Wang, Lan; Zhang, Surong; Yang, Zilin; Zhao, Ziyu; Moudon, Anne Vernez; Feng, Huasen; Liang, Junhao; Sun, Wenyao; Cao, Buyang. (2021). What County-level Factors Influence Covid-19 Incidence in the United States? Findings from the First Wave of the Pandemic. Cities, 118.
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Abstract
Effective control of the COVID-19 pandemic via appropriate management of the built environment is an urgent issue. This study develops a research framework to explore the relationship between COVID-19 incidence and influential factors related to protection of vulnerable populations, intervention in transmission pathways, and provision of healthcare resources. Relevant data for regression analysis and structural equation modeling is collected during the first wave of the pandemic in the United States, from counties with over 100 confirmed cases. In addition to confirming certain factors found in the existing literature, we uncover six new factors significantly associated with COVID-19 incidence. Furthermore, incidence during the lockdown is found to significantly affect incidence after the reopening, highlighting that timely quarantining and treating of patients is essential to avoid the snowballing transmission over time. These findings suggest ways to mitigate the negative effects of subsequent waves of the pandemic, such as special attention of infection prevention in neighborhoods with unsanitary and overcrowded housing, minimization of social activities organized by neighborhood associations, and contactless home delivery service of healthy food. Also worth noting is the need to provide support to people less capable of complying with the stay-at-home order because of their occupations or socio-economic disadvantage.
Keywords
Pandemics; Covid-19; Covid-19 Pandemic; Infection Prevention; Stay-at-home Orders; Structural Equation Modeling; United States; Communicable Disease Prevention; Influential Factors; Lockdown; Structural Equation Modeling (sem); Prevalence; Disease; Healthy Food; Social Activities; Counties; Neighborhoods; Housing; Built Environment; Prevention; Minimization; Socioeconomic Factors; Intervention; Health Care; Vulnerability; Occupations; Coronaviruses; Food Service; Disease Transmission; United States--us
El-Anwar, Omar. (2013). Advancing Optimization of Hybrid Housing Development Plans Following Disasters: Achieving Computational Robustness, Effectiveness, and Efficiency. Journal Of Computing In Civil Engineering, 27(4), 358 – 369.
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Abstract
Following disasters, displaced families often face significant challenges to move from temporary to permanent housing. The Federal Emergency Management Agency is exploring alternative housing pilot programs to evaluate the possibility of providing quickly deployable, affordable housing that can serve both as temporary and permanent housing. Because of the complexities and costs associated with these programs, it is impractical to assume that accelerated permanent housing can fully replace the need for traditional temporary housing, especially in cases of large-scale displacements. A novel methodology was developed to evaluate the socioeconomic benefits of candidate configurations of hybrid housing plans, which incorporates both temporary and accelerated permanent housing developments. This paper presents the computational implementation and performance analysis of this novel methodology to offer a practical decision-support tool to emergency planners. To this end, genetic algorithms and integer-programming optimization models are formulated, and their performances are analyzed based on their effectiveness, efficiency, and robustness. In lieu of developing the integer-programming model, the paper also presents a linear formulation that overcomes the need to use logical operations to model fixed and variable cost components for developing housing projects. Results show the superior performance of integer programming, whereas genetic algorithms offer higher modeling flexibility.
Keywords
Decision Support Systems; Emergency Management; Genetic Algorithms; Integer Programming; Advancing Optimization; Hybrid Housing Development Plans Following Disasters; Achieving Computational Robustness; Achieving Computational Effectiveness; Achieving Computational Efficiency; Federal Emergency Management Agency; Housing Pilot Programs; Temporary Housing; Permanent Housing Developments; Decision-support Tool; Emergency Planners; Integer-programming Optimization Models; Logical Operations; Optimization; Disasters; Housing; Social Factors; Economic Factors; Computation; Hybrid Methods; Disaster Recovery; Accelerated Permanent Housing; Socioeconomic Welfare; Robustness; Effectiveness; Computational Efficiency; 0
Cordoba, Hilton A.; Walter, Rebecca J.; Foote, Nathan S. (2018). The Residential Segregation of San Antonio, Texas in 1910: An Analysis Of Ethno-racial and Occupational Spatial Patterns with the Colocation Quotient. Urban Geography, 39(7), 988 – 1017.
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Abstract
The segregation of cities can be traced to a time when the compartmentalization of space and people was based on factors other than race. In segregation research, one of the limiting factors has always been the geographic scale of the data, and the limited knowledge that exists of segregation patterns when the household is the unit of analysis. Historical census data provides the opportunity to analyze the disaggregated information, and this paper does so with San Antonio during 1910. A spatial analysis of residential segregation based on race, ethnicity, and occupations is carried out with the colocation quotient to map and measure the attraction of residents. Results reveal the presence of residential segregation patterns on different sectors of the city based on households' ethno-racial and occupational attributes; therefore, providing evidence of the existence of residential segregation prior to the commonly cited determinants of segregation of the 20th century.
