Is Farm Real Estate The Next BubbleBrett C. Olsen & Jeffr.docx

Is Farm Real Estate The Next Bubble?
Brett C. Olsen & Jeffrey R. Stokes
Published online: 28 May 2014
# Springer Science+Business Media New York 2014
Abstract The recent increase in farmland prices leads many to
conjecture that a price
bubble exists. A dataset of Iowa farmland prices for three
grades of quality over the last
60 years is examined to address the question whether the
conditions for a rational
expectations bubble are evident. An abnormal component in the
change in farmland
prices is found during the most recent sub-period of the sample.
A novel valuation
model that measures the speculative component of farmland
value as a function of cash
rents shows no speculative component is present. An additional
test of the time series
characteristics of the data provides no evidence of negative
duration dependence.
However, analysis of transition probabilities shows asymmetry
exists most notably in
the low quality farmland data series. Finally, time irreversibility
is shown to be present
at different lags for only the lowest farmland quality grade.
Overall, the results imply
that the low quality grade farmland is the most likely candidate
to exhibit the conditions
necessary to support a rational expectations bubble. In general,

however, the data offer
weak support of a bubble in farmland prices.
Keywords Farmland . Bubbles . Valuation . Abnormal returns
Introduction
The sharp increase in farmland prices over the last few years
has led many to believe
there may be a bubble forming in farmland markets. This belief
naturally leads to the
prediction that the bubble will burst. Indeed, the recent price
increase, in nominal terms,
is distinctively more pronounced than the price increase that
occurred in the 1970s,
which was then followed by the farmland crash in the early to
mid-80s. While a cursory
look at farmland prices may support the presence of a farmland
price bubble, the
determinants of farmland value have also dramatically changed
over the past few
decades. As shown in Fig. 1, while farmland prices have
increased (Panel A), produc-
tivity has nearly doubled (Panel B), and crop prices have risen
sharply as well (Panel C).
The coincident recent rise in farmland prices and corn prices is
difficult to ignore.
J Real Estate Finan Econ (2015) 50:355–376
DOI 10.1007/s11146-014-9469-9
B. C. Olsen (*): J. R. Stokes
Department of Finance, University of Northern Iowa, Cedar
Falls, IA 50614-0124, USA
e-mail: [email protected]

A
B
C
Fig. 1 The average farmland price per acre (Panel A), corn yield
per acre (Panel B), and corn price per bushel
(Panel C) for Iowa farmland from 1950 through 2012
356 B.C. Olsen, J.R. Stokes
Increased world demand for grain and domestic demand for corn
in ethanol production
are two key contributors to the recent increase in corn prices, as
argued by (Stokes and
Cox 2014), who identify low interest rates as another
contributor. These drivers of
farmland prices likely constitute a significant portion of the
farmland’s fundamental
value. Perhaps the rise in farmland values is related to an
increase in the returns on the
farmland’s fundamental value and not speculation at all. While
identifying a bubble or
the timing of its end is challenging at best, understanding the
increase in returns and the
potential for a valuation bubble and its bursting is extremely
important for existing and
prospective buyers and sellers, including farmers and investors.
This study examines rational expectations bubbles as studied by
(Shiller 1978) and

(Blanchard and Watson 1982) and further examined by
(McQueen and Thorley 1994).
Within rational expectations bubbles, asset prices may deviate
from the asset’s funda-
mental value. The bubble component of the fundamental value
relation grows in each
period that it survives. As the bubble component gets larger, it
dominates the funda-
mental component, reducing the likelihood of a negative
innovation. Investors realize
that prices are overvalued, but they believe that prices will
continue to increase. The
probability of a high return compensates for the probability of a
crash. Thus, investors
will remain in overvalued markets. Rational expectations
bubbles imply a nonlinear
pattern in prices. Previous studies examine nonlinear price
patterns within the stock
market ((Shiller 1981); West 1987), the gold market (Blanchard
and Watson 1982), and
the forex market (Evans 1986).
Speculative bubbles within the farmland market are also the
topic of interest for
researchers, especially during periods of sharply increasing
prices as seen in the 1970s
and 2000s (see Fig. 1). The farmland market is a good candidate
for bubbles due to
significant transaction costs (Chavas and Thomas 1999) and
overreaction to changes in
market fundamentals ((Featherstone and Baker 1987); (Lloyd
1994); (Schmitz and
Moss 1996)). (Clark et al. 1993) consider the pattern in
farmland values, testing the
necessary condition that the time-series properties of farmland
values and cash rents

have equal time series representations. Their results do not
support this condition, and
they recommend that models that allow for complexities such as
rational bubbles be
used in future studies. One of the likely reasons for their
conclusion is that while
farmland prices can be observed and potentially change every
time farmland is sold,
cash rents tend to be negotiated at infrequent intervals and are
sluggish to adjust to
changing economic conditions. For example, a farmland owner
and tenant might
negotiate a 3-year lease contract indicating that cash rent will
remain fixed over the
3-year period. In addition, tenants tend to have an informational
advantage about the
productivity of the farmland that they can exploit. Given these
features of the land
owner-tenant relationship, it is not surprising that cash rents
and farmland values do not
have the same time series representation.
(Tegene and Kuchler 1993) investigate the existence of bubbles
in farmland prices in
three Midwest regions using stationarity and cointegration tests.
The authors find no
support for the presence of a speculative bubble in farmland
prices or cash rents for the
period 1921–1989. (Power and Turvey 2010) find that farmland
values have deviated
from fundamental values during the last few years of their
1949–2006 samples. They
use wavelet-based statistical methods and tests on long memory
estimation to show that
price volatility increased and that a short-term bubble exists
over the final 10 years of

their sample period. (Lavin and Zorn 2001) produce mixed
results after employing
Is Farm Real Estate The Next Bubble? 357
different tests to determine if a rational expectations bubble
exists in Iowa and
Nebraska farmland prices from 1910 through 1995. Examining
agricultural commodity
prices rather than farmland prices, (Liu et al. 2013) find
evidence of prices deviating
from fundamental values, but the authors do not find the traits
of speculative bubbles in
five of the six commodities they test.
This paper contributes to the real estate literature and to
contemporary analysis of
asset bubbles. The study extends the knowledge of farmland
pricing and returns by
including varying qualities of farmland in an updated dataset
and directing focus to the
abnormal returns provided by farmland ownership rather than
prices alone. The
contribution of production advances and crop prices as drivers
of abnormal returns
from farmland are also considered.
Following a description of the dataset, this study considers the
rational expectations
bubble and its conditions. Next, the change in the value of Iowa
farmland across the
sample period and during various sub-periods provides an initial
step towards under-
standing the determinants driving any abnormal component of

