efficiency on house prices since it is highly 125unlikely that the valuation of unobserved confounders would change at 126the same time. It also sheds some light on the underlying mechanism or 127driver.128Across the whole period, our models confirm the significant positive 129association between energy efficiency and housing prices which is 130already well-documented in the wider literature [8]. This implies 131around a 1% price premium for each band above D, with a slightly larger 132discount for those below that level, especially those in Bands F/G. 133Crucially, our results indicate that this WTP increased following the 134onset of the energy crisis. While the price premium for properties rated 135Table 2 136Descriptive statistics of variables.137Variable138Value of category139Full sample140Before energy crisis141After energy crisis142Mean/%143Std144Mean/%145Std146Mean/%147Std148Dependent variable149Listing Price (log)15012.1791510.49715212.0751530.49015412.3601550.453156Property-level variables157EPC band158A/B1590.58%1600.56%1610.61%162C16327.70%16424.14%16533.91%166D16754.61%16856.01%16952.18%170E17114.68%17216.42%17311.65%174F/G1752.42%1762.87%1771.64%178EPC score17962.3441809.90318161.55918210.15618363.7111849.291185Tenure186Freehold18754.96%18854.10%18956.44%190Leasehold19145.04%19245.90%19343.56%194Property type195House19691.94%19791.74%19892.29%199Bungalow2008.06%2018.26%2027.71%203Total floor area (log)2044.4572050.2782064.4522070.2762084.4652090.280210Habitable rooms2111–32129.35%2139.61%2148.89%215421633.89%21734.51%21832.81%219522035.35%22134.92%22236.11%2236–722418.79%22518.42%22619.45%2278–102282.62%2292.55%2302.74%231Built form232Detached23316.59%23416.37%23516.96%236Semi-detached23740.84%23840.35%23941.70%240Terraced24142.57%24243.28%24341.33%244Building age245Before 19002468.74%2479.18%2487.97%2491900–192925022.59%25123.10%25221.70%2531930–194925421.22%25521.02%25621.57%2571950–196625815.87%25915.73%26016.11%2611967–198226214.32%26314.05%26414.77%2651983–200226612.28%26712.09%26812.60%2692003–20112704.85%2714.80%2724.93%2732012–20242740.13%2750.01%2760.34%277Modern kitchen278True27923.57%28023.74%28123.27%282Garage283True28433.57%28533.80%28633.18%287Basement288True2891.20%2901.16%2911.25%292Garden293True29487.29%29587.05%29687.72%297School quality2982.8732990.4143002.8743010.4123022.8703030.417304Listing end time305Energy crisis306True30736.49%308Neighbourhood-level variables309Population density (natural log)3103.4633110.8313123.4703130.8323143.4513150.830316Accessibility PC1317−1.2733184.834319−1.2333204.868321−1.3433224.772323Accessibility PC2324−0.0093251.5853260.0033271.593328−0.0293291.572330Income Deprivation3310.1463320.0953330.1473340.0963350.1443360.095337Observations338170,137339108,05134062,086341Y. Ou et al. 342Energy Research & Social Science 135 (2026) 104700 3438 344345346===== PAGE 9 (score 4, numeric-paren 0, table-words 0, var-words 4) =====347348above Band D remains broadly stable (non-significant increases), the 349discount applied to less efficient homes increases significantly in the 350post-crisis period. Using EPC scores rather than bands, we also see a 351significant increase in the premium for energy efficiency overall. This 352result is consistent with a recent UK study based on a more limited 353follow-up period (Jan 2021 to Aug 2022) using transaction price and a 354different research design [39], also finding modest price increases for 355high-rated properties alongside substantially larger price discounts for 356low-rated homes. We find this response emerges gradually rather than 357immediately, suggesting an adaptive response by market participants 358with beliefs about future energy prices evolving over time.359Over the same period as the energy crisis, house prices will have been 360impacted by a variety of economic factors, but we do not believe these 361would have had the same differential impact in relation to energy effi362ciency. In particular, the dramatic rise in interest rates in late 2022 led to 363higher mortgage costs, and likely resulted in reduced housing demand 364and downward pressure on prices overall. However, the rising mortgage 365rates would be expected to affect all properties similarly. More relevant 366perhaps was the introduction of a Minimum Energy Efficiency Standards 367(MEES) for the private rental market, prohibiting landlords from letting 368properties with EPC bands below E which came into force in April 2020. 