Date Thesis Awarded

12-2017

Document Type

Honors Theses -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)

Department

Mathematics

Advisor

Guannan Wang

Committee Members

Ross Iaci

John Parman

Abstract

An accurate prediction to the housing prices is very important to all the real estate market participants: homeowners, mortgage lenders, land agents, investors, real estate appraisers, and insurers. Regression analysis is the most widely used modeling technique to determine the relativeness and strength of the relationship between the response variable and explanatory variables. In this project, we focus on the comparison of different regression methods, including the ordinary least squares method, penalized least squares, and univariate and bivariate smoothing techniques, for predicting the real estate market.

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