Author: Mary Daly

Race Restrictive Covenants in Property Deeds (Edited)

Posted on Author Mary Daly

“No persons of any race except the white race shall use or occupy any building on any lot except that this covenant shall not prevent occupancy by domestic servants of a different race employed by an owner or tenant.”  This language, taken directly from a property deed dated June 10th 1940, in West Hartford’s High Ledge Homes Development, appeared in property deeds in five neighborhoods during the 1940s in West Hartford. In some places in Connecticut such barriers appeared even earlier.

These restrictive covenants, along with ones that may have existed in other Northern states, were implemented in order to prevent minorities from moving into white suburban neighborhoods.  Real estate developers, homeowners, and neighborhood associations wrote these restrictions, called housing covenants, for their developments. Discriminatory covenants excluded certain groups from housing areas not only in Connecticut but throughout the northern United States as well.

Source: UConn Libraries Map and Geographic Information Center. Click for Whole Property Deed.

Population Shift Sparks Unfair Realty Practices

The Great Migration of blacks from the rural South to work in industrial factories in the North increased the minority population in Hartford beginning in the 1920s.  Housing areas became a commodity that whites wanted to protect.  In 1937, the Home Owners’ Loan Corporation (HOLC) issued a map rating Hartford’s neighborhoods on a scale from A-D based on the perceived risk of mortgage defaults in each. The HOLC labeled areas with a high concentration of minorities as riskier; these received a D rating.  Even a small number of minority families living in an area often resulted in it receiving a C rating.  This map vividly documents how the racial composition of a neighborhood influenced the values of homes in the area.

This process—called redlining—exposed the deep concern many whites had about minorities moving into their neighborhoods.  The influx of blacks into the North and the redlining process contributed to “white flight” into the suburbs of Hartford starting in the 1940s.  Real estate agencies and homeowners, concerned about black neighbors causing a decline in property values in their new white suburban enclaves, wrote housing covenants into their property deeds. Due to these covenants, blacks were nearly eliminated from the suburban housing market during the 1940s.

Shelley v. Kraemer: Ending Housing Covenants

Source: UConn Libraries Map and Geographic Information Center. Click image for interactive map.

In 1948, restrictive housing covenants were deemed unenforceable by law in the Supreme Court case Shelley v. Kraemer on the grounds that such restrictions violated the 14th Amendment, which calls for equal treatment under the law for all citizens of the United States.  Privately, however, some would still abide by the restrictive covenants in property deeds, even though they could not be enforced when taken to court.  In many predominantly white areas of Connecticut, such as New Canaan, for example, “gentlemen’s agreements” not to sell homes to blacks, Jews, or other minorities allowed discrimination to quietly persist.  The Fair Housing Act of 1968 did much to discourage continuations of these practices.

Documenting a History of Discrimination

Since it is still legal to have these racially restrictive covenants in property deeds, some still remain in housing deeds in West Hartford (however, they are not enforceable).  Such clauses have been documented for five areas, including the High Ledge Homes Development, by On The Line, a public history web-book by Trinity College professor Jack Dougherty. As part of this effort, The Cities, Suburbs and Schools Project, a collaborative effort involving Trinity faculty and students as well as community members, interviewed citizens of West Hartford in 2011. They asked residents living in homes with property deeds that included race restrictive covenants their thoughts on the matter. Younger, new residents of the area were alarmed to learn that they existed. “It’s not something I would have expected in Connecticut…, “ said one. “I grew up believing that [overt racism] was in the South.” Those who had lived in their neighborhoods for a long time were less surprised. One woman reported knowing of the covenant when she purchased her home in 1970.  These covenants are more than artifacts of an earlier time. They have shaped the present-day nature of communities across Connecticut.

Readers of this article who are aware of racially restrictive covenants in housing deeds are invited to contribute to On The Line’s ongoing research by leaving a comment here. [Will Link to: : http://ontheline.trincoll.edu/maps/]

Learn More:


Everett, Mary. “Oral History Interview on West Hartford and Restrictive Covenants, (with Video).” Oral History Interviews (July 21, 2011). http://digitalrepository.trincoll.edu/cssp_ohistory/23.


