1. 1 Assessing the Effect of Rent Control on Homelessness Grimes, Paul W. & Chressanthis, George A. “Assessing the Effect of Rent Control on Homelessness.” Journal of Urban Economics, Vol. 41,1997, pp. 23-37. Rachel Knutson April 25, 2007

2. 2 Questions addressed in this paper? Main Question: What effect do rent control laws have on the chronically homeless population in the United States?

3. 3 Methods A two-stage model was used. Equation 1: Rent Control i = α + β 1 Density i + β 2 Rental Units i + β 3 Rent i + β 4 ADA i + β 5 Northeast i + β 6 West i + β 7 South i + ε 1i Rent control is modeled as an endogenous variable and is a function of variables assumed to influence voters’ taste for rent controls.

4. 4 Equation 2: Homeless i = θ + γ 1 Rent Control i + γ 2 Rent Gap i + γ 3 Vintage i + γ 4 Population i + γ 5 Density i + γ 6 Poverty i + γ 7 Climate i + γ 8 Crime i + γ 9 Medicade i + γ 10 Group Quarters i + γ 11 Disabled i + γ 12 Female Heads i + γ 13 Veterans i +γ 14 Northeast i + γ 15 West i + γ 16 South i + ε 2i Where i = 1,2,…,200 is the city index In this model homelessness is a function of rent control as well as other factors.

5. 5 Definition of Variables RENT CONTROL: Categorical variable =1 if city enforced a rent control law in 1990; Otherwise = 0. HOMELESS: Persons in city population included in the 1990 census (a) Shelter Count, (b) Street Count, and (c) Total (Shelter + Street) Count, relative to total city population. DENSITY: City population per square mile. RENTAL UNITS: Percent of city’s total housing stock which are renter- occupied units. RENTS: Price of an apartment at the city’s 10th percentile of the rents distribution. ADA: Americans for Democratic Action mean political rating for the city’s U.S. House of Representatives members. 100 point scale with 0 = ‘‘least liberal’’ and 100 = ‘‘most liberal.’’ RENT GAP: Percentage difference between city’s rents at the 10th percentile and the median rent. VINTAGE: Percent of homes in city built prior to 1940. POPULATION: City’s 1990 census population in thousands.

6. 6 Definition of Variables (Cont’d) POVERTY: Percent of city households with income below 1989 poverty line. MEDICAID: Per capita federal Medicaid payments to state and local governments. CLIMATE: Annual degree heating units. CRIME: City’s reported violent crime rate. GROUP QUARTERS: Percent of city population living in supervised group quarters excluding the sheltered homeless. DISABLED: Percent of city population reporting a disability which prevents employment. FEMALE HEADS: Percent of households headed by an adult female. VETERANS: Percent of city population which reports armed service veteran status. NORTHEAST: City located in the Northeast census region = 1; Otherwise = 0. WEST: City located in the West census region = 1; Otherwise = 0. SOUTH City located in the South census region = 1; Otherwise = 0.

7. 7 Methods (Continued) Equation 1 was estimated using probit. The estimates of rent control calculated in equation 1 were then used to calculate equation 2. Three specifications of equation 2 were calculated, for the shelter count, street count, and total (shelter + street) count

8. 8 Data For census purposes an official definition of homelessness does not exist. This study defines the chronic homeless population as: People in emergency shelters for the homeless People in visible street locations From a policy perspective these two populations are important because resources are in many cases devoted to highly visible target groups.

9. 9 Data (Continued) Data used in this study was collected on the night of March 20, 1990, and is called the “S- Night Count” The S-Night Count is not a complete count of the homeless population, but it is used a proxy due to the many difficulties associated with counting this group of people. The S-Night Count for U.S. 200 cities is used and 22 of these 200 cities enforced rent control laws in the 1990 census year.

10. 10 Results: Equation 1 Coefficients all obtained expected signs High pseudo R 2 value indicates this model is a good predictor of Rent Control Most coefficients found to be statistically significant Equation 1: Probit Results VariableRent Control Constant-7.0034*** Density0.0001* Rental Units5.0075** Rents0.0056** ADA0.0187*** Northeast1.2188** West-0.3802 South-0.6316 Pseudo R 2 0.9800 *-Significant at the.10 level **-Significant at the.05 level ***-Significant at the.01 level

11. Results: Equation 2 Equation 2: Regression Results VariableShelter CountStreet CountTotal Count Constant0.00050.00020.0007 Rent Control0.0003***8.2870E-05**0.0004*** Rent Gap2.7510E-05**3.9010E-063.1410E-05** Vintage1.4810E-058.2770E-06*2.3080* Population-1.9820E-101.3120E-10***-6.7040E-11 Density-3.6070E-081.7880E-08*1.8190E-08 Poverty1.6090E-053.6090E-061.9700E-05 Climate3.1580E-09-7.5140E-08**-7.1980E-08 Crime0.0002***-8.6830E-050.0002*** Medicaid-4.3450E-06**-9.1140E-07**-5.2570E-06 Group Quarters0.0199***0.00190.0218*** Disabled-4.62652.5078-2.1188 Female Heads0.0004-0.00060.0002 Veterans-0.00180.00160.0001 Northeast-0.0001-0.0004**0.0005 West0.0006**0.00010.0007** South0.0002-1.2390E-05-6.7040E-11 Adjusted R 2.4192.0897.3615

12. 12 Answer to Main Question The existence of a rent control law is associated with a 0.03% increase in a city’s shelter count and a 0.008% increase in a city’s street count. This indicates that there will be an additional 30 people in shelters and an additional 8 people on the street for every 100,000 residents of a city with rent control laws.

13. 13 Results: Equation 2 (Cont’d) The shelter population is more sensitive to rent controls than the street population. The unavailability of lower end rental housing (rent gap) increases the shelter count. The population and density variables were found to be most significant for the street count, indicating that bigger cities may attract larger street populations. The poverty variable was not found significantly influence the shelter or street count.

14. 14 Results: Equation 2 (Cont’d) Cities with colder climates were found to have smaller street count populations but no significant difference in shelter count populations. Increased government aid for health care, measured through Medicaid, has a negative influence on homelessness. The regional dummy variables indicate that cities in the Northeast have significantly smaller street populations and cities in the West have significantly larger shelter populations compared to cities in the Midwest.

15. 15 Policy Implications Rent control laws provide economic benefits to special interest groups in society and impose social costs by increasing the chronic homelessness rate.