Dynamic Neighborhood Taxonomy A Project of LIVING CITIES By RW Ventures, LLC MacArthur Foundation June 2, 2008
Apr 15, 2017
Dynamic Neighborhood Taxonomy
A Project of
LIVING CITIESBy
RW Ventures, LLC
MacArthur FoundationJune 2, 2008
Agenda
Background: The DNT Project
The Nature of Neighborhood ChangeDigging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of ChangeImplications 1.0: Dynamic, Specialized Neighborhoods
I
Implications 2.0: Specialized Tools - From Diagnostics to Investment
II
III
IV
V
Discussion: What Next? VI
Agenda
I Background: The DNT Project
About Living Cities
“A partnership of financial institutions, national foundations and federal government agencies that invest capital, time and organizational leadership to advance America’s urban
neighborhoods.”
I Background: The DNT Project
AXA Community Investment Program Bank of America The Annie E. Casey Foundation J.P. Morgan Chase & Company Deutsche Bank Ford Foundation Bill & Melinda Gates FoundationRobert Wood Johnson Foundation
The Kresge Foundation John S. and James L. Knight Foundation John D. and Catherine T. MacArthur Foundation The McKnight Foundation MetLife, Inc. Prudential Financial The Rockefeller Foundation United States Department of Housing &Urban Development
LIVING CITIES PARTNERS:
Partners and Advisors
The Urban Institute
I Background: The DNT Project
Participating Cities: Chicago, Cleveland, Dallas and Seattle
… And Over 70 Advisors including Practitioners, Researchers,Funders, Civic Leaders and Government Officials
We Know Where We Want to Go...
Common Goal:
I Background: The DNT Project
Building Healthier Communities
The Challenge: Scarce Resources, Many Options
Community-Based Organizations: select interventions, identify assets and attract investment
Governments: tailor policy and interventions Businesses: identify untapped
neighborhood markets Foundations: target interventions,
evaluate impacts
I Background: The DNT Project
Comprehensive Neighborhood Taxonomy
Business PeopleReal EstateAmenitiesSocial Environment
Improvement or deterioration within type
Gradual vs. Tipping point
From one type to another
Port of entryBohemianRetirementUrban commercialized
EmploymentEducationCrimeHousing stock
Investment activity
I Background: The DNT Project
Agenda
The Nature of Neighborhood ChangeII
Agenda
The Nature of Neighborhood ChangeII
a. Measuring Change: the RSI
b. Overall Patterns
c. Degree and Pace of Change
d. Neighborhoods and Regions
e. Drivers of Change
Agenda
IIa Measuring Change: the RSI
PHYSICAL:Distance from CBD, vacancies, rehab activity, …
Drivers Model and Data
TRANSPORTATION:Transit options, distance to jobs, …
CONSUMPTION:Retail, services, entertainment, …
PUBLIC SERVICES:Quality of schools, police and fire, …
SOCIAL INTERACTIONS:Demographics, crime rates, social capital…
IIa Measuring Change: the RSI
Theoretical Framework
Use Demand for Housing as Proxy for Neighborhood Health
Look at Quality Adjusted Housing Values to Capture Neighborhood Amenities
Look at Change in Quantity of Housing to Account forSupply Effects
Amenities
Structure Rent
Housing Price
IIa Measuring Change: the RSI
The Challenge: Finding a Metric that Works
Issues: Measure change in prices controlling for change in quality
of the housing stock Estimate at very small level of geography Track continuous change over time
Solutions: Repeat Sales to Control for Changes in Neighborhood
Housing Stock Spatial Smoothing: Locally Weighted Regression to account
for “fluid” neighborhood boundaries and address sample size
Temporal Smoothing: Fourier expansions to track change over time
IIa Measuring Change: the RSI
IIa
Developing the Repeat Sales Index (RSI):
Spatial and Temporal Smoothing
Correlations between different RSI Versions p01 p01i p01s p01c p05 p05i p05s p05c p10 p10i p10s p10c nbp01p01i 0.96p01s 0.82 0.89p01c 0.53 0.58 0.82p05 0.94 0.96 0.79 0.49p05i 0.94 0.97 0.82 0.52 0.99p05s 0.83 0.90 0.99 0.77 0.84 0.87p05c 0.53 0.59 0.82 1.00 0.50 0.53 0.79p10 0.92 0.94 0.76 0.47 0.99 0.99 0.82 0.49p10i 0.92 0.95 0.79 0.50 0.98 0.99 0.85 0.51 0.99p10s 0.84 0.90 0.97 0.75 0.85 0.88 1.00 0.77 0.83 0.86p10c 0.53 0.59 0.82 0.99 0.51 0.54 0.79 1.00 0.49 0.51 0.77nb 0.11 0.13 0.11 0.07 0.13 0.13 0.12 0.07 0.13 0.13 0.12 0.07
Number of Fourier Expansions
Measuring Change: the RSI
Optimizing sample size and fluid boundaries through extensive modeling and cross-validation
procedures
Final Product: The DNT RSIRSI Estimation Coverage Using Case/Shiller Method
Time Period: 2000 - 2006 RSI Estimation Coverage Using DNT RSI Method
Time Period: 2000 - 2006
Improving upon traditional repeat sales indices, the DNT RSI can be estimated
for very small levels of geography, and is more accurate, more robust and less volatile.