Hansen, Susan. “Oral History Interview on West Hartford (with Video).” Oral History Interviews (July 22, 2011). http://digitalrepository.trincoll.edu/cssp_ohistory/17.

Jackson, Kenneth T. “Gentleman’s Agreement: Discrimination in Metropolitan America.” In Reflections on Regionalism, edited by Bruce Katz, 185–217. Washington, D.C: Brookings Institution Press, 2000.


Walsh, Debra. “Oral History Interview on West Hartford.” Oral History Interviews (July 21, 2011). http://digitalrepository.trincoll.edu/cssp_ohistory/21

“Federal HOLC “Redlining” Map, Hartford Area, 1937.”University of Connecticut Libraries Map and Geographic Information Center – MAGIC . Web. 19 June 2012. http://magic.lib.uconn.edu/otl/doclink_holc.html.

“Race Restrictive Covenants in Property Deeds, Hartford area, circa 1940.” University of Connecticut Libraries Map and Geographic Information Center – MAGIC . Web. 19 Jun. 2012. http://magic.lib.uconn.edu/otl/doclinkl_covenant.html.

“Racial Change in the Hartford Region, 1900-2010.”University of Connecticut Libraries Map and Geographic Information Center – MAGIC . Web. 20 Jun. 2012. http://magic.lib.uconn.edu/otl/timeslider_racethematic.html.

Shelley V. Kraemer (Syllabus), 100 U.S. 1 (U.S. Supreme Court 1948).

 

How to Lie With Maps

Posted on Author Mary Daly

When analyzing maps, it is important to look at the scale in which the author used to make the map.  In addition to this, the reader should also investigate what the scale does in comparison to what point the author is trying to prove.  In the maps I made below, I used the same statistics of percent minority composition in the different towns in Hartford’s metropolitan area.  Although I used the same data, and the same color scheme, the first map shows a large contrast between Hartford, Bloomfield, East Hartford and Windsor with the rest of the metropolitan area, while the second map portrays a more even minority composition throughout the metropolitan area of Hartford.

 

 

 

 

 

 

To show stark contrast, I made the gradient much darker for those areas whose minority was above a certain percent, and those below much lighter for the above map.  By creating only three gradients for towns to reside in, the contrast between towns became very apparent. This is only because the settings made it so the towns surrounding Hartford, Bloomfield, Windsor and East Hartford all resided under a certain percentage mark, so they all became very light green, appearing that they all have the same minority percentage.  This is untrue of course, but since the towns are all grouped in the same category in this map, it is easy for the reader to make this conclusion.

 

 

 

 

 

 

In the map above, the contrast between the different areas is much less identifiable.  In this map, I adjusted the gradients so there were more categories of around the same tint of green.  Since there are more categories, the difference in percentages of the towns are easier to see, showing an area that looks much more homogeneous than the previous map.

This stark contrast between two maps with the same data exemplifies that the reader must be careful when coming to conclusions about maps.  They should ask themselves questions such as, “Does the scale use rational increments?” and “What is the range of this color–is it small or large?”.  After the reader asks questions such as these, they can start to make conclusions, but it can be dangerous to assume that one color means all the regions with that color have the same minority composition (or whatever is being measured), instead it is wise to look at how large the range is; then the reader can determine how alike the regions actually are.

How to Lie With Maps

Posted on Author Mary Daly

When analyzing maps, it is important to look at the scale in which the author used to make the map.  In addition to this, the reader should also investigate what the scale does in comparison to what point the author is trying to prove.  In the maps I made below, I used the same statistics of percent minority composition in the different towns in Hartford’s metropolitan area.  Although I used the same data, and the same color scheme, the first map shows a large contrast between Hartford, Bloomfield, East Hartford and Windsor with the rest of the metropolitan area, while the second map portrays a more even minority composition throughout the metropolitan area of Hartford.