the change in value. A
model is then developed that examines the fundamental and
speculative component in
farmland value. Finally, several tests scrutinize the pattern of
the farmland returns,
looking for evidence of the characteristics required for a
rational expectations bubble in
Iowa farmland returns.
Data
The analysis that follows uses annual average Iowa farmland
prices from 1950
through 2012 available from Iowa State University Extension
and Outreach.
Farmland price data is obtained through annual surveys of real
estate brokers
and other experts. Survey respondents provide an estimate of
the value of
farmland based on the three grades of quality – high, medium,
and low.
Farmland quality is measured using the Corn Suitability Rating
(CSR), an
index that rates the soil based on its productivity in yielding
row-crops. The
average CSR for a tract of farmland may vary based on soil
type, the slope of
the farmland, and erosion susceptibility. Figure 2 illustrates the
variation in
nominal farmland prices based on production quality. The prices
corresponding
to the three quality grades of high, medium, and low exhibit
little variation
during the first 20 years of the sample. In the 1970s, the prices
noticeably
disperse, rising sharply until reaching a peak in 1981, followed

by a drop to a
low point 5 years later. Prices quickly turn upward from the low
in 1986,
climbing higher and faster through 2012 when high quality
farmland reached
$10,181 per acre. The sharp rise in prices since the farmland
crisis in the 1980s
is feeding the recent interest in research by academics and in
speculation by
media outlets regarding the presence of a farmland price bubble.
Risk-free rates are from the St. Louis Federal Reserve, while
annual average corn
yields (bushels/acre), corn prices (dollars/bushel), and cash
rents are obtained from the
United States Department of Agriculture National Agricultural
Statistics Service
(USDA-NASS). Cash rent data, reflecting the average dollar
payment per acre for
irrigated cropland, is obtained through the annual Cash Rents
Survey conducted by the
USDA. Unfortunately, the cash rent data includes only average
cash rents unrelated to
farmland quality. The arithmetic average is greater than the
USDA-NASS average
suggesting that Iowa farmland quality is skewed toward the high
grade.
To disaggregate the average cash rent data and provide better
estimates of the cash
rents for each quality grade, an entropy model is employed to

estimate the cash rents for
the three farmland quality grades based on the dispersion of
farmland prices among
these grades. The Entropy Concentration Theorem (Jaynes 1957,
1979) states that out
of all of the distributions that satisfy the observed data (i.e., the
moments); a signifi-
cantly large proportion of these distributions will be
concentrated sufficiently close to
the one with maximum entropy. The entropy analysis is
discussed in more detail in the
Appendix and uses the average farmland price for each year of
the sample. Applying
the entropy method to the cash rent data produces the series
depicted in Fig. 3. Since
the cash rents are loosely based on the farmland price data, the
pattern over time is quite
similar to the farmland price series.
Determinants of Farmland Price Changes
The rise in farmland prices in the 1970s eventually ended in the
early part of the
following decade as interest rates doubled and debt tied to
farmland became increas-
ingly difficult to service. While the recent increase in Iowa
farmland prices is effec-
tively illustrated in Fig. 1 Panel A as a distinctively larger
increase than the run-up
experienced in the 1970s, consideration of the possible
similarities of the two time
$0
$2,000

$4,000
$6,000
$8,000
$10,000
$12,000
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
2005 2010
High land quality
Medium land quality
Low land quality
Fig. 2 Nominal farmland prices for Iowa farmland from 1950
through 2012 for the three quality grades of
farmland: high, medium, and low quality
periods should be made to determine if another correction in
farmland values is
imminent. An OLS regression that controls for several
determinants of the change in
farmland prices is used to better understand how the recent
increase in farmland prices
compares to historical trends. The basic model is:
ΔPt ¼ at þ btXt þ εt ð1Þ

The dependent variable, ΔPt, is the change in average value of
Iowa farmland, and
Xt represents a vector of determinants that may influence the
value change. The
determinants used include the changes in corn yields and prices,
the change in the
average cash rents, and the change in the risk-free rate, which
should be highly
correlated to the borrowing costs available to potential farmland
buyers.
Table 1 provides the regression results. Across the entire
period, the changes in corn
price and average cash rents are significantly related (positive)
to the change in
farmland price. Using sub-period regressions where the period
is split into overlapping
20-year sub-periods, a significant intercept in (1) occurs in the
latter periods of the
sample and corresponds closely with the increase in farmland
prices seen since the
1990s. These results imply that farmland produced abnormal
positive increases in
prices only during the most recent portion of the sample period
after controlling for
key determinants. The change in average price per acre during
the 1990 to 2009 sub-
period is driven by the changes in corn prices, average rents,
and the risk-free rate. The
periods that include the 1970s show that the change in average
cash rents has a strong
positive relation to the change in farmland price, but the
farmland price change does not
have an abnormal component (the intercepts are negative and
not significant). While

one cannot conclude from these results that a bubble currently
exists in Iowa farmland,
the findings do show that farmland prices are increasing at a
higher rate than expected.
$0
$50
$100
$150
$200
$250
$300
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
2005 2010
High quality
Medium quality
Low quality
Fig. 3 Nominal cash rents per acre, developed using an entropy
model, for Iowa farmland from 1950 through
2012 for the three quality grades of farmland: high, medium,
and low quality
The Speculative Component of Farmland Returns

While the OLS analysis above indicates that there may be an
abnormal component that
drives farmland price changes in recent years, a more rigorous
examination of the issue
is necessary. Several researchers have shown that the price of
an asset includes a
fundamental component and a rational expectations component
(e.g., (Shiller 1978);
(Blanchard and Watson 1982)).1 The price of an asset can
deviate from its fundamental
value by a rational expectations component, or bubble. With the
equilibrium condition
that the value of an asset is equal to the expected future cash
flows discounted at the
required rate of return r, the bubble component must grow every
period at the rate r.
Examining farmland, a very simple valuation model is used to
consider the presence of
a speculative component, i.e., a bubble, in the price of the
farmland.
Let Ct=C(t) be time t cash rent per acre where Ct evolves
according to a geometric
Brownian motion: dCt=μCtdt+σCtdzt. Here, μ is the expected
rate of growth in cash
rent, σ is the volatility in cash rent, and dzt~N(0,dt) is the
increment of a ℙ -Brownian
motion. The value of farmland per acre, Vt=V(Ct), can be found
by equating the
expected capital gain on the farmland plus the flow of cash rent
with an equilibrium
return on the farmland.2 Mathematically, E dVtð Þ þ Ctdt ¼
ρVtdt; where ρ is the
equilibrium rate of return. Rearranging terms results in the
following second-order

ordinary differential equation:
σ2C2t
2
� �
V
00
t þ μCtV
0
t þ Ct ¼ ρVt
Table 1 OLS regressions of change in farmland value
Full sample
period
1950 to
1969
1960 to
1979
1970 to
1989
1980 to
1999
1990 to
2009
2000 to
2012

