a pooled time-series cross-section analysis of land...

A Pooled Time-Series Cross-SectionAnalysis of Land Prices

Jean-Paul Chavas and C. Richard Shumway

Based on a theoretical formulation of land price formation as an economic rent to a

fixed input, a single equation econometric model is specified and estimated to explainland prices in five Iowa crop reporting districts. It identifies the influence of farm prices,inflationary pressures, and land quality on the price of land.

The determinants of farm land prices havebeen the subject of considerable research inagricultural economics [Wallace; Tweetenand Martin; Herdt and Cochrane; Reynoldsand Timmons]. Much work was concentratedin the 1960's, but the rapid land appreciationexperienced in the last decade has contrib-uted to a resurgence of interest [Harris; Popeet al; Brake and Melichar; Melichar; Reinseland Reinsel]. Indeed, land values in theUnited States tripled in the 1970's.

Pope et al. have recently examined theplausibility of a number of previously pub-lished models [Reynolds and Timmons;Tweeten and Martin; Herdt and Cochrane;Klinefelter] as explanations of recent farmland market events, and studied their predic-tive ability. Reestimation of each model withadditional data produced many sign reversalsand insignificant parameter estimates, par-ticularly in the simultaneous equation mod-els. Their findings suggest important recentstructural changes in the farm land marketnot adequately explained by any of the mod-els. However, the empirical results of thesingle equation (modified Klinefelter) modelwere at least as plausible as the simultaneous

Jean-Paul Chavas is Assistant Professor and C. RichardShumway is Professor of Agricultural Economics at Tex-as A&M University. This research was partially support-ed by a grant from the U.S. - Israel Bilateral Agricul-tural Research and Development Fund (BARD). TexasAgricultural Experiment Station Technical Article No.16085

equation models, and its predictive perform-ance was decidedly better.

The Klinefelter model relates variations inland prices to net returns, average farm size,number of transfers, expected capital gains,and a GNP deflator. Both because of itssimplicity and its predictive performance,the single equation model is particularly at-tractive for further investigation of farm landprices.

A common feature of all the earlier mod-els, whether simultaneous or single equa-tion, has been a reliance either on a singledominant product price [Harris] or on somemeasure of net farm income as a definition ofreturns to land. The latter often requiresextensive adjustments to conventional datasources before it is useful as an indicator ofreturns to land. None of the earlier modelshas attempted to estimate the separate im-pact of various product prices on land priceseven though the composition of commoditiesproduced may have changed markedly overthe data period.

The purpose of this study is to disaggregatethe effects of net income on land prices in anarea characterized by multiple product pro-duction. A simplified single equation modelof land prices is derived from the theoreticalfoundation of price-taking profit-maximizingfirms producing multiple products. The rela-tionship between land value and commodityprices is examined in this context. Estimationis aided by this theoretical formulation as

31

Western Journal of Agricultural Economics

data for the relevant explanatory variables (ortheir close proxies) are readily available. Themodel is used to explain land price variationsover time and across areas in Iowa during theperiod 1967 to 1977.

A Theoretical Approach

Since the supply of land is so inelastic,even over very long adjustment periods, landcan be considered essentially a fixed factor inagricultural production. Thus, the price ofland can be appropriately modeled as aneconomic rent rather than simultaneouslysolving for the intersection of land supply anddemand as though it were a variable factor.While the latter approach has been taken in anumber of prior studies, the key determi-nants of land prices are determinants of arent.

Consider a competitive firm facing a tech-nology represented by a multi-product-multifactor transformation function at time t,

(1) f(Yt, , , S) = 0

where Yt is a (J x 1) vector of outputs, Xt is a(K x 1) vector of variable inputsl , and St is avector of parameters characterizing technolo-gy at time t. Denote by pj the price of yj (thejth output) and by rk the price of xk (the kthinput). Then the profit function of the firm attime t is

J(2) TFt = E Pjt Yjt

j=1

KE rkt Xkt

k=1

Assuming that the objective function of thefirm is to maximize profit, the firm inputdemand and output supply functions are re-

1The transformation function and subsequent optimalsupply-demand equations rightfully include the quanti-ty of fixed factors as an additional argument. However,since land is treated as the main fixed factor and, inaggregate, its quantity does not change much over theperiod of analysis, fixed factors are not included in thetransformation function for notational convenience.

