House Prices and Deed Type
David M. Brasington
Economics Department
Louisiana State University
Robert F. Sarama Jr.
Economics Department
Louisiana State University
February 3, 2005
Abstract
When houses are sold they come with a deed attached that spells out the guarantees the
seller makes about the house. Sometimes the deed tells us about the circumstances
surrounding the sale. Using a 37,043-observation house price hedonic with a Bayesian
spatial error model, we find that the type of deed attached to a housing sale can have a
dramatic correlation with the sale price. Warranty deeds are the most common type, but
ten deed types command a discount and one commands a premium relative to warranty
deeds. Houses with a survivorship deed sell for 1.4% more than houses with warranty
deeds, all else constant. Houses with quit claim deeds sell for 51% less than warranty
deeds, the largest discount we find. Houses with sheriff’s deeds sell for a 31% discount
and houses with foreclosure deeds sell for a 36% discount. The discount for three of our
15 deed types varies significantly across regions. Parameter estimates seem robust to the
exclusion of deed type information. Additionally, we ask whether higher mortgage rates
are associated with riskier deed types, but find no relationship. Finally, we investigate
whether certain deed types are found more often in poor neighborhoods.
Contact information:
David Brasington
Economics Department
Louisiana State University
Baton Rouge, LA 70803
Phone: 225-578-7822
Email: dbrasin@lsu.edu
JEL Classifications: G21, K11, R31
Keywords: mortgage lending, house price hedonic, spatial statistics, real estate law
1
Scores of studies have examined the determinants of house price, yet none has
examined whether the type of deed attached to the house affects its sale price. The deed
attached to a house sometimes contains guarantees of good title, freedom from
encumbrances, and protection from heirs making competing claims against the property;
other deeds make no such guarantees. The type of deed attached to a house can also
impart information about the seller (Hite and ???) and other circumstances surrounding
the sale. For example, a foreclosure deed suggests a lending institution took possession
of the property after the borrower defaulted on the mortgage.
A look at the relation between deed type and house price may be interesting for
several reasons. Real estate agents care about the type of deed attached to the property if
the deed type affects the sale price and their commission. Academics care about deed
type if the exclusion of deed type biases the parameter estimates of environmental quality
or school quality in a house price hedonic. Property tax assessors care about deed type if
the use of this information yields a better prediction of the underlying structural value of
the house than even the sale price does. Knowing the discount for less secure deed types
can help home buyers decide whether it is worth the increased cost to look for a house
with a more secure deed. Discounts and premiums for certain deed types suggest
interesting stories, such as a principal-agent problem for houses sold by guardians of
minors or mentally incapacitated persons. Finally, if traditional lending institutions
refuse to lend for mortgages on houses with risky deed types, sub-prime lenders may fill
the gap and charge higher interest rates to cover the increased risks (Pennington-Cross,
2003a, 2003b).
2
We construct a data set of 37,043 housing sales that have the deed type listed. We
control for outliers, omitted variables, and heteroskedasticity using a Bayesian spatial
error model (LeSage, 1997b). We find that the deed attached to a house can have a
dramatic link with the sale price. The most expensive deed type in our study, a
survivorship deed, commands a 52.4% premium over the price of the typical house sold
with a quit claim deed. This 52.4% premium translates to an average house price
difference of $76,339. Warranty deeds are the most common in our sample. We find
that houses with sheriff’s deeds sell for a 31% discount, and foreclosure deeds a 36%
discount, relative to houses sold with warranty deeds. In all, ten deed types command a
discount and one commands a premium relative to warranty deeds. The value of most
types of deeds is constant across regions, but there are three striking exceptions.
We also check for an indicator of sub-prime lending by regressing the mortgage
rate of the transactions on the deed types. Although our results show that there is no
significant relationship between mortgage rate and deed type, further investigation of this
topic with additional data may be warranted. Lastly, we look at the probability of
observing certain types of deeds in poor neighborhoods. We find that survivorship deeds
are more prevalent in affluent neighborhoods, while we are more likely to observe
fiduciary covenants, quitclaim deeds, and sheriff’s deeds in poverty-stricken
neighborhoods.
Data
Housing data with deed type information is fairly widely available from county
auditors and real estate companies. We collect a sample of 37,043 houses that were sold
3
in Ohio in 2000 that contain information about the type of deed attached to the house at
the time of sale (FARES, 2002). The houses come from five metropolitan areas: Akron,
Cincinnati, Cleveland, Columbus, and Youngstown. All houses are single-family
detached structures that sold for more than $30,000, to help ensure arms’ length
transactions.
We have information about 14 structural characteristics of the houses. These
characteristics are the number of fireplaces, bedrooms, and full and partial bathrooms in
the house; the number of detached structures on the lot; the age of the house; the size of
the house and yard; and dummy variables for whether the house is one-story, made of
brick, and has a deck, patio, garage, or finished basement.
[Insert Table 1 about here]
Our Estimation Approach sections will discuss additional strategies, but the
traditional identification strategy in cross-sectional house price hedonic studies is to
include controls for the characteristics of the neighborhood of the house (e.g., Haurin and
Brasington (1996)). To this end, we match our houses to data from seven different
sources.
A substantial literature investigates the relation between house prices and school
quality (Ross and Yinger, 1999). Brasington (1999) and Brasington and Haurin (2004)
suggest that proficiency test scores are good measures of homeowners’ valuation of the
quality of the public schools. The percent of students in each public school district who
failed the Ohio 12th grade proficiency test is used to capture school quality (or the lack of
it). The other public services in the hedonics measure police protection and
environmental quality, and the tax rate is included as well.
4
Archer, Gatzlaff and Ling (1996) find that growing areas have higher house price
appreciation rates, so fast-growing communities might also have higher sale prices than
slower-growing communities. To this end, a variable HOT MARKET is included to
capture development activity in the community. Other included community descriptors
include the prevalence of single-parent households and racial heterogeneity.
The focus variables are the types of deeds associated with the houses at the time
of sale. Fully 15 deed types are represented in our data set. The definitions, means, and
sources of all our variables are included in Table 1.
Estimation Approach: OLS Models
The traditional ordinary least squares (OLS) house price hedonic takes the
following form:
(1)
ln Vi = X + ,   N(0,2)
where V is the value of house i, X is the matrix of explanatory variables with parameters
 to be estimated, and the error term  is assumed to have a zero mean and constant
variance 2. With the included neighborhood characteristics controlling for the influence
of omitted variables, a house price hedonic for the full sample is performed using
Equation (1). The results appear in the OLS column of results in Table 2.
