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Global warming exacerbates sea level rise and extreme weather events that cause severe flooding, resulting in lost productivity and property damage. To reduce the impact of flooding, residents are avoiding purchasing homes in high-risk areas. There are numerous studies on the relationship between flood hazards and housing prices in developed countries, but few in developing countries. Therefore, this study aims to investigate the relationship between flood hazards and housing prices in Hat Yai, Songkhla, Thailand.

This study uses spatial-lag, spatial error and spatial autoregressive lag and error (SARAR) models to analyze the effect of flood risk on property prices. The main analysis examines the degree of flood risk and housing rental prices from our survey of 380 residences. To test the robustness of the results, the authors examine a different data set of the same samples by using the official property valuation from the Ministry of Finance and the flood risk estimated by the Southern Natural Disaster Research Center.

The SARAR model was chosen for this study because of the occurrence of spatial dependence in both dependent variable and the error term. The authors find that flood risk has a negative impact on property prices in Hat Yai, which is consistent with both models.

This study is one of the first to use spatial econometrics to analyze the impact of flood risk on property prices in Thailand. The results of this study are valuable to policymakers for benefit assessment in cost–benefit analysis of flood risk avoidance or reduction strategies and to the insurance market for pricing flood risk insurance.

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.

The purpose of this paper is two-fold. First, on a theoretical level we introduce a series-type instrumental variable (IV) estimator of the parameters of a spatial first order autoregressive model with first order autoregressive disturbances. We demonstrate that our estimator is asymptotically efficient within the class of IV estimators, and has a lower computational count than an efficient IV estimator that was introduced by Lee (2003). Second, via Monte Carlo techniques we give small sample results relating to our suggested estimator, the maximum likelihood (ML) estimator, and other IV estimators suggested in the literature. Among other things we find that the ML estimator, both of the asymptotically efficient IV estimators, as well as an IV estimator introduced in Kelejian and Prucha (1998), have quite similar small sample properties. Our results also suggest the use of iterated versions of the IV estimators.

Housing affordability in New Zealand is placing significant pressure on the country’s social housing sector with increased demand for public sector accommodation. A common belief suggests that social housing has a negative effect on nearby residential property values. This study aims to develop proximity and concentration measures of social housing to determine if their spatial distribution affects property values.

Using over 32,000 residential sales transactions from Auckland (New Zealand) during a three-year period (2014–2016), this study applies standard hedonic OLS framework with the addition of spatial autoregressive model and spatiotemporal autoregressive model to test if proximity and concentration of social housing influence residential property values.

The research found that private houses that share boundaries with public housing are discounted by 1.7%–3.3% depending on the socio-economic status of the submarket. The authors find that wealthier submarkets are better equipped to absorbing negative externalities attached to social housing. Proximity measures tend to peak at 250 m, with houses discounted up to 5% within that distance. Concentration levels of social housing had a greater influence on the private residential market. At low levels of concentration, houses in areas of high and low socio-economic levels were discounted by approximately 6.5%. The discount does not remain uniform and the gap between the two areas is apparent at medium and high concentration levels. The negative effect was the highest − 23% – in the neighbourhoods that were socially and economically deprived.

The study’s findings can assist policymakers in informing strategies on the future social housing initiatives. The findings suggest that a dispersed development strategy that incorporates a balanced mix of tenure and socio-economic groups should be preferred over a high-density social housing concentrated in already deprived neighbourhoods.

The purpose of this paper is to employ provincial data to study the relationship between several crime typologies, namely murder, robbery, extortion and fraud and economic output in Italy.

The authors propose a spatial econometric approach where the spatial proximity is defined by a measure of physical distance between locations, in order to take into account possible spill-over effects.

The results of the spatial estimation suggest that criminal activities, namely murder and robbery, exhibit a negative impact on Italian gross domestic product while fraud and total crime do not affect economic output and that there are beneficial spill-overs from neighbouring provinces.

The study empirically shows that only violent crimes have a crowding-out effect on economic output.

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.

This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

The paper investigates the prevalence of extreme poverty in a panel of 39 sub-Saharan African (SSA) countries over the period 2000–2018 while accounting for spillover effects.

The study adopts the recently developed spatial dependence-consistent, bias-corrected quasi-maximum likelihood (QML) estimators and the linear dynamic panel regression to control for the potential endogeneity in poverty and corruption spillovers.

The spatial model shows. consistently across all the specifications, that there is a substantial spillover effect of corruption and poverty across the region. Additionally, the study also found that investment in health and education is a significant determinant of poverty in the region. However, the effectiveness of these policy variables to reduce poverty declines in the face of corruption spillovers. More importantly, the empirical analysis shows that poverty does not only exhibit spatial spillovers but also has a persistent effect over time. The results, therefore, suggest that to reduce poverty in the region, sub-Saharan African governments must adopt spatially differentiated policies and programmes by working together to reduce unemployment and corruption in the region, and not the widely adopted spatially mute designs currently in place. The research and policy implications are discussed.

The study accounts for spatial dependency and spillover effects in the analysis of poverty and corruption in SSA