Legacy City Revitalization: The Role of Federal Historic Tax Credit Projects

Transcription

1 Legacy City Revitalization: The Role of Federal Historic Tax Credit Projects Kelly L. Kinahan, AICP Doctoral Candidate Levin College Research Conference August 20, 2015

2 Research Questions (1) What is the distribution of activity across legacy city neighborhood types? (2) What is the relationship between historic tax credit activity and changes neighborhood racial, socioeconomic, and housing characteristics? 2

3 Legacy cities: shrinking, post-industrial, right-sizing, etc. Long-term population loss Economic restructuring Vacancy, abandonment, foreclosures Baltimore, Cleveland, Philadelphia, Richmond, & St. Louis Federal historic rehabilitation tax credits (s): 20% federal income tax credit National Register of Historic Places Income-producing (not owner-occupied) the largest federal program specifically supporting historic preservation (NPS, 2015, p.1) $73 billion of investment for more than 40,000 preservation projects 3

4 Literature Framework: Legacy Cities Demolition: Blight remediation and neighborhood stabilization Not a question of if, but where and to what extent Unknowns: effect on surrounding property values, balance protection of cultural and historic resources, long-term impacts Historic buildings: Core assets for neighborhood revitalization and stabilization Preservation is largely absent from both broader policy discussions and the implemented approaches Recent revitalization trends, particularly downtowns Unknowns: how is preservation activity contributing to revitalization? 4

5 Literature Framework: Urban Preservation Analysis of program effects: standard economic impact analyses at state and federal levels Historic preservation and gentrification preservation efforts are more prone to cause displacement than redevelopment projects involving new construction because property values and rents begin to increase even before the real estate experiences much improvement (Werwath, 1998, p. 489) Limited empirical evidence All types of neighborhood investment? Weak market context of legacy cities? 5

6 Data Historic Tax Credit data: Technical Preservation Services Division of the National Parks Service Federal projects from Geolytics Neighborhood Change Database: Normalized census tract data to 2010 boundaries Pre-intervention: Census 2000 Post-intervention: American Community Survey (5- year estimates) 6

7 Dependent Variables Expected Sign References Race/Ethnicity Share of Non-Hispanic White +/- Bures, (2001); Deng, (2012); Mallach (2015); Podagrosi & Vojnovic, (2008); Swanstrom & Webber, (2014) Share of Non-Hispanic Black +/- Bures, (2001); Hollander (2010); Mallach (2015); Podagrosi & Vojnovic, (2008); Swanstrom & Webber, (2014) Share of Hispanics +/- Socio-economic Bachelor's Degree or greater + Professional/Technical workers + Median Household Income + Share of low-, middle-, and upper income persons +/- Poverty Rate - Households + Housing Median Housing Value + Median Rent + Bures, (2001); Swanstrom & Webber, (2014) Allison, (2005); Coulson & Leichenko, (2004); Deng, (2012); Mallach (2015); Montgomery, (2004) Allison, (2005); Blakely, (2001); Coulson & Leichenko, (2004); Filion, (2010); Florida, (2002); Swanstrom & Webber, (2014) Allison, (2005); Coulson & Leichenko, (2004); Smith, (1998); Mallach (2015); Swanstrom & Webber, (2014); Werwath, (1998) Allison, (2005); Coulson & Leichenko, (2004); Smith, (1998); Mallach (2015); Swanstrom & Webber, (2014); Werwath, (1998) Allison, (2005); Coulson & Leichenko, (2004); Deng (2012); Hollander (2010); Smith, (1998); Swanstrom & Webber, (2014); Werwath, (1998) Birch, (2005); NPS, (2014); Stern & Seifert, (2010); Ryberg-Webster, (2014a, 2014b) Allison, (2005); Bures, (2001); Coulson & Leichenko, (2004); Deng (2012); Smith, (1998); Swanstrom & Webber, (2014); Werwath, (1998) Allison, (2005); Bures, (2001); Coulson & Leichenko, (2004); Deng (2012); Smith, (1998); Swanstrom & Webber, (2014); Werwath, (1998) 7

8 Methods (1) what is the distribution of activity across legacy city neighborhood types and transition patterns? Descriptive statistics, maps 8

