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High Delinquency Rates in Brazil’s Minha Casa Minha Vida Housing Program: Possible Causes and Necessary Reforms

Acolin, Arthur; Hoek-Smit, Marja C.; Eloy, Claudia Magalhaes. (2019). High Delinquency Rates in Brazil’s Minha Casa Minha Vida Housing Program: Possible Causes and Necessary Reforms. Habitat International, 83, 99 – 110.

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Abstract

Brazil's main housing program, Minha Casa Minha Vida (MCMV), has contracted the construction of over 3 million housing units since 2009, providing access to homeownership for low and middle-income households through a combination of credit, subsidies and guarantees. In this paper, we analyze disaggregated delinquency information at the project level for the section of the program that serves households in the lowest income range (Faixa 1). Our analysis of program performance in six metropolitan regions shows an overall level of delinquency of 28% as of the end of 2015. We identify four hypotheses to explain this elevated level of delinquency: the peripheral location of the units, insufficient income to cover ongoing costs, moral hazard in the management of the program, and organized crime in some projects. Our analysis shows that in 4 of the 6 regions, low-income projects in peripheral locations exhibit substantially higher non-payment levels and that lower income households have higher levels of delinquency. Based on our analysis, we recommend modifications to program design, including the inclusion of location criteria in subsidy scaling. The findings provide evidence of the limits of MCMV Faixa l's approach to solving Brazilian low-income housing needs and contribute to an emerging body of literature pointing to the importance of location in housing programs.

Keywords

Income; Urban Growth; Housing; Home Ownership; Moral Hazard; Affordable Housing; Delinquency Rate; Housing Program; Integrated Urban Development; Programa Minha Casa Minha Vida; Social Housing; Mexico; Scale

The Use of Markets in Housing Policy: A Comparative Analysis of Housing Subsidy Programs

Colburn, Gregg. (2021). The Use of Markets in Housing Policy: A Comparative Analysis of Housing Subsidy Programs. Housing Studies, 36(1), 46 – 79.

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Abstract

Many countries use demand-side housing subsidies to support low-income households. Unlike public or social housing programs, demand-side subsidies require recipients to enter the private market to use their benefits. The focus of this study is the experiences of assisted households in the private housing market and the outcomes they achieve. Given the link between policy design and program outcomes and because all housing subsidy programs are not created equal, one might expect the experiences and outcomes of recipients to also vary. To examine this relationship, using data from national housing surveys, this study analyzes cross-national variation in housing support programs and compares the housing and neighbourhood outcomes of subsidized households in the US, the UK, and the Netherlands. The findings of this study highlight that market context and policy design are associated with housing outcomes. In particular, the strong tenant supports and favourable design of housing assistance in the Netherlands is associated with favourable outcomes for subsidized households. In the US and the UK, subsidized households, in general, underperform their unsubsidized peers. This article underscores the importance of institutional context and program design when public assistance programs require recipients to enter the private market to use a benefit.

Keywords

Housing; Housing Subsidies; Comparative Studies; United States; Great Britain; Netherlands; Comparative; Outcomes; Subsidized Housing; Subsidy; Choice Vouchers; Poverty Deconcentration; United-states; Tax Credit; Income; Neighborhoods; Opportunity; Future; Britain; Comparative Analysis; Subsidies; Households; Context; Housing Policy; Design; Subsidies (financial); Housing Market; Low Income Groups; Public Housing; Assistance Programmes; United Kingdom--uk; United States--us

Endowments and Minority Homeownership

Acolin, Arthur; Lin, Desen; Wachter, Susan M. (2019). Endowments and Minority Homeownership. Cityscape, 21(1), 5 – 62.

Abstract

Fifty years after the adoption of the 1968 Fair Housing Act that prohibits discrimination in the housing market, homeownership rates have not increased for Black or Hispanic households. The current homeownership rate for Black households is 42 percent, identical to the 1970 census reported level, and 48 percent for Hispanic households, lower than that in 1970. Using data from the 1989, 2005, and 2013 American Housing Surveys, we identify the extent to which group differences in household endowments account for persistently low minority homeownership levels.

Keywords

Borrowing Constraints; Household Formation; Race; Wealth; Trends; Rates

Toward a Cross-Platform Framework: Assessing the Comprehensiveness of Online Rental Listings

Costa, Ana; Sass, Victoria; Kennedy, Ian; Roy, Roshni; Walter, Rebecca J.; Acolin, Arthur; Crowder, Kyle; Hess, Chris; Ramiller, Alex; Chasins, Sarah. (2021). Toward a Cross-Platform Framework: Assessing the Comprehensiveness of Online Rental Listings. Cityscape, 23(2), 327 – 339.

