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Assessment Growth Limits and Mobility: Evidence from Home Sale Data in Detroit, Michigan


Abstract and Figures

In 1994 the State of Michigan imposed a limit on the growth of property values for tax purposes. The assessment growth cap resulted in the emergence of a differential in effective tax rates between new and long-time property owners. This article examines the degree to which this differential creates a lock-in effect. Using parcel-level data from the City of Detroit, we find that homeowners who have lower effective tax rates are less likely to sell their properties; the average duration of property ownership is 7.5 years longer as a result of the assessment growth cap.
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National Tax Journal, September 2015, 68 (3), 573–600
Timothy R. Hodge, Gary Sands, and Mark Skidmore
In 1994 the State of Michigan imposed a limit on the growth of property values
for tax purposes. The assessment growth cap resulted in the emergence of a dif-
ferential in effective tax rates between new and long-time property owners. This
article examines the degree to which this differential creates a lock-in effect. Using
parcel-level data from the City of Detroit, we nd that homeowners who have lower
effective tax rates are less likely to sell their properties; the average duration of
property ownership is 7.5 years longer as a result of the assessment growth cap.
Keywords: property tax, assessment growth limit, mobility, lock-in effect
JEL Codes: H71
number of states have limitations on the growth rate of property value assessments.1
The primary purpose of these limitations is to ensure that property tax burdens do not
increase too quickly, especially during periods of rapidly rising housing prices. Despite
their popularity, taxable value growth caps have the potential to generate several layers
of undesirable outcomes. In addition to creating both horizontal and vertical inequities
(Skidmore, Ballard, and Hodge, 2010; Hodge et al., forthcoming), there is evidence that
taxable value growth caps reduce household mobility (i.e., create a “lock-in” effect) as
long-time property owners enjoying substantial tax burden reductions, relative to new
homeowners, must forfeit their benets once they move.
Timothy R. Hodge: Ford Motor Credit Company, Dearborn, MI, USA (
Gary Sands: Urban Planning Program, Wayne State University, Detroit, MI, USA (
Mark Skidmore: Department of Agricultural, Food, and Resource Economics and Department of Economics,
Michigan State University, Lansing, MI, USA (
1 O’Sullivan, Sexton, and Sheffrin (1995) offer a comprehensive discussion of tax revolts and the rise of
assessment growth limits. Haveman and Sexton (2008) identify at least 20 states that have some sort of
assessment growth limitation. Such limitations have been referred to as “assessment growth caps,” “taxable
value growth caps,” “assessment growth limits,” and “property value assessment limits.” These terms are
used interchangeably in this article.
National Tax Journal
In this article we examine the degree to which Michigan’s taxable value growth
limit has created a “lock-in” effect in Detroit, Michigan. There are now several studies
that examine the “lock-in” effect in other states and contexts. Our study offers several
contributions to this body of research. First, we use detailed parcel-level data to exam-
ine the degree to which tax savings created by the taxable value growth cap affect the
probability of property sale. Second, we look at this issue in the context of a different
state (Michigan) and in a single jurisdiction (Detroit) in which all properties receive
services from the same overlying government entities. Further, we consider the issue
in the context of a faltering housing market. In Detroit, housing values have fallen
dramatically in the wake of the Great Recession, and yet the differential in effective
tax rates between long-time and new property owners created by the taxable value cap
persists. Finally, we estimate lock-in effect across different property value groups and
areas of the city where housing and neighborhood characteristics may differ substantially.
Although cities across North America have suffered during the Great Recession,
Detroit is among the worst hit. As reported in Table 1, information on the recession’s
impacts for the 100 largest U.S. metropolitan areas from the Brookings Metro Monitor
report (Friedhoff and Kulkarni, 2013) shows that Detroit ranks well below comparable
areas like Cleveland and Pittsburgh; only Youngstown is similar to Detroit.
In addition, 47 percent of property owners were delinquent in their tax payments in
2012 (MacDonald and Wilkinson, 2013). Uncollected taxes amount to $131 million, or
about 12 percent of the City of Detroit’s general fund budget in FY2012. In 2013 Michi-
gan Governor Snyder appointed an Emergency Financial Manager who subsequently
sought bankruptcy protection for the City of Detroit. In December 2014 the City of
Detroit emerged from Chapter 9 bankruptcy, with Judge Stephen Rhodes approving a
plan to reduce $7 billion of the City’s estimated $18 billion debt load. The bankruptcy
came to a close within 15 months, much less time than many experts expected. While
the bankruptcy provided a badly need debt and scal reset, Detroit policymakers are
now turning their attention to larger socio-economic and long-run scal challenges in
order to avoid the reemergence of nancial problems in the future.
In particular, the property tax is of paramount concern. Of relevance for the present
research and as we discuss in greater detail later, assessment practices in Detroit are
equally as important as the taxable value cap in maintaining lower effective tax rates for
owners with long tenure. According to parcel-level data provided by the City of Detroit
Assessment Division, the average selling price of a single-family residential home
in 2011 was just $7,000, evidence of a severely troubled housing market.2 However,
assessments have not kept pace with the rapidly declining housing market. As shown
by Hodge, et al. (forthcoming), assessed values are about ve times higher than they
should be if assessments accurately reected market conditions. The persistence of
lower effective tax rates for long-time homeowners is the result of assessors failing to
2 The gure is based on parcels that sold as “arm’s length” transactions. Specically, parcels that were
transferred to banks or other nancial institutions and all properties with a zero price were excluded.
Assessment Growth Limits and Mobility in Detroit, Michigan
Table 1
The Great Recession in Select Midwest Metropolitan Areas
Recession Rank
Employment Peak
to Trough
(Number of Quarters)
Area Overall Recovery
Product Employment Unemployment
Detroit 98 91 99 98 93 21
Cleveland 60 65 79 71 13 13
Pittsburgh 6 15 20 9 16 7
Youngstown 82 75 96 92 79 16
Source: Friedhoff and Kulkarni, 2013
National Tax Journal
reduce assessments as the housing market declined. It is in the context of bankruptcy,
high delinquency, and misaligned assessments that we examine the degree to which the
taxable value growth cap has reduced mobility and deterred housing market transactions.
Detroit’s current circumstances are extreme, but hardly unique. Detroit is not the only
city that is experiencing scal challenges, which are in large part due to tax base ero-
sion and the underfunding of retiree compensation. As one illustration, Chicago Mayor
Rahm Emmanuel recently stated, “Should Chicago fail to get pension relief soon, we
will be faced with a 2015 budget that will either double city property taxes or eliminate
the vital services that people rely on.”3 Further, many cities, including Chicago, have
some form of assessment growth limit, which may exacerbate the scal challenges
by narrowing the property tax base and creating inefciencies in the housing market.
Understanding the effects of policies like assessment growth caps, even in an extreme
circumstance such as Detroit, provides a useful contribution to policy discussions in
other locations. Our evaluation provides evidence of a lock-in effect even in the midst
of a faltering local housing market; if a lock-in effect persists in such adverse housing
market conditions, it may persist in other markets as well. Further, such knowledge
may be useful in averting crises in other cities.
In order to understand the “lock-in” effect, it is important to know the recent history
of the property tax in Michigan. Prior to 1994 property taxation in Michigan had two
key characteristics. First, public schools were nanced almost exclusively through local
property taxes. The substantial variation among school districts in the value of taxable
property led to large differences among school districts in expenditure per student.
Second, the overall level of property taxation was well above the national average.4
These features were the source of dissatisfaction among voters and prompted a series
of reform measures (Feldman, Courant, and Drake, 2003).