 

 

 

 

 

 

To show stark contrast, I made the gradient much darker for those areas whose minority was above a certain percent, and those below much lighter for the above map.  By creating only three gradients for towns to reside in, the contrast between towns became very apparent. This is only because the settings made it so the towns surrounding Hartford, Bloomfield, Windsor and East Hartford all resided under a certain percentage mark, so they all became very light green, appearing that they all have the same minority percentage.  This is untrue of course, but since the towns are all grouped in the same category in this map, it is easy for the reader to make this conclusion.

 

 

 

 

 

 

In the map above, the contrast between the different areas is much less identifiable.  In this map, I adjusted the gradients so there were more categories of around the same tint of green.  Since there are more categories, the difference in percentages of the towns are easier to see, showing an area that looks much more homogeneous than the previous map.

This stark contrast between two maps with the same data exemplifies that the reader must be careful when coming to conclusions about maps.  They should ask themselves questions such as, “Does the scale use rational increments?” and “What is the range of this color–is it small or large?”.  After the reader asks questions such as these, they can start to make conclusions, but it can be dangerous to assume that one color means all the regions with that color have the same minority composition (or whatever is being measured), instead it is wise to look at how large the range is; then the reader can determine how alike the regions actually are.

How to Lie with Statistics

Posted on Author Mary Daly

Different types of graphs, with different scales, can portray the same set of data in many different ways.  These three images show how one can take the same information but use it in different ways in order to convince people there has been significant change, or almost no change at all.

Source: Dougherty, Jack, Jesse Wanzer, and Christina Ramsay. “Sheff V. O’Neill: Weak Desegregation Remedies and Strong Disincentives in Connecticut, 1996-2008.” Papers and Publications (January 1, 2009). http://digitalrepository.trincoll.edu/cssp_papers/3.

 

The graphs that I made below both use the above bar graph as their data, but appear convey very different messages. One shows very minor changes, which would be used if one wanted to show how little progress has been made.  The second one has a very steep curve, used if one wanted to display an extreme change in percentage of minority students in reduced isolation settings.

 

See how this graph has a scale on the Y axis that goes up to 100. This leads to a very horizontal line–one that looks as if almost no change has happend.  Using a bigger scale is one of the ways people can “lie with statistics” in order to prove the point that they want.

 

This graph on the other hand starts at 8 instead of zero, and ends at just 18 on the Y axis. This leads to a very vertical line, so it looks like there has been a very high increase in percentage of students in reduced isolation settings.  One would use a small scale on the Y axis if they want to falsely prove a point that there has been a lot of change.

 

I was surprised how drastic the difference in graphs was just by changing the axis. So, in conclusion, when looking at graphs, one should always check the numbers on the axis and think about the scale the author is using before coming to conclusions about graphs.

 

How to Lie with Statistics

Posted on Author Mary Daly

Different types of graphs, with different scales, can portray the same set of data in many different ways.  These three images show how one can take the same information but use it in different ways in order to convince people there has been significant change, or almost no change at all.

Source: Dougherty, Jack, Jesse Wanzer, and Christina Ramsay. “Sheff V. O’Neill: Weak Desegregation Remedies and Strong Disincentives in Connecticut, 1996-2008.” Papers and Publications (January 1, 2009). http://digitalrepository.trincoll.edu/cssp_papers/3.

 

The graphs that I made below both use the above bar graph as their data, but appear convey very different messages. One shows very minor changes, which would be used if one wanted to show how little progress has been made.  The second one has a very steep curve, used if one wanted to display an extreme change in percentage of minority students in reduced isolation settings.

 

See how this graph has a scale on the Y axis that goes up to 100. This leads to a very horizontal line–one that looks as if almost no change has happend.  Using a bigger scale is one of the ways people can “lie with statistics” in order to prove the point that they want.

 

This graph on the other hand starts at 8 instead of zero, and ends at just 18 on the Y axis. This leads to a very vertical line, so it looks like there has been a very high increase in percentage of students in reduced isolation settings.  One would use a small scale on the Y axis if they want to falsely prove a point that there has been a lot of change.

 

I was surprised how drastic the difference in graphs was just by changing the axis. So, in conclusion, when looking at graphs, one should always check the numbers on the axis and think about the scale the author is using before coming to conclusions about graphs.