PurposeThis paper aims to determine the specific financial ratio's effects on market value and return of assets for Turkish real estate investment trusts (REITs) traded at Istanbul Stock Exchange (ISE). The paper intends to define liquidity ratios, financial structure ratios, return ratios and stock performance ratios related to market value and return of asset.Design/methodology/approachThe study includes 17 REITs traded in ISE. The period of study is specified as the year from 2009 to 2018. Panel data analysis is applied in this study. Dependent variables are current market value and return of assets, independent variables are 12 financial ratios, which are considered to explain the model significantly. These ratios will be calculated from audited year-end balance sheets for specific periods throughout at least ten years as time series. Two different models and hypotheses have been established to identify the financial ratios that affect the market value and return of assets for REITs.FindingsAccording to the results, long-term financial loans/total assets, return of equity and working capital ratio are negatively correlated with market value, while market value/book value and total assets are correlated positively. On the other hand, market value/book value ratio, price/earning ratio, long-term financial loans/total assets and earnings per share are correlated with return of assets. REITs have high levels of financial leverage, especially in foreign currency. The striking point is that REITs hardly ever do not use financial derivatives to hedge their position again currency and interest rate risk. This approach makes the financial structures of REITs vulnerable and fragile against market volatility.Originality/valueIn Turkey, as an example of an emerging market, financial borrowing does not increase the return rates and market value for REITs due to market's idiosyncratic properties. This finding provides substantial insight into how the debt and equity allocation of Turkish REITs should be structured. Also, it has been observed that forward-looking expectations are considered more than the current situation in the market.

Purpose Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices. Design/methodology/approach The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district. Findings The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation. Research limitations/implications The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon. Practical implications The findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time. Originality/value This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.

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Dependence of the real estate sector on human activity has been unveiled during the COVID-19 pandemic. In addition, it is assumed that trends emitted from the location-based social networks (LBSNs) successfully reflect human activities, hence commercial property trends. This study examined the use of social media to forecast commercial real estate figures during COVID-19 in Istanbul and determined the potential of social media data for forecasting the future rent/price levels of retail properties. Instagram and Twitter, two major LBSN platforms, were selected as social media data sources. First, 17 million geo-tagged Instagram posts and 230 thousand geo-referenced tweets were collected. Then, the data sets were superposed on COVID-19 key points in Turkey and the relationships observed. Finally, the data sets were combined with the commercial real estate data to monitor increases in the accuracy of rent and price predictions. Beşiktaş District of Istanbul was chosen as the pilot region to test the methodology. The results showed that the LBSN-supported models outperformed baseline models most of the time for price predictions and occasionally for rent predictions. Also, both Instagram and Twitter were found essential to the study and could not be omitted. This study demonstrates the significance and leveraging potential of applying human activities to the decision-making processes of the commercial real estate sector under COVID-19 conditions. This is the first study to adopt LBSN data to forecast commercial property prices.