Hurricane winds can cause significant physical damage to residential properties. Pre-storm prediction of wind damage risk allows residents and city emergency officials to plan actions to reduce loss of life and property. In this thesis, I have developed a data-driven machine learning framework to estimate the probability of structural damage risk to a home subject to hurricane force winds. The modeling framework maps a set of predictor variables with the potential to explain structural damage to actual observations of homes damaged by hurricane winds. Widely used wind damage prediction models are parametric and are based on the physics of a struc- ture responding to a wind load. Using a wind damage dataset gathered from about 700,000 residential buildings after Hurricane Ike in 2008 over Harris County, I have built a hybrid machine learning model that combines classification trees and logistic regression. My model is 23.7% more accurate than the physics-based approach at pre- dicting expected damage at the one-kilometer square block level. I demonstrate the robustness of model by using it to predict wind damage to homes in Harris County for simulated hurricanes of category 1 through 5 on the Saffir-Simpson scale. My model produces more accurate pre-storm predictions of wind damage risk which will enable communities to respond to hurricane threats more effectively.