Neglecting neighborhood unobservables or shocks may hinder the identification of causal effects. However, if unobservables are smooth over space and units are paired based on proximity, a neighborhood data transformation can effectively eliminate their influence. This paper studies neighborhood differencing and within-neighborhood estimation strategies in a finite population framework. We establish their asymptotic distribution and provide guidance on standard errors adjustment for within and between neighborhood correlations. We also develop a test for smooth fixed effects, allowing practitioners to select the optimal threshold for data transformation. We examine the behavior of the proposed tools through simulations, showing good finite sample properties. We illustrate the usefulness of our approach using geocoded data from a clustered randomized experiment.