Featured Publications
Linking Landlords to Uncover Ownership Obscurity
Defining the ownership of rental housing can be a difficult task. In recent years there has been an increasing obscurity of ownership in administrative records as more property owners use Limited Liability Companies (LLCs) on deeds and in tax assessment records. In many cases, this obscures the nature and scale of ownership and makes research into property ownership, investors, and landlords more challenging. To overcome these challenges, we compare different text-matching methods within property tax assessment records in Boston, MA from 2004-2019. We show that the source of the difficulty in creating an accurate knowledge of landlords and their portfolios of properties has shifted in the past decade from the scale of data and the messy nature of administrative data to an intentional strategy of obscurity through LLCs. To do so, we incorporate linking to corporate records to uncover intentional ownership obscurity. We assess the prevalence of obscurity among landlords as well as the extent to which it is undermining our ability to observe patterns in rental housing in ways that cannot be accounted for solely with text-matching. These include how obscurity hides not only an increasing consolidation of property ownership in the past decade, but also concentrations of disorder and evictions. In doing so, we demonstrate a comprehensive method for uncovering this obscurity and show how this representation of property ownership can form the basis for understanding inequities in rental housing and the effects of property consolidation.
DOI Code PDF
Defining the ownership of rental housing can be a difficult task. In recent years there has been an increasing obscurity of ownership in administrative records as more property owners use Limited Liability Companies (LLCs) on deeds and in tax assessment records. In many cases, this obscures the nature and scale of ownership and makes research into property ownership, investors, and landlords more challenging. To overcome these challenges, we compare different text-matching methods within property tax assessment records in Boston, MA from 2004-2019. We show that the source of the difficulty in creating an accurate knowledge of landlords and their portfolios of properties has shifted in the past decade from the scale of data and the messy nature of administrative data to an intentional strategy of obscurity through LLCs. To do so, we incorporate linking to corporate records to uncover intentional ownership obscurity. We assess the prevalence of obscurity among landlords as well as the extent to which it is undermining our ability to observe patterns in rental housing in ways that cannot be accounted for solely with text-matching. These include how obscurity hides not only an increasing consolidation of property ownership in the past decade, but also concentrations of disorder and evictions. In doing so, we demonstrate a comprehensive method for uncovering this obscurity and show how this representation of property ownership can form the basis for understanding inequities in rental housing and the effects of property consolidation.
DOI Code PDF
The Choice to Discriminate: How Source of Income Discrimination Constrains Opportunity for Housing Choice Voucher Holders
The “choice” in the Housing Choice Voucher (HCV) program signals the mobility offered to voucher holders. However, some landlords use source of income (SOI) discrimination to exclude voucher holders—limiting their locational choices. We propose several factors likely to influence this landlord strategy including, market competitiveness, stereotypes, and racial discrimination. We examine the prevalence of express SOI discrimination and the effectiveness of SOI antidiscrimination laws. We utilize a novel dataset of 1,107,110 rental listings from the Craigslist pages of 77 mid-sized US cities. We find significant amounts of express SOI discrimination, even where there are SOI antidiscrimination laws. Using multilevel models, we find that landlords are more likely to expressly discriminate in lower-opportunity neighborhoods and when they own market-competitive units. We also find that these factors are moderated by the concentration of voucher holders. These findings underscore how landlord strategies can combine to undermine the choice afforded to voucher holders.
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The “choice” in the Housing Choice Voucher (HCV) program signals the mobility offered to voucher holders. However, some landlords use source of income (SOI) discrimination to exclude voucher holders—limiting their locational choices. We propose several factors likely to influence this landlord strategy including, market competitiveness, stereotypes, and racial discrimination. We examine the prevalence of express SOI discrimination and the effectiveness of SOI antidiscrimination laws. We utilize a novel dataset of 1,107,110 rental listings from the Craigslist pages of 77 mid-sized US cities. We find significant amounts of express SOI discrimination, even where there are SOI antidiscrimination laws. Using multilevel models, we find that landlords are more likely to expressly discriminate in lower-opportunity neighborhoods and when they own market-competitive units. We also find that these factors are moderated by the concentration of voucher holders. These findings underscore how landlord strategies can combine to undermine the choice afforded to voucher holders.
DOI
Different Places, different Problems: Profiles of Crime and Disorder at Residential Parcels
Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and disorder; (2) assessing whether different forms of crime and disorder co-locate at parcels; and (3) determining whether problematic parcels explain crime and disorder across neighborhoods. The study uses 911 and 311 records to quantify physical and social disorder and violent crime at residential parcels in Boston, MA (n = 81,673). K-means cluster analyses identified the typology of problematic parcels and how those types were distributed across census block groups. Cluster analysis identified five types of problematic parcels, four specializing in one form of crime or disorder and one that combined all issues. The second cluster analysis found that the distribution of problematic parcels described the spectrum from low- to high-crime neighborhoods, plus commercial districts with many parcels with public physical disorder. Problematic parcels modestly explained levels of crime across neighborhoods. The results suggest a need for diverse intervention strategies to support different types of problematic parcels; and that neighborhood dynamics pertaining to crime are greater than problematic properties alone.
