r__________ ‘1 ‘FA 56» I". .14. 5.. ABSTRACT FARM REAL ESTATE VALUE PATIERNS IN rm: UNITED STATES, 1930-1962 By George E. Rossmiller The primary objective of this study was to delineate the factors in the farm real estate market which have affected the price of farm real estate between 1930 and 1962. {As an aid in analysis of these factors, a production function model and a residual return model were postulated to estimate the income streams accruing to farm real estate over the period under two different sets of assumptions for 19 different type-of-farming areas in the United States. The estimated annual income streams from both models were capitalized to yield for each area and each year an ex ante or expected price which could be paid for real estate based on the income streams of the past five years and an ex post or actual price which could have been paid based on actual income streams accruing to farm real estate under the assumptions of the models. Further the year-to-year changes in the estimated marginal value products or yearly income streams from the production function model were partitioned into price and pro- ductivity components to further aid in the analysis. Theoretical arguments are employed which indicate that over the period the marginal physical product of farm real estate should have increased primarily due to the technological revolution going on in agriculture during the period which has allowed large increases A: 5‘. Al- of. :1 George E. Rossmiller in agricultural production without the use of increased quantities of land and with the use of much less labor. Fixed asset theory was employed to argue that it is economically sound for farmers to bid up the price of farm real estate even though the returns to their labor may not be comparable to labor returns in the non-farm economy. Both of these arguments were verified by the data although it was found that farmers are influenced by the non-farm wage rate in deter- mining what price they are willing to pay for farm real estate. The net percentage of non-farmer buyers over sellers in the farm real estate market is decreasing due to urbanization and time breaking many of the strong ties a multitude of urban people once had with the rural sector and the increased costs of property taxes and management services involved in farm real estate investments. While non-farmer investor interest is declining the farmer ex- pansion buyer is rapidly becoming more dominant in the farm real estate market. As labor and capital saving technology becomes in- novated excess capacity in these inputs develops and the answer for many farmers is to expand the size of the existing farm unit to make efficient use of the available capital and labor. Many farms are too small to make use of available technology and we find these farm units disappearing and being absorbed in the form of expansion pur- chases by the already larger than average farms. Government programs are found, as expected, to have a greater impact in those areas where farm income levels depend directly and heavily on these programs. Although specific changes in land values George E. Rossmdller were not traceable to specific programs, in general the impact of government programs appeared to be twofold. First, the reduction in uncertainty in the post war period due to price support programs appeared to have some influence in raising land prices in the wheat areas but no influence was detectable elsewhere. Second, through raising farm incomes either by price support or various direct payments farmers? incomes are higher relative to non-farm.incomes and they seem more willing to bid land prices up if their labor incomes are more comparable to non-farm.wages in their area. Further, the data indicate that the productivity component of income streams to land is rising at a rate which suggests that changes in government programs within the limits of political acceptability in the immediate future will probably not cause land MNP‘s to fall but rather will only affect the rate of increase. Finally, the data suggest that current land prices are below what expansion buyers could afford to pay for farm real estate to add to their existing units. Thus, the cautious conclusion that farm real estate market prices will continue their upward trend is advanced. FARM REAL ESTATE VALUE PATTERNS IN THE UNITED STATES, 1930-1962 By (ye, George E. Rossmiller A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1965 PREFACE This study was undertaken as part of a larger project sponsored by Resources for the Future, Inc., and designed to determine the impact of government programs on resource use and allocation in.American agriculture for the period 1917-1962. Other studies contributing to the parent project include two Ph.D. theses completed at Michigan State university--one on labor by Bob F. Jones and one by‘Milburn L. Lerohl on product price expectations. Now in progress here are three theses on labor, land and capital by Venkareddy Chennereddy, Arne Larsen, and C. L. Quance respectively. This study of farm real estate values is independent of the others contributing to the main project and draws inferences and conclusions within its limited scope. When integrated with the other studies, however, the conclusions and inferences which can then be drawn on should have much wider significance. I wish it were possible to acknowledge individually all those who had a part in making my graduate studies a stimulating and rewarding experience. To the faculty and graduate students at Michigan State University with whom I had the pleasure and benefit of crossing minds, I extend a sincere thank you. ,A special thanks are in order to Dr. David H. Boyne, my major professor, whose guidance, encouragement and helpful criticisms throughout my graduate program and particularly with regard to this thesis are gratefully acknowledged. I also thank Dr. Glenn L. Johnson for per- ceptive comments and helpful criticisms during the development of this thesis. For the generous financial support which made graduate work possible I am grateful to Dr. L. L. Boger, Chairman of the Department of Agricultural Economics, Michigan State University, and to Resources for the Future, Inc. Thanks also to‘William.Ruble, Mary‘Merillat, Beth Unger, Arlene King, and Jackie Musell for their assistance and extra efforts in moving my data through the computer, and to Julie Seger who helped in countless ways. Finally I wish to specially thank my wife, Betty, and children, David and Diane for their quiet encouragement and understanding while enduring the trials of graduate school with me. Betty undertook the typing of this manuscript. Any errors either of commission or omission are, of course, entirely my own. George E. Rossmiller ii a . a A w“- TABLE OF CONTENTS Chapter I INTRODUCTION................................ ..... ..... Historical Perspectives............................ Study Objectives................................... Outline of Study................................... 11 THE MODELS AND THE REAL ESTATE VALUE SERIES........... The Data for the Production Function............... The Production Function Model...................... Marginal Value Products............................ Ex Post Real Estate Value Series................... Ex Ante Real Estate Value Series................... Price and Productivity Components of Yearly Changes in Real Estate Marginal Value Products.. The Residual Return Model.......................... The Variables Used in the Residual Model........... III FARM REAL ESTATE MARKET BEHAVIOR.AND COMPOSITION...... Real Estate Value Behavior l930-l96h............... Land Values and Other Economic Indicators.......... The Real Estate Market and the Expansion Buyer..... Land as an Investment.............................. Land Values and Government Programs................ suerOOCOOOOOO...OOOOOOOOOOOOOCOOO00.00.000.00... IV THEORETICAL FRAMEWORK.FOR FARM REAL ESTATE VALUES..... Expected Behavior of Land Marginal Value Products Over Time.............................. Real Estate and Fixed Asset Theory................. Comparative Advantage and Agricultural Production.. Labor and Fixed Asset Theory....................... Implications of Fixed Asset Theory................. Summary and Implications........................... V AN'ANALYSIS OF RESULTS IN HISTORICAL PERSPECTIVE...... Appraisal of the Residual Return Model............. Appraisal of the Production FUnction.Model......... Proposed Use of the Production Function as a Basis for Allocation of Net Farm Income......... Analysis of the Land MVP Series over the Studied Period.................................. Analysis of the Land Value Series.................. VI SUMRYANDCONCLUSIONSOO0.0.0.0000...OOOOOOOOOOOOCOOOO BIBLIOGMPIH.OOOOOOOOOOOOOOOOOIOOOOOCOOOOOOOOOOOOOO0.0.00.0... APPENDHAOOCOOOOOOOCOOOOOOOOOOIOOOOO...OOOOOOOOOOOOOOOOOOOOO. APPENDH BO...00.0.00...O...0....00....OOOOCOOOOOOOOOOCOOOOOOO APPENDH COCOOOCOOOOOOC00......O...OOOOOOOOOIOOOOOOOOOOOOOO... APPENDIX DO...OCOO...0.000000000000000000000000000000000000000 iii 10 10 11 1h 15 2o 22 26 26 68 68 73 75 87 9o 92 92 103 105 107 108 112 112 117 122 121+ 130 131+ 137 141 1112 155 159 Table l 2 LIST OF TABLES Regression Coefficients and Significance Levels from.the Restricted Cobb-Douglas Production FunCtion MOdeIOOOOIOOOOOOOOOOCOOOOOOOIOOOOOOOOOOOOOOOO through Estimated Market Value from Costs and Returns 20 21 Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962........................................ through‘Yearly Marginal Value Products and 39 LO #1 M2 #3 Ah #5 M6 ”7 #8 Related serieSOOOO...OOOOOOOOOOOOOOOOOOOOOOOO0.0.0.0.. Indexes of Average Value Per Acre of Farm.Real Estate, Total Net Farm Income, Consumer Prices, Gross National Product, and Physical Farm Output, United States 1930-196u (1957-1959=1oo) and Selected Spearman Rank Correlation Coefficients... Farm.Expansion Buyers as a Percentage of Total Buyers in the Farm Real Estate Market 19u8-1%3000OOOOOOOOO00.0.00...OOOOOOOOOOOOOOOOOOO0... Number of Farm.Transfers, Number of Expansion Purchases and Expansion Purchases as a Percentage of NUmber of Farms for Selected Years................. Non-Farmer Buyers and Sellers in the Farm Real Estate Market as a Percentage of the Total Market..... Total Farm Real Estate Taxes, Average Per Acre Farm.Real Estate Taxes, and Effective Rate of Taxation 1930-196300000000O...OOOOOIOOOOOOOO00.0.0.0... Ayerage Yearly Rates of Change Within Selected Periods in Real Estate Marginal Physical Products in Constant l9h7-l949 Dollars and in Real Estate Marginal Value Products in Current Dollars............ Changes in Number and Total Dollar Sales of Farms by Gross Sales Categories l950-l959................... Regression Coefficients, Estimated Standard Errors, and Coefficient Significance Levels from the Unrestricted Cobb-Douglas Production Function......... Farm.Labor Salvage Value, Interest Rate, and Per Acre Residual Return for 19 Areas in the U.S.......... iv Page 13 30 through #8 1+9 through 67 69 75 77 79 83 98 150 159 LIST OF APPENDICES Appendix A LOCATION OF THE 1.9 TYPE-OF-FARMING AREAS IN TIE STIJDYIOOO0.0.0.000...OOOOOOOOOOOOOOOOO000...... B TIE PRODUWION FIINCTION mDELOOOOOOOOOOOOOOOOOOOOOOOOO C “PHALIMTION RATEOOOOOOOOOOCOOOOOOOOOOOOOOOOOOO0.... D SUPPIMMAL MTAOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO Page 1111 1112 155 159 EYE: in: ‘1) ('1! E!“ J CHAPTER I INTRODUCTION Farm real estate market values in the U.S. have followed an ever upward trend since the mid 1930‘s despite widely fluctuating and in recent years declining net farm income. This seemingly strange phenomena has been the fuel for a good bit of controversy in recent years and several hypotheses have been advanced purporting to explain it. The reason for the interest is clear. In an industry where income is low relative to incomes in other sectors of the economy and where absolute declines in income in several years during the period are in evidence, it seems a bit peculiar that one of the major inputs should exhibit the price behavior pattern that can be attributed to farm real estate. Interest in explaining the phenomena should be strong for no other reason than that the phenomena exists. But reasons for the interest in this case go deeper. Farm operators and managers are in- terested because the price of real estate and changes in this price affect decisions regarding efficiency of resource allocation and production both within the agricultural sector and between agriculture and non-agricultural sectors of the economy. Further, asset owners are interested because changes in relative prices of assets materially effect relative welfare positions of owners of different types of assets. Taxpayers are interested from.the viewpoint of who benefits by how much from.various government programs which transfer income to l the < of a: ovb‘ e~¢ 5653i amt? 6528' 2 the agricultural sector. And finally policy makers are interested in the causal factors connected with this phenomena from the standpoint of predicting and evaluating consequences of different policies on participating groups in both the agricultural and non-agricultural sectors. But even a cursory look at the available data points out another interesting and important facet of the behavior of farm real estate market values. State and regional aggregates Show the same pattern of increasing values since the mid 1930's, but the increases have been at different rates between states and regions. This addi- tional phenomena does not greatly affect the individual farm operator since his main concern is with the factor price relationships on a specific farm and how to adjust to the changing price relationships on that farm. Even if he is a potential expansion buyer of farm real estate his interest lies in the prices for real estate in the area immediately surrounding the farm unit upon which he is established. The potential investor, however, is not tied nearly so close to any particular area. If, after he decides he wants to invest in farm real estate, he finds the trend in one area to be advancing at a faster rate than those in alternative locations, and if his expectations are that all trends will continue at their individual respective rates, he will, other things being equal, probably invest in that