This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

This dataset is an authoritative inventory of new housing units constructed in the City of Saint Paul from 2010 through the end of Q1 2025. The data originates from two sources: the City's permitting system, and from the City's records on housing affordability. The dataset helps provide a deeper understanding of trends in market rate and affordable housing production. This dataset is updated quarterly, generally by the 15th of the month following the end of each quarter.For the purposes of this dataset, the delineation of "affordable units" is tied to the construction of the new units: does the project — its development financing or the regulatory framework under which it was built — require units be affordable upon the completion of construction?
This definition of affordability does not include units that are affordable only because of a post-construction subsidy or other similar subsequent commitment to affordability, such as through the city's Rental Rehab Loan Program or 4d Affordable Housing Incentive Program. It does, however, include units that are affordable under the terms of zoning district-based density bonuses for affordability. Projects built under a zoning-based density bonus currently comprise a very small portion of the larger total, and are identified in the Notes column of the associated table.This dataset will be updated quarterly, given the manual work currently involved in bringing it up-to-date. It is the product of work over five years across three City departments.Field definitions are available below. In addition to being available for download through the Open Information website, this data is perhaps more easily accessible in an interactive Housing Production Dashboard.This data is designed under a methodology specific to the City of Saint Paul. Other government entities use the same originating permit data, but somewhat divergent methodologies, which can produce very different results. We believe this particular methodology gives the fullest and most timely depiction of housing production available. For specific details, see the "Methodologies Compared" tab at the bottom of the Housing Production Dashboard.Technical detailsThis dataset is generally designed to have one record (row) per building project that creates new units. A project may be the result of one or more building permits. In cases when a project contains both subsidized / affordable and unsubsidized / market rate units, the project is split across two records (rows).

Fields (Columns) Defined

PropertyRSN: An internal unique identifier for the address point with which the permit is associated.

Property Address: The street address at which the permit work took place.

ParcelID: The county-assigned unique identifier for the parcel on which the permit work took place.

Type of Work: The kind of work undertaken at the site. CHOICES: New · Addition · Remodel

Residence Type: What is the physical form of the dwelling units that were created under this building permit? CHOICES: 2-Family/Duplex · Mixed (Commercial/Residential) · Residential (Multi-Fam) · Single Family DwellingDwelling Unit Type: The type of financial structure tied to the new dwelling units created under this permit. CHOICES:Market Rate Unit: Units that did not receive some sort of direct public subsidy or assistance outside normal market sources.Affordable Unit: Units that contractually ensure affordability / access for those in need, at the level of 80% of Area Median Income (AMI) and below. This definition does include units that are affordable under the terms of zoning-based density bonuses, which comprise a very small portion of the overall total. This demarcation of affordable units does not include units that received financial assistance in preparing the site for redevelopment, for activities such as pollution remediation. Further, the affordability included here are only those contractually included at the closing of the development financing of the project, and does not include units restricted as affordable at a later date, such as through the City's 4(d) Affordable Housing Incentive Program, or the Rental Rehab Loan Program.

Commercial to Housing Conversion: The units shown were produced by converting formerly commercial space (including retail, commercial, institutional and industrial type uses) into residential space (including single family, duplex, 3-4 unit, multifamily and congregate-type residential uses). CHOICES:Yes: The housing units shown were converted from commercial space.No: The housing units shown were not converted from commercial space.Project Permit Issue Date: The date the first permit was issued for the project that created the new dwelling units.

Project Permit Issue Year: The year the first permit was issued for the project that created the new dwelling units.

Existing Dwelling Units: The number of dwelling units that existed just prior to the start of the project under the definition of "dwelling unit" in the International Building Code.

New Dwelling Units: The number of new dwelling units created under the building permit(s) under the definition of "dwelling unit" in the International Building Code.

Total Final Dwelling Units: The number of dwelling units existing upon completion of the associated building permit(s), under the definition of "dwelling unit" in the International Building Code.

Notes: This field contains notes on specific unique circumstances. In particular, a few building permits produced both subsidized / affordable and unsubsidized / market rate dwelling units. To make building permits in this scenario function as needed within data systems, we split such permits into two lines, one for each type of unit, and made a notation in this field to reflect that division.

The U.S. Department of Housing and Urban Development (HUD) requires local municipalities that receive Community Development Block Grant (CDBG or CD) formula Entitlement funds to use the 5-year 2016-2020 American Community Survey (ACS) Low and Moderate Income Summary Data (LMISD) data file to determine where CDBG funds may be used for activities that are available to all the residents in a particular area ("CD area benefit" or "CD-eligible area"). A CD-eligible census tract refers to 2020 census tracts where the area is primarily residential in nature and at least 51.00% of the residents are low- and moderate-income persons as per the LMISD data file. For New York City, a primarily residential area is defined as one where at least 50.00% of the total built floor area is residential. Low- and moderate-income persons are defined as persons living in households with incomes below 80 percent of the area median household income (AMI). In addition, floor area percentages have been updated with the most recent floor area data (PLUTO 24v4). Persons who are interested in determining their individual household eligibility for CD-funded programs should refer to HUD's household low- and moderate-income limits for the given year. For more information about how geographic datasets are used for compliance purposes, please refer to the following HUD Office of Community Planning and Development (CPD) Notice CPD-24-04.