Adjusted R-squared for the OLS model of Equation (1) is 0.71. Most of the house
and neighborhood controls have the expected sign. With WARRANTY DEED as the
omitted category, many of the other deed types show up as statistically significantly
related to sale price. A more detailed examination of results is delayed until we reach our
preferred estimation approach.
5
While OLS is the traditional approach, other research suggests that omitted
variables bias the parameter estimates. Ries and Somerville (2004) find that by adding
fixed effect dummy variables, their school quality parameter estimate becomes weaker,
for example. To this end, a dummy variable for each metropolitan area (MSA) is added
to Equation (1), with Akron as the omitted category.1 The MSA dummy variables will
capture the influence of variables that vary at the metropolitan area level, such as
economic conditions. The results are shown in the Fixed Effects OLS column of results
in Table 2.
[Insert Table 2 about here]
The addition of the MSA fixed effects has increased adjusted R-squared slightly
from 0.71 to 0.73. Most of the signs and significance of the parameter estimates remain
unchanged, but there are exceptions. SURVIVORSHIP DEED has become positively
related to house price. Brick houses, outbuildings, and racial heterogeneity have stronger
parameter estimates than before. Market conditions still matter, but the MSA dummies
seem to have siphoned off some of the influence of HOT MARKET. Single parent
households no longer depress house price.
However, the MSA dummy variables are coarse ways of capturing the influence
of omitted variables: they fail to capture a wide range of more localized influences on
house prices like the presence of parks and abandoned houses. To better capture the
influence of omitted variables, we adopt spatial statistics.
Estimation Approach: The Spatial Error Model
6
Spatial dependence is the idea that some things are related to each other over
space: the closer these things are, the more related they are. For example, two nearby
houses have almost equal access to the nearest park; a third house far away from the park
has less access. So there is spatial dependence in access to the park. If access to the park
is valued, all else equal, there will be spatial dependence in the prices of the houses as
well: the two nearby houses will have similar prices, while the third house will have a
less similar price. If park services are omitted from a house price hedonic, there will be
spatial dependence in the error term, invalidating the OLS assumption of independent,
zero-mean errors (LeSage, 1997a). In fact, a likelihood ratio test for spatial dependence
in the residuals of the OLS model of Equation (1) reveals its presence: with a critical chisquared test statistic of 6.6 at the 1% level, the calculated test statistic of 6215.7 rejects
the null hypothesis of no spatial dependence.2
There are two main approaches to modeling spatial dependence. One approach is
the spatial autoregressive (SAR) model, which models spatial dependence by including
an autoregressive term to the set of explanatory variables. Another approach is the
spatial error model (SEM), which models spatial dependence by including an
autoregressive term to the errors of the model. We follow Lacombe (2004) to help decide
which model is more appropriate for our sample. We perform both SAR and SEM
regressions and find that the SEM model captures more spatial dependence than the SAR
model.3 We therefore adopt the SEM model for our hedonic regressions.
The SEM model takes the following form (Anselin, 1988, p. 182):
(2)
Vi = X + 
 = W + ,   N(0,2)
7
The SEM model introduces an autoregressive term W into the error term  of the
house value regression. The parameter  captures the strength of this autoregressive
relation, and the term W is a spatial weight matrix that summarizes the spatial layout of
the data.4
There are many equally valid ways to construct a spatial weight matrix W to show
who the neighbors are for each house. Some studies like Brasington and Hite (2005)
choose an arbitrary number of neighbors. The fewer neighbors one chooses, the more
localized the influences picked up. Rather than choosing a number of neighbors to use,
we rely on a technique called Delauney triangularization (Pace, 2003). The Delauney
triangularization technique chooses the number of neighbors to allow for each house and
assigns the weight each neighboring house is given. For example, house number 1052
might be assigned four neighbors with weights 0.10, 0.30, 0.25, and 0.35, while house
number 15,382 might be assigned six neighbors with weights 0.50, 0.15, 0.15, 0.05, 0.05
and 0.10.5
The spatial error model of Equation (2) is estimated using maximum likelihood.
The concentrated log-likelihood function takes the following form (Anselin, 1988, p. 182;
LeSage, 1999, p. 76):
(3)
L = C – (n/2) ln[(1/n)e(I-W) ( I-W)e] + ln| I-W|
In Equation (3), C is a constant not related to the parameters, n is the number of
observations, and e is the matrix of residuals from least squares estimates for  in the first
part of Equation (2).
The most difficult part of Equation (3) is evaluating the log-determinant of (IW). Computers today are limited to running the SEM model of Equation (2) with a few
8
thousand observations, which takes hours. However, Pace and Barry (1998) and Barry
and Pace (1999) have developed computational tricks that permit larger models to be run.
In fact, the 37,826-observation SEM model takes 62 seconds on a laptop computer with a
2.66 GHz Pentium 4 processor.
The SEM model captures the influence of omitted variables, providing a more
convincing identification strategy than simply including neighborhood characteristics or
including fixed effect dummy variables. The proximity to parks illustration at the
beginning of the section is characteristic of the types of omitted influences that the SEM
model addresses. In fact, proximity to anything is subsumed by the spatial error model.
If proximity to a nuclear power plant, a shopping mall, an interstate highway, and a lake
affect house prices, these omitted influences will be subsumed in the error term, and
normally might adversely influence parameter estimates. But the second part of Equation
(2) recognizes the correlation between the error terms of neighboring houses: if the error
term for house 1 is affected by being near a nuclear power plant, the error term for nearby
house 2 is affected to a similar degree. The error term could also include omitted
variables for income levels, racial composition levels, and other demographic
information. If demographic composition for one house is similar to that of its neighbors,
the spatial error model will capture these influences. In this manner the SEM model
addresses the influence of omitted variables in the house price hedonic. A more complete
intuitive explanation of how spatial statistics addresses omitted variables is found in
Brasington and Hite (2005). A mathematical proof is available in Griffith (1988, p. 94107).
9
The results of the SEM estimation appear in the Spatial Error Model column of
Table 2. Model fit has improved from an adjusted R-squared of 0.71 in the OLS model
to 0.73 in the Fixed Effects model to 0.78 in the SEM model. The spatial error parameter
estimate is 0.56, and it is highly statistically significant. This implies that the average
correlation between the residuals of an observation and that of its neighbors is 0.56.