In December 1993, Michigan voters adopted Proposal A. A key feature of this reform
was the imposition of the taxable value cap, which limited the growth in property value
for tax purposes to the rate of ination or 5 percent, whichever is lower.5 Over time, the
3 Lyman, Rick, 2013. “Chicago Pursues Deal to Change Pension Funding.” The New York Times, December
4 Data from the U.S. Census Bureau for state and local government nances are available online at http:// Before Proposal A became effective, property taxes typically
accounted for about 41 percent of state and local tax revenues in Michigan, well above the national average
of about 30 percent. After Proposal A, property taxes accounted for a percentage of Michigan revenues
that was very close to the national average. Michigan’s property taxes remained slightly above the national
average when measured on a per-capita basis, but the difference between Michigan and the U.S. average
was greatly reduced.
5 Prior to Proposal A, property tax revenues were limited by the Headlee Amendment (1978). The Headlee
Amendment was an earlier attempt at property tax reform (see Skidmore, Ballard, and Hodge (2010) for
further discussion).
Assessment Growth Limits and Mobility in Detroit, Michigan
taxable value cap led to signicant differentials between long-time and new property
owners; this is especially true in places with relatively high property value growth
(Skidmore, Ballard, and Hodge, 2010). Along with the taxable value cap Proposal A also
introduced a distinction between “homestead property” and “non-homestead property,”
where the homestead is dened as the homeowner’s principal residence. For homestead
property, Proposal A limited the statutory property tax millage rate that could be used
for public school operating expenses. This is known as the “homestead exemption,”
since it does not apply to non-homestead property. For homestead properties, average
statutory millage rates were reduced by about one-third.6 However, the reduction in
millage rates varied across jurisdictions; in 2010, homestead properties in the City of
Detroit, where tax rates are particularly high, received a 17.83 mill reduction in their
statutory rate relative to non-homestead properties (a 21 percent reduction).7 The state
government then added a 6 mill “state education tax,” and increased sales and cigarette
taxes to provide for the nancing of elementary and secondary public education.8
Because of the taxable value growth cap, it is important to make a distinction between
the statutory property tax rate and the effective tax rate. The statutory tax rate is the mill-
age rate applied to the tax base. Detroit taxpayers face statutory millage rates that are
much higher than the statewide average; the total 2010 millage rate for owner-occupied
residential properties was 67.32 mills per $1,000 of taxable value (TV).9
For some homeowners, this tax burden is reduced by Proposal A. The effective tax
rate is therefore a more accurate measure of tax burden than is the statutory tax rate.
We dene the Effective Tax Rate for residential property i using
=EffectiveTax Rate TP SEVfTrVC L ()/( )(,, ,,),
ii iiiii
which shows that the Effective Tax Rate for property i is determined by the tax payment
and the state equalized value
per $1,000 of value, where TP is equal to the
6 The homestead exemption effectively equalized the statutory property tax millage rates for local school
operating expenses on homestead properties across the state. This reduced disparities in overall statutory
millage rates across jurisdictions, but it did not eliminate them. Substantial differences in overall millage
rates remain, as a result of differences in millage rates between homestead and non-homestead properties,
differences in the millage rates for school capital expenditures, and differences across municipal govern-
ments, county governments, and special districts.
7 According to Skidmore, Reese, and Kang (2012), the average change in statutory tax rates among 152
communities in the ve county region surrounding Detroit was 12.49 mills.
8 Proposal A also put severe restrictions on the ability of local units to increase property taxes on their own.
Thus, the nancing of operating expenses for K–12 public education became highly centralized, whereas
it had previously been highly decentralized. Also, funding formulas pushed in the direction of more equal
per-student funding for operating expenses, although considerable gaps remain between the highest- and
lowest-spending districts. See Papke (2005) for an excellent analysis of the effects of school nance reform
on education outcomes. In the area of school capital expenses, local school districts still must rely on their
own property taxes. As a result, funding disparities for school capital expenses are much larger than those
for operating expenses.
9 One mill is dened as $1 per $1,000 of taxable value.
National Tax Journal
millage rate multiplied by TV and SEV reects the actual market value of the property.10
Whenever a property is sold or transferred, TV equals SEV and the effective tax rate equals
the statutory rate. Over time, however, effective tax rates for owner-occupied proper-
ties generally become lower than the statutory rate and differ considerably across these
properties, depending on the degree to which the property is protected by the taxable
value cap. Generally, the effective tax rate depends on the length of time an individual
has owned the property
, the rate of ination multiplier (r), the appreciation (or
depreciation) of property value
,11 the characteristics of the property
,12 and the
location of the property
.13 The less a property owner benets from the taxable value
growth cap, the closer the effective rate will be to the full statutory rate.
Over time, the taxable value growth cap created large differentials between TV and
SEV. In Detroit, the largest differential between citywide TV and SEV occurred in
2003, when TV was about 65 percent of SEV. In 2006 the difference in SEV and TV
began to narrow and TV was 86.5 percent of SEV by 2011. Even though SEVs have
fallen in recent years, as of 2010 about 30 percent of property owners had a TV to SEV
gap of 30 percent or more; for an additional 34 percent the gap ranged from 1 percent
to 29 percent. There was no gap between TV and SEV for about 35 percent of Detroit
Figure 1 shows the paths of TV, SEV, and the average sales price of residential proper-
ties in Detroit since 2005. As shown in the gure, sales prices declined more than SEV,
which in turn fell more than TV. Even after the bursting of the housing market bubble, a
signicant gap between aggregate TV and SEV remained for many homeowners. Sales
prices stabilized in 2009 at about 20 percent of the pre-crash levels and have increased
slowly in subsequent years.
Figure 1 gives cause to question assessment practices in Detroit. State assessment
guidelines require local units’ assessments to reect market value. However, Detroit
has not sufciently reduced assessments to fully match the housing value declines. As
shown in Hodge et al. (2015), assessed values of recently sold properties are much higher
than sales prices; the average sale price of properties sold in 2010 was about $7,000
10 According to Michigan assessment guidelines, state equalized value should reect 50 percent of market
11 Because of the taxable value cap, effective tax rates fall as the length of homeownership increases when
property values and thus SEVs are rising faster than the rate of ination.
12 Characteristics such as age of the house, lot size, house size, homestead, etc. are important determinants
of the sale price (related to SEV and TV).
13 The location may inuence the growth in state equalized value since properties in more desirable locations
may experience larger growth in market values relative to properties in less desirable neighborhoods.
14 According to the Case-Schiller Home Price Index, housing prices in the City of Detroit increased by 93
percent from 1994 through 2005, before collapsing during the Great Recession. During the same period
the Case-Schiller National Home Price index rose by 194 percent. Note also that housing prices were on
the rise in Detroit even in the face of substantial population decline.
Assessment Growth Limits and Mobility in Detroit, Michigan
whereas average assessed value of these same properties is about $50,000.15 Detroit
property owners can individually le an appeal of their assessment with the Detroit
Board of Review-Property Assessment and may further appeal an adverse decision
to the Michigan Tax Tribunal. The process is time consuming and expensive, with no
assurance of success. Nevertheless, the Michigan Tax Tribunal received 3,015 petitions
from Detroit homeowners in 2012 (MacDonald and Wilkinson, 2013). If the City were
to assess property in a way that fully reected recent sales, the benets of the taxable
value cap to long-time homeowners would be eliminated in most cases. Given current
assessment practices, even in this faltering housing market many long-time property
owners perceive a tax benet that would be lost if they were to make a decision to move.
Figure 1
Indices of Detroit Residential SEV, TV, and Average Sale Price
Sources: City of Detroit Comprehensive Annual Financial Reports and Michigan Associa-
tion of Realtors. The indices are set equal to 1 in 2005 in order to illustrate and compare
the changes over time.
2005 2006 2007 2008 2009 2010 2011 2012
Ave. Sales Price SEV Taxable Value
15 These are “arms-length” transactions that exclude any zero price property transfers and transfers to a
nancial institution.
National Tax Journal
It should be noted, however, that in 2012 the Michigan State Tax Commission learned
of the potential issue with assessments and ordered an external sample reassessment to
determine whether a full reassessment of all properties within the City was warranted.16
At time this article was written, the State Tax Commission’s investigation was not yet
complete. However, the assessment regime was not in question in 2010–2011 (the
period under consideration), and thus it seems reasonable to think that homeowners
based decisions on the assessment practices and the perceived “tax benets” as shown
on owners’ tax bill, not on state assessment policy guidelines.
