Title Machine learning for solar accessibility : implications for low-income solar expansion and profitability / Sruthi Davuluri, Reň Garc̕a Francheschini, Christopher R. Knittel, Chikara Onda, Kelly Roache

Published Cambridge, Mass. : National Bureau of Economic Research, 2019

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JSTOR Sustainability

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Description 1 online resource (23 pages) : illustrations Series NBER working paper series ; no. 26178 Working paper series (National Bureau of Economic Research) ; no. 26178 Summary 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 Notes "September 2019." Bibliography Includes bibliographical references (pages 19-21) Notes Online resource; title from http://www.nber.org/papers/26178 viewed September 30, 2019 Subject Credit scoring systems -- United States -- Econometric models Low-income consumers -- United States -- Finance, Personal -- Econometric models Consumers -- United States -- Finance, Personal -- Econometric models Solar energy industries -- Economic aspects -- United States -- Econometric models Renewable Resources and Conservation. Electric Utilities. Production, Pricing, and Market Structure Size Distribution of Firms. Forecasting and Prediction Methods Simulation Methods. United States. Form Electronic book Author Francheschini, René Garcia, author Knittel, Christopher R., author Onda, Chikara, author Roache, Kelly, author National Bureau of Economic Research, publisher

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