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The U.S. Congress authorized the creation of real estate investment trusts (REITs) in 1960 so companies could develop publically traded real estate investment portfolios. REITs focus on commercial property, retail property, and rental property. During the last decade, REITs became more active in regional housing markets across the U.S. Single-family rental (SFR) REITs have grown tremendously, buying up residential properties across the country. In some regional housing markets, SFR REITs own noticeable shares of single-family homes. In those settings, SFR REITs take large numbers of housing units off of real estate markets where homeownership transactions occur and manage these properties as part of commercial rental inventories. This has resulted in a new category of multiple property owners, composed of institutional investors as opposed to individual investors, which further exacerbates property wealth concentration and polarization. This study examines the socio-spatial distribution of properties in SFR REIT portfolios to determine if SFR REIT properties tend to cluster in distinct areas. This study will focus on the regional housing market in Nashville, TN. Nashville has one of the most active SFR REIT sectors in the country. County tax assessor records were used to identify SFR REIT properties. These data were joined with U.S. Census data to create a profile of communities. The data were analyzed using SPSS statistical software and GIS software. Our analysis suggests that neighborhoods with clusters of SFR REITs fit the SFR REIT business model. Clusters occur in communities with newer homes, residents with higher levels of educational attainment, and middle to upper-middle incomes. The paper concludes with several recommendations for future research on SFR REITs.

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

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