In Figure 2 we present average effective tax rates of owner-occupied residential
properties at the neighborhood level.17 The high effective tax rates in the extreme west
16 The City of Detroit Assessment Ofce subsequently announced that it would begin the process of reas-
sessing all city properties, and in December of 2014 lowered assessment by 10 percent on average across
the city.
17 Effective tax rates vary substantially across properties within any given neighborhood (see Hodge, et al.
(forthcoming) for a map highlighting parcel-level differences).
Figure 2
Average Eective Tax Rates of Owner-Occupied
Residential Properties by Detroit Neighborhood, 2010
Note: The crosshatch pattern indicates no taxation.
Source: Data provided by the City of Detroit Assessment Division
Assessment Growth Limits and Mobility in Detroit, Michigan
and northeast areas of Detroit are likely the result of a number of factors. The outly-
ing neighborhoods are predominantly single family, newer, and generally of higher
socio-economic status than inner city areas. Perhaps more importantly, these areas
have experienced more stable populations than other parts of the City; population has
even increased in some these areas. Home sales activity in these areas has also been
substantially higher than in the inner city and the TV for many of these properties has
been reset, resulting in a higher effective tax rate.
With this overview of Michigan property tax environment and how policies play out
in practice within Detroit, we now turn to a review of the most relevant literature on
assessment growth limits.
Much of the early empirical research on property tax limits, including taxable value
growth caps, tended to focus on determining the degree to which these emerging scal
constraints limited property tax revenue growth.18 More recently, however, researchers
have turned their attention to the distributional consequences of taxable value growth
limits. We discuss several key articles to demonstrate that there are substantial tax dif-
ferentials that can emerge from taxable value growth caps, thus demonstrating that there
is potential for these differentials to generate housing market distortions. We then turn
our attention to research that specically examines the potential lock-in effect resulting
from taxable value growth caps.
A. Assessment Growth Limits and Equity
Dye, McMillen, and Merriman (2006) address the implications of a recently imposed
assessment growth cap in Cook County, Illinois. They demonstrate that a taxable
value growth cap that protects residential owners (as in Cook County, Illinois) will
lead to increased taxes for industrial and commercial property owners. They also
show that homeowners with property that appreciates at a rate less than the cap will
experience higher effective tax rates to make up for those who are protected. Dye and
McMillen (2007) extend this work by developing a more formal theoretical framework
to evaluate the effects of assessment caps on property taxes. Two key conclusions from
this additional evaluation are that: (1) reassessment upon sale can make it costly for
homeowners to move and may thus affect real estate markets; and (2) assessment limits
can lead to higher taxes for some of the property owners the limit was meant to protect.
Muhammad (2007) evaluates the horizontal and vertical inequities resulting from
the District of Columbia’s taxable value cap policy, imposed in 2002. The District of
Columbia’s tax cap policy imposed a property value assessment growth limit set at 12
18 See Dye, McGuire, and McMillen (2005), Mullins and Joyce (1996), O’Sullivan, Sexton, and Sheffrin
(1995), and Skidmore (1999) for reviews of this literature.
National Tax Journal
percent annually between 2001 and 2005 and 10 percent thereafter. Over the 2001–2007
period, median homestead property values more than tripled in the District of Columbia
(from $128,499 to $400,050), an average annual increase of 20.8 percent. By 2007,
the median property’s nal taxable value differed from the actual market value by over
60 percent. Muhammad demonstrates the degree of horizontal and vertical inequities
resulting from the assessment growth limit. For example, he nds that for homesteads
with a value of $600,000, the effective millage rate can be as high as 79 mills or as
low as 1 mill.
Focusing on Michigan, Skidmore, Ballard, and Hodge (2010) show that just prior
to the real estate market decline beginning in 2008 the taxable value cap in Michigan
reduced effective tax rates statewide by about 18 percent for long-time homeowners rela-
tive to new homeowners. Recently, Hodge et al. (forthcoming) used quantile regression
techniques to evaluate the distributional consequences of Michigan’s taxable value cap
in the City of Detroit. They show that homeowners who have lived in their homes since
1994 (or earlier) face effective property tax rates that are between 19 and 52 percent
lower than property owners who recently purchased their homes.
In 2008 the Lincoln Institute of Land Policy published a comprehensive report on
property tax assessment limits and their use across the U.S. states. The report cov-
ers the institutional/legal aspects of such limits, the implications for the tax base and
local government autonomy, equity issues and the inefciencies that arise. The report
concludes by offering potential alternatives to provide property tax relief to those in
need, indicating that property tax assessment limits are “… the least effective, least
equitable, and least efcient strategies available for providing tax relief (Haveman and
Sexton, 2008, p. 37).”
The body of research discussed above provides ample evidence of the signicant
inequities resulting from taxable value caps. Below, we discuss a closely related literature
that has examined the effects of assessment growth caps on mobility.
B. Assessment Growth Limits and Mobility
The existing research on the potential impacts of assessment growth limits on mobility
has focused on California and Florida. Specically, O’Sullivan, Sexton, and Sheffrin
(1995), Nagy (1997), Stohs, Childs, and Stevenson (2001), Wasi and White (2005),
Ferreira (2009), and Ferreira, Gyourko, and Tracy (2010) study the potential lock-in
effect resulting from California’s Proposition 13, and Stansel, Jackson, and Finch (2007)
and Ihlanfeldt (2011) focus on Florida’s Save Our Homes program. Consider rst the
California studies.
O’Sullivan, Sexton, and Sheffrin (1995) use simulation methods to evaluate the
potential effects of California’s Proposition 13, concluding that assessment growth caps
reduce mobility. Nagy (1997) and Wasi and White (2005) use data on mobility rates
before and after the imposition of Proposition 13 in California relative to communi-
Assessment Growth Limits and Mobility in Detroit, Michigan
ties in other states without a taxable value cap. With tax benets accruing to station-
ary homeowners, the expectation was that relative mobility rates would decline after
Proposition 13. However, the ndings of the two studies differed, with Nagy failing to
nd evidence for a lock-in effect and Wasi and White (2005) supporting its existence.
Stohs, Childs, and Stevenson (2001) use a cross-sectional approach to evaluate the
lock-in effect. Specically, they compare home sales rates in California metropolitan
areas with metropolitan areas in other states, nding that sales rates are relatively lower
in California — a result that is consistent with a lock-in effect. However, in the context
of estimating effects using aggregated cross-sectional data, as pointed out by Ihlanfeldt
(2011), omitted variable bias is a concern.
Ferreira (2009) also examined residential mobility after Proposition 13, but focused on
two amendments allowing transferability of the tax benets to a new home for those who
are age 55 or older. In a comparison of two age groups, he found that mobility for those
over age 55 is about 25 percent higher than the mobility for those age 54 and younger.
There are also two studies that focus on Florida’s assessment growth cap. Stansel,
Jackson, and Finch (2007) compared average home tenure of full-time homeowners in
2002 and 2006 — before and after the implementation of the assessment growth limit.
The researchers hypothesized that average home tenure would be longer in 2006 because
homeowners would have accumulated tax savings and would be less willing to move.
However, their comparisons failed to support this hypothesis. Ihlanfeldt (2011) used
parcel-level data from Duval and Miami-Dade counties to examine the probability of
home sale before and after the imposition of Amendment One, which enable homestead
property owners to apply a portion of the tax savings from the assessment growth cap
to a new home. He nds evidence that household mobility increased in the periods
following the passage of Amendment One.
