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Dynamic Neighborhoods: New Tools for Community and Economic Development

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Chicago TREND (Transforming Retail Economics of Neighborhood Development) combines innovative predictive analytics, deal brokering and financial products to support "retail on the leading edge" of emerging neighborhood markets. The new initiative - including partnerships with ICSC, Nielsen, Econsult Solutions and leading retailers and developers - aims to enable retailers, developers, investors and neighborhoods to better target particular types of retail to specific changing neighborhoods offering retail opportunity that will help drive the neighborhood change. The initiative is led by Lyneir Richardson. To discuss potential retail development and partnership opportunities, please contact him at lyneir@rw-ventures.com.

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Dynamic Neighborhoods: New Tools for Community and Economic Development

  1. 1. Dynamic Neighborhood Taxonomy A Project of LIVING CITIES By RW Ventures, LLC MacArthur Foundation June 2, 2008
  2. 2. Agenda Background: The DNT Project The Nature of Neighborhood Change Digging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of Change Implications 1.0: Dynamic, Specialized Neighborhoods I Implications 2.0: Specialized Tools - From Diagnostics to Investment II III IV V Discussion: What Next?VI
  3. 3. Agenda I Background: The DNT Project
  4. 4. 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 Foundation Robert 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:
  5. 5. 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
  6. 6. We Know Where We Want to Go... Common Goal: I Background: The DNT Project Building Healthier Communities
  7. 7. 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
  8. 8. Comprehensive Neighborhood Taxonomy Business People Real Estate Amenities Social Environment  Improvement or deterioration within type  Gradual vs. Tipping point  From one type to another  Port of entry  Bohemian  Retirement  Urban commercialized  Employment  Education  Crime  Housing stock  Investment activity I Background: The DNT Project
  9. 9. Agenda The Nature of Neighborhood ChangeII
  10. 10. 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
  11. 11. Agenda IIa Measuring Change: the RSI
  12. 12. 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
  13. 13. 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 for Supply Effects Amenities Structure Rent Housing Price IIa Measuring Change: the RSI
  14. 14. 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
  15. 15. 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 nb p01 p01i 0.96 p01s 0.82 0.89 p01c 0.53 0.58 0.82 p05 0.94 0.96 0.79 0.49 p05i 0.94 0.97 0.82 0.52 0.99 p05s 0.83 0.90 0.99 0.77 0.84 0.87 p05c 0.53 0.59 0.82 1.00 0.50 0.53 0.79 p10 0.92 0.94 0.76 0.47 0.99 0.99 0.82 0.49 p10i 0.92 0.95 0.79 0.50 0.98 0.99 0.85 0.51 0.99 p10s 0.84 0.90 0.97 0.75 0.85 0.88 1.00 0.77 0.83 0.86 p10c 0.53 0.59 0.82 0.99 0.51 0.54 0.79 1.00 0.49 0.51 0.77 nb 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
  16. 16. Final Product: The DNT RSI RSI 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
  17. 17. Looking at Particular Tracts: Appreciation in Greater Grand Crossing IIa Measuring Change: the RSI
  18. 18. 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
  19. 19. Chicago Neighborhood Change, 1990-2006 1990 IIb Overall Patterns
  20. 20. Chicago Neighborhood Change, 1990-2006 1991 IIb Overall Patterns
  21. 21. Chicago Neighborhood Change, 1990-2006 1992 IIb Overall Patterns
  22. 22. Chicago Neighborhood Change, 1990-2006 1993 IIb Overall Patterns
  23. 23. Chicago Neighborhood Change, 1990-2006 1994 IIb Overall Patterns
  24. 24. Chicago Neighborhood Change, 1990-2006 1995 IIb Overall Patterns
  25. 25. Chicago Neighborhood Change, 1990-2006 1996 IIb Overall Patterns
  26. 26. Chicago Neighborhood Change, 1990-2006 1997 IIb Overall Patterns
  27. 27. Chicago Neighborhood Change, 1990-2006 1998 IIb Overall Patterns
  28. 28. Chicago Neighborhood Change, 1990-2006 1999 IIb Overall Patterns
  29. 29. Chicago Neighborhood Change, 1990-2006 2000 IIb Overall Patterns
  30. 30. Chicago Neighborhood Change, 1990-2006 2001 IIb Overall Patterns
  31. 31. Chicago Neighborhood Change, 1990-2006 2002 IIb Overall Patterns
  32. 32. Chicago Neighborhood Change, 1990-2006 2003 IIb Overall Patterns
  33. 33. Chicago Neighborhood Change, 1990-2006 2004 IIb Overall Patterns
  34. 34. Chicago Neighborhood Change, 1990-2006 2005 IIb Overall Patterns
  35. 35. Chicago Neighborhood Change, 1990-2006 2006 IIb Overall Patterns
  36. 36. Initial Conditions and Appreciation APPRECIATION 14.9 -0.17 MEDIAN HOUSING PRICES 1990 Up 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
  37. 37. 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.
