Abstract:e application of UrbanSim requires land or real estate price data for the study area. ese can be difficult to obtain, particularly when tax assessor data and data from commercial sources are unavailable. e article discusses an alternative method of data acquisition and applies hedonic modeling techniques in order to generate the required data. Many studies have highlighted that ordinary least square (OLS) regression approaches lack the ability to consider spatial dependency and spatial heterogeneity, consequently leading to biased and inefficient estimations. erefore, a comprehensive data set is used for modeling residential asking rents by applying and comparing OLS, spatial autoregressive, and geographically weighted regression (GWR) techniques. e latter technique performed best with regard to model t, but the issue of correlated coefficients favored a spatial simultaneous autoregressive model. Overall, the article reveals that when housing markets are a particular concern in UrbanSim applications, signi cant efforts are needed for the price data generation and modeling. e study concludes with further development potentials for UrbanSim.