The present study adds to the literature in several ways. First, despite the fact that a
number of studies demonstrate that a lock-in effect exists, there is still some question
regarding the conditions under which a lock-in effect may emerge; our study focuses
on the effects of a faltering real estate market, a context that is quite different than other
studies. Second, we examine the issue using parcel-level data in a single city, Detroit,
where all property owners interact with the same governmental units. Of the studies
discussed above, only Ihlanfeldt (2011) uses parcel-level data.19 An advantage of our
parcel-level data is that we have detailed information about the parcel that includes tax
payment, tax rate, and property characteristics. This allows us to calculate the precise
tax saving resulting from the assessment growth cap for each parcel. A limitation is
19 Ferreira (2009) and Ferreira, Gyourko, and Tracy (2010) use a sample of households from the American
Household Survey, whereas Wasi and White (2005) use a sample of household level data from the Integrated
Public Use Micro Data (IPUMS) for years 1970, 1980, 1990, and 2000. A limitation of these studies is
that property tax savings from the assessment growth cap are generated from reported property taxes and
housing values provided by the respondent and are not actual values.
National Tax Journal
that we do not have much information about the owner except the number of years they
have owned their property. Finally, we examine the lock-in effect on different home
value groups and different locations within Detroit.
As described in the literature review, effective tax rate differentials resulting from an
assessment growth cap may generate inefciencies; specically, they have the potential
to inhibit normal levels of residential market transactions and homeowner mobility.
This phenomenon has come to be known as the “lock-in” effect. A homeowner who
enjoys a substantial tax benet may be less likely to sell because the cost of holding the
property is low(er) and tax benets are lost once the property is sold. Before turning to
the empirical analysis, we rst offer a detailed description of the data we use.
A. Data
The City of Detroit’s Assessment Division provided parcel-level data for this research.
The raw data include information for 444,183 real and personal property parcels in
2010, of which we focus on owner-occupied (i.e., homestead) residential properties.
However, since our focus is on determining factors that inuence a voluntary move, we
exclude all property transfers to a nancial institution due to mortgage foreclosure or a
government entity due to tax foreclosure.20 In total, there are 103,610 owner-occupied
residential properties included in our evaluation. In order to match the previous owners’
effective tax rate with sales activity, we combine the 2010 parcel-level property char-
acteristics data with 2011 residential property sales data.21 In addition to the property’s
effective tax rate, we include other variables such as years owned, house age and size,
lot size, and indicator variable for whether a property is delinquent on property taxes,
and three proxy measures for whether a homeowner is “underwater” on his/her mort-
gage (the amount owed exceeds the market value of the home). Since additional years
of ownership lead to reductions in effective tax rates in the presence of an assessment
growth cap, we must control for years of ownership in order to isolate the impact of
the assessment growth cap.
In a declining market those who purchased their homes more recently are more
likely to be underwater and incur greater difculty in selling their property. However,
in the case of Detroit, property values dropped so dramatically in the wake of the real
estate crisis that they were too low for purchasers to obtain a typical home loan. In
fact, nearly half of home sales in Detroit are for cash. Thus, as years of ownership
20 To focus our evaluation on the effects of the assessment growth cap, we also exclude 328 homestead
properties that received some sort of tax abatement.
21 Note that houses sold in 2010 do not experience the “pop up” in taxable value until the 2011 tax year.
Therefore, using the properties that sold in 2011 with the previously discussed tax data allows us to measure
how reduced effective tax rates affect the probability of sale.
Assessment Growth Limits and Mobility in Detroit, Michigan
decrease, the probability of selling increases due to a lower benet from the assessment
cap but may
decrease d
ng ma
t. We therefore must also control for
whether a homeowner is underwater. Unfortunately, we do not have a direct measure
of whether a homeowner is underwater, so we must create proxy variables. To generate
the underwater proxies, we must estimate the percent equity a homeowner has in his/her
home, which we do using zip code level housing price index data obtained from Core
Logic in combination with information on the length of ownership and the last sales
price. The estimated percent equity a homeowner has in his/her property is based on
a 30-year mortgage amortization schedule with an 8.5 percent annual rate of interest,
and a 10 percent down payment.22 Since in recent years about half of home sales have
been cash transactions, we designate all home sales under a price of $40,000 during
the period of analysis to be cash sales and thus have full equity.23 This variable tends
to be negative for homes purchased just before the housing market collapse. From this
calculation of equity, we also create two indicator variables: (1) underwater (yes=1,
no=0); (2) positive equity but less than 100 percent equity (yes=1, no=0); and (3) 100
percent equity (yes=1, no=0). We present estimates of sale probability using the nega-
tive equity indicator variable, the percent equity variable, and the negative equity and
partial equity indicator variables (with full equity as the omitted category). Summary
statistics for all of the variables we use in the analysis are provided in Table 2, with
denitions and details provided in Appendix 1.
B. Empirical Analysis
To examine the average change in length of ownership resulting from the assessment
growth cap, a two-step procedure is required. First, we estimate the change in effective
tax rates as a result of the taxable value growth limit. Then we estimate the effects of the
effective tax rate on the probability of a property being sold. With these two estimates,
we can calculate the effect of the taxable value growth limit on the length of ownership.
Recall that the taxable value growth cap may lower effective tax rates the longer a
property owner retains ownership. The rst step is to estimate the effect of years of
ownership on effective tax rates. We note that the relationship between years of owner-
ship and effective tax rates is likely non-linear.24 We therefore use the natural logarithm
of years of ownership as our key independent variable. Consider Table 3, which reports
22 The choice of the 8.5 percent interest rate is about the average interest rate for a 30-year xed rate mortgage
over the 1970–2010 period. More detail on the calculation of the percent equity measure is available from
the authors upon request.
23 Traditional lending institutions are reluctant to lend amounts of less than $40,000. For example, in our data
if one purchased a $40,000 property in 2007 with cash, they would be designated as having full equity;
there were many such sales in our data.
24 The change in effective tax rates depends on the growth in a property’s SEV over time and the date at which
an owner purchased the property. We thank the editor for offering this insight. Note that in this regression
we cap the number of years owned at 16 because tax savings associated with ownership begin to accrue
after the imposition of the taxable value cap in 1994.
National Tax Journal
an effective tax rate regression in which the key variable of interest, ln(Years Owned
Capped), is negative and statistically signicant.25 That is, the effective tax rate is lower
the longer an owner retains the property. For homestead properties owned since 1994
(the date of taxable value growth cap implementation), effective tax rates are about 30
mills (or 48 percent) lower than they would have been otherwise. As discussed earlier,
a property owner stands to lose the tax benet he/she currently enjoys upon sale of a
property. Given that the average tax payment is about $1,500, this amounts to an annual
tax savings of about $600. According to this rst part of our evaluation, it seems that
there is a substantial incentive to retain ownership for many homeowners.
Table 2
Summary Statistics
Variable Mean
Sales Price 34,746 2,361,732
State Equalized Value 28,310 13,315
Taxable Value 21,017 11,925
Effective Tax Rate
(or millage using assessed value as base)
49.60 14.43
Effective Tax Rate
(or millage using adjusted last sales price as base)
25.21 274.18
Sold in 2011 (yes=1, no=0) 0.043 0.204
Living Area (in square feet) 1,108 390.6
Lot Size (in square feet) 831.9 193.0
Age (in decades) 6.706 1.485
Tax Delinquent (yes=1, no=0) 0.353 0.478
Underwater Indicator (yes=1, no=0) 0.262 0.440
Percent Equity (yes=1, no=0) 0.491 0.560
Negative Equity (yes=1, no=0) 0.262 0.440
Partial Equity (yes=1, no=0) 0.310 0.462
Years Owned Uncapped 27.72 19.86
Observations 103,512
25 See Hodge et al. (forthcoming) for a more detailed analysis and explanation of the determinants of effective
tax rates in the Detroit context.
Assessment Growth Limits and Mobility in Detroit, Michigan
The control variables are of interest as well. Older and larger homes have lower
effective tax rates, whereas properties with larger lots have higher effective tax rates.
