Re-Thinking Commercial Real Estate Market Segmentation

Franz Fuerst, G. Marcato
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Abstract

Identifying groups of comparable individual assets for a relative comparison of investment performance presents a major difficulty for direct real estate investors. The old adage ‘no two properties are exactly the same’ expresses this problem, yet investment managers require reliable this information to assess the performance of individual assets. The most common segmentation currently used in real estate investment analysis combines property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes, including the equivalent yield and size of the property, as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are also able to better predict the resulting financial performance of the assets than the old two-dimensional segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude that each of the two approaches is valid depending on the specific task at hand. While our new clusters are more suitable for identifying investment opportunities and risks, the old sector-region classification is sufficient for describing the broad characteristics of a real estate portfolio.
商业地产市场细分的再思考
为投资业绩的相对比较确定可比较的个人资产组是直接房地产投资者面临的一个主要困难。“没有两处房产是完全相同的”这句老话表达了这个问题,但投资经理需要可靠的信息来评估单个资产的表现。目前在房地产投资分析中最常用的分割方法是将房地产行业和地理区域相结合。在本文中,我们比较了现有行业分类与一种新型细分的预测能力,使用聚类分析对一些相关的物业属性,包括物业的等效产量和规模,以及租赁条款、租户数量和租户集中度的信息。随着时间的推移,新的片段显示出不同的和相对稳定的。在分析的第二阶段,我们测试新生成的细分是否也能够比旧的二维细分更好地预测资产的最终财务绩效。通过判别分析和神经网络分析,我们发现了支持这一假设的混合证据。总的来说,我们得出结论,根据手头的具体任务,这两种方法中的每一种都是有效的。虽然我们的新集群更适合于识别投资机会和风险,但旧的部门-地区分类足以描述房地产投资组合的广泛特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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