Journal article 64 views
Returns to solar panels in the housing market: A meta learner approach
Energy Economics, Start page: 107768
Swansea University Authors: Ilias Asproudis , Cigdem Gedikli , Okan Yilmaz
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DOI (Published version): 10.1016/j.eneco.2024.107768
Abstract
This paper aims to estimate the returns to solar panels in the UK residential housing market. Our analysis applies a causal machine learning approach to Zoopla property data containing about 5 million observations. Drawing on meta-learner algorithms, we provide strong evidence documenting that solar...
Published in: | Energy Economics |
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ISSN: | 0140-9883 |
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Elsevier BV
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa64339 |
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v2 64339 2023-09-03 Returns to solar panels in the housing market: A meta learner approach da7667a22ea7ad12af360650b733406f 0000-0002-8332-1832 Ilias Asproudis Ilias Asproudis true false c83614936b5df640b1409eda0676aa44 0000-0002-0055-6397 Cigdem Gedikli Cigdem Gedikli true false bb42de9bf10d32bda4695327b3aa0470 0000-0002-0553-8518 Okan Yilmaz Okan Yilmaz true false 2023-09-03 SOSS This paper aims to estimate the returns to solar panels in the UK residential housing market. Our analysis applies a causal machine learning approach to Zoopla property data containing about 5 million observations. Drawing on meta-learner algorithms, we provide strong evidence documenting that solar panels are directly capitalized into sale prices. Our results point to a selling price premium above 6% (range between 6.1% to 7.1% depending on the meta-learner) associated with solar panels. Considering that the average selling price is £230,536 in our sample, this corresponds to an additional £14,062 to £16,368 selling price premium for houses with solar panels. Our results are robust to traditional hedonic pricing models and matching techniques, with the lowest estimates at 3.5% using the latter. Despite the declining trend, the additional analyses demonstrate that the positive premium associated with solar panels persists over the years. Journal Article Energy Economics 0 107768 Elsevier BV 0140-9883 Solar panels; Residential housing market; Sale prices; Machine-learning; Meta-learners 9 7 2024 2024-07-09 10.1016/j.eneco.2024.107768 COLLEGE NANME Social Sciences School COLLEGE CODE SOSS Swansea University SU Library paid the OA fee (TA Institutional Deal) 2024-07-10T16:07:06.7646691 2023-09-03T18:03:43.6622633 Faculty of Humanities and Social Sciences School of Social Sciences - Economics Ilias Asproudis 0000-0002-8332-1832 1 Cigdem Gedikli 0000-0002-0055-6397 2 Oleksandr Talavera 3 Okan Yilmaz 0000-0002-0553-8518 4 |
title |
Returns to solar panels in the housing market: A meta learner approach |
spellingShingle |
Returns to solar panels in the housing market: A meta learner approach Ilias Asproudis Cigdem Gedikli Okan Yilmaz |
title_short |
Returns to solar panels in the housing market: A meta learner approach |
title_full |
Returns to solar panels in the housing market: A meta learner approach |
title_fullStr |
Returns to solar panels in the housing market: A meta learner approach |
title_full_unstemmed |
Returns to solar panels in the housing market: A meta learner approach |
title_sort |
Returns to solar panels in the housing market: A meta learner approach |
author_id_str_mv |
da7667a22ea7ad12af360650b733406f c83614936b5df640b1409eda0676aa44 bb42de9bf10d32bda4695327b3aa0470 |
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da7667a22ea7ad12af360650b733406f_***_Ilias Asproudis c83614936b5df640b1409eda0676aa44_***_Cigdem Gedikli bb42de9bf10d32bda4695327b3aa0470_***_Okan Yilmaz |
author |
Ilias Asproudis Cigdem Gedikli Okan Yilmaz |
author2 |
Ilias Asproudis Cigdem Gedikli Oleksandr Talavera Okan Yilmaz |
format |
Journal article |
container_title |
Energy Economics |
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container_start_page |
107768 |
publishDate |
2024 |
institution |
Swansea University |
issn |
0140-9883 |
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10.1016/j.eneco.2024.107768 |
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Elsevier BV |
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Faculty of Humanities and Social Sciences |
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Faculty of Humanities and Social Sciences |
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Faculty of Humanities and Social Sciences |
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School of Social Sciences - Economics{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Social Sciences - Economics |
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description |
This paper aims to estimate the returns to solar panels in the UK residential housing market. Our analysis applies a causal machine learning approach to Zoopla property data containing about 5 million observations. Drawing on meta-learner algorithms, we provide strong evidence documenting that solar panels are directly capitalized into sale prices. Our results point to a selling price premium above 6% (range between 6.1% to 7.1% depending on the meta-learner) associated with solar panels. Considering that the average selling price is £230,536 in our sample, this corresponds to an additional £14,062 to £16,368 selling price premium for houses with solar panels. Our results are robust to traditional hedonic pricing models and matching techniques, with the lowest estimates at 3.5% using the latter. Despite the declining trend, the additional analyses demonstrate that the positive premium associated with solar panels persists over the years. |
published_date |
2024-07-09T16:07:05Z |
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1804205058043150336 |
score |
11.016861 |