Housing Recovery Research Articles

Today in Thailand: multidisciplinary perspectives on the current tsunami disaster risk reduction

Abstract Thailand is one of the many countries in Asia that fell victim to the monstrous 2004 Indian Ocean tsunami. To date, there has been tons of effort to reduce disaster risk in Thailand. Nevertheless, there is still a considerable margin for improvement. This paper aims to review previous efforts and measures that have been taken to reduce Thailand's risk to disasters, and highlight possible areas for improvement. We use the disaster risk management as a thought-framework for our review, where we discuss Thailand's current disaster risk reduction (DRR) measures from a multi-perspective: the tsunami numerical simulation, tsunami warning system, tsunami evacuation sign, manuals and guidelines, evacuation shelters, housing recovery, hotel business and recovery, public awareness and media, and disaster education. In understanding the current state of disaster risk efforts, we will then highlight potential areas for improvement.

Comparing three approaches to developing concentrated rural settlement after the Lushan Earthquake

Housing reconstruction is critical following natural disasters, especially in rural areas of developing countries. Concentrated rural settlement (CRS) is an important method adopted by local governments in China to promote post-disaster rural housing recovery. However, the effectiveness and selection of CRS approaches under different situations often puzzles local governments, even though various approaches to CRS development have been available since the Wenchuan Earthquake on May 12, 2008. This study therefore compares three approaches to developing CRS (unified planning/unified reconstruction, unified planning/joint reconstruction, and unified planning/self-reconstruction), based on three case studies in Lushan County of Ya’an City in Sichuan Province of China. The factors considered include the triggering event, participants, process, and outcome. Research information and data were collected using interviews, a questionnaire survey, and content analysis. The comparison reveals a transformation from a government-led pattern to a resident-led pattern in the rural housing reconstruction after the Lushan Earthquake. When comparing the effectiveness of these three approaches, each approach must be considered according to its prerequisites to ensure the sustainability of reconstruction. Based on the comparative analysis, the study also offers a map for future initiatives, providing valuable insights for local governments to develop sustainable CRS in post-disaster reconstruction.

Effects of pre-disaster household social vulnerability on housing recovery

Effects of pre-disaster household social vulnerability on housing recovery

RecovUS: An Agent-Based Model of Post-Disaster Household Recovery

The housing sector is an important part of every community. It directly affects people, constitutes a major share of the building market, and shapes the community. Meanwhile, the increase of developments in hazard-prone areas along with the intensification of extreme events has amplified the potential for disaster-induced losses. Consequently, housing recovery is of vital importance to the overall restoration of a community. In this relation, recovery models can help with devising data-driven policies that can better identify pre-disaster mitigation needs and post-disaster recovery priorities by predicting the possible outcomes of different plans. Although several recovery models have been proposed, there are still gaps in the understanding of how decisions made by individuals and different entities interact to output the recovery. Additionally, integrating spatial aspects of recovery is a missing key in many models. The current research proposes a spatial model for simulation and prediction of homeowners’ recovery decisions through incorporating recovery drivers that could capture interactions of individual, communal, and organizational decisions. RecovUS is a spatial agent-based model for which all the input data can be obtained from publicly available data sources. The model is presented using the data on the recovery of Staten Island, New York, after Hurricane Sandy in 2012. The results confirm that the combination of internal, interactive, and external drivers of recovery affect households’ decisions and shape the progress of recovery.

Monetary Policy and US Housing Expansions: The Case of Time-Varying Supply Elasticities

We challenge the assumption in the literature of constant housing supply elasticities across housing expansions. Using a time-varying parameter (TVP)-VAR model on monthly US data since the early 1990s, we find that the response of housing supply to an expansionary monetary policy shock relative to the response of house prices has declined substantially since the Great Financial Crisis (GFC). Our findings are consistent with research suggesting that land-use regulation has tightened. Absent major reversions in regulation, our results point to a post-COVID-19 housing recovery characterised by a sluggish response of housebuilding to demand, but a relatively stronger response of house prices.

