Neighborhood Change Indicators September 19, 2024

HUD PD&R Contract #: 86614623c00008

Alena Stern and Claudia Solari

Agenda

 Background & motivation

 Types of neighborhood change measured

 Input data for predictions

 Results and machine learning models

 Limitations & next steps

 Audience thoughts and Q&A

 How might you use this in your work?

2

Background & Motivation

Background & Motivation

 Stern 2021

 Machine learning models of neighborhood change on 4 metro

areas in DC and OH

 Expand

 Nationwide

 Data sources beyond HUD Administrative data & ACS

 Incorporate learnings from pilot and user engagement

4

User Engagement

 Project Advisory Group (N=11)

 Geographic coverage: Denver, Phoenix, Charlotte, New Orleans,

Philadelphia, Rochester, Detroit, DC, rural areas

 Various roles: Lived experience of neighborhood change, local

practitioners/advocacy groups, NNIP affiliates, local government, scholar

 Subject Matter Experts

 Rural areas, tribal areas, climate impacts, historical preservation,

equity, fairness 5 Implementation Process

Obtain Sub-

HUD Orientation & DUA License PAG Co-Design

Meeting and Literature Session

Review

Input Dataset and PAG and HUD Draft Model Documentation Feedback on Specification and Drafting and Neighborhood Training Finalization Change Definitions

PAG Feedback on Code + Spatial Model Finalization Dataset Drafting, Draft Results and and Evaluation Finalization, and Spatial Dataset Disposition

6

Types of Neighborhood Change

Types of Neighborhood Change - Goals

 Provide actionable information

 Prevent displacement of incumbent residents

 Target resources to mitigate displacement

 Address the “missing middle” from outmigration

 Not stereotyping/stigmatizing communities

 Act of change (e.g., population loss) vs label (e.g., declining)

 Understandable definitions

 Split data into rural and urban subgroups for modeling

8

Selected Types of Neighborhood Change (% tracts 2022)

 Displacement due to price pressures (only urban areas) (2.2%)

 Lower income at period start, increased housing cost, increased income, decreased

public benefits

 Population loss due to economic disinvestment (4.2%)

 Decreased households, decreased income, decreased college-educated

 Inclusive growth (2.2%)

 Lower income at period start, increased income, stable housing cost, stable voucher

tenants, increased population

 Mutually exclusive, with “Change not measured” (91.4%)

9

Time Periods of Change and Prediction

Period of Change Predicted Date Prediction Made

2017-2022 December 2021

2016-2021 December 2020

2015-2020 December 2019

2014-2019 December 2018

2013-2018 December 2017

10

Neighborhood Change Classifications in Trinidad/NoMa

Mt. Olivet

Cemetery

Gallaudet

University

Neighborhood Change

H St NE

11

Input Data for Prediction

Neighborhood Change Dimensions and Data Sources

 Demographic composition (e.g., race, ethnicity, age)

 Income and education composition (e.g., education, household income)

 Land and dwelling use (e.g., vacancy, tenure, housing choice voucher tenants)

 Cultural and Institutional characteristics (e.g., libraries, historic sites)

 Quality of housing stock (e.g., housing costs, mortgage activity, disaster

shocks)

 Economic investment (e.g., jobs, commute time)

13

Feature Generation

 Create new variables capturing changes prior to prediction date

 Change in variable in the given tract

 Change in variable in the neighboring tracts

 Change in the rate of change of a variable in the given tract

 Consistency of change in a variable in the given tract

 Drop variables that have no variation, high degrees of missingness after

imputation, and are highly correlated with each other

 From 449 raw variables, results in 261 (rural) and 249 (urban) features for

training

14

Time Periods of Prediction and Data Availability

Period of Change Most Recent ACS 5-year Date Prediction Made Predicted Available for Features

2017-2022 December 2021 2020

2016-2021 December 2020 2019

2015-2020 December 2019 2018

2014-2019 December 2018 2017

2013-2018 December 2017 2016

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Results and Machine Learning Models

Modeling Approach

 Randomly split tracts into training and testing sets (within

each subgroup)

 Try many combinations of model types and specifications on

the training data for each subgroup

 Evaluate the performance and fairness of the models

 Select the best-performing model for each subgroup and apply

to the test data

17

Modeling Results on Test Data for 2022

(urban)

18

Fairness Metrics on

Test Data for Urban

Tracts in 2022

19

Neighborhood Change Classifications

Mt. Olivet

Cemetery

Gallaudet

University

Predicted:

displacement -

price pressure

H St NE

*At top 5% threshold

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Limitations & Next Steps

 Limitations

 National focus excluded useful local data

 Include more datasets

 Test more model variations

 User guide

 Conversation-starter

 How to use the information on neighborhood change

21

Recommendations

 Communicate the change type & the probability of change

 Show multiple years

 Allow user feedback

 Overlay more data for deeper context (e.g., climate change,

transportation, rural neighborhood assets, R/ECAP)

22

Areas for Further Research

 Explore other modeling set-ups to enhance model’s ability to target

changing tracts (e.g., continuous change, change relative to county, only

include “eligible” tracts, binary classification)

 Test other modeling approaches and specifications (e.g., deep learning

models)

 Explore other subgroup divisions (e.g., rural, suburban, urban)

 Add more datasets &variables tailored to subgroups and types of

change

 Test prediction of change further out than 1 year

 Explore persistently unchanging neighborhoods 23

Audience Thoughts

and Q&A

Appendix

Displacement due to price pressures (only urban areas)

 Median household income is below county median household income in the

starting year, suggesting a population at risk of displacement.

