Local Hedonic House-Price Modelling for Urban Planners: Advantages of Using Local Regression Techniques

Abstract

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 /-value of parameter estimates (^-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 i-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.

Use of Geographically Weighted Regression (GWR) Method to Estimate the Effects of Location Attributes on the Residential Property Values

“…Thus, by including longitude and latitude coordinates (ui, vi) to Eq. (1) above, the general form of the HPM can be mathematically expressed at location i in space as follows [Crespo and Grêt-Regamey 2013]…”

“…According to Eq. 2, the location-specific parameters βk (ui, vi) are estimated using weighted least squares and can be expressed as follows [Crespo and Grêt-Regamey, 2013]:…”

Does Accessibility to the Central Business District (CBD) Have an Impact on High-Rise Condominium Price Gradient in Kuala Lumpur, Malaysia?

“…In the real estate research, a number of studies have attempted to take advantage of GWR technique to investigate and appropriately modeled household demand for residential property attributes whether in general (for example, Lehner, 2011;McCord et al, 2012;Crespo and Grêt-Regamey, 2013) or specific terms such as public transport (for example, Du and Mulley, 2006;Banister, 2007;Dziauddin et al, 2015), road improvement (for example, Mulley, 2013), parks (for example, McMullen, 2011), environmental quality (for example, Carruthers and Clark, 2009) and neighbourhood quality (for example, Sunak and Madlener, 2012). In all of these studies, the authors report GWR performed better than OLS as indicated by a higher adjusted R 2 , a lower AIC and large differences in parameter estimates.…”

“…Since HPM provides a basis for GWR, by including longitude and latitude coordinates (u i , v i ) to the equation (1) above, the general form of a HPM can be mathematically expressed at location i in space as follows (Crespo and Grêt-Regamey, 2013: p. 667):…”

“…The advantage of using GWR in comparison to other spatial methods such as multilevel modelling is that each observation is treated as an individual observation at a specific geographic point. Thus, the maps produced will not be limited within an artificially bounded geographical area such as political or administrative boundaries as normally required when modelling spatial data (Du and Mulley, 2012;Crespo and Grêt-Regamey, 2013).…”