IIa Measuring Change: the RSI
Looking at Particular Tracts: Appreciation in Greater Grand Crossing
IIa Measuring Change: the RSI
Contour Model of 2004-06 Prices around Mt. Prospect Property
Spatial Distribution of Housing Values – Mount Prospect
1450 S. Busse
Sales Transactions
Tract Boundaries
IIa Measuring Change: the RSI
Chicago Neighborhood Change, 1990-2006
1990
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1991
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1992
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1993
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1994
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1995
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1996
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1997
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1998
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
1999
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
2000
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
2001
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
2002
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
2003
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
2004
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
2005
IIb Overall Patterns
Chicago Neighborhood Change, 1990-2006
2006
IIb Overall Patterns
Initial Conditions and Appreciation
APPRECIATION14.9
-0.17
MEDIAN HOUSING PRICES 1990Up to $38,000
$38,001 - $63,750
$63,751 - $107,500
Over $107,500
IIb Overall Patterns
Up to 52,600
$52,601 – $72,125
$72,126 – $108,175
Over $108,175
Change in Price: Poor Neighborhoods Present the Most Opportunities for Investment
IIb Overall Patterns
Many of the poorest neighborhoods are the ones that
grew the most, outperforming wealthier communities in each
of the four sample cities.
Partly Due to Lack of Information,These Areas Are Also the Most Volatile
TEMPORAL VOLATILITY OF INDEX0.14 - 0.93
0.94 - 1.33
1.34 - 2.54
2.55 and above
APPRECIATION14.9
-0.17
IIb Overall Patterns
By increasing the availability of information on these markets, we could reduce risk, increase
market activity, and help stabilize these communities, further strengthening their
performance.
0.25 – 0.53
0.53 – 0.60
0.60 – 0.79
0.79 – 11.2
Applications and New Capacities
Develop the “S&P/Case Shiller Home Price Index” Equivalent for Neighborhood Markets
Maintain DNT RSI and Expand it to New Markets Create a Real Estate Information Company to
Market and Distribute the RSI Specialize in Guiding Investment in Workforce
Housing
IIb Overall Patterns
Beyond Change in Price: Relationship of Price and Quantity
Tract 016501: 17% Developable Parcels in 1990Tract 016501: 17% Developable Parcels in 1990
Tract 002900: 0.05% Developable Parcels in 1990Tract 002900: 0.05% Developable Parcels in 1990
IIb Overall Patterns
Development of new housing in a neighborhood can keep prices more affordable, as places with greater constraints on new construction
experience faster appreciation.
IId Degree and Pace of Change
Neighborhood Change Is a Slow ProcessNeighborhood Mobility by Time Interval
5 Years 10 Years 15 YearsNo Change 1 Quintile2 or More Quintiles
58%
33%
8%
64%
30%6%
71%
25%4%
IId Degree and Pace of Change
Even over 15 years, most neighborhoods do not change their
position relative to other neighborhoods in the region.
Yet Substantial Change Occursin Select Neighborhoods
Median Sales Price Transition MatrixCleveland, 1990-2004
Final QuintileInitial Quintile 1 2 3 4 5
1 76.9% 15.4% 7.7% 0.0% 0.0%2 5.1% 51.3% 25.6% 15.4% 2.6%3 2.6% 26.3% 26.3% 39.5% 5.3%4 7.7% 2.6% 28.2% 23.1% 38.5%5 7.7% 5.1% 10.3% 23.1% 53.8%
IId Degree and Pace of Change
In Cleveland, 13% of all the tracts at the bottom of the distribution
in 1990 moved up to the top 2 quintiles 15 years later.
IIe Neighborhoods and Regions
Neighborhoods Tend to Move with their Regions
IIe Neighborhoods and Regions
Most neighborhoods follow their region closely, but there are important exceptions.
Regional Effects Vary by Area
Across Cities, 35% of NeighborhoodChange is Accounted for by Regional Shifts
R Squared from Regression Models of Tract RSI on Region
IIe Neighborhoods and Regions
Cleveland Chicago Dallas Seattle
Regional Effects Vary by Area
Across Cities, 35% of NeighborhoodChange is Accounted for by Regional Shifts
R Squared from Regression Models of Tract RSI on Region
IIe Neighborhoods and Regions
Cleveland Chicago Dallas Seattle
81%57%
28%7%
Neighborhood performance depends upon neighborhood characteristics,
regional trends, and linkages between the two
Drivers of Change: The Big Picture
Drivers of Change: Modeling Strategy Two Dependent Variables:
– Change in Quantity of Residential Units – Quality-Adjusted Change in Price (RSI)
Three Sets of Models: – 1990-2000 Decennial Model – 1994-2004 Time Series– 1999-2004 Time Series
Three Specifications: – Regress Change on Initial Conditions – Random Effects (Time Series Including both Initial
Conditions and Change for Independent Variables)– Fixed Effects (Time Series with Time-Variant
Independent Variables Only) Models Across Four Cities Supplemented with City-
Specific Models for Chicago
IIe Drivers of Change
The Big Picture: Urban Neighborhoods Are Coming Back
Chicago Cleveland Dallas Seattle
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
Average Tract Appreciation Rate (1990-2004)
Suburbs
City
IIe Drivers of Change
Over the past 15 years, tracts in the central citygrew faster than tracts in nearby suburbs.
