Farmland is a major component of wealth in the farm sector as well as wealth of farm households. This study contributes to our knowledge of variations in farmland prices by examining the extent to which farmland values are spatially correlated and to what extent that this spatial correlation can be explained by income to farmland.land values, spatial correlation, Land Economics/Use,

Livanis, Grigorios T.

Mishra, Ashok K.

Moss, Charles B.

English

Research Papers in Economics

1 
 
Impact of Capital Gains and Urban Pressure on Farmland Values: A 
Spatial Correlation Analysis  
 
Charles B. Moss (University of Florida), Ashok K. Mishra (United States Department of 
Agriculture), and Grigorios Livanis (Northeastern University) 
 
 
 
 
 
Presented at the American Agricultural Economics Association Meetings 
in Portland, Oregon 2007 
 
 
 
Copyright 2007 by Charles B. Moss, Ashok K. Mishra, and Grigorios Livanis. All rights 
reserved. Readers may make verbatim copies of this document for non-commercial purposes by 
any means, provide that this copyright appears on all such copies. 2 
 
Impact of Capital Gains and Urban Pressure on Farmland Values: A 
Spatial Correlation Analysis 
Charles B. Moss (University of Florida), Ashok K. Mishra (United States Department of 
Agriculture), and Grigorios Livanis (Northeastern University) 
Abstract: Farmland is a major component of wealth in the farm sector as well as wealth of farm 
households.  This  study  contributes  to  our  knowledge  of  variations  in  farmland  prices  by 
examining the extent to which farmland values are spatially correlated and to what extent that 
this spatial correlation can be explained by income to farmland. 
Keywords: land values, spatial correlation 
Introduction 
This study examines changes in the spatial correlation of farmland values over time using the 
spatial  correlation  measure  proposed  by  Theil,  Moss,  and  Chen  (1996).  The  stability  of  the 
agricultural balance sheet in the United States since World War II has been intimately linked to 
farmland  values.  Over  this  time  period,  farmland  values  have  accounted  for  on  average  70 
percent of all agricultural asset values. In addition, financial crises such as the one occurring in 
the mid 1980s have been linked to weaknesses in the agricultural balance sheet resulting from 
falling farmland values coupled with the shortfalls in operating returns. Thus, analysis of factors 
contributing to the variation in farmland values is important in predicting the economic viability 
of the sector and potential need for policy response. Changes in farmland values may have a 
major impact  not  only  on farm sector wealth, but  also  on the economic well-being of farm 
households  through  its  linkages  to  consumption  and  investment.  Further,  large  changes  in 
farmland values may also affect the overall level of risk on farm sector investment. This research 
is closely related to the dynamics of farm and rural economy and in particular the economic well-3 
 
being  and  agricultural  market  and  factor  markets.  Results  from  this  research  will  identify 
methods to improve measurement of farmland values at the farm and sector level. 
Our results indicate that the spatial correlation fallen by 50 percent since the 1950s. Given 
this  decline,  we  examine  the  possibility  that  this  decline  could  be  explained  by  market 
fundamentals. Specifically, we examine whether changes in returns to farmland have also decline 
through time. Again, our results indicate that the spatial correlation fell by 35 percent over the 
same time period. Thus, the decline in spatial correlation of farmland values is consistent with 
the decline in spatial correlation in returns. 
Measure and Data 
Theil, Moss, and Chen (1996) suggest that 
2 2
11
2
NN
i j i j
ij
p p x x                      (1) 
where  i p  is the share of the population in state  i,  i x  is the variable of interest (in Theil, Moss, 
and  Chen  income  per  capita  or  in  our  application  farmland  values  per  acre),  and 
2  is the 
variance of that variable. To demonstrate their conjecture we start from the conclusion and work 
backwards 
2 22
1 1 1 1
22
1 1 1 1
2 2 2
1
2 2 2
11
2 2 2 2 2
2
2
2
2
2
2
2
N N N N
i j i j i j i i j j
i j i j
N N N N
i j i i j j j j
i j j j
N
i i i
i
NN
i i i i
ii
p p x x p p x x x x
p p x x p x p x
p x x x x
p x x p x x
x x x
             (2) 4 
 
