Using Text Mining To Analyze Real Estate Classifieds

TL;DR

This paper introduces a two-stage regression model that leverages textual data from real estate classifieds to predict property prices and identify influential keywords, demonstrating improved accuracy over traditional models.

Contribution

The paper presents a novel two-stage regression approach that effectively utilizes textual features from classifieds for price prediction and keyword analysis, outperforming existing models.

Findings

Model achieves lower root mean squared error across datasets.

Textual features significantly improve price prediction accuracy.

Keywords identified influence property prices positively or negatively.

Abstract

Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In this paper we propose a two-stage regression model that exploits the textual data in real estate classifieds. We show how our model can be used to predict the price of a real estate classified. We also show how our model can be used to highlight keywords that affect the price positively or negatively. To assess our contributions, we analyze four real world data sets, which we gathered from three different property websites. The analysis shows that our model (which exploits textual features) achieves significantly lower root mean squared error across the different data sets and against variety of regression models.