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Residency Requirements and House Prices: A Natural Experiment from Ohio
Carolyn A. Dehring
Insurance, Legal Studies and Real Estate University of Georgia
and
Elizabeth N. Fisher Insurance, Legal Studies and Real Estate
University of Georgia [email protected]
March 21, 2014
Abstract: In the last 20 years many US cities have removed their residency requirements in response to municipal employee demands to choose where they live. We examine the effect of Ohio’s residency requirement ban on housing prices in the city and suburban markets of Cleveland, Akron, and Dayton, Ohio. These cities did not comply with the 2006 law until 2009, when the Ohio Supreme Court upheld the ban. We find house price declines in cities with residency requirements, which suggests that municipal workers left relatively poor performing public school districts for better quality suburban school districts following the Supreme Court ruling. Keywords: Residency Requirement, Housing Prices, School Quality
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INTRODUCTION
A residency requirement is a law that forces municipal employees to reside in the city
they work for as a condition of employment. An employee’s residence is defined in terms of
where the employee normally eats, sleeps and maintains his or her personal effects (Hirsch &
Rufalo, 1985). Residency requirements emerged during the industrial revolution at the turn of the
20th century, when it was common to staff the police force by appointing local residents
(Eisinger, 1983). Today residency requirements can cover from one up to all of municipality’s
employees.
The objective of this paper is to test the effect of the State of Ohio’s residency
requirement ban on housing prices in those major cities that had previously required the majority
of their municipal workers to live in the city. While cities provide many desirable goods and
services which attract residents, the public schools are often of relatively poor quality, and
private school alternatives are frequently expensive. Thus, with the elimination of residency
requirements, it is reasonable to expect that city worker households will move. We seek to
determine whether the predicted exodus of households from the cities in which they work leads
to house price declines in those cities following the elimination of residency requirements. We
also test for an increase in the demand for housing in higher quality suburban school districts.
Finally, we conduct a natural experiment in two counties with residency requirements, where we
observe some houses located within the city limit that are zoned for a suburban school district,
and test for differences in house price effects based on school district.
After controlling for general housing market conditions, we find property values in the
Ohio cities of Cleveland, Akron and Dayton declined by 8.4%, on average, relative to the
suburban housing markets of these same cities in the 2.5 years following the Ohio Supreme
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Court’s 2009 ruling that upheld a ban on residency requirements. In Cincinnati, OH, where was
no city-wide residency requirement, we find no such decline in the city relative to the suburbs
during this same time period. We also examine suburban housing markets surrounding the cities
of Cleveland, Dayton and Akron, respectively, and find evidence of house prices increases
following the residency requirement ban that are associated with increases in public school
performance. Together these results suggest a flight out of cities into better performing public
school districts following the state Supreme Court’s ruling. Lastly, following the elimination of
residency requirements, we find that house prices decline between 11% and 12% on those city
properties that are also zoned for the city school district.
BACKGROUND
Over the last 100 years, residency requirements have fallen in and out of favor with
American cities as social and economic climates have changed. Between 1920 and 1960,
widespread opposition to residency laws arose, with many arguing that residency requirements
prevented the most qualified candidates from being hired. Accordingly, during this time many
cities abolished or stopped enforcing the laws (Eisinger, 1983). By 1960, only Buffalo, NY,
Milwaukee, WI, Philadelphia, PA and a few other major cities had residency laws (Fogelson,
1977).
Residency laws gained momentum again in the 1970s in response to suburbanization,
which saw many upper and middle class residents move out of the city to the suburbs. Cities
used residency requirements to bring some of these residents back to the city in order to help
raise the standard of living in the city, reduce crime, and increase the tax base and local
spending. It was thought that municipal employees such as police would work harder and have a
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better understanding of the city and its problems if they lived within the city. Also, having
emergency workers available in the city would potentially decrease response times to
emergencies. Some proponents claimed residency requirements would decrease tardiness and
absenteeism from work (Eisinger, 1983). Reduced energy use by city employees in their journey
to work was also argued in support of residency requirements. Finally, it was thought that
residency requirements would see more minorities and unemployed hired, thus reducing the cost
of some city services, while at the same time reducing competition for central city minorities
against white metropolitan workers. Accordingly, the 1970s saw many US cities reenact their
residency requirements, or enforce those that were already in place. By 1976, 29 of the 50 largest
cities in America had residency requirements (Hirsch & Rufalo, 1985), and by 1980, two-thirds
of all US cities over 250,000 had residency requirements (Eisinger, 1983).
Today, residency requirements have again fallen out of favor. Municipal employees
oppose residency requirements because they limit housing choice, and in doing so may restrict
access to quality public schools. Many police and firefighters perceive that residency
requirements hurt recruitment efforts and the ability to keep qualified staff. Moreover, if
municipal workers are forced to live within the city boundaries, this necessitates a wage
premium. (Duncan, 2005). Most legal cases against city residency requirements have been
argued on citizens’ right to commute.1
Of the largest fifty cities in America today, only a handful have maintained their
residency requirements; these include Boston, MA, Chicago, IL, and Philadelphia, PA. In
January 2014, a Milwaukee County Circuit Judge declared Milwaukee’s longstanding residency
1 Shapiro v. Thompson, Graham v. Richardson, Dunn d. Blumstein, Donnelly v. City of Manchester, Memorial Hospital v. Maricopa County, Kent v. Dulles, Korematsu v. United States.
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rule void and unenforceable.2 In Milwaukee, municipal workers and Milwaukee Public School
District workers were covered by these requirements, or approximately 6% of all city
households.3 Milwaukee officials had opposed the action in part because of the expected effect
on already depressed property values and the tax base.4
The Ohio Experience
In 2003, 125 of the state of Ohio’s 243 cities had some sort of residency requirement
(Ohio Municipal League, 2003). The exact manner in which these requirements were applied
varied by municipality; some cities required only the City Manager to live in the city, while other
cities like Cincinnati and Columbus allowed employees to live within the county or surrounding
counties, respectively.5 However, the cities of Cleveland, Dayton, and Akron required all city
employees to live within the municipality borders.
