Housing Price Prediction

This paper explores the question of how house prices in five different counties are affected by housing characteristics (both internally, such as number of bathrooms, bedrooms, etc. and externally, such as public schools’ scores or the walkability score of the neighborhood). Using data from sold houses listed on Zillow, Trulia and Redfin, three prominent housing websites, this paper utilizes both the hedonic pricing model (Linear Regression) and various machine learning algorithms, such as Random Forest (RF) and Support Vector Regression (SVR), to predict house prices. The models’ prediction scores, as well as the ratio of overestimated houses to underestimated houses are compared against Zillow’s price estimation scores and ratio. Results show that SVR gives a better price prediction score than the Zillow’s baseline on the same dataset of Hunt County (TX) and RF gives close or the same prediction scores to the baseline on three other counties. Moreover, this paper’s models reduce the overestimated to underestimated house ratio of 3:2 from Zillow’s estimation to a ratio of 1:1. This paper also identifies the four most important attributes in housing price prediction across the counties as assessment, comparable houses’ sold price, listed price and number of bathrooms.