Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45616
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dc.contributor.authorGEPTS, Bieke-
dc.contributor.authorNUYTS, Erik-
dc.contributor.authorVERBEECK, Griet-
dc.date.accessioned2025-03-12T07:56:07Z-
dc.date.available2025-03-12T07:56:07Z-
dc.date.issued2025-
dc.date.submitted2025-02-28T12:28:57Z-
dc.identifier.citationSustainability, 17 (3) (Art N° 1235)-
dc.identifier.urihttp://hdl.handle.net/1942/45616-
dc.description.abstractRetrofitting existing buildings is a cornerstone of Europe's strategy for a sustainable built environment. Therefore, accurate short-term forecasts to evaluate policy impacts and inform future strategies are needed. This study investigates the short-term predictive modelling of renovation activity in Belgium, focusing on overall renovation activity (RA) and energy-specific renovation activity (EA). Using data from 2012 to 2023, linear modelling was employed to analyze the relationships between RA/EA and macroeconomic indicators, market confidence, building permits, and loan data, with model performance evaluated using Mean Absolute Percentage Error (MAPE). Monthly data and time lags of up to 24 months were considered. The three best-performing models for RA achieved MAPE values between 2.9% and 3.1%, with validated errors ranging from 0.1% to 4.1%. For EA, the best models yielded MAPE values between 4.4% and 4.6% and validated errors between 8.9% and 14%. Renovation loans and building permits emerged as strong predictors for RA, while building material prices and loans were more relevant for EA. The time lag analysis highlighted that renovation processes typically span 15-24 months following loan approval. However, the low accuracy observed for EA underscores the need for further refinement. This explorative effort forms a solid base, inviting additional research to enhance our predictive capabilities and improve short-term modelling of the (green) residential renovation market.-
dc.language.isoen-
dc.publisherMDPI-
dc.rights2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).-
dc.subject.otherrenovation forecasting-
dc.subject.otherenergy retrofits-
dc.subject.othermacroeconomic indicators-
dc.subject.otherpredictive modelling-
dc.subject.othersustainable built environment-
dc.titleExplorative Short-Term Predictive Models for the Belgian (Energy) Renovation Market Incorporating Macroeconomic and Sector-Specific Variables-
dc.typeJournal Contribution-
dc.identifier.issue3-
dc.identifier.volume17-
local.format.pages23-
local.bibliographicCitation.jcatA1-
dc.description.notesVerbeeck, G (corresponding author), Hasselt Univ, Fac Architecture & Arts, Agoralaan Bldg E, B-3590 Diepenbeek, Belgium.-
dc.description.notesbieke.gepts@uhasselt.be; erik.nuyts@uhasselt.be;-
dc.description.notesgriet.verbeeck@uhasselt.be-
local.publisher.placeMDPI AG, Grosspeteranlage 5, CH-4052 BASEL, SWITZERLAND-
dc.relation.referencesEuropean Commission. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the Energy Performance of Buildings (Recast). 2010. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32010L0031 (accessed on 23 October 2023). European Commission; Directorate-General for Energy. EU Energy in Figures—Statistical Pocketbook 2022; Publications Office of the European Union: Luxembourg, 2022; Available online: https://data.europa.eu/doi/10.2833/334050 (accessed on 10 December 2024). European Commission. 