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DO BELOW MARKET HOUSING MANDATES WORK? EVIDENCE FROM CALIFORNIA Based on my research with Ben Powell and Tom Means Edward Stringham, Ph.D. Department of Economics San Jose State University www.sjsu.edu/stringham Overview The problem of housing affordability Below market housing mandates as a proposed solution Assessing below market housing mandates How effective have they been at producing units? How much do below market units cost and who pays? Economics of below market housing mandates Regression results on how below market housing mandates affect housing prices and housing quantity Conclusion www.sjsu.edu/stringham When I went to grad school in Virginia, I lived in in this luxury highrise with a classmate for $655 per person. When I got a job in California, I figured I could live an equivalent building like this one. www.sjsu.edu/stringham The Problem: Housing prices are very high In San Francisco the Median Priced Home sells for $773,500 In Santa Clara County “the suburbs”, the Median Priced Home sells for $689,000 Source: DataQuick Information Systems, www.dqnews.com, October 18, 2007 www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham The Problem: Housing prices are very high That means housing payments for these median priced homes in Santa Clara County are $4,355 per month, or roughly $52,250 per year, or $143 per day! (Assuming a 30 year mortgage at 6.5 percent) www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham High prices preclude many from buying www.sjsu.edu/stringham Why are prices high? Supply has not kept up with demand Are we running out of land? Housing is unaffordable because of zoning laws (Harvard/Wharton study) “Exclusionary” zoning laws mandate minimum lot sizes, minimum density, and other restrictions that prevent the market from supplying more housing The proposed solution Inclusionary zoning A mandatory inclusionary zoning ordinance as practiced in California is an affordable housing mandate that requires builders to sell a certain percentage of their homes at below market rates www.sjsu.edu/stringham The goals of inclusionary zoning The program is touted as a way to make housing more affordable The program is touted as a way to provide housing for all income levels, not just the rich Helps create diverse socio-economic communities www.sjsu.edu/stringham How inclusionary zoning ordinances work Varies by city, but most California ordinances require 10-20 percent of new units to be sold at prices affordable to low income families (defined as a certain percentage of median income) For example, in Tiburon, California a low income family can only afford to pay $109,800 for a home so: 10 percent of new homes in Tiburon must be sold at $109,800 90 percent can be sold at market rates www.sjsu.edu/stringham Where do they have it? Most popular in California Also in place in New Jersey, Virginia, and Maryland and are being considered in many other places including New York, Los Angeles, and Chicago. www.sjsu.edu/stringham California cities with inclusionary zoning ordinances www.sjsu.edu/stringham What are the results? www.sjsu.edu/stringham Examples of below market rate developments Examples of below market rate developments Examples of below market rate developments Examples of below market rate developments Examples of below market rate developments www.sjsu.edu/stringham Examples of below market rate developments Looks good right? Many people say the programs are a success and should be implemented in more cities www.sjsu.edu/stringham Assessing inclusionary zoning How do advocates measure success? What evidence do they provide that the ordinances are good? What’s the normative standard? www.sjsu.edu/stringham Number of inclusionary zoning Figure 1: Number of the Bay Area ordinances inBay Area Cities With Inclusionary Zoning ities with Inclusionary 50 45 40 35 30 Zoning 25 Number of Bay Area C 20 15 10 5 0 70 74 84 88 98 02 72 76 78 80 82 86 90 92 94 96 00 19 19 19 19 19 19 19 19 19 19 19 20 19 19 19 19 20 Role of Economic Analysis Just because a policy is becoming more popular does not mean it is a good idea Hoping something is a good idea does not make something a good idea Some policies may not be the best means of achieving the desired ends of increasing housing affordability www.sjsu.edu/stringham Role of Economic Analysis Inclusionary zoning sounds good to many people, but my coauthor and I decided to investigate the actual results of the policy rather than just looking at the expressed intent What does economics have to say? www.sjsu.edu/stringham Some research questions Is inclusionary zoning helping increase the supply of affordable housing in California? How costly is inclusionary zoning? Are there any drawbacks that have not been