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Gentrification’s Effect on Crime Rates

By Mischa-von-Derek Aikman   Gentrification’s Effect on Crime Rates


Many scholars have explored the behavior of crime rates within neighborhoods that are considered to have been completely gentrified, or are still currently undergoing the process of gentrification. They do this largely by studying the changes in crime trends in numerous neighborhoods that display typical characteristics of gentrification. This literature survey pays careful attention to the definition used to select examples of gentrified neighborhoods for examination. It will also exert the claim that crime rates of particular categories seem to rise on average within these neighborhoods upon the commencement of gentrification. It will look at the models used to normalize crime rates across neighborhoods of different populations and densities, as well as those that account for the issue of crime rates regressing towards the mean. Using these normalized statistics, the survey will outline conjectured reasons as to why crime rates seem to rise in gentrified neighborhoods.

Defining and Selecting Gentrifying Neighborhoods

The issue of neighborhood selection proves to be inherently complex as the literature quickly realizes that one of the root difficulties is that what is considered a “gentrified/gentrifying” neighborhood is subject to many different definitions and interpretations. When selecting “gentrifying” neighborhoods for study, it is important to differentiate between neighborhoods that are simply experiencing a cycle of appreciation, and those that are truly gentrifying (Taylor, 1989). In other words the average increase in dollar value of houses and land alone do not define a neighborhood that is gentrifying per se as this can be the result of inflation in the larger housing market (McDonald 1986). Additionally, McDonald (1986) distinguishes between gentrification and “incumbent upgrading” in which current residents improve housing stock, and there is no apparent population change. Rather, Taylor (1989) defines gentrification as “the migration of younger, middle-, and perhaps upper-income households into centrally located urban neighborhoods and the accompanying upgrading of the worn-out housing stock that previously had “filtered down” to lower-income occupants.” It is also commonly accepted that gentrification is accompanied by the inevitable displacement of lower-income residents who previously resided in these neighborhoods (Taylor 1989).

Even with this relatively common definition, methods of choosing neighborhoods for study vary between authors. McDonald (1986) selects a sample of fourteen neighborhoods in which “gentrification has been reported.” These neighborhoods were all located in Boston, New York, San Francisco, Seattle, and Washington, D.C. The literature studied these particular neighborhoods based on various principles. Most importantly, they were chosen due to the availability of time-series crime statistics between 1970 and 1984, as well as an attempt to capture neighborhoods that underwent both commercial and residential gentrification (McDonald, 1986). However, this methodology used by McDonald did not go on to compare gentrifying neighborhoods with non- gentrified neighborhoods, and was generally arbitrary in its selection process (Taylor 1989).

Negative Impacts of Displacement

Given the definition of gentrification used in this survey, displacement proves to be a necessary byproduct. Atkinson (2002) uses cross-sectional data in gentrified neighborhoods where population outflow exceeds citywide averages to determine the extent to which displacement becomes an issue. Atkinson (2002) argues that displacement is typically short lived, but may be prolonged depending on the rate of inflow of new residents. Although using less quantitative methods, Atkinson outlines issues that arise from displacement, which contribute to an environment conducive to increased crime. These include evictions due to the inability to afford the rising price of rent associated with gentrification. This inevitably leads to increased homelessness directly through the loss of Single Room Occupant (SRO) dwellings (Atkinson 2002). However, Atkinson admits that there was no conclusive evidence confirming that the loss of SRO’s were caused by gentrification directly, and not by occurrences in the wider housing market. Atkinson’s research on crime directly produced contradicting results (which we explore in more detail). While crime seemed to fall in some gentrified neighborhoods, others showed that crime actually increased within certain categories (Atkinson 2002). There is also the issue of social conflict sparked by the presence of new residents with “different cultural backgrounds” (Atkinson 2002).