Relative to the Fixed Effects OLS model, the SEM model has made PATIO lose
statistical significance, while BEDROOMS and SINGLE PARENTS become statistically
significant. The strength of the relation between house prices and HOT MARKET has
strengthened, while it has weakened for ONE STORY, BRICK, OUTBUILDINGS,
DECK, and RACIAL HETEROGENEITY. The SEM model also appreciably alters the
relation between house prices and the age of a house. We are almost ready to interpret
the focus variables at length, but to be more certain of our results we make one final
modification to our estimation technique by adopting a Bayesian approach.
Estimation Approach: The Bayesian Spatial Error Model
Because we wish to make the most correct inferences possible, we must be wary
of heteroskedasticity and outliers. Heteroskedasticity will cause inefficient parameter
estimates and invalidate hypothesis testing, and outliers can dramatically alter parameter
estimates. The preceding OLS, Fixed Effects, and SEM models ignore these problems,
while in fact White’s test rejects the null of homoskedasticity at the 1% level.6
Anselin (1988) suggests a heteroskedasticity correction for maximum likelihood
spatial routines like that of Equation (3), but there are many reasons to favor a spatial
Bayesian SEM model instead (LeSage, 1999, p. 141). A Bayesian model does not
10
require a restrictive specification for the heteroskedastic disturbance term. Bayesian
models are more robust to outliers than maximum likelihood. Maximum likelihood
methods require normally distributed errors, which may be an erroneous assumption.
And although the Bayesian approach uses conditional distributions in its sampling
methodology, with a large sample of draws it converges in the limit to the true joint
posterior distributions of the parameters.
The Bayesian spatial error model is that of LeSage (1997b). It takes the same
form as Equation (2) except for the more complex specification of the disturbance term:
(4)
Vi = X + 
 = W + ,   N(0,2V)
V = diag(v1, v2,…, vn)
r/vi  2(r) / r
1/2  (,)
A big difference between Bayesian and non-Bayesian estimation is the use of prior
information. We allow diffuse priors for , , and 2. Ordinarily, the term r in Equation
(4) would be distributed gamma with two parameters. Instead, following LeSage (1999,
p. 121), we set r = 4 as our informative prior on vi. This particular prior yields relatively
constant estimates of vi in the presence of homoskedasticity, while at the same time
accommodating non-constant error variances in the presence of heteroskedasticity and
outliers.7 Again, the computational tricks of Barry and Pace (1999) and Pace and Barry
(1998) must be used to allow the large sample to run in a reasonable amount of time.8
The Bayesian spatial error model in Equation (4) depends on having a large
number of draws to converge to the true joint posterior distribution of the parameters. If
11
you don’t have enough draws, you can’t trust your parameter estimates. Although
convergence diagnostics are available, the true test of convergence is when the estimates
don’t change with added draws. We run a model with 300 draws (with 30 additional
burn-in draws) and a model with 1000 draws (with 100 additional burn-in draws) and
achieve similar results, suggesting that 300 draws is sufficient.
One way to see if the Bayesian technique improves on the non-Bayesian spatial
error model is to compare the 2 estimates. The estimate of 2 has fallen from 0.0682 to
0.0486, suggesting some benefit to using the Bayesian approach (LeSage, 1999, p. 121).9
Adjusted R-squared is 0.78 in the non-Bayesian model and 0.77 in the Bayesian model,
suggesting that the non-Bayesian model tried to fit outliers to a minor extent. Going from
the non-Bayesian to the Bayesian model made ADMINISTRATOR’S DEED become
statistically significant for the first time, CORPORATION DEED increase its statistical
significance from the 10% to the 1% level, and SINGLE PARENTS have a somewhat
stronger negative relation with house price. The remaining parameter estimates change
little. Having settled on our theoretically preferred econometric model, we are ready to
interpret the key parameter estimates.
Deed type estimation results:
Our omitted deed category, WARRANTY DEED, is among the most valued by
the housing market. Only survivorship deeds command a premium relative to warranty
deeds. All else constant, houses with survivorship deeds sell for 1.4% more than
warranty deeds. With a mean house price of 145,685, the 1.4% premium for survivorship
deeds translates to almost $2040. The survivorship deed is one in which a joint tenant
12
maintains ownership rights following the death of another joint tenant. Because the
survivorship deed prevents the heirs of the deceased from making claims against the
property, the property becomes less risky for the final owner.
Three deed types have insignificant parameter estimates, implying that we cannot
reject the null hypothesis that houses with these types of deeds sell for anything different
than houses with warranty deeds. These three types are execution deeds, trustee’s deeds,
and final distribution deeds. Although statistically significant results are found for three
even less numerous deed types, the relatively small number of execution deeds (12) and
final distribution deeds (21) may contribute to their statistical insignificance.
The remaining ten deed types are associated with lower house prices, all else
constant. In most cases the lower house prices are associated with the level of risk
involved with holding a particular deed. The largest discount (51%) is attached to houses
with quit claim deeds. Quit claim deeds do not state the nature of the rights conveyed in
a transaction and provide no warranties of ownership. These deeds merely convey the
grantor’s rights or interests in the real estate. For these reasons, a property selling with a
quit claim deed has a higher chance of previous owners surfacing and claiming title to the
property than a deed that provides more warranties of ownership.
Other discounts may be attributed to a level of urgency associated with selling a
particular real asset. The second-largest discount is for houses with foreclosure deeds
(36%), followed closely by sheriff’s deeds (31%). A foreclosure occurs when a mortgage
goes into default and the lender acquires ownership rights to the property. The lender
then sells the asset quickly, usually by auction. As a result, the property sells for a
discount compared to what it would have sold for had it been left on the market longer.
13
A sheriff’s deed is issued when a court orders the conveyance of a property to satisfy
judgment. As in the foreclosure case, the receiving party of a sheriff’s deed may have an
interest in extracting the monetary value from the property as quickly as possible. Such
an action would cause the asset to sell for discount.
Guardian deeds command a 27% discount. This means that the typical house
would sell for nearly $39,335 more if it had a warranty deed rather than a guardian deed.
The “urgency factor” may also play a role in the discount associated with guardian deeds.
A guardian deed is used to convey the property of an infant or incompetent. As an
example, a minor may reach the predetermined age of conveyance of the property and
need the monetary value of the asset rather than the physical value. Such a situation can
occur when a minor reaches the age of eighteen and needs the money invested in a house
to pay for college. This situation would merit the quick sale of the house in order to
extract the monetary value in a timely fashion. In some cases of guardian deed sales
there may also be a principal-agent problem: guardians don’t get paid any more for
selling the house for a high price, and therefore may wish to sell as quickly as possible to
lessen the amount of time they have to spend selling the property.
