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首页> 外文期刊>Growth and Change >Are Homes Near Water Bodies and Wetlands Worth More or Less? An Analysis of Housing Prices in One Connecticut Town
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Are Homes Near Water Bodies and Wetlands Worth More or Less? An Analysis of Housing Prices in One Connecticut Town

机译：水体和湿地附近的房屋价值多少？康涅狄格州一个城镇的住房价格分析

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Proximity to wetlands and water bodies can be considered an amenity (for open space and recreation value), as well as a possible nuisance (with the potential for flooding or development restrictions), although the overall effect may be different depending on location. Studies of the impacts of wetlands and water on housing prices can also be prone to spatial autocorrelation problems arising from omitted unobservables. McMillen and Redfearn explain that locally weighted regressions (LWRs) can address spatial autocorrelation. In addition to ordinary least squares (OLS), we use LWR to control for spatial effects and analyze how proximity to water bodies and wetland areas impact real sales prices of homes in one Connecticut town in 2000-2009. With OLS regressions, proximity to wetland areas and water bodies are insignificant determinants of the real sale price of homes. When we control for spatial effects with a nonparametric (i.e., LWR) approach, the significance of the water variable is different than from OLSwhile greater distance from wetlands leads to an insignificant relationship with housing price, the water distance effect becomes negative and significant. These results imply that incorporating potential spatial heterogeneity in the data is crucial for accurately estimating the direction, magnitudes, and statistical significance of the relationships between environmental variables and housing prices.
机译：接近湿地和水体可以被认为是一种便利（对于开放空间和娱乐价值），并且可以被认为是令人讨厌的事物（具有潜在的洪灾或开发限制），尽管总体效果可能因位置而异。研究湿地和水对房价的影响也容易出现因遗漏的不可观测因素而引起的空间自相关问题。 McMillen和Redfearn解释说，局部加权回归（LWR）可以解决空间自相关。除了普通最小二乘（OLS），我们使用LWR来控制空间影响并分析水体和湿地区域的邻近程度如何影响2000-2009年康涅狄格州一个城镇的房屋实际销售价格。通过OLS回归，与湿地和水体的接近程度对房屋实际销售价格的影响不大。当我们使用非参数（即LWR）方法控制空间效应时，水变量的意义不同于OLS，而与湿地的距离越大则与房价的关系不显着，水距效应就变得负面而显着。这些结果表明，在数据中纳入潜在的空间异质性对于准确估算环境变量与房价之间关系的方向，大小和统计意义至关重要。

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