Stanford, CA 94305
Institutional Affiliation: Stanford University
NBER Working Papers and Publications
|September 2019||Machine Learning for Solar Accessibility: Implications for Low-Income Solar Expansion and Profitability|
with Sruthi Davuluri, René García Franceschini, Christopher R. Knittel, Kelly Roache: w26178
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 techniqu...