The next highest discounts belong to houses with limited warranty deeds (19%),
administrator’s deeds (16%), and special warranty deeds (14%). The limited warranty
provides a warranty for only the period during which the seller held the title, leaving the
buyer open to claims from previous periods. This deed is less risky than the quit claim
deed, but more risky than the warranty deed. The special warranty deed requires the
grantor to defend title against claims of only those related to the grantor in the some way.
This means that only claims brought by the grantee and those claiming under him are
14
guaranteed to be defended by the grantor. In both the limited and special warranty deeds,
the buyer of the house must rely on title insurance for protection. An administrator’s
deed is used to convey the property of a person who dies without a will. The discount for
this deed type may be associated with the “urgency factor.” Two cases in which a person
would die without a will occur when the death is unexpected or when the person has no
close heirs to leave the asset. In both cases, the property might be sold quickly. For
example, if a wife’s husband dies in a car accident, the wife may downsize to a smaller
residence that doesn’t constantly remind her of her husband’s tragic death. Another
example may include the possibility of a distant relative passing away, and the asset
being passed down to someone who hardly knew the deceased. In this case, the heir
might be anxious to sell the property and realize the unexpected profit quickly, before
someone surfaces to contest the administrator’s judgment.
Houses sold with corporation deeds sell for an 11% discount relative to warranty
deeds. If a corporation relocates an employee, it may buy the employee’s house at
market price to speed the employee’s relocation. But having no interest in holding real
estate, the corporation sells the property quickly at a discount, possibly to another
employee new to the area.
The weakest discounts belong to houses with fiduciary (9%) and executor’s deeds
(8%). A fiduciary covenant is not as safe as a warranty deed because it does not
specifically and absolutely provide warranty against all claims against the title, which
would explain the 9% discount. An executor’s deed conveys the property of one who has
died with a will. This is similar to the administrator’s deed in that the grantor is now
dead. However, unlike an administrator’s deed, an executor’s deed is issued when the
15
deceased grantor has a will. The presence of a will would decrease the “urgency factor,”
and the remaining discount could be attributed to the fact that the executor’s deed simply
provides less protection against claims to the title than does a warranty deed.
Implications of omitting deed types
House price hedonic studies are published fairly often by economists and real
estate researchers. The preceding analysis suggests that the type of deed associated with
a house can have a dramatic effect on the price it sells for, either because of the type of
deed itself or because the type of deed signals something about the quality of the house or
the motivation level of the seller. A failure to include deed type in house price hedonics
may cause biased parameter estimates and incorrect inference about parameters of public
policy interest, like that of school quality, environmental quality, or police services. The
Bayesian spatial error model is performed again, this time omitting information on deed
type.
The Bayesian SEM w/o Deeds column of Table 2 suggests that excluding deed
types does not appreciably alter any parameter estimates. Many parameter estimates are
completely unchanged, in fact. So the exclusion of deed type information seems not to
hurt inferences made from house price hedonics. It is only necessary to include deed type
information if one is researching deed types themselves.
Individual MSA regressions
Performing house price hedonics for the full sample provides a concise way of
examining the relation between deed type and house price across the state of Ohio. And
16
the use of spatial statistics helps control for the influence of omitted variables that affect
cities. But a pooled regression may mask differences in discounts associated with deed
types across different markets. To this end, separate regressions are run for houses in the
Akron, Cincinnati, Cleveland, Columbus, and Youngstown metropolitan areas.
The
results are summarized in Table 3 below.
[Insert Table 3 about here]
Although all deed types are defined in the Ohio Revised Code, and are therefore
recognized across the state, our sample does not have all deed types represented in every
metropolitan area. In fact, our Akron sample has only four deed types represented. The
other areas have all but two deed types represented. And because each metropolitan area
necessarily has fewer observations than the full sample, fewer deed types attain statistical
significance. Still, a few observations can be made.
The discount for having an executor’s deed is 10% in Cincinnati but only 5% in
Cleveland. The discount for having a quit claim deed varies from 35% in Akron to 61%
in Cleveland, a difference in mean sale price of nearly $38,000 between the two regions.
And fiduciary deeds command a discount of only 7.6% in Cincinnati but a 17% discount
in Columbus. The discount for sheriff’s deeds, survivorship deeds, guardian deeds,
limited warranty deeds and foreclosure deeds is fairly constant across regions. Still, the
discount varies widely between regions for certain deed types.
What kind of houses have these deeds?
Having found that deed type is related to the sale price of a house, and that the
discount of having certain deed types is more pronounced in some regions than others,
17
we now ask a new question: are houses in poor neighborhoods more likely to have
certain deed types? We estimate probit models with each of the fourteen unusual deed
types as dependent variables.
The explanatory variables used to characterize a
neighborhood include census block group measures of per capita income, percentage of
the population with graduate degrees, poverty level, unemployment rate, total residential
value, and the level of urbanization. The results are summarized in Table 4 below, and
several observations can be made from the output.
[Insert Table 4 about here]
The negative signs on the coefficients of the unemployment, urbanization, and
poverty parameters indicate that survivorship deeds are less prevalent in poor urban
neighborhoods. Also, there is a greater probability of observing a foreclosure deed in an
urbanized neighborhood with lower per capita income.
The neighborhoods with a higher probability of having sheriff’s deeds are located
in more rural areas, have a higher poverty rate, and less people with graduate level
educations. The probability of observing fiduciary covenants or quit claim deeds is
higher in poverty stricken neighborhoods than in affluent ones, all else constant.
Another interesting observation is that there is a lower probability of observing a
corporation deed in a neighborhood inundated with inhabitants with graduate degrees.
This may indicate that corporations supply houses primarily for their blue collar workers,
while the executives are left to find their own homes. Alternatively, executives who were
transferred to another city lived in central city neighborhoods close to corporate
headquarters, where education levels were lower.
18
The influence of deed types on mortgage interest rates
One could hypothesize that individuals who buy houses with riskier deed types
would pay higher mortgage interest rates than individuals who take out mortgages on
houses with warranty deeds. In fact, traditional lending institutions may refuse to issue
mortgages on houses with shaky collateral. Buyers of houses with riskier deed types may
need to turn to sub-prime lending institutions and pay higher interest rates. We regress
mortgage interest rates as a function of deed types and control for the mortgage amount,
frequency of mortgage payments, racial heterogeneity of the neighborhood, the size of
the house, and the quality of public schooling. The results can be seen in Table 5.