The Big Picture:Neighborhood Change = Changing Neighbors?
Ratio of HMDA Borrower Income (2000-2005) to Census Income (2000)
IIe Drivers of Change
The primary mechanism of change overall appears to be the movement of people.
Exploring the Relative Importance of Different Drivers of Change
(1994-2004 Random Effects Model, Standardized Coefficients with 95% Confidence Interval)
IIe Drivers of Change
The Big Picture:Drivers of Neighborhood Change
Mobility is the key mechanism of change Movers are attracted to areas with undervalued
housing but sound economic fundamentals (employment, income, education, young adults)
Being connected is important: proximity to job centers, access to transit, lower commuting times are positive
Cultural and Recreational Amenities (art galleries, bars and restaurants) help, but are not the main event
“The Goldilocks Theory” …
IIe Drivers of Change
… Neighborhoods of Opportunity are “Just Right.”
The Big Picture:Drivers of Neighborhood Change
Race is still a factor: even controlling for income, influx of minorities in a neighborhood leads to lower appreciation – but there are important exceptions
Neighborhood Spillovers are important: what happens in your neighborhood reflects what happens in the neighborhoods around you
Context matters: substantial variation by type and stage; current conditions have large effects on degrees and patterns of change
IIe Drivers of Change
DETROIT—Notorious for its abandoned buildings, industrial warehouses, and gray, dilapidated roads, Detroit's Warrendale neighborhood was miraculously revitalized this week by the installation of a single, three-by-four-foot plot of green space.
The green space, a rectangular patch of crabgrass located on a busy median divider, has by all accounts turned what was once a rundown community into a thriving, picturesque oasis, filled with charming shops, luxury condominiums, and, for the first time ever, hope.
The Johansens, who just moved to Warrendale, enjoy some outdoor time.
3'-By-4' Plot Of Green Space Rejuvenates NeighborhoodFEBRUARY 11, 2008 | ISSUE 44•07
The “Little” Picture: Few Silver Bullets
IIe Drivers of Change
The “Little” Picture: Few Silver Bullets
IIe Drivers of Change
What matters varies a great deal by type of neighborhood.
Preliminary – For Illustration Purposes Only Preliminary – For Illustration Purposes Only
Agenda
Background: The DNT Project
The Nature of Neighborhood ChangeDigging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of ChangeImplications 1.0: Dynamic, Specialized Neighborhoods
I
Implications 2.0: Specialized Tools - From Diagnostics to Investment
II
III
IV
V
Discussion: What Next? VI
Agenda
Specialized DriversIII
The Effect of LIHTC Projects:Good in Moderation
[Overall Model Result: Non Significant]
Specialized Drivers: Looking at Particular FactorsIII
LIHTC projects help, up to a point: as the concentration of LIHTC units exceeds a certain threshold, the effect becomes less
positive
Effe
ct o
n D
NT
RSI
, Par
tial R
esid
uals
(GAM Output based on 1994-2004 Fixed Effects Model, All Cities)
The Effect of Sub-Prime Lending: A Four Year Fallout
Specialized Drivers: Looking at Particular FactorsIII
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
1 2 3 4 5
Year
Effect of Subprime Lending on Neighborhood Appreciation
Reg
ress
ion
Coe
ffici
ent,
DN
T R
epea
t Sal
es In
dex
[Overall Model Result: Positive and Significant]
(199
4-20
04 R
ando
m E
ffect
s M
odel
, All
Citi
es)
By monitoring the levels of sub-prime activity over the past few years, practitioners can have a good sense of how the effects will play out in the years to
come.
Applications and New Capacities
Detailed Analysis of Particular Factors of Interest: e.g. Access to and Use of Credit, Different Types of Business Establishments, Arts and Cultural Centers, etc.
Further Investigation of the Relationships between Drivers of Change: e.g. Examine Interactions between Different Drivers (e.g. Crime and Transit)
Return on Investment Analysis: Tie Magnitude of Effect to Cost of Interventions to Assess which Strategies are Most Cost Effective
Specialized Drivers: Looking at Particular FactorsIII
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
Neighborhood ConvergenceBeta Convergence in Cook County, 1990 - 2005
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
Neighborhoods Now Tend to Naturally Catch Up to Each Other
But not All Neighborhoods Follow this Trend...
