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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.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

There is a lack of understanding in the literature on the spatial relationships between crime and house price. This paper aims to test the impact of spatial effects in the housing market, how these are related to the incidence of crime and whether effects vary by the type of crime.

The analysis initially explores univariate and bivariate spatial patterns in crime and house price data for the Belfast Metropolitan Area using Moran’s I and Local Indicator Spatial Association (LISA) models, and secondly uses spatial autoregression models to estimate the role of crime on house prices. A spatially weighted two-stage least-squares model is specified to analyse the joint impact of crime variables. The analysis is cross sectional, based on a panel of data.

The paper illustrates that the pricing impact of crime is complex and varies by type of crime, property type and location. It is shown that burglary and theft are associated with higher-income neighbourhoods, whereas violence against persons, criminal damage and drugs offences are mainly associated with lower-priced neighbourhoods. Spatial error effects are reduced in models based on specific crime variables.

The originality of this paper is the application of spatial analysis in the study of the impact of crime upon house prices. Criticisms of hedonic price models are based on unexplained error effects; the significance of this paper is the reduction of spatial error effects achievable through the analysis of crime data.

This study aims to examine the impact of relative importance of local characteristics, distance from the city centre and unobservable spatial relation in explaining values of constant‐quality apartment units in Vienna.

Drawing on recent developments in spatial econometrics and spatial hedonic house price modelling, the rent gradient hypothesis is examined by means of hedonic regression and spatial hedonic regression. Spatial autocorrelation tests are applied in order to assess possible presence of spatial dependence. The authors borrow Florax et al.'s specification search strategy in order to choose the most appropriate spatial model specification.

This research shows that local characteristics – or particularities – proxied by district and distance from the city centre are important location variables with regard to the Viennese apartment market. The spatial analysis suggests that the apartment prices are spatially autocorrelated and the Viennese apartment market has a distance‐based neighbourhood structure. The main finding is, however, that residents are willing to bid more for constant‐quality apartment units that are close to the centre of the city.

Rent gradient hypothesis is usually tested within non‐spatial hedonic frameworks: this study estimates a spatial hedonic model additionally in order to allow for comparison of results. This is also the first article to apply recent developments in spatial econometrics to examine explicitly rent gradient theory in the context of the Viennese apartment market.

The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level.

The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018.

The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered.

The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model can be easily estimated using maximum likelihood. The benefits of such a model are that it yields an improved fit to the data and more accurate spatial spill-over estimates.

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.

This paper focusses on analysing the impact of crime on the housing market in Los Angeles (LA) County. By looking at different types of crime instead of general crime measures and controlling by spatial dimension of prices and crime as well as endogeneity, a model is developed that allows for the understanding of how a specific crime impacts the housing market transaction price. To perform the analysis, the paper merges different data sets (crime, housing transaction and census data) and then computes the distances to crucial transport modes to control the accessibility features affecting housing prices. The latter allows estimating the association of housing prices and crime in the distance and estimating the impact on housing depending on it.

This paper focusses on the following crimes: aggravated assault, burglary (property crime), narcotics, non-aggravated assault and vandalism. The paper shows firstly how incidents of reported crime are distributed across space and how they are related to each other – thus highlighting crime models with spatial influences. Secondly, the research utilises instrumental variables within the methodology to estimate house prices using spatial analysis techniques while controlling for endogeneity. Thirdly, it estimates the direct impact of crime on house prices and explores the impact of housing and neighbourhood features.

Results suggest that house transaction prices and crime are closely correlated in two senses. Housing prices are endogenously negatively associated with the levels of narcotics and aggravated assaults. For narcotics, the impact of distance is shorter (1,000 m). However, for burglary, vandalism and non-aggravated assaults, the price reaction suggests a positive association: the further away the crime occurs, the higher the prices. The paper also shows the large spatial association of different crimes suggesting that they occur together and that their accumulation would make negative externalities appear affecting the whole neighbourhood.

The use of a huge database allows interesting findings, but one limitation can be to not have longer time observations to identify the crime evolution and its impact on housing prices.

Large implications as the relationship identified in this paper allow defining precise policies to avoid crime in different areas in LA. In addition, crime has significant but quantitative small effects on LA housing transaction prices suggesting that the effect depends on the spatial scale as well as lack on information about where the crimes are committed. Lack on information suggests low transparency in the market, affecting the transaction decision-taken process, affecting the risk perception and with relevant implications over household welfare.

This paper relates the spatial association among crimes defining the hotspots and their impacts on housing transaction prices.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

This paper aims to explore the relationship between market pricing and design quality within the development industry. Currently, there is a lack of research that examines real estate at the property level. Development quality is widely believed to have diminished over the past decades, while many investors seem uninterested in the design process. The study aims to address these issues through a pricing model that integrates design attributes. It is hoped that empirical findings will invite broader stakeholder interest in the design process.

The research establishes a framework for assessing spatial compliance across residential developments within London. Compliance is assessed across ten boroughs, with technical space guidelines used as a proxy for design quality. Transaction prices and spatial assessments are aligned within a hedonic pricing model. Empirical findings are used to establish whether undermining spatial standards presents a significant development risk.

Findings suggest a relationship between sale time and unit size, with “compliant” units typically transacting earlier than “non-compliant” units. Almost half of the 1,600 apartments surveyed appear to undermine technical guidelines.

It is suggested that an array of design attributes be explored that extend beyond unit size. Additionally, future studies may consider the long-term implications of design quality via secondary transaction prices.

Practical implications include the development of a more scientific approach to design valuation. This may enhance the position of product design management within the development industry and architectural services.

Social implications may include improvement in residential design.

An innovative approach combines a thorough understanding of both design and economic principles.

A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.

Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.

The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.

This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.