Michael J. McCord, Sean MacIntyre, Paul Bidanset, Daniel Lo and Peadar Davis
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…
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.
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Paul Bidanset, Michael McCord, Peadar Davis and Mark Sunderman
The purpose of this study is to enhance the estimation of vertical and horizontal inequity within property valuation. Property taxation is a crucial source of finance for local…
Abstract
Purpose
The purpose of this study is to enhance the estimation of vertical and horizontal inequity within property valuation. Property taxation is a crucial source of finance for local government around the world – based on a presumptive tax base underpinned by estimates of property value, inaccurate real estate valuations used for such ad valorem or value-based property tax calculations potentially lead to a variety of costs, both financial and other, for tax payers and governments alike. More common are increased costs in time, staff and, in some cases, legal fees. Some governments are even bound by acceptability thresholds to promote fairness, equitability and overall government accountability with respect to valuation.
Design/methodology/approach
There exist a number of vertical inequity measurements that have undergone academic testing and scrutiny within the property tax industry since the 1970s. While these approaches have proved successful in detecting horizontal and vertical inequity, one recurring disadvantage pertains to measurement error/omitted variable bias, stemming largely from a failure to accurately account for location. A natural progression within property tax research is the application of a more spatially local weighted modelling approach to examine vertical and horizontal inequity. This research, therefore, specifies a geographically weighted regression (GWR) methodology to detect and measure vertical inequity in property valuations.
Findings
The findings show the efficacy of using more applied spatial approaches for vertical tax estimation and indeed the limitations of employing conditional mean estimates coupled with delineated boundaries for assessing property tax inequity. The GWR model findings highlight the more fluctuating nature of vertical inequity across the Belfast market for the apartment sector both in a progressive and regressive sense and at different magnitudes. Moreover, the results reveal spatial clustering in the effects and are indicative of systematic inequities related to location inferring that spatial (horizontal) tax inequities are not random. The findings further show increased GWR model predictability overall.
Originality/value
This research adds to the existing literature base for evaluating both vertical and horizontal inequity in value-based property taxation at the intra-neighbourhood level. This is accomplished by modifying the Birch–Sunderman approach by transforming the traditional OLS model architecture to a GWR model, thereby allowing coefficient estimates of inequity to vary not only across a jurisdiction, but also at a more local level, while incorporating property characteristic variables. This arguably allows assessors to identify specific geographical areas of concern, saving them money, time and resources on identifying, addressing and correcting for inequity.
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Michael James McCord, John McCord, Peadar Thomas Davis, Martin Haran and Paul Bidanset
Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an…
Abstract
Purpose
Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity.
Design/methodology/approach
Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria.
Findings
The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error.
Originality/value
Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.
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Michael James McCord, Peadar Thomas Davis, Paul Bidanset, William McCluskey, John McCord, Martin Haran and Sean MacIntyre
Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern…
Abstract
Purpose
Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is problematic to understand the nature of the relationship between house prices and the accessibility to services and prominent neighbourhood landmarks and amenities. Therefore, this paper aims to quantify and measure the (dis)amenity effects on house pricing levels within particular geographic housing sub-markets.
Design/methodology/approach
Most hedonic models are estimated using regression techniques which produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price. This paper uses a quantile regression (QR) approach that provides a “more complete” depiction of the marginal impacts for different quantiles of the price distribution using sales data obtained from 3,780 house sales transactions within the Belfast Housing market over 2014.
Findings
The findings emerging from this research demonstrate that housing and market characteristics are valued differently across the quantile values and that conditional quantiles are asymmetrical. Pertinently, the findings demonstrate that ordinary least squares (OLS) coefficient estimates have a tendency to over or under specify the marginal mean conditional pricing effects because of their inability to adequately capture and comprehend the complex spatial relationships which exist across the pricing distribution.
Originality value
Numerous studies have used OLS regression to measure the impact of key housing market externalities on house prices, providing a single estimate. This paper uses a QR approach to examine the impact of local amenities on house prices across the house price distribution.