Neighborhood ConvergenceBeta Convergence in Cook County, 1990 - 2005
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
Characteristics of Poorer Neighborhoods that Tend to Converge
Closer to the Central Business District In neighborhoods with more turnover and
potential for redevelopment– More apartment buildings; lower homeownership;
less residential stability Near Neighborhoods with more amenities and
social capital– Proximity to Transit, Grocery Stores, Art Galleries
and Eating Establishments
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
Improvement With High and Low Turnover
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
Improvement With High and Low Turnover
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
What characterizes the neighborhoods that improved with less displacement?
Drivers of “Improvement in Place”
Improvement with Low Turnover Is Associated With:
High Home Ownership Rates Low Vacancy Rates Access to Transit Reduction in Unemployment Presence of Employment Services High Social Capital High Percentage of Young Adults
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
Applications and New Capacities
Targeting Interventions: Identify neighborhoods that are more likely to
converge, prioritize affordability preservation efforts Focus development interventions to places that are
less likely to be “rediscovered” by the market.Further Investigation of Specific
Neighborhood Segments: Analysis of Stable Mixed-Income Communities Analysis of Drivers of Improvement in Place for
Specific Subsets of Neighborhoods (e.g. Immigrant, Startup Family, etc.)
Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
Overall Summary Implications
Neighborhoods are highly specialized Neighborhood change is a function of people and
money moving in and out This in turn varies based on neighborhood type and
stage of development – different people and investors choose different neighborhoods in the context of larger markets and systems
As a result, what matters varies by place. For any given neighborhood, the goal could be continuity or change in type, and implementation entails understanding who you want to stay or move in, and what factors matterto them
Summary Implications
Two major implications:
1. We need a framework for understanding neighborhoods as dynamic, specialized, and nested in larger systems
2. We need much better tools for customizedanalysis of local economies
Agenda
Background: The DNT Project
The Nature of Neighborhood ChangeDigging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of ChangeImplications 1.0: Dynamic, Specialized Neighborhoods
I
Implications 2.0: Specialized Tools - From Diagnostics to Investment
II
III
IV
V
Discussion: What Next? VI
Agenda
Dynamic, Specialized Neighborhoods (with Special Thanks to Andy Mooney!)IV
Neighborhoods are Complex
Dynamic, Specialized Neighborhoods (with Special Thanks to Andy Mooney!)IV
Neighborhoods are Complex
Dynamic, Specialized NeighborhoodsIV
Neighborhoods are Dynamic
Dynamic, Specialized NeighborhoodsIV
Neighborhoods are Dynamic
Dynamic, Specialized NeighborhoodsIV
Neighborhoods are Nested in Larger Systems Which Drive the Flows of People and Capital
Dynamic, Specialized NeighborhoodsIV
Neighborhoods are Nested in Larger Systems which drive the Flow of Capital and People
Dynamic, Specialized NeighborhoodsIV
Functioning Neighborhoods Connect Residents and Assets to Larger Systems
Poverty Productivity
Connectedness
Isolation
Dynamic, Specialized NeighborhoodsIV
Functioning Neighborhoods Connect Residents and Assets to Larger Systems
Employment networksEntrepreneurial opportunities
Business, real estate investment
Expanded products and services
Productive, healthycommunities
Undervalued, underutilized assets
Poverty Productivity
Connectedness
Isolation
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 1A: What type of neighborhood do you
want to be?
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 1A: What type of neighborhood do you
want to be?
Starter Home Community
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 1A: What type of neighborhood do you
want to be?STEP 1B: What drivers will get you there?
Starter Home Community
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 1A: What type of neighborhood do you
want to be?STEP 1B: What drivers will get you there?
Starter Home Community
• Specific Retail Amenities• Child Care• Schools• Safety• Affordability
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 1A: What type of neighborhood do you
want to be?STEP 1B: What drivers will get you there?
Starter Home Community
• Specific Retail Amenities• Child Care• Schools• Safety• Affordability
Dynamic, Specialized NeighborhoodsIV
ECONOMIC SYSTEM
Applying the FrameworkSTEP 2: Identify Relevant System
Retail Markets
Starter Home Community
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 3: Identify Change Levers Within System
Starter Home Community
ECONOMIC SYSTEM
Retail Markets
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 3: Identify Change Levers Within System
Starter Home Community
Commercial Land Assembly (production –
costs)Specialized Market Data(exchange – finding costs)
ECONOMIC SYSTEM
Retail Markets
Dynamic, Specialized NeighborhoodsIV
Applying the FrameworkSTEP 4: Specify Interventions
Starter Home Community
Commercial Land Assembly (production –
costs)Specialized Market Data(exchange – finding costs)
ECONOMIC SYSTEM
Retail Markets
Dynamic, Specialized NeighborhoodsIV
Agenda
Background: The DNT Project
The Nature of Neighborhood ChangeDigging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of ChangeImplications 1.0: Dynamic, Specialized Neighborhoods
I
Implications 2.0: Specialized Tools - From Diagnostics to Investment
II
III
IV
V
Discussion: What Next? VI
Developing New Tools for the Field
Question/Goal ToolHow will a specific intervention affect its surrounding area? Impact Estimator
Enabling investment in inner city real estate markets RSI REIT
What drivers differentiate neighborhoods with respect to a specific outcome of interest? CART
Identify comparable neighborhoods based on drivers of change and other key characteristics Neighborhood Typology
What neighborhoods are similar along particular factors of interest? Custom Typologies
How does the impact of an intervention vary in different places?