considered? www.sjsu.edu/stringham First let’s compare an estimate of housing need to how many units inclusionary zoning produces www.sjsu.edu/stringham 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 Portola Valley Fairfax Yountville Tiburon San Anselmo Corte Madera Los Altos Calistoga Mill Valley Sebastopol Larkspur Clayton San Carlos Los Gatos Benicia Need by City Half Moon Bay Cotati Hercules Healdsburg Pleasant Hill Sonoma Danville Emeryville Menlo Park San Leandro Petaluma Palo Alto outh San Francisco Berkeley East Palo Alto Rio Vista Union City San Rafael www.sjsu.edu/stringham Cupertino Rohnert Park Morgan Hill Richmond San Mateo Novato Mountain View Napa Sunnyvale Brentwood Pleasanton Livermore Dublin Fremont Santa Clara Santa Rosa San Francisco Association of Bay Area Government’s Needs 2001-2006 Estimated Affordable Housing zoning. years by multiplying of Bay Area inclusionary Governments year produced Determination". needs according average units per "Affordable" units produced through Five year housing zoning times 5.5.) under inclusionary to the Association (Calculated for 5.5 "Regional Housing How many units does inclusionary zoning produce? www.sjsu.edu/stringham 2,000 4,000 6,000 8,000 0 10,000 12,000 14,000 D Portola Valley Fairfax Y ountville Tiburon San Anselmo Corte Madera etermination". Los Altos Calistoga Mill Valley Sebastopol Larkspur Clayton San Carlos Los Gatos Benicia Half Moon Bay Cotati Hercules Healdsburg Pleasant Hill Sonoma units per year produced under inclusionary z Danville Emeryville units by Bay Area city Menlo Park "Affordable" units produced through inclusionary z San Leandro Petaluma Palo Alto South San Francisco oning. (C Berkeley oning times 5.5.) East Palo Alto Rio Vista Union City San Rafael Five year housing needs according to the Association of Bay Area G Cupertino Rohnert Park Produced Under Inclusionary Zoning Morgan Hill Richmond San Mateo Novato Mountain View overnments "R Napa Sunnyvale Brentwood Pleasanton Livermore egional H Dublin Fremont Santa Clara Santa Rosa San Francisco alculated for 5.5 years by multiplying average Need versus actual production of affordable ousing Needs Need versus actual production of affordable units by Bay Area city Figure 2: Housing Needs Versus Expected Units Fewer than Produced Under Inclusionary Zoning "Affordable" units produced through inclusionary zoning. (Calculated for 5.5 years by multiplying average 7,000 units in units per year produced under inclusionary zoning times 5.5.) Five year housing needs according to the Association of Bay Area Governments "Regional Housing Needs 30 years Determination". Only 228 14,000 12,000 Annually 10,000 Average city 8,000 produces fewer 6,000 than 15 per 4,000 year after 2,000 0 adopting a Los Altos Portola Valley Fairfax San Anselmo Yountville Tiburon Corte Madera Calistoga Mill Valley Sebastopol Larkspur Clayton San Carlos Los Gatos Benicia Half Moon Bay Cotati Hercules Healdsburg Sonoma Pleasant Hill Danville Union City Emeryville Menlo Park San Leandro Palo Alto Petaluma South San Francisco East Palo Alto Berkeley Rio Vista Cupertino San Rafael Rohnert Park Richmond Morgan Hill San Mateo Novato Napa Sunnyvale Mountain View Brentwood Livermore Pleasanton Dublin Santa Clara Santa Rosa Fremont San Francisco program www.sjsu.edu/stringham Production Compared to Need 4% 96% "Affordable" units produced through inclusionary zoning. (Calculated for 5.5 years by multiplying average units per year produced under inclusionary zoning times 5.5) Shortfall of affordable units not produced through inclusionary zoning. (Data is only for cities with inclusionary zoning.) www.sjsu.edu/stringham Why does inclusionary zoning do a poor job? Despite its attractive sounding name, inclusionary zoning is nothing more than a price control If economists agree on anything, its that price controls (price ceilings) on housing reduce the quantity and/or quality of housing supplied www.sjsu.edu/stringham Economics of Affordable Housing Mandates Price ceiling on a percentage of units Essentially a tax on the remainder of units Increases prices for the vast majority of homebuyers Decreases quantity of housing produced www.sjsu.edu/stringham Economics of Affordable Housing Mandates Price ceiling on a percentage of units Essentially a tax on the remainder of units Increases prices for the vast majority of homebuyers Decreases quantity of housing produced www.sjsu.edu/stringham Inclusionary Zoning Creates Two Markets: First the Price Controlled Market PRICE OF Supply of Housing HOUSING P1 Affordability Control Demand for Housing Qs w/ control Qd w/control QUANTITY OF Qs w/out control= HOUSING Qd w/out control Inclusionary Zoning Creates Two Markets: First the Price Controlled Market PRICE OF Supply of Housing HOUSING P1 Affordability Shortage