dynamic neighborhoods: new tools for community and economic development

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.”


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


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:


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


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


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…


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


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

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.


Looking at Particular Tracts: Appreciation in Greater Grand Crossing


Contour Model of 2004-06 Prices around Mt. Prospect Property

Spatial Distribution of Housing Values – Mount Prospect

1450 S. Busse

Sales Transactions

Tract Boundaries


Chicago Neighborhood Change, 1990-2006

1990

IIb Overall Patterns


1991



1992



1993



1994



1995



1996



1997



1998



1999



2000



2001



2002



2003



2004



2005



2006


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


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


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


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


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


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%


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%


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


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


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


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


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)


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)


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” …


… 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


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


http://www.theonion.com/content/index/4407

The “Little” Picture: Few Silver Bullets


What matters varies a great deal by type of neighborhood.

Preliminary – For Illustration Purposes Only Preliminary – For Illustration Purposes Only

Agenda



I


II

III

IV

V


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


-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.


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 Neighborhood SegmentsIII

Neighborhood ConvergenceBeta Convergence in Cook County, 1990 - 2005


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


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


Improvement With High and Low Turnover


Improvement With High and Low Turnover


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



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.)


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



I


II

III

IV

V


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


Neighborhoods are Nested in Larger Systems Which Drive the Flows of People and Capital


Neighborhoods are Nested in Larger Systems which drive the Flow of Capital and People


Functioning Neighborhoods Connect Residents and Assets to Larger Systems

Poverty Productivity

Connectedness

Isolation


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


Applying the FrameworkSTEP 1A: What type of neighborhood do you

want to be?



want to be?

Starter Home Community



want to be?STEP 1B: What drivers will get you there?




want to be?STEP 1B: What drivers will get you there?


• Specific Retail Amenities• Child Care• Schools• Safety• Affordability


ECONOMIC SYSTEM

Applying the FrameworkSTEP 2: Identify Relevant System

Retail Markets



Applying the FrameworkSTEP 3: Identify Change Levers Within System


ECONOMIC SYSTEM

Retail Markets


Applying the FrameworkSTEP 3: Identify Change Levers Within System


Commercial Land Assembly (production –

costs)Specialized Market Data(exchange – finding costs)

ECONOMIC SYSTEM

Retail Markets


Applying the FrameworkSTEP 4: Specify Interventions


Commercial Land Assembly (production –

costs)Specialized Market Data(exchange – finding costs)

ECONOMIC SYSTEM

Retail Markets


Agenda



I


II

III

IV

V


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


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

epea

t Sal

es In

dex,

1 =

Fur

thes

t Aw

ay


Applying the Estimator to a Specific Project: New Shopping Center in Chicago

New Shopping CenterNew Shopping Center


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


Sample CART: Foreclosures

40 Variables Tested


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


... And Why

CART Output: Chicago Segments


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


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


A Preliminary Taxonomy of Neighborhoods


Key Dimensions:

•People•Income•Age•Foreign Born

•Place•Land Use•Housing Stock•Business Types

Variables




Key Dimensions:



Variables

Type 7: “Homeowners”Type 7: “Homeowners”




Key Dimensions:



Variables

Type 8: Type 8: ““Young Professionals”Young Professionals”


Taxonomy Structure: “Genus, Phylum, Species”



Chicago Neighborhood Types



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”


• 177 Tracts in Chicago• 20% of Total


Moving Down the Taxonomy: From “Phylum” to “Species”



Applying the Taxonomy...




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:



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



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:



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


Example: Tracking the Price Mix

Strong Overall Appreciation, Strong Overall Appreciation, Range of Housing Options Is NarrowingRange of Housing Options Is Narrowing


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


Percentage of Affordable Homes, 1990-2006

19901990


19911991



19921992



19931993



19941994



19951995



19961996



19971997



19981998



19991999



20002000



20012001



20022002



20032003



20042004



20052005



20062006



Sample “Affordability Reports”


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


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


Zooming In: Wicker Park Area


Pattern “Spreading” to Nearby Tracts


Possible Application: Anticipating and Managing Gentrification

Different Appreciation Patterns Found in the DNT RSI


“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


“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


One Neighborhood... Or Three?


One Neighborhood... Or Three?

Distinct Patterns of Change in Different Parts of the Neighborhood


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


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


Summary: Applying the Findings and Tools

Quick Case Study – using the tools for neighborhood development stategies

Other Applications – using the tools to target interventions


Applying Metrics, Findings and Tools: Auburn Gresham


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


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


Other Applications: Selecting Target Areas


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


0

-94

Other Applications: Using Affordability Reports to Prioritize Housing Work


0

-94

0%

-100%

Agenda



I


II

III

IV

V


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


Dynamic Neighborhood Taxonomy

A Project of

LIVING CITIESBy

RW Ventures, LLC

MacArthur FoundationJune 2, 2008

dynamic neighborhoods: new tools for community and economic development

Economy & Finance