Geographically Weighted Regression
Track affordability and neighborhood housing mix Housing Diversity Metric
Anticipate and manage neighborhood change Pattern Search Engine
Identify “true” neighborhood boundaries Locally Weighted Regression
Specialized Tools - From Diagnostics to InvestmentV
Developing New Tools for the Field
Question/Goal ToolHow will a specific intervention affect its surrounding area? Impact Estimator
Enabling investment in inner city real estate markets RSI REIT
What drivers differentiate neighborhoods with respect to a specific outcome of interest? CART
Identify comparable neighborhoods based on drivers of change and other key characteristics Neighborhood Typology
What neighborhoods are similar along particular factors of interest? Custom Typologies
How does the impact of an intervention vary in different places?
Geographically Weighted Regression
Track affordability and neighborhood housing mix Housing Diversity Metric
Anticipate and manage neighborhood change Pattern Search Engine
Identify “true” neighborhood boundaries Locally Weighted Regression
Specialized Tools - From Diagnostics to InvestmentV
Impact Estimator
What It Does: Estimate impact of an intervention on
surrounding housing values (or on other outcome, e.g. crime)
Possible Applications: Evaluate the impact of a development policy Choose among alternative interventions
based on estimated benefits to the surrounding community
Advocate for a specific interventionSpecialized Tools - From Diagnostics to InvestmentV
Example: What is the effect over time and space of LIHTC housing?
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
Specialized Tools - From Diagnostics to InvestmentV
Monte Carlo Simulation to Estimate Impact Variation with Distance
Homes within 1000 ft of an LIHTC site appreciate at a 4%
higher rate than homes between 1000 ft and 2000 ft.
Impact of LIHTC on Surrounding Properties
Estimated Distance Decay Function – LIHTC ProjectsEstimated Distance Decay Function – LIHTC Projects
Distance from Project Location (in Miles)Distance from Project Location (in Miles)
DN
T R
epea
t Sal
es In
dex,
1 =
Fur
thes
t Aw
ay
DN
T R
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es In
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1 =
Fur
thes
t Aw
ay
Specialized Tools - From Diagnostics to InvestmentV
Applying the Estimator to a Specific Project: New Shopping Center in Chicago
New Shopping CenterNew Shopping Center
Specialized Tools - From Diagnostics to InvestmentV
Estimated benefits to the community: $29 million in increasedproperty values, or an average of $1,300 per home owner.
Classification and Regression Tree (CART)
What It Does: Identify similar neighborhoods with respect
to an outcome of interest and its driversApplications: Identify leverage points to affect the desired
outcome Meaningful comparison of trends and best
practices across neighborhoods
Specialized Tools - From Diagnostics to InvestmentV
Sample CART: Foreclosures
40 Variables Tested
Specialized Tools - From Diagnostics to InvestmentV
What Neighborhoods Have Similar Numbers of Foreclosures...
Sample CART: Foreclosures
40 Variables TestedOutcome: Number of Foreclosures (2004)
Drivers:% Subprime Loans in Previous Years
Mean Loan Applicant Income
% FHA Loans% Black Borrowers
Specialized Tools - From Diagnostics to InvestmentV
... And Why
CART Output: Chicago Segments
Specialized Tools - From Diagnostics to InvestmentV
Cluster 8: Defining Traits and Risk Factors
Segment Profile: Isolated, underserved, predominantly African
American communities. High rates of unemployment and sub-prime lending activity.
Primary Risk Factor: Percentage of sub-prime
loans (primary driver offoreclosures) is at itshighest and still onthe rise
Specialized Tools - From Diagnostics to InvestmentV
Percentage of Sub-prime Loans by Year
DNT Neighborhood Typology
What It Does: Identifies distinct neighborhood types based
on drivers of change and other characteristics
Possible Applications: Tailor strategies to specific neighborhood
types Benchmark neighborhood performance Peer analysis and relevant best practices Facilitate impact analysis
Specialized Tools - From Diagnostics to InvestmentV
A Preliminary Taxonomy of Neighborhoods
Specialized Tools - From Diagnostics to InvestmentV
Key Dimensions:
•People•Income•Age•Foreign Born
•Place•Land Use•Housing Stock•Business Types
Variables
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
A Preliminary Taxonomy of Neighborhoods
Specialized Tools - From Diagnostics to InvestmentV
Key Dimensions:
•People•Income•Age•Foreign Born
•Place•Land Use•Housing Stock•Business Types
Variables
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
A Preliminary Taxonomy of Neighborhoods
Specialized Tools - From Diagnostics to InvestmentV
Key Dimensions:
•People•Income•Age•Foreign Born
•Place•Land Use•Housing Stock•Business Types
Variables
Type 7: “Homeowners”Type 7: “Homeowners”
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
A Preliminary Taxonomy of Neighborhoods
Specialized Tools - From Diagnostics to InvestmentV
Key Dimensions:
•People•Income•Age•Foreign Born
•Place•Land Use•Housing Stock•Business Types
Variables
Type 8: Type 8: ““Young Professionals”Young Professionals”
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
Taxonomy Structure: “Genus, Phylum, Species”
Specialized Tools - From Diagnostics to InvestmentV
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
Chicago Neighborhood Types
Specialized Tools - From Diagnostics to InvestmentV
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
Example: Type 3, “Low Income Stable”
Cluster Profile: These are lower income neighborhoods with a high
percentage of single family homes and a stable resident base. These areas are also characterized by a significant presence of vacant land and very few retail and service establishments. Unemployment rates are lower than in other low income neighborhoods, and neighborhood residents tend to be employed in sales, service and production occupations.