Supply Chain Modeling in the Aftermath of a Disaster: A System Dynamics Approach in Housing Recovery

Severe catastrophic natural disasters adversely affect housing stock and regional capacity to build and repair houses, with unfavorable impacts on the business continuity of industrial and business organizations resting on that affected community. Restoring industrial capacity and business continuity is crucial for the organizations’ survival in the aftermath of a disaster but the process takes time while the affected region faces an unexpected surge in the demand for housing. Modeling approaches largely used in the supply chain management realm may support humanitarian logistics and policy-making by providing a testbed in which supply chain elements of the recovery process can be examined before implementing. This research presents a system dynamics model that considers the problem of housing recovery from the demand and supply perspective and provides significant insights for policymakers into how the production of permanent housing depends upon the uncertainties and feedback effects of material and labor. In order to highlight its utility, a hypothetical scenario in which a hurricane hits the U.S. Hampton Roads region, which is a major population center along the mid-Atlantic coast, has been configured by using actual data collected from both governmental and academic sources.

Recovery home networks as social capital.

Ensuring recovery home residents' social integration into a home environment is important for preventing early dropout and facilitating sustained recovery. Social capital theory may provide an explanation for how recovery homes may protect residents and improve recovery rates. However, little is known about how social capital in recovery home environments is structured and accessed. Recovery homes may increase social capital by sharing bonds through friendships, lending money, and advice-seeking. The current study describes social network cross sectional data obtained from a study of 42 Oxford House recovery homes, in three locations in the US (North Carolina, Texas, and Oregon). The residents rated each member of their house on the dimensions of friendship, money loaning, and advice seeking to assess how each resident views one another on these dimensions. The research used baseline data from a larger longitudinal study, and although some data were presented for the full sample (APL, isolates, mean reciprocity and density), the results primarily focused on case studies for three of the participating Oxford Houses-with examples of low, median, and high "connected" houses respectively. Standard measures of network structures were calculated for each home. Although all Oxford Houses follow the same house rules, they were found to vary in network structure. Findings indicated a considerable range of interconnectedness among residents in these houses, with friendship being the most common relationship, willingness to lend money less common, and advice-seeking the least common. The findings on friendship, willingness to lend, and advice-seeking provide promising leads about what occurs among the social networks within these complex eco-systems, and may provide ways to better understand and facilitate resident social integration into these settings.

Tobacco Use among Recovery Home Residents: Vapers Less Confident to Quit.

Objectives: In this study, we provide estimates of the prevalence of tobacco use among residents in Oxford House (OH) recovery homes. We explore predictors for smoking cessation on future intentions to quit tobacco use, such as self-efficacy, motivation to quit, and level of nicotine dependence. We focus on examining smoking-related differences between 2 groups of tobacco users: (1) residents who only vape, and (2) residents who only smoke or who smoke and vape (ie, dual users). Methods: Data come from a research project examining the social networks of residents in OH homes at Wave 5 (N = 270) when questions were asked about tobacco use and cessation. We conducted multiple logistic regression analysis to assess the predictors on intentions to quit tobacco in the next 6 months. Results: Among residents, 60.4% report that they either smoke cigarettes, vape, or use both products. The odds of quitting are 92.5% lower for residents who only use e-cigarettes than for those who only smoke cigarettes or who smoke cigarettes and vape. Motivation to quit was associated with more than double the odds for self-efficacy on intentions to quit with odds ratios of 3.23 (95% CI: 1.81, 5.78; p = .00) and 1.47 (95% CI: 1.05, 2.07; p = .03) respectively. Conclusion: The high prevalence of smoking and vaping in this population makes it an important target for interventions.

