Publications

@mastersthesis{TUW-268467,

title = {Use of Pictures from Social Media to Assess the Local Attractivity as an Indicator for Real Estate Value Assessment},

author = {Christopher Kmen},

url = {http://publik.tuwien.ac.at/files/publik_268467.pdf},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

school = {Department for Geodesy and Geoinformation, FB Geoinformation},

abstract = {In recent years there has been a massive increase in the production and collection of data (Goodchild 2007). Especially in the field of social media an overflowing quantity of pictures is produced. Therefore, the question is raised, if spatial models could be derived from these images. Or, in other words, is it possible to use social media data for spatial and/or semantic purposes?

In recent studies by Hochmair (2009) and Alivand (2013) it was found that people tend to make more pictures in places which appear more attractive than in those which seem less appealing. Other Studies (Brunauer et al. 2013) and (Helbich et al. 2013) come to the conclusion that those areas that appear more appealing have higher real estate prices.

This study will link all these components together. Images are collected from social media and classified based in their focus - social interaction or documentation of the surrounding. Images in the later case will be used for further analysis. A neural network will be used for classification. As area for the study Vienna is chosen.

In the next step another big amount of social media images with geo location features is gathered and filtered with the newly trained neural network. Then the location information of the valid images is stored. Out of these data a heat map is created, with the density of the images taken as indicator.

For the validation of the created model the company DataScience Service GmbH compares the heat map with their real estate price model to see if there is a link between social media output and real estate prices.},

keywords = {Pictures, Property, social media, TEnsor Flow, Value},

pubstate = {published},

tppubtype = {mastersthesis}

}