In 2006, allegedly in an effort to get rid of some collective bargaining rights of police and
firefighters, Ohio enacted a state-wide ban on the enforcement of residency requirements to give
municipal workers more freedom of choice. Legislators had the power to involve themselves
with residency requirements because such requirements affected the comfort and general welfare
of municipal workers. After the 2006 ban, various cities began filing lawsuits against the state,
including Dayton, Akron, and Cleveland. They argued the General Assembly ruling was in
2 The residency rule had not been enforced since the signing of 2013-2015 state budget by Wisconsin governor Scott Walker. 3 The Business Journal (2013) 4 Under the terms of the plan, non-emergency workers and school district employees could live anywhere, but emergency workers would have to live within 15 miles of the city border. Journal Interactive 03 Jun. 2013. 5 Cincinnati requires only administrative managers, such as the city manager, police and fire chief, etc. to reside within the city.
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violation of home rule.6 On June 11, 2009, the state ban on residency requirements was upheld
by Ohio Supreme Court, with the majority opinion supporting the legislative ban.
In the time period between the 2006 General Assembly ban and the 2009 Supreme Court
ruling, it was public knowledge that Cleveland, Dayton and Akron were fighting the 2006 ban,
and that residency requirements would continue to be enforced in those cities. For example, in
Cleveland, the Mayor issued a press release four months prior to the ban announcing the city
would continue to enforce its residency requirements (Sweet 2008). While city employees may
have tried to move moved, they did so at their own risk. Sweet (2008) notes that four city of
Cleveland employees were fired in 2007 for living in suburban Cuyahoga County, where
supposedly investigations into the employees began before the new state law went into effect.
After the 2006 ban was passed, employees for the City of Akron were warned by the Mayor that
anyone who moved out of the city would be fired, citing Akron’s charter. Similarly, the Mayor
of the City of Dayton sent a letter to all employees notifying them that the city’s residency
requirement would continue to be enforced.
The objective of this paper is to determine whether house prices decrease in cities after
residency requirements have been abolished. In order for this to come about, employees must
actually move out. A 2011 report by the Milwaukee Legislative Reference Bureau suggests that
employees do leave when residency requirements end. In a survey of 5 cities in which residency
requirements had been repealed and for which data was made available, the percent of city
workers living outside the city limits ranged from 17% to 70%.7
6 Cleveland missed the deadline to join the suit; however, the court did allow oral arguments from Cleveland. Fields, Reginald. "Cleveland Will Get to Argue in Ohio Supreme Court for Residency Requirement." The Plain Dealer. Cleveland.com, 18 Jan. 2009. Web. 06 Mar. 2013. However in another lawsuit Cleveland filed against the state, Cleveland argued the ban on residency requirements would do “irreparable financial harm” to the city due to employees seeking residency in surrounding communities (Sweet). 7 “Survey of Residency Requirements in Various Jurisdictions.” City of Milwaukee Legislative Reference Bureau Memorandum. March 22, 2011.
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The housing sale data we use to test this this hypothesis is from the time period 2007-
2011. During this time, like in many American housing markets, house prices were declining in
the Ohio counties we examine. Also, like in many American cities, homeowner mobility was
constrained by negative equity and tightening credit standards during this time. Finally, any
number of personal constraints might prevent a household from moving in the years immediately
following the removal of a residency requirement. Nevertheless, anecdotal evidence suggests
that Ohio city workers moved in response to the 2009 ruling. We surveyed police and fire
departments in Cleveland, Akron and Dayton concerning the residency status of their respective
forces.8 As of spring 2013, 40% of Cleveland firefighters and 35% of Cleveland Police had left
the city.9,10 In Dayton the numbers are similar, with 44% of firefighters and 37% of police
having left the city limits since mid-2009.11,12 In Akron, police numbers were not provided, but
far fewer firefighters, just 13%, had left the city as compared to Dayton and Cleveland.13
LITERATURE
Despite the perceived link between property values and residency requirements, the
literature on residency requirements includes no such studies linking the two. This literature
instead focuses on changes in labor productivity and determinants of residency requirements, and
has produced mixed results on the effectiveness of residency requirements. Using data from a
8 We were unable to obtain information concerning the residence of all city employees in Cleveland, Dayton and Akron. Similarly the Akron Police department would not release their residency information. 9 In the City of Cleveland, 469 out of 790 firefighters continue to reside in the City of Cleveland. Information provided by Sherri Paskett, City of Cleveland. June 4, 2013 telephone call. 10 In the City of Cleveland, 1,045 of approximately 1,600 patrolmen continue to reside in the City of Cleveland as of April 2013. Patrolmen constitute approximately 80% of the police force. Information provided by David Wagner, Cleveland Police and Patrolmen Association, June 16, 2013 telephone call. 11 In the City of Dayton, 37% of sworn police officers have moved out of the city. Information provided by Sergeant John Ross, Dayton Police Department, telephone call 5/22/2013. 12 In the City of Dayton, 120 of 277 firefighters now reside outside the city limits. Information provided by Anita, Dayton Firefighters Local 136, telephone call 5/22/2013. 13 Residency of Akron police is at 87%. Information provided by Captain Al Bragg. Telephone call 5/21/2013.
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survey of citizens’ experiences with police serving their neighborhood, as well as traditional
crime and clearance data, Smith (1980) studies the re-emergence of regulations requiring
municipal employees to reside in the cities they serve. Smith addresses whether police
involvement in the community has a positive effect on police performance. Smith shows that
residency is positively related to the percentage of citizens, who rate their police force
“outstanding,” who have been assisted by the police, who perceive restraint in police use of
force, and who perceive fairness and courtesy in police treatment of local citizenry. Smith also
finds that residency does appear to be related to crimes reported by the police, but finds positive
and significant clearance rates, especially of lesser crimes. Smith also looks at residency and
officer attitudes towards the community and finds resident police have a more positive view of
the community. Smith then compares middle-sized versus small-size cities and finds the police
perception of citizen support is higher in middle-sized cities than smaller cities, suggesting
residency requirements might not be a one-size-fits-all policy.
Eisinger (1983) studies the revival of residency requirements in the 1970s and the
resulting effect on local economies. Eisinger theorizes that residency laws will be passed or
enforced in cities that are suffering financial distress and economic deterioration. On the other
hand, economically and fiscally healthy cities would rescind residency requirements or stop
enforcing them. He measures economic distress using population changes, unemployment levels
and decline in the size of public sector work, a frost-belt location (highly related to
unemployment), and a dummy variable for whether the city has a mayor. Eisinger concludes that
residency laws are weak policy responses to economic decay, and that their economic effect is
unproven.
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Hirsch and Rufalo (1985) study the effects of residency requirements on a municipality’s
demand for police, police labor pool, and police wage. They also examine what city
characteristics lead to a residency requirement. Their results show a significant decrease in total
compensation for police subjected to a residency requirement, and insignificant results in the
total number of police employed.