Expectations Surrounding Gentrification’s Effect on Crime

Rational expectations about gentrification’s effect on crime can be made in either direction. We can expect a general decrease in crime due to the fact that statistically, middle to upper income residential spaces typically have lower crime rates (Taylor 1989). Additionally, the more affluent people migrating into the neighborhood are more likely to have more political influence, and can therefore successfully request a heightened police presence (Taylor 1989). We can also reasonably expect increased crime in gentrified areas due to the fact that displaced young adults may move to neighborhoods within close proximity of their original homes, and may view their wealthier replacers as more attractive targets (McDonald 1986). Another practical reason for crime rates to rise is that the presence of richer residents living among those who would typically be below the poverty line could feed an atmosphere of social conflict (McDonald 1986). This occurrence has the potential to manifest itself in physical violence between cohabitants.


As mentioned before, McDonald (1986) utilized the time-series data from 14 arbitrarily chosen gentrified neighborhoods to determine plausible effects of gentrification on crime. While his findings are also discussed in the ‘Results’ portion, this section will look more closely the approach used in Taylor’s (1989) study. Taylor (1989) utilizes census data available for all 277 Baltimore City neighborhoods, and 1979 – 1980 Part I crime data for the same neighborhoods. To obtain the ‘beginning of the decade’ and ‘end of the decade’ crime counts for each offense, 2-year averages were used (1970 and 1971 for beginning, and 1979 and 1980 for the end). Crime counts were then divided by respective total neighborhood population in order to calculate crime rates per 100,000 (except for burglary which was divided by number of households).

In order to capture neighborhood dynamics ‘in the context of what was happening in other neighborhoods,’ both the predictor and outcome scales were made relative. Therefore, both beginning and end of decade crime rates were transformed to weighted percentile scores. With this information, Taylor (1989) was able to rank all the neighborhoods (relative to one another) while accounting for each respective population size. This rank is essentially an ordinal representation of the various crime rates for all the neighborhoods. Taylor (1989) found this measurement attractive, as its skewness (measure of the asymmetry of the probability distribution) is lower than that of logged or raw crime rates.

As opposed to simply taking the difference between scores to determine change in crime rates from year to year, residualized change scores were used (Pt = A + BPt-1 + e). The residual itself (e) should theoretically represent the unexpected change from year to year as it was (as per the assumptions of regression) uncorrelated with the predicted scores. Additionally, each parameter value for 1980 was regressed on its respective 1970 score. The residual generated from this regression was used as the gauge of change (Taylor 1989). Ultimately, these regressions controlled for fluctuating population levels throughout the time period, as well as for each neighborhood’s respective initial level of crime.

Furthermore, in an attempt to provide a more “clear-cut” method of identifying gentrified neighborhoods, Taylor (1989) utilizes a single (not multiple) indicator of gentrification. This measure uses a census-based item whereby households were polled, and asked to provide the current market value of the homes. Using this information, a ‘dynamic index’ representing the appreciation in neighborhood house values was constructed to determine a house-value percentile score for each neighborhood (Taylor 1989). This was done by using a regression model similar to that used in the development of percentile scores for the crime rates. Calculating the percentile changes in house values produced residuals that accounted for the unexpected increases or decreases given the neighborhood’s initial house-value score. Controlling for initial levels accounts for the “regression to the mean” issue, and since the only relevant factor is the ordered ranking of the neighborhoods at any given point in the time-series, inflation is also accounted for (Taylor 1989).

Finally, it was determined that neighborhoods with very high residualized relative house- value scores were those neighborhoods that truly “gentrified” (and not merely experienced appreciation). This obviously presented the issue of determining a “cut off point” (how far down that list would be considered as gentrified neighborhoods?). For this reason, the study was conducted with the top 15, as well as the top 20 scoring neighborhoods (Taylor 1989).


Using the regression analysis outlined in Taylor’s (1989) paper, it was concluded that gentrification was associated with unexpected increases in both larceny and robbery. This result held true both when using the top 20 gentrifying neighborhoods, as well as the top 15.