Control Demand for Housing Qs w/ control Qd w/control QUANTITY OF Qs w/out control= HOUSING Qd w/out control Economics of Affordable Housing Mandates Price ceiling on a percentage of units Essentially a tax on the remainder of units Increases prices for the vast majority of homebuyers Decreases quantity of housing produced www.sjsu.edu/stringham Inclusionary Zoning Creates Two Markets: Second the “Market” Rate Units Supply of Housing w/ IZ tax PRICE OF HOUSING Supply of Housing P w/ tax (for market buyers) P1 Demand for Housing Q w/ Q1 QUANTITY OF tax HOUSING Our research was the first attempt to quantify the cost of the program Without knowing the cost of a program policymakers have little idea whether better ways of helping low income households exists www.sjsu.edu/stringham Sample Calculations of Cost Associated with Providing Units for “Low” Income "Low" price control ost C associated with selling "Low" unit $800,000 $700,000 $600,000 $500,000 $400,000 $300,000 $200,000 $100,000 $0 Alameda Napa Marin Solano Sonoma Francisco San Mateo Santa Clara Contra Costa San www.sjsu.edu/stringham $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000 $800,000 $900,000 $1,000,000 $1,100,000 $1,200,000 $1,300,000 Cotati Emeryville Rohnert Park Healdsburg Richmond Petaluma Santa Rosa Santa C lara Hercules San Francisco East Palo Alto Sebastopol Rio Vista San Leandro Sonoma So. SF Novato C alistoga price controlled unit Fairfax Morgan Hill Yountville Union C ity Sunnyvale Napa Brentwood Livermore Mountain View www.sjsu.edu/stringham San Rafael Dublin San Anselmo San Mateo Pleasant Hill C orte Madera Fremont Half Moon Bay Berkeley Benicia Larkspur Pleasanton C layton San C arlos C upertino Los Gatos Mill Valley Danville Palo Alto Average cost associated with selling each Menlo Park Tiburon Los Altos Portola Valley units $25,000,000 $50,000,000 $75,000,000 $100,000,000 $125,000,000 $150,000,000 $175,000,000 $200,000,000 $225,000,000 $250,000,000 $0 Sebastopol Pleasant Hill Sonoma Yountville Half Moon Bay Emeryville Novato Corte Madera Napa C alistoga Tiburon East Palo Alto Dublin Menlo Park San C arlos Berkeley San Mateo www.sjsu.edu/stringham Larkspur C layton Danville San Francisco Los Altos Santa Rosa San Leandro Livermore Morgan Hill C upertino Pleasanton Petaluma price controlled unit times the number of Palo Alto Average cost associated with selling each San Rafael Mill Valley Sunnyvale Who pays for the below market units? Because government does not write a check for the below market units, the affordable housing mandate is essentially a tax on new housing: There is no free lunch here but unfortunately the tax is hidden This hidden tax must be borne by some combination of market rate homebuyers, builders, and landowners www.sjsu.edu/stringham Inclusionary Zoning Acts as a Tax on New Homes (Cost per BMR unit)(% BMR Units) = Tax Per Market Unit (% Market Units) For example in Mill Valley one out of ten units must be sold at a lost of $750,000 so: ($750,000)(10%) = $83,000 Tax Per Market Rate Unit (90%) In other words, in a 10 unit development the $750,000 cost would be spread over the 9 market rate units. Inclusionary Zoning Acts as a Tax on New Homes (Cost per BMR unit)(% BMR Units) = Tax Per Market Unit (% Market Units) For example in Mill Valley one out of ten units must be sold at a loss of $750,000 so: ($750,000)(10%) = $83,000 Tax Per Market Rate Unit (90%) In other words, in a 10 unit development the $750,000 cost would be spread over the 9 market rate units. Inclusionary Zoning Acts as a Tax on New Homes (Cost per BMR unit)(% BMR Units) = Tax Per Market Unit (% Market Units) For example in Mill Valley one out of ten units must be sold at a loss of $750,000 so: ($750,000)(10%) = $83,000 Tax Per Market Rate Unit (90%) In other words, in a 10 unit development the $750,000 cost would be spread over the 9 market rate units. Inclusionary Zoning Acts as a Tax on New Homes What is the magnitude of the tax in San Francisco Bay Area cities? www.sjsu.edu/stringham $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 $160,000 $180,000 $200,000 $220,000 $0 Cotati E meryville R ichmond Rohnert Park Santa Clara H ercules H ealdsburg San Pleasant H ill io R Vista San Leandro Sonoma Novato Petaluma Santa R osa Fairfax Morgan H ill Sunnyvale Napa Brentwood Livermore Mountain San R afael San Anselmo San Mateo orte C Madera ast E Palo Alto www.sjsu.edu/stringham Y ountville Sebastopol Benicia D ublin So. San Larkspur Union C ity C layton Calistoga San C arlos units caused by inclusionary zoning Fremont Los G atos Mill Valley D anville Menlo Park Pleasanton alf H Moon Berkeley Effective tax imposed on new market-rate C upertino Tiburon Los Altos Palo Alto Portola Valley $225,000 $200,000 $150,000 $175,000 $125,000 $100,000 $50,000 $75,000 $25,000 $0 Cotati