Like Eisinger, Mehay and Seiden (1986) also study the revival of residency requirements.
They research how residency requirements affect results of popular votes in municipalities. They
find residency requirements bring voters to a city that will disproportionately gain from high
levels of public spending. While Mehay and Seiden acknowledge that municipal employees that
reside within the city they work for may be more productive or have lower information costs
then workers who live outside the city, the empirical results show any gains from increased
productivity are washed out by the public employees voting for increased spending.
In their examination of residency requirements, Gonzalez, Mehay, and Duffy-Deno
(1991) assume that public employee labor markets are characterized by conditions of excess
supply, and that public services are produced under non-competitive conditions. They find that
residency requirements increase the demand for police labor and has little effect on supply, thus
increasing total police employment but with no signifigant effect on wages. O’Brien, (1997)
compares four different hypotheses for a residency requirement’s effect on compensation and
employment. O’Brien finds residency laws do not effect compensation or employment for police
or firefighters, and he concludes that residency requirements have no effect on labor markets.
Finally, Duncan, (2005) examines how residency requirements affect neighborhood
choice by examining municipal wages and a family’s decision to enroll their children in private
school. Duncan theorizes that families will either enroll their children in private school or move
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to neighborhoods with better schools and hence pay more in taxes and rent. Duncan’s findings
showed that a comprehensive residency requirement attracts middle class families and
discourages urban flight, but in order for the city to achieve these desired results the city must
institute a wage premium of an additional dollar an hour.
MODELS
We employ hedonic pricing models to test our main hypotheses concerning house price
effects in the cities and suburbs of Cleveland, Dayton, and Akron following the Ohio Supreme
Court’s ruling upholding the ban on residency requirements. Specifically, the hedonic models are
designed to reveal the structure of house prices both before and after the 2009 residency
requirement ruling. In the first model, we look at both city and suburban housing markets to see
whether house prices fall more in cities after the residency requirement ruling. The first model is
presented in Equation 1:
𝑃𝑅𝐼𝐶𝐸𝑖𝑗𝑘𝑙 = 𝑒𝑥𝑝 �𝛽𝑃𝑖 + 𝛿𝐷𝑗 + �𝛿𝑘𝐶𝑂𝑈𝑁𝑇𝑌𝑘
2
𝑘=1
+ �𝜙𝑙
4
𝑙=1
𝑌𝐸𝐴𝑅𝑙 + 𝛾1𝑃𝑂𝑆𝑇 + 𝛾2𝐶𝐼𝑇𝑌 + 𝛾3𝑃𝑂𝑆𝑇𝐶𝐼𝑇𝑌 + 𝜀𝑖�. [1]
Where i is the home that sold in school district j, in county k and in year l. The dependent
variable is sales price, the β’s, δ’s, 𝜙’s, and 𝛾’s are parameters to be estimated and 𝜀 is a
composite error term. The explanatory variables are classified into vectors which describe the
physical attributes of the property, P, and the socio-economic characteristics of the school
district in which the parcel is located, D. The vector P includes the size of the lot in acres,
ACRES, total square feet of living space of the house, AREA, the number of bathrooms, BATHS,
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and the age of the house, AGE.14 Similar to Black (1999), we use socio-demographic variables to
control for neighborhood (district) attributes. The vector D includes a number of socio-economic
variables that describe the school district in which the parcel is located. The variables P_LATINO
and P_BLACK indicate the percent of the district that is Black or Latino, respectively.
P_AGE_0_9 and P_AGE_65 indicate the percent of household with children aged 0 to 9 years,
and the percent with adults over 65 years of age, respectively. District median income is revealed
by the variable MEDINC, and the percent of female-headed households is indicated by
P_FEMHEAD. Education attainment levels by district are measured with P_BACHELORS,
P_GRAD, and P_NOHIGHSHC which indicate the percent of households with bachelor’s
degrees, graduate degrees, and no high school diplomas respectively. In Model 1 we also control
for the county in which the parcel is located with two county indicator variables, COUNTY, and
for overall macroeconomic effects during the sample period with four year-specific dummy
variables, denoted YEAR.
We control for house price effects following the 2009 Supreme Court ruling by using a
POST dummy variable. This variable indicates that the sale occurred following the June 11, 2009
Supreme Court decision. The variable CITY distinguishes houses located within the city limits
from those in the suburbs. The coefficient γ2 reveals whether, in general, city house prices are
different from suburban house prices. The interaction term POSTCITY denotes sales in the city
that occur after the 2009 Ohio Supreme Court ruling. The coefficient γ3 is the percentage change
in house prices in the city relative to the suburbs after the residency requirement ban is upheld. If
the residency requirement has no effect on house prices in the city, we expect γ3 will be equal to
14 We do not use number of bedrooms because it is highly correlated with both area and number of bathrooms and often has a negative coefficient (Sirmans, Macpherson and Zietz, 2005).
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zero. If households leave the city for the suburbs after the 2009 ruling, we expect γ3 to be
negative.
In the second model, we look only at suburban housing markets surrounding those cities
with residency requirements, where house prices changes are related to school quality. Model 2,
which tests whether there are house price effects in higher quality suburban public school
districts, is presented in Equation 2:
𝑃𝑅𝐼𝐶𝐸𝑖𝑗𝑘𝑙 = 𝑒𝑥𝑝 �𝛽𝑃𝑖 + 𝛿𝐷𝑗 + �𝛿𝑘𝐶𝑂𝑈𝑁𝑇𝑌𝑘 + �𝜙𝑙𝑌𝐸𝐴𝑅𝑙
4
𝑙=1
+ (𝛾1 + 𝛾2𝑃𝑂𝑆𝑇)𝐼𝑁𝐷𝐸𝑋𝑗 + 𝜀𝑖
2
𝑘=1
�. [2]
School quality is measured by INDEX, an Ohio State Index measuring the education
quality of school districts. From Model 2 above, the coefficient 𝛾R1 is the percentage change in
house price with a one unit change in the school quality index. Recent literature on the
capitalization effects of school quality suggests price effects from school quality are small to
non-existent. (Ries and Somerville 2010, Bayer, Ferreira and McMillan 2007) Prior to the 2009
Ohio Supreme Court ruling, we expect school quality to be positively capitalized into house
prices after controlling for property and socio-economic characteristics. The coefficient 𝛾R2
reveals any additional effect on house prices following the Ohio Supreme Court’s 2009 ruling.