32

spectively, xt- = xkt (Rt, Pt, St) and yt = yjt(Rt, Pt, St) where Rt = (rt, ... ,rKt) and Pt =

(Pit, . . ,PJt). Substituting these optimum in-puts and outputs into equation (2) gives thefirm indirect profit function

J K(3) 7Tf = E pjt yt - E rkt xkt

j=1 k=1

Equation (3) is the firm quasi-rent at timet. It is linear homogeneous in prices, anincreasing function of output prices, and adecreasing function of input prices [Varian].The quasi-rent is the income that is left afterall variable inputs have been paid. Thus, it isthe remuneration of the fixed factors. Landremains essentially fixed in aggregate supplylonger than other farm inputs and so is typi-cally regarded as the major fixed factor thatobtains an economic rent. However, thereare other relevant fixed factors also, e.g.,management, operator labor and structures.If we denote by ott the expected proportion ofnet returns, or quasi-rent, of farming ac-tivities that goes to land then attit* is theexpected value of return to land in year t.

Since land provides a service over time,the value of land at time t= 0 is the presentvalue 2

T(4) L= E '

t=1 (1+ i)t

where T is the length of the planning horizonand i is the discount rate reflecting timepreference. Of course, different land qual-ities would imply different production func-tions, different rents and different landvalues. Also, since time is involved in equa-tion (4), it follows that the current economic

2The quasi-rent for the first time period is also discount-ed under the assumptions that present value is com-puted for the beginning of the first time period, incomeis received at the end of the period, and interestcharges on costs incurred during the period are sub-tracted along with other costs from current incomebefore discounting.

July 1982

Analysis of Land Prices

situation as well as expectations about thefuture concerning product and variable inputprices and technology may influence landvalues.3 To illustrate, assume that the returnto land, OtTFt*, is expected to increase over

time at a constant rate, B3, i.e. oXttt* =

(l+ P)t cxoro*, where P3<i. Then, when Tbecomes large, the value of land in (4) is

(5) L= * a0too*i-P3

Clearly, from (5) an increase in current re-turns increases land price. Also, since

aL 1+i * >

ap (i- [3)2 00>

it follows that any factor that tends to in-crease 3 will increase current land value. Forexample, any expectation of future outputprice increases, variable input price de-creases, or future technological progresswould put upward pressure on land prices.

Melichar has shown that the proportion ofthe total return that goes to production assetshas risen sharply over the last two decades.He argues that the substitution of capital forlabor may be one of the major factors ex-plaining this trend. Since this substitution islargely due to technological and market pricechanges, it appears reasonable to assume thatthe parameter at is determined by such vari-ables. In this case, substituting (3) into (4)yields an equation of the general form

(6) L = f[R1, R2,...,RT;

P1, P2,... ,PT; S, S2,... ,ST; i]

where Rt, Pt (t= 1,. . . ,T) are the expectedfuture price vectors, and S is a set of parame-ters measuring technology. Expression (6)gives the price the firm is willing to pay for agiven piece of land that has a multi-productuse according to (1).

3Melichar has emphasized that an expected growth inreturns to land can play a prominent role in increasingland prices.

Application to Iowa Land Prices

Expression (6) provides a basis for the in-vestigation of land prices. The case of Iowahas been chosen for two reasons. First, cross-section and time-series data of good qualityare available in Iowa [Harris, et al.]. Second,the Iowa economy is predominantly agricul-tural, so the impact of urban activities onland prices is expected to be small. Ratherthan formally testing the latter hypothesis, itsvalidity will be assessed by the explanatorypower and performance of the estimationequation which excludes variables directlymeasuring urban competition for land.

