Trends in Real Estate Investment Trust Ownership of US Health Care Properties.
Ontology highlight
ABSTRACT:
SUBMITTER: Bruch JD
PROVIDER: S-EPMC9107031 | biostudies-literature | 2022 May
REPOSITORIES: biostudies-literature
Publications
Trends in Real Estate Investment Trust Ownership of US Health Care Properties.
Bruch Joseph Dov JD Katz Tamar T Ramesh Tarun T Appelbaum Eileen E Batt Rosemary R Tsai Thomas C TC
JAMA health forum 20220513 5
This cross-sectional study examines the prevalence and characteristics of real estate investment trust-owned health care properties in the US health care sector. ...[more]
Similar Datasets
Project description:Retrofitting existing buildings is crucial for achieving Net Zero emissions. Institutional real estate owners play a key role because of their significant ownership, especially of large buildings. We utilize an interdisciplinary approach to evaluate cost-optimal decarbonization conditions for three Swiss real estate portfolios owned by a global institutional investor. We leverage a bottom-up optimization framework for building asset retrofitting, scaled to the portfolio-level, to study the effect of policy scenarios and implementations. Results indicate that achieving Net Zero necessitates significant investments, largely through thermal energy efficiency measures and low-CO2 energy systems, as early as possible to avoid locked-in emissions. Owners will be challenged to smooth long-term capital investments, pointing to a potential liquidity crisis. Consequently, hard-to-decarbonize assets are unable to reach regulatory benchmarks largely because of lingering embodied emissions. To lower transition risk, we recommend that policymakers move toward average CO2 benchmarks at the real estate portfolio-level, emulating automotive fleets.
| S-EPMC10165412 | biostudies-literature
Project description:BackgroundTrust has been introduced as the cornerstone of the public and health providers' relation. Public trust in primary health care (PHC) is crucial and must be measured. The aim of this study was to develop and validate PHC trust measurement tool.MethodsThis was a psychometric study to develop PHC trust measuring tool done in Tabriz, East-Azerbaijan with participation of 600 households in 2016. Item generation was done through literature review and experts opinions. The content validity, reliability and construct validity of the PHC trust tool were assessed using several statistical methods including modified Kappa, Kendall's Tau and intra-class correlation coefficient (ICC) as well as exploratory factor analysis (EFA). Data were analyzed using STATA 14 statistical software package.ResultsA 30-item questionnaire was developed. The Modified Kappa coefficient as an indicator of content validity assessment was 0.94. With respect to reliability assessment, a high internal consistency was observed with 0.98 Cronbach-Alpha score and the test-retest reliability for overall scale (assessed by ICC) was 0.94 (CI: 0.87-0.97). Exploratory factor analysis emerged 2 factors. Factor 1 consisted of 25 items accounting for 74.1% of the variance (eigenvalue = 22.47) followed by Factor 2 consisting of 5 items accounting for 19.2% of the variance (eigenvalue = 1.6).ConclusionPHC trust measuring tool could be used as a valid and reliable tool by health systems in Iran and similar contexts to investigate how they are trustful from the public viewpoint.
| S-EPMC6642567 | biostudies-literature
Project description:The dataset represents a collection of real estate announcements published in 2018 in the Latvian leading advertisement website www.ss.com [1]. In the Latvian case, mentioned advertisement website is alternative information source in contrast with several (5-7) large real estate agencies. The mentioned advertisement website has no important competitors in Latvia, closer competitor reklama. lv [2] is 4-5 times smaller. Advertisement website www.ss.com represents information from small and medium size agencies, as well from individuals, who want to take part in the real estate market. The collected dataset reflects the observation dynamics of 12 months during 2018, including in total 238 thousand observations. Dataset has 24 dimensions, such as in announcement mentioned price for real estate, deal types, dimensions of location of real estate, such as region, district, address; characteristics of real estate, such as real estate type (land, flat and so on), size and main characteristics for each real estate type, such as land area or bad rooms in apartments. The dataset is hosted in Data Archiving and Networked Services (DANS) repository [3].
| S-EPMC7096678 | biostudies-literature
Project description:With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.
