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Article
Publication date: 16 November 2018

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…

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.

Details

Journal of European Real Estate Research, vol. 11 no. 3
Type: Research Article
ISSN: 1753-9269

Keywords

Article
Publication date: 30 March 2020

Joseph Awoamim Yacim and Douw Gert Brand Boshoff

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Property Management, vol. 38 no. 2
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 7 June 2021

Daniel Lo, Nan Liu, Michael James McCord and Martin Haran

Information transparency is crucially important in price setting in real estate, particularly when information asymmetry is concerned. This paper aims to examine how a…

Abstract

Purpose

Information transparency is crucially important in price setting in real estate, particularly when information asymmetry is concerned. This paper aims to examine how a change in government policy in relation to information disclosure and transparency impacts residential real estate price discovery. Specially, this paper investigates how real estate traders determined asking prices in the context of the Scottish housing market before and after the implementation of the Home Report, which aimed to prevent artificially low asking prices.

Design/methodology/approach

This paper uses spatial lag hedonic pricing models to empirically observe how residential asking prices are determined by property sellers in response to a change in government policy that is designed to enhance market transparency. It uses over 79,000 transaction data of the Aberdeen residential market for the period of Q2 1998 to Q2 2013 to test the models.

Findings

The empirical findings provide some novel insights in relation to the price determination within the residential market in Scotland. The spatial lag models suggest that spatial autocorrelation in property prices has increased since the Home Report came into effect, indicating that property sellers have become more prone to infer asking prices based on prior sales of dwellings in close vicinity. The once-common practice of setting artificially low asking prices seems to have dwindled to a certain extent statistically.

Originality/value

The importance of understanding the relationship between information transparency and property price determination has gathered momentum over the past decade. Although spatial hedonic techniques have been extensively used to study the impact of various property- and neighbourhood-specific attributes on residential real estate market in general, surprisingly little is known about the empirical relationship between spatial autocorrelation in real estate prices and information transparency.

Details

International Journal of Housing Markets and Analysis, vol. 15 no. 2
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 25 February 2014

M. McCord, P.T. Davis, M. Haran, D. McIlhatton and J. McCord

Accounting for locational effects in determining price is of fundamental importance. The demise of the mainstream property market has culminated in increasing appetite and…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/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.

Details

International Journal of Housing Markets and Analysis, vol. 7 no. 1
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 13 February 2007

Sherif Roubi and Ashraf Ghazaly

The purpose of this paper is to focus on inter‐neighbourhood variation in the rental apartment market in Greater Cairo, Egypt, and its potential influence on property…

1143

Abstract

Purpose

The purpose of this paper is to focus on inter‐neighbourhood variation in the rental apartment market in Greater Cairo, Egypt, and its potential influence on property prices and performance of hedonic pricing models.

Design/methodology/approach

The paper delves into the issue of whether comparables from different neighbourhoods are homogeneous enough to be aggregated in hedonic pricing models. This paper extends the research on rental‐property market segmentation by investigating the existence of apartment submarkets determined by neighbourhoods.

Findings

Results show that parameters are unstable across neighbourhoods and Chow test provides further support for utilising spatial hedonic pricing models.

Originality/value

This paper provides further support for spatial hedonic pricing models using empirical evidence from Greater Cairo, Egypt. The paper finds that explanatory and predictive powers of hedonic pricing models are improved when separate hedonic equations are estimated for each neighbourhood in Greater Cairo. The paper does not provide an elaborate solution for implementing spatial models in Greater Cairo but rather supports the notion that one has to be developed.

Details

Property Management, vol. 25 no. 1
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 3 October 2016

David McIlhatton, William McGreal, Paloma Taltavul de la Paz and Alastair Adair

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…

1069

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

International Journal of Housing Markets and Analysis, vol. 9 no. 4
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 17 August 2021

Evgeniy M. Ozhegov and Alina Ozhegova

A common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices

Abstract

Purpose

A common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices are decomposed into internal characteristics of an apartment, apartment characteristics and external characteristics. To account for the unobserved quality of the surrounding environment, price models include spatial price correlation factors, where the distance is usually measured as the distance in geographic space. In determining the price, a seller focuses not only on the observed and unobserved factors of the apartment and its environment but also on the prices of similar marketed objects that can be selected both by geographic proximity and by characteristics similarity. The purpose of this study is to show the latter point empirically.

Design/methodology/approach

This study uses an ensemble clustering approach to measure objects' proximity and test whether the proximity of objects in the property characteristics space along with spatial correlation explain the significant variation in prices.

Findings

In this paper, the pricing behaviour of sellers in a reselling market in Perm, Russia is studied. This study shows that the price transmission mechanism includes both geographic and characteristics spaces.

Practical implications

After testing on market data, the proposed framework for the distance construct could be used to obtain higher predictive power for price predictive models and construction of automated valuation services.

Originality/value

This study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces.

Details

International Journal of Housing Markets and Analysis, vol. 15 no. 5
Type: Research Article
ISSN: 1753-8270

Keywords

Abstract

Details

Freight Transport Modelling
Type: Book
ISBN: 978-1-78190-286-8

Article
Publication date: 4 December 2019

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.

Details

International Journal of Housing Markets and Analysis, vol. 13 no. 5
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 1 January 2013

Michael T. Norton, Calum Turvey and Daniel Osgood

The purpose of this paper to develop an empirical methodology for managing spatial basis risk in weather index insurance by studying the fundamental causes for differences…

1661

Abstract

Purpose

The purpose of this paper to develop an empirical methodology for managing spatial basis risk in weather index insurance by studying the fundamental causes for differences in weather risk between distributed locations.

Design/methodology/approach

The paper systematically compares insurance payouts at nearby locations based on differences in geographical characteristics. The geographic characteristics include distance between stations and differences in altitude, latitude, and longitude.

Findings

Geographic differences are poor predictors of payouts. The strongest predictor of payout at a given location is payout at nearby location. However, altitude has a persistent effect on heat risk and distance between stations increases payout discrepancies for precipitation risk.

Practical implications

Given that payouts in a given area are highly correlated, it may be possible to insure multiple weather stations in a single contract as a “risk portfolio” for any one location.

Originality/value

Spatial basis risk is a fundamental problem of index insurance and yet is still largely unexplored in the literature.

Details

The Journal of Risk Finance, vol. 14 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

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