An aggregate net returns measure hasbeen used to represent commodity prices inmost prior land value studies. Such an ap-proach has the disadvantage that the impacton land prices of different growth patternsamong commodities cannot be differentiated.Between 1967 and 1977, corn price in Iowaincreased by 75 percent, while soybean priceincreased by 161 percent and hog price by112 percent [Iowa Farm Outlook Charts].These changes have been associated withimportant modifications in commodity pro-portions. For instance, the proportion of Io-wa harvested acreage in soybeans has variedfrom less than 20 percent to more than 40percent in the last two decades. Since, asargued in the previous section land value isinfluenced by both the level and expectedgrowth of net returns, it appears important toidentify the different sources of expectedgrowth in a model of land prices. This is donein this paper by introducing commodity-specific information in the modeling ap-proach. The analysis concerns land prices infive of the nine crop reporting districts ofIowa: (1) Northwest, (2) North Central, (3)West Central, (4) Central and (5) East Cen-tral. They have been chosen because thethree major commodities produced in Iowa,corn, soybeans and hogs, are also the majoragricultural commodities in each of thesedistricts. Thus, the price vector P in equation(6) includes corn price, soybean price andhog price.

Although Iowa is one of the major agricul-

33

Chavas and Shumway


tural states in the U.S., both prices for itsproducts and prices for its variable inputs aredetermined within the larger national mar-ket. Because the aggregate supply of indi-vidual inputs in such a market is likely notperfectly elastic, input prices may be func-tionally related to product prices via theirderived demands. To demonstrate, the par-tial equilibrium industry demand function forthe kth input, denoted by xd (Rt, Pt, St) issimply the sum of the input demand func-tions xjt over all firms in the industry. Con-sidering the market supply functions for in-puts, xs (rkt), k= 1,...,K, the equilibriuminput prices can then be obtained by solvingthe input market supply and demand func-tions for Rt:

(7) Rt = R(Pt, St)

Equation (7) gives the equilibrium inputprices at time t as they adjust through themarket to changes in output prices and tech-nology. Thus, substituting (7) into (6) gives,

(8) L = f[R(PI, SI),... ,R(PT, ST);P1 ,...,PT; S1, S2,.. .,ST; i]

Expression (8) shows that the "total effect"of an output price change on land value(dL/dpjt) is the sum of the direct effects(aL/apjt) and indirect effects (8L/Rlt)(aRt/apjt). Such "total effect" measures can beused as a predictive device (Buse). Their usein policy analysis has been illustrated recent-ly by Gardner in the context of multiproductsupply response. In our case, we will focuson the relationship between product pricesand land value. Consequently, in order todirectly estimate the total effects of outputprices, variable input prices are not includedin the model, as expression (8) is a functiononly of technology and product prices P, i.e.corn, soybean and hog prices.

Technological progress over time, St, isrepresented initially by corn yield.4 Dummy

4 Precise measures of technology are not available. Yieldspick up input substitution effects as well as changes in

34

variables for crop reporting districts are in-cluded as proxies for cross-sectional differ-ences in land quality. Two other measuresare included in revisions of the model: thedummy variables are replaced by the "cornsuitability rating", and soybean yield is in-cluded along with corn yield to reflect addi-tional temporal changes in technology. The"corn suitability rating" is a variable de-veloped by soil and crop scientists in mid-western states [Fenton; Fenton, et al.] tomeasure changes in soil and climatic qualityover space. These ratings provide an indexfor comparing farm land in different locationsin Iowa. They measure the integrated effectsof numerous factors that influence the yieldpotential and frequency of soil use for cornand soybean production at a specified man-agement level. More specifically, they reflectsoil, slope and weather differences, and dif-ference in response to modern technology[Fenton, et al.].

Inflation is expected to have some influ-ence on land prices either directly or indi-rectly through the discount rate i in ex-pression (8). 5 For this reason, the consumerprice index is introduced in the model as ameasure of general inflationary pressures.Prices are not deflated by the consumer price

the underlying technology. The major alternative, thetime variable, typically is used to represent constantabsolute or relative technical change over time but isnot well suited to measuring lumpy or irregular techni-cal change.

5Inflation (or expected inflation) may result in eitherhigher or lower farm land prices depending on how itaffects after tax net returns to agriculture, the propor-tion of net returns that goes to land, and the discountrate. If it affects only the discount rate, an increase ininflation tends to decrease land values. However, if anincrease in food prices constitutes a part of the infla-tionary pressure, one may expect net returns fromfarming to increase with inflation. If variable inputprices do not rise as rapidly as product prices, it is alsopossible for inflation to yield a higher proportion of netreturns as a quasi-rent to land. Thus, whether inflation(or the expectation of it) increases or decreases landvalues is an empirical question and cannot be unambi-guously signed as a theoretical hypothesis in the ab-sence of further assumptions.

