Examining the spatial relationship between environmental health factors and house prices

Abstract

Purpose Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place. Design/methodology/approach A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM). Findings The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source. Originality/value Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China

“…Geographically weighted regression (GWR) model, which is an extension of traditional regression model (e.g., ordinary least squares, OLS) (Ștefănescu et al, 2017;Tripathi et al, 2019aTripathi et al, , 2019bXue et al, 2020), has become one of the crucial spatial heterogeneity modeling tools (Lu et al, 2020). In recent years, many domestic and foreign scholars have carried out in-depth and extensive research in various fields by using GWR model, including social environmental factors and regional economy, regional house prices and pollution (McCord et al, 2018;Xu et al, 2019), the impacts of environmental heterogeneity and land-use change on wild animal distribution (Liu indicated the water factor largely influenced the potential distribution of these species. These results would contribute to a more comprehensive understanding of the potential geographical distribution pattern and the distribution of suitable habitats of some rare and endangered plant species in Northwest Yunnan and would be helpful for implementing long-term conservation and reintroduction for these species.…”

Establishing Dynamic Impact Function for House Pricing Based on Surrending Multi-Attributes: Evidence From Taipei City, Taiwan

“…The second category is considered to greatly affect housing prices because of the impact of the surrounding facilities (Droes & Francke, 2018;. Similar studies can be also seen in examining the spatial relationship among environmental health factors, neighborhood amenities, and house prices McCord, MacIntyre, Bidanset, Lo, & Davis, 2018). These considerations regarding parametric and non-parametric measurement can be regarded as factors that influence house prices based on distance.…”

A methodology for identifying critical factors influencing land value in urban areas: a case study of Kerala, India

“…This argument is supported by the findings reported by Simons and Saginor (2006), by which the land value decreases when the proximity to contamination source is high. To date, many studies have demonstrated the negative influencing factors affecting land value (Kashian and Rockwell, 2013; Ozdenerol et al , 2015; McCord et al , 2018). The variable “proximity to animal farms, slaughterhouse, fish and meat market” is ranked 8 with an RII value of 0.91.…”

“…The most significant variable affecting land value from this subcategory is “noise and pollution levels in the neighborhood.” The variable is selected with an RII value of 0.86 and ranked 21 among all categories. Proximity to pollution sources and their impact on land value is studied by various researchers establishing the negative effect on property prices (Szczepańska et al , 2014; Ozdenerol et al , 2015; McCord et al , 2018).…”