Following the 2009 court ruling, if there is no increase in demand for better quality suburban
school districts, we would expect 𝛾R2, the coefficient on the interaction term between the INDEX
and POST variables, to be equal to zero. On the other hand, if there is a positive housing demand
shock in higher quality suburban school districts, we would expect 𝛾R2 to be positive.
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It may be that any house price effects in the suburbs that are increasing in public school
quality are unrelated to residency requirements. For example, in a weak economy, households
may be less able to afford private schools, which can increase the housing demand in better
public school districts. To test this hypothesis Model 3 accommodates different price effects in a
suburban housing market having no residency requirements in the city. This is presented in
Equation 3:
𝑃𝑅𝐼𝐶𝐸𝑖𝑗𝑘𝑙 = 𝑒𝑥𝑝 �𝛽𝑃𝑖 + 𝛿𝐷𝑗
+ �𝛿𝑘𝐶𝑂𝑈𝑁𝑇𝑌𝑘 + �𝜙𝑙𝑌𝐸𝐴𝑅𝑙
4
𝑙=1
+ (𝛾1 + 𝛾2𝑃𝑂𝑆𝑇 + 𝛾3𝑅𝑅𝑃𝑂𝑆𝑇)𝐼𝑁𝐷𝐸𝑋𝑗 + 𝜀𝑖
3
𝑘=1
�.
[3]
In Model 3, the variable RR indicates sales in a housing market that has residency
requirements in the city. We again expect coefficient 𝛾R1 > 0, suggesting that school quality is
positively capitalized into house prices prior to the 2009 ruling. The coefficient 𝛾R2 reveals any
change in suburban house prices associated with school quality following the ban, regardless of
whether a city residency requirement is in force. The interaction term RRPOSTINDEX indicates
post-ruling sales in a housing market with residency requirements. If house price increases in the
suburbs are due to the residency requirement ban, then we would expect γ3 > 0. On the other
hand, if γ3 is not significantly different than zero, this would suggest that any post-ruling house
price effects found in Model 2 may be unrelated to residency requirements.
As in Model 1, in Model 4 we look at both city and suburban housing markets to see
whether house prices fall more in cities relative to suburbs following the Ohio Supreme Court’s
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ruling upholding the ban on residency requirements. However, we distinguish between houses
located in the city and also zoned for the city school district with those in the city but zoned for a
suburban school district. The major cities in the counties of Cuyahoga and Montgomery,
Cleveland and Dayton, respectively, had these anomalies, such that the school districts for these
cities (Cleveland Municipal School District and Dayton City School District) did not incorporate
the entire city.15 This model thus measures whether houses that sold within the boundaries of the
city but outside the boundary of the city school district (CSD) experienced the same drop in
house prices after the ruling. Model 4 is presented below:
𝑃𝑅𝐼𝐶𝐸𝑖𝑗𝑘𝑙 = 𝑒𝑥𝑝 �𝛽𝑃𝑖 + 𝛿𝐷𝑗 + �𝜙𝑙
4
𝑙=1
𝑌𝐸𝐴𝑅𝑙 + 𝛾1𝑃𝑂𝑆𝑇 + 𝛾2𝐶𝐼𝑇𝑌 + 𝛾3𝐶𝑆𝐷
+ (𝛾4 + 𝛾5𝐶𝑆𝐷)𝐶𝐼𝑇𝑌𝑃𝑂𝑆𝑇 + 𝜀𝑖�.
[4]
Here the variable CITY indicates whether a house is within the boundaries of Cleveland
or Dayton. The variable CSD indicates whether the house is located in the Cleveland Municipal
Schools District or the Dayton City School District, respectively. Thus, γ2 is the percentage
change in house price for a city location, and γ3 is the percentage change in price for a city
school district location. The coefficient on CITYPOST, γ4, reveals any house price changes in the
city generally following the 2009 ruling on residency requirements, while γ5 indicates any
additional percentage change in price for city houses that are zoned for a suburban school
district. Thus, the total percentage change in price for city houses zoned in suburban school
15 Specifically the City of Cleveland includes part of Shaker Heights City School District and the City of Dayton includes parts of Huber Heights City School District, Jefferson Township Local School District, Mad River Local School District, Northridge Local School District, and Trotwood-Madison City School District.
15
districts is γ4 , and the total percentage change in price for city houses in the city school district
is γ4 + γ5. If, after the residency requirement ban, households leave cities for reasons
independent of school district quality, we would expect γ5 to be zero. On the other hand, we
expect γ5 to be negative if the decrease in housing demand is due to a city school district
location, rather than a city location generally.
DATA
To measure the effect of Ohio’s statewide residency requirement ban on house prices we
use housing sales data from January 2007 to December 2011 from multiple Ohio counties. This
five-year window, which takes place during the national housing market downturn, provides us
with two and one half years of sales both before and after the mid-2009 residency requirement
ruling.16 Housing transactions are collected from the auditor’s offices of Summit County,
Cuyahoga County, Montgomery County, and Hamilton County, where the cities of Akron,
Cleveland, Dayton and Cincinnati are located, respectively.17 As discussed earlier, citywide
residency requirements continued to be enforced in the cities of Cleveland, Akron and Dayton
until the mid-2009 ruling. On the other hand, the City of Cincinnati required its employees to
reside within Hamilton County rather than within the city of Cincinnati. The housing transaction
data includes information on sale price, sale date, sale validity, location, and various housing
characteristics. In total, the four-county sample contains 64,570 housing transactions.18
16 The sample period is from January 1, 2007 December 31, 2011, with the exception of Cuyahoga County, where sales are only available through September 2011. 17 Thanks to Shirley DeCheco (Summit County), Paula Drake (Hamilton County), Bill Buckholtz (Cuyahoga County) and Elaine Johnson (Montgomery County) for helping us collect this information. 18 To make sure mobile homes are not in the sample we restrict minimum lot size to be one-tenth of an acre and we eliminate any sales with less than 500 square feet of living space. We also eliminate sales of homes having a sale price of less than $10,000.
16
To measure school district quality we use data from the Ohio Department of Education,
which provides public school performance metrics by school district. Specifically, we use the
Ohio performance index to measure school quality (INDEX) from the 2009-2010 school year.19
The INDEX is based on a 120 point system, and measures students’ achievement levels for each
school district.
