High-resolution assessment of gentrification across cities
Gentrification is notoriously difficult to measure, particularly across jurisdictions (Loukaitou-Sideris et al., 2019). Gentrification is a complex process which includes physical (e.g., building rehabilitation) and economic (e.g., business types/ownership) transitions as well as social (wealth, education) and cultural (community organizations) transitions. Traditional measures of gentrification are either qualitative or quantitative (Preis et al., 2020). Qualitative measures typically provide a robust look at economic and cultural transitions in particular neighborhoods, while quantitative measures typically offer a clearer look at economic and social trends across entire cities (bunten et al., 2023; Preis et al., 2020). While a few isolated projects have sought to integrate these into mixed methods indicators (Pettit et al., 2019), scholars continue to search for indicators that bridge the strengths of qualitative and quantitative assessment - a way to map gentrification at a large scale that offers an early warning system as well as a way to measure the effects over time.
I was awarded a Poverty Solutions fellowship in the summer of 2023 to pursue improved solutions for modeling gentrification across jurisdictions, particularly in the context of amenity-driven gentrification, which we have worked on previously. For this work, I explored the utility of nationwide point data for households and businesses as a way to create very high resolution information on gentrification processes, outcomes, and futures. I built on the work of bunten et al. (2023) who documented that measuring the gap between income in a neighborhood and home value in a neighborhood can foreshadow transitions. Using household-level data provided by InfoGroup, a marketing firm, I assessed the physical and social transitions associated with gentrification with income and home value data, including the development of an early warning system for gentrification. This novel early warning system for gentrification can be calculated at the household level for any city in the United States. It can then be combined with business data, also available at the level of individual structures, and census data to consider the predictiveness of our early warning system and the implications for future planning. For the purposes of this project, we focus on coastal Michigan as a case study in early warning system development, work which sets the stage for broader analysis of tourism-driven gentrification on the “pleasant peninsula.”
Developing the early warning system
Gentrification scholars have long theorized that the primary driver behind the phenomenon is the so-called rent gap (Smith, 1979). Although the exact definition and operationalization of a rent gap has long been debated (Hammel, 1999), the general principle is that a rent gap occurs when there is some distance between the potential and realized value of a parcel. While some scholars argue that land use and land rent theory are incompatible, others point out that a rent gap can appear even under the complexities of mixed land use. Building on bunten et al. (2023), this work assumes that a rent gap occurs and can be measured anytime the expectations for a plot of land differ from the realized value extracted. Specifically, we explore the bounds of bunten’s expectations-based measure which argues that an emerging gap between local home values and local incomes demonstrates the high potential for near-term investment and neighborhood change.
While bunten et al. model and test this hypothesis in a number of large urban centers, we have extended this model in two key ways. First, we operationalized the model at the household level, allowing extreme flexibility in the scope and scale of assessment. Second, we applied this model to large towns and small cities in Michigan, exploring the potential of this expectations-based metric to offer early warnings of forms of gentrification that occur outside large urban agglomerations (Cocola-Gant, 2018).
To do this, we employed three key datasets: 1. a household-level dataset provided by InfoGroup, 2. business data provided by InfoGroup, and 3. US census data. Data cleaning and processing occurred in R; code are available as an R markdown document upon request and data can be shared with University of Michigan affiliates under the library license. The scope of analysis was coastal urban areas in Michigan, following the Census Bureau’s definition of urban as at least 2,000 housing units or at least 5,000 people (2020 Census Urban Areas FAQs, 2022). Within each of those coastal urban areas, we calculated household income and home value percentiles before assessing the gap between these. In our base case (based on the work of bunten et al.), a percentile gap higher than 25% marked a house as susceptible to gentrification, and neighborhoods where more than ⅓ of homes met this criterion were considered to be in the early stages of gentrification. Sensitivity analysis of both of these thresholds is discussed below. We also test how much lag appears between the appearance of the rent gap and other documented changes in neighborhoods.
Using the expectations measure to predict future changes
To determine which percentile gap and time delay were most predictive of community change, we searched for trends in the correlation between rent gap variables and known indicators of gentrification. Specifically, we considered seven different values for the gap size, seven values for the percent of the neighborhood reaching a gap, and four values for the number of years after a gap was identified.