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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/2/276 |
| _version_ | 1797621997020119040 |
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| author | Sergio Copiello Simone Coletto |
| author_facet | Sergio Copiello Simone Coletto |
| author_sort | Sergio Copiello |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:04:06Z |
| format | Article |
| id | doaj.art-72d2954b84034fd8a685686538fe8613 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T09:04:06Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-72d2954b84034fd8a685686538fe86132023-11-16T19:30:16ZengMDPI AGBuildings2075-53092023-01-0113227610.3390/buildings13020276The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization ApproachSergio Copiello0Simone Coletto1Department of Architecture and Arts, University IUAV of Venice, Dorsoduro 2206, 30123 Venice, ItalyDepartment of Architecture and Arts, University IUAV of Venice, Dorsoduro 2206, 30123 Venice, ItalyThe 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.https://www.mdpi.com/2075-5309/13/2/276building energy efficiencygreen buildingsreal estate marketproperty priceprice premiumenergy rating bands |
| spellingShingle | Sergio Copiello Simone Coletto The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach Buildings building energy efficiency green buildings real estate market property price price premium energy rating bands |
| title | The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach |
| title_full | The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach |
| title_fullStr | The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach |
| title_full_unstemmed | The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach |
| title_short | The Price Premium in Green Buildings: A Spatial Autoregressive Model and a Multi-Criteria Optimization Approach |
| title_sort | price premium in green buildings a spatial autoregressive model and a multi criteria optimization approach |
| topic | building energy efficiency green buildings real estate market property price price premium energy rating bands |
| url | https://www.mdpi.com/2075-5309/13/2/276 |
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