July 1982


index since that would restrict the profitfunction to be homogeneous of degree zeroin product prices and general price level.While there are some valid theoretical argu-ments to support such a restriction whenboth product and variable input prices areincluded in the estimation equation, noneexist to justify imposing zero homogeneity inproduct prices and general price level alone.

Finally, (8) involves expectations about thefuture. Ideally, one would like to formulatean operational notion of future prices basedon rational expectations arguments (Muth)because all learning evident in the market isincorporated in such expectations. However,because of the difficulty of specifying struc-tures generating rational expectations, a sim-ple adaptive structure is assumed here. Onthis basis, the following model has beenspecified

(9) Ldt = oo+ cl [b PCORNt_1+(1- b)PCORNt_ 2]

+ oL2 [b PSOYt- I + (1 - b)PSOYt_ 2] + a [b PHOGt 1

+ (1- b)PHOGt_2] + o4YCORNt 1 + x5 CPIt_ 1

+ t6 DV2 + o7 DV3 + s8 DV4 +0t9 DV 5 ,

where t denotes time; Ld denotes the landprice in the dth district, PCORN, PSOY andPHOG are, respectively, the Iowa farm pricefor corn, soybeans and hogs; YCORN is theIowa corn yield; CPI is the consumer priceindex, and DVd is a dummy variable takingthe value of 1 for the dth district and zerootherwise. All variables are in logarithmicform.

The specification of the lag structure in (9)assumes a one-year delay in the adjustmentof land price to the yield variable and theCPI. The lag distribution for the price vari-ables is slightly more complex. First, in pre-liminary tests, attempts to introduce priceslagged more than two years gave insignificantcoefficients. On this basis, assuming a one-year delayed response to price, prices lagged

1 and 2 years were included in the model.Second, we tested whether the shape of thelag structure was the same for corn, soybeansand hog prices and failed to reject this hy-pothesis at the 5 percent significance level.Thus, the specification presented in (9) as-sumes an identical distributed lag shape forthe three prices.

Equation (9) provides a simple specifica-tion of land price behavior. It is constructedto estimate the total effects of major agricul-tural product prices and to determinewhether it is possible to explain farm landprices in this market from a competitivemodel of agricultural production. Using an-nual pooled observations from 1967 to 1977for the five regions, it is estimated by non-linear regression, using the Marquardt al-gorithm. The results are presented in Table1.

The model explains 99.2 percent of thevariations in land prices within the estima-tion period. All variables except DV5 aresignificant at the 5 percent level. The latter issignificant at the 10 percent level. Table 1shows that the product prices lagged twoyears have more impact on land prices thanproduct prices lagged one year, as the esti-mate of b is .3611. This suggests a fairly slowadjustment.

All the elasticities of land price with re-spect to output prices have the expectedsign. They are .42, .49 and .37 for corn,soybeans, and hogs, respectively. The rela-tive magnitude of these elasticities appearsplausible. The corn price elasticity is largerthan the hog price elasticity, which corres-ponds to the rank ordering of their values ofproduction: in 1977, Iowa value of produc-tion for corn and hogs was $2.2 and $2.0billion respectively. Soybean price has agreater elasticity although the value of soy-bean production was lower ($1.4 billion forIowa in 1977). This may be due to the growthfactors associated with the sharp increase insoybean acreage in Iowa in the last twentyyears. A one percent sustained increase inthe price of all commodities would increaseland price by 1.27 percent. Thus, proportion-

35

Chavas and Shumway


ate increases in major product prices accom-panied by the corresponding adjustments ininput prices are translated into a greaterrelative increase in land prices.

The elasticity of land price with respect tocorn yield is .89 which suggests that techno-logical progress has a very strong and signifi-cant impact on land rent. Also, a one percentincrease in the consumer price index is as-sociated with a .54 percent increase in landprice. Thus, while it is possible to have gen-eral inflation without a corresponding in-crease in farm land prices, such does notappear to be the case in Iowa; land prices arepositively correlated with the consumer priceindex.

