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Spatial Analysis with Applications on Real Estate Market Price Prediction
Zheng, Yujing
Zheng, Yujing
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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2017-12-01
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Mathematics