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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.

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.

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.

Between 2013 and 2016 Western countries experienced a nearly 600% increase in terrorist attacks. Among the most significant shift in terrorism trends during this time is the recent focus on civilians in crowded places as a frequent target. Although crowded places have become critical targets for terrorist attacks, there remains a dearth of research studying crowded places or the built environment practitioner's role in creating crowded places that are as resilient as possible against terrorism.

This paper presents the results from 33 in-depth, semi-structured, one-hour interviews with property developers, property investors, property managers, security consultants, designers, planners and government/policy officials in Sydney, Canberra and Melbourne, Australia. A purposive, snowball sampling method was used to identify participants in the study.

This research extends the existing literature base on counterterrorism protective security, a distinctly under-researched component of the terrorism research discourse, by developing a baseline of threat considerations considered during the planning, design and development process. This paper presents the Australian results of a first-of-its-kind international study that connects the planning, design and development of real estate in crowded places with planning for protective counterterrorism, and investigates what, when and how counterterrorism protective security (CTPS) is considered in the development process of crowded places. The findings show that a series of common threats were identified across the stakeholder groups, including development risk, development location/site selection, natural phenomena and human-induced issues.

This research extends the current knowledge base on CTPS and has the potential to influence decision-makers in both the counterterrorism policy landscape and those influential in developing standards for the planning, design, construction and management of real estate assets.

An original contribution of this research is detailing the significant range of threats, impacts of events and organisational influences that exist in informing the real estate development process.

Tobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach.

The research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1‐Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area.

This investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market.

This study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.

The purpose of this paper, which is the first of a two-part series, is to build upon the established research on environmental economics and sustainability theory developed by Ramsey (1928), Weitzman (2007) and Gollier (2010). The Ramsey-Weitzman-Gollier model, with the contribution of Howarth (2009) and Nordhaus (2007a, b), focuses on discount rate development for environmental and long-term assets, linking discounted utility analysis embedded in the CCAPM model of Lucas (1978) to the policy concerns associated with the valuation of public and sustainable resources. This paper further investigates these issues to the rates structure appropriate for exhaustible resources with a particular emphasis on urban land, based upon the differentiation of strong and weak form sustainability concepts constrained by the objectives of the sustainable criterion of Daly and Cobb (1994).

The paper integrates the concepts of discount rate development for environmental and long-term assets and discounted utility analysis to the policy concerns associated with the valuation of public and sustainable resources. It develops new theoretical insight in order to allow the theoretical formulation of discount and capitalization rates that can be empirically applied and tested.

The paper provides theoretical support for a new approach concerned with the development of capitalization and discount rates in the valuation of non-renewable resources. A key concern of valuing non-renewable or limited resource endowments (in space or time) is the problem of irreversible investment or irrevocable decision implementation as suggested by Arrow-Fisher (1974), Krautkraemer (1985) and Daly and Cobb (1994). It investigates the challenge with developing capitalization rates and valuation of depleting resources temporally, within the constraints of sustainability. To achieve this, an optimal control discounting procedure subject to a sustainable objective statement is employed – in this context it suggests that sustainability should be treated as an alternative to traditional growth and the maximization of near-term returns.

This paper extends the construct of developing rates structures appropriate for the valuation of exhaustible resources. It places a conceptual emphasis on urban land development. The measures developed and the insights gained may serve as a basis for future research on the optimal levels of sustainable development appropriate for different nations.

The aim of this study is to add to the emerging knowledge base in the UK and be of relevance to land use planners and all stakeholders in property taxation. Urban green open spaces are valuable environmental resources often associated with positive influences for quality of life and property value.

Using a hedonic pricing specification, this paper measures the proximate effect of public green space on residential property value. It examines the relationship between 3,854 residential sales transactions and public green spaces across the Belfast housing market gathered from Land and Property Services throughout the year 2011 showing the percentage effect on property value with respect to distance to public green spaces.

The results show that, ceteris paribus, urban green space has a significant positive impact on proximate residential properties sale price for the terrace and apartment sectors and that terrace and apartment property located closer to public green spaces achieved increases in sale price of up to 49 per cent. Adjacency to green open space produced significant property value premiums in only two of the four housing types analysed, with limited statistically significant proximate effects evident for the detached and semi-detached sectors, a finding which has important social and public policy implications.

A number of empirical studies have demonstrated that public green space, such as urban parks, have a positive impact on property values. However, there is a paucity of empirical research on this relationship in the UK. This study serves to address this gap by examining the effect of public green spaces on house price within the medium-sized regional city in the UK.

The purpose of this study is to examine the impact of crime on apartment prices for Hamburg, Germany, for the years 2012 to 2017.

The authors use a panel data setting with fixed effects estimators and temporal lags to moderate the endogeneity concerns related to crime. The authors consider the effect of total crime, violent and property crime and some sub-categories of crime.

The estimates show that it takes two to three years for prices to react, with the longer run elasticity reaching −0.12 for total crime, −0.15 for property crime and −0.06 for violent crime. The elasticities are much larger in high-crime areas (−0.22 for total crime, −0.28 and −0.09 for property and violent crime) and elevated also in low-income areas.

The finding that property crime matters more in terms of quantitative impact for housing values than violent crime provides reasonable grounds for rethinking the resource allocation of public spending on crime clearance and prevention in Germany. Far more emphasis on preventing property crime appears in order and especially so in the lower income or higher crime areas, which are significantly more affected by crime and in particular property crime than those in high income or low crime areas.

The estimates for Hamburg provide the first detailed results of the impact of crime on real estate prices in Germany. It is also the first study for Continental Europe using panel data.

Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City.

The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable.

Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence.

There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices.

This study aims to analyse the effect of gun-related violence on housing values, controlling for the area's crime levels and locational factors. Previous studies that aimed to find a causal connection between crime and housing values used instrument variables to solve the endogeneity problem. Here, the authors have instead been able to take advantage of the fact that shootings have occurred in random time and space. This has made it possible to estimate models to create windows around the shooting (event) and to estimate the causal effects of the shootings. Thus, the authors aim to contribute to the regression discontinuity design method in this context to estimate the short-term effects.

Using the regression discontinuity design method, the authors can estimate the short-term effects of shootings.

Findings from the analysis indicate that shootings directly affect those who are impacted by shootings and indirectly affect the environments where shootings occur. The indirect effect of shootings is momentary as it is capitalised directly in housing values in the immediate area. The effect also appears to be relatively long-term and persistent as housing values have not returned to the price level before the shooting 100–200 days after the shooting. The capitalisation effect is higher the closer one gets to the central parts of the city. On the other hand, the capitalisation effect is not higher or lower in areas with a higher crime rate per capita.

The article contributes to the previous literature in several ways. First and foremost, it provides an explicit analysis of shootings in built-up areas and their hypothesised effect on property prices through the impact on attractiveness and perceived safety. As far as the authors know, no study has analysed this issue on the international level or in Sweden. In this way, the authors aim to develop a study that can provide critical knowledge about one of the adverse effects of shootings. The authors also contribute to the literature by utilising unique data material, which allows the authors to merge information from the police about the exact location of shootings in the Stockholm area with data on sales of apartments in the same residential areas. In addition to the exact location of the shootings (coordinates), the authors also have access to data about whether the shootings led to injuries or deaths. Thus, the authors have separated the effect of shootings and fatal shootings, which has not been done before. Finally, the authors set out to highlight the results as a contribution to the debate on shootings.