The coefficients of the DVd variables esti-mate the impact of all regional differencesfrom region 1, i.e., soil, climate, etc., on landprices. Because soil and crop scientists inIowa and other Midwestern states have de-veloped an explicit measure of soil andclimatic quality for each county in the state,the "corn suitability rating" is examined as analternative to the dummy variables. To de-termine the extent to which this measure isCZ

E correlated with the estimates of regionalS~a) ~ price differences, the latter (estimated coeffi-

4U cients of DVd's) are regressed on the simpleE average of county corn suitability ratingsCZ (CSR) in each district. The regression results

~a) ~ are presented in Table 2. They show that the_ corn suitability ratings explain 83 percent of0 ~a) ~ the variations in land prices between dis-U) tricts. Although there are few degrees of

freedom, both the intercept and the slope(D coefficient have low standard errors, imply-

CZX ~ ing that a reasonably narrow confidence in-t. terval could be established for predictingc)

0 TABLE 2. Regression of the Coefficients ofa) the DVd's on the Corn Suitability

'E!*on~ ~ Ratings.aCo

IC INT CSRin

o 68.446 54.020o°H (1.292) (14.202)0.E R2 = .83

Standard errors are in arentheses below the reres-aStandard errors are in parentheses below the regres-sion estimates.

CM 0)

O O~

o c

0 C)

0 0)

CMOC

O

0'

O .O

co CO00 CO

1- °

CM CM

LO coCO CD

v-

0

c00

10 O

cc CO

:-

Q

LC

0

CD

0

0

0

CO

I03

CM0L

ETz

0s

UO

o'

E

fi

m0

I=

0P

36

July 1982

-J- - .-- --- - -- -- . - ---. - I


regional price cllterences based only on theCSR.

To further examine the value of the CSRvariable in the estimation of land prices andto determine its impact on the other parame-ter estimates, it is substituted for the districtdummy variables in equation (9), and themodel is reestimated. The results are report-ed in Table 3 (revised model A). Except forthe intercept, all parameter estimates are thesame as in the original model to the thirddecimal place. This suggests fairly robustestimates of the elasticities. Standard errorsare a little higher, but all parameters aresignificant at the 5 percent level. Goodness offit as measured by R2 is only slightly lower.These results provide an explicit estimate ofthe influence of soil quality, as measured bythe CSR, on land prices: a 1 percent increasein the CSR increases land prices by 1.05percent. Testing the hypothesis that the elas-ticity of land price with respect to CSR isequal to 1, we conclude that, given a 5percent level of significance, land price is alinear homogeneous function of the CSR.

At the suggestion of one of the reviewers, asecond technology variable, soybean yield(YSOY), was included along with corn yieldand the CSR in a further revision. The pa-rameter estimate on soybean yield was notsignificant at the 10 percent level, and stan-dard errors on most other variables weresubstantially increased (see table 3, revisedmodel B). Thus, while high collinearity 0i

among independent variables was a potential oproblem in all three models, it appears tohave affected the quality of the estimatesonly in revision B. Although prices and the >CPI show considerable correlation, the lowstandard errors on all parameters estimated Mimply that collinearity is not a major problem 0in either model. Along with the high R2

values and parameter consistency, the quali- X

ty of estimates in those equations appears to -C)

be very good. W

Finally, in order to validate the model, C

predicted land prices from the models are mcompared with the actual prices. This com-

.. · . ...

parison is charted in Figure 1 for actual and

r- 0000 000) 0)

(00 Lo (0't (O ·

CO - CUJ CM

d0 V 0000 't '

LO (0 o

C) (D 7-- 00ot (D (0 'LO- -N

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* CD )C)

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00 0t CL (0 (f (0

co- cO CM C

CO (D (D1

N~~ (O No(V)'D (0(0

00 C) CO -'-t (D CM

00 0 C)D NT- N 'ItCC)

C~'- COJ~

( · C) 0)

C) r, ,00

< c

(N

cr

n

0i

0

0C)

O

0-

0U)C)a0

0:

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-) 0(O ,

.O~

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Ca

a,cCQ)

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37

Chavas and Shumway

- -- 1 - -- - - - L -- .-I


Price

ACTUAL

PREDICTED

/ t (Revised Model A)

/!

