Proposal for a Forecasting Methodology to Predict Commercial Real Estate Values in Istanbul Using Social Big Data

This paper provides a new forecasting methodology for commercial real estates in Istanbul using social big data. Big data has gained popularity as a tool for the growth of real estate research in recent years. Location-based social networks (LBSNs), in particular, provide an excellent potential to demonstrate the characteristics of metropolitan cities and human activities within. Whilst there is relatively limited research on the relationship between social big data and real estate values, most of the existing research focuses on residential properties. This paper aims to discover the potential of social media data to forecast the future rent/price levels of retail properties in stanbul. Two different LBSN platforms, Instagram and Twitter, are chosen as the social media data sources. For the timeframe, June 2019 - May 2021, 16 million geo-tagged Instagram posts and 230 thousand geo-referenced tweets from a total of 174 thousand venues are collected by the authors. The data set is clustered by relevant districts of Istanbul and the spatial distribution of social media content is observed. Finally, the data sets are combined with the commercial real estate data temporally for the districts. Multivariate time-series analyses are conducted to obtain the optimum prediction model and interval. This method increases the accuracy in rent and/or price predictions by selecting the best exogenous variables and forecasting models for each district, where applicable. This paper demonstrates the significance and the leveraging potential of adapting human activities to the decision-making processes of the commercial real estate sector.