  38. 38. Partly Due to Lack of Information, These Areas Are Also the Most Volatile TEMPORAL VOLATILITY OF INDEX 0.14 - 0.93 0.94 - 1.33 1.34 - 2.54 2.55 and above APPRECIATION 14.9 -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
  39. 39. 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
  40. 40. 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.
  41. 41. IId Degree and Pace of Change
  42. 42. Neighborhood Change Is a Slow Process Neighborhood Mobility by Time Interval 5 Years 10 Years 15 Years No Change 1 Quintile 2 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.
  43. 43. Yet Substantial Change Occurs in Select Neighborhoods Median Sales Price Transition Matrix Cleveland, 1990-2004 Final Quintile Initial 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.
  44. 44. IIe Neighborhoods and Regions
  45. 45. Neighborhoods Tend to Move with their Regions IIe Neighborhoods and Regions Most neighborhoods follow their region closely, but there are important exceptions.
  46. 46. Regional Effects Vary by Area Across Cities, 35% of Neighborhood Change is Accounted for by Regional Shifts R Squared from Regression Models of Tract RSI on Region IIe Neighborhoods and Regions Cleveland Chicago Dallas Seattle
  47. 47. Regional Effects Vary by Area Across Cities, 35% of Neighborhood Change 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
  48. 48. Drivers of Change: The Big Picture
  49. 49. 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
  50. 50. 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 city grew faster than tracts in nearby suburbs.
  51. 51. 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.
  52. 52. Exploring the Relative Importance of Different Drivers of Change (1994-2004 Random Effects Model, Standardized Coefficients with 95% Confidence Interval) IIe Drivers of Change
  53. 53. 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.”
  54. 54. 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
  55. 55. 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 Neighborhood FEBRUARY 11, 2008 | ISSUE 44•07 The “Little” Picture: Few Silver Bullets IIe Drivers of Change
  56. 56. 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
  57. 57. Agenda Background: The DNT Project The Nature of Neighborhood Change Digging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of Change Implications 1.0: Dynamic, Specialized Neighborhoods I Implications 2.0: Specialized Tools - From Diagnostics to Investment II III IV V Discussion: What Next?VI
  58. 58. Agenda Specialized DriversIII
  59. 59. 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 EffectonDNTRSI,PartialResiduals (GAM Output based on 1994-2004 Fixed Effects Model, All Cities)
  60. 60. 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 RegressionCoefficient,DNTRepeatSalesIndex [Overall Model Result: Positive and Significant] (1994-2004RandomEffectsModel,AllCities) 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.
  61. 61. 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
  62. 62. Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
  63. 63. Neighborhood Convergence Beta Convergence in Cook County, 1990 - 2005 Specialized Drivers: Looking at Particular Neighborhood SegmentsIII Neighborhoods Now Tend to Naturally Catch Up to Each Other
  64. 64. But not All Neighborhoods Follow this Trend... Neighborhood Convergence Beta Convergence in Cook County, 1990 - 2005 Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
  65. 65. 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
  66. 66. Improvement With High and Low Turnover Specialized Drivers: Looking at Particular Neighborhood SegmentsIII
  67. 67. Improvement With High and Low Turnover Specialized Drivers: Looking at Particular Neighborhood SegmentsIII What characterizes the neighborhoods that improved with less displacement?
