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Home My WebLink About20250533 26 Finley St Site Plan Neighborhood Impacts from Affordable Housing - Large City vs Small City 0925202520250913, Neighborhood impacts from Affordable Housing - Large city vs small city.docx Page 1 of 4 Neighborhood Impacts of Affordable & Supported (Non-Supervised) Housing: Large Cities vs. Small Cities/Suburbs Executive Summary • Across settings, rigorous studies generally do not find that affordable or supported housing increases violent or property crime; many show neutral effects or modest crime reductions in lower-income areas. • Property value effects are context-dependent: in distressed or lower-income neighborhoods (often in large cities or inner-ring suburbs), nearby values tend to be neutral to positive after LIHTC/supportive housing opens. In affluent, low-minority areas (often suburban), very localized price dips (within a few hundred feet) have been observed for some LIHTC sites. • Supported housing for people with psychological disabilities (apartment-based with services, not constant supervision) typically shows no adverse neighborhood effects. In New York City, properties within ~500 feet of supportive housing did not decline and showed steady growth after openings; in Denver, violent/property crime did not systematically rise, though disorderly-conduct reports increased within ~500 feet (interpret with caution). • Implementation quality (scale, design, management, services) and baseline neighborhood conditions are the primary moderators of outcomes. Distance bands matter: very-local effects (<500 ft) can differ from broader neighborhood-scale trends (1,000–2,000 ft). How Impacts Differ by Context A. Large Cities (e.g., NYC, Denver, Chicago) • Affordable housing (LIHTC): neutral or positive impacts on nearby values in lower-income neighborhoods; documented reductions in some crimes in poorer areas after LIHTC development. • Supported housing (NYC): no drop in values within ~500 feet; steady appreciation relative to comparable properties after opening. • Supported housing (Denver): no systematic rise in violent/property crime; localized increase in disorderly-conduct reports within ~500 feet (authors caution about interpretation). • Housing vouchers across 10 large cities: when controlling for tract fixed effects and trends, voucher presence does not cause higher crime. 20250913, Neighborhood impacts from Affordable Housing - Large city vs small city.docx Page 2 of 4 B. Small Cities & Suburbs (e.g., Mount Laurel, NJ; Orange County, CA) • Mount Laurel (Ethel Lawrence Homes): no increases in crime, no declines in property values, and no rise in taxes compared with similar nearby towns. • Orange County, CA (largely suburban): after affordable housing opens, nearby sale prices rise modestly and crime falls or stays the same; effects are strongest in higher-poverty tracts and are neutral elsewhere. • Evidence on group homes/supported settings in suburban contexts generally finds no adverse effect on nearby property values. Practical Guidance for a Balanced, Fair Assessment • Report outcomes by neighborhood type (high-income/low-minority vs. lower-income/distressed) and by distance bands (<500 ft vs. 1,000–2,000 ft). • Require strong property-management plans and integrated support-services protocols for supported housing; document maintenance and nuisance-response procedures. • Use before/after plus matched-control designs (or difference-in-differences) when evaluating proposed sites to mirror the strongest methods in the literature. • When communicating with stakeholders, present both the average (neutral/positive) findings and the small, localized exceptions observed in certain high-income areas very near some sites. 20250913, Neighborhood impacts from Affordable Housing - Large city vs small city.docx Page 3 of 4 Annotated Citations (with links) Study Setting / Population Design / Data Neighborhood Effects Notes / Limits Link Diamond & McQuade (2019), JPE – LIHTC spillovers Multiple metros; LIHTC sites (often urban) Equilibrium diff-in-diff estimator; crime & prices Low-income areas: prices up (~6.5%), crime down; high-income, low-minority areas: small price dips very near sites Heterogeneous effects; very-local negatives are small & context-specific Diamond & McQuade (2019) Ellen, Schwartz, Voicu & Schill (2007), JPAM – NYC subsidized rentals New York City; subsidized multifamily Hedonic difference-in-differences No typical depressant effect; many areas show increases in nearby values NYC-specific; focuses on values rather than crime outcomes Ellen, Schwartz, Voicu & Schill (2007) Ellen, Lens & O’Regan (2012), Housing Policy Debate – vouchers & crime 10 large U.S. cities; voucher households Panel models with tract FE & trends Voucher presence does not cause higher crime once trends are controlled Addresses earlier correlation/causation confusion Ellen, Lens & O’Regan (2012) Santiago, Galster & Pettit (2003), Urban Studies – scattered-site public housing Denver; 38 dispersed assisted sites Pre/post models with spatial controls No post-development increase in reported crime near dispersed sites Older period; Denver-specific; crime reporting considerations Santiago, Galster & Pettit (2003) Furman Center (Been et al., 2008) – NYC supportive housing New York City; 123 supportive housing sites Before/after property value analysis No drop within 500 ft; steady growth relative to comps after opening Focuses on values (not crime) in NYC context Furman Center (Been et al., 2008) HUD/Urban Institute (1999) – Denver supportive housing Denver; supportive housing (non-institutional) Econometric models for values & crime; focus groups No systematic rise in violent/property crime; higher disorderly-conduct reports within ~500 ft; mixed-to-positive value effects beyond ~1,000–2,000 ft Localized disorderly-conduct uptick; interpretation cautions (reporting, clustering) HUD/Urban Institute (1999) Albright, Derickson & Massey (2013), City & Community – Mount Laurel Suburban NJ; Ethel Lawrence Homes Multiple time-series with matched controls No increase in crime; no decline in values; no tax increase vs. similar towns Suburban context; strong quasi-experimental design Albright, Derickson & Massey (2013) 20250913, Neighborhood impacts from Affordable Housing - Large city vs small city.docx Page 4 of 4 Study Setting / Population Design / Data Neighborhood Effects Notes / Limits Link UCI Livable Cities Lab (2022) – Orange County impact study Largely suburban Orange County, CA; affordable & PSH Countywide administrative data (prices & crime), before/after comparisons Nearby sale prices rise modestly; crime falls or stays the same (strongest gains in higher-poverty tracts) Regional study; effects vary by poverty level & distance UCI Livable Cities Lab (2022) Boydell, Trainor & Pierri (1989), Hosp. & Community Psychiatry – group homes Toronto area; group homes for mentally ill adults Quasi-experimental property value comparisons No negative effect on nearby residential property values Older study; property values only Boydell, Trainor & Pierri (1989) Ryan & Coyne (1985) – group homes & neighborhood property values Midwestern suburbs; 13 group homes Sales around openings; before/after & distance No significant adverse effects on price/time-on-market metrics Focus on market indicators; 1980s context Ryan & Coyne (1985)