Intercept 0.013 0.006 −0.010 −0.006 −0.004 0.044 *** 0.076 **
Corn yield 0.018 0.073 0.314 * −0.065 −0.068 0.036 −0.044
Corn price 0.190 *** 0.108 0.231 * 0.041 0.141 0.126 ** 0.220
*
Average rents 0.811 *** 0.515 1.086 *** 1.019 ** 0.632 0.824
*** 0.462
Risk-free rate 0.002 −0.026 −0.049 0.112 0.149 0.035 *** 0.019
Adj R2 0.429 0.032 0.462 0.383 0.214 0.671 0.539
This table provides the OLS regression results wherein the
dependent variable is the change in the average
price per acre of Iowa farmland. Corn yield is the change in the
yield of corn, in bushels per acre; Corn price is
the change in the per bushel price of corn; Average rents is the
change in cash rent received per acre; and Risk-
free rate is the change in the rate of return on the 10-year
Treasury. The change in each variable from period t-1
to period t is calculated as ln(Xt/Xt−1).
*** (** * ) indicates significance at the 1 % (5 % 10 %) level
1 (Camerer 1989) provides a thorough review of asset bubbles
and provides a more explicit definition of the
rational speculative bubble model.
2 While it is theoretically possible to develop the appropriate
hedging arguments to cast the pricing model
presented here in the context of risk-neutral pricing, the
approach used here opts for a simpler pricing model
that does not depend on such assumptions.

The general solution to this equation is
V Ctð Þ ¼ K1Cβ1t þ K2Cβ2t þ K3Ct
where K1, K2, K3, β1>1, and β2<0 are all constants and
β1;2 ¼
− μ−
σ2
2
� �
�
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffiffiffiffiffiffi
μ− σ2
2
� �2 þ 2ρσ2q
σ2
As Ct→0, it should be the case that Vt→0, which will happen if
and only if K2=0
since β2<0. Using similar reasoning, as Ct→∞, a bounded
solution representing
fundamental value will happen if and only if K1=0 since β1>0.
However, (Dixit and Pindyck 1994) have shown that capital
gain speculation can
result for some Ct≫0 in equilibrium. If capital gain speculation
is present, an investor

could buy farmland at a price above fundamental value in
anticipation of a large capital
gain. This would result in a farmland valuation model where
V Ctð Þ ¼ K1Cβ1t þ K3Ct ð2Þ
With the interpretation that the first term is a speculative
component of farmland
value while the second term is the fundamental value taken as a
perpetuity. The
parameter K3 in this case can be shown to equal (ρ−μ)
−1. It follows that by
regressing observed farmland values on observed cash rents, the
significance of K1
can be empirically tested under the null hypothesis that K1=0
and there is no
component of farmland value attributable to speculation.
A nonlinear least squares approach estimates the parameters
from the value relation
(2), and the results are presented in Table 2. As shown, the
parameter K1 is not
statistically significant for any farmland quality although the
parameters β1 and K3
are highly significant at the 1 % level. The Wald, F, and
Likelihood Ratio (LR) test
statistics also confirm that there is no statistical evidence of a
speculative component in
Iowa farmland prices. Capitalization rates (i.e., ρ−μ ¼ bK−13 )
are also shown from OLS
Table 2 Nonlinear Least Squares and Ordinary Least Squares
parameter estimates for low, medium, and high
quality Iowa farmland

Nonlinear Least Squares Test statistics Ordinary Least Squares
Farmland Quality K1 β1 K3 Wald F LR K3 Cap rate
Low 0.000240 3.25476 *** 6.9318 *** 0.19466 136.4 107.1
15.6292 *** 6.398 %
Medium 0.000016 3.73286 *** 11.8710 *** 0.13256 135.7
106.8 19.6264 *** 5.095 %
High 0.000017 3.59539 *** 14.1488 *** 0.10677 108.0 95.4
22.0974 *** 4.453 %
This table provides (1) the parameter estimates from the
farmland value relation V Ctð Þ ¼ K1Cβ1t þ K3Ct
using nonlinear least squares; (2) the Wald, F, and Likelihood
Ratio (LR) test statistics; and (3) the estimated
capitalization rates (Cap Rate) from the relation V(Ct)=K3Ct,
where ρ−μ ¼ bK−13 . *** indicates significance at
the 1 % level
regressions with the restriction that K1=0 in (2), and the results
are consistent with
higher (lower) quality farmland having a lower (higher) cap
rate.
It is important to note that while these results are inconsistent
with speculation in
farmland, they cannot be unencumbered from the pricing model
itself. That is, the
statistical tests are a joint test of the efficacy of the structural

model and the hypothesis
in question. This means that rejecting speculation may just be a
rejection of the
structural model. The results of efforts to this point to
determine the potential for a
speculative bubble in the Iowa farmland market are insufficient.
Therefore, additional
tests are proposed to eliminate the potential influence of the
structural model. In what
follows, as shown in (2), the fundamental value of farmland is
assumed to be perpe-
tuity, namely,
V Ctð Þ ¼ K3Ct ¼
Ct
ρ −μ
ð3Þ
implying that the perpetuity grows exponentially at rate μ and is
discounted at
equilibrium rate ρ. Also, it follows directly that
dV Ctð Þ ¼
dCt
ρ−μ
¼ μVtdt þ σVtdzt
As shown, when cash rents follow geometric Brownian motion,
the fundamental value
of farmland also follows geometric Brownian motion. The
implication here is that
farmland values are assumed to be lognormally distributed, and
the time series of
farmland values is necessarily nonstationary.

As noted above, hedging arguments may make it possible to
unencumber the
fundamental value from the unobservable parameter ρ.
However, the selected approach
here estimates ρ from observable data by noting that it is akin to
a weighted average
cost of capital. Most farmland is financed with a mixture of
debt and equity capital.
More specifically, farmland purchases typically require about
one-third of the purchase
price as down payment so that ρ=(1/3)Re+(2/3)i, where Re (i)
represents the cost of
equity (debt) capital. Stock market returns are assumed to
represent the opportunity cost
of investing equity capital in farmland, and the cost of debt
capital is assumed to
approximate 10-year Treasury bond yields plus a premium. The
premium is estimated
to be 1.73 % using Farm Credit System data on interest rates
charged for loans
collateralized by farm real estate.
Patterns in the Time Series Data
Additional tests that are not influenced by the structural models
studied above are
deployed to evaluate the patterns in the time series data. These
tests examine different
traits of the time series to determine if the conditions for a
rational expectations bubble
are present. Previous studies focus on the capital gains earned
on farmland. This
perspective ignores the income received from owning, i.e., cash
rents, which represent
a significant component of the total return. The OLS regressions