  68. 68. 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
  69. 69. 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
  70. 70. 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 matter to them
  71. 71. 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 customized analysis of local economies
  72. 72. Agenda Background: The DNT Project The Nature of Neighborhood Change Digging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of Change Implications 1.0: Dynamic, Specialized Neighborhoods I Implications 2.0: Specialized Tools - From Diagnostics to Investment II III IV V Discussion: What Next?VI
  73. 73. Agenda Dynamic, Specialized Neighborhoods (with Special Thanks to Andy Mooney!)IV
  74. 74. Neighborhoods are Complex Dynamic, Specialized Neighborhoods (with Special Thanks to Andy Mooney!)IV
  75. 75. Neighborhoods are Complex Dynamic, Specialized NeighborhoodsIV
  76. 76. Neighborhoods are Dynamic Dynamic, Specialized NeighborhoodsIV
  77. 77. Neighborhoods are Dynamic Dynamic, Specialized NeighborhoodsIV
  78. 78. Neighborhoods are Nested in Larger Systems Which Drive the Flows of People and Capital Dynamic, Specialized NeighborhoodsIV
  79. 79. Neighborhoods are Nested in Larger Systems which drive the Flow of Capital and People Dynamic, Specialized NeighborhoodsIV
  80. 80. Functioning Neighborhoods Connect Residents and Assets to Larger Systems Poverty Productivity Connectedness Isolation Dynamic, Specialized NeighborhoodsIV
  81. 81. Functioning Neighborhoods Connect Residents and Assets to Larger Systems  Employment networks  Entrepreneurial opportunities  Business, real estate investment  Expanded products and services  Productive, healthy communities  Undervalued, underutilized assets Poverty Productivity Connectedness Isolation Dynamic, Specialized NeighborhoodsIV
  82. 82. Applying the Framework STEP 1A: What type of neighborhood do you want to be? Dynamic, Specialized NeighborhoodsIV
  83. 83. Applying the Framework STEP 1A: What type of neighborhood do you want to be? Starter Home Community Dynamic, Specialized NeighborhoodsIV
  84. 84. Applying the Framework STEP 1A: What type of neighborhood do you want to be? STEP 1B: What drivers will get you there? Starter Home Community Dynamic, Specialized NeighborhoodsIV
  85. 85. Applying the Framework STEP 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
  86. 86. Applying the Framework STEP 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
  87. 87. ECONOMIC SYSTEM Applying the Framework STEP 2: Identify Relevant System Retail Markets Starter Home Community Dynamic, Specialized NeighborhoodsIV
  88. 88. Applying the Framework STEP 3: Identify Change Levers Within System Starter Home Community ECONOMIC SYSTEM Retail Markets Dynamic, Specialized NeighborhoodsIV
  89. 89. Applying the Framework STEP 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
  90. 90. Applying the Framework STEP 4: Specify Interventions Starter Home Community Commercial Land Assembly (production – costs) Specialized Market Data (exchange – finding costs) ECONOMIC SYSTEM Retail Markets Dynamic, Specialized NeighborhoodsIV
  91. 91. Agenda Background: The DNT Project The Nature of Neighborhood Change Digging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of Change Implications 1.0: Dynamic, Specialized Neighborhoods I Implications 2.0: Specialized Tools - From Diagnostics to Investment II III IV V Discussion: What Next?VI
  92. 92. Developing New Tools for the Field Question/Goal Tool How 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
  93. 93. Developing New Tools for the Field Question/Goal Tool How 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
  94. 94. 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 intervention Specialized Tools - From Diagnostics to InvestmentV
  95. 95. 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. Homes within 1000 ft of an LIHTC site appreciate at a 4% higher rate than homes between 1000 ft and 2000 ft.
  96. 96. 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) DNTRepeatSalesIndex,1=FurthestAwayDNTRepeatSalesIndex,1=FurthestAway Specialized Tools - From Diagnostics to InvestmentV
  97. 97. 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 increased property values, or an average of $1,300 per home owner.
  98. 98. Classification and Regression Tree (CART) What It Does:  Identify similar neighborhoods with respect to an outcome of interest and its drivers Applications:  Identify leverage points to affect the desired outcome  Meaningful comparison of trends and best practices across neighborhoods Specialized Tools - From Diagnostics to InvestmentV
  99. 99. Sample CART: Foreclosures 40 Variables Tested Specialized Tools - From Diagnostics to InvestmentV What Neighborhoods Have Similar Numbers of Foreclosures...
