2008 V36 4: pp. 717–751 REAL ESTATE ECONOMICS Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model Stephen A. Samaha ∗ and Wagner A. Kamakura ∗∗ In this study we consider the problem of sellers, buyers and real estate apprais- ers in determining the price for a house, taking into account the characteristics of the house and its location as well as the goals of these three different parties. The appraiser’s job is to determine the fair market value of the house, while the buyer and seller want to find, respectively, the lowest and highest feasible price for it. We combine recent developments in geography and economet- rics to develop an approach that determines local estimates of property values from the perspectives of the buyer, seller and appraiser, taking into account the characteristics of the house as well as its location. We illustrate our approach analyzing closing prices in one residential real estate market. In this study we propose to take the perspectives of the seller and the buyer in uncovering the lowest price that the seller should accept or the highest price the buyer should pay for the real estate property, while simultaneously factoring in the critical aspects of market imperfections and location. Because real estate transaction prices are often settled in a less-than-perfect negotiation process, and, consequently, the estimates of property values based on past prices should leave room for negotiation, we develop a geographically weighted stochastic frontier model that takes the seller’s perspective of determining the potential buyer’s reservation value (i.e., the highest possible price she should expect for the house) as well as the buyer’s perspective of determining the seller’s reservation value (i.e., the lowest feasible price to bid on a house). We then apply and illustrate our model using data from one real estate market. It is difficult to overstate the central role that the hedonic price regression framework has had in valuing residential and commercial real estate proper- ties. This framework (Griliches 1961, Rosen 1974, Epple 1987) assumes that the prices sold for each property represent the market clearing prices, and therefore the results of the regression should provide an unbiased estimate for the fair market value of each house. However, there is an increasing body ∗ Universityof Washington, Seattle, WA 98195 or ssamaha@u.washington.edu. ∗∗ Duke University, Durham, NC 27708 or kamakura@duke.edu. C  2008 American Real Estate and Urban Economics Association