Chaix, Basile; Duncan, Dustin; Vallee, Julie; Vernez-moudon, Anne; Benmarhnia, Tarik; Kestens, Yan. (2017). The Residential Effect Fallacy in Neighborhood and Health Studies Formal Definition, Empirical Identification, and Correction. Epidemiology, 28(6), 789 – 797.
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Abstract
Background: Because of confounding from the urban/rural and socioeconomic organizations of territories and resulting correlation between residential and nonresidential exposures, classically estimated residential neighborhood-outcome associations capture nonresidential environment effects, overestimating residential intervention effects. Our study diagnosed and corrected this residential effect fallacy bias applicable to a large fraction of neighborhood and health studies. Methods: Our empirical application investigated the effect that hypothetical interventions raising the residential number of services would have on the probability that a trip is walked. Using global positioning systems tracking and mobility surveys over 7 days (227 participants and 7440 trips), we employed a multilevel linear probability model to estimate the trip-level association between residential number of services and walking to derive a naive intervention effect estimate and a corrected model accounting for numbers of services at the residence, trip origin, and trip destination to determine a corrected intervention effect estimate (true effect conditional on assumptions). Results: There was a strong correlation in service densities between the residential neighborhood and nonresidential places. From the naive model, hypothetical interventions raising the residential number of services to 200, 500, and 1000 were associated with an increase by 0.020, 0.055, and 0.109 of the probability of walking in the intervention groups. Corrected estimates were of 0.007, 0.019, and 0.039. Thus, naive estimates were overestimated by multiplicative factors of 3.0, 2.9, and 2.8. Conclusions: Commonly estimated residential intervention-outcome associations substantially overestimate true effects. Our somewhat paradoxical conclusion is that to estimate residential effects, investigators critically need information on nonresidential places visited.
Keywords
Self-rated Health; Record Cohort; Physical-activity; Transportation Modes; Built Environment; Activity Spaces; Research Agenda; Risk-factors; Associations; Exposure
Walter, Rebecca J.; Caine, Ian. (2019). The Geographic And Sociodemographic Transformation Of Multifamily Rental Housing In The Texas Triangle. Housing Studies, 34(5), 804 – 826.
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Abstract
This study catalogues the location, clustering and sociodemographic distribution of the development of multifamily rental housing over the last five decades in the Texas Triangle, one of the fastest growing megaregions in the United States. The research reveals prior to the 1970s, apartments clustered in downtown areas; throughout the 1980s and 1990s, the development of apartments expanded to the suburbs and along major interstates; and in the 2000s, apartment growth continued in the peripheral areas while returning downtown. During this time period, apartments were developed most often in majority white, high-income and low-poverty neighbourhoods. These geographic and sociodemographic characteristics challenge widespread conceptions that equate multifamily rental housing with central city locations and low-income populations. The findings suggest that multifamily rental housing offers a powerful tool to increase residential density in downtown and suburban locations, while also accommodating a sociodemographically diverse population.
Keywords
Sociodemographic Factors; Rental Housing; Neighborhoods; Home Ownership; Housing Development; Apartments; Locational Patterns; Multifamily Rental Housing; Sociodemographics; Suburban Infill; Texas Triangle; City Centres; Central Business Districts; Housing; Poverty; Suburban Areas; Residential Density; Suburbs; Transformation; Catalogues; Density; Clustering; Income; Multiple Dwellings; Low Income Groups; Rentals; Catalogs; Texas; United States--us
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