Neighborhood Affordability and Housing Market Resilience

Problem, research strategy, and findings: Although many researchers have examined factors associated with vulnerability to foreclosure, few have investigated the role neighborhood affordability plays in foreclosures in metropolitan areas. In this study, we examine the effects of location affordability (i.e., housing and transportation affordability combined) on resilience to foreclosure in more than 300 U.S. metropolitan areas during the U.S. housing recovery period. Using hierarchical linear regression with changes in zip code–level home foreclosure rates, our findings suggest the relationship between affordability and foreclosure resilience varies according to urban form (central/high-density city versus suburban low-density area) and types of metropolitan housing markets (boom–bust versus strong versus weak). In the national analysis, where location affordability was high, home foreclosure rates dropped substantially in central/high-density areas but not in suburban low-density areas. When we disaggregated the zip codes according to the market type, location affordability contributed to recovery in central cities in strong and weak metros and in the suburbs of boom–bust metros. There was no positive association in the suburbs of strong and weak metros. With improved data, future studies could measure an association between affordability and lower income renter households.Takeaway for practice: Our study of the affordability crisis that followed the foreclosure crisis shows that planners can foster resilient and affordable housing markets by expanding and densifying affordable neighborhood locations and considering interactions between the costs of housing and transportation. Planners can improve neighborhood affordability with local and regional strategies based on the local residential density and the type of metropolitan housing market.

A promising recovery housing model for American Indian communities.

In 2016, two Oxford House (OH) recovery homes were established for the Suquamish Tribal reservation. A group of researchers interviewed house members and key individuals responsible for the creation of these two unique OHs. Because American Indians are an at-risk population for substance use disorders, our study explored whether an OH-type recovery home model could be successfully adapted to this population, given the specific nature of tribal cultures. Findings indicated that the residents, composed of both American Indians and non-American Indians, found these OHs to be supportive of recovery in general and with the values of American Indians. The tribal community's positive attitudes toward these recovery homes may have been due to similarities between the Suquamish Tribal Government and OH's democratic structures, with all individuals having a voice in the decision making process. The overall findings suggest that these types of culturally modified recovery settings on American Indian tribal lands could be an important resource for individuals dealing with substance use disorders.

Effect of the Los Angeles Soft-Story Ordinance on the post-earthquake housing recovery of impacted residential communities

Post-earthquake recovery models can be used to quantify the resilience-related benefits of policies intended to mitigate building seismic risk. An assessment of the effect of the Los Angeles Soft-Story Ordinance on the post-earthquake housing recovery of residential communities is presented. An inventory of approximately 8000 buildings located in five Los Angeles neighborhoods is considered. The neighborhoods vary based on the percentage of soft-story buildings, population density and the fraction of renter- and owner-occupied residences. Archetype buildings that are representative of the target inventory are developed based on a building-by-building survey performed using Google Street View. Variations in the number of stories and presence and layout of the soft first story are considered in the development of the archetypes. Analytical building-level damage fragility curves are developed using the results from nonlinear analyses of structural models representing each archetype. A scenario-based damage assessment is performed using shaking intensities generated from the Southern California ShakeOut scenario, and a discrete-time state-based stochastic process model is used to represent post-earthquake recovery. The quantified effect of the Ordinance retrofit varied based on the considered recovery metric. For instance, the initial loss of occupancy for the entire inventory is reduced by 25%. However, if the time to restore 90% occupancy is used as the recovery performance metric, the Ordinance retrofit leads to a 64% reduction.