area in preference to another. So the potential investor is interested both in how farm real estate values behave relative to other types of in- vestments and in any area differentials in real estate values within the agricultural sector. inc: rela of h 95:6 thIC Cthe area theS 191a knaw eve: and he c; 3 The taxpayer is interested primarily in the distribution of income transfers to agriculture via government programs as between owners of assets of different types. He is probably not too interested in the distribution of benefits to individuals within a particular group of owners of a specific type of asset. That is, it may be sufficient for the taxpayer‘s purposes for him to know that some por- tion of the income transfer benefits accrue to real estate holders in the form.of higher capitalized real estate values. Although, if the income transfers go primarily to large landholders who are already relatively well off, the taxpayer may be more concerned about the use of his taxes for the program than if transfers went to relatively low income farmers and contributed toward raising their levels of living to standards society deems acceptable. But the policy maker is concerned with all facets of the real estate value phenomena-~aggregate changes in farm.real estate values through time, changes in real estate values relative to values of other inputs, differential changes in real estate values as between areas, and most important, the causal relationships and changes in these relationships over time which contribute to both absolute and relative real estate values in agriculture. With more comprehensive knowledge of these relationships policy makers can make more accurate evaluation and prediction of consequences of future proposed programs, and if deemed appropriate, can develop programs to counteract effects of changes in these relationships through time. The policy maker needs specific kinds of information to develop informed programs for which he can predict effects on real estate values both in absolute terms and in relative terms between different areas of the country. This svste: of the €512: pEIEZE center 7‘” ,u arr‘ct orie: Stud: Value iflg P Priiu h information should include definition of the variables in the economic system which determine the price of farm real estate, specification of the relationships of these variables to each other and to farm real estate prices, and determination of temporal changes in relevant parameters. Existing studies in this general area will be cited as their contents dictate throughout this study, but some general comments are appropriate at this point. Most of the literature which attempts to explain the increasing farm real estate value phenomena deals with farmland in general. That is, the aggregate increase in value of farm- land is considered without regard for differential rates of increase in different areas. Literature dealing with relationships between real estate values and production is normally couched in terms of land used in the production of a specific commodity and how various commodity oriented government programs will affect these values. The aggregate studies then leave the problem of differential rates of change in values for areas within the aggregate untouched while the studies relat- ing production and land values are oriented toward land used in the production of an individual commodity. But farms in the United States are generally molti-enterprise operations with the land being used for more than one crop and in most cases also used to sustain at least one type of livestock enterprise. Different areas throughout the country can be delineated within which enterprise combinations are broadly similar and between which they are quite different. To be optimally useful to policy makers enumeration of the variables, relationships, and parameters should be couched in terms which would point out the differential impacts of potential 5 programs on real estate values in various heterogeneous areas charac- terized by farms with relatively homogeneous enterprise combinations ‘within each area. After all, we are interested in land values not for how land itself is affected but to determine how people, including landowners, are affected through these value relationships. And only by understanding the historical relationships of the past can we assess our present position, and attempt to see, however dimly, into the future. The following chapters then will look into the past--back to about l930--and in so doing attempt to describe some of the relationships and trends which have contributed to our present situation with regard to farm real estate values. Our starting point, the early 1930's is chosen because for several reasons that date is near the beginning of the historical era of which the present is a part. Historical Perspectives U.S. land policy went through four distinct phases starting back in 178A with an emphasis on cash receipts for the federal govern- ment from sale of the public domain.1 Next came the policy for rapid settlement of the public domain for agricultural purposes. The 1862 Homestead Act was the primary motivating force and this phase lasted until 1891, although full settlement was not accomplished until the second decade of the 190023. The farm population reached its peak in 1916, due to federal land disposal policy, declined to 1930, rose again through 1933 due to the Depression, and has declined thereafter. * 1Marion Clawson and Held,Burnell, The Federal Lands: Their Use and Management, (Baltimore: John Hopkins Press, 1957), chap. 1. tr; 1e tr 6 Between 1891 and the beginning of the New Deal era in 1932, land policy emphasis turned to reservation and conservation implemented through various legislation including the Forest Reservation Act (1891), the Newlands Reclamation Act (1902), Federal Water Power Act (1920) and the Clark McNary Forest and Watershed Improvement.Act_(l924). The present phase of land policy began in 1933 with the New Deal and is characterized by public land management of the remaining public domain and legislative assistance to agriculture through various commodity, trade, and credit policies. 