According to McDonald’s (1986) study on the 14 neighborhoods (See attached table for details on crime rates for each neighborhood), every gentrified neighborhood studied had total Index crime rates above the average of their respective cities. It most be noted, however, that these observations were based on ‘per capita’ crime rates. This poses an issue since the population of almost all these neighborhoods declined during the period of observation. Therefore, these rates could have been just as influenced by population fluctuations as they could be by the actual shift in number of crime incidents (McDonald 1986). More specifically, McDonald (1986) notes that presence of higher crime rates in the gentrified neighborhoods were actually lower for personal crimes, but higher for property crimes (with a few insignificant exceptions). Table 1 (attached in the appendix) indicates that the effect of gentrification on crime is not of a linear nature. The crime rates rise to a significant climax in 1980, and then subside again shortly after (McDonald 1986).

This tells us that the time frame of the observations plays a crucial role in the results one gets. In an attempt to correct for this, McDonald (1986) calculates each neighborhood’s crime rate as a ratio of their respective citywide rate (these values can be seen in Table 2). The results show significant declines in personal crime from what they were in 1970 in 6 of the 14 neighborhoods (McDonald 1986). The analysis of property crime rates showed just the opposite result. Property crime rates for all but one neighborhood showed a decline (McDonald 1986). Finally, it was the general observation that despite the apparent decline in personal crime rates, most of the gentrified neighborhoods maintained crime rates higher than their citywide averages (McDonald 1986).

Questions Moving Forward

Obviously, the results of these two studies produce slightly contradicting results (Atkinson 2002). Whereas McDonald observes a decline in personal crimes and an increase in property crimes, Taylor observes an increase in both. An interesting exercise would be to conduct McDonalds’ methodology of determining trends in crime rates on those neighborhoods selected using Taylor’s (1989) regression analysis. Additionally, studying time-series data for more than a decade could shed light on the results’ sensitivity to the span of time we noticed in McDonald’s piece. Lastly, it would be interesting to explore the existence of any implemented policies used as a response to heightened crime in gentrifying neighborhoods, as well as any influences these policies might have on the crime rates themselves.


Table 1: Table Showing Crime Rates in Selected Cities and Neighborhoods, 1970-84 (McDonald)

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Table 2: Table Showing Crime Rates of Selected Neighborhoods, Indexed to the Crime Rates of Their Cities, 1970- 84 (McDonald)

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  1. Atkinson, Rowland, Dr. “Does Gentrification Help or Harm Urban Neighbourhoods? An Assessment of the Evidence-Base in the Context of the New Urban Agenda.”ESRC Centre for Neighbourhood Research (2002): n. CNR. Web. 06 Feb. 2014.
  2. Covington, Jeanette, and Ralph B. Taylor. “GENTRIFICATION AND CRIME Robbery and Larceny Changes in Appreciating Baltimore Neighborhoods During the 1970s.” Urban Affairs Quarterly 25 (1989): n. pag. Sage Publications, Inc. Web. 06 Feb. 2014.
  3. McDonald, Scott C. “Does Gentrification Affect Crime Rates?” Chicago Journals(1986): n. The University of Chicago Press. Web. 06 Feb. 2014.

1 Comment

  1. It is also interesting to note the differences between gentrification of a neighborhood and gentrification of a city (that is, the different stages of the gentrification). With Durham, for instance, one could argue that the gentrification that is occurring is causing Durham as a city to be an up-and-coming location for younger populations, while with some of the more “established” cities in the McDonald study that you referenced, the focus is on gentrifying neighborhoods in cities that are already well-populated. It would be interesting to see a comparison of various gentrifying cities based on how far along in the gentrification process that city is in order to assess the economic effects generated by the different stages of gentrification. This would shed light on where crime falls into the equation, i.e. if an externality of gentrification is the factor that is causing changes in crime rates.

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