Price segment indexing in southern office markets

Abstract

Real estate is traditionally defined as the space market but before any investor hones in on a geographic area or asset class in which to invest, they must understand their capital limitations. If an investor only has $1 OM to invest and an asset requires $60M in equity, it is reasonable to assume that it is unattainable. Considering such financial limitations the real estate market can be defined in terms of capital investment levels in addition to the traditional delineations of the space market. Investors typically analyze space markets and may benchmark asset prices to an index but these tools do not define the nuances of their particular capital investment level but rather depict a mean of previous or projected investment dynamics. This thesis explores the price dynamics of specific price segments in the Southern office markets of the United States. Regression analyses are used to tease out the marginal differences between price segments. Modeling commercial real estate price dynamics is typically done with a standard OLS repeat-sales regression and we will do the same here for a controlled baseline analysis. However, in order to comprehend the price dynamics of specific price segments within a market this thesis will use a quantile regression model to parse the price market into deciles. This model revealed significant varying degrees of price volatility across the deciles that increased from the lowest price cohort to the highest, confirming the hypothesis.