REIT Geospatial Analytics

This listing offers detailed analytics on REITs at the REIT level, Geographic Level and Property Level. Additionally, the analytics provide counts of the number of grocery stores, highway exists, cofffee shops, restaurants (by type), gas stations and other business types at varying levels of proximity to REIT owned properties.

The data is structured across three tables:

- REIT Level Metrics: Provides Herfindahl-Hirschman Index (HHI) concentration values as a metric for geographic concentration risk for each REIT along with a metrics for REIT portfolio concentration in by Urban, Suburban and Rural

- Geography Level Metrics: REIT property counts and portfolio proportions for different levels of political and urban geography to include: Country, State, County, MSA, Zip code. Breakdowns of portfolio concentration by Urban, Suburban and Rural classification is also provided down at State, County, MSA geographic levels

- Property Level Metrics: Provides locational details of specific REIT owned properties plus counts of the number of grocery stores, highway exits, coffee shops and other tenant desired features at different levels of proximity to REIT owned properties.

This offering is more than a mere investment aid; RDM's analytics provides a nuanced understanding of the inherent risks involved in the allocation of assets in specific geographical locations, enabling users to make well-informed decisions to optimize risk management and asset diversification. Data tables can help answer questions like, Is our investment strategy giving us true diversification? Or is the geographic overlap of different REITs actually over exposing our portfolio to same risks type despite attempts for asset diversification?

The Property level data are counts for hundreds of business types in close proximity to each REIT owned property, which allows subscribers to understand quality of a property's location. For example, data can be use do determine what REITs are located closest to features associated with higher rents, lower crime or logistical nodes like highway exits?

By leveraging this data, users can refine their risk assessment models, enhance their analytical capabilities, and gain a clearer perspective on the geographic concentration risks involved in REITs.

Fields Included:

- QY (QuarterYear)
- REIT
- GEOGRAPHY_LEVEL
- GEOGRAPHY
- ATTRIBUTE
- VALUE
- PROPERTY_ID

本数据集提供了房地产投资信托基金（Real Estate Investment Trust, REIT）层面、地理层面以及物业层面的详细分析数据。此外，该分析还统计了与REIT持有物业不同距离范围内的杂货店、高速公路出入口、咖啡店、分类餐饮商户、加油站及其他商业业态的数量。

数据以三张数据表的形式组织：
- REIT层面指标：提供赫芬达尔-赫希曼指数（Herfindahl-Hirschman Index, HHI）集中度数值，作为衡量各REIT地理集中度风险的指标，同时包含REIT投资组合在城市、郊区、乡村三类区域的集中度指标。
- 地理层面指标：涵盖不同政治及城市地理层级的REIT物业数量与投资组合占比，包含国家、州、郡、大都市统计区（Metropolitan Statistical Area, MSA）、邮政编码。同时在州、郡、大都市统计区层级，还提供了按城市、郊区、乡村分类的投资组合集中度细分数据。
- 物业层面指标：提供REIT持有特定物业的区位详情，以及与该物业不同距离范围内的杂货店、高速公路出入口、咖啡店及其他租户偏好配套设施的数量。

本数据集不仅可作为投资辅助工具，RDM的分析还能帮助用户深入理解特定地理区域资产配置中存在的固有风险，助力用户做出明智决策，以优化风险管理与资产分散化策略。相关数据表可解答如下问题：我们的投资策略是否实现了真正的分散化？抑或是尽管尝试了资产分散化，但不同REIT的地理重叠实则让投资组合暴露于同类风险之中？

物业层面的数据统计了REIT持有物业周边数百种商业业态的分布数量，可帮助订阅者了解物业区位质量。例如，可通过该数据判断哪些REIT的物业紧邻高租金、低犯罪率或高速公路出入口这类物流节点等优质配套。

通过利用该数据集，用户可优化风险评估模型、提升分析能力，并更清晰地认知REIT投资所涉及的地理集中度风险。

包含字段如下：
- 季度年份（QuarterYear, QY）
- REIT
- 地理层级（GEOGRAPHY_LEVEL）
- 地理区域（GEOGRAPHY）
- 属性类别（ATTRIBUTE）
- 数值（VALUE）
- 物业ID（PROPERTY_ID）

搜集汇总

背景与挑战

背景概述

该数据集通过REIT、地理和物业三个层级的指标，提供房地产投资信托基金的地理集中度风险分析和周边商业设施统计。数据支持用户评估资产多样化程度和物业区位质量，优化风险管理和投资决策。