E meryville Richmond Rohnert Park Santa Clara Hercules Healdsburg San Francisco Pleasant Hill Rio Vista San Leandro Sonoma Novato Petaluma Santa Rosa Fairfax Morgan Hill Sunnyvale Napa Brentwood Livermore Mountain View San Rafael San Anselmo San Mateo the Costs Assuming 50%of tax is borne by consumers Assuming 100% of tax is borne by consumers Corte Madera East Palo Alto Yountville Sebastopol Benicia Dublin South San Francisco www.sjsu.edu/stringham Larkspur Union City Clayton Calistoga San Carlos Fremont Los G atos Mill Valley Danville Menlo Park Pleasanton Half Moon Bay Berkeley Cupertino Tiburon Los Altos Assuming 84%of tax is borne by consumers Palo Alto Portola Valley Increases in Price of New Homes Caused by Inclusionary Zoning (Under Three Different Assumptions About Who Bears Economic theory says: Below market housing mandates are essentially a tax on the remainder of units Taxes on a product makes that product more expensive Taxes and price controls reduce the quantity supplied www.sjsu.edu/stringham In contrast California courts say: The program is legal because “the ordinance will necessarily increase the supply of affordable housing” --California Courts of Appeal in Home Builders Association of Northern California v. City of Napa, 2001 www.sjsu.edu/stringham Whose theory is right? What kind of research is done? www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham Whose theory is right? “These debates, though fierce, remain largely theoretical due to the lack of empirical research.” --California Coalition for Rural Housing and Non-Profit Housing Association of Northern California (2003, p.3) www.sjsu.edu/stringham Empirical tests Get data including price and quantity for all California cities Get data for when cities adopted ordinance Investigate whether adopting ordinance affects price or quantity of housing www.sjsu.edu/stringham Description of Data Sources: Census data for California cities for 1990 and 2000. RAND California Statistics for 1990 and 2000 Construction Industry Research Board Housing Permit Data 1970-2005 California Coalition for Rural Housing and Non-Profit Housing Association of Northern California survey on policy adoption dates www.sjsu.edu/stringham Variable Observations Mean Standard Minimum Maximum Deviation Table 1: Summary Statistics Population 2000 N=446 65,466 (197,087) 10,007 3,694,834 Population 1990 N=431 58,468 (187,014) 1,520 3,485,398 Households 2000 N=446 22,251 (68,673) 1,927 1,276,609 Households 1990 N=431 20,512 (66,074) 522 1,219,770 Housing Units 2000 N=446 23,278 (71,843) 2,069 1,337,668 Housing Units 1990 N=431 21,745 (70,331) 597 1,299,963 Density 2000 N=446 7.62 (6.06) 0.42 37.32 (persons/acre) Density 1990 N=431 6.87 (5.88) 0.08 37.01 (persons/acre) Median Household N=446 52,582 (21,873) 16,151 193,157 Income 2000 Median Household N=431 38,518 (14,543) 14,215 123,625 Income 1990 Per Capita Income N=446 23,903 (13,041) 7,078 98,643 2000 Per Capita Income N=431 16,696 (8,070) 4,784 63,302 1990 Rents/Income 2000 N=446 27.60% (3.1%) 14.4% 50.1% Rents/Income 1990 N=431 28.9% (2.7%) 14.9% 35.1% Average Home Price N=360 300,594 (235,436) 49,151 2,253,218 2000 Average Home Price N=352 206,754 (112,804) 52,858 1,018,106 1990 Specification of the model for price Suppose we only had data for one year and our regression was: Yit = β0 + d1IZyrit + β1Xit + ai + vit HousingPrice = Intercept + Policy Dummy + Controls + Error Terms, which includes (ai) the unobserved city specific effects. www.sjsu.edu/stringham Specification of the model for price And suppose the regression showed: the intercept to be β0 = $300,000 and the policy variable coefficient to be d1=$180,000 HousingPrice= ($300,000)+($180,000)(PolicyDummy) +…. Problem: Unless we could get data for relevant control variables, we would be unable to tell if cities with the policy have $180,000 higher housing prices because of the policy or because of some unobserved differences (ai) between cities that adopted policy and those that did not. www.sjsu.edu/stringham Or consider the same problem for a cross sectional regression on housing quantity www.sjsu.edu/stringham Specification of the model for quantity Suppose we only had data for one year and our regression was: Yit = β0 + d1IZyrit + β1Xit + ai + vit HousingQuantity = Intercept + Policy Dummy + Controls + Error Terms, which includes (ai) the unobserved city specific effects. www.sjsu.edu/stringham Specification of the model for quantity And suppose the regression showed the intercept to be β0 = 70,000 and the policy variable coefficient to be d1=40,000 HousingQuanity= (70,000)+(40,000)(PolicyDummy) +…. Problem: Unless we could get data for relevant control variables, we would be unable to tell if cities with the policy have 40,000 more units because of the policy or because of