where  x  is the population weighted mean of the sample. 
Theil, Moss, and Chen and Moss (1996) then propose a decomposition of the variance 
based on that result. Specifically, they propose two measures 
2
2
1
:
1
:
i j i j B i j
BB B
i j i j N i j
NN N
MSBD p p x x p p
MSNB p p x x p p
                  (3) 
where  B  denotes regions that share a common boarder or are contiguous and  N  denotes those 
regions that are not contiguous; hence,  MSBD denotes the mean square error across contiguous 
regions,  MSND denotes the mean square difference across non-contiguous regions,  B  is the 
share of the population in contiguous regions, and  N  is the population share in non-contiguous 
regions. This specification decomposes into unit sum rule 
1 BN H                        (4) 
where  H  is the Hirshman-Hefindahl concentration measure (
2
1
N
i i Hp ). Hence, given that 
2
0 ii xx  for all i, Equations 1 and 3 imply that 
2 2
11
2
NN
i j i j B N
ij
p p x x MSBD MSNB .               (5) 
Based on the implicit decomposition of the overall variance in Equation 5, Theil, Moss, 
and Chen and Moss use the ratio 
MSBD
r
MSNB
                          (6) 
as  a  measure  of  the  significance  of  spatial  correlation.  Intuitively,  the  higher  r ,  the  more 
dissimilar contiguous counties are compared to non-contiguous counties. Thus, the higher r , the 
less spatial correlation. 5 
 
This study uses U.S. Department of Agriculture, Economic Research Service state-level 
data for 48 states (excluding Alaska and Hawaii) across 10 farm production regions from 1950 to 
2005.    These  annual  data  on  land  values,  interest  rates,  returns  to  farm  assets,  government 
payments, and debt servicing ratios are derived from a variety of sources such as the Census of 
Agriculture, various USDA agencies, Federal Deposit Insurance Corporation (FDIC) call reports, 
and the Farm Credit System. All prices and income are deflated using the Personal Consumption 
Expenditure Component of the Implicit Gross Domestic Product deflator. This study defines the 
return to farmland as the gross revenues per acre less the expenditures on variable inputs as 
described  by  Erickson,  Mishra,  and  Moss  (2003).  This  definition  is  less  complete  than  the 
alternative specification (i.e., the definition of returns offered by Melichar [1979]). However, as 
demonstrated  by  Mishra,  Moss,  and  Erickson  (2004),  these  more  complete  formulations  of 
imputed returns may introduce measurement error problems if other quasi-fixed assets or labor 
are trapped in agriculture. Average real interest rate is the average interest rate on farm business 
debt (i.e., ratio of interest expenses minus interest expenses associated with operators dwelling 
expenses to average farm debt).   
Results 
Table 1 presents the spatial correlation results for both farmland values and returns to farmland 
using the definition by returns. The results in Columns (2) through (4) of Table 1 show a slight 
concentration of farmland across contiguous states with  B  increasing from 0.116 in 1950 to 
0.136 in 2003. These results also indicate that most of this gain comes from non -contiguous 
shares ( N ). This limited change is consistent with the slow change in overall distribution of 
farmland. Changes in the distribution of farmland result primarily from the loss of farmland to 
urban growth, but other factors such as the conversion of farmland to environmental set asides. 6 
 
The results depicted in Table 1 are consistent  with the effect of inflation on farmland 
values  (Moss  1997).  Specifically,  the  both  MSBD  and  MSND   are  based  on  nominal  asset 
values. However, given that we are primarily interested in the ratio between  MSBD and MSND , 
the spatial correlation coefficient itself is unaffected. Turning to spatial correlation measure, we 
see that share of spatial correlation in farmland values fell by fifty percent between 1950 and 
2003. Given our discussion above, this implies that the overall spatial correlation in farmland 
values has increased radically since 1950. 
The increase in spatial  correlation in farmland values could be the result of numerous 
factors  including  as  discussed  by  Livanis  et  al.  2006.  Specifically,  the  increased  spatial 
correlation  could  be  the  result  of  increased  spatial  correlation  in  returns  to  agriculture  or 
increased spatial  correlation in  urban pressures.  To examine the significant  of both  of these 
alternatives, we next computed the change in spatial correlation in returns to farmland over time. 
As depicted in Table 1, the measure of spatial correlation fell from 0.777 in 1950 to 0.505 in 
2003,  or  to  65  percent  of  1950  level.  Thus,  the  evidence  suggests  that  large  portion  of  the 
increase in spatial correlation could be explained by changes in agricultural returns. 
To  compare  the  spatial  correlation  results  for  farmland  values  and  returns  with  urban 
growth, we computed the spatial correlation coefficient for the logarithmic change in each state’s 
residential population divided by the number of acres in agriculture. These results are presented 
in  Table  2.  Regressing  the  spatial  correlation  coefficient  of  farmland  values  on  the  spatial 
correlation coefficient for agricultural returns and urban pressure yields 
2 0.1589 0.6217 0.1138 0.57
(0.0790) (0.0941) (0.0529)
L I u r r r R
.                 (7) 
Hence, the regression results suggest that the spatial correlation in farmland values is driven 
primarily by both the spatial correlation in agricultural returns and the spatial correlation in urban 7 
 