369This policy is likely to have had some impact on energy efficiency price 370premiums at the bottom end and may help explain the increases in the 371“brown discount” observed for EPC F/G properties in 2020. However, it 372is unlikely to account for the much steeper rise in the “brown discount” 373seen from 2022 onwards.374Crucially, our results help to relieve the long-standing concern of 375omitted variable bias in estimates of the WTP for housing energy effi376ciency, which has weakened causal claims and limited the credibility of 377policy prescriptions. By utilising the natural experiment of the energy 378crisis, we conclude that coefficients on the EPC capture WTP for energy 379efficiency itself, at least to some extent. This confirmation provides a 380more credible message to the market that buyers do value more energy- 381efficient homes. The confirmation of the causal relationship allows more 382informed decision-making for policy (including government subsidy 383sizes, energy efficiency standards, green mortgage design, etc), busi384nesses (in more effective investment decisions), and households (in 385choosing housing products).386Furthermore, this result also indicates that people are at least partly 387driven to pay for more efficient homes by energy cost savings. Previous 388research analysing relationship between energy prices and WTP for 389efficient homes have found little influence of energy prices [27,37], 390suggesting the price premium does not come from energy cost consid391erations. This study, on the contrary, finds that energy costs do influence 392people's WTP for more energy-efficient homes. When energy prices are 393higher, the returns on investment in energy efficiency rise both through 394reduced operational costs and through increased house values. Other 395than expectations of high energy prices, one study found that people are 396highly uncertain about future trend of gas and electricity prices [63], 397which could be another reason for customers to choose more energy 398efficient homes in order to reduce risks. This finding might encourage a 399greater policy focus on the monetary benefits of energy efficiency 400measures instead of environmental ones. However, recognising the 401importance of energy cost savings highlights the inequality between 402high- and low-income households. While the former can afford energy 403efficient improvements, the latter cannot and thereby suffer from higher 404burdens from energy costs. It is therefore important for Governments to 405support low-income households to minimise the gap.406Our finding of about a 1% price premium per band translates into 407£1900 or £2200 in housing price capitalisation based on the median and 408mean housing price respectively for listings in this study. The average 409cost to improve dwellings with EPC band of D to an EPC band of C or 410above is estimated at £6018 per dwelling [64] albeit with a wide range. 411For those planning to sell a dwelling in the near future, there still ap412pears to be little market incentive to make significant energy efficiency 413improvements. For those planning to remain for some time, however, 414the increase in capital value does increase the overall returns on such 415investments. One analysis [65] suggests that the average annual energy 416Fig. 3. Distribution of key attributes at LSOA11 level.417Y. Ou et al. 418Energy Research & Social Science 135 (2026) 104700 4199 420421422===== PAGE 10 (score 115, numeric-paren 57, table-words 9, var-words 19) =====423424cost saving of improving from EPC D to C was around £680 per year at 425the peak of the energy crisis. The capital value gains from energy 426Table 3 427Main regression results.428Value of category429Model 1: EPC bands430Model 2: EPC score431Coef.432[95% CIs]433Coef.434[95% CIs]435Intercept4369.9234379.842438Property-level variables439EPC band440A/B4410.022***442[0.011, 0.033]443–444–445C4460.013***447[0.011, 0.015]448–449–450E451−0.015***452[−0.018, −0.013]453–454–455F/G456−0.041***457[−0.047, −0.035]458–459–460EPC score461–462–4630.001***464[0.001,0.001]465Tenure466Leasehold467−0.007***468[−0.009, −0.005]469−0.007***470[−0.009, −0.005]471Property type472Bungalow4730.094***474[0.090, 0.097]4750.094***476[0.090, 0.097]477Total floor area (log)4780.496***479[0.491, 0.501]4800.496***481[0.491, 0.501]482Habitable rooms48344840.014***485[0.010, 0.017]4860.013***487[0.010, 0.017]48854890.054***490[0.051, 0.058]4910.054***492[0.051, 0.058]4936–74940.126***495[0.121, 0.130]4960.126***497[0.121, 0.130]4988–104990.196***500[0.189, 0.203]5010.196***502[0.188, 0.203]503Built form504Detached5050.174***506[0.171, 0.177]5070.174***508[0.171, 0.177]509Terraced510−0.172***511[−0.175, −0.170]512−0.172***513[−0.175, −0.170]514Building age515Before 19005160.001517[−0.003, 0.005]5180.002519[−0.002, 0.006]5201900–1929521−0.032***522[−0.035, −0.028]523−0.031***524[−0.034, −0.027]5251930–19495260.014***527[0.011, 0.017]5280.015***529[0.012, 0.018]5301967–19825310.026***532[0.023, 0.029]5330.025***534[0.022, 0.029]5351983–20025360.099***537[0.095, 0.103]5380.098***539[0.094, 0.102]5402003–20115410.132***542[0.127,0.137]5430.128***544[0.123,0.133]5452012–20245460.139***547[0.115, 