2023. Available online: https://ec.europa.eu/commission/presscorner/detail/en/ip_23_1581 (accessed on 22 October 2024). Meijer, F.; Itard, L.; Sunikka-Blank, M. Comparing European residential building stocks: Performance, renovation and policy opportunities. Build. Res. Inf. 2009, 37, 533–551. [Google Scholar] [CrossRef] Atanasiu, B.; Maio, J. Europe’s Buildings Under the Microscope. A Country-by-Country Review of the Energy Performance of Buildings; BPIE: Brussels, Belgium, 2011; Available online: https://www.researchgate.net/publication/271200847 (accessed on 10 December 2024). Karakosta, C.; Mylona, Z.; Papathanasiou, J.; Psarras, J. Integrating Existing Knowledge to Accelerate Buildings Renovation Rates in Europe. In International Transactions in Operational Research; Springer: Cham, Switzerland, 2023; pp. 2128–2129. [Google Scholar] [CrossRef] Sonnleithner, M. New Opportunities for Increasing the Renovation Rate of Buildings. Arch. Pap. Fac. Arch. Des. STU 2021, 26, 2–9. [Google Scholar] [CrossRef] Verbeeck, G.; Housmans, K. Renovatiebarometer Vlaanderen; VEKA (Vlaams Energie & Klimaat Agentschap): Brussels, Belgium, 2017. [Google Scholar] Filippidou, F.; Nieboer, N.; Visscher, H. Are we moving fast enough? The energy renovation rate of the Dutch non-profit housing using the national energy labelling database. Energy Policy 2017, 109, 488–498. [Google Scholar] [CrossRef] Yu, J.; Chang, W.-S.; Dong, Y. Building Energy Prediction Models and Related Uncertainties: A Review. Buildings 2022, 12, 1284. [Google Scholar] [CrossRef] Filippidou, F.; Sandberg, N.H.; Sartori, I.; Nieboer, N.; Vestrum, M.I.; Næss, J.S.; Brattebø, H. Energy renovation rates in the Netherlands–comparing long and short term prediction methods. Arch. Built Environ. 2018, 14, 167–194. [Google Scholar] [CrossRef] Kurvinen, A.; Huovari, J.; Lahtinen, M.; Sen, T.; Saari, A. A dynamic model for estimating the long-term need for repairs and renovations in residential buildings. Build. Res. Inf. 2024, 52, 533–550. [Google Scholar] [CrossRef] Sartori, I.; Sandberg, N.H.; Brattebø, H. Dynamic building stock modelling: General algorithm and exemplification for Norway. Energy Build. 2016, 132, 13–25. [Google Scholar] [CrossRef] Sandberg, N.H.; Sartori, I.; Brattebø, H. Using a dynamic segmented model to examine future renovation activities in the Norwegian dwelling stock. Energy Build. 2014, 82, 287–295. [Google Scholar] [CrossRef] Cohen, V.; Burinskas, A. The Evaluation of the Impact of Macroeconomic Indicators on the Performance of Listed Real Estate Companies and Reits. Ekonomika 2020, 99, 79–92. [Google Scholar] [CrossRef] Correia, L.; Ribeiro, M.J. Macroeconomics and the Construction Sector: Evidence from Portugal. Athens J. Bus. Econ. 2022, 9, 9–26. [Google Scholar] [CrossRef] Mughal, N.S. Reviewing the Impact of the Macroeconomic Components on the Performance of the Construction Industry. Int. J. Bus. Manag. Sci. 2023, 4, 105–127. [Google Scholar] Oladipo, F.; Oni, O. Review of Selected Macroeconomic Factors Impacting Building Material Prices in Developing Countries—A Case Of Nigeria. Ethiop. J. Environ. Stud. Manag. 2012, 5, 131–137. [Google Scholar] [CrossRef] Puci, J.; Demi, A.; Kadiu, A. Impact of macroeconomic variables on the construction sector. Corp. Bus. Strat. Rev. 2023, 4, 22–30. [Google Scholar] [CrossRef] Lynn, T.; Rosati, P.; Egli, A. Deep Renovation: Definitions, Drivers and Barriers. In Disrupting Buildings Digitalisation and the Transformation of Deep Renovation; Palgrave Macmillan: Cham, Switzerland, 2023; pp. 1–22. [Google Scholar] [CrossRef] Wilson, C.; Crane, L.; Chryssochoidis, G. Why do homeowners renovate energy efficiently? Contrasting perspectives and implications for policy. Energy Res. Soc. Sci. 2015, 7, 12–22. [Google Scholar] [CrossRef] Achtnicht, M.; Madlener, R. Factors influencing German house owners’ preferences on energy retrofits. Energy Policy 2014, 68, 254–263. [Google Scholar] [CrossRef] Essencia Marketing. Renovatieonderzoek. 