  100. 100. Sample CART: Foreclosures 40 Variables Tested Outcome: 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
  101. 101. CART Output: Chicago Segments Specialized Tools - From Diagnostics to InvestmentV
  102. 102. 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 of foreclosures) is at its highest and still on the rise Specialized Tools - From Diagnostics to InvestmentV Percentage of Sub-prime Loans by Year
  103. 103. 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
  104. 104. 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
  105. 105. 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
  106. 106. 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”Type 7: “Homeowners”Type 7: “Homeowners” PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
  107. 107. 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” Type 8:Type 8: ““Young Professionals”Young Professionals” PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
  108. 108. Taxonomy Structure: “Genus, Phylum, Species” Specialized Tools - From Diagnostics to InvestmentV PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
  109. 109. Chicago Neighborhood Types Specialized Tools - From Diagnostics to InvestmentV PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
  110. 110. 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”
  111. 111. Example: Type 3, “Low Income Stable” Specialized Tools - From Diagnostics to InvestmentV • 177 Tracts in Chicago • 20% of Total PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
  112. 112. Moving Down the Taxonomy: From “Phylum” to “Species” Specialized Tools - From Diagnostics to InvestmentV PRELIMINARY – FOR ILLUSTRATION PURPOSES ONLY
  113. 113. Applying the Taxonomy... Specialized Tools - From Diagnostics to InvestmentV
  114. 114. Applying the Taxonomy... Specialized Tools - From Diagnostics to InvestmentV Tract 680800Tract 680800 Type 3-B,Type 3-B, “Vacancies and“Vacancies and Social Capital”Social Capital” Change in Value 6%6% 125%125% Median Income $26,319 $21,600 Vacant Units 18.5% 14.9% Social Capital 1.5 4.2 Unemployment 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:
  115. 115. Applying the Taxonomy... Specialized Tools - From Diagnostics to InvestmentV Tract 680800Tract 680800 Type 3-B,Type 3-B, “Vacancies and“Vacancies and Social Capital”Social Capital” Change in Value 6%6% 125%125% Median Income $26,319 $21,600 Vacant Units 18.5% 14.9% Social Capital 1.5 4.2 Unemployment 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
  116. 116. Applying the Taxonomy... Specialized Tools - From Diagnostics to InvestmentV Type 6-CType 6-C New DevelopmentNew Development Cluster 8Cluster 8 YoungYoung ProfessionalsProfessionals Cluster 7Cluster 7 HomeownersHomeowners 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:
  117. 117. Applying the Taxonomy... Specialized Tools - From Diagnostics to InvestmentV Type 6-CType 6-C New DevelopmentNew Development Cluster 8Cluster 8 YoungYoung ProfessionalsProfessionals Cluster 7Cluster 7 HomeownersHomeowners 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?
  118. 118. 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
  119. 119. 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
  120. 120. 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, but Range of Housing Options Is Still WideRange of Housing Options Is Still Wide Specialized Tools - From Diagnostics to InvestmentV
  121. 121. Percentage of Affordable Homes, 1990-2006 19901990 Specialized Tools - From Diagnostics to InvestmentV
  122. 122. 19911991 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  123. 123. 19921992 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  124. 124. 19931993 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  125. 125. 19941994 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  126. 126. 19951995 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  127. 127. 19961996 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  128. 128. 19971997 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  129. 129. 19981998 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  130. 130. 19991999 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  131. 131. 20002000 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  132. 132. 20012001 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  133. 133. 20022002 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  134. 134. 20032003 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  135. 135. 20042004 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  136. 136. 20052005 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  137. 137. 20062006 Percentage of Affordable Homes, 1990-2006 Specialized Tools - From Diagnostics to InvestmentV
  138. 138. Sample “Affordability Reports” Specialized Tools - From Diagnostics to InvestmentV
  139. 139. 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
  140. 140. 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
  141. 141. Zooming In: Wicker Park Area Specialized Tools - From Diagnostics to InvestmentV
