The effect of resource constraints on housing recovery simulations

Costa, Rodrigo; Haukaas, Terje

doi:10.1016/j.ijdrr.2021.102071

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…Costa and Haukaas [14] simulated how the limited number of contractors in a community extends the recovery time of single-family residential buildings in Vancouver, Canada. They developed models of permitting, engineering assessment, financing, contracting and willingness to repair as well D r a f t as models that account for the effect of socioeconomic factors on housing recovery.…”

Section: Introductionmentioning

confidence: 99%

A Demand-Supply Framework for Evaluating the Effect of Resource and Service Constraints on Community Disaster Resilience

Blagojević¹,

Stojadinović²

2022

Preprint

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Tools that quantify community disaster resilience are essential for informed decision-making on community disaster resilience improvement measures. One of the major research gaps in quantifying community disaster resilience are community disaster recovery simulations. Such simulations enable an insight into factors that enable a rapid and efficient community disaster recovery and vice versa. The iRe-CoDeS framework presented in this paper, simulates community disaster recovery as a time-stepping loop, where at each time step the interplay of demand and supply of community components for various resources and services dictates components’ ability to operate and recover. Disaster resilience of a community is then quantified using a multi-dimensional metric, where each dimension represents the unmet demand of a community regarding a certain resource or a service, labelled as Lack of Resilience (LoR). This paper presents how such a demand/supply approach can be applied to account for resource and service constraints, impeding factors, that prolong component recovery and thus decrease community disaster resilience. Housing resilience of north-east San Francisco exposed to a Mw7.2 earthquake on the San Andreas Fault is quantified to illustrate the proposed approach. rWhale application framework recently developed at the NHERI SimCenter is used for this purpose, presenting how such a regional simulation on the effect of natural disasters on communities can be extended using the iRe-CoDeS framework to simulate community disaster recovery and quantify community disaster resilience. It is shown that housing resilience quantification results obtained in the Case Study focused on a part of San Francisco are in accordance with the existing estimates of housing resilience. The evolution of the post-disaster community-level supply and demand for recovery resources and services is obtained, identifying how and when the unmet demand for these resources and services impedes community recovery. Lastly, the effect of community’s ability to mobilize resources and services needed for its recovery on its disaster resilience is investigated.

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Section: Introductionmentioning

confidence: 99%

A Demand-Supply Framework for Evaluating the Effect of Resource and Service Constraints on Community Disaster Resilience

Blagojević¹,

Stojadinović²

2022

Preprint

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“…In this context, computational simulations are a powerful tool to support planning. Some scholars have proposed simulation models for housing recovery and highlighted the relevance of pre-planning for workforce demand (Alisjahbana and Kiremidjian, 2021; Costa and Haukaas, 2021). However, these models focus on simulating the allocation of the existing workforce.…”

Section: Introductionmentioning

confidence: 99%

Simulating post-disaster temporary housing needs for displaced households and out-of-town contractors

2022

Self Cite

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Residential damage from major disasters often displaces local residents out of their homes and into temporary housing. Out-of-town contractors assisting in post-disaster housing reconstruction also need housing, creating additional pressure on the local housing stock. Communities should thus prepare for a surge in temporary housing demand to minimize the impact on the local residents and to expedite housing recovery efforts. Computational models can support recovery planning. This article introduces an agent-based simulation framework to estimate the workforce demand and the joint temporary housing needs of contractors and displaced households. The main agents are households seeking to repair their homes, local contractors, and out-of-town contractors. Out-of-town contractor agents come into the community if the labor and housing markets are favorable. The framework can be used to evaluate the resulting challenges and benefits of interventions aimed at attracting out-of-town contractors to expedite housing recovery. We present a case study on the housing recovery of the city of San Francisco after hypothetical M 6.5, M 7.2, and M 7.9 earthquakes. A shortage of contractors is shown to bottleneck the reconstruction if no out-of-town contractors are recruited. Conversely, out-of-town contractors increase the likelihood of temporary housing shortages. These results highlight the need to plan for shortages of reconstruction labor and temporary housing during recovery.

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“…The model takes into account number of damaged structures, their dispensation and limited resources when recovery starts. Costa and Haukaas (2021) simulated the effects of limited resources on post-earthquake housing recovery. Results of this research showed that ownership status and median housing income have significant effects on recovery period.…”

Section: Introductionmentioning

confidence: 99%

Empirical study of housing recovery and property abandonment following valley fire in California: insights from neighborhood characteristics and building attributes

Rezakhani

2022

IJDRBE

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PurposeThis paper aims to examine how neighborhood characteristics (income, population composition) and individual building attributes (ownership) affect the recovery period of single-family housing and determine their correlations with property abandonment and changes in residential land use after natural disaster.Design/methodology/approachThis empirical study focuses on Valley Fire, one of the California’s most destructive wildfires in 2015, and uses assessor, community, demographic and sales data to measure recovery of a panel of single-family houses located in Lake County in California between 2012 and 2020. Several regression and correlation models will be developed to test different hypotheses.FindingsThis study found that: Recovery period is longer than what expected in most existing literature; ownership status significantly affects recovery period; income level is not a significant factor for shortening the recovery period; and minorities may need more assistance for constant recovery. Findings of this research will help identify at risk communities to avoid uneven housing recovery and lower the rate of property abandonment.Originality/valueHousing recovery is key to revitalizing communities following major natural disasters. The sociodemographic characteristics of each neighborhood have significant impact on the duration of recovery and possible property abandonment. Understanding how home and neighborhood characteristics affect recovery will help planners prevent long-lasting adverse effects of natural disasters.

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The effect of resource constraints on housing recovery simulations

Cited by 11 publications

References 31 publications

A Demand-Supply Framework for Evaluating the Effect of Resource and Service Constraints on Community Disaster Resilience

A Demand-Supply Framework for Evaluating the Effect of Resource and Service Constraints on Community Disaster Resilience

Simulating post-disaster temporary housing needs for displaced households and out-of-town contractors

Empirical study of housing recovery and property abandonment following valley fire in California: insights from neighborhood characteristics and building attributes

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