The crisis of the mortgage market and the mortgage-backed security (MBS) market in 2008 had dramatic negative effects in dragging down all of the economy on a worldwide scale. Many researches have, therefore, attempted to explore the influencing factors on mortgage default risk. This project, in cooperation with the company EnerScore, revolves around discovering a correlation between portfolios of mortgages to underlying energy expenditures. EnerScore€™s core product provides an internal dataset related to home energy efficiency for American homes and gives their corresponding home energy efficiency rating to every home, which is called an €œEnerScore.€� This project involves discovering a correlation between default within portfolios of mortgages based on underlying energy expenditures. The goal is to show that energy efficient homes potentially have lower default risks than standard homes because the homes which lack energy efficiency are associated with higher energy costs. This leaves less money to make the mortgage payment, and thereby increases default risk. The first phase of this project involves finding a foreclosure dataset that will be used to design the quantitative model. Due to limited availability and constraints related to default data, Google search query data is used to develop a broad based and real-time index of mortgage default risk and establish a meaningful scientific correlation. After analyzing several statistical models to explore this correlation, the regression tree model showed that the EnerScore is a strong predictor for mortgage default risk when using city-level mortgage default risk data and EnerScore data.
Worcester Polytechnic Institute
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Ren, Qingyun, "Quantitative Risk Assessment for Residential Mortgages" (2017). Masters Theses (All Theses, All Years). 628.
loan lenders, peer-to-peer, home energy efficiency, mortgage default risk, regression