[Insert Table 5 about here]
Of the 37,043 houses with deed types, only ??? have the necessary mortage data.
While our data limit us from doing an exhaustive study of this issue, we conclude that
there appears to be no significant relationship between more risky deed types and
mortgage rates. Although four of the deed types are statistically significant, the signs of
the coefficients do not tell a consistent story. As expected, special warranty deeds have
higher interest rates and survivorship deeds have lower interest rates. However, sheriff’s
and foreclosure deeds have lower interest rates than warranty deeds. This leads us to
reject the hypothesis that houses with riskier deed types have higher mortgage rates than
safer deed types.
Conclusion
The type of deed attached to a house often has a marked influence on sale price.
All else constant, the difference between the sale price of a house with the most valued
19
deed type and the least valued is 52.4%. The most common type of deed is a warranty
deed. The premium or discount for the average house of each deed type is summarized in
Table 6 below:
[Insert Table 6 about here]
Some of the discounts reflect the added risk that a buyer incurs when buying a
house with a quit claim deed, for instance, which does not guarantee that the seller has a
clear title to the house. Other discounts reflect motivated sellers who are willing to get
rid of the house without waiting for a higher bidder, like a guardian deed. Some
discounts indicate motivated sellers and possibly the condition of the house, like a
foreclosure deed or a sheriff’s deed.
The current study provides estimates of the trade-off between higher sale price
and higher seller risk for a number of deeds. A quit claim deed sells for about $54,000
less, or 37% of the price of the average house, than a special warranty deed but also
entails additional risk. It also provides guidance for real estate agents: all else equal,
commissions are 19% larger for houses with warranty deeds than for houses with limited
warranty deeds.
Property tax assessors may obtain a more reliable estimate of the underlying value
of a house by factoring in the type of deed. For example, the discount for a corporation
deed may stem from unique seller motives and not from the structural condition of the
house. So houses with corporation deeds may be worth about 11% more than the sale
price of the house, which affects property tax collections from the buyer. States like
California assess taxes based on the sale price of a home. If assessment were based on
sale price, Ohio would have under-collected $60,680,570 in property taxes in 2000 for
20
houses whose deed types command a discount. On the other hand, it would have overcollected $23,789,312 in property taxes for houses whose deed types command a
premium over warranty deeds.10 And states like Ohio that use comparable house sales
for assessment also mis-estimate house value by not accounting for the non-traditional
deed types of comparable house sales. In our sample, 30% of houses are sold with nonwarranty deeds.
In some cases, the mortgage rates attached to real estate transactions can provide
evidence of sub-prime lending. We investigated whether or not there is a relationship
between the more risky deeds and the mortgage rates. The analysis performed with our
limited data set suggests that there is no relationship between the more risky deed types
and the mortgage rates.
Our investigation into the probability of observing certain deed types in poor
neighborhoods yields some interesting results. We find that some deeds, such as
quitclaim and sheriff’s, are more common in poverty-stricken neighborhoods than in
affluent ones; while others like survivorship deeds are more common in more affluent
neighborhoods. Our results show that there is a relationship between neighborhood types
and deed types, and those relationships may be further investigated in future studies.
Additional work could investigate the discount or premium for sale of houses by
banks or between family members (Hite & ???). However, our investigation suggests
that neither the relation between house price and structural characteristics nor the relation
between house price and neighborhood characteristics or public services is affected by
the omission of the type of deed attached to the sale of a house.
21
Table 1: Variable Definitions, Sources, and Means
Variable Name
LN HOUSE PRICE
Definition (Source)
ONESTORY
Sale price of house in 2000 in U.S. dollars (1); natural log
is used in hedonic regressions, but unlogged means are
shown
Dummy variable = 1 if house is one story (1)
BRICK
Dummy variable = 1 if house is constructed of brick (1)
FINISHED
BASEMENT
GARAGE
Dummy variable = 1 if house has a finished basement (1)
FIREPLACES
Number of fireplaces the house has (1)
OUTBUILDINGS
Number of exterior buildings on the lot (1)
BEDROOMS
Number of bedrooms the house has (1)
FULLBATHS
Number of full bathrooms the house has (1)
PARTBATHS
Number of partial bathrooms the house has (1)
