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Returns to solar panels in the housing market: A meta learner approach

Ilias Asproudis Orcid Logo, Cigdem Gedikli Orcid Logo, Oleksandr Talavera, Okan Yilmaz Orcid Logo

Energy Economics, Volume: 137, Start page: 107768

Swansea University Authors: Ilias Asproudis Orcid Logo, Cigdem Gedikli Orcid Logo, Okan Yilmaz Orcid Logo

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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...

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Published in: Energy Economics
ISSN: 0140-9883
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa64339
first_indexed 2023-09-03T17:11:28Z
last_indexed 2024-11-25T14:13:52Z
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spelling 2024-08-01T15:35:23.0208009 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 137 107768 Elsevier BV 0140-9883 Solar panels; Residential housing market; Sale prices; Machine-learning; Meta-learners 1 9 2024 2024-09-01 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) Swansea University 2024-08-01T15:35:23.0208009 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 64339__31030__f8885b3afea246c38e31b1cb3c753a40.pdf 64339.VoR.pdf 2024-08-01T15:32:28.3130120 Output 514169 application/pdf Version of Record true © 2024 The Author(s). This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
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
author_id_fullname_str_mv 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
container_volume 137
container_start_page 107768
publishDate 2024
institution Swansea University
issn 0140-9883
doi_str_mv 10.1016/j.eneco.2024.107768
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Social Sciences - Economics{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Social Sciences - Economics
document_store_str 1
active_str 0
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 2024-09-01T11:26:25Z
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