performed above that
found cash rents are a significant determinant of changes in
farmland prices supports
the total return perspective of farmland ownership. The data
series is further refined by
calculating the excess abnormal returns from farmland, defined
as the returns provided
by the capital gain and income yield over the change in the
fundamental value of the
farmland minus the return on a risk-free asset. More formally,
excess abnormal returns,
Rt
A, in period t are defined as:
RAt ¼ ln
Pt
Pt−1
� �
þ Ct
Pt
� �
−ln
Vt
Vt−1
� �
−Rf ;t ð4Þ

where ln(Pt/Pt−1) is the nominal capital gains yield based on
the change in farmland
price from period t−1 to t; (Ct/Pt) represents the income yield,
measured as the current
cash rent divided by the farmland price; ln(Vt/Vt−1) represents
the discrete change in
fundamental value, found using (3) above; and Rf,t is the risk-
free rate in period t, which
is obtained from CRSP.3 It is important to note that previous
studies such as (Lavin and
Zorn 2001) examine only the capital gains yield (the first term
in (4)), while this study
focuses on the excess abnormal returns.
Runs
Within the rational expectations bubble structure, the
probability of a negative return
conditional on a series of prior positive returns decreases with
the length of the run. In
other words, the longer the bubble continues over time, the less
likely that a crash will
occur. This phenomenon is called negative duration dependence.
Alternatively, positive
duration dependence contends that the probability of a run
ending increases with the
length of the run (Kiefer 1988). (McQueen and Thorley 1994)
examine the duration
dependence of stock price patterns, stating that the duration
dependence pattern test is
unique to bubbles. Bubbles can induce positive autocorrelation,
skewness, and kurtosis,
but these parameters are also associated with the fundamental
component of prices.
(Lavin and Zorn 2001) find evidence of positive duration

dependence with their Midwest
farmland price data, a result inconsistent with the presence of a
bubble. Examining 28 different commodities, (Went et al. 2009)
test the duration
dependence and find eleven that exhibit the traits of rational
expectations bubbles.
The duration dependence method used here applies the methods
of (McQueen and
Thorley 1994) and (Lavin and Zorn 2001) to the three quality
grades of farmland in
Iowa, testing for negative duration dependence using a hazard-
function specification,
which measures the probability of an unexpected return
decrease given a sequence of
prior return increases. From the annual abnormal return data, a
series of positive and
negative run lengths is constructed. The resulting dataset is a
set (St) of J observations
of random run length T. The hazard rate, ht, represents the
probability that a run ends at
period t given that the run lasts until t, or [ht≡Pr(T=t|T≥t)]. For
bubbles, ht+1<ht for all T, which implies that as a positive run
increases in length, the
probability of the run ending in the next period decreases?
To estimate the hazard rate, the Weibull model, a commonly
used parametric
representation of the hazard, is used. The Weibull model is
given as
ht ¼ λp λtð Þp −1 ð5Þ
3 Obtained from Ken French’s website at

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_lib
rary.html.
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_lib
rary.html
where λ and p are parameters to be estimated. The parameter p
takes on particular
importance in that its magnitude determines the type of duration
exhibited by the data.
For p>1 (p=1) positive (constant) duration dependence results,
while p<1 is indicative
of negative duration dependence. Also, when p=1, the Weibull
model reduces to the
exponential model, another frequently used parametric
(constant) duration model.
As shown in Table 3, low quality farmland exhibits the longest
runs of positive
abnormal returns (median=3.82 years). Also shown in the table
and consistent with past
research on farmland price changes, the parameter p from the
Weibull model in (5) is
statistically significantly greater than one for all farmland
qualities, providing evidence of
positive duration dependence. The results in Table 3 clearly
show that negative duration
dependence, a characteristic of rational expectations bubbles, is
not present in the data.
Transition Probabilities
If farmland returns follow a random walk, the probability of an

increase in returns will
not depend on the previous year’s return. To evaluate the null
hypothesis that excess
Table 3 Run frequencies and tests of duration dependence
Land Quality
Run Length Low Medium High
1 9 11 13
2 8 8 9
3 5 6 6
4 5 4 2
5 4 4 2
6 4 3 2
7 3 2 2
8 2 2 1
9 2 1 1
10 2 1 1
11 1 – –
12 1 – –
Median run length (years) 3.82 3.09 2.60

Weibull parameters and standard errors:
λ 0.2049 0.2546 0.2944
σλ 0.0213 0.0276 0.0397
p 1.4906 1.5041 1.3762
σp 0.2400 0.2601 0.2673
H0:p=1 2.0442
** 1.9379 ** 1.4071 *
H0:p≤0 6.2105
*** 5.7825 *** 5.1475 ***
This table provides run frequencies for positive excess
abnormal returns across the three farmland quality
grades. The estimated Weibull model is: ht=λp(λt)
p−1 where ht is the hazard rate at time t, λ and p are
parameters, and σλ and σp are standard errors for the parameter
estimates.
*** (** * ) indicates rejection of the
null hypothesis at the 1 % (5 % 10 %) level
abnormal returns follow a random walk, a Markov chain
technique is used. The
Markov chain approach requires that the abnormal return series
is stationary but does
not restrict the series to be normally distributed as do regression

tests. For a random
walk series, the transition probabilities from one period to the
next will be statistically
identical, resulting in a symmetrical pattern. In a Markov chain
setting, the transition
probabilities may vary based on the sequence of prior returns,
an advantage over other
time series tests.
(Lavin and Zorn 2001) find asymmetry in the transition
probabilities from one
period to the next for capital gains. Their two-state Markov
chain examining farmland
prices for Iowa and Nebraska show asymmetry for sequences of
farmland price changes
over 3 years. In other words, the probability of a price increase
following two periods of
price decreases differs from the probability of a price decrease
after two price increases.
A finite two-state Markov process {It}, based on previous work
((Neftçi 1984);
(McQueen and Thorley 1991); (Lavin and Zorn 2001)), is used
and is defined as:
It ¼ 1 if R
A
t > 0
0 if RAt ≤ 0
�
where Rt
A represents the excess abnormal returns in period t. The
derived series {It}

