Search results

From a theoretical point of view, a spatial econometric model can contain both a spatially lagged dependent variable (spatial lag) and a spatially autocorrelated error term (spatial error). However, such models are rarely used in practice. This is because (assuming a lattice model approach is used for both the spatial lag and spatial error) the model is difficult to estimate¹ unless the weight matrices are different for the spatial lag and the spatial error.

Baltagi and Li (2001) derived Lagrangian multiplier tests to jointly test for functional form and spatial error correlation. This companion paper derives Lagrangian multiplier tests to jointly test for functional form and spatial lag dependence. In particular, this paper tests for linear or log-linear models with no spatial lag dependence against a more general Box-Cox model with spatial lag dependence. Conditional LM tests are also derived which test for (i) zero spatial lag dependence conditional on an unknown Box-Cox functional form, as well as, (ii) linear or log-linear functional form given spatial lag dependence. In addition, modified Rao-Score tests are also derived that guard against local misspecification. The performance of these tests are investigated using Monte Carlo experiments.

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.

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 aims to address the following questions: is good liner shipping connectivity a requisite for merchandise imports plus exports? What is the average of merchandise imports plus exports of the countries neighboring China? Do the merchandise imports plus exports of these countries correspond to each country’s own merchandise imports plus exports or liner shipping connectivity index (LSCI)?

The authors spatially analyze liner shipping connectivity and merchandise imports plus exports using 2016 data and a common framework for linear regression to establish the relationship amongst a country’s LSCI and its merchandise imports plus exports and between its merchandise imports plus exports and those of its neighbors. Merchandise imports plus exports of countries are not necessarily independent of each other, and countries that are contiguous may produce similar observations.

North America and Western Europe comprised clusters of countries that participated more actively in the international trading system, while Africa’s countries had less international trade than average. The study identifies and quantifies the geographical ripple of transport infrastructure on merchandise trade from a national perspective. Notably, a spatially lagged term improved the model’s ability to account for variations in merchandise imports plus exports across countries.

The spatial lag of merchandise imports plus exports can contribute to specifying the spread of merchandise imports plus exports beyond what the authors would anticipate from a country’s network of liner shipping.

The purpose of this paper is to investigate the role of spatial dependency in the construction of a price index for the transactions of whole office buildings. It examines transactions of office buildings over a 15-year period and addresses an under-researched area in investment property analysis.

The study examines data relating to transactions of all office buildings in the Melbourne (Australia) central business district between 2000 and 2015. The methodology uses a spatial weights matrix to construct a hedonic model, spatial error model, spatial lagged model and an office building transactional price index.

The findings confirm the existence of spatial dependency for the transactions of office buildings. In addition, incorporating the effect of spatial dependency by constructing spatial error and spatial lagged model improved the accuracy of the estimated transactional price index for office buildings.

These findings make an important contribution to the literature by highlighting the importance of the issue of spatial autocorrelation in the estimation of valuation models and price indexes for office buildings. Until now the focus has predominantly been on individual office units rather than whole office buildings, where the barrier has traditionally been access to comprehensive data. The analysis did not consider leasing details as this information is not accessible in the Australian market.

The research will assist stakeholders including valuers, investors and market regulators to improve their understanding of movements in the office property transactional market. The findings provide an insight into trends associated with the transfer of office buildings. It will assist future decisions about the location of a new office building developments in order to optimise their proximity to transport and other buildings.

The study will assist planners to ensure the location of office buildings are optimised from a social sustainability perspective. This equates to buildings located in close proximity to transport facilities and also supporting the development of office buildings in locations, which are associated with lower future risk.

The construction of an accurate and reliable property index is critically important for practitioners to understand the movement in both the property market and also in the broader economy. A substantial increase in whole office building acquisitions has been observed in recent years, especially after the 2007 Global Financial Crisis (Lizieri and Pain, 2014) although there has remained limited research undertaken in this area.

Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial‐temporal interconnections between house price movements across the Australian capital cities.

Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects.

The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information.

This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted.

This paper aims to analyse the spatial effect of school input – “student–teacher ratio” – on property prices in Taipei Metropolis, Taiwan. The falling fertility rate inevitably changes educational system since more less-experienced part-time teachers are hired for the sake of schools’ budget. Hence, in addition to full-time teachers, part-time teachers are included in measuring the student–teacher ratio to see if an increase in part-time teachers, indicating the possible plunge of school quality, may decrease the value of nearby properties.

Three types of spatial regressions (including spatial lag, spatial error and SARAR models), which incorporate different kinds of spatial dependencies into hedonic models, are applied to reveal the relationship between two measures of student–teacher ratios and property values.

Conventional variables, including housing attributes, demographics and local facilities, demonstrated their consistent and expected influence on property prices. More importantly, the significant “student–teacher ratio 2” (both full-time and part-time teachers) indicated that low-paid, less-experienced and overworked part-time teachers can hardly deliver quality instruction, which inevitably causes harm to school credit and potential buyers’ confidence in valuing neighbouring properties.

Facing the decrease in children and the shrinking budget, the solution to maintain teacher’s quality is to remove the unnecessary administrative chores from full-time teachers and let them do their jobs rather than hiring part-time teachers. Good school input quality should add value to nearby properties, which in return appeals more students to enrol in this school and further elevate schools’ financial burden.

This paper is one of the few studies that consider part-time teachers in capitalising school-input quality into property prices. The increase in part-time teachers, which may lead to an illusion that each student could have higher degree of individual attention from teachers, actually lowers the education quality distributed to all the students. It provides a different perspective in defining the importance of teaching quality to property values in Chinese culture.

This paper revisits the joint and conditional Lagrange multiplier tests derived by Debarsy and Ertur (2010) for a fixed effects spatial lag regression model with spatial autoregressive error, and derives these tests using artificial double length regressions (DLR). These DLR tests and their corresponding LM tests are compared using an empirical example and a Monte Carlo simulation.

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.