Socio-economic data for the analysis is obtained from the American Community Survey
and the U.S. Census Bureau. Socio-economic data are the 2009 five-year estimates which the
ACS weights by school district. Table 1 lists each variable and its definition. Tables 2 and 3
provide descriptive statistics for the four counties of Cuyahoga (Cleveland), Montgomery
(Dayton), Summit (Akron) and Hamilton (Cincinnati). Specifically, Table 2 provides summary
statistics for all variables while Table 3 provides summary statistics for school performance
variables by county.
Descriptive statistics for the four counties combined are presented in Table 2. The
average sale price in the study area sold for $140,616.20 On average, lots (ACRES) are 0.40
acres. Houses in the sample are, on average, 60 years old, around 1,500 square feet in size, and
have 1.5 baths. The Ohio Supreme Court ruling occurs almost exactly mid-way through the
sample period, and around half of all sales, 46%, occur after this date (POST). The majority of
house sales were in the suburbs, with 26% of sales occurring in one of the major cities in the
analysis (CITY). In total, 43% of home sales were in Cuyahoga County, 19% were in
Montgomery County, 7% were in Summit County, and the remaining 30% of sales were in
Hamilton County.
19 We choose to use the 2009 – 2010 school year both because it is mid-sample period and because that is the most recent school year before the June 2009 Ohio State Supreme Court decision. 20 When an imposed maximum of $400,000 was used to eliminate outliers the results on the variables of interest did not change.
17
The majority of the population in our study area is white. Table 2 shows the average
percent of Latinos (not Black) in a district is 2.4%, and the percent of Blacks (not Latino) is
21.6%. Less than 13% of households have children under the age of 10, and the mean number of
households with a person aged 65 or older is 14%. The average median household income was
$55,794. About 25% of households (with children) are headed by a female. On average, under
19% of households have a bachelor’s degree, over 11% of households have a graduate degree
and over 11% of households didn’t graduate high school.21 The average Ohio Performance Index
(measuring school quality) for a household was 92.1, with a sample minimum of 72.5, and a
maximum of 110.5.
Table 3 displays school performance measures for each county’s major city school
districts, the comparable measure for that county’s suburban districts (on average), and provides
the city school district ranking within the county. In addition to the performance index measure
(INDEX), Table 3 includes other measures of school performance at the district level, including
average SAT and ACT scores, average attendance and graduation rates. From Table 3 we see
that the city school districts of Cleveland, Dayton and Akron, are among the worst in their
respective counties across all school quality measures. In Cuyahoga County, the Cleveland
Municipal City School District is ranked last in the county in both attendance and graduation
rates, and is near the bottom for the performance index, ACT and SAT scores. In Montgomery
County, the Dayton City School District is ranked last in the county across all 5 performance
measures provided. In Summit County, the Akron City School District is ranked last in the
performance index, graduation rate and ACT scores, and next to last in attendance and SAT
scores. Certainly, the relatively poor performance of the Cleveland, Dayton and Akron public
21 The socio demographic variables in Table 2 are weighted by the number of home sales that occurred in the school district.
18
school districts in comparison to the surrounding suburban school districts may be a major factor
in a municipal employee’s decision to leave the cities of Cleveland, Akron or Dayton following
the residency requirement ban.22 By contrast, in Hamilton County, where there was no residency
requirement, the Cincinnati City School District is in the top half of Hamilton County districts in
SAT scores and attendance rates (although last in the county in graduation rates, and near the
bottom in ACT scores and the Ohio performance measure).
ESTIMATION AND RESULTS
To estimate the models a logarithmic transformation is used. We also include zip code
fixed effects to control for differences in neighborhood quality that may affect home values or
bias the results for school quality if better schools are located in nicer neighborhoods (Black,
1999).
The results from Model 1 are presented in Table 4. We find consistent results across all
models concerning the housing attributes variables; house prices increase with lot size, building
area and the number of baths, and decrease with building age. We find consistent results across
all models concerning the socio-demographic variables, although these can vary across
individual counties. Housing prices are generally lower the higher the percentage of Blacks and
Latinos in a district, respectively, the higher the percentage of households with children under 10
years of age, and the higher the percentage of female-headed households. A greater percentage of
households with bachelors and graduate degrees increases house prices, but, curiously, so does 22Shaker Heights City School District was ranked 18th in Cuyahoga the Ohio performance index measuring school quality out of 31 possible school districts. There were 299 house sold in our sample period that were in Cleveland but not Cleveland Municipal School District. Huber Heights City School District was rank 10th, Jefferson Township Local School District was ranked 15th, Mad River Local School District was ranked 11th, Northridge Local School District, was ranked 13th and Trotwood-Madison City School District was ranked 14th out of the 16 school districts in Montgomery county in the Ohio Performance index measuring school quality. There were 96 houses that sold in Dayton not in the Dayton City School district in our sample.
19
the percentage of households with no high school diploma. Finally, as expected, house prices are
falling over the 2007-2011 sample period.
The first column of Table 4 shows the regression results for all the counties with
residency requirements combined. The combined results from Cuyahoga, Summit, and
Montgomery Counties indicate an average post-ruling house price change of -8.4% in cities.
Columns 2 through 4 display results for each residency requirement county individually. The
POSTCITY variable is negative in all three counties, but is only statistically significant in
Cuyahoga and Montgomery Counties, where the estimated house price changes are -3.8% in
Cleveland, and -11.1% in Dayton. In contrast, in Column 5 we see that in Hamilton County,
where there were no city-wide residency requirements, the coefficient on POSTCITY is positive.
Our results suggest that the ban on residency requirements may have caused house prices in the
cities to decline relative to suburban housing prices after controlling for differences between
these markets generally.
Results from Model 2 are presented in Table 5. Prior to the residency requirement ruling,
we find some capitalization of public school quality in the suburban markets of Cuyahoga,
Montgomery and Summit Counties. Prior to the 2009 ruling, house prices are 0.69% higher with
every additional point in Ohio Performance Index measuring school district quality. However,
following the 2009 ruling, we find an additional increase in house price of 0.17% with every
additional point in this same index. Our results suggest an increase demand for houses in higher
quality suburban school districts following the Ohio Supreme Court ruling. These findings are
consistent with those from Model 1, and support a flight to quality public schools on the part of
municipal employees.
20
As discussed earlier, it may be that these suburban house price effects are due to an
increase in demand for better quality public schools that is unrelated to residency requirements.
Thus, we must test whether house prices increases in the suburbs are more pronounced in
markets in cities with residency requirements following the 2009 ruling. These results, based on
the combined suburban markets of Cuyahoga, Summit, Montgomery and Hamilton Counties, are
presented in Table 6.
