The Implicit Pricing of Property Attributes

Investment theory dictates that capitalisation rates for freehold property should be determined by the risk free nominal rate of return plus the risk premium less the expected growth rate. The capitalisation rate will therefore vary depending on the growth potential and risk attached to investment returns. The risk premium will be governed by factors based around the location, physical characteristics and leasing, including both the quality of the tenant and existing lease structures. In reality, capitalisation rates within market valuations are often determined by reference to direct comparison with other similar property investments, especially in mature, transparent markets.The purpose of this micro-level study is to examine the pricing of property investment focusing on the determination of capitalisation rates and the property attributes that determine the risk premium. A robust micro-level hedonic model is developed to attempt to disaggregate the capitalisation rates observed for office properties to examine the location and property specific factors that influence the risk premia set by investors. The cross-sectional inter-temporal analysis employs a dataset composed of property transactions that occurred in the London office sector over the preceding decade and contains property specific information not previously released by CoStar. It is widely recognised that multiple factors affect the risk attached to the expected income stream of a property. A review of the sparse literature reveals that this area of property pricing has received little attention. This ongoing, exploratory study could take an important step forward in this field by identifying and measuring the impact that factors such as location, physical characteristics, leasing details and tenant characteristics have on the risk premium and pricing of direct property.Little is known about the derivation of property risk premium, despite its importance in pricing. This project will attempt to bridge this knowledge gap by identifying the drivers of capitalisation rates and help to develop a better understanding of how investors perceive individual property attributes. This explorative research should be important to practice and academia, not least because it will employ a revealed preference method and transaction dataset that that have not been used before to examine the pricing of commercial property investments.