Returns to Solar Panels in the Housing Market: A Meta Learner Approach

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

Discussion Papers from Department of Economics, University of Birmingham

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 fortifying that solar panels are directly capitalized into sale prices. Our results point to a selling price premium above 6 percent (range between 6.2 percent to 6.9 percent depending on the meta-learner) associated with solar panels. Considering that the average selling price is 230,536 GBP in our sample, this corresponds to an additional 14,293 GBP to 15,906 GBP selling price premium for houses with solar panels. Our results are robust to traditional hedonic pricing models and matching techniques.

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)
Pages: 31 pages
Date: 2023-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-ure
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https://repec.cal.bham.ac.uk/pdf/23-01.pdf

Related works:
Journal Article: Returns to solar panels in the housing market: A meta learner approach (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:bir:birmec:23-01

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