AGE
Age of house in hundreds of years (1)
HOUSE SIZE
Thousands of square feet of building size (1)
YARD SIZE
PATIO
Size of yard of house in acres, where 1 acre = 43,560
square feet (1)
Dummy variable = 1 if house has a patio (1)
DECK
Dummy variable = 1 if house has a deck (1)
BAD SCHOOL
Percent of students in school district who are below
proficient on Ohio 12th grade math proficiency test in
2000-2001 school year (6)
Single-parent returns as a percentage of total returns in
school district, for 1999 income tax returns (7)
value of new agricultural and residential (class 1) buildings
constructed between 1999 and 2000 per pupil in school
district in tens of thousands of U.S. dollars (8)
Tax year 2000 class 1 (agricultural and residential) tax rate
in school district in effective mills (2)
Air releases in Census tract of the house in hundreds of
SINGLE PARENTS
HOT MARKET
TAX RATE
AIR POLLUTION
Dummy variable = 1 if house has a garage (1)
Full Sample
Means ()
145,685
(116,841)
0.46
(0.50)
0.41
(0.49)
0.08
(0.27)
0.57
(0.49)
0.52
(0.59)
0.01
(0.09)
3.12
(0.75)
1.49
(0.63)
0.47
(0.54)
0.41
(0.31)
1.68
(0.73)
0.53
(1.93)
0.10
(0.30)
0.12
(0.33)
36.7
(12.9)
10.9
(5.2)
0.24
(0.38)
32.0
(5.6)
0.30
22
RACIAL
HETEROGENEITY
POLICE
PROTECTION
WARRANTY DEED
EXECUTION DEED
ADMINISTRATOR’S
DEED
CORPORATION
DEED
EXECUTOR’S DEED
QUIT CLAIM DEED
SHERIFF’S DEED
TRUSTEE’S DEED
FINAL DIST DEED
SURVIVORSHIP
DEED
FIDUCIARY DEED
GUARDIAN DEED
SPECIAL
WARRANTY DEED
LIMITED
WARRANTY DEED
FORECLOSURE
DEED
MORTGAGE
AMOUNT
MORTGAGE
FREQUENCY
MORTGAGE RATE
PER CAP INCOME
GRADUATE
DEGREE
thousands of pounds (3)
Leik index of racial heterogeneity of Census block group
of the house, where 0 is racially homogeneous, 1 is racially
heterogeneous (4)
Number of police officers per 1000 residents in police
district in 1997 (5)
Dummy variable = 1 if house is sold with a warranty deed
(1)
Dummy variable = 1 if house is sold with an execution
deed (1)
Dummy variable = 1 if house is sold with an
administrator’s deed (1)
Dummy variable = 1 if house is sold with a corporation
deed (1)
Dummy variable = 1 if house is sold with an executor’s
deed (1)
Dummy variable = 1 if house is sold with a quit claim deed
(1)
Dummy variable = 1 if house is sold with a sheriff’s deed
(1)
Dummy variable = 1 if house is sold with a trustee’s deed
(1)
Dummy variable = 1 if house is sold with a final
distribution deed (1)
Dummy variable = 1 if house is sold with a survivorship
deed (1)
Dummy variable = 1 if house is sold with a fiduciary deed
(1)
Dummy variable = 1 if house is sold with a guardian deed
(1)
Dummy variable = 1 if house is sold with a special
warranty deed (1)
Dummy variable = 1 if house is sold with a limited
warranty deed (1)
Dummy variable = 1 if house is sold with a foreclosure
deed (1)
Amount of mortgage in dollars (1)
Frequency, in years, with which the mortgage rate can
change (1)
Interest rate of mortgage in percentage points (1)
Per-capita income of households in census block group in
dollars (4)
Percentage of census block group residents who have a
graduate school degree (4)
(3.56)
0.10
(0.10)
15.3
(14.2)
0.70
(0.46)
0.00032
(0.018)
0.00024
(0.015)
0.0023
(0.048)
0.011
(0.10)
0.0090
(0.095)
0.0052
(0.072)
0.0012
(0.034)
0.00056
(0.024)
0.24
(0.43)
0.015
(0.12)
0.00029
(0.017)
0.00048
(0.022)
0.0037
(0.061)
0.0022
(0.047)
104,833.28
(77,000.58)
0.35
(1.26)
1.16
(3.17)
24,973.49
(10,267.62)
9.66
(8.58)
23
TOT RESIDENTIAL
VALUE
UNEMPLOYMENT
Total residential property value in school district (2)
953,365,362
(965,749,967)
Percentage of adults in census block group that is
3.85
unemployed (4)
(3.62)
URBAN
Percentage of population in census block group that is
91.26
living in an urbanized area or urban cluster (4)
(24.19)
POVERTY
Percentage of persons in census block group living below
6.83
the appropriate threshold poverty income level (4)
(8.14)
Sources: (1) First American Real Estate Solutions (2002); (2) Ohio Department of Taxation (2003);
(3) U.S. Environmental Protection Agency (2002); (4) GeoLytics CensusCD 2000 (2002); (5)
GeoLytics Crime Reports CD (2000); (6) Ohio Department of Education (2002); (7) Ohio
Department of Taxation (2002); (8) Ohio Department of Taxation (2000)
24
Table 2: Full Sample Regression Results
Dependent Variable is LN HOUSE PRICE
Explanatory Variable
EXECUTION DEED
ADMINISTRATOR’S
DEED
CORPORATION DEED
EXECUTOR’S DEED
QUIT CLAIM DEED
SHERIFF’S DEED
TRUSTEE’S DEED
FINAL DIST DEED
SURVIVORSHIP
DEED
FIDUCIARY DEED
GUARDIAN DEED
SPECIAL
WARRANTY DEED
LIMITED
WARRANTY DEED
FORECLOSURE DEED
ONESTORY
BRICK
FINISHED
BASEMENT
GARAGE
FIREPLACES
OUTBUILDINGS
OLS
Fixed
Effects OLS
Spatial
Error Model
-0.030
(0.35)
-0.13
(1.30)
-0.11**
(3.45)
-0.085**
(5.68)
-0.48**
(29.80)
-0.34**
(15.77)
-0.001
(0.023)
-0.0089
(0.14)
0.0052*
(1.38)
-0.098**
(7.77)
-0.21*
(2.35)
-0.17*
(2.39)
-0.17**
(6.78)
-0.32**
(9.49)
0.058**
(14.42)
0.025**
(7.08)
0.034**
(5.85)
0.098**
(27.5)
0.10**
(30.65)
0.049**
(2.75)
-0.012
(0.14)
-0.12
(1.26)
-0.089**
(2.81)
-0.072**