A spatially explicit model of postdisaster housing recovery

AbstractAlthough postdisaster housing recovery is an important player in community recovery, its modeling is still in its infancy. This research aims to provide a spatial regression model for predicting households’ recovery decisions based on publicly available data. For this purpose, a hierarchical Bayesian geostatistical model with random spatial effects was developed. To calibrate the model, households’ data that were collected from Staten Island, New York, in the aftermath of Hurricane Sandy were used. The model revealed that on the scale of census tract, residents with higher income or larger household size were significantly less likely to reconstruct. In contrast, odds of reconstruction rose with increase of long‐term residents. The model outputs were also employed to develop a reconstruction propensity score for each census tract. The score predicts probability of reconstruction/repair in each tract versus others. The model was validated through comparison of the propensity scores with the distribution of Community Development Block Grant Disaster Recovery assistance and its resultant reconstruction. The validation indicated capability of the model to predict the potential hotspots of reconstruction. Accordingly, the propensity score can serve as a decision‐support tool to tailor recovery policies.

Census characteristics of Illinois sober living homes: A geographic information systems study

Recovery homes currently provide a supportive, cohesive setting following addiction treatment to thousands of individuals who often have the least resources and consequently a high risk of relapse. Such homes are particularly important regarding reducing inequality, particularly for those with substance use disorders exiting inpatient treatment or jail/prison, and many of these individuals have experienced homelessness and high rates of psychiatric co-morbidity. Yet, the success of these houses and their residents is likely related to the ability of the residents to obtain employment. This study geocoded data from 52 Oxford House recovery homes throughout Illinois. Data indicated that Oxford Houses in Illinois were often located in areas with the highest unemployment activity in the state. Placing homes in communities with higher job opportunities and employment rates could increase employment for individual residents and, subsequently, reduce inequality by increasing their odds of long term abstinence.

The Evolution of Localized Housing Recovery in Japan

Localized disaster recovery housing refers to housing made using local resources (materials, people, and skills). Using local resources to build temporary and permanent housing for people who lost their homes helps revitalize the disaster-affected area by contributing to the local economy and supporting local businesses. In the context of Japan, localized housing recovery can be understood as using some or all of the following: timber materials; traditional/local wood construction methods; and local companies and craftspeople. The term ‘localized housing recovery,’ chiikigata juutaku fukkou in Japanese, was first used in the recovery of the Yamakoshi area of Nagaoka City after the 2004 Chuetsu Earthquake, where local timber and carpenters were included from the post-disaster planning through rebuilding phases. While wooden temporary housing was rarely used since the 1995 Great Hanshin Earthquake in Kobe, after the 2011 Great East Japan Earthquake a “localized type” of construction used local natural resources for the construction of wooden temporary housing or recovery housing, including over 6,000 units of wooden temporary housing in Fukushima Prefecture alone. A similar approach was used after the 2011 Flood Disaster in the Kii Peninsula. This paper provides an overview of the evolution process of this localized recovery housing in Japan.

User involvement in housing recovery after typhoon Haiyan from a capability perspective

Typhoon Haiyan hit the Philippines on 8th November 2013 destroying more than 1 million houses and affecting mostly the poor. This paper discusses user involvement in housing recovery from a capability perspective. The paper also analyses possibilities for extension of the housing solutions. Based on the Sphere handbook and the theoretical framework, a Model for user involvement in evolutionary housing recovery has been developed and applied to analyse empirical data from two qualitative case studies in the cities of Tanauan and Ormoc. Prospective users that have attained different opportunities and choices through the projects whilst being actively involved in some stages of the process seem to have enhanced capacities such as self-construction skills, self-esteem, leadership and community organizing. Users were not involved or only consulted during the planning and design stage, which derived in generic housing lacking qualities for extension. The findings from both case studies support the theoretical construct underlying the model. The implications of the model is that it can be applied to design, carry out or assess housing recovery processes in other developing contexts.

