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Paper eres2017_212:
A Contemporary Sentiment Analysis Approach: Algorithm-Based Analysis of News Items within the Direct Real Estate Market in the US

id eres2017_212
authors Lang, Marcel; Jessica Ruscheinsky, Jochen Hausler
year 2017
title A Contemporary Sentiment Analysis Approach: Algorithm-Based Analysis of News Items within the Direct Real Estate Market in the US
source 24th Annual European Real Estate Society Conference in Delft, Netherlands
summary Among others, Ghysels et al. (2007) found, that fundamental economic indicators alone are not able to fully explain the dynamics of commercial real estate returns in the United States. Even more clearly, Khadjeh Nassirtoussi et al. (2014) state that investors often change their purchasing behavior according to irrational and emotional assumptions. This work tries to investigate aspects of these yet insufficiently researched factors influencing the direct real estate market in more detail.With news being one of the major information sources for investors, it can be assumed that they might affect decision making processes and hence may also influence prices. This behavior should be especially interesting in the direct real estate market, as the buying process, compared to stocks, for example, is comparatively long. Accordingly, the question arises as to whether, media can be used to explain market dynamics when sentiments are extracted from news items with different algorithmic approaches. This idea is tested by looking at the commercial real estate market in the US. Thus a data set of about 40,000 SNL news items was collected covering the time span from from 2005 until 2015 where all news articles had to contain the keyword “Real Estate” and were geographically limited to being published in the United States. First of all, a Naïve Bayes classifying algorithm is applied to extract market sentiment, as it is among the most promising ones in performing neuro-linguistic programming tasks, according to Antweiler and Frank (2004). Outcomes are further compared to the results of a support vector machine algorithm developed by Cortes and Vapnik (1995), which, in short, creates a linear decision surface to allow for a sentiment classification of text samples. Both algorithms count among the most popular supervised learning methods.In order to quantify the impact of sentiment in the commercial real estate market, both the CoStar Composite Repeat Sale Indices, which represents all property classes and types, as well as the Moody’s/RCA Commercial Property Price Indices are applied to investigate the relationship between media sentiment and property returns in different sectors of the Real Estate market.To the best of our knowledge, this paper is the first work applying algorithm-based textual analysis on news articles to create a measure of media sentiment on the direct commercial real estate market in the US.
keywords Sentiment measurement; Textual Analysis; Algorithm based evaltuation;
series ERES:conference
type paper session
discussion No discussions. Post discussion ...
ratings
session Real Estate Markets & Economics
last changed 2017/11/18 16:20
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