represents 1 for return increases, and 0 for non-positive return
changes. If returns follow
a random walk, then the probability of a return increase or
decrease does not depend on
the previous sequence of returns, providing the following
transition probabilities for a
first-order Markov chain.
λ00 ¼ Pr It ¼ 0
��It−2 ¼ 0; It−1 ¼ 0h i
λ01 ¼ Pr It ¼ 0
��It−2 ¼ 0; It−1 ¼ 1h i
λ10 ¼ Pr It ¼ 0
��It−2 ¼ 1; It−1 ¼ 0h i
λ11 ¼ Pr It ¼ 0
��It−2 ¼ 1; It−1 ¼ 1h i
Of course, there is no particular reason to believe that abnormal
returns follow a first-
order Markov chain. (McQueen and Thorley 1991) use a second-
order representation for
annual stock returns, noting the consistency with previous
studies ((Fama and French
1988); (Poterba and Summers 1988)) and the results of
identification tests (e.g.,
Likelihood Ratio). (Lavin and Zorn 2001) assume that capital
gains follow a second-
order Markov chain based on the large proportion of one- and 2-
year run lengths in their
sample of Iowa and Nebraska farmland prices. Examining the
nature of the data series,
this study estimates transition probabilities for the three
farmland qualities for first-,

second-, and third-order Markov chains using the Akaike
Information Criterion (AIC)
and LR tests to determine the appropriate order of the chain. In
each case, the transition
probabilities are chosen to maximize the log of the likelihood
function as shown by
(Neftçi 1984). The criterion statistics are presented in Table 4
Panel A. The AIC statistics
suggest that, in general, low quality farmland is best modeled as
a first-order Markov
chain. For medium quality farmland, the order of the Markov
chain is most likely third-
order. For high quality farmland, the order of the Markov chain
is most likely second-
order, but the AIC for the first-order chain is only slightly
larger.
Table 4 Panel B presents the results of more rigorous tests of
the order of the Markov
chains, including the LR test statistics under various hypothesis
tests. These results
show that the first-order Markov chain cannot be rejected in
favor of the second-order
or third-order Markov chain at any reasonable significance
levels for low quality
farmland. For medium quality farmland, the first-order chain is
rejected in favor of
the second-order (third-order) chain at the 10 % (5 %) level. At
the 10 % level of
significance, the second-order Markov chain can be rejected in
favor of the third-order

Markov chain. For high quality farmland, the first-order chain
cannot be
rejected in favor of the second-order, and the first-order Markov
chain is
rejected in favor of the third-order Markov chain, yet only at
the 10 % level
of significance. Based on the results in both panels of Table 4,
the following
Markov chains are investigated: the first-order Markov chain
for low quality
farmland, the second- and third-order Markov for medium
quality, and the first-
and third-order for high quality.
Next, tests of symmetry and the random walk hypothesis are
conducted for each
farmland quality grade using the appropriate Markov chain
order. Shown in Table 5 are
the estimated transition probability matrices for each farmland
quality grade and corre-
sponding Markov chain order. Symmetry implies that the
probability that the abnormal
return of farmland increases given a previous increase is the
same as the probability that the
abnormal return of farmland decreases given a previous
decrease. Thus, in the case of
symmetry, the diagonal elements of the transition probability
matrix would be identical. For
low quality farmland and assuming a first-order Markov chain,
as shown in Table 5, the
probability of a(n) decrease (increase) in excess abnormal
returns following a(n) decrease
(increase) is 42.18 % (83.37 %). A similar disparity in the
transition probabilities is seen for
high quality farmland if a first-order Markov chain (39.33 % vs.
68.86 %) is assumed.

Using a third-order Markov chain, the disparity is much smaller
and not statistically
Table 4 Markov chain order for abnormal returns
Panel A: Akaike Information Criterion
1st order 2nd order 3rd order
Low quality 66.52 67.49 67.93
Medium quality 77.22 74.87 73.50
High quality 82.98 82.66 83.73
Panel B: Likelihood Ratio tests
H0: 1st order
H1: 2nd order
H0: 1st order
H1: 3rd order
H0: 2nd order
H1: 3rd order
Low quality LR=3.0257 LR=10.5916 –
Medium quality LR=6.3477 * LR=15.7227 ** LR=9.3750 *
High quality LR=4.3231 LR=11.2502 * –
This table provides results of tests to determine the rank order
of the Markov Chains for the abnormal returns
series of Iowa farmland by farmland quality. Panel A presents
the Akaike Information Criterion. Panel B

presents the Likelihood Ratio (LR) test statistics. *** (** * )
indicates significance at the 1 % (5 % 10 %) level
significant. The transition probabilities for medium quality
farmland, regardless of the
Markov chain order, are also not significantly different.
LR tests are performed, testing the null hypothesis that the
transition prob-
abilities are equal, and the results are reported in Table 6 Panel
A. For low
(high) quality farmland, the null is rejected at the 1 % (5 %)
level of
significance, implying that the excess abnormal returns series
appears to be
asymmetric. For medium quality farmland, however, the null
hypotheses that
the transition probabilities are identical cannot be rejected,
indicating symmetry.
The results from the Markov chain analysis examining
symmetry suggest that
the conditions required for a rational expectations bubble are
present for low
and high quality farmland, but not present for medium quality
farmland.
In Table 6 Panel B LR statistics are presented for the test of the
random walk
hypothesis, which implies that all of the transition probabilities
are equal. As shown,
this hypothesis is rejected for all three levels of farmland
quality at the 1 % significance

level. A review of the transition probabilities in Table 5 support
this finding. The results
from the Markov chain analysis testing the random walk theory
suggest that the
conditions required for a rational expectations bubble are
present for all three farmland
qualities, but the conditions are most prominent for the low and
high farmland quality
grades.
Time Reversibility
Another symmetry application is the “time reversible” nature of
the data series.
(Ramsey and Rothman 1996) introduce time reversibility as a
unified frame-
work for the alternative definitions of data series asymmetry. A
data series is
“time reversible” if the covariance relationship of the series is
the same going
forward in time as it is going backward in time. If true, the data
series is
symmetrical and would not be conducive to the formation of
bubbles. Within
the Markov chain framework above, asymmetric transition
probability matrices
imply time irreversible processes. The analysis begins with the
null hypothesis
that abnormal returns data series are time reversible. (Ramsey
and Rothman
1996) develop a time reversibility test and apply the test to
several economic
data series. (Lavin and Zorn 2001) use the time reversibility
method to examine
the patterns of farmland prices in Iowa and Nebraska, finding
irreversibility

patterns at different lags in the time series.
The equality of individual moments from the joint probability
distribution of
the data series, {Xt}, are tested, following the approach of
(Ramsey and
Rothman 1996) and (Lavin and Zorn 2001). The symmetric-
bicovariance func-
tion, γ2,1, of a stationary data series {Xt} is defined as the
difference between
two bicovariances, or γ2,1(k)=E[Xt
2Xt−k]−E[XtXt−k
2 ] for all integer values of lag
k. The data series {Xt} is time reversible if γ2,1 = 0 for all lags
k in ℕ, i.e., the
moments are equal. Sample estimates of the bicovariances for
the stationary
series {Xt} with T observations are
bB2;1 ¼ 1
T−k
X
t¼kþ1
T
X 2t X t−k
T