A framework and case study for integrating household decision-making into post-earthquake recovery models

Prior empirical research has demonstrated that the decisions of affected populations can significantly influence housing recovery outcomes following a natural hazard event. The current study seeks to develop an integrated post-earthquake recovery model that explicitly accounts for household decision-making. An empirical probabilistic utility-based decision model is developed using data from a survey of Los Angeles households. The results from a multinomial logistic regression showed that the time in residence, neighborhood evacuation level, physical damage to residence, duration of utility disruption and loss of access to the building, household income and earthquake insurance coverage had a statistically significant association with homeowners’ decisions. For renter decision-making, only physical damage to the residence and duration of utility disruption are found to be statistically significant. In addition to household decision-making, the integrated model incorporates probabilistic building performance assessment and a discrete-state stochastic process representation of post-earthquake housing recovery. The results from a case study incorporating three Los Angeles neighborhoods (Koreatown, East Hollywood and Lomita) show that the influence of household decision-making on occupancy-based recovery trajectories is amplified as the scale of damage increases.

Quality of life as a predictor of social relationships in Oxford House

Improved access to housing and recovery support is a low-cost, high-potential opportunity to help people recovering from alcohol and substance use sustain their recoveries. Oxford House (OH) recovery homes represent a recovery-favorable social environment for at least some people, but it is still unclear which resident characteristics and relational dynamics affect the social integration of residents. In the current study, OH residents in three geographic locations completed a social network instrument and self-rated their quality of life (QOL). The instruments were administered to the current (per wave) residents of 42 OHs at three time points over a period of a year. Findings indicated that those with a higher QOL were more likely to form friendships with those with a lower QOL than with their similar QOL peers, and vice versa. This finding would not have been predicted based on relationship mechanisms typical of broader social contexts, where homophily (similarity-based assortativity) is common. The self-governance model that characterizes OH residences, in which success among residents is necessarily viewed as mutually dependent and therefore mutually beneficial, seems a likely explanation for our result. Specifically, and aligned with current knowledge about what works in peer oriented recovery, our results suggest the governance mechanisms of OH favor relationships between those more stable in their recovery and those who are at a higher risk of dropout or relapse. This study reveals a potential research avenue examining an important ingredient for the effectiveness of OH.

Housing recovery outcomes after typhoon Haiyan in the Philippines: a critical realist perspective

ABSTRACTTyphoon Haiyan damaged around a million houses in the Philippines in November 2013. It especially affected the poor. Using a realist laminated ontology, this paper explains how multiple causal mechanisms triggered unexpected outcomes in the housing recovery programme in the province of Leyte. The proposed causal mechanisms were: the intentional causal agency of the Filipino power elite; the collaboration–competition dilemma among individual agents and social structures; the constraints faced by the National Housing Authority with regards to procurement and budget; unfreedoms for resilient resettlement; secondary side effects of land and resettlement policies; and the under the table practice of bribery. The combination of these multiple causal mechanisms generated emergent outcomes such as low output of permanent post-disaster housing, generic housing solutions that cannot be extended, reproduction of pre-disaster vulnerabilities, recovery of valuable land by the Filipino elite and allocation of public funding to a limited range of recipients.

Using discrete event simulation to build a housing recovery simulation model for the 2015 Nepal earthquake

Models of hazards and disasters are increasingly used to help to develop disaster-related policies and plans. Unfortunately, readily available modelling tools, such as the U.S. Federal Emergency Management Agency's Hazus model provide inadequate representation of housing recovery. Most disaster-related housing models are focused on the immediate impacts of earthquakes in terms of physical damage and economic losses. This paper introduces a new simulation modelling programming library called DESaster (discrete event simulation of disaster recovery) for building disaster recovery simulation models, which currently focuses exclusively on housing recovery. A brief overview of DESaster is provided prior to describing its application to model housing recovery after the 2015 Nepal earthquake. The DESaster library was found to be flexible enough to construct a specific model of housing recovery in Nepal. More specifically, it was found to be particularly useful for better understanding resource needs, such as construction materials and skilled labourers, through comparison of different allocation scenarios.

Anchors of Social Network Awareness Index: A Key to Modeling Postdisaster Housing Recovery

AbstractReestablishment of housing is a crucial component of the recovery process and has a domino effect on the overall timing of recovery. Anchors of social networks, such as schools and churches...