(4.94)
-0.50**
(31.46)
-0.30**
(14.48)
0.035
(0.80)
0.0098
(0.15)
0.019**
(4.96)
-0.076**
(6.11)
-0.23**
(2.61)
-0.19**
(2.72)
-0.17**
(6.95)
-0.37**
(11.26)
0.05**
(12.59)
0.052**
(14.52)
0.024**
(4.23)
0.066**
(17.94)
0.099**
(30.39)
0.085**
(4.87)
-0.025
(0.34)
-0.14
(1.62)
-0.077*
(2.26)
-0.085**
(6.65)
-0.49**
(35.22)
-0.31**
(16.78)
-0.036
(0.94)
-0.04
(0.74)
0.017**
(5.14)
-0.092**
(8.48)
-0.27**
(3.53)
-0.14*
(2.27)
-0.16**
(7.40)
-0.38**
(13.16)
0.029**
(8.02)
0.035**
(10.39)
0.019**
(3.22)
0.059**
(14.31)
0.071**
(23.43)
0.047**
(2.98)
Bayesian
Spatial
Error Model
-0.015
(0.23)
-0.16*
(2.00)
-0.11**
(3.93)
-0.078**
(6.35)
-0.51**
(27.60)
-0.31**
(17.63)
-0.034
(0.85)
-0.018
(0.28)
0.014**
(4.35)
-0.090**
(7.89)
-0.27**
(3.33)
-0.14**
(2.51)
-0.19**
(7.45)
-0.36**
(11.37)
0.037**
(9.43)
0.031**
(9.00)
0.015**
(2.67)
0.066**
(16.27)
0.068**
(21.71)
0.050**
(3.06)
Bayesian
SEM w/o
Deeds
0.035**
(8.81)
0.030**
(9.84)
0.017**
(2.84)
0.067**
(16.73)
0.069**
(21.57)
0.056**
(3.63)
25
BEDROOMS
0.0039*
-0.0021
0.0090**
0.0084**
0.008**
(1.39)
(0.76)
(3.58)
(3.62)
(2.96)
FULLBATHS
0.059**
0.064**
0.046**
0.046**
0.048**
(16.04)
(17.83)
(14.06)
(13.03)
(13.23)
PARTBATHS
0.058**
0.054**
0.041**
0.038**
0.039**
(15.80)
(15.11)
(12.64)
(12.95)
(11.79)
AGE
-0.36**
-0.36**
-0.62**
-0.61**
-0.61**
(20.34)
(20.18)
(32.88)
(32.83)
(31.94)
AGE SQUARED
0.12**
0.11**
0.28**
0.27**
0.27**
(8.79)
(7.88)
(19.41)
(18.82)
(17.77)
HOUSE SIZE
0.45**
0.44**
0.37**
0.39**
0.39**
(53.43)
(53.65)
(49.95)
(44.95)
(44.59)
HOUSE SIZE
-0.020**
-0.019**
-0.017**
-0.017**
-0.017**
SQUARED
(14.54)
(14.45)
(14.01)
(11.33)
(11.03)
YARD SIZE
0.040**
0.040**
0.042**
0.047**
0.045**
(29.11)
(29.87)
(31.51)
(24.92)
(23.98)
YARD SIZE
-0.00019** -0.00019** -0.00021** -0.00023** -0.00022**
SQUARED
(18.54)
(19.16)
(20.57)
(15.97)
(16.06)
PATIO
-0.023**
0.026**
-0.00018
-0.0039
-0.0036
(4.22)
(4.64)
(0.034)
(0.75)
(0.74)
DECK
0.027**
0.068**
0.028**
0.029**
0.032**
(5.41)
(13.51)
(5.99)
(7.08)
(7.06)
BAD SCHOOL
-0.0051**
-0.0063**
-0.0060**
-0.0055**
-0.0054**
(25.50)
(29.33)
(21.50)
(19.04)
(19.48)
SINGLE PARENTS
-0.0044**
0.000019
-0.0035**
-0.0050**
-0.0050**
(8.85)
(0.04)
(4.30)
(6.92)
(6.82)
HOT MARKET
0.033**
0.020**
0.040**
0.026**
0.027**
(6.64)
(4.03)
(4.95)
(4.31)
(4.28)
TAX RATE
0.0025**
0.0024**
0.0032**
0.0030**
0.0031**
(7.80)
(7.29)
(8.51)
(6.61)
(6.76)
AIR POLLUTION
-0.00088*
-0.00016
0.00015
-0.000034
-0.00009
(2.00)
(0.37)
(0.23)
(0.06)
(0.14)
RACIAL
-0.26**
-0.38**
-0.21**
-0.22**
-0.22**
HETEROGENEITY
(15.34)
(23.13)
(8.52)
(9.28)
(9.45)
POLICE PROTECTION
0.0017**
0.0012**
0.0016**
0.0014**
0.0014**
(14.46)
(10.48)
(8.82)
(8.47)
(9.02)
CONSTANT
11.00**
11.10**
11.24**
11.23**
11.22**
(604.64)
(564.78)
(9381.15)
(498.15)
(497.17)
0.56**
0.49**
0.48**
Spatial error parameter 
(334.29)
(99.59)
(253.39)
Adjusted R-Squared
0.71
0.73
0.78
0.77
0.76
Number of observations = 37,043 housing transactions. Parameter estimates shown with absolute
value of (asymptotic) t-ratios in parentheses below. ** = statistically significant at 1% level; * =
statistically significant at 10% level. WARRANTY DEED is the omitted deed category. Fixed
Effects OLS includes dummy variables for metropolitan area with Akron as the omitted category;
dummy parameter estimates suppressed in output but available upon request.
26
Table 3: Metropolitan Area Regressions Summary
Dependent Variable is LN HOUSE PRICE
Variable
EXECUTION DEED
Akron
Cincinnati
Cleveland
Columbus Youngstown
-0.10
0.10
0.052
0.008
(0.91)
(0.81)
(0.41)
(0.03)
ADMINISTRATOR’S
-0.09
-0.34
-0.31
DEED
(0.84)
(1.98)
(1.27)
CORPORATION DEED
-0.10**
-0.05
(3.31)
(0.20)
EXECUTOR’S DEED
-0.099**
-0.05*
0.05
-0.11
(6.22)
(1.99)
(1.02)
(0.95)
QUIT CLAIM DEED
-0.35**
-0.46**
-0.62**
-0.57**
-0.39**
(6.78)
(15.40)
(21.97)
(12.64)
(4.98)
SHERIFF’S DEED
-0.30**
-0.35**
-0.28**
-0.26**
(11.28)
(9.42)
(3.71)
(5.21)
TRUSTEE’S DEED
0.003
-0.08
0.17
-0.039
(0.06)
(0.95)
(0.52)
(0.32)
FINAL DIST DEED
-0.023
(0.36)
SURVIVORSHIP
-0.17
0.012**
0.019**
0.027**
0.045**
DEED
(0.49)
(3.00)
(3.28)
(2.91)
(3.41)
FIDUCIARY
-0.079**
-0.08**
-0.18**
-0.11*
COVANENT
(6.54)
(3.53)
(3.11)
(2.97)
GUARDIAN DEED
-0.29**
-0.24*
(2.61)
(2.30)
SPECIAL
-0.17
-0.21**
-0.015
WARRANTY DEED
(1.56)
(2.48)
(0.14)
LIMITED
-0.18**
-0.26**
-0.0039
WARRANTY DEED
(4.99)
(6.64)
(0.07)
FORECLOSURE DEED
-0.44**
-0.36**
-0.30**
(5.64)
(9.29)
(4.54)
0.30**
0.48**
0.35**
0.49**
0.30**
Spatial error parameter 
(9.27)
(77.94)
(22.83)
(36.76)
(8.53)
Adjusted R-Squared
0.74
0.75
0.77
0.83
0.71
Number of observations
1767
18,589
9163
5776
1748
Parameter estimates shown with absolute value of (asymptotic) t-ratios in parentheses below. ** =
statistically significant at 1% level; * = statistically significant at 10% level. WARRANTY DEED
is the omitted deed category. Other controls from Table 2 also included but suppressed in output;
full set of results available from authors. Model used is Bayesian spatial error model.
27
Table 4: Neighborhood Characteristics and Deed Type Probit Analysis.
