Space‐Varying Regression Coefficients: A Semi‐parametric Approach Applied to Real Estate Markets
提出一种半参数方法，通过让回归系数随地理位置变化来估计房屋价值，缓解特征价格模型中遗漏变量的问题，并用洛杉矶县交易数据展示了市场细分和边际价值的空间差异。
This paper presents a method for estimating home values by non‐parametrically incorporating the physical location of the properties. Specifically, I allow the parameters of the observed covariates to vary in space. This approach mitigates one of the biggest deficiencies inherent in hedonic pricing models–omitted variables. I demonstrate the advantages of the proposed method using real estate transaction data from Los Angeles County. The estimation finds a substantial spatial variation of the marginal values of the hedonic characteristics and provides an insight into the segmentation of the market. The proposed method is an extension of semi‐parametric multi‐dimensional k‐nearest‐neighbor smoothing. It alleviates a fundamental problem known as the curse of dimensionality by incorporating parametric components into a non‐parametric estimation.