ESTIMATED

/

x

f

S^-----x-- ------ -------------------- V~Year

YRED aTE

2500

2000

1500

1000

1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979

Figure 1. Actual and Predicted Land Prices in Iowa-Central District.

38

July 1982

Ica[


predicted land prices in one district usingone model over the estimation period (1967-77) and for two additional forecast years(1978-79). The model tracks actual landprices very closely over the estimationperiod, substantially underpredicts in 1978and then closely forecasts actual 1979 price.The large forecast error for 1978 is not sur-prising given the lagged structure of themodel and the unusual single-year downturnin 1977 following several years of rapid ap-preciation in land prices.

Predictions using both models are con-trasted with actual land prices in all districtsfor the forecast years 1978-79 in Table 4.They consistently under-predict 1978 landprices but give more accurate predictions for1979. The average forecast error for bothyears is 11.1 percent for Revised Model Aand 9.2 percent for Revised Model B. Whilethese magnitudes seem high, they are lowerthan the best predictive performance of anymodel examined by Pope, et al. for the 1973-75 period. Given the unusual circumstancesof the forecast period, these simple modelsperform generally quite well.

Comparison with Previous Research

Because of specification, location and timedifferences, comparison of our results withearlier studies is difficult. For example, con-

sidering only specification differences,Klinefelter and also Reynolds and Timmonsuse deflated prices and income while Herdtand Cochrane and our model use the generalprice level as a separate explanatory variable.While Tweeten and Martin, and Reynoldsand Timmons include net farm income intheir model, Herdt and Cochrane use theratio of the index of prices received by farm-ers to the index of prices paid by farmers toexplain the variations in land prices. Also, inorder to capture the influence of expectationsabout the future, Klinefelter, and Reynoldsand Timmons introduce a capital gain vari-able, while Herdt and Cochrane, and ourmodel use a distributed lag on farm prices.Finally, although our approach uses a simplespecification, it is the only one that explicitlyexamines the effects of multiple-product useof the land. Thus, only limited comparisonsof results follow.

Our high estimated elasticity of land pricewith respect to corn yield is in agreementwith Herdt and Cochrane's finding that pro-ductivity is an important factor influencingland prices. Also, our results give furtherinferential evidence about the positive rela-tionship existing between support-pricepolicies and land values. From a policy point-of-view, it has the advantage of providing adirect estimate of the influence of a change in

TABLE 4. Model Predictions of Land Prices.

District -------- District ~Average Pre-1 2 3 4 5 diction Error

($/acre) (%)1978:

Actual 1932 2146 1623 2078 1826Predicted 1556 1748 1450 1807 1543 15.44Revised Model APredicted 1615 1814 1504 1875 1602 12.25Revised Model B1979:

Actual 2363 2548 1887 2438 2163Predicted 2043 2296 1904 2372 2026 6.67Revised Model APredicted 2284 2566 2128 2652 2265 6.06Revised Model B

39

Chavas and Shumway


a particular commodity price on land prices.For example, a change in the corn supportrate that raises its expected price 10 percentcan be expected to increase land prices be-tween 2.5 and 4.2 percent.

Our estimated elasticities of Iowa landprices with respect to the general price levelare lower than the .93 estimated by Pope etal. (modified Klinefelter model for 1970, esti-mation period 1946-1972). The fact that suchresults appear to depend on the model speci-fication suggests that the exact role of infla-tion in the determination of land value isunclear.

Finally, by pooling time series and crosssection data, our model provides a quantita-tive measure of the influence of land quality(as measured by CSR) on land prices. Thisfeature, which is not present in the othermodels just discussed, has potential applica-tions for the valuation of land by appraisers.

Summary

Based on a theoretical formulation of landprice formation as an economic rent to a fixedinput in a multi-product production system,a single equation econometric model hasbeen specified and estimated to explain landprices in five Iowa crop reporting districts.The data period for estimation was 1967 to1977. This period was one of major pricechanges.