0.162]5480.132***549[0.109, 0.156]550Property facilities551Modern kitchen5520.043***553[0.041, 0.045]5540.043***555[0.041, 0.045]556Garage5570.057***558[0.055, 0.060]5590.058***560[0.055, 0.060]561Basement5620.051***563[0.043, 0.059]5640.052***565[0.044, 0.060]566Garden5670.073***568[0.070, 0.076]5690.073***570[0.070, 0.075]571School quality5720.008***573[0.003, 0.012]5740.008***575[0.003, 0.012]576Time577Yes578Yes579Neighbourhood-level variables580Population density (natural log)581−0.006582[−0.023, 0.010]583−0.006584[−0.023, 0.010]585Accessibility PC15860.017***587[0.015, 0.019]5880.017***589[0.015, 0.019]590Accessibility PC2591−0.004592[−0.011, 0.003]593−0.004594[−0.011, 0.003]595Income Deprivation596−1.589***597[−1.694, −1.484]598−1.590***599[−1.694, −1.484]600σ2601ϵ6020.0316030.031604σ2605u6060.0536070.053608σ2609f6100.2156110.215612VPC61363.10%61463.10%615Pseudo R261689.60%61789.60%618Observations619170,137620170,137621Note: 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 622level 2 random effect variance divided by level 1 and level 2 random effect variance: VPC = σ2623u/624( σ2625u + σ2626ϵ627)628. The Pseudo R2 is calculated based on framework [61]: R2 =629σ2630f + σ2631u/632(633σ2634f + σ2635u + σ2636ϵ637)638where σ2639f is the variance explained by level 1 and level 2 predictors. *** p < .001. ** p < .01. * p < .05.640Table 4 641Price premium of energy efficiency with change after energy crisis.642Variable643Model 3: 644Interaction with EPC bands645Model 4: 646Interaction with EPC score647Coef.648[95% CIs]649Coef.650[95% CIs]651Variables of interest652EPC A/B6530.020*654[0.006, 0.034]655After energy crisis * EPC A/B6560.005657[−0.017, 0.028]658EPC C6590.012***660[0.009, 0.015]661After energy crisis * EPC C6620.001663[−0.003, 0.005]664EPC E665−0.013***666[−0.016, −0.010]667After energy crisis * EPC E668−0.008**669[−0.013, −0.003]670EPC F/G671−0.036***672[−0.043, −0.030]673After energy crisis * EPC F/G674−0.019**675[−0.032, −0.006]676EPC score6770.0012***678[0.0011, 0.0013]679After energy crisis * EPC score6800.0003***681[0.0001, 0.0005]682VPC68363.02%68463.02%685Pseudo R268689.60%68789.60%688Observations689170,137690170,137691Note: Only variables of interest are presented. *** p < .001. ** p < .01. * p < .05.692Y. Ou et al. 693Energy Research & Social Science 135 (2026) 104700 69410 695696697===== PAGE 11 (score 22, numeric-paren 10, table-words 4, var-words 1) =====698699Fig. 4. Price premium for EPC bands before and after the energy crisis. 700Note: 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 701that of the corresponding interaction term. The confidence interval of “coefficients after energy crisis” is calculated based on [62]. The p-values indicate the statistical 702significance of interaction components EPC band*Energy crisis i.e., difference between coefficients before and after the crisis. *** p < .001. ** p < .01. * p < .05.703Fig. 5. Estimates of price premium after different years. 704Note: The coefficient estimates and confidence intervals are calculated using the same approach as specified in the note of Fig. 4.705Table 5 706Robustness check: main regression on matched and complete listings.707Model708Variable709Matched listings 710(2017.09–2023.10)711All listings 712(2017.09–2023.10)713EPC band714A/B7150.018* [0.001, 0.035]7160.019** [0.007,0.031]717C7180.012*** [0.009, 0.015]7190.012*** [0.010, 0.014]720E721−0.014*** [−0.018, −0.011]722−0.016*** [−0.018, −0.013]723F/G724−0.045*** [−0.053, −0.037]725−0.040*** [−0.046, −0.035]726EPC score727Score7280.001*** [0.001, 0.001]7290.001*** [0.001, 0.001]730Notes: N = 64,292 for matched listings and N = 149,526 for all listings.731Y. Ou et al. 732Energy Research & Social Science 135 (2026) 104700 73311 734735736===== PAGE 12 (score 7, numeric-paren 0, table-words 0, var-words 7) =====737738efficiency improvement are therefore equivalent to around three years 739of running cost savings so forms a significant additional incentive.740There are some limitations to this study. One key limitation is the 741focus on the second-hand housing market in one UK metropolitan area, 742although we have no reason to doubt that results here would apply 743across the rest of the UK at least. Given the energy crisis is global in scale, 744generating different impacts across regions [66] and housing markets, 745future studies could analyse cross-regional or international markets to 746assess how the energy crisis influences the relationship between energy 747efficiency and housing prices more widely. Another limitation lies in 748potential heterogeneity in WTP for housing energy efficiency across 749different socio-demographic groups, property types, etc. In reality, 750preferences to invest in energy efficiency improvements may vary 751significantly [67,68]. Ignoring this heterogeneity may lead to over752generalised conclusions and limit the applicability of the findings to 753granular policy or market interventions. Therefore, a priority for future 754research is to explore and examine the heterogeneous WTP for housing 755energy efficiency.7567. Conclusions757Taking the dramatic price increase from the global energy crisis as an 758opportunity, this study investigates whether residents' willingness-to- 759pay for more energy-efficient homes increased following the crisis. 