Redlining and Blockbusting

3 Key excerpts on "Redlining and Blockbusting"

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  Race and Crime

  Geographies of Injustice

  • Elizabeth Brown, George Barganier(Authors)
  • 2018(Publication Date)
  • University of California Press

    (Publisher)

  Instead, the power of the private market combined with the federal government to create even more wealth accumulation in white communities. By contrast, nonwhite, especially black, communities experienced systematic disinvestment and disenfranchisement, and the power of geography served to stymie gains acquired through the politi-cal process. Real Estate and Lending Practices Blockbusting emerged at the turn of the twentieth century as a common prac-tice among real estate agents. Blockbusting was a practice that profited off white racial fears and black economic exclusion. Demand for housing was high, 172 Housing Inequality and Racial Segregation while supply was tight. This positioned realtors to make considerable profits. In mixed neighborhoods, agents would go to white houses telling the owners of an impending “black invasion” and offering to buy the home quickly and easily. In this way, realtors were able to obtain homes for much less than their market value. Once the neighborhood turned over, agents would subdivide the houses into multiple units. The units would be sold to members of the black commu-nity, who because of the short supply of available housing and Jim Crow laws criminalizing homelessness were desperate for any shelter. Now, however, the selling price for only a fraction of the home was exorbitant, particularly when compared to the rock-bottom price paid by the realtor (Orser 2015). This is how realtors “busted” the racial heterogeneity of many urban neighborhoods and turned blocks over to one racial group or another. Though the national realty associations never explicitly endorsed blockbust-ing practices, in 1922 the National Association of Real Estate Brokers (NAREB) published principles that included the caution that “the purchase of property by certain racial types is very likely to diminish the value of other property”, thereby encouraging realtors to guide clients to a particular racialized set of neighborhoods (Power 1983, 318).

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  Place, Exclusion and Mortgage Markets

  • Manuel B. Aalbers(Author)
  • 2011(Publication Date)
  • Wiley-Blackwell

    (Publisher)

  1 Harris and Forrester (2003) demonstrate that, in Canada, the origins of redlining are not in the inner city but in the less desirable suburbs. “Black-balling,” as redlining was known in Canada in the first half of the twentieth century, typically hit fringe territories that were weakly regulated and largely unserviced. In the second half of the century, however, lenders clearly preferred to finance new, suburban areas over inner cities (Murdie 1986).

  Grime and Smith (1982) and Jones and Maclennan (1987) find no evidence of redlining by building societies in Manchester and Glasgow, respectively, but both studies do see clear differences in the spatial distribution of mortgage finance. Boddy (1976), Weir (1976), P. Williams (1978) and Bassett and Short (1980) demonstrate the redlining policies in Newcastle, Birmingham, London, and Bristol, respectively, practiced by British building societies who mostly redline neighborhoods dominated by relatively low-priced pre-1919 terraced properties, usually as “working-class areas” deemed unstable in social and economic terms, and often targeted under urban renewal schemes such as General Improvement Areas or Housing Action Areas . These are also often areas inhabited by black households or students (Weir 1976). Head offices rather than local offices designated the redlined neighborhoods (Boddy 1976). In the absence of building societies, “banks and finance houses have moved in to provide finance in these areas. These institutions have done so because lending in these areas represents a highly profitable activity. The repayment terms are short and the interest rates high” (P. Williams 1978: 27). Engels (1994) documents a similar process in Sydney, Australia, where “fringe” or marginal lenders fill the gap with higher-priced loans.

  P. Williams (1976) shows the dynamics of redlining and gentrification in the London district of Islington, which was avoided by the larger building societies, banks, and insurance companies – but not by a number of small building societies – in the 1950s and 1960s. He argues that a combination of regulatory pressure and a rise in local demand slowly pushed more and more lenders to discontinue their redlining practices in Islington: “The new attitude adopted by the building societies, and the financial institutions can be seen as a direct outcome of the activities of estate agents, government policy and the ‘invading’ middle class” (P. Williams 1976: 78).

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  Linear Models with R

  • Julian J. Faraway(Author)
  • 2016(Publication Date)
  • Chapman and Hall/CRC

    (Publisher)

  Chapter 12 Insurance Redlining — A Complete Example In this chapter, we present a relatively complete data analysis. The example is inter-esting because it illustrates several of the ambiguities and difficulties encountered in statistical practice. Insurance redlining refers to the practice of refusing to issue insurance to certain types of people or within some geographic area. The name comes from the act of drawing a red line around an area on a map. Now few would quibble with an insur-ance company refusing to sell auto insurance to a frequent drunk driver, but other forms of discrimination would be unacceptable. In the late 1970s, the US Commission on Civil Rights examined charges by sev-eral Chicago community organizations that insurance companies were redlining their neighborhoods. Because comprehensive information about individuals being refused homeowners insurance was not available, the number of FAIR plan policies written and renewed in Chicago by zip code for the months of December 1977 through May 1978 was recorded. The FAIR plan was offered by the city of Chicago as a default policy to homeowners who had been rejected by the voluntary market. Information on other variables that might affect insurance writing such as fire and theft rates was also collected at the zip code level. The variables are: race racial composition in percentage of minority fire fires per 100 housing units theft thefts per 1000 population age percentage of housing units built before 1939 involact new FAIR plan policies and renewals per 100 housing units income median family income in thousands of dollars side north or south side of Chicago The data come from Andrews and Herzberg (1985) where more details of the variables and the background are provided. 12.1 Ecological Correlation Notice that we do not know the races of those denied insurance. We only know the racial composition in the corresponding zip code.

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