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This paper develops an estimation strategy for and then applies a spatial autoregressive multinomial probit model to account for both spatial clustering and cross-alternative correlation. Estimation is achieved using Bayesian techniques with Gibbs and the generalized direct sampling (GDS). The model is applied to analyze land development decisions for undeveloped parcels over a 6-year period in Austin, Texas. Results suggest that GDS is a useful method for uncovering parameters whose draws may otherwise fail to converge using standard Metropolis-Hastings algorithms. Estimation results suggest that residential and commercial/civic development tends to favor more regularly shaped and smaller parcels, which may be related to parcel conversion costs and aesthetics. Longer distances to Austin’s central business district increase the likelihood of residential development, while reducing that of …