These results are generally in line with expectations. Old, large homes are less likely
to be sold and thus do not experience “pop up” effects as often, which results in lower
effective tax rates. On the other hand, larger lots are more desirable and are more likely
to be sold and thus have higher effective tax rates. Note that we will use the coefcient
on years of ownership to calculate the millage reduction resulting from the taxable value
cap, which will in turn be used to determine the effect of the effective tax rate on the
probability of selling a property, which is discussed next.
We estimate the probability of selling a property as a function of effective tax rates
while controlling for a number of property and neighborhood characteristics. To inform
and guide our empirical strategy we present a simple model put forth by Hanushek
and Quigley (1978) and Ihlanfeldt (2011), where the probability of a property selling
is equal to the probability that an owner will put his/her property up for sale (P(U))
Table 3
Eective Tax Rate Regression Results
Independent Variable Coefcient
Living Area (in square feet) –0.0006***
Lot Size (in square feet) 0.0023***
Age (in decades) –0.7289***
ln(Years Owned Capped) –10.654***
Constant 78.371***
Neighborhood Effects Yes
Observations 103,512
Notes: Standard errors are in parentheses and all regressions are corrected for heteroskedas-
ticity. Asterisks denote signicance at the 1% (***), 5% (**), and 10% (*) levels.
National Tax Journal
times the conditional probability that if the property is for sale, a buyer is found
PS PU PB U() () (/).
The probability of offering a property for sale depends on the loss of utility from being
at a suboptimal level of housing consumption (H*) and moving costs (MC).
The property owner puts his/her property up for sale when the utility loss from living
at a suboptimal level of housing exceeds moving costs,
=−>PU UH UH MC()1if(*) ()
=−<PU UH UH MC() 0if(*) () .
To determine the effect of the taxable value cap on the probability of moving, we include
a range of variables that control for differences in P(U) across property owners, prop-
erty characteristics, and neighborhood characteristics. The tax savings resulting from
the taxable value cap are embedded in MC; moving requires that one give up any tax
benet that has been acquired over time.26
We estimate the probability of a property being sold with a standard probit estimation
procedure. The core results are presented in Table 4 where, in addition to the effective
tax rate, we include as control variables property characteristics, years of ownership,27
and neighborhood indicator variables. Table 4 presents three regressions where each
includes one of three proxy variables for being underwater: An underwater indicator
variable (Column 1), percent equity (Column 2), and two indicator variables that indicate
being underwater and having partial equity (Column 3) where full equity is the omitted
reference indicator variable.
Before considering the variable of interest, Effective Tax Rate, we rst discuss the
control variables. Larger homes are more likely to be sold than smaller homes, all else
equal. Properties that are delinquent on their property taxes are less likely to be sold.
One possible explanation for this result is that tax delinquency may serve as a barrier
to selling until such time that back-taxes are paid. Consider now the estimated effects
of equity on the probability of a home selling. In Column 1, we include an indicator
variable that equals 1 if the property is underwater, and 0 otherwise. In Column 2,
26 This tax benet depends also on assessments, particularly in the context of falling housing prices. If
assessments kept pace with falling prices, the tax benets would have disappeared for most property
owners. However, as noted in the discussion of Figure 1, assessments have not kept pace with property
value declines. The differentials between long-time and new property owners should have dissipated, but
they have not. Thus, under the existing assessment regime, long-time properties still receive a tax benet
relative to new property owners.
27 Recall that the longer a property owner retains ownership, the lower the effective tax rate. However, years
of ownership also serves as a proxy for the owners’ general propensity of move (long-time property own-
ers are generally less likely to sell). We therefore include years of ownership as an explanatory variable
to control for this important factor.
Assessment Growth Limits and Mobility in Detroit, Michigan
Table 4
Home Sale Probit Estimation Results with Eective Tax Rates
Calculated Using SEV as the Base
Independent Variable (1) (2) (3)
Living Area (in square feet) 0.00005** 0.00006** 0.00005***
(0.00002) (0.00002) (0.00002)
Lot Size (in square feet) –0.00007 –0.00007 –0.00007
(0.00005) (0.00007) (0.00005)
Age (in decades) –0.0086 –0.0102 –0.0085
(0.0069) (0.0069) (0.0069)
Tax Delinquency (yes=1, no=0) –0.175*** –0.191***
Underwater Indicator –0.153***
– –
Percent Equity – 0.229***
Negative Equity Indicator – –0.0173
Some Equity Indicator – 0.186***
Effective Tax Rate 0.0074*** 0.00074*** 0.00078***
(0.0007) (0.0007) (0.0007)
ln(Years Owned Uncapped) –1.003*** –0.109*** –0.0218***
(0.0074) (0.0071) (–2.08)
Constant –1.853*** –2.004*** –2.103***
(0.1222) (0.1233) (0.126)
Neighborhood Effects Yes Ye s Yes
Observations 103,512 103,512 103,512
Pseudo R20.029 0.035 0.031
Marginal Effect on Probability of Sale (dy/dx)
Effective Tax Rate 0.0006 0.0006 0.0007
Notes: Standard errors are in parentheses and all regressions are corrected for heteroskedasticity. Asterisks
denote signicance at the 1% (***), 5% (**), and 10% (*) levels.
National Tax Journal
we replace the underwater indicator variable with estimated equity in the home, and
in Column 3 we include the negative equity indicator as well as a partial equity indi-
cator variable. In Column 1 we see that being underwater decreases the likelihood of
selling relative to those with partial or full equity. In Column 2 we obtain a similar
result: those with more equity are more likely to sell. These results are consistent with
previous research that shows that low or negative equity tends to create a lock-in effect.
In Column 3, however, a more nuanced result emerges. Here we see that the negative
equity indicator is statistically insignicant, whereas the partial equity indicator is
positive and highly signicant. That is, those with partial equity are more likely to sell
relative to those with full equity. This result may in part be the due to the fact that many
of the recent sales have been for cash. Finally, the longer a property is owned, the lower
is the probability of home sale. In our analysis, controlling for years owned is important
in order to obtain an unbiased estimate of the effect of the taxable value growth cap on
the probability of home sale.28 While the coefcient estimates on the control variables
are of interest, the focus in this article is on the role the assessment growth cap plays
in mobility. We now turn our attention to this question.
Controlling for other factors, we see that the coefcient on effective tax rates is
positive and statistically signicant in all three regressions. That is, a lower effective
tax rate reduces the probability of home sale. From these estimates, it appears that
the lock-in effect is occurring. The size of the coefcient on the effective tax rate is
similar across all three columns. The marginal effects generated from the effective tax
rate coefcients are also presented at the bottom of Table 4; the marginal effect of the
effective tax rate from the homestead property regression ranges from 0.0006 to 0.0007.
A 1 mill increase (decrease) in the effective tax rate increases (decreases) the average
probability of selling a property by 0.06 to 0.07 percent. Though the magnitude of the
coefcient may seem inconsequential upon initial examination, the size of the effect
is in fact large. However, several steps are required to assess the magnitude of the
By combining the results from Tables 3 and 4, we can calculate the change in tenure
as a result of the assessment growth cap. From the estimates in Table 4, Columns 2 and
3, the probability of selling a property decreases by about 0.06 percent as a result of the
reduced effective tax rate generated from the assessment growth limit (a 30 mill reduction
times the 0.0006 coefcient = 0.018. That is, property owners who have owned their
property since 1994 and thus receive an average 30 mill reduction in the effective tax
rate due to the taxable value growth cap have a 1.8 percent lower turnover rate. We must
now compare this reduction with the average turnover rate for homestead properties in
the City of Detroit. Following the method presented in Stohs, Childs, and Stevenson
(2001), average tenure length is calculated by taking the reciprocal of the property
28 The estimates in Table 4 conrm the notion that the longer a property owner retains the property, the less
likely he/she will sell a property. We note that omitting this variable leads to a larger coefcient on the ef-
fective tax rate variable. This result provides some assurance that we are generating an unbiased estimate
of the lock-in effect.
Assessment Growth Limits and Mobility in Detroit, Michigan
turnover rate. For example, if the turnover rate (percent of homes sold) in a given year
is 10 percent, then on average a household remains in a home for 10 years (1.00/0.10).
