Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability / Sruthi Davuluri, René García Franceschini, Christopher R. Knittel, Chikara Onda, Kelly Roache.

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Davuluri, Sruthi

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TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w26178.Publication details: Cambridge, Mass. National Bureau of Economic Research 2019.Description: 1 online resource: illustrations (black and white)Subject(s):

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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.

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September 2019.

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.