0n the international scene partly as an outgrowth of events leading up to Werld War I, which disrupted existing patterns of world trade and partly as a result of the role the U.S. played during and after the war as a large supplier of industrial and agricultural products to the allied powers for waging the war and for the later reconstruction, we moved from being a debtor to being a creditor nation in 1919. ‘We were hesitant in accepting the creditor nation role and along with it, if we wanted to maintain our export rate, accepting more imports or extending more credit. As a result, we discontinued our wartime credit to the Allies causing a catastrophic drop in foreign demand for products produced by an over-expanded U.S. agricultural industry.2 Finally, due to the industrial revolution, the terms of trade turned against agriculture in favor of industry, in terms of both wages and goods. Prices of farm products rose from an index of 100 in 19lh to a peak of 225 in early 1920. They then tumbled beginning in the fall of 1920 to an index of 12h in 1921. The high product prices during the 2Murray R. Benedict, Farm Policies of the United States 1790- 1220, (New York: The Twentieth Century Fund, 1953), p. 169. t: (II 7 war along with pent up savings by farmers in the form of Liberty Bonds and other liquid assets caused a land boom starting in 1916 and lasting through 1920 which raised land prices by 56 percent during the period.3 From.the 1920 peak, land prices declined steadily until the low was reached in 1933. So, hit by a Blackened foreign demand, saddled by large mortgage debts contracted in the post war boom.and by a suddenly tight money policy, all in the face of over expansion due to the war, the American farmer saw agricultural prices and incomes slump farther and stay down longer than their industrial counterparts. With the agricultural depression of the early twenties came the major growth of the national farm organizations, which were to influence the New Deal legislation of the 1930’s and maintain a strong voice in agricul- tural policy to the present time. The depressed agricultural situation in the 1920‘s resulted in governmental action in the form of farm credit legislation but the federal government was not ready to commit itself to full scale support of the agricultural industry as evidenced by the fate of the five McNary-Haugen bills introduced in Congress between l92h and 1928. Two of the five bills were passed by Congress and both.were killed by presidential veto. Finally by 1930, U.S. agriculture was well on its way toward conversion from horse and mule power to tractors and motor vehicles and the larger scale machinery which went with them. So the early 193093 represent the door to our agricultural era in terms of land settlement, mechanization trends, agricultural policy and legislation, farmers organizations, and farm population trends. 3Agricultural Statistics, (1937), pp. h03-h06. ‘(II Vi b1 III Ir) Study Objectives The primary objective of this study then is the delineation of the factors in the farm real estate market which have affected the price of farm real estate since 1930 and analysis of the differential impact of these factors on different agricultural areas of the United States through time. In order to accomplish this objective secular marginal value product series for the period 1930-1962 for 19 commercial farming areas are derived by using a production function model. These MVP?s are capitalized to form an ex post and an ex ante real estate value series for each area. The ex post series shows what could have been paid for farm real estate under the assumptions of the model and the ex ante series are derived by a mechanical expectation model and shows what a potential buyer could have paid given the assumptions of the model. Under a different set of assumptions and using a residual return model, yearly returns to farm real estate are calculated for each of the areas by subtracting from.net farm.income an imputed return to labor and capital. The residual which is left is allocated to real estate and capitalized in the same way as the MVP‘S from.the production function model to derive the ex ante and ex post real estate value series. Differences and changes over time within and between the four derived series and the market value series given in the data for each area are analyzed and related to their causal factors. Outline of Study Chapter II deals with the development of the two models and the derivation of ex pgst and ex ante farm real estate value series in n u «c P015 St: t'n. 9 from each. These series are presented in Tables 2-20 beginning on page 30. Marginal value products and related series from the produc- tion function model are presented in Tables 21-39 beginning on page #9. Chapter III is a historical account of farm real estate price behavior and its relationship to the rest of the economy during the studied period. The changing composition of the farm real estate market and factors contributing to these changes are also discussed. Chapter IV presents theoretical arguments for expecting land price rises even though the other productive factors may be receiving relatively low returns. This is the theoretical framework within which the data derived in Chapter II are analyzed and conclusions drawn. Chapter V contains an appraisal of the usefulness and relevance of both models used in the study and an analysis of the data derived from these models against the historical events occurring during the studied period. Chapter VI presents the summary and conclusions of the total study. . Sale .1.) m CHAPTER II THE MDDELS.AND THE REAL ESTATE VALUE SERIES As an aid in analyzing the factors in the farm real estate market affecting farm real estate prices a production function and a residual return model are developed to estimate income streams accruing to farm real estate over the period 1930-1962. Each model is applied to data compiled for 19 commercial farm areas in the United States by the U.S. Department of Agriculture and published in its Costs and Returns on Commercial Farms series.1 The production function model estimates the yearly income streams to farm real estate in the form of marginal value products while the residual return model subtracts an imputed return to labor and non-real estate capital to arrive at a residual which is allocated as a return to farm real estate. The series thus derived are compared with each other and with the current market value of farm real estate estimates found in the Costs and Returns series which is estimated by a market condition or comparable sales method. The Data for the Production Function The U.S.D.A.3s Costs and Returns series contains detailed data on commercial farms in 37 different areas in the U.S. designated by 1U.S. Department of Agriculture, ERS, Costs and Returns on Come mercial Farms, 1930-19:1, Statistical Bulletin No. 297, 1961, and U.S. Dept. of.Agriculture, ERS, Farm Costs and Returns, Agriculture Informa- tion Bulletin No. 230 series which updates the data in Statistical Bulletin 297 through 1962. Hereafter these data sources will be referred to as the Costs and Returns data. 10 d. ' P\ 11 the type of farm organization most commonly found in each area. The data are presented as yearly costs and returns figures for a typical farm unit representative of the area-type commercial farm organization. The representative farm unit data are built up with each figure being the geometric mean of the particular statistic calculated from sample commercial family operated farms in the specific area and type. Area- type farms which are too large or too small to be considered family operated commercial farms are excluded from the calculations. The extremes differ slightly from area to area but in general they are defined as farms whose size in terms of acres differ more than three standard deviations in either direction from.the geometric mean. Eliminating the extremes makes the series more uniform.and representative. The Production Function Model The function fit to the sample data is a restricted Cobb- Douglas linear in logarithms type of the general form ‘Y=aX1]’_1x2-b2. . .xgn where z_is the dependent variable-~output, 5.13 a constant, x1 . . . Xh are the independent variables--inputs, and b1 . . . bn are constants measuring the elasticity of'XLwith respect to the correspondingjxi. The function is fit to combined cross-sectional and time-series data from representative type farms in 19 different areas over the 33-year period 1930-1962. As explained in Appendix B a restricted form of the general Cobb-Douglas model is employed because, while statistically it is no different from the general form, it yields more economically reasonable estimates of the functional parameters. The coefficient of multiple determination adjusted by the degrees of freedom.