pressure. Numerically, the effect of agricultural returns appears somewhat larger than the effect 
of urban pressure, but further analysis such as the decomposition into the bits of information 
(Theil 1987, Moss 1997) would be required to develop the contribution of each variable. Further, 
the direction of the effects is appropriate. Increases in r  coefficient are associated with relatively 
higher  differences  in  adjacent  states.  Thus,  as  the  border  differences  in  returns  on  farmland 
increases, so does the border differences in farmland values. 
Conclusions and Implications 
Our results indicate that the spatial correlation in farmland values fell by 50 percent from 0.582 
in 1950 to 0.281 in 2003. Undoubtedly this decline can be attributed to several factors. One 
possibility is that the decline in spatial differences can be explained by changes in factor returns 
(or by market fundamentals). Focusing on returns to farmland, we again see that the spatial 
dispersion of in returns to farmland have also declined over time. In 1950, the spatial correlation 
in returns was 0.777 which by 35 percent of this value (to 0.505 in 2003). Regressing the spatial 
correlation in farmland values on the spatial correlation in agricultural returns and the spatial 
correlation  in  urban  growth  indicates  that  the  spatial  nature  of  farmland  values  by  both 
agricultural  returns  and  urban  growth.  However,  the  relative  magnitude  of  the  estimated 
coefficient and the statistical significance indicates that agricultural returns are somewhat more 
important  in  determining  the  spatial  correlation  in  farmland  prices.  This  result  is  somewhat 
askew from the growing consensus that the spatial dimension of urbanization is a significant 
factor in determining farmland values. However, this divergence may be primarily attributed to 
the level of aggregation used in this study. Specifically, the urban variable used by Livanis et al. 
focuses on the population within a fifty mile radius  of a piece of  farmland. This  degree of 
specificity is far below the state-level data used in this study. 8 
 
References 
Erickson,  K.,  A.K.  Mishra,  and  C.B.  Moss.  (2003)  “Cash  Rents,  Imputed  Returns,  and  the 
Valuation  of  Farmland  Revisited.”  In  Government  Policy  and  Farmland  Markets:  The 
Maintenance of Farmer Wealth Edited by Moss, C.B. and A. Schmitz Ames, Iowa: Iowa 
State University Press, 223–35. 
Livanis, G., C.B. Moss, V.E. Breneman, and R.F. Nehring. (2006) “Urban Sprawl and Farmland 
Prices.” American Journal of Agricultural Economics 88(4), 915–29. 
Mishra, A.K., C.B. Moss, and K. Erickson. (2004) “Valuing Farmland with Multiple Quasi-
Fixed Inputs.” Applied Economics 36(15)(2004): 1655–69. 
Moss, C.B. (1996) “The Spatial Correlation of Presidential Elections.” In Festschrift Honoring 
Johan Koerts  Edited by Bode,  B., van  Dalen,  J., and Klomp,  L., Rotterdam: Erasmus 
University, 15–18. 
Moss,  C.B.  (1997)  “Returns,  Interest  Rates,  and  Inflation:  How  They  Explain  Changes  in 
Farmland Values.” American Journal of Agricultural Economics 79(4), 1311–8. 
Theil, H., C.B. Moss, and D. Chen. (1996) “The Spatial Autocorrelation of Per Capita GDPs.” In 
Studies in Global Econometrics by Theil, H. Boston: Kluwer Academic Publisher, Inc., 
47–52. 9 
 