This very simple model explained about 99percent of the variation of land prices withinthe estimation period. It identified the indi-vidual impact on land price of price changesin the three major commodities produced.For example, a 10 percent increase in soy-bean price corresponds approximately to a 5to 6 percent increase in land prices in Iowa.The sum of the elasticities with respect tooutput prices was found to be greater thanone. In addition to rising farm commodityprices, technological progress appears to beone of the major causes of rising land prices.Further, land price differences among dis-tricts can be largely explained by differencesin the "corn suitability rating" estimated by

40

crop and soil scientists. It is found that landprices are a linear homogeneous function ofthe "corn suitability rating". Thus, a majorimplication of this work is that an economet-ric model using a minimum of data may bequite useful for estimating regional and timedifferences in land values. While far moresophisticated and complex models of landprices could be formulated, both the theoret-ical underpinnings and the performance ofthis simple model make it an attractive alter-native for further examination. Although for-mal tests were not conducted on the influ-ence of input prices, urban pressures, ormany other possible variables on land prices,the high statistical quality of the estimatesand the predictive performance of the modelgive little reason to suspect that excludedvariables have had a strong independent in-fluence on farm land values in this particularmarket.

References

Brake, J. R. and E. Melichar. "Agricultural Finance andCapital Markets." A Survey of Agricultural EconomicsLiterature, Vol. 1, ed. L. R. Martin Minneapolis:University of Minnesota Press, 1977.

Buse, Rueben C. "Total Elasticities -A PredictiveDevice." Journal of Farm Economics 40(1958):881-91.

Fenton, T. E. "Use of Soil Productivity Ratings inEvaluating Iowa Agricultural Land." Journal of Soiland Water Conservation 30, no. 5 (1975):237-40.

Fenton, T. E., G. A. Miller and P. E. Rosenberry."Iowa's Agriculture Land Use, Cropland ProductionBase, and Prime Agriculture Land." The University ofIowa, The Institute of Urban and Regional Research.January 1979.

Gardner, B. L. "Determinants of Supply Elasticity inInterdependent Markets." American Journal of Ag-ricultural Economics 61, (1979):463-75.

Harris, D. G., "Inflation-Indexes, Price Supports, andLand Values." American Journal of Agricultural Eco-nomics 59, (1977):489-95.

Harris, D., T. Lord, and J. Weirich. "1978 Iowa LandValue Survey." Iowa Agricultural Extension Service,FM1762, March 1979.

July 1982


Herdt, R. W. and W. W. Cochrane. "Farmland Pricesand Technological Advance." Journal of Farm Eco-nomics 48(1966):243-63.

Iowa Farm Outlook Charts, Cooperative ExtensionService, Iowa State University, Econ. Inf. 190.

Klinefelter, D. A. "Factors Affecting Farmland Values inIllinois." Illinois Agricultural Economics 13(1973):27-33.

Melichar, E. "Capital Gains versus Current Income inthe Farming Sector." American Journal ofAgricultur-al Economics 61(1979):1085-92.

Muth, J. F. "Rational Expectations and the Theory ofPrice Movements." Econometrica 24(1961):315-35.

Pope, R. P., R. A. Kramer, R. D. Green and B. D.Gardner. "An Evaluation of Econometric Models ofU.S. Farmland Prices." Western Journal of Agricul-tural Economics 4(1979):107-19.

Reinsel, R. D. and E. K. Reinsel. "The Economics ofAsset Values and Current Income in Farming." Amer-ican Journal of Agricultural Economics 61(1979):1093-97.

Reynolds, T. E. and J. F. Timmons. Factors AffectingFarmland Values in the United States. Iowa Agricul-tural Experiment Station Research Bulletin 566, 1969.

Tweeten, L. G. and J. E. Martin. "A Methodology forPredicting U.S. Farm Real Estate Price Variation."Journal of Farm Economics 48(1966):378-93.

Varian, H. R. Microeconomic Analysis. New York: Nor-ton & Co., 1978.

Wallace, "Comparative Farm-Land Values in Iowa."Journal of Land and Public Utility Economics2(1926):385-92.

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Chavas and Shumway

July 1982 Western Journal of Agricultural Economics

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a pooled time-series cross-section analysis of land...

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