(R2) is 12 .8093 indicating that approximately 81 percent of the variance in output is "explained" by the variance of the independent variables included in the function.2 The production function coefficients and their significance levels are presented in Table 1. With the restricted model, standard errors of the regression coefficients were not readily available. Therefore, the normal significance level test for the individual regression coefficients using the £_statistic was not possible. The method used to determine. whether the individual coefficients were statistically different from zero was to run the function in the computer first with only the restriction on the sum.of the relevant bi' Then with the original restriction and an additional restriction holding each bi in the func- tion equal to zero in turn, the run was repeated, once for each bi“ The F statistic was then used to test the null hypothesis for each individual bi that Pi = 0. The test statistic was W, a - m1 ESSr/N-Krl Essb is the error sum of squares for the function run with both re- strictions, ESSr is the error sum.of squares for the function run with only the original restriction, P is the number of degrees of freedom for the numerator and is equal to the number of restrictions (in this case 1), and N-Krl is the denominator degrees of freedom with N the number of observations and K the number of independent variables.3 -__ gAppendix.A shows the location and type of farm designation for the 19 areas included in the study. .Appendix B is a detailed discussion of the production function model, the assumptions, and the variables included in the model. 3This general procedure is described in Richard F. Foote, "-1‘IVal-u nih- zflwflxjcn'fiuAuAUuU HUflUUUH,“thVz 93h“ .Cn.vtulu. ~ «- _ u . .FLH‘ .r..Mw.—i 5.. u..~...;...~ . . Q ~ .u -9 . a. . Cw~wh 5H 0):: rtlul I . 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Gama ***mma. uHom auoo «wcwoouumh moomuwom #Ho.u mmma **owo. pawn ahoo «waflmfimm moomumom Hoo.u wmmH ***:na. uHom cuoo «huwmnnwom omo.u Emma ***mma. muomoacfiz «momuhuflmn ***>wa.n mmma *mzo. huwmn camaoomws ououmos mzo.u nmmH ***mmo. khan: :Hmaoomws :uoummu ***oHH.u :mmH no: Amowaabn wou< How omwmvuhufimn .u.z kuuaoo ***NOH.1 mmmH mafiaasn mou< mHo.n NmmH ***nmm. monounxu wcwumuomo mHo.n Hmma ***mwm. uonmq “mafiassn ***mnm. mumumm Haws no: «EHH you ommmv omma muamcH Hmowmmsm mowaann mafia mum. acmumaoo mucofioammooo oHanuw> wuaoaowmmooo macawum> :onmouwom scammoumom mmHmsonnnnoo wouowuumom «nu Bonn mHo>QA monouwmacmwm can muoofiuwmmooo :owmmoummm H Homo: aoeuunom nowuoswoum H oHan I: < re ("A ’1'! 1h Marginal Value Products All physical inputs and output are entered in the production function in either constant dollar or physical terms. Hence, marginal physical product series can be derived for real estate in each area for each year by converting the estimated Yi from the production b . functiontn antilogs and substituting them.into the formula 1 Y1] xlij where b1 is the real estate regression coefficient in the function, ‘Yij is the estimated physical output in year i.on the representative farm from area j, andjxij is the number of acres in the representative farm.in the ith year and 1th area. In specifying the dependent variable, conversion to constant dollars is accomplished by dividing current value of total output by the index of prices received in each area. This leaves total output in physical quantities multiplied by base price terms. The marginal physical products obtained from the production function then are also in these terms. The procedure can be reversed by multiplying the marginal physical products thus obtained by the prices received index to obtain secular marginal value product series for real estate. The calculated real estate MVP for each year and each area is the point at which the farm is operating on its static production function. The statistical function with time and area dummy variables included to shift the function for each area and year pinpoints the Analytical Tools for StudyiggLDemand and Price Structures, Agricultural Handbook No. M, £38, U.S.D.A. (Washington: U.S. Government Printing (foice, 1958), pp. l79-l8h. The specific application of the general procedure used here was developed in a discussion between William liable, Agricultural Experiment Station, Michigan State University and t:he author. 15 location of the firm in a given area on its yearly static production h So the MVP‘s obtained from this statistical function are function. secular in that they apply through time. The calculated MVP for each year is the only known point on the firm‘s static function for that year. Ex Post Real Estate Value Series NOW the secular MVP series derived above can be considered as the price which can be paid each year for the productive services of the real estate input. In order to develop land value data to compare with the real estate values found in the Costs and Returns series estimated by the market value or comparable sales method, the secular MVPfs or income streams are capitalized to obtain a present value for real estate for each year. {A capitalization formula is needed which.will allow both the net annual return or secular MVP and.the capitalization rate to change from year to year. ‘As the data are only available through 1962 the formula is couched in such a way that the last term contains a 5-year average of the MVP?s and interest rates from 1958 through 1962. This term will repeat itself so there are always 3h terms in the formula even though a term per year is dropped at the beginning of the formula as values are capitalized for the years beginning with 1930 and moving to 1962. A 5-year average for the last term is used under the assump- tion that the best clue to the future is the immediate past. That is, expectations about future income streams to farm real estate under #The role of the time and area dummy variables is discussed in Appendix B. this a firth that bale: 161': the Vher IEt: If a incj 3552 Ss.‘ ‘q 16 this assumption are based on the average of the actual income streams for the past 5 years. Thirty-four terms in the formula for each year‘s value is chosen because there are 33 years in the period and the 3hth term allows the formula to be generalized to include the year 1930 when 'MVP?s for all 33 years can be used. Also, it is a long enough period that the addition to value from the last term provides a reasonable balance between approaching zero and weighting the last 5-year constant term too heavily in calculating the real estate value series. The formula presented is comparable to the first 3% terms of the infinite series a]. 