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.
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