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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.

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.

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.

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.

Accounting for locational effects in determining price is of fundamental importance. The demise of the mainstream property market has culminated in increasing appetite and investment activity within the private rental sector. The primary purpose of this paper aims to analyse the local variation and spatial heterogeneity in residential rental prices in a large urban market in the UK using various geo-statistical approaches.

Applying achieved price data derived from a leading internet-based rental agency for Belfast Northern Ireland is analysed in a number of spatially based modelling frameworks encompassing more traditional approaches such as hedonic regressive models to more complex spatial filtering methods to estimate rental values as a function of the properties implicit characteristics and spatial measures.

The principal findings show the efficacy of the geographically weighted regression (GWR) technique as it provides increased accuracy in predicting marginal price estimates relative to other spatial techniques. The results reveal complex spatial non-stationarity across the Belfast metropole emphasizing the premise of location in determining and understanding rental market performance. A key finding emanating from the research is that the high level of segmentation across localised pockets of the Belfast market, as a consequence of socio-political conflict and ethno-religious territoriality segregation, requires further analytical insight and model specification in order to understand the exogenous spatial and societal effects/implications for rental value.

This study is one of only a few investigations of spatial residential rent price variation applying the GWR methodology, spatial filtering and other spatial techniques within the confines of a UK housing market. In the context of residential rent prices, the research highlights that a soft segmentation modelling approaches are essential for understanding rental gradients in a polarised ethnocratic city.

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals.

This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided.

Results show that the spatial autocorrelation problem is under control as shown by low Moran's I indexes. Moreover, this geo‐statistical approach provides coefficients on environmental amenities that are still highly significant by capturing only the remaining spatial autocorrelation.

The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.

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.

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).

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.

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.

Investigations of urban public services remain confined to western settings while research on urban public services in non-western cities focuses mainly on the availability and delivery of basic services. Using the case study of Calcutta, this study is an empirical investigation of the evolution, spatial distribution, and changes in spatial patterns of public libraries for the period 1850–1991. It seeks to demonstrate the provision and accessibility to public libraries at the intraurban scale thereby extending research of urban service delivery to a non-western city. Within the context of urban service delivery – who benefits and why, the location of libraries in three time periods are analyzed. The study finds that the urban morphology of the colonial city continues to exert a strong influence on the growth and spatial distribution of public libraries. Empirical evidence suggests that there is no locational bias based on physical accessibility in the distribution of public libraries. No progressive or regressive spatial arrangement based on socioeconomic variables is indicated.

Statistical methods of spatial analysis are often successful at either prediction or explanation, but not necessarily both. In a recent paper, Dearmon and Smith (2016) showed that by combining Gaussian Process Regression (GPR) with Bayesian Model Averaging (BMA), a modeling framework could be developed in which both needs are addressed. In particular, the smoothness properties of GPR together with the robustness of BMA allow local spatial analyses of individual variable effects that yield remarkably stable results. However, this GPR-BMA approach is not without its limitations. In particular, the standard (isotropic) covariance kernel of GPR treats all explanatory variables in a symmetric way that limits the analysis of their individual effects. Here we extend this approach by introducing a mixture of kernels (both isotropic and anisotropic) which allow different length scales for each variable. To do so in a computationally efficient manner, we also explore a number of Bayes-factor approximations that avoid the need for costly reversible-jump Monte Carlo methods.

To demonstrate the effectiveness of this Variable Length Scale (VLS) model in terms of both predictions and local marginal analyses, we employ selected simulations to compare VLS with Geographically Weighted Regression (GWR), which is currently the most popular method for such spatial modeling. In addition, we employ the classical Boston Housing data to compare VLS not only with GWR but also with other well-known spatial regression models that have been applied to this same data. Our main results are to show that VLS not only compares favorably with spatial regression at the aggregate level but is also far more accurate than GWR at the local level.

This paper seeks to explore the dynamics of the spatial distribution of landed residential property values in Singapore in the 1990s. Topics covered include: spatial patterns that can be discerned in the distribution of landed property values; how property values change over time; and how government intervention influenced this dynamic property value surface. Data are collected from the Singapore Institute of Surveyors and Valuers property transaction database, and are analysed by using the geographic information system, parametric and non‐parametric statistics. Findings of this paper contribute to the understanding of the urban dynamics of an Asian metropolis, especially in terms of its residential property market and internal spatial structure.

Prosociality may in part determine sustainability behavior. Prior research indicates that pro-environmental behavior correlates with prosocial attitudes, and separately, that prosociality correlates with social support in homes and communities. Therefore, prosociality may constitute a keystone variable linking human well-being with pro-environmental behavior. The purpose of the paper is to test this conjecture.

Data from a multi-year student survey at the University of Maine on environmental behavior, prosociality and experienced social support are used. A two-stage least-squares regression is applied to explore the relationships between these variables, and sub-scale analysis of the pro-environmental responses is performed. Additionally, spatial statistics for the student population across the state are computed.

The data corroborate previous findings and indicates that social support within a community may bolster the prosociality of its members, which in turn may increase pro-environmental behaviors and intentions.

Cross-sectional data do not permit the imputation of causality. Self-reported measures of behavior may also be biased. However, student prosociality surveys may provide an effective and low-cost sustainability metric for large populations.

The results of this study corroborate prior research to suggest that pro-environmental and prosocial behaviors may both be enhanced by bolstering social support efforts at the community level.

It is suggested that prosociality could become a keystone sustainability indicator. The study’s results extend the understanding of the connections between prosociality, social support and pro-environmental behavior. The results of this study suggest that efforts to simultaneously improve the well-being and environmental status might focus on building prosociality and social support systems at the community level.

The paper aims to analyze the behavior of the Galfenol rods under bending conditions that are employed in a vibration energy harvester by illustrating the spatial variations in stress and magnetic field.

This paper describes a 3‐D static finite element model of magnetostrictive materials, considering magnetic and elastic boundary value problems that are bidirectionally coupled through stress and field dependent variables. The finite element method is applied to a small vibration‐driven generator of magnetostrictive type employing Iron‐Gallium alloy (Galfenol).

The 3‐D static finite element modeling presented here highlights the spatial variations in magnetic field and relative permeability due to the corresponding stress distribution in the Galfenol rods subjected to transverse load. The numerical calculations show that about 1.1 T change in magnetic flux density is achieved which demonstrates the effectiveness of the inspected vibration‐driven generator in voltage generation and energy harvesting. The model predictions agree with the experimental results and are coherent with the magnetostriction phenomenon.

This paper fulfils the behavior analysis of Galfenol rods under transverse load that includes both compression and tension. The compressive and tensile stresses contributions to change in magnetic flux densities in the Galfenol rods were calculated by which the effectiveness of the inspected vibration‐driven generator in voltage generation and energy harvesting is demonstrated.