Published in:Environment and Planning B: Planning and Design
Language:
English
DOI:
10.1068/b38093
ISSN:
0265-8135;
1472-3417
Origination:
Footnote:
Description:
<jats:p> Hedonic house-price models have long been used in urban studies to investigate important factors characterizing cities (eg, the demand for amenities or housing submarkets). Traditionally, the formulation of hedonic models has been solved using global spatial econometric techniques. The development of local regression methods brought new insights into urban planning as the relationships between house prices and their determinants can be estimated locally and therefore mapped across space. Such maps provide planners and policy makers with valuable location-specific information to support their decision-making processes. A feature that is frequently overlooked when performing spatial local analysis is testing the statistical significance of local parameter estimates over space. This can be done by mapping the t-value of parameter estimates ( t-surfaces). In this study we propose the use of a mixed geographically weighted regression (mixed-GWR) technique to estimate a hedonic house-price model in Zurich. Mixed-GWR is an extended version of GWR by which some parameters are allowed to vary over space, while others remained fixed. To obtain spatially explicit results in a more meaningful way, we propose the use of t-surfaces to explore the statistical significance of selected local parameter estimates over space. We also follow the Bonferroni correction to overcome the problem of multiple hypothesis testing in local regression modelling. Results reveal interesting patterns in the spatial variability of local estimates for planners. For instance, areas are identified over which public policies such as house taxing have little or no effect on house pricing. Similarly, economic distortions in the housing market can be examined through the variability of residents' willingness to pay for larger dwellings. Also, urban development processes such as densification of cities can be supported by spatially exploring relevant socioeconomic variables. </jats:p>