82 83 at V a + fl + + 0 O O + —— + 0 (H1151 (1H2) (1H3)3 (“Tat where V is the capitalized present value, a_is the expected net annual return, and 5.13 the capitalization rate, and £_approaches infinity. If a and E are assumed constant, the formula collapses to V = a/r. By including only the first 3% terms of the infinite series, under an assumed constant £.of 5 percent, the calculated value is biased down- ward from.the true value calculated by V = a/r to approximately 81 per- cent of the true level.5 The higher the capitalization rate the smaller the percentage of downward bias. The downward bias can be interpreted as using a higher capitalization rate than the one stated in the formula. In this study with that interpretation the effective rate is approximately 1 percent higher than the series actually used 6 here. 5Robert C.‘Ueast (ed.), Standard'Mathematical Tables 13th Student Edition, (Cleveland: Chemical Rubber 00., 196E}, pp. h83-h99. 6The magnitude of the differential between the ex post series PFC? 17 The capitalization rate used in the formula is the rate charged for new loans on January 1 of each year by the Federal Land Bank in each of the 19 farming areas studied.7 The Formula is: k-t+1 3h vt a .21 MVPt+i-l + k2 2‘MVPc 1= '= -t+ '—"'—"' (1+Tt+i-1)i J (life)3 (I) Where: V = Per acre real estate t = Year 1930,...,l962 value in year E_ k = 1962 MVP = Secular MVP or income stream 1 = l,...,k-t+1 r = Capitalization rate j = k-t+2,...,3h MVPc = EM“1958-4962 5 . these are constants r = Er1958-1962 ° 5 The yearly MVP's entering this formula are in current dollars for that year. If the general price level remains stable over the period, no inflationary or deflationary influences become capitalized into the real estate values and the series will reflect the price which a buyer could afford to pay for farm real estate in any year in that year's current dollars. In other words the purchasing power of a and ex ante series in addition to being due to the difference in assump- tions is partly due to the difference of approximately one percent in effective capitalization rates in calculating the two series. This would cause a constant percentage differential, however, and other factors dis- cussed later account for changes in relationships between the two series. 7For a discussion of the rationale for choosing this particular interest rates series see Appendix 0. Appendix D presents the interest rate schedule used for each of the 19 areas. 1 Ar CL. as gen the t0 yea be: A; 'u "‘ H. Q 18 dollar would be the same throughout the time period. This, of course, has not been the case. As evidenced by the Consumers Price Index the general price level fell in the early 1930?s, reached a low in 1933 and increased between 1933 and 1962, except for a slight dip between 1938 and 1940 and again in 19h9 and 1955. Thus, since 1933, the purchasing power of the dollar has generally declined. In 1962 it took.$2.33 to buy the same bundle of goods and services as $1.00 would buy in 1933. .A land MVP in 1962 then would need to be 133 percent higher than in 1933 in order for the purchasing power of the income streams to be comparable and the landowner to be equally well off in the two years. When the income streams are capitalized back to 1933 without being adjusted for the rising price level the capitalized price in 1933 includes a factor attributable to the rising price level. If a buyer paid this price he would be paying in dollars with relatively high purchasing power for future income streams of dollars with rela- tively lower and declining purchasing power. With the rising price level his asset position would deteriorate through time because price calculations had been based on dollar streams of declining purchasing power. In order to be able to compare the ex post land value series with the Costs and Returns estimates of current market prices paid for farm real estate, the income streams must be adjusted to reflect their purchasing power in the particular year for which the g§_pg§£_value is being calculated. In other words, the ex post series must be adjusted for the effect of future price level changes. To accomplish this, the Ab yea inc .fl. uh. CE 19 MVP series is deflated by the Consumers Price Index based at 100 in the year for which the real estate value is derived. That is, the formula incorporates a general price level deflator in the form of the consumer price index as follows: 1962 k-tfl 31‘ Z MVP CPI ICPI I5 Vt a z MVPt+i-1(CPIt/cp1t+i_1) + 2 (H958 d( t d) i=1 (1+1- 1 mi j=k-t+2 wag H - 1+ z: rd/5 )1 d=l958 (II) where: w II V = Per acre real estate 1962 value in year 5 i = l,...,k-t+l MVP = Secular MVP or income stream. - k-t+2,...,3h c... l r = Capitalization rate d l958,...,l962 CPI = Consumer Price Index t = Year l930,...,l962 Here the MVP’s for all future years are expressed in terms of constant purchasing power in the year for which price is being calculated so the buyer would neither gain nor loose due to general price level changes. The real estate values derived from the formula are ex pest values in the sense that they use actual data on future income streams or'MVP’s in finding the capitalized value in year t_rather than predicted data on expectations of future income streams. .Admittedly the values are based on less fact and more prediction as t moves from 1930 toward 1962 but the point of complete prediction is not reached until £_= 1963. The value derived from the formula for a given year Cd: ca; I??? Wh 31:. 20 can be interpreted as the price a farm real estate buyer could have paid if he had known the size of the future income stream and if his capitalization rate coincided with that used in the formula. gngntg_Real Estate Value Series The other series developed is called the ex ante series. It is a purely mechanical behavioral model which assumes that the land buyer looks at the average of the past 5 year's MVP‘s and interest rates in determining how much to pay for land. The formula for the capitalized value based on the preceding 5-year averages is: 5 Z t 5 Z rt-i/5 (III) i=1 where: V = Per acre real estate value in year t_ t MVP = Secular MVP or income stream r = Capitalization rate t = year, l935,...,1962 1=1,...,5 This value is interpreted as the price the buyer estimates he can pay for farm real estate on the basis of the returns to that factor during the past 5 years. The buyer is assuming that the past 5 years yield a reasonable estimate of prices and output levels which will prevail in the future. The model does not use all the information available to a buyer such as trends in general economic conditions or 21 rapidly increasing demand conditions due to wars, or changes in price or production outlook due to introduction of different government programs. Therefore, it will not adjust readily to abrupt changes in I output and it cannot foresee turning points in prices or output. The range of land value estimazes