2013 Theses Master's

Rental Price Adjustment, Volatility and Clustering

Tian, Baiyue

Since last decades, researchers and practitioners became more and more interested in strategically allocating real estate assets in regions with common features instead of viewing each individual market separately. They conducted researches to develop their own classification systems which can be used in this strategic asset allocation process. Following a similar logic in grouping markets with common features together, I started a series of researches in developing a classification system on office space markets under three market conditions - rising markets, turbulent markets and recovering markets, and comparing the stability or the potential structural changes in the classification system under different market conditions. This study as the first research of a series of three researches intends to identify and estimate U.S. office space market's clustering dynamic between 2007 and 2012, which is developed as a classification system under turbulent market condition. The commonalities in this research are defined and measured by minimum average of all distances between the pair of observations from the pair markets based on three metrics – average effective rent, standard deviation of effective rent, and rental price elasticity. The outcomes are then tested through splitting dataset to validate the stability of this classification system. The deliverables from this research are a framework, which can be applied to all similar researches focused on market segments, as well as a classification system containing seven groups of markets, which sets up the foundation for future researches.