2021. Available online: http://essenciamarketing.be/en/page/market-information-construction-Belgium (accessed on 10 December 2024). Vlaams Energie Agentschap, Renovatiepact. 2014. Available online: https://www.energiesparen.be/sites/default/files/atoms/files/goedgekeurdeconceptnotaRenovatiepact_0.pdf (accessed on 10 December 2024). Gepts, B.; Nuyts, E.; Verbeeck, G. Renovation rate as a tool towards achieving SDGs 11 and 13. IOP Conf. Ser. Earth Environ. Sci. 2020, 588, 042010. [Google Scholar] [CrossRef] Alaloul, W.S.; Musarat, M.A.; Rabbani, M.B.A.; Iqbal, Q.; Maqsoom, A.; Farooq, W. Construction Sector Contribution to Economic Stability: Malaysian GDP Distribution. Sustainability 2021, 13, 5012. [Google Scholar] [CrossRef] Chiang, Y.; Tao, L.; Wong, F.K. Causal relationship between construction activities, employment and GDP: The case of Hong Kong. Habitat Int. 2015, 46, 1–12. [Google Scholar] [CrossRef] Kikwasi, G. Causes and Effects of Delays and Disruptions in Construction Projects in Tanzania. Australas. J. Constr. Econ. Build.-Conf. Ser. 2013, 1, 52–59. [Google Scholar] [CrossRef] Bolkol, H.K. Causal Relationship between Construction Production and GDP in Turkey. Int. J. Res. Bus. Soc. Sci. (2147-4478) 2015, 4, 42–53. [Google Scholar] [CrossRef] Subramanian, J.; Simon, R. Overfitting in prediction models—Is it a problem only in high dimensions? Contemp. Clin. Trials 2013, 36, 636–641. [Google Scholar] [CrossRef] Ying, X. An Overview of Overfitting and its Solutions. J. Phys. Conf. Ser. 2019, 1168, 022022. [Google Scholar] [CrossRef] Sabilla, S.; Nopember, I.T.S.; Sarno, R.; Triyana, K.; Utara, U.G.M.S. Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals. Int. J. Intell. Eng. Syst. 2019, 12, 81–90. [Google Scholar] [CrossRef] Ratner, B. The correlation coefficient: Its values range between +1/−1, or do they? J. Target. Meas. Anal. Mark. 2009, 17, 139–142. [Google Scholar] [CrossRef] Palmer, P.B.; O’connell, D.G. Research Corner: Regression Analysis for Prediction: Understanding the Process. Cardiopulm. Phys. Ther. J. 2009, 20, 23–26. [Google Scholar] [CrossRef] Wong, J.M.; Chan, A.P.; Chiang, Y. Construction manpower demand forecasting: A comparative study of univariate time series, multiple regression and econometric modelling techniques. Eng. Constr. Arch. Manag. 2011, 18, 7–29. [Google Scholar] [CrossRef] Tso, G.K.F.; Yau, K.W.K. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy 2007, 32, 1761–1768. [Google Scholar] [CrossRef] Phyo, P.-P.; Byun, Y.-C.; Park, N. Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression. Symmetry 2022, 14, 160. [Google Scholar] [CrossRef] Liu, J.; Love, P.E.D.; Sing, M.C.P.; Carey, B.; Matthews, J. Modeling Australia’s Construction Workforce Demand: Empirical Study with a Global Economic Perspective. J. Constr. Eng. Manag. 2015, 141, 05014019. [Google Scholar] [CrossRef] Zhao, Y.; Qi, K.; Chan, A.P.; Chiang, Y.H.; Siu, M.F.F. Manpower forecasting models in the construction industry: A systematic review. Eng. Constr. Arch. Manag. 2021, 29, 3137–3156. [Google Scholar] [CrossRef] Lafit, G.; Meers, K.; Ceulemans, E. A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models. Psychometrika 2022, 87, 432–476. [Google Scholar] [CrossRef] Li, J. Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what? PLoS ONE 2017, 12, e0183250. [Google Scholar] [CrossRef] Koutsandreas, D.; Spiliotis, E.; Petropoulos, F.; Assimakopoulos, V. On the selection of forecasting accuracy measures. J. Oper. Res. Soc. 2021, 73, 937–954. [Google Scholar] [CrossRef] de Myttenaere, A.; Golden, B.; Le Grand, B.; Rossi, F. Mean Absolute Percentage Error for regression models. Neurocomputing 2016, 192, 38–48. [Google Scholar] [CrossRef] Sun, Y.; Haghighat, F.; Fung, B.C. A review of the-state-of-the-art in data-driven approaches for building energy prediction. Energy Build. 2020, 221, 110022. [Google Scholar] [CrossRef] Al-Hamadi, H.; Soliman, S. Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electr. Power Syst. Res. 2005, 74, 353–361. [Google Scholar] [CrossRef] Heim, J.J. The Impact of Consumer Confidence on Consumption and Investment Spending. J. Appl. Bus. Econ. 2010, 11, 37–54. [Google Scholar] Dees, S.; Brinca, P.S. Consumer confidence as a predictor of consumption spending: Evidence for the United States and the Euro area. Int. Econ. 2013, 134, 1–14. [Google Scholar] [CrossRef] Abreu, M.I.; Oliveira, R.; Lopes, J. Attitudes and Practices of Homeowners in the Decision-making Process for Building Energy Renovation. Procedia Eng. 2017, 172, 52–59. [Google Scholar] [CrossRef] Biere-Arenas, R.; Spairani-Berrio, S.; Spairani-Berrio, Y.; Marmolejo-Duarte, C. One-Stop-Shops for Energy Renovation of Dwellings in Europe—Approach to the Factors That Determine Success and Future Lines of Action. Sustainability 2021, 13, 12729. [Google Scholar] [CrossRef] Nieboer, N. Improving energy performance of Dutch homes: Coping with general investment behaviours. Int. J. Build. Pathol. Adapt. 2017, 35, 488–500. [Google Scholar] [CrossRef] Broers, W.; Vasseur, V.; Kemp, R.; Abujidi, N.; Vroon, Z. Decided or divided? An empirical analysis of the decision-making process of Dutch homeowners for energy renovation measures. Energy Res. Soc. Sci. 2019, 58, 101284. [Google Scholar] [CrossRef] Kastner, I.; Stern, P.C. Examining the decision-making processes behind household energy investments: A review. Energy Res. Soc. Sci. 2015, 10, 72–89. [Google Scholar] [CrossRef] Szymańska, E.J.; Kubacka, M.; Woźniak, J.; Polaszczyk, J. Analysis of Residential Buildings in Poland for Potential Energy Renovation toward Zero-Emission Construction. Energies 2022, 15, 9327. [Google Scholar] [CrossRef]-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.artnr1235-
dc.identifier.doi10.3390/su17031235-
dc.identifier.isi001419648300001-
local.provider.typewosris-
local.description.affiliation[Gepts, Bieke; Nuyts, Erik; Verbeeck, Griet] Hasselt Univ, Fac Architecture & Arts, Agoralaan Bldg E, B-3590 Diepenbeek, Belgium.-
local.description.affiliation[Gepts, Bieke] Essencia, Lichtveld 24, B-3980 Tessenderlo, Belgium.-
local.description.affiliation[Nuyts, Erik] PXL Univ Coll, PXL Healthcare, Elfde Liniestr 24, B-3500 Hasselt, Belgium.-
local.description.affiliation[Nuyts, Erik] PXL Univ Coll, PXL MAD Sch Arts, Elfde Liniestr 25, B-3500 Hasselt, Belgium.-
local.uhasselt.internationalno-
item.contributorGEPTS, Bieke-
item.contributorNUYTS, Erik-
item.contributorVERBEECK, Griet-
item.fullcitationGEPTS, Bieke; NUYTS, Erik & VERBEECK, Griet (2025) Explorative Short-Term Predictive Models for the Belgian (Energy) Renovation Market Incorporating Macroeconomic and Sector-Specific Variables. In: Sustainability, 17 (3) (Art N° 1235).-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
crisitem.journal.eissn2071-1050-
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