from the model will be much greater than if it were able to incorporate additional information of this kind. But under normal circumstances the model should be expected to yield values consistent in direction (even though the magnitude may be greater or less) with the more sophisticated approach an actual buyer would use. One modification of the mechanical model is possible, however, and is included before the series are derived. The general price level change is taken into account in a manner similar to that used in deriving the ex post series. If the price level does not change, the purchasing power of each dollar in the income stream is constant over the past 5 years which are used as a base for the estimated price in the present year. But if the general price level increases over these 5 years the purchasing power of the dollar declines. Therefore, due to the price level increase alone the buyer should expect to pay a higher price in current dollars than the capitalization formula will estimate. Of course, if the price level declines the capitalization formula over estimates the price the buyer should be willing to pay. Thus, assuming a reasonably well informed potential buyer would make this kind of adjustment a price level adjustment is included in the capitalization formula and it becomes: 22 5 2 MVP _ (CPI ICPI - l5 v = 1:1 t 1 t t 1;) t 5 I Z rt-i 5 IV i=1 ( ) where: Vt = Per acre real estate value in year t_ MVP = Secular MVP or income stream r = Capitalization rate CPI = Consumer Price Index t = Year 1935,...,1962 i = l,...,5 The interpretation of the value derived from this formula is as discussed above except that price level changes are considered. The real estate value series resulting from the above ex pest and ex ante calculations using formulas (II) and (IV) located on pages 19 and 22 respectively are presented in columns 2 and 3 of Tables 2-20 for the period 1930-1962 for the 19 farming areas in the study. Column 1 of these tables presents the estimated current market value of farm real estate in the respective areas from.the Costs and Returns data. Price and Productivity Components of Yearly Changes in Real Estate Marginal Value Products Finally, in order to facilitate interpretation of the MVP data derived from the production function model several manipulations are performed on these series. The marginal physical product and marginal value product series are converted to indexes based on l9h7-19h9 = 100. These series along with the prices received index on the same base from 51 cc: or. to ta Re pr V : 1.9. . < “J “Us 1" 0 U Hill 23 the Costs and Returns data are presented in the first 3 columns of Tables 21 through 39--a set of series per table for each area in the study. The fourth column in each table is the MVP series for each area derived from the production function. The fifth, sixth, and seventh columns deal with year-te-year changes in the MVP series and are offset one-half space downward to show that they are calculated between one year and the next. The fifth column is merely the change in MVP from one year to the next. wa since MVP is equal to MPP times the product price, a change in MVP may be caused by a change in MPP, a change in product prices, or some combination of changes in both. The change in MVP is partitioned into that portion of the total change due to change in MPP and that portion due to change in price. These two portions of the MVP change are called the productivity component and the price component respectively and are presented in columns 6 and 7 of the tables. The theoretical basis for the procedure is presented by Boyne in Michigan State University Technical Bulletin 29h, "Changes in the Real Wealth Position of Farm Operators, 19110-196038 Essentially the procedure is based on the Taylor series expansion of the function V = PQ where for a change in value between time period.£_and time period t +’l the partitioning into the various price-quantity components of the change is: 8David H. Boyne, Changes in the Real Wealth Position of Farm Operators, 1940-1969, Technical Bulletin No.29h, (Michigan State University, Agricultural Experiment Station, 196%), pp. 31-33 and 70- 71. bct': rate othe But for. Cur: a p; qua: 2h lav = PtAQ+QtAPtAPAQ Substituting MPP for Q and MVP for V we have (1) (2) (3) The productivity component is term (1) plus half of term‘(3) and the price component is term (2) plus the other half of termw(3). This assumes along with Boyne9 that (l) the rate of change in both price and MPP during each period is constant, (2) the constant rates of change for price and MVP are not necessarily equal to each other, (3) the rates of change in a given time period are not neces- sarily equal to the rates of change in other time periods. Normally we could derive either the price or the productivity component and subtract it from the total change to find the other. But in order to accomplish either method directly from the above formula we would need MPP expressed in pure physical and price in current dollar terms. Since price in this case is only available as a price index with 1947-19h9 = 100 and MPP is only available in quantity times base (l9h7-l9h9 = 100) price or constant dollar terms a slight variation of the formula is needed. . In our case MVP is equal to the price index (based on l9h7-l9h9 = 100) multiplied by MPP in constant dollars also based on l9h7-l9h9 = 100. Now remembering that MPP is in quantity times base price terms we can multiply term (1) of the above formula by 'MPPt to get MPP: MVPt MP1’t-i-1"M:l’1’t\. This converts the change in MPP between periods _t_ meek ‘/ 91bid, p. 71 and Hhe: tai: EV the peri beca 10‘ M?.. 3301 25 and t +.1 to percentage terms based on the level of MPP in year 5. When this is done the base price terms or constant dollar scalar con- tained in MPP drops out.10 MVPt is in current £_year dollars, so MVPt times the percentage change in MPP between.£_and t+l is equal to the change in MVP between E and t +-1 due to change in MPP during that period. This is a productivity component but is downward biased because it contains none of term (3) in the above formula while it should contain half of it to be unbiased. To correct for this bias we can calculate a productivity com- ponent which, in addition to containing term (1), contains all of term (3). This productivity component will be upward biased by the same amount as the one just calculated is downward biased. An average of the two then will yield an unbiased productivity component which is equal to term (1) plus half of term.(3) of the above formula. The upward biased productivity component is calculated using MVPt+1 and percentage change in MPP based on the level onMPPb+1. The formula then for the unbiased productivity component of a change in MVP between year £_and t +~l is: MVPt MI’Pt-i-l - MPPt + MVPt+1 MPPt-l-l ' MPPt 2 and the price component is the change in.MVP minus the productivity component. 