 
Table 1. Spatial Correlation of Farmland Values and Income to Farmland 
Year 
(1) 
Contiguous 
(x100) 
(2) 
Hirschman-
Herfindahl 
(x100) 
(3) 
Non-
Contiguous 
(x100) 
(4) 
MSBD 
(5) 
MSNB 
(6) 
r 
(7) 
2 
(8) 
MSBD 
(9) 
MSNB 
(10) 
r 
(11) 
2 
(12) 
1950  11.6  4.0  84.4  2414.1  4147.4  0.582  1890.1  46.8  60.2  0.777  28.1 
1955  11.8  4.1  84.1  4641.5  7742.8  0.599  3530.1  25.5  42.6  0.600  19.4 
1960  12.0  4.2  83.8  9709.6  16041.0  0.605  7302.2  48.4  66.7  0.725  30.8 
1965  12.3  4.2  83.4  16125.9  27972.6  0.576  12663.9  125.7  185.8  0.676  85.3 
1970  12.6  4.3  83.1  18321.1  38952.9  0.470  17339.2  142.5  180.1  0.791  83.8 
1975  12.9  4.4  82.7  72051.7  152406.0  0.473  67673.8  930.6  1553.0  0.599  702.2 
1980  12.9  4.5  82.6  363028.3  721713.8  0.503  321560.9  1915.7  3019.7  0.634  1371.1 
1985  13.0  4.5  82.5  168158.2  375306.0  0.448  165714.1  3397.2  5528.0  0.615  2500.8 
1990  13.3  4.5  82.2  229481.0  640367.9  0.358  278346.1  3999.9  7266.4  0.550  3251.1 
1995  13.5  4.7  81.9  291154.1  934829.3  0.311  402239.1  3812.3  8916.5  0.428  3906.4 
2000  13.6  4.7  81.8  493265.1  1644427.6  0.300  705645.2  924.9  1972.9  0.469  869.2 
2001  13.6  4.7  81.7  550518.7  1866739.4  0.295  800249.2  871.3  1879.1  0.464  827.1 
2002  13.6  4.7  81.7  614648.0  2125961.2  0.289  910572.9  760.4  1531.2  0.497  677.4 
2003  13.6  4.7  81.7  707249.4  2515028.3  0.281  1075051.1  734.9  1455.2  0.505  644.1 
 10 
 
 
Table 2. Spatial Correlation for Urban Pressure 
Year 
(1) 
Contiguous 
(x100) 
(2) 
Hirschman-
Herfindahl 
(x100) 
(3) 
Non-
Contiguous 
(x100) 
(4) 
MSBD 
(x10000) 
(5) 
MSNB 
(x10000) 
(6) 
r 
(7) 
2
 
(x10000) 
(8) 
1951  11.7  4.0  84.3  4.3  6.7  0.634  6.2 
1955  11.8  4.1  84.1  2.8  5.8  0.488  5.2 
1960  12.0  4.2  83.8  2.8  4.5  0.630  4.1 
1965  12.3  4.2  83.4  1.5  3.3  0.470  2.9 
1970  12.6  4.3  83.1  1.0  1.7  0.575  1.5 
1975  12.9  4.4  82.7  9.6  21.8  0.440  19.3 
1980  12.9  4.5  82.6  2.2  2.7  0.788  2.5 
1985  13.0  4.5  82.5  2.0  3.9  0.518  3.5 
1990  13.3  4.5  82.2  1.5  2.6  0.589  2.4 
1995  13.5  4.7  81.9  1.7  2.2  0.800  2.0 
2000  13.6  4.7  81.8  5.4  6.0  0.911  5.6 
2001  13.6  4.7  81.7  1.2  1.3  0.897  1.2 
2002  13.6  4.7  81.7  0.7  1.0  0.691  0.9 
2003  13.6  4.7  81.7  9.3  11.3  0.821  10.5 
   

Cash Rents, Imputed Returns, and the Valuation of Farmland Revisited.”

Returns, Interest Rates, and Inflation: How They Explain Changes in Farmland Values.”

The Spatial Autocorrelation of Per Capita GDPs.”

The Spatial Correlation of Presidential Elections.” In Festschrift Honoring Johan Koerts Edited by

Urban Sprawl and Farmland Prices.”

Impact of Capital Gains and Urban Pressure on Farmland Values: A Spatial Correlation Analysis

Impact of Capital Gains and Urban Pressure on Farmland Values: A Spatial Correlation Analysis

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