some unobserved differences (ai) between cities that adopted policy and those that did not. www.sjsu.edu/stringham Specification of the model With city data, so many city specific characteristics are unobservable and they will be end up in the error term (ai) www.sjsu.edu/stringham Solution Panel data with first difference model Data from all California cities in different years Our approach is estimate a first difference model to eliminate the fixed effect We also specify a semi-log model, which makes the interpretation of the coefficient for the policy variable as the approximate percentage change due to the change in policy www.sjsu.edu/stringham First difference model Start with the level model from 2000 lnYi,2000 = d0 + d1IZyri,2000 + β1Xi,2000 + vi,2000 + ai and the level model from 1990 lnYi,1990 = d0 + d1IZyri,1990 + β1Xi,1990 + vi,1990 + ai www.sjsu.edu/stringham First difference model Difference the two gives us: lnYi,2000 - lnYi,1990 = d0 + (d1IZyri,2000 – d1IZyri,1990) + (β1Xi,2000 – β1Xi,1990) + (vi,2000 – vi,1990) + ai - ai which can be rewritten as: ln(Yi,2000/Yi,1990) = d0 + d1ΔIZyri,t+ β1ΔXi,t + vi,t Notice that the city specific unobserved differences (ai) that remain constant over time drop out, because we are comparing cities from 1990 to themselves in 2000. For example, San Diego is still by the ocean and still has nice weather in both 1990 and 2000. www.sjsu.edu/stringham First difference model ln(Yi,2000/Yi,1990) = d0 + d1ΔIZyri,t+ β1ΔXi,t + vi,t For the regressions on price it can be interpreted as: Change in Price = Intercept + Change in Policy Variable + Change in Other Control Variables + Error Term For the regressions on quantity it can be interpreted as: Change in Quantity = Intercept + Change in Policy Variable + Change in Other Control Variables + Error Term www.sjsu.edu/stringham Interpreting the first difference model for housing price The first difference model looks at housing price for each city in 1990 and housing price for each city in 2000, and compare the two for each city. So if prices in City A went from $200,000 to $300,000 and prices in City B went from $400,000 to $600,000, the fact that City B has better weather and starts with higher prices is irrelevant if we are just looking at housing appreciation which is 50% for both of these cities. www.sjsu.edu/stringham Interpreting the first difference model for housing quantity Or for housing quantity, the first difference model looks at quantity for each city in 1990 and housing price for each city in 2000, and compare the two for each city. So if quantity of homes in City A went from 100,000 to 102,000 and quantity of homes in City B went from 200,000 to 204,000, the fact that City B is more metropolitan is irrelevant if we are just looking at production rates which are 2 percent increases for both of these cities. www.sjsu.edu/stringham Interpreting the first difference model’s policy variable coefficient What we are interested in is if changes in the policy variable (cities that adopted the policy) are associated with different percentage changes in prices or different percentage changes in quantity. Do cities adopting inclusionary zoning mitigate economy wide price increases or do they end up with even higher prices? www.sjsu.edu/stringham Interpreting the first difference model’s policy variable (ΔIZ) ln(Yi,2000/Yi,1990) = d0 + d1ΔIZyri,t+ β1ΔXi,t + vi,t In our data we code a 0 for cities that do not have a below market housing mandate and 1 for cities that do (for each specific year) Since our model is a first difference model, our variable (ΔIZ) looks at how a cities changes from time period 1 to time period 2 www.sjsu.edu/stringham Possible codings for the policy variable ΔIZ (Changes in Inclusionary Zoning) ΔIZ=0 if a city has no IZ in Period 1 or 2 ΔIZ=1 if a city has no IZ in Period 1 but has IZ in Period 2 ΔIZ=-1 if a city has IZ in Period 1 but eliminates IZ in Period 2 ΔIZ=0 if a city has IZ in Period 1 and keeps the policy in Period 2 The second case is most important to us. If a city’s policy variable (ΔIZ) is positive it indicates a city adopted inclusionary zoning between period 1 and period 2 www.sjsu.edu/stringham Interpreting the regression’s coefficient (d1) for the policy variable (ΔIZ) in the price regressions ln(Yi,2000/Yi,1990) = d0 + d1ΔIZyri,t+ β1ΔXi,t + vi,t The coefficient d1 estimates what percentage the dependent variable (price in our first set of regressions) changes due to the adoption of inclusionary zoning www.sjsu.edu/stringham Interpreting the regression’s coefficient (d1) for the policy variable (ΔIZ) in the price regressions ln(Yi,2000/Yi,1990) = d0 + d1ΔIZyri,t+ β1ΔXi,t + vi,t E.g. If the regressions looking at housing prices show that d1 were -0.05 it would mean adopting inclusionary zoning decreased prices