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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, 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
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first_indexed 2023-09-03T17:11:28Z
last_indexed 2023-09-03T17:11:28Z
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spelling 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
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 0
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 0
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 2024-07-09T16:07:05Z
_version_ 1804205058043150336
score 11.016079

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