This paper introduces the Special Issue on Cascading Effects in Disaster Risk Management. It reviews the contributions and highlights their multi-disciplinary interpretations of cascades. It proceeds to discuss whether the on-going unfolding of the COVID-19 pandemic illustrates the cascades metaphor.

We argue that housing affordability is as much about incomes as it is about house prices. Consequently, a comprehensive analysis of housing affordability should be conducted in which incomes and house prices are determined through the specification of labor, capital and product markets in addition to housing markets. A spatial econometric model for Israel is used to study the effects on the regional distribution of housing affordability of income generating shocks in labor and capital markets, as well as supply shocks in housing markets. Particular attention is paid to the effects on affordability of planning delays in tendering land for housing construction and the issue of building permits. Spatiotemporal impulse responses for housing affordability show that region-specific shocks, such as accelerated planning permission and the provision of regional investment grants, percolate across the economy as a whole. Implications for place-based regional policy are discussed.

Informed regional policy needs good regional data. As regional data series for key economic variables are generally absent whereas national-level time series data for the same variables are ubiquitous, we suggest an approach that leverages this advantage. We hypothesize the existence of a pervasive “common factor” represented by the national time series that affects regions differentially. We provide an empirical illustration in which national FDI is used in place of panel data for FDI, which are absent. The proposed methodology is tested empirically with respect to the determinants of regional demand for housing. We use a quasi-experimental approach to compare the results of a “common correlated effects” (CCE) estimator with a benchmark case when absent regional data are omitted. Using three common factors relating to national population, income and housing stock, we find mixed support for the common correlated effects hypothesis. We conclude by discussing how our experimental design may serve as a methodological prototype for further tests of CCE as a solution to the absent spatial data problem.

A new empirical approach to identify local housing markets (LHM’s) is proposed, which focuses on the spatial correlation between local house price indices constructed from repeat sales data. It extends the work of Pryce who claimed that if housing in different locations are perfect substitutes, their house price indices should be perfectly correlated over time. Pryce’s work represents a paradigmatic change in identifying local housing markets using revealed preferences rather than hedonic pricing. It requires spatial panel data for house prices which we construct using repeated sales data to generate house price indices for Tel Aviv (1998–2014) for over 100 census tracts. These price indices are used to define LHMs.  The number of LHMs varies inversely with the degree to which house prices in locations belonging to the same LHM, are expected to be correlated. It also varies directly with the order of contiguity of these locations. Results point to considerable spatial heterogeneity in house price movement. This belies the popular impression that the Tel Aviv housing market is relatively homogeneous, characterised by expensive housing and uniform house price movements.

Building damage probabilities are invaluable for assessing short-term losses from natural hazards. In many countries however, the individual building level data required for assessing reliable damage are usually unavailable. This paper shows how the post-processing of aggregate HAZUS earthquake damage assessments can yield building-level damage probabilities. On the basis of three plausible scenarios for Northern Israel, we generate and visualize a building-level combined damage probability index. We use the tools of exploratory spatial data analysis to purge any causal influences in the spatial pattern of these calculated damage probabilities. The costs and benefits of our approach are discussed.

This monograph deals with spatially dependent nonstationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using spatial panel data instead of assuming it to be exogenously fixed. This is followed by a discussion of spatial nonstationarity in spatial cross-section data, and a full exposition of non-stationarity in both single and multi-equation contexts, including the estimation and simulation of spatial vector autoregression (VAR) models and spatial error correction (ECM)  models. 
The book reviews the literature on panel unit root tests and panel cointegration tests for spatially independent data, and for data that are strongly spatially dependent. It provides for the first time critical values for panel unit root tests and panel cointegration tests when the spatial panel data are weakly or spatially dependent. 
The volume concludes with a discussion of incorporating strong and weak spatial dependence in non-stationary panel data models. All discussions are accompanied by empirical  testing based on a spatial panel data of house prices in Israel.

This paper revisits the claim that a causal link exists between the inflexibility of land‐use planning and low elasticity of housing supply. A theoretical model of factors impacting district‐level planning is presented alongside a model of the impact of planning delay on housing supply. The models are estimated for the Tel Aviv district using detailed data covering a 12‐year period. Results show significant impacts on planning delay due to plan, municipality and district planning commission characteristics. House prices exert a positive effect on planning speed and municipal funding acts as a supply constraint. While planning delay does not impact housing supply, delay uncertainty has a negative impact and acts as a constraint.

Demand for household insurance is intuitively perceived as contributing to household and community resilience. However the causality in this relationship is not clear. This paper examines household insurance expenditure and the generation of urban resilience as jointly determined. Potential endogeneity is purged by estimating this relationship as a system and using an instrumental variable approach. Empirical analysis based on aggregated Israeli household expenditure data is used. Results show that instrumenting makes a difference, that a distinction needs to be drawn between personal resilience and environmental resilience and that insurance coverage has an independent effect on resilience different to that of classic social (personal) and economic (property and place-based) characteristics. The policy context of the findings are discussed.

The value of most ecosystem services invariably slips through national accounts. Even when these values are estimated, they are allocated without any particular spatial referencing. Little is known about the spatial and distributional effects arising from changes in ecosystem service provision. This paper estimates spatial equity in ecosystem services provision using a dedicated data disaggregation algorithm that allocates ‘synthetic’ socio-economic attributes to households and with accurate geo-referencing. A GIS-based automated procedure is operationalized for three different ecosystems in Israel. A nonlinear function relates household location to each ecosystem: beaches, urban parks and national parks. Benefit measures are derived by modeling household consumer surplus as a function of socio-economic attributes and distance from the ecosystem. These aggregate measures are spatially disaggregated to households. Results show that restraining access to beaches causes a greater reduction in welfare than restraining access to a park. Progressively, high income households lose relatively more in welfare terms than in low income households from such action. This outcome is reversed when distributional outcomes are measured in terms of housing price classes. Policy implications of these findings relate to implications for housing policies that attempt to use new development to generate social heterogeneity in locations proximate to ecosystem services.

A structural spatial econometric model for nine regions of Israel is estimated using non-stationary spatial panel data during 1987–2015. The model focuses on the relation between regional markets in labour, housing and capital when there is imperfect internal migration between regions, when capital is imperfectly mobile between regions, and when building contractors operate across regions. Since the regional panel data are non-stationary, the econometric methodology is based on spatial panel cointegration. The estimated model is used to simulate the temporal and spatial propagation of regional shocks induced, for example, by regional policy (land for housing, regional investment grants). Impulse responses are temporally and spatially state dependent. They are also highly persistent because of longevity in housing and capital.