Latent Topic Analysis of the Post Property for Sales to Predict a Selling Price of Second-Hand Condominiums

Abstract

This research objective is to study the latent topics analysis in selling post of real estate of second-hand condominium by using Latent Dirichlet Allocation (LDA) and build a price prediction model of second-hand condominium using multiple linear regression and artificial neural networks by measuring and comparing the performance of the second hand condominium price prediction model with root mean square error (RMSE). This experiment included four variables are room size, number of bathroom, number of bedroom and latent topics from LDA. The result of LDA indicated that selling post of real estate can be separated into 4 topics, in which finding the factors that affect the price use the regression analysis method to get five variables are room size, number of bathroom, floors, topic 2 and topic 4. The RMSE based on the multiple linear regression analysis was 1.349, while the RMSE based on artificial neural network was 1.156. Thus, it can be concluded that the predictive model using the artificial neural networks is superior to multiple linear regression.