Other Characteristics: Neighborhoods in this cluster had the highest
foreclosure rates. Stable type, though 4% transitioned to type 7
“Homeowners”
Example: Type 3, “Low Income Stable”
Specialized Tools - From Diagnostics to InvestmentV
• 177 Tracts in Chicago• 20% of Total
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
Moving Down the Taxonomy: From “Phylum” to “Species”
Specialized Tools - From Diagnostics to InvestmentV
PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
Applying the Taxonomy...
Specialized Tools - From Diagnostics to InvestmentV
Applying the Taxonomy...
Specialized Tools - From Diagnostics to InvestmentV
Tract 680800Tract 680800Type 3-B, Type 3-B, “Vacancies “Vacancies and Social and Social Capital”Capital”
Change in Value 6%6% 125%125%Median Income $26,319 $21,600Vacant Units 18.5% 14.9%Social Capital 1.5 4.2Unemployment Rate 46.5% 19.3%Turnover (% Moved in Last Five Years) 47.4% 37.4%Educational Attainment – More than High School
25% 29.8%
Comparing Within Type:
Applying the Taxonomy...
Specialized Tools - From Diagnostics to InvestmentV
Tract 680800Tract 680800Type 3-B, Type 3-B, “Vacancies “Vacancies and Social and Social Capital”Capital”
Change in Value 6%6% 125%125%Median Income $26,319 $21,600Vacant Units 18.5% 14.9%Social Capital 1.5 4.2Unemployment Rate 46.5% 19.3%Turnover (% Moved in Last Five Years) 47.4% 37.4%Educational Attainment – More than High School
25% 29.8%
Comparing Within Type:
Identify Factors Affecting
Neighborhood Performance Compared to
Peers
Applying the Taxonomy...
Specialized Tools - From Diagnostics to InvestmentV
Type 6-CType 6-CNew New DevelopmentDevelopment
Cluster 8Cluster 8Young Young ProfessionaProfessionalsls
Cluster 7Cluster 7HomeowneHomeownersrs
Age 19-34 35% 49% 24%Stability (Less than 5) 69% 71% 39%Single Family Housing 27% 11% 71%Commercial Land Use 8.4% 7.7% 3.9%School Quality (reading test scores)
76 55 60
Homeownership 33% 34% 69%
Comparing Across Types:
Applying the Taxonomy...
Specialized Tools - From Diagnostics to InvestmentV
Type 6-CType 6-CNew New DevelopmentDevelopment
Cluster 8Cluster 8Young Young ProfessionaProfessionalsls
Cluster 7Cluster 7HomeowneHomeownersrs
Age 19-34 35% 49% 24%Stability (Less than 5) 69% 71% 39%Single Family Housing 27% 11% 71%Commercial Land Use 8.4% 7.7% 3.9%School Quality (reading test scores)
76 55 60
Homeownership 33% 34% 69%
Comparing Across Types:
What Would it Take to Become a Different Type of Neighborhood?
