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Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability

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  • Sruthi Davuluri
  • René García Franceschini
  • Christopher R. Knittel
  • Chikara Onda
  • Kelly Roache

Abstract

The solar industry in the US typically uses a credit score such as the FICO score as an indicator of consumer utility payment performance and credit worthiness to approve customers for new solar installations. Using data on over 800,000 utility payment performance and over 5,000 demographic variables, we compare machine learning and econometric models to predict the probability of default to credit-score cutoffs. We compare these models across a variety of measures, including how they affect consumers of different socio-economic backgrounds and profitability. We find that a traditional regression analysis using a small number of variables specific to utility repayment performance greatly increases accuracy and LMI inclusivity relative to FICO score, and that using machine learning techniques further enhances model performance. Relative to FICO, the machine learning model increases the number of low-to-moderate income consumers approved for community solar by 1.1% to 4.2% depending on the stringency used for evaluating potential customers, while decreasing the default rate by 1.4 to 1.9 percentage points. Using electricity utility repayment as a proxy for solar installation repayment, shifting from a FICO score cutoff to the machine learning model increases profits by 34% to 1882% depending on the stringency used for evaluating potential customers.

Suggested Citation

  • Sruthi Davuluri & René García Franceschini & Christopher R. Knittel & Chikara Onda & Kelly Roache, 2019. "Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability," NBER Working Papers 26178, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26178
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    References listed on IDEAS

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    4. Keys, Benjamin J. & Mukherjee, Tanmoy & Seru, Amit & Vig, Vikrant, 2009. "Financial regulation and securitization: Evidence from subprime loans," Journal of Monetary Economics, Elsevier, vol. 56(5), pages 700-720, July.
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    Cited by:

    1. Papineau, Maya & Rivers, Nicholas, 2022. "Experimental evidence on heat loss visualization and personalized information to motivate energy savings," Journal of Environmental Economics and Management, Elsevier, vol. 111(C).

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation

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