9 Neigbborhood Type Black, Stressed, & Disadvantaged Collapsed Urban Core Competitive & Educated some Distress Declining & Black Educated Newcomers Established & Stable Homeowners Highly Bifurcated: Success & Distress White Immigrants Neigbborhood Category Highly Distressed Highly Distressed Stable Highly Distressed Stable Stable Stable Highly Distressed Description low-value housing;low-income families with little educational attainment; residents are primarily black, unemployed, and living below the poverty line long-term renters, high rates of poverty and public assistance among renters; high vacancy rates, weak housing values, and low educational attainment; large share of the population is under 18, black, and unemployed; higher-than average rents; some white residents high-value housing, well-educated singles, and higher-than-average income; white residents, rents higher than city and MSA averages; high rates of poverty and public assistance among renters weak housing values, paired with low educational attainment, high rates of public assistance, among low-income black families high housing values with well-educated, highincome singles high levels of homeownership, low levels of poverty and public assistance, people that have lived in their homes and the neighborhood for an extended period of time, low vacancy, and little multifamily housing high rates of poverty and public assistance, transient renters; high-value housing occupied by well-educated singles; lowincome renters very old housing stock; primarily white residents; some foreign born and Hispanic residents Share of Distribution 19% 7% 8% 19% 13% 16% 7% 11% Highest Neighborhood Concentration Ratio City Census Year STL (1.20) BAL (1.24) PHI (1.32) RVA (1.19) RVA (1.49) STL (1.30) RVA (2.11) PHI (1.28) 2010 (1.21) 1990 (1.10) 2010 (1.11) 2000 (1.21) 1970 (1.30) 1970 (1.14) 2000 (1.21) 1970 (1.20) Gaining/Losing Share, * +7.7% -0.1% +1.4% +4.1% -8.1% -2.9% +2.1% *Based on the share of all 1970 neighborhoods in the neighborhood type compared to the share of 2010 neighborhood in the type. -4.2% Most Common Transition Pattern Collapsed Urban Core; Declining & Black (all years) Black, Stressed & Disadvantaged Bifurcated: Success & Distress; Educated Newcomers (all years) Black, Stressed, & Disadvantaged Established & Stable Homeowners ( ); Declining & Black ( ) Educated Newcomers( ); White Immigrant ( ) Competitive & Educated some Distress Black, Stressed & Disadvantaged 9

10 Highly Distressed Black, Distressed, & Disadvantaged Collapsed Urban Core Declining and Black White Immigrants Subtotal Stable Competitive & Educated, some Distress Educated Newcomers Established and Stable Homeowners Highly Bifurcated Subtotal Total with Activity Total Total Projects Total Investment Baltimore Projects Investment 28% 29% 17% $ 50,306,545 8% 5% 2% $ 13,313,764 18% 7% 3% $ 32,476,179 5% 0% 0% $ - 59% 41% 22% 15% 11% 37% 43% $ 305,166,688 10% 2% 1% $ 2,636,555 14% 2% 1% $ 6,483,810 6% 17% 34% $ 210,387,420 41% 59% 78% 85% 21% $620,770,963 Cleveland Projects Investment Philadelphia Projects Investment 27% 32% 10% $ 55,023,634 18% 8% 2% $ 13,028,492 8% 12% 6% $ 20,383,484 5% 2% 2% $ 882,086 19% 16% 4% $ 6,599,639 23% 22% 16% $ 112,600,212 10% 4% 1% $ 34,201 13% 0% 0% $ - 64% 64% 21% 13% 59% 31% 20% 10% 5% 12% 48% $ 480,377,334 13% 43% 64% $ 677,656,517 5% 0% 0% $ - 9% 8% 3% $ 35,045,262 18% 0% 0% $ - 13% 2% 1% $ 6,688,452 9% 24% 31% $ 63,162,515 6% 16% 13% $ 453,973,524 36% 36% 79% 87% 41% 69% 80% 90% 15% 14% $625,580,807 $1,299,874,545 Highly Distressed Black, Distressed, & Disadvantaged Collapsed Urban Core Declining and Black White Immigrants Subtotal Stable Competitive & Educated, some Distress Educated Newcomers Established and Stable Homeowners Highly Bifurcated Subtotal Total with Activity Total Total Projects Total Investment Richmond Projects Investment St. Louis Projects Investment 27% 36% 38% $ 179,310,603 26% 15% 3% $ 20,650,408 3% 4% 0% $ 4,196,399 9% 15% 23% $ 148,329,832 21% 11% 1% $ 4,778,212 17% 12% 18% $ 26,205,470 2% 0% 0% $ - 7% 9% 4% $ 11,902,295 53% 50% 39% 30% 59% 50% 48% 16% 0% 0% 0% $ - 8% 24% 26% $ 782,145,803 14% 14% 5% $ 40,854,688 5% 0% 0% $ - 15% 0% 0% $ - 18% 3% 0% $ 391,795 18% 36% 56% $ 395,821,709 9% 24% 26% $ 316,499,079 47% 50% 61% 70% 41% 50% 52% 84% 42% 32% $624,961,613 $1,306,124,683 10