In 2011 the turnover rate for Detroit homestead property not in tax or mortgage
foreclosure was 4.7 percent. Based on this turnover rate, the average length of tenure
is about 21 years.29 A 1.8 percent reduction in the probability of home sale means that
the turnover rate for these homeowners is reduced to 2.9 percent. This translates into
an increase in the average duration of property ownership from 21 years to 34.5 years
(1.00/0.029) for property owners who have owned their property since 1994, which
is an increase in tenure length of about 64 percent. However, the average effect for all
property owners (not just those who have owned their property since 1994) is an increase
from 21 to 28.5 years, or about 35.7 percent. This estimate of the average effect is larger
than the 25 percent estimate of Ferreira (2009) and the more modest 7 percent estimate
from Ihlanfeldt (2011). This analysis suggests a meaningful increase in the length of
property ownership as a result of the assessment growth cap — evidence of a lock-in
Given that the estimated magnitude of the effect is larger than the earlier studies,
we are cautious in drawing denitive conclusions, and examine the data further. As
noted earlier, the work of Hodge et al. (2015) demonstrates that city assessments over
this period were much higher than actual market prices. It could therefore be that the
coefcient estimate on our measure of effective tax rates dened as tax payment/(state
equalized value/1000) is systematically affected by the inaccurate assessments. We
therefore calculate an alternative measure of effective tax rates that is dened as tax
payment/[(0.5*HPI adjusted sales price)/1000],30 where the adjusted sales price is equal
to the sales price at the time the property was last sold brought into 2009 terms using
the City of Detroit housing price index data obtained from Core Logic. This method
produced many signicant outliers, primarily because there were so many extremely
low sales prices and thus extremely high effective tax rates. We therefore applied the
standard trimming method suggested by the International Association of Assessing
Ofcers (IAAO) to remove outliers. Specically, the IAAO procedure for trimming is
to delete observations outside 1.5 multiplied by the interquartile range (IQR), where the
IQR is the difference between the rst and third quartiles. After trimming, we are left
with 67,986 observations, with 2.3 percent of the properties selling. Note, however, that
this procedure only removed high effective tax rate observations. Recall that with the
original data about 4.7 percent of properties sold; the trimming procedure eliminated
relatively more recently sold properties than non-sold properties. This outcome is due
29 The rate of non-foreclosed property turnover in 2011 was lower than in previous years and the turnover
rate with home foreclosures was 5.16 percent. Note that the calculated tenure length based on the rate of
home sales is about nine years longer than the average years of ownership from the summary statistics
reported in Table 3.
30 The adjusted sales price is multiplied by 0.5 in order make this measure of effective tax rates consistent
with the original measure; recall that in the original measure state equalized value is dened as 50 percent
of estimated market value.
National Tax Journal
to the fact that many of the extremely low sales prices occurred during the period of
The estimates using this approach are presented in Table 5. Before focusing on the
revised effective tax rate variable, there are two notable issues that should be discussed
with regard to the control variables. The rst is that the coefcient on the underwater
variable reverses signs and is positive (Column 1). However, as shown in Column 2
the coefcient on percent equity is still positive, indicating that those with more equity
are more likely to sell than those with less equity. These two results seem to contradict
each other, but the Column 3 estimate may reconcile the issue. These results indicate
that both those with negative equity and partial equity are less likely to sell than those
with full equity. However, the absolute magnitude of the coefcient on the partial equity
variable is larger than the coefcient on the negative equity variable. That is, while both
groups are less likely to sell than those with full equity, those with negative equity are
more likely to sell than those with partial equity. The result in Column 1 is driven by
the relative difference in the coefcient estimates between negative and partial equity.
Taken together, we conclude that those with more equity are less likely to sell than those
with less as shown in Column 2. The second difference is that the coefcient on years
of ownership becomes positive. That is, the longer one owns a home the more likely
he/she is to sell it. We are unsure of what is leading to the reversal in sign, but speculate
that it may be due to the trimming procedure, which eliminated a larger proportion of
recently sold homes.31
Turning to the effective tax rate measure, we see that the coefcient is still positive
and highly signicant, indicating that those with higher effective tax rates are more
likely to sell. Note that the coefcients in all three columns are about half the size of
those presented in Table 4. However, the proportion of properties sold in the trimmed
data set is also much smaller (reduced to 2.3 percent from 4.7 percent), and thus the
magnitude of the estimated lock-in effect in percentage terms is similar in both sets of
The effect of the assessment growth cap on home sale probability can also be examined
across different property value groups. Table 6 presents the coefcient and marginal
effects for the original effective tax rate variable as used in Table 4 across property
value quintiles where properties are ordered by sales price and are then grouped into
the ve categories. We expect the higher valued properties to be most susceptible to
the lock-in effect as these properties are most likely to enjoy larger tax reductions as a
result of the taxable value cap.
The results reported in Table 6 show that property owners with higher valued homes
(as measured by SEV) are locked into ownership for longer periods than owners with
lower valued homes. Specically, the effective tax rate begins to be important in the
31 To explore this issue further, we re-estimated the Table 4 results using the subsample that generated the
Table 5 estimates. The resulting coefcient on the effective tax rate is 0.0025***, which is similar to the
Table 4 coefcient estimates. This additional information suggests that data trimming is leading to the
difference in the magnitude of the coefcient.
Assessment Growth Limits and Mobility in Detroit, Michigan
Table 5
Home Sale Probit Estimation Results with Eective Tax
Rates Calculated Using Adjusted Last Sale Price as the Base
Independent Variable (1) (2) (3)
Living Area (in square feet) 0.00013*** 0.00013*** 0.00010***
(0.00004) (0.00004) (0.00004)
Lot Size (in square feet) –0.00012 –0.00001 –0.00012
(0.00008) (0.00007) (0.00008)
Age (in decades) –0.009 –0.0119 –0.0080
(0.0107) (0.0107) (0.0107)
Tax Delinquency (yes=1, no=0) –0.260*** –0.275***
Underwater Indicator 0.0653***
– –
Percent Equity – 0.201***
Negative Equity Indicator – –0.262***
Some Equity Indicator – –0.398***
Effective Tax Rate 0.0035*** 0.0036*** 0.0043***
(0.0004) (0.0004) (0.0004)
ln(Years Owned Uncapped) 0.184*** 0.075*** 0.0063***
(0.0309) (0.0223) (0.0286)
Constant –2.272*** –2.391*** –2.152***
(0.188) (0.170) (0.184)
Neighborhood Effects Yes Ye s Yes
Observations 67,986 67,986 67,986
Pseudo R20.032 0.036 0.041
Marginal Effect on Probability of Sale (dy/dx)
Effective Tax Rate 0.00017 0.00018 0.00021
Notes: Standard errors are in parentheses and all regressions are corrected for heteroskedasticity. Asterisks
denote signicance at the 1% (***), 5% (**), and 10% (*) levels.
National Tax Journal
second quintile and the magnitude of the coefcient increases in the higher quintiles.
However, variation in the magnitude of the effect across property value classes may not
be solely attributable to the lock-in effect resulting from the assessment growth limit.
The observed differences across property value groups may also be inuenced by the
fact that less expensive homes tend to be “starter” homes and thus average length of
tenure may be less than more expensive “dream” homes.
In addition to evaluating differential effects by property value class, we also consider
differential effects across 10 geographic sub-areas within the city (called Neighborhood
Clusters by the Detroit Planning and Development Department).32 Given that housing
price growth and decline varies across the city, we expect differential lock-in effects
across space as well. In Table 7, we present the estimates of 10 separate regressions,
Table 6
Probit Estimation Results — Marginal Eects of Eective Tax Rates
on the Sale Probability, by SEV Quintile
(Quintile 1 — Lowest Sales Prices; Quintile 5 — Highest Sales Prices)
Quintile 1 2 3 4 5
Effective Tax Rate –0.00003 0.0005*** 0.0013*** 0.0018*** 0.0013***
Notes: All regressions are corrected for heteroskedasticity. Asterisks denote signicance at the 1% (***),
5% (**), and 10% (*) levels. These estimates include all control variables and neighborhood effects.