Housing Diversity Metric
What It Does: Tracks the affordability and mix of the
housing stock (distribution, not just median) Applications: Enables tracking the range of housing
available in the neighborhood Better indicator of possible displacement
than median prices alone
Specialized Tools - From Diagnostics to InvestmentV
Example: Tracking the Price Mix
Strong Overall Appreciation, Strong Overall Appreciation, Range of Housing Options Is NarrowingRange of Housing Options Is Narrowing
Specialized Tools - From Diagnostics to InvestmentV
Example: Tracking the Price Mix
Strong Overall Appreciation, Strong Overall Appreciation, Range of Housing Options Is NarrowingRange of Housing Options Is Narrowing
Strong Overall Appreciation, butStrong Overall Appreciation, butRange of Housing Options Is Still WideRange of Housing Options Is Still Wide
Specialized Tools - From Diagnostics to InvestmentV
Percentage of Affordable Homes, 1990-2006
19901990
Specialized Tools - From Diagnostics to InvestmentV
19911991
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19921992
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19931993
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19941994
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19951995
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19961996
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19971997
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19981998
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
19991999
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
20002000
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
20012001
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
20022002
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
20032003
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
20042004
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
20052005
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
20062006
Percentage of Affordable Homes, 1990-2006
Specialized Tools - From Diagnostics to InvestmentV
Sample “Affordability Reports”
Specialized Tools - From Diagnostics to InvestmentV
Pattern Search
What It Does: For identified patterns of change, finds other
neighborhoods that have been through or are going through the same pattern
Applications: Enables identifying comparable neighborhoods
with respect to particular patterns of change in order to identify key factors and effects
Enables anticipating and managing particular patterns of change
Specialized Tools - From Diagnostics to InvestmentV
Pattern Search Example: Gentrification in Chicago Goal: Anticipating Neighborhood Change How it Works: Define a Pattern and Find Matching
Cases Example: Possible Gentrification Pattern Defined
Based on a Neighborhood in Chicago
Specialized Tools - From Diagnostics to InvestmentV
Zooming In: Wicker Park Area
Specialized Tools - From Diagnostics to InvestmentV
Zooming In: Wicker Park Area
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Pattern “Spreading” to Nearby Tracts
Specialized Tools - From Diagnostics to InvestmentV
Possible Application: Anticipating and Managing Gentrification
Different Appreciation Patterns Found in the DNT RSI
Specialized Tools - From Diagnostics to InvestmentV
Possible Application: Anticipating and Managing Gentrification
Different Appreciation Patterns Found in the DNT RSI
Specialized Tools - From Diagnostics to InvestmentV
Possible Application: Anticipating and Managing Gentrification
Different Appreciation Patterns Found in the DNT RSI
Specialized Tools - From Diagnostics to InvestmentV
Possible Application: Anticipating and Managing Gentrification
Different Appreciation Patterns Found in the DNT RSI
Specialized Tools - From Diagnostics to InvestmentV
“LWR+” (Locally Weighted Regression +)
What It Does: Much more granular examination of
neighborhood dynamics Showing what areas share common trends;
identify “true” neighborhood boundaries
Applications: Better tailor interventions to areas
undergoing different kinds of change Assess appropriate geographic scope of
interventionsSpecialized Tools - From Diagnostics to InvestmentV
Example: Logan Square
“Standard” Look at a Community Area:
Overall Increase in Median Home Values
Specialized Tools - From Diagnostics to InvestmentV
“Dissecting” Community Trends: Using LWR to Uncover Local Dynamics
Rapid change in Southeast section, which went from cheapest to most expensive area
in the neighborhood
Specialized Tools - From Diagnostics to InvestmentV
One Neighborhood... Or Three?
Specialized Tools - From Diagnostics to InvestmentV
One Neighborhood... Or Three?
Specialized Tools - From Diagnostics to InvestmentV
One Neighborhood... Or Three?
Specialized Tools - From Diagnostics to InvestmentV
One Neighborhood... Or Three?
Distinct Patterns of Change in Different Parts of the Neighborhood
Specialized Tools - From Diagnostics to InvestmentV
Filling In the Picture: Real Estate Dynamics
Higher Levels of Rehab and New Construction in Rapidly Appreciating
SectionSpecialized Tools - From Diagnostics to InvestmentV
Filling In the Picture: Demographic Shifts
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
Overall Northwest Central Southeast
Percent Change in Hispanic Population, 1990-2000
0%
20%
40%
60%
80%
100%
120%
140%
Overall Northwest Central Southeast
Percent Change in Household Income, 1990-2000
Higher income, White households moving into Southeast area possibly pushing original Hispanic
population Northwest Specialized Tools - From Diagnostics to InvestmentV
Filling In the Picture: Business Patterns
Specialized Tools - From Diagnostics to InvestmentV
Filling In the Picture: Business Patterns
Specialized Tools - From Diagnostics to InvestmentV
Filling In the Picture: Business Patterns
Specialized Tools - From Diagnostics to InvestmentV
Filling In the Picture: Business Patterns
Specialized Tools - From Diagnostics to InvestmentV
Filling In the Picture: Business Patterns
Number of bars and restaurants doubled in Southeast section, while it remained the same in
the rest of the neighborhood
Specialized Tools - From Diagnostics to InvestmentV
Summary: Applying the Findings and Tools
Quick Case Study – using the tools for neighborhood development stategies
Other Applications – using the tools to target interventions
Specialized Tools - From Diagnostics to InvestmentV
Applying Metrics, Findings and Tools: Auburn Gresham
Specialized Tools - From Diagnostics to InvestmentV
Step One: Figure Out Who You Are (Challenges and Opportunities)
Type 3-A, “Single Parents:” Low income, single family homes, stable resident base. More children and single parent households.
Underperforming relative to peers: 61% growth in RSI compared to 129% for peer group
“Red Flags”:– Population Loss: 6% decline between 1990 and 2000– Business Attrition: Lost over 50% of its retail and service
establishments between 1990 and 2006– Financial Distress: high percentage of credit past due, high
foreclosure rates Positive Signs:
– Decline in Unemployment: from 20% to 14% between 1990 and 2000
– Low Crime: crime rates are half of peer group’sSpecialized Tools - From Diagnostics to InvestmentV
Step Two: What to Expect and Where You Can Go What to Expect Based on Model Findings:
– Unlikely to Converge: Targeted Development Interventions Needed to Spearhead Change
– High Foreclosure and Sub-Prime Lending Rates, on the Rise Through 2005: Negative Effects Should “Bottom Out” around 2009-2010
– (Apply Pattern Search Tool: Identify Other Neighborhoods with Similar Patterns of Change, see what happened next.)