In Table 6 we again see some capitalization of public school quality in the suburban
markets, but a post-ruling house price decrease in school districts with each additional point on
the performance index. Together the parameter estimates on INDEX and POSTINDEX suggest a
total change in price of 1.1% with every additional point in the performance index in the suburbs
of Hamilton County following the 2009 ruling. There is an additional effect of 0.27% per point
following the 2009 ruling in those suburbs with residency requirements, for a total house price
increase of 1.4% per performance index point following the ruling.
The results for Model 4 are displayed in Table 7. Here, we perform separate regressions
on Cuyahoga and Montgomery Counties, respectively. In Cuyahoga County, as seen by the
parameter on CITYPOST, we find no significant price changes for houses in the City of
Cleveland following the 2009 residency requirement ruling, and thus no price changes to city
houses that are zoned for a suburban school district. However, the coefficient on CITYCSDPOST
is -0.1182, and suggests an additional house price decline of 11.1% for these Cleveland houses
that are also in the Cleveland Municipal School District following the 2009 ruling. In
Montgomery County, we find a price increase for houses in the City of Dayton that are zoned in
a suburban school district following the 2009 residency requirement ruling. The additional house
price declines of 34% for City of Dayton houses that are in the Dayton City School District, for a
21
total post-ruling change in house prices of -12.0% for these properties. The results of this natural
experiment suggest that public school quality, rather than a city address, is what affects a
municipal employee’s decision to move when residency requirements are lifted.
CONCLUSION
Residency requirements, which force municipal employees to live in or near the cities in
which they work, were once common practice in America’s largest cities. Today they have fallen
out of favor, in large part because they restrict housing choice. In 2006, the Ohio General
Assembly banned residency requirements, a controversial action which was ultimately upheld by
the Ohio Supreme Court in 2009.
We test the effect of Ohio’s residency requirement ban on housing markets of Cleveland,
Dayton and Akron, all of which maintained their residency requirements until the 2009 ruling.
Following the 2009 ruling, we find that house prices decrease in these cities relative to their
suburbs, where public school quality tends to be higher. In Cincinnati, where there was no city-
wide residency requirement, we find no such effect following the Ohio Supreme Court ruling.
We also examine suburban housing markets surrounding the cities of Cleveland, Dayton and
Akron, respectively, and find evidence of house prices increases in the suburbs that are
associated with increases in public school performance. We find evidence of price increases in
the suburbs of Cincinnati as well, although the effect is of a smaller magnitude. Finally, we
conduct a natural experiment in Cuyahoga and Montgomery counties, where there are houses
located within the city limits that are zoned for a suburban school district. Following the
residency requirement ban, we find that house prices decline between 11% - 12% on those city
properties that are zoned for the city school districts. Our findings suggest there are housing
22
market effects from municipal workers leaving cities and their underperforming public schools
for the suburbs when residency requirements are eliminated.
As mentioned earlier, house prices were declining during the sample period. These house
price declines, which broadly commenced in 2006, resulted in many homeowners with negative
equity in their homes, and unable to move. Moreover, tightening credit standards following the
housing market crash also may have prevented those homeowners with marginal credit quality
wishing to leave the cities from doing so. Thus, we suspect our estimates of house price effects
from the ruling on residency requirements are conservative. In addition, because our post-
window is only 2.5 years, we are only measuring short-terms effects from the ban on residency
requirements. For example, some households with school-aged children may choose to wait until
their children are of a certain age before moving to avoid disruption in the child’s school
experience.
Our findings have several policy implications. First, for cities like Milwaukee, WI, a
decrease in property values from the removal of the city’s residency requirement is a valid
concern. On the other hand, because most major cities have abolished residency requirements,
our findings can be used to argue for their reinstatement as a means to bolster a tax base
weakened by the current economic climate. Finally, our results suggest that allowing municipal
workers subject to residency requirements flexibility in school district choice may lessen the
extent of adverse economic impacts when such requirements are eliminated.
23
Table 1
Variable Definitions Variable Definition PRICE Sale price of the property in dollars ACRES Number of acres of the lot AREA Square feet of house BATHS Number of bathrooms AGE Age of house RR Dummy for if house sold in a county with a city with a major residency requirement POST Dummy for if house was sold after June 11th 2009 CITY Dummy for if house is in city limits of Cleveland, Akron, Dayton, or Cincinnati. CUY Dummy for if house is in Cuyahoga county HAM Dummy for if house is in Hamilton county MON Dummy for if house is in Montgomery county SUM Dummy for if house is in Summit county YEAR2008 Dummy for if house was sold in 2008 YEAR2009 Dummy for if house was sold in 2009 YEAR2010 Dummy for if house was sold in 2010 YEAR2011 Dummy for if house was sold in 2011 P_LATINO Percent Latino (not Black) in school district P_BLACK Percent Black (not Latino) by school district P_AGE_0_9 Percent of households with children age 0 to 9 years by school district P_AGE_65 Percent of households with adults over 65 years by school district MEDINCOME Median Income of households of school district P_FEMHEAD Percent of households with children and female only head of household by school district P_BACHELORS Percent of households with bachelor’s degree by school district P_GRAD Percent of households with graduate degree by school district P_NOHIGHSCH Percent of households with no high school diploma INDEX Ohio State School District Index measuring school quality based out of 120 points. Sources. Housing transactions were collected from the auditor’s offices of Summit County, Cuyahoga County and Montgomery County, where Akron, Cleveland, and Dayton are located, respectively. Public school data comes from the Ohio Department of Education website. Socio-demographic information is obtained from the American Community Survey 2009 – 5 year estimates.