  142. 142. Zooming In: Wicker Park Area Specialized Tools - From Diagnostics to InvestmentV
  143. 143. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  144. 144. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  145. 145. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  146. 146. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  147. 147. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  148. 148. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  149. 149. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  150. 150. Pattern “Spreading” to Nearby Tracts Specialized Tools - From Diagnostics to InvestmentV
  151. 151. Possible Application: Anticipating and Managing Gentrification Different Appreciation Patterns Found in the DNT RSI Specialized Tools - From Diagnostics to InvestmentV
  152. 152. Possible Application: Anticipating and Managing Gentrification Different Appreciation Patterns Found in the DNT RSI Specialized Tools - From Diagnostics to InvestmentV
  153. 153. Possible Application: Anticipating and Managing Gentrification Different Appreciation Patterns Found in the DNT RSI Specialized Tools - From Diagnostics to InvestmentV
  154. 154. Possible Application: Anticipating and Managing Gentrification Different Appreciation Patterns Found in the DNT RSI Specialized Tools - From Diagnostics to InvestmentV
  155. 155. “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 interventions Specialized Tools - From Diagnostics to InvestmentV
  156. 156. Example: Logan Square “Standard” Look at a Community Area: Overall Increase in Median Home Values Specialized Tools - From Diagnostics to InvestmentV
  157. 157. “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
  158. 158. One Neighborhood... Or Three? Specialized Tools - From Diagnostics to InvestmentV
  159. 159. One Neighborhood... Or Three? Specialized Tools - From Diagnostics to InvestmentV
  160. 160. One Neighborhood... Or Three? Specialized Tools - From Diagnostics to InvestmentV
  161. 161. One Neighborhood... Or Three? Distinct Patterns of Change in Different Parts of the Neighborhood Specialized Tools - From Diagnostics to InvestmentV
  162. 162. Filling In the Picture: Real Estate Dynamics Higher Levels of Rehab and New Construction in Rapidly Appreciating Section Specialized Tools - From Diagnostics to InvestmentV
  163. 163. 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
  164. 164. Filling In the Picture: Business Patterns Specialized Tools - From Diagnostics to InvestmentV
  165. 165. Filling In the Picture: Business Patterns Specialized Tools - From Diagnostics to InvestmentV
  166. 166. Filling In the Picture: Business Patterns Specialized Tools - From Diagnostics to InvestmentV
  167. 167. Filling In the Picture: Business Patterns Specialized Tools - From Diagnostics to InvestmentV
  168. 168. 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
  169. 169. 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
  170. 170. Applying Metrics, Findings and Tools: Auburn Gresham Specialized Tools - From Diagnostics to InvestmentV
  171. 171. 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’s Specialized Tools - From Diagnostics to InvestmentV
  172. 172. 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
  173. 173. 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
  174. 174. Other Applications: Using CART to Improve Child Care Interventions Outcome:Outcome: • Number of Child CareNumber of Child Care Slots per ChildSlots per Child Drivers:Drivers: • Distance to Downtown (+)Distance to Downtown (+) • Percent of Foreign Born andPercent 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
  175. 175. 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
  176. 176. Other Applications: Using Affordability Reports to Prioritize Housing Work Specialized Tools - From Diagnostics to InvestmentV 0 -94
  177. 177. Other Applications: Using Affordability Reports to Prioritize Housing Work Specialized Tools - From Diagnostics to InvestmentV 0 -94 0% -100%
  178. 178. Agenda Background: The DNT Project The Nature of Neighborhood Change Digging Deeper: Specialized Drivers By Factors, Types of Neighborhood and Patterns of Change Implications 1.0: Dynamic, Specialized Neighborhoods I Implications 2.0: Specialized Tools - From Diagnostics to Investment II III IV V Discussion: What Next?VI
  179. 179. Agenda What Next?VI
  180. 180. 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 Housing What Next?VI
  181. 181. 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
  182. 182. 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
  183. 183. 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
  184. 184. Core Project Team Christopher Berry, The University of Chicago Graeme Blair, Econsult Corporation Riccardo Bodini, RW Ventures, LLC Michael He, RW Ventures, LLC Richard Voith, Econsult Corporation Robert Weissbourd, RW Ventures, LLC I Background: The DNT Project
  185. 185. Dynamic Neighborhood Taxonomy A Project of LIVING CITIES By RW Ventures, LLC MacArthur Foundation June 2, 2008

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