IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i18p3523-d266981.html
   My bibliography  Save this article

Quantifying the Measurement Error on England and Wales EPC Ratings

Author

Listed:
  • Jenny Crawley

    (UCL Energy Institute, University College London, London WC1H 0NN, UK)

  • Phillip Biddulph

    (UCL Energy Institute, University College London, London WC1H 0NN, UK)

  • Paul J. Northrop

    (Department of Statistical Science, University College London, London WC1H 0NN, UK)

  • Jez Wingfield

    (UCL Energy Institute, University College London, London WC1H 0NN, UK)

  • Tadj Oreszczyn

    (UCL Energy Institute, University College London, London WC1H 0NN, UK)

  • Cliff Elwell

    (UCL Energy Institute, University College London, London WC1H 0NN, UK)

Abstract

Domestic Energy Performance Certificates (EPCs) are used in the UK to provide energy efficiency ratings for use in policy and investment decisions on individual dwellings and at a stock level. There is evidence that the process of creating an EPC introduces measurement error such that repeat assessments of the same property give different ratings, compromising their reliability. This study presents a novel error analysis to estimate the size of this effect, using repeated EPC assessments of 1.6 million existing dwellings in England and Wales. A statistical model of how measurement error contributes to variation between repeated measurements is set out, and exploratory data analysis is used to decide how to apply this model to the available data. The results predict that the one standard deviation measurement error decreases with EPC rating, from around ± 8.0 EPC points on a rating of 35 to ±2.4 on a rating of 85. This predicted error is higher than the limit recommended in UK guidance except in very efficient buildings; it can also result in dwellings being rated in the wrong EPC band, for example it was estimated that 24% of band D homes are rated as band C.

Suggested Citation

  • Jenny Crawley & Phillip Biddulph & Paul J. Northrop & Jez Wingfield & Tadj Oreszczyn & Cliff Elwell, 2019. "Quantifying the Measurement Error on England and Wales EPC Ratings," Energies, MDPI, vol. 12(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3523-:d:266981
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/18/3523/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/18/3523/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jenkins, David & Simpson, Sophie & Peacock, Andrew, 2017. "Investigating the consistency and quality of EPC ratings and assessments," Energy, Elsevier, vol. 138(C), pages 480-489.
    2. Hardy, A. & Glew, D., 2019. "An analysis of errors in the Energy Performance certificate database," Energy Policy, Elsevier, vol. 129(C), pages 1168-1178.
    3. Kelly, Scott & Crawford-Brown, Doug & Pollitt, Michael G., 2012. "Building performance evaluation and certification in the UK: Is SAP fit for purpose?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6861-6878.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    5. Collins, Matthew & Curtis, John, 2018. "Bunching of residential building energy performance certificates at threshold values," Applied Energy, Elsevier, vol. 211(C), pages 662-676.
    6. Fuerst, Franz & McAllister, Patrick & Nanda, Anupam & Wyatt, Peter, 2015. "Does energy efficiency matter to home-buyers? An investigation of EPC ratings and transaction prices in England," Energy Economics, Elsevier, vol. 48(C), pages 145-156.
    7. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    8. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
    2. Chaudhuri, Kausik & Huaccha, Gissell, 2023. "Who bears the energy cost? Local income deprivation and the household energy efficiency gap," Energy Economics, Elsevier, vol. 127(PA).
    3. Didem Gunes Yilmaz & Fatma Cesur, 2023. "A Study for the Improvement of the Energy Performance Certificate (EPC) System in Turkey," Sustainability, MDPI, vol. 15(19), pages 1-24, September.
    4. Ferentinos, Konstantinos & Gibberd, Alex & Guin, Benjamin, 2021. "Climate policy and transition risk in the housing market," Bank of England working papers 918, Bank of England.
    5. Ferentinos, Konstantinos & Gibberd, Alex & Guin, Benjamin, 2023. "Stranded houses? The price effect of a minimum energy efficiency standard," Energy Economics, Elsevier, vol. 120(C).
    6. Ellen Webborn & Jessica Few & Eoghan McKenna & Simon Elam & Martin Pullinger & Ben Anderson & David Shipworth & Tadj Oreszczyn, 2021. "The SERL Observatory Dataset: Longitudinal Smart Meter Electricity and Gas Data, Survey, EPC and Climate Data for over 13,000 Households in Great Britain," Energies, MDPI, vol. 14(21), pages 1-37, October.
    7. Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
    8. Jenny Crawley & Despina Manouseli & Peter Mallaburn & Cliff Elwell, 2022. "An Empirical Energy Demand Flexibility Metric for Residential Properties," Energies, MDPI, vol. 15(14), pages 1-18, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pasichnyi, Oleksii & Wallin, Jörgen & Levihn, Fabian & Shahrokni, Hossein & Kordas, Olga, 2019. "Energy performance certificates — New opportunities for data-enabled urban energy policy instruments?," Energy Policy, Elsevier, vol. 127(C), pages 486-499.
    2. Li, Y. & Kubicki, S. & Guerriero, A. & Rezgui, Y., 2019. "Review of building energy performance certification schemes towards future improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    3. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    4. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    5. Hyunju Son & Youyi Fong, 2021. "Fast grid search and bootstrap‐based inference for continuous two‐phase polynomial regression models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    6. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2017. "Generalized partially linear regression with misclassified data and an application to labour market transitions," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 145-159.
    7. Zi Ye & Giles Hooker & Stephen P. Ellner, 2021. "Generalized Single Index Models and Jensen Effects on Reproduction and Survival," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 492-512, September.
    8. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    9. Nagler Thomas & Czado Claudia & Schellhase Christian, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
    10. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    11. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.
    12. Morteza Amini & Mahdi Roozbeh & Nur Anisah Mohamed, 2024. "Separation of the Linear and Nonlinear Covariates in the Sparse Semi-Parametric Regression Model in the Presence of Outliers," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    13. Wahba, Jackline & Schluter, Christian, 2009. "Illegal migration, wages and remittances- semi-parametric estimation of illegality effects," Discussion Paper Series In Economics And Econometrics 913, Economics Division, School of Social Sciences, University of Southampton.
    14. Feng, Yuanhua & Härdle, Wolfgang Karl, 2020. "A data-driven P-spline smoother and the P-Spline-GARCH models," IRTG 1792 Discussion Papers 2020-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    15. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    16. Clark, Andrew E. & Etilé, Fabrice, 2011. "Happy house: Spousal weight and individual well-being," Journal of Health Economics, Elsevier, vol. 30(5), pages 1124-1136.
    17. Hannes Matuschek & Reinhold Kliegl & Matthias Holschneider, 2015. "Smoothing Spline ANOVA Decomposition of Arbitrary Splines: An Application to Eye Movements in Reading," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-15, March.
    18. Michaelides, Michael & Spanos, Aris, 2020. "On modeling heterogeneity in linear models using trend polynomials," Economic Modelling, Elsevier, vol. 85(C), pages 74-86.
    19. Lu, Qiang (Steven) & Yang, Yupin & Yuksel, Ulku, 2015. "The impact of a new online channel: An empirical study," Annals of Tourism Research, Elsevier, vol. 54(C), pages 136-155.
    20. Nadja Klein & Thomas Kneib & Giampiero Marra & Rosalba Radice & Slawa Rokicki & Mark E. McGovern, 2018. "Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes," CHaRMS Working Papers 18-06, Centre for HeAlth Research at the Management School (CHaRMS).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3523-:d:266981. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.