Communications Question

Write a formal, 6 – 10 page APA research paper on the housing market crash and where we stand today. The paper should describe the scope and principal features of the field of study (such as Real Estate Broker, Agent, Appraiser, etc.), citing core theories and practices, and offer a similar explication of a related field. This means, explain how the perspective you chose relates to the field of Real Estate in general and the other areas within Real Estate.

You may write your paper from any number of perspectives:

-Appraisers and their job or role-The lending market-Generational effects on the housing market-A career as a broker or sales agent or other careers in Real Estate-Land use-The government’s role in Real Estate-Laws that will affect Real Estate-Other subjects that may have affected the history and today’s situations in the Real Estate Market

A minimum of three references are required. Two must be from the APUS Library (See Course Guide for help). Wikipedia is NOT a valid Academic source. Dictionaries and encyclopedias may be used; however, they do not count towards your minimum two library references.

J Real Estate Finan Econ (2015) 50:355–376
DOI 10.1007/s11146-014-9469-9
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), productivity 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.
B. C. Olsen (*) : J. R. Stokes
Department of Finance, University of Northern Iowa, Cedar Falls, IA 50614-0124, USA
e-mail: brett.olsen@uni.edu
356
B.C. Olsen, J.R. Stokes
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
Is Farm Real Estate The Next Bubble?
357
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 fundamental 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 fundamental 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
358
B.C. Olsen, J.R. Stokes
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 understanding 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
Is Farm Real Estate The Next Bubble?
359
$12,000
$10,000
High land quality
Medium land quality
Low land quality
$8,000
$6,000
$4,000
$2,000
$0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Fig. 2 Nominal farmland prices for Iowa farmland from 1950 through 2012 for the three quality grades of
farmland: high, medium, and low quality
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 significantly 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 increasingly difficult to service. While the recent increase in Iowa farmland prices is effectively 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
360
B.C. Olsen, J.R. Stokes
$300
$250
High quality
Medium quality
Low quality
$200
$150
$100
$50
$0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
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
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 bt X t 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 subperiod 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.
Is Farm Real Estate The Next Bubble?
361
Table 1 OLS regressions of change in farmland value
Intercept
Full sample
period
1950 to
1969
1960 to
1979
0.013
0.006
−0.010
1980 to
1999
1990 to
2009
2000 to
2012
−0.006
−0.004
0.044 ***
0.076 **
*
−0.065
−0.068
0.036
−0.044
0.041
0.141
0.126 **
***
0.462
Corn yield
0.018
0.073
0.314
Corn price
0.190 ***
0.108
0.231 *
***
1970 to
1989
***
1.019
**
0.220 *
Average rents
0.811
0.515
1.086
0.632
0.824
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 Riskfree 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
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, EðdV t Þ þ C t dt ¼ ρV t dt; where ρ is the
equilibrium rate of return. Rearranging terms results in the following second-order
ordinary differential equation:

2 2
σ Ct
00
0
V t þ μC t V t þ C t ¼ ρV t
2
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.
362
B.C. Olsen, J.R. Stokes
The general solution to this equation is
β
β
V ðC t Þ ¼ K 1 C t 1 þ K 2 C t 2 þ K 3 C t
where K1, K2, K3, β1 >1, and β2