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Benchmark for REIT Performance in Malaysia Using Hedonic Regression Model

机译：马来西亚利用储层回归模型的基准

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This paper focuses on the setting of a benchmark for the REIT performance within the REIT industry to achieve a sector induced national REIT index for Malaysia. The study has as objectives to (i) explore literatures on performance and benchmarking; (2) appraise REIT performance analysis as presented in the past studies; and (3) propose Hedonic Regression Model analysis towards setting a benchmark for REIT. The study adopted the quantitative research and analysis method. Three conventional REITs were purposively selected to reflect diversity in portfolio and location. Data were extracted from the annual reports of Three (3) REIT companies (AmFirst REIT, Starhill REIT AmanahRaya REIT) through their websites for period of five years (2008–2012). Hedonic regression was performed on the collected data from the REITs Company to forecast benchmark for the REITs based on individual capacity as reflected by the economic and operational indices. Thereafter the average of the return forecast for the three selected REIT represents an aggregate benchmark for the REIT industry in Malaysia. The study found that the M-REIT do outperform the KLCI but performed lower than the industry set return for 2013 by the study. The limitation of the study is twofold, first the sample for the study is small (3 out of the 12 conventional REITs) and secondly the study did not cover the Islamic REIT and there are 3 Islamic REITs in Malaysia. The identified limitations will be addressed in future research.
机译：本文重点介绍了Reit行业中Reit性能的基准，以实现一个诱导马来西亚国家Reit指数。该研究作为（i）探索表现和基准的文献; （2）评估过去研究中提出的重新绩效分析; （3）提出河口回归模型分析为REIT设置基准。该研究采用了定量研究和分析方法。三种传统的Reits被任意地选择，以反映投资组合和位置的多样性。通过他们的网站，从三（3）个Reit公司（Amfirst Reit，Starhill Reit Amanahraya Reit）的年度报告中提取了数据（2008 - 2012年）。对REITS公司的收集数据进行了宿舍回归，以根据经济和业务指数反映的单个能力来预测房地产资源基准的基准。此后，三个所选REIT的返回预测的平均值代表了马来西亚REIT行业的总基准。该研究发现，M-Reit确实表现优于KLCI，但是通过该研究表现低于2013年的行业设定返回。研究的限制是双重的，首先进行研究的样品很小（三种传统的REITS中有3个），其次，这项研究没有覆盖伊斯兰的REIT，在马来西亚有3个伊斯兰皇家店员。未来的研究将解决已确定的限制。

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