Keywords
Housing Tax Credit; Local Indicators; New York; Association; Indexes; Cities; Scale; City; Differentiation; Environment; Residential Segregation; Colocation Quotient; San Antonio; Spatial Analysis
Acolin, Arthur. (2020). Housing Wealth and Consumption over the 2001-2013 Period: The Role of the Collateral Channel. Journal Of Housing Research, 29(1), 68 – 88.
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Abstract
This study estimates changes in the relationship between housing wealth and consumption among homeowners during the recent housing boom and bust in the United States, focusing on the period 2001-2007, during which house prices increased and financial innovations led to an increased availability of products enabling households to extract home equity; and on the period 2007-2013, during which house prices declined and home equity withdrawal products became largely unavailable. The estimated elasticity of consumption with regard to housing wealth increased in 2004 and 2007 (.06) relative to 2001 (.04). The estimated elasticities then decreased in 2010 and 2013 (to below .04). In addition, the increase was larger among borrowing constrained households than unconstrained households. No relationship between housing prices and consumption was found among renters. These additional tests for subpopulations support the hypothesis that the increase in consumption out of housing wealth occurred through the collateral channel.
Keywords
Consumption (economics); Wealth; Product Elimination; Equity (real Property); Home Prices; Home Ownership; United States; Collateral Channel; Credit And Consumption; Housing Wealth Effects; Housing; Housing Costs; Estimates; Prices; Households; Consumption; Equity; Borrowing; Hypotheses; Innovations; Elasticity Of Demand; Propensity To Consume; Housing Prices; Lines Of Credit; Mortgages; Subpopulations; Collateral; United States--us
Hall, Joshua C.; Lacombe, Donald J.; Neto, Amir; Young, James. (2022). Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets. Journal Of Economics & Finance, 46(2), 360 – 373.
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Abstract
Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. [ABSTRACT FROM AUTHOR]; Copyright of Journal of Economics & Finance is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Keywords
Housing Market; Multilevel Models; Test Scoring; Cincinnati (ohio); Ohio; Bayesian Methods; Slx Model; Spatial Econometrics; Spatial Hierarchical Models
El-Anwar, Omar. (2013). Maximising the Net Social Benefit of the Construction of Post-Disaster Alternative Housing Projects. Disasters, 37(3), 489 – 515.
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Abstract
The widespread destruction that follows large-scale natural disasters, such as Hurricane Katrina in August 2005, challenges the efficacy of traditional temporary housing methods in providing adequate solutions to housing needs. Recognising these housing challenges, the Congress of the United States allocated, in 2006, USD 400 million to the Department of Homeland Security to support Alternative Housing Pilot Programs, which are intended to explore the possibilities of providing permanent and affordable housing to displaced families instead of traditional temporary housing. This paper presents a new methodology and optimisation model to identify the optimal configurations of post-shelter housing arrangements to maximise the overall net socioeconomic benefit. The model is capable of quantifying and optimising the impacts of substituting temporary housing with alternative housing on the social and economic welfare of displaced families as well as the required additional costs of doing so. An application example is presented to illustrate the use of the model and its capabilities.
Keywords
Public Housing; Temporary Housing; Hurricane Katrina, 2005; Natural Disasters; Socioeconomic Factors; Mathematical Models; Mathematical Optimization; United States; Alternative Housing Pilot Programs; Optimisation; Socioeconomic Benefit; Disasters
Lacoe, Johanna; Bostic, Raphael W.; Acolin, Arthur. (2018). Crime and Private Investment in Urban Neighborhoods. Journal Of Urban Economics, 108, 154 – 169.
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Abstract
The question of how best to improve neighborhoods that lag behind has drawn considerable attention from policy-makers, practitioners, and academics, yet there remains a vigorous debate regarding the best approaches to accomplish community development. This paper investigates the role crime policy plays in shaping the trajectory of neighborhoods. Much of the existing research on neighborhood crime was conducted in rising-crime environments, and the evidence was clear: high levels of crime have adverse effects on neighborhoods and resident quality of life, This study examines how private investment in neighborhoods in two cities Chicago and Los Angeles changed as the incidence of neighborhood crime changed during the 2000s, a period when crime was declining city-wide in both places. Using detailed blockface-level data on the location of crime and private investments between 2006 and 2011, the analysis answers the question: Do changes in crime affect private development decisions? The results show that private investment, as represented by building permits, decreases on blocks where crime increases in the past year. We also find that the relationship between crime and private investment is not symmetric private investment appears to only be sensitive to crime in rising crime contexts. The result is present in both cities, and robust to multiple definitions of crime and the elimination of outliers and the main commercial district. These results suggest that crime-reduction policies can be an effective economic development tool, but only in certain neighborhoods facing specific circumstances.