Table 7
Probit Estimation Results — Marginal Eects of
Eective Tax Rates on the Sale Probability, by Cluster
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
Effective Tax Rate 0.0004** 0.0017*** 0.0003* 0.0001 0.0003**
Observations 11,455 13,443 8,617 1,555 8,442
Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster 10
Effective Tax Rate 0.0001 0.0012*** 0.0008*** 0.0007*** 0.0005***
Observations 6,148 16,265 13,598 14,845 9,242
Notes: All regressions are corrected for heteroskedasticity. Asterisks denote signicance at the 1% (***),
5% (**), and 10% (*) levels. These estimates include all control variables but neighborhood effects are
not included.
32 A neighborhood cluster map can be found at
Assessment Growth Limits and Mobility in Detroit, Michigan
one for each of the 10 geographic clusters in the city. We nd positive and statistically
signicant coefcients on the effective tax rate in eight of the 10 clusters, with the larg-
est effect in Cluster 2. However, the 10 clusters are relatively large and heterogeneous,
making it somewhat difcult to interpret the estimates in Table 7. Generally, smaller
effects were found in areas of the city that have been most severely affected by the
collapse of the housing market. Clusters 1, 3, 5, and 6 have been subject to extensive
foreclosures and increasing abandonments among single family homes. Cluster 4 has
too few sales (and too few homestead properties) to produce signicant results. The
remaining areas of the city have been somewhat more stable, but all submarkets are to
some degree stressed.
Our general nding of decreased likelihood of property sale as a result of the assess-
ment growth cap mirrors the conclusions of several previous studies, but our estimated
effect is larger than other studies. Our examination has the advantage of using parcel-
level data that include the actual tax benets that homeowners enjoy as a result of the
taxable value cap. These data enable us to consider differential effects across property
value classes, as well as across space. Measuring the effects at the parcel level also offers
a more precise measure of the impact because the benet that properties receive from
an assessment growth cap varies substantially within a single jurisdiction. Further, we
examine the lock-in effect in the context of a declining urban area that is in the midst
of a real estate crisis.
In the context of Detroit’s current property assessment regime, our examination shows
that the taxable value cap increases tenure length by about 36 percent. We also note that
the effects differ by property value — no lock-in effect is found in the lowest quintile,
but decreases in mobility are evident in the second to fth property value quintiles,
with large changes occurring in the third to fth quintiles. These results show that the
impacts of the taxable value growth cap differ substantially depending on circumstances.
Generally, the ndings suggest that the taxable value growth cap generates a signicant
reluctance among some property owners to sell their properties.
As with any empirical analysis, there are some caveats that should be discussed.
First, there is considerable evidence that assessed values are substantially higher than
they should be, given actual current market values. More accurate assessments would
eliminate differences between SEV and TV for most properties. The unwillingness on
the part of policymakers and assessors to allow assessed values to fully fall to reect
market conditions is in part due to incentives created by the taxable value cap.33 Since
increases in TV are limited to the rate of ination (or 5 percent), applying subsequent
33 In January 2015, Mayor Duggan announced the assessments were reduced by 10 percent on average across
the city, while the city undergoes a full reassessment of property. While this reduction is a positive move,
the work of Hodge, et al. (2015) suggests that assessments should be reduced much further to bring as-
sessment in line with market values.
National Tax Journal
percentage increases to a lower base will mean that property tax revenues will not
recover to pre-decline levels when housing prices begin to trend upward; there is
therefore a strong incentive on the part of local ofcials to keep TV and SEV from fall-
ing. This aspect of assessment practices may lead to bias in our estimated impact of
effective tax rates on home tenure length. However, in robustness analysis where we
modify the effective tax rate variable using actual sales price data, a lock-in effect still
emerges. Second, even though we control for years of ownership, being underwater, and
property/neighborhood characteristics, there is still the possibility of omitted variable
bias for our coefcient on effective tax rates. While some caution is warranted in the
interpretation of our ndings, they offer new evidence of lock-in effect in the context
of a faltering housing market.
As previously noted, our estimates are larger than those reported in previous stud-
ies. While we are unsure why our estimated effects are larger, we offer two possible
explanations. First, Detroit has one of the highest property tax rates in the country. It
may be that the perceived tax benets of the assessment growth cap and thus the lock-in
effect is magnied compared to lower tax jurisdictions. In addition, average household
income in Detroit is just $25,000; as a percent of income the tax savings generated by
the assessment growth cap is substantial and this again may serve to magnify the lock-in
While Detroit is in some ways an exceptional case, this analysis may be of interest
to other struggling urban areas around the country. Importantly, with unemployment
near 20 percent, the taxable value growth cap may inhibit appropriate moves to obtain
employment, thus exacerbating the spatial mismatch/unemployment problem. The
efciency costs of the taxable value growth cap in a city experiencing signicant eco-
nomic challenges are potentially substantial. While there is a temptation to think that
reduced mobility might be good in the context of signicant population out-migration,
in a broader context the taxable value growth cap inhibits moves that could ultimately
improve both labor and housing market outcomes.
We thank the Lincoln Institute of Land Policy for nancial support. We thank Fred
Morgan of the City of Detroit Assessment Division for providing detailed parcel-level
data and Councilman Kenneth Cockrel for inviting us to work on property tax challenges
in Detroit. We also thank the editors and anonymous referees for valuable comments
and suggestions.
The authors have no nancial arrangements that might give rise to conicts of interest
with respect to the research reported in this paper.
Assessment Growth Limits and Mobility in Detroit, Michigan
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Assessment Growth Limits and Mobility in Detroit, Michigan
Variable Name Variable Denition
Sales Price Sales price of the property
State Equalized Value State equalized value of a parcel is equal to (0.5) ×
(estimated market value of property)
Taxable Value Taxable value of a property is state equalized value at the
time of purchase, but taxable value grows at the annual rate of
ination or 5 percent since the date of purchase, whichever is
less. Taxable value is adjusted to state equalized value at the
time of sale.
Effective Tax Rate
(using assessed value as base)
[(Statutory tax rate per 1,000 of taxable value) ×
(taxable value)]/(state equalized value)
Effective Tax Rate (using last
sale price as base)
[(Statutory tax rate per 1,000 of taxable value) ×
(taxable value)]/[(0.5) × (last sales price adjusted by Housing
Price Index)]
Living Area Size of residential structure in square feet
Lot Size Size of lot in square feet
Age Age of residential structure in 10-year intervals
Tax Delinquent Indicator variable equal to 1 if the property owner is tax
delinquent and 0 otherwise
Underwater Proxy Estimated equity generated from zip code level housing price
index data (see detailed description in data section of paper)
Underwater Indicator variable equal to 1 if the home owners is underwater
as measured by Underwater Proxy, and 0 otherwise
Partial Equity Indicator variable equal to 1 if the home owners has partial
but not full equity as measured by Underwater Proxy, and 0
Full Equity Indicator variable equal to 1 if the home owners has full
equity as measured by Underwater Proxy, and 0 otherwise
Years Owned Uncapped Number of years owned by the current property owner.
Years Owned Capped Number of years owned by the current owner since the
adoption of Proposal A in 1994
... That is, the homeowner may change their capitalization payment if they don't expect to live in their home for the duration of the program. However, this is unlikely given the average length homeownership in Detroit is approximately 22 years, longer than program benefits are granted (Hodge et al., 2015). 10 See Feldman et al. (2003) for an extensive discussion of Proposal A. 11 The full capitalization rate also depends on the level of improvements a homeowner undertakes to qualify for the program. ...
