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Past applications of spatial regression models have frequently interpreted the parameter estimates of models that include spatial lags of the dependent variable incorrectly. A discussion of issues surrounding proper interpretation of the estimates from a variety of spatial regression models is undertaken. We rely on scalar summary measures proposed by LeSage and Pace (Introduction to spatial econometrics. Taylor Francis/CRC Press, Boca Raton, 2009) who motivate that these reflect a proper interpretation of the marginal effects for the nonlinear models involving spatial lags of the dependent variable. These nonlinear spatial models are contrasted with linear spatial models, where interpretation is more straightforward. One of the major advantages of spatial regression models is their ability to quantify spatial spillovers. These can be defined as situations where nonzero cross-partial derivatives exist that reflect …