11 Methods (1) what is the distribution of activity across legacy city neighborhood types and transition patterns? Descriptive statistics, maps (2) what is the relationship between historic tax credit activity and neighborhood racial, socioeconomic, and housing characteristics? Difference-in-difference regression model understand whether rehabilitation activity accelerated neighborhood changes or if they continued at the same rate baseline differences between and non- tracts are accounted for 12

12 Non- Non Non-Hispanic Whites 43.4% 37.8% 44.1% 33.3% Non-Hispanic Blacks 46.1% 52.2% 42.2% 54.0% Hispanic 4.3% 5.6% 5.9% 7.6% Households 1,341 1, Bachelor's Degree or Greater 27.0% 15.9% 36.9% 19.2% Professional/Technical Workers 24.2% 17.8% 28.0% 19.0% Poverty 28.4% 23.8% 28.7% 25.7% Median Household Income $36,009 $39,507 $38,319 $36,322 Very low-income (30% of city MHI or less) 23.5% 19.4% 17.9% 15.8% Low-income (31-50% of city MHI) 9.2% 8.8% 12.6% 13.9% Moderate income (51-80% of city MHI) 12.9% 13.7% 11.0% 12.4% Middle income (81-120% of city MHI) 13.9% 14.5% 15.2% 16.4% Upper income (120% of city MHI or greater) 40.8% 43.6% 43.3% 41.2% Share of Housing Units 50 years or older 63.8% 57.6% 73.5% 74.5% Median Rent $680 $656 $847 $774 Median Housing Value $121,406 $88,471 $211,250 $137,811 13

13 Statistical Model Yit=α+β1(i)+β2(Postt)+β3( Post)it+ β4(city)+ϵit where: α = intercept β = coefficient = dummy variable for neighborhoods with historic tax credit investment between 1998 and 2007 Post = dummy variable for the post treatment period of 2010 City= Citywide fixed effects Y = Value (in log form) for revitalization indicators in census tract i in year t ϵ = a random error term with the usual assumed statistical properties 14

14 Treatment Group Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 All tracts Above median tracts Below median tracts Stable (2000) tracts All tracts Above median tracts n Comparison Group All non- tracts All non- tracts and below median tracts All non- tracts and above median tracts All non- tracts and all non-stable tracts Matched comparison tracts Matched comparison tracts and below median tracts n Total observations

15 Dependent Variables Model 1 Model 2 Model 3 Model 4 Model 5 Full Model Above Median Below Median Stable (2000) and Matched Comp. Model Model 6 Above Median and Matched Comparison Group Race/Ethnicity Percent Hispanic * Percent Non-Hispanic Black ** * * Percent Non-Hispanic White Socio-economic Income Groups Very-low income Low-income ** *** Moderate-income * * ** Middle-income ** * ** Upper-income * * Median Houshold Income ** ** *** Percent Bachelor's or more Percent Professional/Technical workers * Poverty Rate Housing Households ** ** Median Housing Value *** ** * *** Median Rent Observations All Dependent variables are in natural log form. ***p<0.01, **p<0.05, *p<0.1 20

16 Key Findings & Policy Implications incentivizes investment in weak market conditions projects are contributing to important LC neighborhood revitalization goals- attracting more upper-income earners and professional/technical workers as well as increased median household income and median housing values Negative change effects- loss of non-hispanic black and lowand moderate-income residents. Most evident in Stable neighborhoods These differences vary, however, based on the scale of investment, the status of the neighborhood prior to activity, and the composition group of comparison tracts 21

17 Key Findings & Policy Implications Legacy city policy: Including s in strategic targeting frameworks for neighborhood stabilization Federal/state policy: Requiring greater coordination with local planning efforts; training for real estate developers on program use Evidence that activity does play a significant role in the complex neighborhood change processes of legacy cities 22

18 Legacy City Revitalization: The Role of Federal Historic Tax Credit Projects Kelly L. Kinahan, AICP Doctoral Candidate Levin College Research Conference August 20, 2015