Where You Can Go (Based on Typology): Type 3 neighborhoods tend to remain the same type, but can transition to Type 7, “Homeowners” (stable neighborhood, similar housing stock, but higher income).
What to Aim For: Housing Stock and Demographics Make This Neighborhood a Good Candidate for Improvement in Place (Which Would Help Transition to Type 7)Specialized Tools - From Diagnostics to InvestmentV
Step Three: How Can You Get There? Key Areas of Focus (Based on Model Findings):
– Homeownership– Build on Positive Employment Trends– Improve Connection to Jobs– Address Population Loss and Vacancies (possibly related to foreclosure issues) – (Social Capital and Retail)
Possible Interventions: – Perfect Location for Center for Working Families?
Possible Additional Steps:– Apply Model Findings to Assess Cost-Benefit of Alternative Interventions– Apply Impact Analyst to Evaluate Sites and Programs– Apply LWR to identify which neighboring communities you particularly need to work with
Specialized Tools - From Diagnostics to InvestmentV
Other Applications: Using CART to Improve Child Care Interventions
Outcome:Outcome: • Number of Child Care Number of Child Care
Slots per ChildSlots per Child
Drivers:Drivers:• Distance to Downtown (+)Distance to Downtown (+)• Percent of Foreign Born and Percent of Foreign Born and
Hispanics (-)Hispanics (-)• Educational Attainment (+)Educational Attainment (+)• Home Ownership (-)Home Ownership (-)
Segmentation Based on Child Care Capacity
0.49
0.27
0.25
0.19
0.18
0.11
Segment Child Care Slots per Child
Specialized Tools - From Diagnostics to InvestmentV
Other Applications: Selecting Target Areas
Specialized Tools - From Diagnostics to InvestmentV
Convergence Model Results Can be Used to Identify Areas
that Are in Greater Need of Interventions
Other Applications: Using Affordability Reports to Prioritize Housing Work
Specialized Tools - From Diagnostics to InvestmentV
0
-94
Other Applications: Using Affordability Reports to Prioritize Housing Work
Specialized Tools - From Diagnostics to InvestmentV
0
-94
0%
-100%
Agenda
Background: The DNT Project
The Nature of Neighborhood ChangeDigging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of ChangeImplications 1.0: Dynamic, Specialized Neighborhoods
I
Implications 2.0: Specialized Tools - From Diagnostics to Investment
II
III
IV
V
Discussion: What Next? VI
Agenda
What Next?VI
Open Source: Multiple Parties Are Already Interested in Moving the Work Forward LISC – Analysis of Impact of LIHTC Projects UMI/NNIP – Embed Tools in Existing Web Platforms Preservation Compact – Affordability Reports and Real Estate
Metrics Chicago Department of Children and Youth Services – Targeting
Location of Youth Programming Metropolitan Mayor’s Caucus/Chicago Metropolis 2020 –
Affordability Reports, Real Estate Metrics, Patterns of Change Analysis
MCIC – RSI and Other Housing Values Indicators MDRC – Analysis of Impact of New Communities Program Case Western – Analysis of Mixed-Income Communities Ansonia Properties, LLC and CityView – Apply Tools to Guide
Investment in Workforce HousingWhat Next?VI
Building on the DNT Foundation:Possible Next Steps
Make the tools available to practitioners and investors (e.g. by embedding them in existing web-based data platforms)
Apply the work to particular neighborhoods and interventions (and improve the analysis and tools based on repeated application)
Maintain and update core metrics (particularly RSI) going forward
Extend the capacity to rest of the region and other cities: expand data, metrics, models and tools
Create a “learning network”?
What Next?VI
Building on the DNT Foundation
Additional Possible Directions of Further Work: Build on Typology Results to Identify Drivers by Type Analyze the Impact of Specific Development Interventions
(from employment centers to safety initiatives to foreclosure prevention and remediation)
Develop a Gentrification Early Warning System Identify What Amenities Attract Different Demographics Identify Opportunities for Workforce Housing Business Evolution: What are the Stages of Business
Development in Neighborhoods? ...
What Next?VI
Discussion
General Comments and Questions What are You Trying to Better Understand About
Neighborhoods? What Impacts are You Trying to Achieve and
Measure? What Tools and Applications Would Be Most Useful? Partners: Corollary Research, Tool Development
and Testing, Other?
DiscussionVI
Core Project Team
Christopher Berry, The University of Chicago
Graeme Blair, Econsult Corporation Riccardo Bodini, RW Ventures, LLCMichael He, RW Ventures, LLCRichard Voith, Econsult CorporationRobert Weissbourd, RW Ventures, LLC
I Background: The DNT Project
Dynamic Neighborhood Taxonomy
A Project of
LIVING CITIESBy
RW Ventures, LLC
MacArthur FoundationJune 2, 2008