Modelling hedonic residential rents for land use and transport simulation while considering spatial effects

Abstract

?eapplication ofUrbanSimrequires land or real estate price data for the study area. ?ese can be difficult to obtain, particularly when tax assessor data and data fromcommercial sources are un- available. ?earticle 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 hetero- geneity, consequently leading to biased and inefficient estimations. ?erefore, a comprehensive data set is used formodeling residential asking rents by applying and comparingOLS, spatial autoregressive, and geographically weighted regression (GWR) techniques. ?e latter technique performed best with re- gard to model ?t, but the issue of correlated coefficients favored a spatial simultaneous autoregressive model. Overall, the article reveals that when housingmarkets are a particular concern inUrbanSimap- plications, signi?cant efforts are neededfor thepricedata generationandmodeling. ?estudy concludes with further development potentials forUrbanSim.