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

摘要

the application of UrbanSim requires land or real estate price data for the study area. ăesencan be difficult to obtain, particularly when tax assessor data and data from commercial sources are un-navailable. ăe article discusses an alternative method of data acquisition and applies hedonic modelingntechniques in order to generate the required data. Many studies have highlighted that ordinary leastnsquare (OLS) regression approaches lack the ability to consider spatial dependency and spatial hetero-ngeneity, consequently leading to biased and inefficient estimations. ăerefore, a comprehensive data setnis used formodeling residential asking rents by applying and comparingOLS, spatial autoregressive, andngeographically weighted regression (GWR) techniques. ăe latter technique performed best with re-ngard to model đt, but the issue of correlated coefficients favored a spatial simultaneous autoregressivenmodel. Overall, the article reveals that when housing markets are a particular concern in UrbanSim ap-nplications, signiđcant efforts are needed for the price data generation andmodeling.ăe study concludesnwith further development potentials for UrbanSim.