Academic journal article The Journal of Real Estate Research

Testing the Waters: A Spatial Econometric Pricing Model of Different Waterfront Views

Academic journal article The Journal of Real Estate Research

Testing the Waters: A Spatial Econometric Pricing Model of Different Waterfront Views

Article excerpt


This study contrasts the pricing of waterfront view amenities in a South Carolina lakefront community between the bubble and the post-bubble phases of the real estate cycle. Testing for spatial autocorrelation reveals the asymmetrical operation of spatial processes between the two periods. We find that empirical results for quality of view, view aspect orientation, and origin of buyer are affected by the recent recession. Specifically, prices for non-premium view properties suffer disproportionately. These results are confirmed by the construction of a spatial error model that provides an improved fit compared to a standard OLS spatial model.

The objective of this study is to evaluate the price for waterfront amenities as revealed in the market through transactions between buyers and sellers covering an 11-year period from January 1, 2000 to the end of 2010. The focal point of this study is the Reserve at Lake Keowee, a 3,900-acre master-planned community in South Carolina started in December 1999. A Jack Nicklaus signature golf course was constructed in 2002 to complement the Reserve's abundant lake amenities creating a rich spatial environment with highly differentiating water, mountain, and golf course views. Geographic information systems (GISs)-based data are used to measure spatial variables and estimate a spatial hedonic price model for 589 vacant lots sold over this period. From a theoretical perspective, we find that spatial variables, such as percent slope, feet of shoreline, and view orientation, can increase the predictive power of a hedonic model in the valuation of real estate pricing premiums. The focus of this study on vacant lots means that the results are not distorted by the variance of housing factors, such as the age of a house.

Lake Keowee is a pristine, blue water lake covering 18,500 acres in upstate South Carolina. It was created in the late 1960s as part of an energy generation partnership between Duke Energy and South Carolina, culminating in the opening of the Oconee Nuclear Power Plant on Lake Keowee in 1973 (Hembree and Jackson, 2004). The land around Lake Keowee is renowned as having outstanding recreational opportunities, with 300 miles of shoreline, potable water, a temperate climate, and close proximity to the Blue Ridge Mountains.

Traditional neo-classical economics researchers examine property transactions in terms of a static, efficient market with rational agents having full information. Such assumptions discount the possibility of speculative bubbles and informational asymmetries. In contrast, in this study we use GIS-based statistical analysis to investigate whether buyers' behavior is substantively different between the two dominants phases of the cycle: the boom phase from 2000 to 2006 and the bust phase from 2007 onwards. Congruent with national property trends, the Reserve at Lake Keowee experienced a spectacular property bubble and commensurate bust that adds a dynamic perspective to the study.

First, GIS analysis is used to research idiosyncratic buyer behavior such as preferences for view aspects. Properties with a south-western view perspective are nominally less desirable in the South due to their sun exposure and accompanying heat in the summer. This is the first known study to examine whether any differences in pricing for alternative view aspects is found between in-state and out-of-state buyers. Secondly, GIS is used to introduce a new spatial variable, VIEWSCOPE. We use this variable to measure the distance of unobstructed water view for waterfront properties in order to estimate pricing premiums for different types of views for vacant lots. Previous research has indicated the existence of a hierarchy of pricing of views. In this study, we examine whether this pricing hierarchy is stable over the duration of the real estate cycle. Finally, we examine whether the inclusion of the GIS-based spatial data eliminates the presence of spatial autocorrelation. …

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