ab
le
5
T
ra
ns
it
io
n
pr
ob
ab
il
it
y
es
ti
m
at
es
1s
t
or
de
r
2n
d
or
de
r
3

rd
or
de
r
L
ow
qu
al
it
y
p
dd
¼
0
:4
2
1
8
p
di
¼
0
:5
7
8
2
p
id

¼
0
:1
6
6
3
p
ii
¼
0
:8
3
3
7
–
–
M
ed
iu
m
qu
al
it
y
–
p
dd

d
¼
0
:6
9
7
3
p
dd
i
¼
0
:3
0
2
7
p
di
d
¼
0
:1
7
5
1
p
d
ii
¼

0
:8
2
4
9
p
id
d
¼
0
:1
8
5
7
p
id
i
¼
0
:8
1
4
3
p
ii
d
¼
0
:2
4

8
5
p
ii
i
¼
0
:7
5
1
5
2 6 4
3 7 5
p
d
dd
d
¼
0
:6
0
0
0
p
d
dd
i
¼
0

:4
0
0
0
p
dd
id
¼
0
:0
0
0
0
p
dd
ii
¼
1
:0
0
0
0
p
di
dd
¼
0
:5
0
0

0
p
d
id
i
¼
0
:5
0
0
0
p
di
id
¼
0
:2
2
2
2
p
di
ii
¼
0
:7
7
7
8

p
id
dd
¼
1
:0
0
0
0
p
id
di
¼
0
:0
0
0
0
p
id
id
¼
0
:1
2
5
0
p
id
ii

¼
0
:8
7
5
0
p
ii
dd
¼
0
:1
2
5
0
p
ii
di
¼
0
:8
7
5
0
p
ii
id
¼
0

:2
6
0
9
p
ii
ii
¼
0
:7
3
9
1
2 6 6 6 6 6 6 6 6 6 4
3 7 7 7 7 7 7 7 7 7 5
H
ig
h
qu
al
it
y
p
dd
¼
0
:3
9
3

3
p
di
¼
0
:6
0
6
7
p
id
¼
0
:3
1
1
4
p
ii
¼
0
:6
8
8
6
–
p

d
dd
d
¼
0
:6
0
0
0
p
d
dd
i
¼
0
:4
0
0
0
p
dd
id
¼
0
:0
0
0
0
p
dd

ii
¼
1
:0
0
0
0
p
di
dd
¼
0
:3
3
3
3
p
d
id
i
¼
0
:6
6
6
7
p
di
id
¼

0
:3
0
0
0
p
di
ii
¼
0
:7
0
0
0
p
id
dd
¼
0
:6
6
6
7
p
id
di
¼
0
:3

3
3
3
p
id
id
¼
0
:2
2
2
2
p
id
ii
¼
0
:7
7
7
8
p
ii
dd
¼
0
:2
2
2
2

p
ii
di
¼
0
:7
7
7
8
p
ii
id
¼
0
:3
5
2
9
p
ii
ii
¼
0
:6
4
7
1
2 6 6 6 6 6 6 6 6 6 4

3 7 7 7 7 7 7 7 7 7 5
T
h
is
ta
bl
e
p
ro
vi
de
s
tr
an
si
ti
on
pr
ob
ab
il
it
y
es
ti
m
at
es
fo
r
ea
ch

fa
rm
la
nd
qu
al
it
y
gr
ad
e
ap
pl
yi
ng
th
e
ap
pr
op
ri
at
e
or
de
r
M
ar
ko
v
ch
ai
ns

id
en
ti
fi
ed
in
T
ab
le
4
.
S
u
bs
cr
ip
ts
i
an
d
d
re
fe
r
to
an
in
cr
ea
se
o
r

d
ec
re
as
e
in
ex
ce
ss
ab
no
rm
al
re
tu
rn
s
bB1;2 ¼ 1
T−k
X
t¼kþ1
T
X tX
2
t−k

The test statistic, bγ2;1 kð Þ , which is the difference between
the sample bicovariance
estimates, is estimated as
bγ2;1 kð Þ ¼ bB2;1 kð Þ−bB1;2 kð Þ ð6Þ
for various integer values of k.
To estimate the test statistic in (6), a time series model is fit to
each data series. While
prior studies fit ARMA models to their data ((Ramsey and
Rothman 1996), e.g.), the
possibility of heteroscedastic error variances should be
considered. (Engle 1982, 1983),
among others, show that variances may cluster, requiring a
different approach to analyzing
time series data. (Serrano and Hoesli 2010) focus on the
heteroscedastic nature of securi-
tized real estate returns relative to stock prices. As prices
change rapidly, the variance of the
prices is most likely not constant. To capture the effects of this
excess volatility, each
farmland quality data series is tested for ARCH effects using
the white-noise test for the
squared time series. The null hypothesis is that the population
autocorrelation functions for
the squared time series are equal to zero. Examining the p-value
for the Ljung-Box
modified statistic within the Chi-square distribution, both the
high and medium quality
farmland data series are found to exhibit ARCH effects, while
the low quality farmland
data series does not. An EGARCH framework from (Nelson
1991) is applied to
recognize the potential for an asymmetric property of the
volatilities. An EGARC

Table 6 Markov chain tests for excess abnormal returns
Panel A: Tests of symmetry and transition probabilities
1st order
H0: pdd=pii
H1: pdd≠pii
2nd order
H0: pddd=piii
H1: pddd≠piii
3rd order
H0: pdddd=piiii
H1: pdddd≠piiii
Low quality LR=9.0708 *** – –
Medium quality – LR=0.0914 LR=0.3706
High quality LR=4.9761 ** – LR=0.0366
Panel B: Tests of the random walk assumption
1st order
H0: all pjk=pkj
H1: not all pjk=pkj
2nd order
H0: all pjkl=plkj
H1: not all pjkl=plkj
3rd order
H0: all pjklm=pmlkj
H1: not all pjklm=pmlkj