... Second, it is unknown what effect this program has had on vacancy and mobility rates, and thus the benefits may continue to be under-or overestimated. Especially with Michigan's taxable value cap policy, any reduced mobility created by the NEZH tax relief program may lead to lost revenue the city would have otherwise received (Hodge et al., 2015). However, this reduced revenue may be offset by reduced vacancy rates. ...
In this paper we examine the degree to which the tax benefits of the Neighborhood Enterprise Zone Homestead (NEZH) program are capitalized into the value of residential property. Although Enterprise Zone (EZ) programs and the studies examining these place-based policies are abundant, the NEZH program is unique because it offers tax abatements directly to the homeowner. Specifically, NEZH beneficiaries receive an 11.5 mill reduction (out of 66.61 total mills) for 12 years (excluding the value of land), and a phase-in to full taxation through year 15. We use residential sales data for properties sold in Detroit between 1997 and 2010 and a difference-in-differences model to estimate how much households are paying for the tax savings. Our primary results (intent-to-treat estimates) show all sales within NEZHs were 6 − 10 percent higher compared to those outside a zone. Furthermore, treatment-on-the-treated estimates highlight program beneficiaries paid 39 percent more for their property. With a full capitalization rate of 8.05 percent, these results indicate NEZH benefits are being overcapitalized by Detroit residents.
... properties. Non-beneficiaries may also be anticipating participation in the program and capitalize the "potential" benefits they will receive once they are issued a NEZH (Hodge, Skidmore, and Sands, 2015). However, this reduced revenue may be offset by reduced vacancy rates. ...
This study considers if major league sports facilities help the property tax base in their vicinity recovery from recession. The Great Recession is treated as an exogenous shock to the property tax base, and proximity to three facilities in Los Angeles County, California is used to predict the speed at which nearby properties’ taxable assessed value recovers. California is a useful context because state law provides a consistent and uncontroversial way to measure property tax base recovery. Using parcel‐level data from 2006 through 2019 for properties within three‐miles of the Staples Center, Dodger Stadium, and Dignity Health Sports Park, only proximity to Dodger Stadium impacts recovery speed. The results indicate that an average property a mile closer to the stadium recovers in 87 percent of the time an otherwise similar property a mile further away does, a finding that is robust to placebo. The study’s implications are discussed.
Detroit recently decreased assessments for 3 consecutive years, 2014–2016, with 2 possible types of reduction: a decrease in assessment without a decrease in property tax bill or a decrease in both. This article examines the effect of each type of reduction on property tax delinquency using parcel-level data from 2013 to 2016. Overall, results indicate that lowering a property owner’s assessment without a decrease in their tax bill reduced delinquency by 1.7%. Lowering both assessment and tax bill reduced delinquency by 2.7%. Additional analyses examine the levels of assessment reduction that triggered changes in delinquency, the relationship between assessment reductions and taxes owed, the persistence of reduced delinquency without corresponding tax decreases, and how the results vary by property owner type (i.e., homestead and residency status). Property owner type is of particular interest because it provides possible explanations for why delinquency declined without lower taxes. Not only is this evaluation important for Detroit, but understanding what methods are effective in reducing property tax delinquencies is beneficial to officials in other jurisdictions struggling to collect property taxes.
This article uses data on 3,788 vacant land sales to explore the pattern of land values in the city of Detroit, Michigan. The analysis provides evidence of a U-shaped land value gradient. Land values are relatively high in and near the central business district (CBD), but the land value gradient is very steep; estimated land values drop precipitously to less than $1,000 for typical sized lot in a vast "donut" area surrounding the CBD. However, land values begin to rise near the city's border. © 2017 by the Board of Regents of the University of Wisconsin System.
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In 1991, a property tax limitation measure was imposed in five Illinois counties. Dye and McGuire (1997) studied its short-term impact. With the limit now in effect for over a decade and extended to many more counties, we assess its long-term impact. Because jurisdictions brought under the limitation since 1997 have done so after a county-option referendum, our estimation strategy treats the measure as endogenous. We find that the restraining effect of the limit on the growth of property taxes is stronger in the long run than the short run, and that the growth of school expenditures is slowed by the measure.
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In this paper we examine the degree to which Michigan’s property value assessment growth cap has eroded the tax base and created substantial differences in effective tax rates among residential properties within the City of Detroit. While the analysis focuses on a specific city with significant tax base erosion challenges, it is relevant to other cities in Michigan and across the nation, particularly in states that impose assessment growth limits. Using quantile regression techniques, we examine how an assessment growth cap alters effective tax rate distributions within and across property value groups. Results show that the cap creates a wide range of effective tax rates across properties of similar value (horizontal inequity), and similar tax payments for properties of differing values (vertical inequity).
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We examine the change in the distribution of property tax payments resulting from Michigan's imposition of a property tax assessment growth cap in 1994. The cap restricts growth in property value for tax purposes to the infl ation rate, for those maintaining continuous ownership. Upon sale, however, the tax base is adjusted to refl ect market value. Using data from a survey conducted in 2008, we fi nd that long-time homeowners enjoy an average reduction in effective tax rates (relative to new homeowners) of 19 percent. The cap also appears to have reduced effec-tive property tax rates for older homeowners, and for those with higher incomes.
We examine the degree to which assessment practices in the City of Detroit have created substantial inequities in property tax payments across residential properties. Two key contributions of this article include: (1) inequities created by assessment practices are examined in a collapsed real estate market, and (2) quantile regression techniques are used to determine how assessment practices have altered assessment distributions within and across property value groups. Results show that current practices have created a wide range of property tax payments across properties with similar value (horizontal inequity), and similar tax payments for properties of differing values (vertical inequity).
The property tax savings provided by assessment caps are generally lost when homeowners move. There is, therefore, a concern that homeowners get "locked-in" to their current home. Using data from Florida, the results presented in this paper show that the lock-in effect is nontrivial in magnitude, especially for homeowners in single-family homes (in comparison to condominiums) and those located in jurisdictions with relatively low property tax rates.
The purpose of this study is to empirically examine the effect on ownership tenure for residential housing under an acquisition-based property tax system. The state of Florida has an acquisition-based property tax system whereby annual assessments are capped to protect existing homeowners; the assessment is only adjusted to reflect market value upon resale. We hypothesize that such a system would lengthen the average tenure of residential home ownership, since the transactions costs of intra-state moves are magnified by the lost property subsidy. A sample of 20 Florida counties is used to examine average and median residential housing tenure at two distinct points in time to investigate changes in housing tenure. The results do not support the hypothesis. Possible mitigating factors included increased residential housing demand from a large population influx, escalating residential property values, low interest rates and easy credit availability, and homeowners adjusting to the acquisition-based property tax system.
In this article we examine the relationship between property tax rates and tax base growth in southeast Michigan using data for all 152 communities in the five counties surrounding Detroit over the 1983–2002 period. To address endogeneity, we exploit the adoption of Proposal A in 1994, which resulted in substantial exogenous and differential changes to property tax rates and school spending across all communities. This major state imposed intervention enables us to examine the tax rate-tax base relationship using appropriate instrumental variable techniques. We find that both tax rate and school spending changes have statistically significant effects on tax base growth. We also examine the effects of competitor community policies on own community tax base growth using a unique approach to define competitors. Specifically, competitor communities are determined by migration flows as opposed to the more traditional methods based on contiguity or population. We find significant regional competition effects; changes in tax rates and school spending relative to competitors are also important to tax base growth.
In 1978, the voters of California cut the property tax paid by home owners whenthey passed Proposition 13. However, home owners lose much of this tax savings if they sell their homes and buy others, because, under Proposition 13, recently purchased property is assessed at a higher rate. This may create a lock-in effect, and as a result home owners may be less likely to move from their present homes. Using the Census Bureau's Annual Housing Surveys (1975, 1978, 1982), I compare home owner mobility rates after Proposition 13 with rates immediately prior to the initiative. I find that mobility did decline in the years immediately after the introduction of Proposition 13. However, the data suggest the decline in mobility in California may be simply a part of a national decline in mobility, because there was a concurrent decline in mobility in the rest of the nation.