24
Table 2
Housing and demographic characteristics descriptive statistics for Cuyahoga, Hamilton, Montgomery and Summit Counties
Variable N Mean Std Dev Minimum Maximum PRICE 64,570 140,616 141,530 10,001 7,000,000 ACRES 64,570 0.40 1.22 0.10 117.05 AREA 64,570 1,476 716 502 13,914 BATH 64,570 1.45 0.66 1.00 6.00 AGE 64,570 60.00 25.85 0.00 202.00 RR 64,570 0.70 0.46 0.00 1.00 CITY 64,570 0.26 0.44 0.00 1.00 POST 64,570 0.46 0.50 0.00 1.00 CUY 64,570 0.43 0.50 0.00 1.00 MON 64,570 0.19 0.39 0.00 1.00 HAM 64,570 0.30 0.46 0.00 1.00 SUM 64,570 0.07 0.25 0.00 1.00 INDEX 64,570 92.05 11.83 72.50 110.50 P_LATINO 64,570 2.38 2.21 0.00 8.70 P_BLACK 64,570 21.60 21.31 0.00 92.80 P_AGE_0_9 64,570 12.47 1.60 7.10 18.20 P_AGE_65PLUS 64,570 14.01 3.55 5.90 33.60 MEDINC 64,570 55,794 21,348 24,768 122,074 P_FEMHEAD 64,570 25.24 17.30 0.00 71.20 P_BACHOLORS 64,570 18.20 8.06 3.80 39.90 P_GRAD 64,570 11.74 7.96 1.10 38.40 P_NOHIGHSCH 64,570 11.53 6.48 1.50 29.20 YEAR2008 64,570 0.20 0.40 0.00 1.00 YEAR2009 64,570 0.20 0.40 0.00 1.00 YEAR2010 64,570 0.18 0.39 0.00 1.00 YEAR2011 64,570 0.16 0.37 0.00 1.00
25
Table 3 School District Statistics
Suburban Cuyahoga
County
Cleveland Municipal
City School District
Rank (Out of 31)
Index 98.0 71.5 29 Attendance 95.3 90.7 31 Graduation Rate 93.6 54.3 31 Mean ACT 21.7 16 30 Mean SAT 1048.3 864 28
Suburban Montgomery
County
Dayton City School District
Rank (Out of 16)
Index 97.6 75.9 16 Attendance 95.0 91.3 16 Graduation Rate 94.4 79.8 16 Mean ACT 21.7 17 16 Mean SAT 1018.9 904 16
Suburban Summit County
Akron City School District
Rank (Out of 17)
Index 102.0 84.5 17 Attendance 95.3 93.2 16 Graduation Rate 95.7 76.0 17 Mean ACT 22.1 19 17 Mean SAT 1086.3 1046 16
Suburban Hamilton County
Cincinnati City School
District
Rank (Out of 22)
Index 99.9 87.3 21 Attendance 95.1 95.2 10 Graduation Rate 94.2 80.4 22 Mean ACT 21.6 19 18 Mean SAT 1041.8 1042 10 Note. Note suburban county excludes major city in mean calculation.
26
Table 4
Cuyahoga, Montgomery, Summit and Hamilton Counties- Cities and Suburbs
Variable Res. Req. Counties
Combined
Cuyahoga County
Montgomery County
Summit County
Hamilton County
Intercept 11.3917 *** 11.7727 *** 12.6358 *** 10.4599 *** 10.8924 *** (0.2612)
(0.1082)
(0.229)
(0.4855)
(0.1297)
LNACRE 0.1129 *** 0.1424 *** 0.1267 *** 0.0694 *** 0.1983 *** (0.017)
(0.0076)
(0.0091)
(0.0104)
(0.0065)
AREA 0.0003 *** 0.0003 *** 0.0003 *** 0.0002 *** 0.0003 *** (0.00002)
(0.00001)
(0.00001)
(0.00001)
(0.00001)
BATH 0.085 *** 0.0813 *** 0.0528 *** 0.1222 *** 0.2098 *** (0.0126)
(0.0096)
(0.0106)
(0.0161)
(0.0055)
AGE -0.0042 ** -0.0039 *** -0.007 *** -0.0069 *** -0.0135 *** (0.002)
(0.0007)
(0.001)
(0.0009)
(0.0004)
AGE_SQ -0.000008
-0.000016 *** 0.000007
0.000016 *** 0.000061 *** (0.00001)
(0.00001)
(0.00001)
(0.00001)
(0.000003)
MON -0.159 *** (0.0406) SUM -0.2479 *** (0.0457) POST 0.1752 *** 0.1892 *** 0.0974 *** 0.0853 ** 0.049 ***
(0.0197)
(0.0156)
(0.0218)
(0.0383)
(0.0146) CITY 0.0464
-0.1216 *** 0.0598 ** -0.3412 *** -0.8612 ***
(0.0728)
(0.0336)
(0.0267)
(0.0997)
(0.0378) POSTCITY -0.0875 ** -0.0391 ** -0.118 *** -0.0528
0.0424 ***
(0.0423)
(0.0199)
(0.0193)
(0.0493)
(0.0121) P_LATINO -0.0093
-0.0579 *** 0.0242
0.0447
-0.1226 ***
(0.0142)
(0.0066)
(0.0184)
(0.058)
(0.0079) P_BLACK -0.0068 *** -0.0092 *** 0.002 * 0.0058 * 0.0179 ***
(0.0011)
(0.0004)
(0.0011)
(0.0031)
(0.001) P_AGE_0_9 -0.0012
-0.0523 *** -0.0407 *** -0.0669 *** 0.1207 ***
(0.012)
(0.0064)
(0.0111)
(0.0186)
(0.0073) P_AGE_65PLUS 0.0079
-0.01 *** -0.0286 *** 0.0366 *** 0.0368 ***
(0.006)
(0.0021)
(0.0077)
(0.0141)
(0.0037) MEDINC -0.000004 * -0.000008 *** -0.000006 ** 0.000007 * -0.000016 ***
(0.000002) (0.000001)
(0.000003)
(0.000004)
(0.000002)
P_FEMHEAD -0.0024
-0.0087 *** -0.0088 *** -0.0015
-0.0744 *** (0.0017)
(0.0008)
(0.0018)
(0.0016)
(0.0062)
P_BACHOLORS 0.0193 *** 0.0402 *** 0.0202 *** 0.0308 *** -0.0134 *** (0.0055)
(0.0019)
(0.0052)
(0.0098)
(0.0028)
P_GRAD 0.0124 *** 0.0194 *** 0.0108 ** -0.0183
0.051 *** (0.0044)
(0.0014)
(0.0055)
(0.0146)
(0.0045)
P_NOHIGHSCH -0.0024
0.0639 *** 0.0089
0.0405 ** 0.0276 *** (0.0111)
(0.0043)
(0.0062)
(0.0206)
(0.005)
YEAR2008 -0.1939 *** -0.2548 *** -0.0857 *** -0.0985 *** 0.036 *** (0.0239)
(0.0099)
(0.0118)
(0.0235)
(0.009)
27
YEAR2009 -0.352 *** -0.4063 *** -0.229 *** -0.2404 *** -0.0281 ** (0.0297)
(0.0144)
(0.0197)
(0.0343)
(0.0134)
YEAR2010 -0.4055 *** -0.3954 *** -0.4145 *** -0.2021 *** -0.0328 ** (0.0316)
(0.0184)
(0.0248)
(0.0449)
(0.0166)
YEAR2011 -0.4953 *** -0.5051 *** -0.4614 *** -0.2816 *** -0.183 *** (0.0335)
(0.0192)
(0.0249)
(0.0446)
(0.0165)
R-SQUARE 0.568
0.566
0.658
0.590
0.674 # OBS. 45,348
28,356
12,572
4,420
19,750
Note. The dependent variable is the natural log of the price the home sold for. Zip code level fixed effects are used. Standard errors are in parentheses.