Low quality LR=23.4306 *** – –
Medium quality – LR=19.0789 *** LR=24.2950***
High quality LR=6.9706 *** – LR=14.062***
This table provides tests of symmetry and transition
probabilities (Panel A) and tests of the random walk
assumption (Panel B). Subscripts i and d refer to an increase or
decrease in excess abnormal returns. Subscripts
j, k, l, and m may represent an increase or decrease in excess
abnormal returns. *** (** * ) indicates significance
at the 1 % (5 % 10 %) level and rejection of the null
H(2,1) provides the best model structure for both the high and
medium quality farmland
series.4 An ARMA(1,1) provides the best fit to the low quality
farmland series.
Armed with the time series models for the data, a Monte Carlo
simulation is
performed, and the sample bicovariance estimates from (6) are
calculated. Next, a plot
is constructed comparing the sample bicovariance, bγ2;1 kð Þ ,
which is calculated directly
from each data series, to the boundary conditions of +/− two
standard deviations of the
sample bicovariance determined by the Monte Carlo simulations
(N=10,000 iterations).
The comparison is illustrated in Fig. 4. The high and medium
farmland quality data

series are clearly time reversible time series as the +/− two
standard error boundaries are
not breached. These two farmland qualities, then, are time
reversible to order 3 and
degree 20.5 However, the low quality farmland plot shows that
the test statistic exceeds
the boundaries at lags k=2, 3, 4, and 5. Only the abnormal
returns series from low
quality farmland exhibits evidence of time irreversibility, or
asymmetry. The standard-
ized time reversibility statistic, bγ2;1 kð Þ=Var bγ2;1 kð Þ�
�1=2 (Ramsey and Rothman 1996),
provided in Table 7, corroborates the Fig. 4 illustrations.
Applying an EGARCH(2,1) to
the low quality farmland data series provides very similar
results.
Conclusions
Farmland prices in recent years have risen sharply, inviting
speculation of the presence
of a bubble in the farmland market. While there have been many
studies of the behavior
of farmland prices, most concentrate on the capital gains
associated with farmland in
the 1970s, a period of well documented farmland price
increases. Using an updated
sample (1950–2012) of Iowa farmland prices and cash rents
segregated by farmland
quality, several empirical tests are deployed using farmland
capital gains and excess
abnormal returns to determine whether the more recent
increases in farmland value are
consistent with the formation of a bubble in rural Iowa farmland
markets.

From OLS regressions, after controlling for crop prices, crop
yields, cash rents to the
owners, and interest rates, a significant intercept is produced
only during the most
recent portion of the data sample. Despite the abnormal change
in farmland prices (the
intercepts) in recent years, no statistically significant evidence
is found to support a
speculative component in the value of Iowa farmland. This
finding, however, cannot be
unencumbered from the specified structural model. Therefore,
additional empirical tests
are necessary to examine patterns in the excess abnormal
returns data for the conditions
that support the existence of a rational expectations bubble.
Negative duration depen-
dence, a characteristic of rational expectations bubbles, is not
evident for the excess
abnormal returns series of any of the three farmland quality
grades. Markov chain
analysis finds that the pattern of excess abnormal returns is
asymmetric for low and
high quality farmland, although the high quality farmland
results are not persistent
across higher order Markov chains. The null hypothesis that
excess abnormal returns
follow a random walk is rejected for all farmland quality
grades. Lastly, only the low
quality farmland data series exhibits time irreversibility
patterns at different lag points.
4 Details for the models for all three farmland quality data
series are available from the authors.

5 Describing the time reversibility results, the order refers to
i+j for γi,j, and the degree refers to the maximum
lag, which is 20.
A
B
-0.010
-0.008
-0.006
-0.004
-0.002
0.000
0.002
0.004
0.006
0.008
0.010
0 5 10 15 20
Lag

High quality land
-0.006
-0.004
-0.002
0.000
0.002
0.004
0.006
0 5 10 15 20
Lag
Medium quality land
C
-0.002
-0.001
-0.001
0.000
0.001
0.001

0 5 10 15 20
Lag
Low quality land
The key finding of this study is that in general, Iowa farmland
markets, despite the
strong increases in farmland prices in recent years, is likely not
consistent with the
formation of a farmland market bubble. At best, if a bubble has
formed or is forming, it
is more likely to be in the market for low quality Iowa farmland
and not the higher
quality grades. This could occur because of the much greater
values being placed on the
higher quality grades. More participants are able to bid on the
lowest farmland quality,
increasing the likelihood of the rational expectations bubble,
while fewer players
bidding on the higher quality grades lowers the bubble
probability.
One potential weakness of this study, indeed a weakness of
potentially all farmland
value studies, is the aggregate nature of the data. Increasing the
frequency of the returns
�Fig. 4 The results of testing the null hypothesis that the excess
abnormal return data series are time reversible.
The solid line represents the sample bicovariance estimate, γ2;
1k , for lags k=1–20. The dotted lines identify

the upper and lower boundaries at +/− two standard deviations
of the bicovariance estimate calculated from
Monte Carlo simulations. Points above the upper boundary or
below the lower boundary indicate lags at which
the data series is time irreversible
Table 7 Time reversibility test statistics
High quality farmland Medium quality farmland Low quality
land
k
1 −0.0158 −0.0430 −0.7620
2 −0.1634 −0.2852 −2.9420 ***
3 −0.3780 −0.6428 −4.1240 ***
4 −0.2379 −0.3590 −1.9581 *
5 −0.3491 −0.6243 −2.8628 ***
6 −0.0757 −0.1310 −1.3804
7 0.1429 0.1160 −0.9123
8 0.1556 0.1610 −0.3254
9 0.4974 0.7290 0.7533
10 0.0827 −0.1337 −1.1715
11 0.0794 0.1118 0.0004
12 −0.0363 −0.1234 −0.6328
13 −0.2204 −0.4008 −1.3349
14 −0.1610 −0.2275 −0.8643
15 −0.1055 −0.1373 −0.4493
16 −0.0866 −0.1223 −0.2919
17 −0.0505 −0.1041 −0.1361

18 −0.3506 −0.5074 −1.2326
19 −0.6328 −0.8934 −1.3837
20 −0.6405 −0.7820 −1.4247
This table provides the results from testing whether the three
data series are time reversible to order 3 and
degree 20. The test statistic, bγ2;1 kð Þ=Var bγ2;1 kð Þ� �1=2
is generated from the Gaussian innovations from the
time series models. The numerator is the sample bicovariance
estimate calculated directly from the time series.
The denominator is the standard deviation of the sample
bicovariance estimates calculated using Monte Carlo
simulations. k represents the lag. *** (** * ) indicates
significance at the 1 % (5 % 10 %) level and rejection of
the null that the time series is reversible
data would add statistical power to the tests examining the
patterns. A second potential
weakness is the source of the farmland value data, the surveys
of real estate brokers and
other related experts. Requesting an assessment of farmland
values only once each
calendar year may introduce a familiarity bias, for example,
related to chronology (the
ease of recalling more recent information). Although elusive,
high frequency, transactions-
level farmland price data would allow for a more comprehensive
farmland value analysis.
Acknowledgments The authors wish to thank C.F. Sirmans
(editor) and the reviewer for their helpful
comments, as well as seminar participants at the University of

