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  "description": "The Research Context After entering their contact information, the patron completes the following fields: Focus Metro Area: Atlanta MSADataset Framework: Neighborhood AffordabilityService Tier: PremiumData Emphasis Style: Data Coverage Through the selections above, this particular patron has requested a Neighborhood Affordability Dataset focused on the Atlanta MSA. They also chose a premium dataset that includes rental… ",
  "path": "/neighborhood-affordability-datasets-the-example/",
  "publishedAt": "2026-06-11T20:57:00+00:00",
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  "textContent": "The Research Context After entering their contact information, the patron completes the following fields: Focus Metro Area: Atlanta MSADataset Framework: Neighborhood AffordabilityService Tier: PremiumData Emphasis Style: Data Coverage Through the selections above, this particular patron has requested a Neighborhood Affordability Dataset focused on the Atlanta MSA. They also chose a premium dataset that includes rental and home-buying affordability data and have emphasized data coverage over data quality. With respect to the latter, this means that the dataset will include the maximum number of neighborhoods, although the reliability scores for some neighborhoods will fail to meet established acceptability standards. With the requested dataset in hand, the patron can explore one of their fundamental questions: Which neighborhoods can renting households making 50 percent of Atlanta’s median household income afford?From the data provided, the patron knows that 50 percent of the median household income (MHI) is equivalent to $47,742 (contextually updated). This translates to households at this income level affording to pay $1,194 per month in rent and utilities without being cost-burdened. When households at this income level are matched with the neighborhood closest to their rental affordability threshold, the patron identifies a Fulton County neighborhood with the following statistical profile: Based on a standardized way of stratifying neighborhoods, the patron finds that this particular neighborhood is classified as a semi-disadvantaged neighborhood. These neighborhoods are positioned below middling neighborhoods and above disadvantaged neighborhoods on the stratification scale. On the overall scale that ranges from 0.58 to 14.49, the closest match neighborhood for households at 50 percent of MHI (50MHI) has a 2.51 stratification score. It should also be noted that this particular neighborhood is 95.5 percent Black American and 3.8 percent Latino. When the patron broadens the neighborhood context to include semi-disadvantaged neighborhoods as a category, they get a better overall picture of semi-disadvantaged neighborhoods and what living in such a neighborhood looks like quantitatively. For example, while the closest match neighborhood for 50MHI households was over 95 percent Black, semi-disadvantaged neighborhoods as a category are 45.0 percent Black. This shows that relying solely on the closest match neighborhood could give a slanted portrait of what semi-disadvantaged neighborhoods look like in reality. The patron also sees via the dataset that 24.6 percent of Atlanta MSA households live in semi-disadvantaged neighborhoods. Additionally, when we calculate the statistical averages for a wider set of measures, the following profile is revealed: Overall, it is quite clear that semi-disadvantaged (SDIS) neighborhoods fare better, on average, than the closest match neighborhood. The poverty rate is lower for SDIS neighborhoods, the MHI is higher, and homeownership rate is much higher. The one advantage that the closest match neighborhood has over the SDIS category is a higher median home value. So, now the patron knows that the closest match neighborhood has some interesting characteristics that do not exactly match the broader pattern for the type of neighborhoods that 50MHI households can afford. This begs the question of how to broaden beyond the closest match neighborhood in a way that is more targeted. Their question becomes what is the neighborhood context for the SDIS neighborhoods that are at and below the affordability threshold for 50MHI households. After finding and isolating these 59 neighborhoods, the patron finds that they are only 29.0 percent Black, on average, and 61.9 percent White. Already, this is a puzzling development given the findings up to this point. Going deeper into the numbers reveals the following statistical profile: The number one thing that stands out when it comes to these within-50MHI-affordability SDIS neighborhoods is the disparity in the contrast between the median gross rents for these neighborhoods compared to general SDIS neighborhoods and the contrast between the other measures. In essence, the rent in these particular neighborhoods is $550 cheaper than the SDIS-wide rent without much of a dip in the MHI of the residing households or in median home values. In fact, the poverty rate is lower and the homeownership rate is higher. With genuine curiosity and firm determination, the patron takes a deeper look at the data to make sense of these developments. Eventually, they take a closer look at county dynamics. They notice that out of the 59 neighborhoods that were simultaneously affordable to 50MHI households and classified as SDIS, 84.7 percent are in non-core Metro Atlanta counties. This contrasts significantly with the SDIS-wide figure (44.0 percent) for non-core counties. Although the fact that this subset of neighborhoods lies mostly outside of the urban core explains some things, it doesn’t explain everything. Several questions linger for the patron with respect to how land is valued in counties that are just beyond the perimeter of the urban core and what role race and ethnicity might be playing in this dynamic. Overall, the patron is pleased with having a dataset that is compatible with their sense of curiosity and care for the community. They look forward to exploring the next question they have in mind. Your own data-rich experience is right around the corner. To learn more about this endeavor and stay in touch with the next developments, be sure to sign up below. Name* Email* Organization* By submitting this form you agree to receive emails from us and understand that your contact information will be saved. Submit",
  "title": "Neighborhood Affordability Datasets: The Example"
}