by 5%. E.g. If the regressions looking at housing prices show that d1 were 0.01 it would mean adopting inclusionary zoning increased prices by 1%. www.sjsu.edu/stringham Interpreting the regression’s coefficient (d1) for the policy variable (ΔIZ) in the quantity regressions ln(Yi,2000/Yi,1990) = d0 + d1ΔIZyri,t+ β1ΔXi,t + vi,t The coefficient d1 estimates what percentage the dependent variable (quantity in our second set of regressions) changes due to the adoption of inclusionary zoning www.sjsu.edu/stringham Interpreting the regression’s coefficient (d1) for the policy variable (ΔIZ) in the quantity regressions ln(Yi,2000/Yi,1990) = d0 + d1ΔIZyri,t+ β1ΔXi,t + vi,t E.g. If the regressions looking at housing quantity show that d1 were 0.02 it would mean adopting inclusionary zoning increased quantity by 2%. E.g. If the regressions looking at housing quantity show that d1 were -0.01 it would mean adopting inclusionary zoning decreased quantity by 1%. www.sjsu.edu/stringham How does inclusionary zoning affect the price of housing? www.sjsu.edu/stringham Table 3: Summary of Policy Coefficients from 15 Regressions on the Price of Housing by Model and by Lag Year Dependent Variable: ln(Price) Level models for 2000 data First difference models (2000- Level models for 1990 data 1990) Policy variable Coefficient of Policy variable Coefficient of Policy variable Coefficient of Policy Variable Policy Variable Policy Variable iz1985 .389 Iz1995 .627 Iz95delta .312 iz1986 .431 Iz1996 .642 Iz96delta .298 iz1987 .431 Iz1997 .637 Iz97delta .278 iz1988 .442 Iz1998 .637 Iz98delta .270 iz1989 .457 Iz1999 .642 Iz99delta .265 www.sjsu.edu/stringham Table 5: Regression Results of How Below Market Housing Mandates Affect Price of Housing: First Difference Model with Control Variables Dependent Variable: ln(average price 2000/1990) Coefficients and Coefficients and Independent Variable (Standard Errors) (Standard Errors) N=431 N=431 0.001 -0.009 Constant (0.025) (0.025) 0.228*** iz95delta (0.038) 0.217*** iz99delta (0.037) 0.173*** 0.178*** Median income (0.0126) (0.0125) -0.007 -0.008 density (0.011) (0.011) -0.0017 -0.00112 population (0.00661) (0.00662) -0.002 -0.003 rent % (0.005) (0.005) Adj. R-Squared 0.4332 0.4300 Note: *, **,*** denotes significance at the .10, .05, .01 levels, two-tailed test. Table 5: Regression Results of How Below Market Housing Mandates Affect Price of Housing: First Difference Model with Control Variables Dependent Variable: ln(average price 2000/1990) Coefficients and Coefficients and Independent Variable (Standard Errors) (Standard Errors) N=431 N=431 0.001 -0.009 Constant (0.025) (0.025) 0.228*** iz95delta (0.038) 0.217*** iz99delta (0.037) 0.173*** 0.178*** Median income (0.0126) (0.0125) -0.007 -0.008 density (0.011) (0.011) -0.0017 -0.00112 population (0.00661) (0.00662) -0.002 -0.003 rent % (0.005) (0.005) Adj. R-Squared 0.4332 0.4300 Note: *, **,*** denotes significance at the .10, .05, .01 levels, two-tailed test. Table 5: Regression Results of How Below Market Housing Mandates Affect Price of Housing: First Difference Model with Control Variables Dependent Variable: ln(average price 2000/1990) Coefficients and Coefficients and Independent Variable (Standard Errors) (Standard Errors) N=431 N=431 0.001 -0.009 Constant (0.025) (0.025) 0.228*** iz95delta (0.038) 0.217*** iz99delta (0.037) 0.173*** 0.178*** Median income (0.0126) (0.0125) -0.007 -0.008 density (0.011) (0.011) -0.0017 -0.00112 population (0.00661) (0.00662) -0.002 -0.003 rent % (0.005) (0.005) Adj. R-Squared 0.4332 0.4300 Note: *, **,*** denotes significance at the .10, .05, .01 levels, two-tailed test. Summary of results Cities that imposed inclusionary zoning on average increased prices by 20 percent www.sjsu.edu/stringham Economics of Affordable Housing Mandates Price ceiling on a percentage of units Essentially a tax on the remainder of units Increases prices for the vast majority of homebuyers Decreases quantity of housing produced www.sjsu.edu/stringham How does inclusionary zoning affect the quantity of housing? www.sjsu.edu/stringham Table 4: Summary of Policy Coefficients from 15 Regressions on the Quantity of Housing by Model and by Lag Year Dependent Variable: ln(Housing Units) Level models for 2000 data First difference models (2000- Level models for 1990 data 1990) Policy variable Coefficient of Policy variable Coefficient of Policy variable Coefficient of Policy Variable Policy Variable Policy Variable iz1985 .777 iz1995 .665 Iz95delta -.045 iz1986 .751 iz1996 .614 Iz96delta -.024 iz1987 .751 iz1997 .585 Iz97delta -.027 iz1988 .679 iz1998 .585 Iz98delta -.038 iz1989 .653 iz1999 .618 Iz99delta -.051 www.sjsu.edu/stringham Table 6: Regression Results of How Below Market Housing Mandates Affect Quantity of Housing: First Difference Model with Control