{"title":"Forecasting commercial real estate indicators under COVID-19 by adopting human activity using social big data","authors":"Maral Taşcılar, Kerem Yavuz Arslanlı","doi":"10.1007/s41685-022-00254-7","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":36164,"journal":{"name":"Asia-Pacific Journal of Regional Science","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asia-Pacific Journal of Regional Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s41685-022-00254-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 1
Abstract
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.
期刊介绍:
The Asia-Pacific Journal of Regional Science expands the frontiers of regional science through the diffusion of intrinsically developed and advanced modern, regional science methodologies throughout the Asia-Pacific region. Articles published in the journal foster progress and development of regional science through the promotion of comprehensive and interdisciplinary academic studies in relationship to research in regional science across the globe. The journal’s scope includes articles dedicated to theoretical economics, positive economics including econometrics and statistical analysis and input–output analysis, CGE, Simulation, applied economics including international economics, regional economics, industrial organization, analysis of governance and institutional issues, law and economics, migration and labor markets, spatial economics, land economics, urban economics, agricultural economics, environmental economics, behavioral economics and spatial analysis with GIS/RS data education economics, sociology including urban sociology, rural sociology, environmental sociology and educational sociology, as well as traffic engineering. The journal provides a unique platform for its research community to further develop, analyze, and resolve urgent regional and urban issues in Asia, and to further refine established research around the world in this multidisciplinary field. The journal invites original articles, proposals, and book reviews.The Asia-Pacific Journal of Regional Science is a new English-language journal that spun out of Chiikigakukenkyuu, which has a 45-year history of publishing the best Japanese research in regional science in the Japanese language and, more recently and more frequently, in English. The development of regional science as an international discipline has necessitated the need for a new publication in English. The Asia-Pacific Journal of Regional Science is a publishing vehicle for English-language contributions to the field in Japan, across the complete Asia-Pacific arena, and beyond.Content published in this journal is peer reviewed (Double Blind).