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

Elias Asproudis, Cigdem Gedikli, Oleksandr Talavera () and Okan Yilmaz

Energy Economics, 2024, vol. 137, issue C

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.

Keywords: Solar panels; Residential housing market; Sale prices; Machine-learning; Meta-learners (search for similar items in EconPapers)
JEL-codes: Q42 Q5 R21 R31 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Returns to Solar Panels in the Housing Market: A Meta Learner Approach (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:137:y:2024:i:c:s0140988324004766

DOI: 10.1016/j.eneco.2024.107768

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