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

By Michael Löchl, Kay W. Axhausen. 2010

115 citations recorded by the source corpus.

The application of UrbanSim requires land or real estate price data for the study area. These can be difficult to obtain, particularly when tax assessor data and data from commercial sources are unavailable. The 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. Therefore, a comprehensive data set is used for modeling residential asking rents by applying and comparing OLS, spatial autoregressive, and geographically weighted regression (GWR) techniques. The latter technique performed best with regard to model fit, 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, significant efforts are needed for the price data generation and modeling. The study concludes with further development potentials for UrbanSim.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/03ae226b461f39ce77988b8e944f5ba8259156d3

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In Economics and Econometrics

In Transportation

Citations in the corpus

Modelling transport and real-estate values interactions in urban systems (2012, methodology): “Furthermore, when integrated in Land Use/Transport Interaction models (LUTI models) hedonic models can help simulate the complex interactions caused in an urban systemwhere location choices for housing or companies depend very strongly on the real estate market (Löchl and Axhausen, 2010; Waddell et ”
Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014, methodology): “…2010), (2) the expansion method (Kestens et al. 2006, Bitter et al. 2007), (3) multivariate moving window kriging (Páez et al. 2008) and (4) spatial simultaneous autoregressive regression (Löchl and Axhausen 2010), where models (2) and (3) can similarly account for non-stationary relationships.”