Dependent Variable is Deed Type
DEED
Per Cap
Income
Graduate
degree
Tot
Residential
Value
Unemployment
Urban
Poverty
FORECLOSURE
-0.00004**
0.0139*
-1.43E-11
0.0117
0.0040*
-0.009
SURVIVORSHIP
1.97E-6*
-0.0022
-1.96E-10**
-0.0126**
-0.0035**
-0.0064**
FIDUCIARY
-1.95E-8
-0.0034
-7.51E-11**
-0.0016
0.0003
0.0050*
QUIT CLAIM
-9.02E-6*
-0.0041
2.06E-11*
-0.0024
0.0015
0.00756*
SHERIFFS
-1.02E-6
-0.0099*
-1.43E-10**
-0.0013
-0.0022*
0.0094*
CORPORATION
8.18E-6
-0.04**
-8.35E-11
-0.0107
0.0015
0.0048
FINAL DIST
9.05E-6
-0.0104
2.2E-10**
0.0164
0.0014
-0.0192
SWARRANTY
5.27E-6
-0.0134
-2.73E-10*
0.0071
-0.0029
0.0115
TRUSTEES
7.46E-6*
0.0064
7.87E-12
0.0151
-0.00003
-0.0036
LWARRANTY
8.95E-6**
-0.0052
2.85E-11
0.0029
-0.0007
0.0073*
GUARDIAN
0.00002
-0.0403*
-2.45E-11
-0.0390
0.8520
0.0159
ADMINISTRATORS
-0.00004
-0.0167
1.05E-12
-0.0021
0.0086
-0.0149
EXECUTORS
2.17E-6
-0.00653*
1.39E-11
-0.0060
-0.0002
-0.0010
EXECUTION
-1.6E-6
-0.0033
3.32E-11
-0.0540
-0.0027
0.0032
Parameter estimates shown. ** = statistically significant at 1% level; * = statistically
significant at 10% level. Number of observations = 34,219
28
Table 5: Interest Rates and Deed Types
Dependent Variable is MORTGAGE RATE
Explanatory Variable
Variable Means
OLS Results
(Standard Deviation)
CONSTANT
1.0
8.74
(0.0)
(79.11)
RACIAL HETEROGENEITY
0.09
1.88
(0.09)
(7.48)
MORTGAGE AMOUNT
150,052.07
-0.000002
(108,623.33)
(4.78)
MORTGAGE FREQUENCY
2.55
-0.124
(2.49)
(7.79)
BAD SCHOOL
34.155
0.024
(12.48)
(11.58)
HOUSE SIZE
1.95
-0.222
(0.91)
(5.11)
CORPORATION DEED
0.001
-0.731
(0.032)
(1.43)
EXECUTION DEED
0.0004
0.398
(0.02)
(0.54)
EXECUTORS DEED
0.009
-0.069
(0.0965)
(0.22)
FIDUCIARY COVANENT
0.016
0.068
(0.124)
(0.42)
FINAL DIST DEED
0.00061
0.089
(0.025)
(0.15)
FORECLOSURE
0.0002
-1.345**
(0.014)
(20.47)
GUARDIAN DEED
0.00061
0.482
(0.025)
(1.07)
LIMITED WARRANTY DEED
0.004
0.063
(0.065)
(0.26)
QUITCLAIM DEED
0.006
0.323
(0.078)
(1.26)
SHERIFF’S DEED
0.003
-0.579*
(0.059)
(2.35)
SURVIVORSHIP DEED
0.289
-0.230**
(0.45)
(4.54)
SPECIAL WARRANTY DEED
0.0002
1.221**
(0.014)
(33.61)
TRUSTEE’S DEED
0.0014
0.560
(0.038)
(1.01)
Parameter estimates shown with absolute value of t-ratios in parentheses below. ** =
statistically significant at 1% level, * = 10% level. WARRANTY DEED is the omitted
deed category. Number of observations = 4893 Adjusted R-squared = 0.20.
29
Table 6: Premium or Discount for Each Deed Type
Relative to Warranty Deed
Survivorship Deed
Warranty Deed
Execution Deed
Trustee’s Deed
Final Distribution Deed
Executor’s Deed
Fiduciary Deed
Corporation Deed
+ 1%
0%
0%
0%
0%
- 8%
- 9%
- 11%
Special Warranty Deed
Administrator’s Deed
Limited Warranty Deed
Guardian Deed
Sheriff’s Deed
Foreclosure Deed
Quit Claim Deed
- 14%
- 16%
- 19%
- 27%
- 31%
- 36%
- 51%
30
References
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The MSA dummy parameter estimates for Cincinnati and Youngstown are negative, Cleveland’s is
positive, and Columbus’ is statistically insignificant.
2
The test is the lratios test of LeSage (2004), described in LeSage (1999, p. 73).
3
The spatial parameter from the SAR model is 0.16 with an asymptotic t-ratio of 89.7, while the spatial
parameter from the SEM model is 0.56 with an asymptotic t-ratio of 334.29. In addition, the explanatory
1
33
power of the SEM model exceeds that of the SAR model: adjusted R-squared is 0.71 for SAR and 0.78 for
SEM.
4
An excellent, intuitive discussion of the spatial weight matrix W is given in LeSage (1997a).
5
This is just an illustration. The actual weights come from the product of wmat*wmat*A, where wmat has
elements i = 1/ (square root of the sum of the elements of the i-th row), and A is the adjacency matrix from
Voronoi tessellation.
6
The calculated chi-square test statistic is 1525.9 for the full sample. The null is rejected at the 1% level
for each of the individual MSA samples as well, with test statistics of 90.8 for Akron, 1039.0 for
Cincinnati, 393.8 for Cleveland, 230.2 for Columbus, and 143.9 for Youngstown.
7
An alternative is to set the two parameters of the gamma distribution for r to the informative priors of 8
and 2. Nearly identical estimates are achieved either way.
8
The full sample Bayesian SEM with 300 draws took 543 seconds, and the one with 1000 draws took 1738
seconds.
9
By contrast it is 0.0888 for the OLS model.
10
Assessment in Ohio is based on sale prices of comparable houses. Ohio faces similar problems as states
that only use the sale price of the house itself, but only to the extent that these comparable houses sell with
atypical deed types (30% of the time, according to our sample means). These calculations assume the
discounts and premiums are unrelated to the quality of the house and reflect only seller motivation and
uncertainty of title. It also assumes the distribution of deed types in our sample is representative of the
state. The dollar figures are based on the average effective millage rate in 2000 of 49.81 and the
$119,281,000,000 of assessed residential property value in 2000.