Abstract
In the formulation of hedonic models, in addition to locational factors and building structures which affect the house prices, the generation of the omitted variable bias is thought to occur in cases when local environmental variables and the individual characteristics of house buyers are not taken into consideration. However, since it is difficult to obtain local environmental information in a small neighborhood unit and to observe individual characteristics of house buyers, these variables have not been sufficiently considered in previous studies. We demonstrated that non-negligible levels of omitted variable bias are generated if these variables are not considered.
Highlights
Economic growth and the progress of urbanization have promoted high-density land use and the construction of high-rise buildings, thereby generating urban problems such as traffic jams, the obstruction of insolation and ventilation, and impaired views
“Survey on Land Use in Tokyo” is the average area of buildings existing in a mesh regardless of ownership, whereas the average area observed in the national census means the floor space attributed to a household, which is different from the former area index
To estimate hedonic price functions, a focus was placed on the problem of omitted variable bias and an attempt was made to clarify the effect of incorporating neighborhood effect variables as a means of reducing this bias
Summary
Economic growth and the progress of urbanization have promoted high-density land use and the construction of high-rise buildings, thereby generating urban problems such as traffic jams, the obstruction of insolation and ventilation, and impaired views. In terms of methods for addressing the problem of omitted variable bias accompanying unobservable variables, attempts have been made to estimate hedonic functions by performing market segmentation [5,6]. As a representative parametric method, an estimation method has been proposed that aims to increase flexibility of fit by means of a high-dimensional polynomial equation using coordinate values (latitude, longitude) This is the so-called Parametric Polynomial Expansion model proposed by Jackson [7], which inputs the squares and cubes of coordinate values and a multi-dimensional cross term into explanatory variables. Estimation methods that take spatial correlation of the error term into account have been proposed All of these approaches do no more than indirectly resolve the problem of unobservable variables by devising estimation methods. By comparing a model incorporating these variables with a model that does not, we clarify whether or not omitted variable bias is generated
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