Keywords
Enterprise Zones; Crime; Investment; Neighborhoods
Acolin, Arthur. (2020). Owning vs. Renting: The Benefits of Residential Stability? Housing Studies, 37(4), 644-647.
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Abstract
In housing research, owning, as compared to renting, is generally depicted as more desirable and associated with better outcomes. This paper explores differences in outcomes between owners and renters in 25 European countries and whether these differences are systematically smaller in countries in which owners and renters have more similar levels of residential stability (smaller tenure length gap). The results indicate that the direction of the relationship between tenure type and the selected outcomes is largely similar across countries. Owners generally exhibit more desirable outcomes (including life satisfaction, civic participation, educational outcomes for children, and physical and mental health). However, when looking at the relationship between outcomes and country level differences in tenure length gap, findings suggest that renters have outcomes that are more similar to owners in countries in which tenure length gaps are smaller. These results point to the potential benefits of policies that would increase residential stability, particularly for renters.
Keywords
European Union; Homeownership Benefits; Length Of Residence; Tenure; Home-ownership; Homeownership
Acolin, Arthur; Colburn, Gregg; Walter, Rebecca J. (2022). How Do Single-Family Homeowners Value Residential and Commercial Density? It Depends. Land Use Policy, 113.
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Abstract
This paper develops estimates of the relationship between local density and single-family home values using 2017 transactions for five U.S. metropolitan regions: Chicago, Los Angeles, Minneapolis, Philadelphia, Seattle. Proposals to build new commercial and residential development projects that would increase local density commonly face opposition from local homeowners. Academic literature links the response from homeowners to concerns that higher density is associated with lower property values but there is limited empirical evidence establishing this relationship at the local level. We find a positive and significant relationship between density and house value in the core area of the five metropolitan regions we analyze. Within 7.5 miles of the center of these metropolitan regions, a 10% increase in surrounding built area density is associated with a 1.1–1.9% increase in house prices per square foot. For outlying areas, the estimates are smaller and even negative in several cases. We instrument density based on topographic and soil characteristics and find similar results. These findings point to the need for a more nuanced discussion of the relationship between local density and housing values.
Keywords
Population Density; Soil Density; Single Family Housing; Home Ownership; Housing Development; Housing Discrimination; Home Prices; Los Angeles (calif.); Density; Single-family House Value; Urban Form; Residential Development; Real Estate; Property Values; Residential Density; Development Programs; Housing; Estimates; Metropolitan Areas; Development Projects; Empirical Analysis; Families & Family Life; Soil Characteristics
El-Anwar, Omar; Chen, Lei. (2013). Computing a Displacement Distance Equivalent to Optimize Plans for Postdisaster Temporary Housing Projects. Journal Of Construction Engineering And Management, 139(2), 174 – 184.
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Abstract
Residence in temporary housing is a critical period for the social, economic, and psychological recovery of displaced families following disasters. Temporary housing locations define the displacement distance between families and their essential needs. The objective of this paper is to develop a novel methodology to capture the specific proximity needs and preferences of displaced families. This paper proposes a displacement distance equivalent as an objective metric to evaluate the performance of temporary housing locations in meeting the needs of displaced families. Moreover, the paper describes the development of an integer programming optimization model capable of optimizing temporary housing assignments to minimize total displacement distance equivalent while meeting budget constraints. The main contribution of this paper to the body of knowledge is in transforming the purpose of temporary housing programs from offering general accommodation to providing customized housing solutions tailored to the individual proximity needs of each household using the proposed displacement metric. In addition, the proposed optimization model enables decision makers to set budget constraints to ensure the economic feasibility of identified temporary housing solutions. DOI: 10.1061/(ASCE)CO. 1943-7862.0000601. (C) 2013 American Society of Civil Engineers.
Keywords
Disasters; Emergency Management; Integer Programming; Social Sciences; Displaced Families; Customized Housing Solutions; Decision Makers; Displacement Metric; Budget Constraints; Integer Programming Optimization Model; Objective Metric; Temporary Housing Locations; Post-disaster Temporary Housing Projects; Displacement Distance Equivalent Computation; Multiobjective Optimization; Optimization; Temporary Housing; Disaster Recovery; Displacement Distance; Housing Sites