Northern Iowa.
Appendix
Since only annual average cash rent is reported, an entropy-
based model, based on
(Golan 2006), is used to determine likely cash rents for low,
medium, and high quality
farmland using the most likely distribution of farmland by
quality. Most of Iowa
farmland is medium to high quality and, as a result, the simple
average of farmland
values is not the same as the reported average value. Therefore,
the following optimi-
zation model is specified to recover the most likely proportion
of farmland of each
quality grade that would constitute the reported average. More
clearly, the following
framework is solved for each t:
max E sð Þ ¼
X
i
sitln sitð Þ
s.t.
X
i
sitPit ¼ P̄ t;
X
i
sit ¼ 1;

sit ≥0
Where i is an index of farmland quality, and sit is the time t
proportion or share of
total Iowa farmland that is categorized by quality i. E(s) is the
entropy of the distribu-
tion of the unobserved proportions. The first constraint is a
moment matching equation
wherein the weighted average farmland value is forced to equal
the reported average,
Pt. The second constraint ensures that the proportions sum to
one, while the third set of
constraints ensures the proportions are non-negative. If the
reported average appearing
in the right-hand side of the first constraint is the simple
average, the entropy would be
maximized with a uniform distribution of proportions equal to
one-third each.
Once the proportions are uncovered, they are applied to the
reported average cash
rent per acre. However, it is typically the case that cash rent for
high (medium) quality
farmland is about 20 % higher than cash rent for medium (low)
quality farmland.
Applying this simple rule allows for the development of cash
rent data for each
farmland quality that is consistent with the distribution of
farmland prices by quality.
One drawback of this construction is the fact that μ, the rate of
growth in cash rents,

will not vary by farmland quality even though it likely does in
reality. However, the
obvious advantage is that reported farmland values by quality
can be fully utilized to
construct excess abnormal returns.
References
Blanchard, O. J., & Watson, M. W. (1982). Bubbles, rational
expectations, and financial markets. Crisis in the
economic and financial system. Lexington: Lexington Books.
Camerer, C. (1989). Bubbles and fads in asset prices: a review
of theory and evidence. Journal of Economic
Surveys, 3(1), 3–41.
Chavas, J. P., & Thomas, A. (1999). Dynamic analysis of land
prices. American Journal of Agricultural
Economics, 81(4), 772–784.
Clark, J. S., Fulton, M., & Scott, J. T., Jr. (1993). The
inconsistency of land values, land rents, and
capitalization formulas. Journal of Agricultural Economics,
75(1), 147–155.
Dixit, A., & Pindyck, R. (1994). Investment under uncertainty.
Princeton: Princeton University Press.
Engle, R. (1982). Autoregressive conditional heteroscedasticity
with estimates of the variance of United
Kingdom inflations. Econometrica, 50(4), 987–1008.
Engle, R. (1983). Estimates of the variance of U.S. inflation
based on the ARCH model. Journal of Money,
Credit and Banking, 15(3), 286–301.

Evans, G. W. (1986). A test for speculative bubbles in the
sterling dollar exchange rate: 1981–1984. American
Economic Review, 76(4), 621–636.
Fama, E. F., & French, K. R. (1988). Permanent and temporary
components of stock prices. Journal of
Political Economy, 96(2), 246–273.
Featherstone, A. M., & Baker, T. G. (1987). An examination of
farm sector real asset dynamics: 1910–85.
American Journal of Agricultural Economics, 69(3), 532–546.
Golan, A. (2006). Information and entropy econometrics: a
review and synthesis. Foundations and Trends in
Econometrics, 2(1–2), 1–145.
Jaynes, E. (1957). Information theory and statistical mechanics
II. Physical Review, 180(2), 171–190.
Jaynes, E. (1979). Where do we stand on maximum entropy? In
R. Levine & M. Tribus (Eds.), The maximum
entropy formalism (pp. 15–118). Cambridge: The MIT Press.
Kiefer, N. M. (1988). Economic duration data and hazard
functions. Journal of Economic Literature, 26(2),
646–679.
Lavin, A. M., & Zorn, T. S. (2001). Empirical tests of the
fundamental-value hypothesis in land markets.
Journal of Real Estate Finance and Economics, 22(1), 99–116.
Liu, X., Filler, G., & Odening, M. (2013). Testing for
speculative bubbles in agricultural commodity prices: a
regime switching approach. Agricultural Finance Review, 73(1),
179–200.
Lloyd, T. A. (1994). Testing a present value model of

agricultural land values. Oxford Bulletin of Economics
and Statistics, 56(2), 209–223.
McQueen, G., & Thorley, S. (1991). Are stock returns
predictable? A test using Markov chains. Journal of
Finance, 46(1), 239–263.
McQueen, G., & Thorley, S. (1994). Bubbles, stock returns, and
duration dependence. Journal of Financial
and Quantitative Analysis, 29(3), 379–401.
Neftçi, S. H. (1984). Are economic time series asymmetric over
the business cycle? Journal of Political
Economy, 92(2), 307–328.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset
returns: a new approach. Econometrica, 59(2),
347–370.
Poterba, J., & Summers, L. (1988). Mean reversion in stock
prices: evidence and implications. Journal of
Financial Economics, 22(1), 27–59.
Power, G. J., & Turvey, C. G. (2010). US rural land value
bubbles. Applied Economics Letters,
17(7), 649–656.
Ramsey, J. B., & Rothman, P. (1996). Time irreversibility and
business cycle asymmetry. Journal of Money,
Credit and Banking, 28(1), 1–21.
Schmitz, A., & Moss, C.B. (1996). Aggregate evidence of
boom/bust cycles in domestic agriculture. Applied
Economics Working Paper AEWP 96–1, University of Florida.
Serrano, C., & Hoesli, M. (2010). Are securitized real estate

returns more predictable than stock returns?
Shiller, R. S. (1978). Rational expectations and the dynamic
structure of macroeconomic models: a critical
review. Journal of Monetary Economics, 4(1), 1–44.
Shiller, R. J. (1981). Do stock prices move too much to be
justified by subsequent changes in dividends?
American Economic Review, 71(3), 421–436.
Stokes, J.R., & Cox, A.T. (2014). The speculative value of farm
real estate. Journal of Real Estate Research,
forthcoming.
Tegene, A., & Kuchler, F. R. (1993). Evidence on the existence
of speculative bubbles in farmland prices.
Went, P., Jirasakuldech, B., & Emekter, R. (2009). Bubbles in
commodities markets. SSRN working paper.
West, K. D. (1987). A specification test for speculative bubbles.
Quarterly Journal of Economics, 102(3),
553–580.
Reproduced with permission of the copyright owner. Further

reproduction prohibited without
permission.
c.11146_2014_Article_9469.pdfIs Farm Real Estate The Next
Bubble?AbstractIntroductionDataDeterminants of Farmland
Price ChangesThe Speculative Component of Farmland
ReturnsPatterns in the Time Series DataRunsTransition
ProbabilitiesTime ReversibilityConclusionsAppendixReferences
https://www.nreionline.com/finance-investment/how-broad-
economic-trends-are-affecting-us-real-estate

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