Abstract

Research on rental housing markets in the United States has traditionally relied on national or local housing surveys. Those sources lack temporal and spatial specificity, limiting their use for tracking short-term changes in local markets. As rental housing ads have transitioned to digital spaces, a growing body of literature has utilized web scraping to analyze listing practices and variations in rental market dynamics. Those studies have primarily relied on one platform, Craigslist, as a source of data. Despite Craigslist's popularity, the authors contend that rental listings from various websites, rather than from individual ones, provide a more comprehensive picture. Using a mixed-methods approach to study listings across various platforms in five metropolitan areas, this article demonstrates considerable variation in both the types of rental units advertised and the features provided across those platforms. The article begins with an account of the birth and consolidation of online rental platforms and emergent characteristics of several selected websites, including the criteria for posting, search parameters, search results priority, and first-page search results. Visualizations are used to compare features such as the 40th percentile of rent, rent distribution, and bedroom size based on scraped data from six online platforms (Padmapper, Forrent.com , Trulia, Zillow, Craigslist, and GoSection8), 2020 Fair Market Rents, and 2019 American Community Survey data. The analyses indicate that online listing platforms target different audiences and offer distinct information on units within those market segments, resulting in markedly different estimates of local rental costs and unit size distribution depending on the platform.

Optimal Composition of Hybrid/Blended Real Estate Portfolios

Ametefe, Frank Kwakutse; Devaney, Steven; Stevenson, Simon Andrew. (2019). Optimal Composition of Hybrid/Blended Real Estate Portfolios. Journal Of Property Investment & Finance, 37(1), 20 – 41.

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Abstract

Purpose The purpose of this paper is to establish an optimum mix of liquid, publicly traded assets that may be added to a real estate portfolio, such as those held by open-ended funds, to provide the liquidity required by institutional investors, such as UK defined contribution pension funds. This is with the objective of securing liquidity while not unduly compromising the risk-return characteristics of the underlying asset class. This paper considers the best mix of liquid assets at different thresholds for a liquid asset allocation, with the performance then evaluated against that of a direct real estate benchmark index. Design/methodology/approach The authors employ a mean-tracking error optimisation approach in determining the optimal combination of liquid assets that can be added to a real estate fund portfolio. The returns of the optimised portfolios are compared to the returns for portfolios that employ the use of either cash or listed real estate alone as a liquidity buffer. Multivariate generalised autoregressive models are used along with rolling correlations and tracking errors to gauge the effectiveness of the various portfolios in tracking the performance of the benchmark index. Findings The results indicate that applying formal optimisation techniques leads to a considerable improvement in the ability of the returns from blended real estate portfolios to track the underlying real estate market. This is the case at a number of different thresholds for the liquid asset allocation and in cases where a minimum return requirement is imposed.

Keywords

Hedge-fund-replication; Volatility Dynamics; Tracking Error; Stock; Performance; Property; Returns; Markets; Private; Model; Open-ended Funds; Real Estate Liquidity; Portfolio Optimization; Blended Real Estate; Defined Contribution Pensions

Searching for Housing in the Digital Age: Neighborhood Representation on Internet Rental Housing Platforms across Space, Platform, and Metropolitan Segregation

Hess, Chris; Acolin, Arthur; Walter, Rebecca; Kennedy, Ian; Chasins, Sarah; Crowder, Kyle. (2021). Searching for Housing in the Digital Age: Neighborhood Representation on Internet Rental Housing Platforms across Space, Platform, and Metropolitan Segregation. Environment And Planning A-economy And Space, 53(8), 2012 – 2032.

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Abstract

Understanding residential mobility, housing affordability, and the geography of neighborhood advantage and disadvantage relies on robust information about housing search processes and housing markets. Existing data about housing markets, especially rental markets, suffer from accuracy issues and a lack of temporal and geographic flexibility. Data collected from online rental platforms that are commonly used can help address these issues and hold considerable promise for better understanding the full distribution of available rental homes. However, realizing this promise requires a careful assessment of potential sources of bias as online rental listing platforms may perpetuate inequalities similar to those found in physical spaces. This paper approaches the production of rental advertisements as a social process driven by both contextual and property level factors. We compare data from two online platforms for the 100 most populated metropolitan areas in the United States to explore inequality in digital rental listing spaces and understand what characteristics are associated with over and underrepresentation of advertisements in certain areas. We find similar associations for socioeconomic measures between platforms and across urban and suburban parts of these metropolitan areas. In contrast, the importance of racial and ethnic composition, as well as broader patterns of segregation, for online representation differs substantially across space and platform. This analysis informs our understanding of how online platforms affect housing search dynamics through their biases and segmentation, and highlights the potential and limits in using the data available on these platforms to produce small area rental estimates.

Keywords

Fair Market Rents; Cities; Opportunity; Residential Mobility; Online Rental Listings; Rental Housing Markets; Housing Search; Inequality

College of Built Environments’ unique Inspire Fund aims to foster research momentum in underfunded pursuits college-wide. And it’s working.

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Gregg Colburn’s new book presents opportunity to ‘rethink housing’

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2022 CBE Inspire Fund awardees announced

In 2021 the College of Built Environments launched the CBE Inspire Fund, designed to support CBE research activities for which a relatively small amount of support can be transformative. The second year of awards have just been announced, supporting five projects across 4 departments within the college as they address topics such as food sovereignty, anti-displacement, affordable housing, and health & wellbeing. This year’s awardees include:  Defining the New Diaspora: Where Seattle’s Black Church Congregants Are Moving and Why Rachel…