Variables Dependent Variable: ln(units 2000 - 1990) Coefficients and Coefficients and Independent Variable (Standard Errors) (Standard Errors) N=431 N=431 -0.056** -0.054** Constant (0.023) (0.023) -0.104** iz95delta (0.042) -0.097** iz99delta (0.041) 0.0683*** 0.0660*** Median income (0.0132) (0.0131) 0.113* 0.114* Density (0.011) (0.011) 0.0233* 0.0230* Population (0.00729) (0.00729) Adj. R-Squared 0.2921 0.2911 Note: *, **,*** denotes significance at the .10, .05, .01 levels, two-tailed test. Table 6: Regression Results of How Below Market Housing Mandates Affect Quantity of Housing: First Difference Model with Control Variables Dependent Variable: ln(units 2000 - 1990) Coefficients and Coefficients and Independent Variable (Standard Errors) (Standard Errors) N=431 N=431 -0.056** -0.054** Constant (0.023) (0.023) -0.104** iz95delta (0.042) -0.097** iz99delta (0.041) 0.0683*** 0.0660*** Median income (0.0132) (0.0131) 0.113* 0.114* Density (0.011) (0.011) 0.0233* 0.0230* Population (0.00729) (0.00729) Adj. R-Squared 0.2921 0.2911 Note: *, **,*** denotes significance at the .10, .05, .01 levels, two-tailed test. Table 6: Regression Results of How Below Market Housing Mandates Affect Quantity of Housing: First Difference Model with Control Variables Dependent Variable: ln(units 2000 - 1990) Coefficients and Coefficients and Independent Variable (Standard Errors) (Standard Errors) N=431 N=431 -0.056** -0.054** Constant (0.023) (0.023) -0.104** iz95delta (0.042) -0.097** iz99delta (0.041) 0.0683*** 0.0660*** Median income (0.0132) (0.0131) 0.113* 0.114* Density (0.011) (0.011) 0.0233* 0.0230* Population (0.00729) (0.00729) Adj. R-Squared 0.2921 0.2911 Note: *, **,*** denotes significance at the .10, .05, .01 levels, two-tailed test. Summary of results Cities that imposed inclusionary zoning on average decreased quantity by 10 percent www.sjsu.edu/stringham Economics of Affordable Housing Mandates Price ceiling on a percentage of units Essentially a tax on the remainder of units Increases prices for the vast majority of homebuyers Decreases quantity of housing produced www.sjsu.edu/stringham Bottom line Inclusionary zoning actually reduces the amount of housing and makes housing less affordable www.sjsu.edu/stringham Inclusionary zoning does not make housing more affordable Inclusionary zoning is counterproductive Is there any silver lining? www.sjsu.edu/stringham Do price controls have any silver lining? Our report has been moderately successful at putting some constraints on the claims by those who advocate inclusionary zoning Advocates of price controls no longer claim inclusionary zoning is a full solution as they used to, but those who still advocate the ordinance claim it’s a partial solution. They say that producing a few units is better than none They say at least it can benefit a few people www.sjsu.edu/stringham Do price controls have any silver lining? Inclusionary zoning increases prices for most people, but could it at least benefit me? www.sjsu.edu/stringham www.sjsu.edu/stringham www.sjsu.edu/stringham $300,000 Looks like a great deal right? www.sjsu.edu/stringham $300,000 Looks like a great deal right? What advocates of inclusionary zoning often fail to tell people: this San Francisco condo will have resale price restrictions for the next 55 years I am age 32, so that means I would not be able to sell it at market rate until I am age 87 Meanwhile, the already higher market rate units appreciate at normal rates creating further disparity between neighbors www.sjsu.edu/stringham $300,000 Looks like a great deal right? What advocates of inclusionary zoning often fail to tell people: this San Francisco condo will have resale price restrictions for the next 55 years I am age 32, so that means I would not be able to sell it at market rate until I am age 87 Meanwhile, the already higher market rate units appreciate at normal rates creating further disparity between neighbors www.sjsu.edu/stringham Appreciation of Housing Under Inclusionary Zoning (Assuming market rate homes appreciate at 3% per year) $4,500,000 $4,000,000 $3,500,000 Price Controlled Home Resale Price $3,000,000 $2,500,000 Market Rate Home $2,000,000 $1,500,000 $1,000,000 $500,000 $0 2005 2011 2017 2023 2029 2035 2041 2047 2053 2059 Year www.sjsu.edu/stringham Appreciation of Housing Under Inclusionary Zoning (Assuming market rate homes appreciate at 7% per year) $35,000,000 $30,000,000 Price Controlled $25,000,000 Home Resale Price $20,000,000 Market Rate Home $15,000,000 $10,000,000 $5,000,000 $0 2005 2011 2017 2023 2029 2035 2041 2047 2053 Year 2059 www.sjsu.edu/stringham Other questions aspects of affordable housing mandates Is it really ownership if a person cannot gain any appreciation? Is it really ownership if a person cannot give their home to their