| S-EPMC11232574 | biostudies-literature
Project description:Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.Supplementary informationThe online version contains supplementary material available at 10.1007/s00181-020-01992-3.
| S-EPMC7805573 | biostudies-literature
Project description:As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilize two Neuro-Inspired COmpanion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 ( p=0.02 ) between self-assessed trust and trust measured from the game, and a positive impact of non-verbal communication on trust ( p=0.0008 ) and robot perception for anthropomorphism ( p=0.007 ) and animacy ( p=0.00002 ). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human's trust in robots.
| S-EPMC8211980 | biostudies-literature
Project description: Not available
| S-EPMC10928537 | biostudies-literature
Project description:ImportanceThe home health agency (HHA) market has seen increasingly more change in ownership transactions. Little is known about the organizational characteristics and quality of care of HHAs after ownership has changed.ObjectiveTo examine whether an HHA change in ownership was associated with different quality-of-care outcomes, patient volume, and staffing levels.Design, setting, and participantsUsing current HHA change of ownership files linked to publicly available Medicare HHA data, this staggered difference-in-differences analysis evaluated ownership change transactions of Medicare-certified HHAs from quarter 1 of 2016 to quarter 4 of 2019. Pretransaction and posttransaction HHA characteristics and quality-of-care outcomes were compared between HHAs that changed ownership and up to 8 matched controls. The transactions in the main analysis included (1) when an HHA remained as for-profit with a change in ownership and (2) when an HHA changed from nonprofit/public to for-profit ownership. The data were analyzed between November 2023 and September 2024.Main outcomes and measuresThe primary outcomes were HHA-quarter measures of star ratings, the individual quality measures that compose the star ratings based on the Outcome and Assessment Information Set, and claims-based quality metrics, such as hospitalizations and emergency department visits. The secondary outcomes included HHA year measures of Medicare patient volume, per capita payments, and staffing levels (full-time equivalents and minutes per visit).ResultsThe main dataset included 294 Medicare-certified HHAs that changed ownership, matched with 2330 controls. In 3 years after an ownership change, quarterly star ratings increased by 0.18 (95% CI, 0.05-0.31) relative to matched controls, with greater increases among HHAs that converted from nonprofit/public to for-profit. No significant improvement was observed in the 60-day rates of hospital admissions or outpatient emergency department visits. Further, no significant changes were observed in the number of Medicare beneficiaries, but per capita payments increased within 2 years post-ownership change. Significant reductions were observed in full-time equivalents of registered nurses (-17% [95% CI, -31% to -3%]) and home health aides (-26% [95% CI, -39% to -13%]), as well as per-visit minutes for skilled nursing care (-5% [95% CI, -9% to -1%]), physical therapy (-3% [95% CI, -5% to 0%]), and home health aide care (-11% [95% CI, -15% to -6%]).Conclusions and relevanceIn this difference-in-differences analysis of Medicare-certified HHAs, ownership change was associated with higher star ratings and Medicare per capita payments, but not with claims-based quality measures. Reduction in staffing levels after ownership change raises concerns about implications for quality of care.
| S-EPMC11530943 | biostudies-literature
Project description:With the rapid increase of downward pressure on China's economy, the stability of the property market, as an important part of the economic transformation process, also has a far-reaching impact on enterprises' R&D investment. We select the data of Chinese large and medium-sized industrial enterprises from 1998 to 2015 as our research sample and propose a new combination measurement model based on closeness degree to measure the real estate bubble level in China accurately. The structural vector autoregressive (SVAR) theory is utilized to empirically test the dynamic relationship between the real estate bubble, corporate liquidity, and R&D investment. The results indicate that the real estate bubble level in China is increasing, and a certain risk of deviating from the safety interval in the future exists; The rapid expansion of the real estate bubble has a continuing negative impact on corporate R&D investment, that is, its "credit mitigation effect" is much smaller than the "capital relocation effect," and industrial enterprises will fall into the so-called "low-tech lock-in" state. In other words, to a certain extent, the development of this kind of real estate bubble will not be conducive to the transformation and upgradation of enterprises and long-term economic growth.
| S-EPMC8432813 | biostudies-literature
Project description: Not available
| S-EPMC1440636 | biostudies-literature