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Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and disorder; (2) assessing whether different forms of crime and disorder co-locate at parcels; and (3) determining whether problematic parcels explain crime and disorder across neighborhoods. The study uses 911 and 311 records to quantify physical and social disorder and violent crime at residential parcels in Boston, MA (n = 81,673). K-means cluster analyses identified the typology of problematic parcels and how those types were distributed across census block groups. Cluster analysis identified five types of problematic parcels, four specializing in one form of crime or disorder and one that combined all issues. The second cluster analysis found that the distribution of problematic parcels described the spectrum from low- to high-crime neighborhoods, plus commercial districts with many parcels with public physical disorder. Problematic parcels modestly explained levels of crime across neighborhoods. The results suggest a need for diverse intervention strategies to support different types of problematic parcels; and that neighborhood dynamics pertaining to crime are greater than problematic properties alone.
DOI
A Multisource Database Tracking the Impact of the COVID-19 Pandemic on the Communities of Boston, MA, USA
A pandemic, like other disasters, changes how systems work. In order to support research on how the COVID-19 pandemic impacted the dynamics of a single metropolitan area and the communities therein, we developed and made publicly available a “data-support system” for the city of Boston. We actively gathered data from multiple administrative (e.g., 911 and 311 dispatches, building permits) and internet sources (e.g., Yelp, Craigslist), capturing aspects of housing and land use, crime and disorder, and commercial activity and institutions. All the data were linked spatially through BARI’s Geographical Infrastructure, enabling conjoint analysis. We curated the base records and aggregated them to construct ecometric measures (i.e., descriptors of a place) at various geographic scales, all of which were also published as part of the database. The datasets were published in an open repository, each accompanied by a detailed documentation of methods and variables. We anticipate updating the database annually to maintain the tracking of the records and associated measures.
DOI Code Data PDF
A pandemic, like other disasters, changes how systems work. In order to support research on how the COVID-19 pandemic impacted the dynamics of a single metropolitan area and the communities therein, we developed and made publicly available a “data-support system” for the city of Boston. We actively gathered data from multiple administrative (e.g., 911 and 311 dispatches, building permits) and internet sources (e.g., Yelp, Craigslist), capturing aspects of housing and land use, crime and disorder, and commercial activity and institutions. All the data were linked spatially through BARI’s Geographical Infrastructure, enabling conjoint analysis. We curated the base records and aggregated them to construct ecometric measures (i.e., descriptors of a place) at various geographic scales, all of which were also published as part of the database. The datasets were published in an open repository, each accompanied by a detailed documentation of methods and variables. We anticipate updating the database annually to maintain the tracking of the records and associated measures.
DOI Code Data PDF
The Emergence and Evolution of Problematic Properties: Onset, Persistence, Aggravation, and Desistance
Objectives: Scholars and practitioners have paid increasing attention to problematic properties, but little is known about how they emerge and evolve. We examine four phenomena suggested by life-course theory that reflect stability and change in crime and disorder at properties: onset of issues; persistence of issues; aggravation to more serious types of issues; and desistance of issues. We sought to identify the frequency and dynamics of each. Methods: We analyze how residential parcels (similar to properties) in Boston, MA shifted between profiles of crime and disorder from 2011 to 2018. 911 dispatches and 311 requests provided six measures of physical disorder, social disorder, and violence for all parcels. K-means clustering placed each parcel into one of six profiles of crime and disorder for each year. Markov chains quantified how properties moved between profiles year-to-year. Results: Onset was relatively infrequent and more often manifested as disorder than violence. Pathways of aggravation led from less serious profiles to a mixture of violence and disorder. Desistance was more likely to occur as de-escalations along these pathways then complete cessation of issues. In neighborhoods with above-average crime, persistence was more prevalent whereas desistance less often culminated in cessation, even relative to local expectations. Conclusions: The results offer insights for further research and practice attentive to trends of crime and disorder at problematic properties. It especially speaks to the understanding of stability and change; the role of different types of disorder; and the toolkit needed for problem properties interventions.
DOI
Objectives: Scholars and practitioners have paid increasing attention to problematic properties, but little is known about how they emerge and evolve. We examine four phenomena suggested by life-course theory that reflect stability and change in crime and disorder at properties: onset of issues; persistence of issues; aggravation to more serious types of issues; and desistance of issues. We sought to identify the frequency and dynamics of each. Methods: We analyze how residential parcels (similar to properties) in Boston, MA shifted between profiles of crime and disorder from 2011 to 2018. 911 dispatches and 311 requests provided six measures of physical disorder, social disorder, and violence for all parcels. K-means clustering placed each parcel into one of six profiles of crime and disorder for each year. Markov chains quantified how properties moved between profiles year-to-year. Results: Onset was relatively infrequent and more often manifested as disorder than violence. Pathways of aggravation led from less serious profiles to a mixture of violence and disorder. Desistance was more likely to occur as de-escalations along these pathways then complete cessation of issues. In neighborhoods with above-average crime, persistence was more prevalent whereas desistance less often culminated in cessation, even relative to local expectations. Conclusions: The results offer insights for further research and practice attentive to trends of crime and disorder at problematic properties. It especially speaks to the understanding of stability and change; the role of different types of disorder; and the toolkit needed for problem properties interventions.
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