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Returns to solar panels in the housing market: A meta learner approach
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Cigdem Gedikli ,
Oleksandr Talavera,
Okan Yilmaz
Energy Economics, Volume: 137, 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 |
| Published: |
Elsevier BV
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64339 |
| 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 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. |
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| Keywords: |
Solar panels; Residential housing market; Sale prices; Machine-learning; Meta-learners |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Swansea University |
| Start Page: |
107768 |