760The findings show that energy-efficient properties command a positive 761price premium of above 1% per band, and that this has increased in the 762post-crisis period. By documenting an increase of WTP after the crisis, 763we argue that the causal interpretation of the relationship between 764housing energy efficiency and housing prices is strengthened. Further765more, the results indicate that energy cost considerations play a sub766stantive role in this WTP, rather than the effect reflecting only 767environmental concern or signalling. Overall, this study provides new 768insights on the price premium associated with energy-efficient homes in 769the context of the energy crisis, offering a valuable contribution to the 770existing literature.771The findings have important policy and practical implications. First, 772the strengthened evidence of WTP for more energy-efficient homes 773could increase investors' willingness to finance retrofit works, enabling 774more homeowners to upgrade. It should also encourage policy makers to 775consider introducing minimum energy efficiency standards for the 776housing sales markets. Second, the identification of the association be777tween WTP and energy costs suggests that policymakers could place 778more emphasis on energy bill reduction when promoting energy effi779ciency measures in policy design. Moreover, it is important for policy780makers to recognise the disparity of the energy transition process among 781high- and low-income households, to provide targeted subsidies to 782ensure more equal outcomes. Finally, the increase in WTP following the 783recent energy crisis highlights its potential role as a catalyst for behav784ioural change, offering policymakers an opportunity to leverage this 785opportunity to reach broader sustainability objectives.786CRediT authorship contribution statement787Yunbei Ou: Writing – original draft, Visualization, Validation, 788Software, Methodology, Investigation, Formal analysis, Data curation, 789Conceptualization. Nick Bailey: Writing – review & editing, Supervi790sion, Methodology, Funding acquisition, Conceptualization. David 791Philip McArthur: Writing – review & editing, Supervision, Methodol792ogy, Funding acquisition, Conceptualization. Qunshan Zhao: Writing – 793review & editing, Supervision, Methodology, Funding acquisition, 794Conceptualization.795Declaration of Generative AI and AI-assisted technologies in the 796writing process797During the preparation of this work, the authors used ChatGPT-5 in 798order to improve language and writing. After using this tool/service, the 799authors reviewed and edited the content as needed and take full re800sponsibility for the content of the published article.801Declaration of competing interest802The authors declare that they have no known competing financial 803interests or personal relationships that could have appeared to influence 804the work reported in this paper.805Acknowledgement806This work was made possible by the ESRC-funded Advanced Quan807titative Methods (AQM) Doctoral Studentship [ES/P000681/1] and 808ESRC’s on-going support for the Urban Big Data Centre [ES/L011921/1 809and ES/S007105/1]. Dr Qunshan Zhao has received the support from 810the Royal Society International Exchange Scheme [IEC\NSFC\223042]. 811We would like to thank the reviewers and attendees at the Housing 812Studies Association Conference 2025 for providing useful feedback.813Appendix A. Supplementary data814Supplementary data to this article can be found online at https://doi. 815org/10.1016/j.erss.2026.104700.816Data availability817The Zoopla data with UPRN is not openly available due to privacy 818laws, links to other data sources are provided in text and the code is 819available at: https://github.com/YunbeiOu/WTP_EPC_EnergyCrisis.820References821[1] IEA, “Tracking clean energy Progress 2023 – analysis”, IEA, 2023. https://www.ie 822a.org/reports/tracking-clean-energy-progress-2023.823[2] M. del M. Sol`a, A. de Ayala, I. Galarraga, M. Escapa, Promoting energy efficiency at 824household level: a literature review, Energy Eff. 14 (1) (2020) 6.825[3] C. Wang, J. Wang, D. Norb¨ack, A systematic review of associations between energy 826use, fuel poverty, energy efficiency improvements and health, Int. J. Environ. 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