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

A-Tier
Journal: Energy Economics
Year: 2024
Volume: 137
Issue: C

Authors (4)

Asproudis, Elias (not in RePEc) Gedikli, Cigdem (not in RePEc) Talavera, Oleksandr (University of Birmingham) Yilmaz, Okan (not in RePEc)

Score contribution per author:

1.005 = (α=2.01 / 4 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:eee:eneeco:v:137:y:2024:i:c:s0140988324004766
Journal Field
Energy
Author Count
4
Added to Database
2026-01-29