Using Co-Ordinate Systems in Hedonic Housing Regressions

Steven B. Caudill, Neela D. Manage, Franklin G. Mixon
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Abstract

Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities and dis-amenities. As an alternative to these, relatively recent research advocates a latitude–longitude co-ordinate system for incorporating distance information into hedonic house price regressions. This study shows that many of the claims made in this research, particularly those referencing the elimination or diminution of “biases of coefficients of non-distance variables”, are given the particulars of the Monte Carlo experiments, not possible to investigate. We further show, both analytically and with our simulations, that there is no omitted variable bias present in their simulations because their randomly generated non-distance variable is uncorrelated with any of the other variables used in their regression models.
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在价格住房回归中使用协同系统
对价房价研究通常通过以下两种方式纳入位置信息:一种是采用一组虚拟变量来代表被称为 "邻里 "的单个地点,另一种是采用一组距离(或旅行时间)变量来描述地点与便利设施和不便利设施的接近程度。除此以外,相对较新的研究主张采用经纬度坐标系,将距离信息纳入保值型房价回归中。本研究表明,该研究中的许多主张,特别是那些关于消除或减少 "非距离变量系数偏差 "的主张,由于蒙特卡洛实验的特殊性,不可能得到证实。我们通过分析和模拟进一步证明,他们的模拟不存在遗漏变量偏差,因为他们随机生成的非距离变量与回归模型中使用的任何其他变量都不相关。
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