* Significant at the 10% level ** Significant at the 5% level *** Significant at the 1% level
28
Table 5
Cuyahoga, Montgomery and Summit Counties - Suburbs
Variable Coefficient INTERCEPT
11.1814 *** (0.1356) LNACRE
0.1138 ***
(0.0046) AREA
0.0003 *** (0.00001) BATH
0.0923 ***
(0.0064) AGE
-0.0041 *** (0.0004) AGE_SQ
-1.00E-06 (0.000003) MON
-0.1355 ***
(0.0228) SUM
-0.1282 *** (0.0296) INDEX
0.0069 ***
(0.0013) POSTINDEX
0.0017 *** (0.0001) P_LATINO
-0.015 ***
(0.0058) P_BLACK
-0.0058 *** (0.0005) P_AGE_0_9
-0.0521 ***
(0.0045) P_AGE_65PLUS
-0.0081 *** (0.0019) MEDINC
-0.00001 ***
(0.000001) P_FEMHEAD
-0.0084 *** (0.0006) P_BACHOLORS
0.0311 ***
(0.0015) P_GRAD
0.0143 *** (0.0011) P_NOHIGHSCH
0.04 ***
(0.0028) YEAR2008
-0.1972 *** (0.0078) YEAR2009 -0.3561 ***
29
(0.0109) YEAR2010
-0.3779 *** (0.0144) YEAR2011
-0.4763 ***
(0.0148) R-SQUARE 0.5901 Note. There are 34,836 housing transactions. The dependent variable is the natural log of the price the house sold for. Zip code level fixed effects are used. Standard errors are in parentheses. * Significant at the 10% level ** Significant at the 5% level *** Significant at the 1% level
30
Table 6
Cuyahoga, Montgomery, Summit and Hamilton Counties - Suburbs Only
Variable Coefficient Intercept 10.7058 *** (0.119) LNACRE 0.1557 *** (0.0039) AREA 0.0003 *** (0.000005) BATH 0.1679 *** (0.0048) AGE -0.0077 *** (0.0003) AGE_SQ 0.00002 *** (0.000002) HAM -0.1805 *** (0.021) MON -0.2319 *** (0.0256) SUM 0.0852 * (0.0436) INDEX 0.012 *** (0.0011) POSTINDEX -0.0012 *** (0.0001) RR_POSTINDEX 0.0027 *** (0.0002) P_LATINO -0.0201 ***
(0.0042) P_BLACK -0.0035 ***
(0.0004) P_AGE_0_9 -0.0121 ***
(0.0031) P_AGE_65PLUS -0.0022 (0.0015) MEDINC -0.00001 ***
(0.0000004) P_FEMHEADCHILD -0.0062 ***
(0.0004) P_BACHOLERS 0.023 ***
(0.0012) P_GRAD 0.0151 ***
(0.0009) P_NOHIGHSCH 0.0244 ***
31
(0.0022) YEAR2008 -0.1456 ***
(0.0064) YEAR2009 -0.2986 ***
(0.0091) YEAR2010 -0.3137 ***
(0.0118) YEAR2011 -0.4248 *** (0.012) R-SQUARE 0.6132 Note. There are 47,983 housing transactions. The dependent variable is the natural log of the price the house sold for. Zip code level fixed effects are used. Standard errors are in parentheses. * Significant at the 10% level ** Significant at the 5% level *** Significant at the 1% level
32
Table 7 Variable Cuyahoga County Montgomery County Intercept 11.7566 *** 12.6036 ***
(0.1136) (0.2211) LNACRE 0.1431 *** 0.1284 ***
(0.0076) (0.009) AREA 0.0003 *** 0.0003 ***
(0.00001) (0.00001) BATH 0.0811 *** 0.0526 ***
(0.0096) (0.0106) AGE -0.0039 *** -0.0067 ***
(0.0007) (0.001) AGE_SQ -0.00002 *** 0.00001 (0.00001) (0.00001) POST 0.1892 *** 0.0968 ***
(0.0156) (0.0218) CITY -0.1451 *** 0.0215 (0.0479) (0.0293) CSD 0.0028 -0.0795 (0.0635) (0.0591) CITYPOST 0.1432 0.2887 ***
(0.0906) (0.0784) CITYCSDPOST -0.1882 ** -0.4168 ***
(0.0913) (0.0785) P_LATINO -0.0559 *** 0.0326 *
(0.0068) (0.0184) P_BLACK -0.0092 *** 0.0018 (0.0004) (0.0013) P_AGE_0_9 -0.0521 *** -0.0352 ***
(0.0064) (0.0124) P_AGE_65PLUS -0.0102 *** -0.0246 ***
(0.0021) (0.0086) MEDINC -0.00001 *** -0.00001 *
(0.000001) (0.000003) P_FEMHEAD -0.0088 *** -0.0086 ***
(0.0008) (0.0019) P_BACHOLORS 0.0407 *** 0.0178 ***
(0.002) (0.0061) P_GRAD 0.0195 *** 0.0092 *
(0.0014) (0.0053) P_NOHIGHSCH 0.065 *** 0.0049 (0.0047) (0.0069) YEAR2008 -0.2548 *** -0.0853 ***
(0.0099) (0.0117) YEAR2009 -0.4064 *** -0.2298 ***
33
(0.0144) (0.0197) YEAR2010 -0.3956 *** -0.4128 ***
(0.0184) (0.0248) YEAR2011 -0.5046 *** -0.4611 ***
(0.0192) (0.025) R-SQUARE 0.566 0.6589 # Observations 28,355 12,572 Note. The dependent variable is the natural log of the price the home sold for.
Zip code level fixed effects are used. Standard errors are in parentheses. * Significant at the 10% level ** Significant at the 5% level *** Significant at the 1% level
34
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