children unless their children are also low income? Is a program that creates two tiers of ownership really good for low income families? How costly are these programs to monitor? What will be the long run effects? www.sjsu.edu/stringham What do “owners” of these price controlled units have to say? www.sjsu.edu/stringham How Should We Deal With High Prices? Worst Idea….price controls Inclusionary zoning has many problems that will only get worse over time Inclusionary zoning does not address the real reason why housing has become so unaffordable www.sjsu.edu/stringham How Should We Deal With High Prices? Worst Idea….price controls! Inclusionary zoning has many problems that will only get worse over time Inclusionary zoning does not address the real reason why housing has become so unaffordable www.sjsu.edu/stringham Just say no to price controls! PRICE OF Supply of Housing HOUSING P1 Affordability Control Demand for Housing Qs w/ control Qd w/control QUANTITY OF Qs w/out control= HOUSING Qd w/out control As an alternative to price controls how can we encourage more affordable housing? Better idea… www.sjsu.edu/stringham Allowing supply to keep up with demand PRICE OF Supply of Housing 1 HOUSING P1 Demand for Housing Q1 QUANTITY OF HOUSING Allowing supply to keep up with demand PRICE OF Supply of Housing 1 HOUSING Supply of Housing 2 P1 P1 Demand for Housing Q1 Q2 QUANTITY OF HOUSING Real Solutions (as alternatives to price controls) Eliminate Exclusionary Zoning, Eliminate Growth Boundaries, Eliminate Permit Moratoria, and Eliminate Inclusionary Zoning…. www.sjsu.edu/stringham My favorite quote on this subject “Production is the key for being able to have a wide range of housing options,” said Michael Houlemard, executive director of the Fort Ord Reuse Authority. “If we encourage production….that alone is going to either stabilize or drive down home prices in the area.” (The Californian, Salinas, CA, January 19, 2004) www.sjsu.edu/stringham My favorite quote on this subject “Houlemard draws his assessment directly from a study done by two San Jose State economists.” (The Californian, Salinas, CA, January 19, 2004) www.sjsu.edu/stringham DO BELOW MARKET HOUSING MANDATES WORK? EVIDENCE FROM CALIFORNIA Based on my research with Ben Powell and Tom Means Edward Stringham, Ph.D. Department of Economics San Jose State University www.sjsu.edu/stringham Inclusionary Zoning Advocates Speak “The price of housing is not a function of its development cost. Rather, housing price, be it rents or sale prices, are solely a function of market demand” (David Paul Rosen 2004). “Even if their profits are not maximized, developers will still realize acceptable profits. Therefore, developers will still develop” (Padilla 1995). Institute for Local Self Government states that inclusionary zoning helps, “Offset the demand on housing that is created by new development.” www.sjsu.edu/stringham Advocates of inclusionary zoning speak “High enough density bonuses create affordable units at no cost to landowners, developers, or other homeowners” (Padilla 1995). "Most inclusionary rules are actively sought by developers, and can hardly be considered taxes" Dietderich (1996). “Developers often fail to participate because they do not understand the economics of the program” Kautz (2002). www.sjsu.edu/stringham Long-Term Controls $50,000 Income $45,000 Targeting $40,000 Mobility $35,000 Improvements Income Per Person $30,000 Median Income Administration $25,000 Mean Income $20,000 $15,000 $10,000 $5,000 $0 15-24 25-34 35-44 45-54 55-64 65+ www.sjsu.edu/stringham Age Reaction to our research Research has been featured in over seventy papers, including favorable stories in San Francisco Chronicle, San Jose Mercury News, Sacramento Bee, and Miami Herald In the past twelve months the report has been downloaded from Reason’s website 73,364 times www.sjsu.edu/stringham Reaction The Critics: “Their paper suggests that the “market” will solve our housing problems. Funny that it hasn’t yet!” - Gary Patton, LandWatch “It theorizes but offers no proof, that developers pass the costs of the IH units to market-rate consumers... In reality, developers are not philanthropies and will charge the highest price the market will bear, with or without IH.” - Rob Wiener, California Coalition for Rural Housing. www.sjsu.edu/stringham Reaction The Best: “At best, using IZ to provide low-income housing is at like fighting a forest fire with a garden hose. Under the harsh light this new study shines on the policy that hose may be spraying fuel, rather than water, on the fire.” - Daniel Weintraub, Sac Bee. www.sjsu.edu/stringham DO AFFORDABLE HOUSING MANDATES WORK? Benjamin Powell and Edward Stringham Reason POLICY STUDY www.sjsu.edu/stringham 318

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