The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach

The energy issue has given rise to a prolific research field, which branches into several strands. One of these strands focuses on the role played by building energy features in shaping property prices. Indeed, market players are expected to show a higher willingness to pay for building units charac...
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Autor*in:	Sergio Copiello [verfasserIn] Simone Coletto [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	building energy efficiency green buildings real estate market property price price premium energy rating bands

Übergeordnetes Werk:	In: Buildings - MDPI AG, 2012, 13(2023), 2, p 276
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:2, p 276

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/buildings13020276

Katalog-ID:	DOAJ080995381

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language	English
source	In Buildings 13(2023), 2, p 276 volume:13 year:2023 number:2, p 276
sourceStr	In Buildings 13(2023), 2, p 276 volume:13 year:2023 number:2, p 276
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institution	findex.gbv.de
topic_facet	building energy efficiency green buildings real estate market property price price premium energy rating bands Building construction
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authorswithroles_txt_mv	Sergio Copiello @@aut@@ Simone Coletto @@aut@@
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callnumber-first	T - Technology
author	Sergio Copiello
spellingShingle	Sergio Copiello misc TH1-9745 misc building energy efficiency misc green buildings misc real estate market misc property price misc price premium misc energy rating bands misc Building construction The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach
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topic_title	TH1-9745 The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach building energy efficiency green buildings real estate market property price price premium energy rating bands
topic	misc TH1-9745 misc building energy efficiency misc green buildings misc real estate market misc property price misc price premium misc energy rating bands misc Building construction
topic_unstemmed	misc TH1-9745 misc building energy efficiency misc green buildings misc real estate market misc property price misc price premium misc energy rating bands misc Building construction
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title	The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach
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title_full	The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach
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title_sort	price premium in green buildings: a spatial autoregressive model and a multi-criteria optimization approach
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title_auth	The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach
abstract	The energy issue has given rise to a prolific research field, which branches into several strands. One of these strands focuses on the role played by building energy features in shaping property prices. Indeed, market players are expected to show a higher willingness to pay for building units characterized by higher energy performance. The study of the so-called price premium for building energy efficiency has flourished in the last decade or so; plenty of evidence is now available concerning its occurrence, although its magnitude is still debated. The literature relies on the methodological frameworks of statistical modeling and multiple regression, primarily employing hedonic price models. Lately, spatial autoregressive models have also been adopted. Here, we propose to deal with estimation of the price premium by adopting an innovative perspective. In particular, we use a methodological framework in which regression models are complemented with a multi-criteria optimization approach. Using a spatial autoregressive model first, and with D as the reference energy rating band, we find the following price premiums: 55% for A4, 42% for A3 to A, 20% for B or C, −14% for F, and −29% for G. The multi-criteria optimization approach proves efficient in estimating the price premium. The estimates above are essentially confirmed: the results converge for all the energy rating bands except for G.
abstractGer	The energy issue has given rise to a prolific research field, which branches into several strands. One of these strands focuses on the role played by building energy features in shaping property prices. Indeed, market players are expected to show a higher willingness to pay for building units characterized by higher energy performance. The study of the so-called price premium for building energy efficiency has flourished in the last decade or so; plenty of evidence is now available concerning its occurrence, although its magnitude is still debated. The literature relies on the methodological frameworks of statistical modeling and multiple regression, primarily employing hedonic price models. Lately, spatial autoregressive models have also been adopted. Here, we propose to deal with estimation of the price premium by adopting an innovative perspective. In particular, we use a methodological framework in which regression models are complemented with a multi-criteria optimization approach. Using a spatial autoregressive model first, and with D as the reference energy rating band, we find the following price premiums: 55% for A4, 42% for A3 to A, 20% for B or C, −14% for F, and −29% for G. The multi-criteria optimization approach proves efficient in estimating the price premium. The estimates above are essentially confirmed: the results converge for all the energy rating bands except for G.
abstract_unstemmed	The energy issue has given rise to a prolific research field, which branches into several strands. One of these strands focuses on the role played by building energy features in shaping property prices. Indeed, market players are expected to show a higher willingness to pay for building units characterized by higher energy performance. The study of the so-called price premium for building energy efficiency has flourished in the last decade or so; plenty of evidence is now available concerning its occurrence, although its magnitude is still debated. The literature relies on the methodological frameworks of statistical modeling and multiple regression, primarily employing hedonic price models. Lately, spatial autoregressive models have also been adopted. Here, we propose to deal with estimation of the price premium by adopting an innovative perspective. In particular, we use a methodological framework in which regression models are complemented with a multi-criteria optimization approach. Using a spatial autoregressive model first, and with D as the reference energy rating band, we find the following price premiums: 55% for A4, 42% for A3 to A, 20% for B or C, −14% for F, and −29% for G. The multi-criteria optimization approach proves efficient in estimating the price premium. The estimates above are essentially confirmed: the results converge for all the energy rating bands except for G.
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title_short	The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach
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