Academic journal article Real Estate Economics

Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach

Academic journal article Real Estate Economics

Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach

Article excerpt

The absolute location of each real estate parcel in an urban housing market has a unique location-value signature. Accessibility indices, distant gradients and locational dummies cannot fully account for the influence of absolute location on the market price of housing because there are an indeterminable number of externalities (local and nonlocal) influencing a given property at a given location. Furthermore, the degree to which externalities affect real estate values is not only unique at each location but highly variable over space. Hence, absolute location must be viewed as interactive with other determinants of housing value. We present an interactive variables approach and test its ability to explain price variations in an urban residential housing market. The statistical evidence suggests that the value of location, as embodied in the selling price of housing units, may not be separable from other determinants of value. It is recommended that housing valuation models, therefore, be specified to allow site, structural and other independent attributes to interact with absolute location--{x, y} coordinates--when accounting for intraurban variation in the market price of residential housing. This approach is especially useful when estimating the value of housing for geographic areas where very little is known a priori about the neighborhoods or submarkets.

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Determining the underlying value of location in urban land and housing markets is inherently problematic because real estate values are simultaneously affected by a variety of site, structural and locational attributes. The most influential site and structural attributes typically are observable and can be easily included in house price regressions. Information about a property's location attributes is much more difficult to observe and quantify because numerous external effects (positive and negative) act upon a parcel of land at a given location. Moreover, these effects are reflected in a parcel's value.

We refer to the composite external effect at a given point in space as the location-value signature (LVS) of that parcel. Each parcel's relative location, and therefore its LVS, is unique with respect to influential nodes, districts, axes, corridors, competitive land uses and externalities found within an urban system. Thus, a parcel's LVS plays a role in the determination of the market value of a house residing on that parcel. This paper develops a hedonic pricing model that allows a parcel's unique LVS to be estimated.

Our proposed model is based on two fundamental assumptions. First, because locational characteristics are capitalized into the price of land, models for improved properties that incorporate location variables as additive terms, as is standard practice, are likely misspecified because the price of land (i.e., the coefficient on lot size) is held spatially constant across the urban area. Other structural attributes, such as age and size of dwelling unit, also often display identifiable spatial patterns. The "spatial regularities" of the patterns strongly suggest that the hedonic price of the attributes also vary spatially. Thus, a more appropriate specification is to interact location variables with variables such as lot size, age and dwelling size. Unlike additive specifications, interactive specifications allow these attribute prices to vary spatially.

Second, although the search for alternative modeling frameworks has produced a rich literature on hedonic pricing models, few have directly incorporated parcel-specific locational information in a nonarbitrary form--such as the use of Cartesian {x, y} coordinates. (1) Yet, there is a need to explore the full potential of these frameworks given that (1) no two parcels or properties occupy the same location in space, and (2) housing, land, locational accessibility and composite external effects are jointly purchased.

The inclusion of {x,y} coordinates in an interactive capacity should have a significant impact on a model's ability to explain house price variation. …

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