 Monthly median housing costs as a share of mean second quintile income in

the start year increases by at least 10 percent, suggesting housing price

pressures.

 The proportion of the tract's population using public benefits decreases,

suggesting a displacement of the lowest income community members.

 The tract's median household income increases, suggesting a displacement of

lower income households by higher income households.

26

Population loss due to economic disinvestment

 The proportion of the population with a bachelor's degree or

higher declines, suggesting a loss of the more highly educated

population.

 Median household income declines by at least 5 percent, suggesting a

decline in economic outcomes.

 A decrease in the number of households, suggesting population loss.

27

Inclusive growth

 Median household income is below the county median household income in

the starting year, suggesting a sufficient low-income at the start point.

 The inflation-adjusted (to 2022 dollars) income for the first and second quintile

of residents both increase, suggesting that income increases are shared by the

lower quintiles of the income distribution.

 Monthly median housing costs as a share of mean second quintile income in

the start year increases by less than 5 percent, suggesting limited price

pressures.

 The number of tenant-based voucher holders does not decline during the

period, suggesting that landlords are not refusing to rent to voucher holders.

 The number of households grows during the period, indicating population

growth. 28 Why we determined these definitions?

 Heavily informed by the literature

 But, the literature is inconsistent in how it operationalizes

neighborhood change types

 Wanted to focus on the process rather than loaded

phenomena that can add complexity and nuance

 Definitions can vary by locality but ours needs to apply

more broadly across the county.

 Have simple definitions that are understandable

29

Prevalence of Neighborhood Change by Type

Change Type 2018 2019 2020 2021 2022

Displacement Due to Price Pressures 1.2% 1.9% 2.4% 2.4% 2.2%

Population Loss Due to Disinvestment 4.2% 3.3% 4.0% 3.9% 4.2%

Inclusive Growth 2.4% 2.4% 2.2% 2.0% 2.2%

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Model Training

31

Input Data Sources

Neighborhood Relevant Datasets (Variables Measuring Factors) Change Factor

Demographic  American Community Survey (race, ethnicity, age, foreign born, languages composition spoken)

Income and  American Community Survey (household income, educational attainment, education health insurance coverage) composition

Land and  American Community Survey (tenure, units in structure) dwelling use  HUD USPS vacancy data (residential and business vacancy, no-stat, and

active addresses)

 HUD administrative data (housing choice voucher tenants and projects)

Cultural and  IMLS Public Libraries Survey (library openings and closures) institutional  National Register of Historic Places (historic preservation designation) characteristics

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Input Data Sources

Neighborhood Change Factor Relevant Datasets (Variables Measuring Factors)

Quality of housing stock  Home Mortgage Disclosure Act (mortgage activity,

home prices).

 American Community Survey (median gross rent, home

value, number of bedrooms, ratio income to home value)

 Comprehensive Housing Affordability Strategy

(housing problems).

 FEMA Disaster Data (disaster shocks)

Economic Investment  LEHD Origin-Destination Employment Statistics

(Jobs/workers at different income points).

 American Community Survey (broadband access,

commute time, employment rate)

33

Input Data Source Publication Lag

Data Source Earliest Year Latest Year Publication

Available Available* Lag (Years)

ACS 5-Year 2013 2022 1

HUD USPS 2008 2023 0

HUD Administrative 2008 2023 0

HMDA 2013 2022 1

FEMA 2009 2022 1

LODES 2011 2021 2

IMLS PLS 2012 2022 1

HUD CHAS 2011 2020 3

National Register of Historic Places 2014 2022 1

*As of December 2023

34

Most important features: urban model

 Previous neighborhood change:

 inclusive growth, displacement due to price pressures, depopulation due to disinvestment  Income

 80th percentile income, previous change in household income, mean first quintile income,

100th percentile income, 2nd quintile share of aggregate income

 Education

 Change in the share with a bachelor’s degree  Housing

 median mortgage loan amount, previous change in number of households, number of

owner-occupied units, change in housing costs, proportion of owner-occupied units that are in 1-unit buildings

 Public assistance

 change in proportion receiving public assistance, change in the number of tenant-based

voucher households, share of population receiving SNAP

35

Most important features: rural model

 Previous neighborhood change:

 inclusive growth, depopulation due to disinvestment  Income

 Whether the tract is below county median income, change in the share below the federal

poverty limit, change in median household income, mean and upper bound of first income quintile, share of total income held by third quintile, 100th percentile income

 Education:

 Share with education more than a bachelor’s degree, share with a bachelor’s degree  Housing

 median housing cost, previous change in number of households, share of units built before

1960, change in housing costs, proportion of owner-occupied units that are in 1-unit buildings, count of active residential addresses

 Economic:

 Number of Hispanic workers

36