10'That is, letting Q stand for the physical quantity portion and Pb stand for the base price portion of MPP, MPPt-c-1 ' MPPI: a Q::+11’b " ‘1th 3 Qt+1'Qt = mp1: ‘1th Qt in physical MPP without any bias entering from the base price scalar. the percentage change C.» IE5 an C3 f0 26 The Residual Return Model One method of determining value used extensively by real estate appraisers is the income capitalization method. The approach generally starts with net farm income and subtracts an imputed return to the operator and family labor and non-real estate capital inputs. The residual or amount left over is the return or payment in any given year for the productive services of the real estate input. If the data are available residual returns are usually calculated for several years and averaged to level out year-to-year fluctuations. .A capitali- zation rate is decided upon and then the return is capitalized to yield the present value of the future income streams accruing to land on the basis of the residual calculations. The Variables Used in the Residual Calculations Data for the representative farms from the same 19 agricultural areas chosen for the production function are used for the residual calculations. The variables are defined as follows: Charge for non-real estate capital. The Costs and Returns data for each type of farm for each year contains an estimate of the current value of non-real estate capital. The crop and livestock inventory portion is valued using the prices at which these items could be sold, January 1 of each year. The machinery and equipment portion is valued at replacement cost minus depreciation value as of the beginning of the year. Thus the capital value each year is assumed to be the price which sale of the capital items on the open market would bring. Any charge for farm capital investment must be an arbitrary one as discussed in.Appendix C. Farm assets vary greatly in type and 27 productive life span and are purchased at different times. For our purpose here we use the interest rate charged on January 1 for new loans by the Federal Land Banks in the respective areas both because it is easily available and because it is assumed to be the opportunity cost interest rate that most farmers would look at during the period in assessing alternatives for capital investment. No charge is made 'for operating expense capital. Charge for operator and family labor. Since we want to use a charge for operator and family labor which reflects an-off farm opportunity cost return we need a series of annual wage rates in the non-farm economy which will approximate the return the farm operator could expect from full-time non-farm employment. Jones developed such a series based on national averages of annual income per employed factory worker adjusted by the national non-farm sector unemployment rate. Jones? calculations are adjusted to reflect the factory wage rates prevailing in each year within each of the 19 type of farming areas. This was accomplished by using state averages of factory wage rates weighted by number of factory workers for states in which each of the 19 areas fall to obtain a factory wage representative of each farm.type area. The national unemployment rate for each year was used for all area calculations.12 11For a rationale for using wage series for factory workers and method of adjustment for the unemployment rate see Bob P. Jones, "Farm- Non-Farm.Labor Flows, 1917-1962," (unpublished Ph.D. dissertation, Mich. State University, 196M), pp. 136-1h7. For a further discussion of the unemployment rate weights used in Jonesf calculations see Earl 0. Heady and Luther G. Tweeten, Resource Demand and Structure of the Agricultural Industry, (Ames: Iowa State University Press, 1963), pp. 2h3-252. 12These factory wage rates adjusted by the national unemployment rate are presented for each area in.Appendix D. 28 The residual return to real estate in any given year is net farm income for that year minus the imputed charges for capital and labor. The return to land is then put on a per acre basis by dividing the residual by the number of acres in the representative farm in that year. The residual return model assumes an opportunity cost or salvage value return for labor and capital. If returns to these factors in their present agricultural use drop below the opportunity cost returns calculated for labor and capital these factors would be expected to move out of their present use and into their opportunity cost use. Thus these are the minimum returns which would be expected to keep these inputs in their present use. Therefore, the return to farm real estate calculated with the variables defined as above should establish the upper bound for farm real estate values derived by the residual model. The yearly residual returns are capitalized into ex post and ex ante real estate value series in the same way that the production function MVPfs are converted to value series. Columns h and 5 of Tables 2-20 present the resulting series derived from.the residual model, again using formulas (II) and (IV) on pages 19 and 22 respectively. Negative residual returns to land were found in some years. They were entered into the capitalization formula on the same basis as positive returns. A negative capitalized value is interpreted by a potential buyer as meaning that he should be willing to "buy” if in addition to acquiring ownership of the land he also receives at the or} {O i111 SE 0 P'V‘ qu: V85 Bl; fr: und be 29 same time at least the negative capitalized value along with it. In other words the negative capitalized value is what he would need now to compensate him for the negative income streams or losses he will incur in the future from ownership of the land. Conversely a potential seller under the assumptions of the residual model should be willing to pay a buyer the negative capitalized value for taking over ownership of the property. We would, no doubt, silently question the extent of mental derangement of a seller if he offered to make such a transaction, then quickly take him.up on his proposition; particularly if his property ‘was located in the hog-beef raising area of the Corn Belt or the Texas Black Prairie cotton area--both of which turn out to have negative real estate values in the residual model. Further discussion of the results from this model is found in Chapter V where we argue that while the underlying assumptions of the model are correct the model itself must be used with extreme caution for estimating real estate values. 30 Table 2 Central Northeast Dairy--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs afii_Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 37 --- 151 --- 39 1931 33 --- 139 --- 36 1932 27 --- 129 --- 32 1933 28 --- 123 --- 30 1931 29 --- 132 --- 31 1935 30 81 138 28 32 1936 30 83 110 39 30 1937 30 89 150 15 30 1938 30 100 119 52 27 1939 30 111 119 59 25 1910 30 128 151 62 25 1911 32 119 162 63 21 1912 33 190 131 67 25 1913 35 230 192 73 25 1911 39 263 191 51 27 1915 13 299 191 16 23 1916 19 353 206 52 27 1917 51 #33 231 99 21 1918 58 175 215 122 17 1919 62 181 237 173 7 1950 60 158 210 86 1 1951 63 163 260 183 0 1952 70 113 261 150 - 5 1953 70 122 263 117 - 7 1951 68 387 268 16 - 1 1955 75 370 268 33 - 5 1956 78 372 276 23 - 7 1957 81 369 288 - 1 - 8 1958 86 371 298 - 2 -10 1959 92 367 297 2h -13 1960 91 373 301 19 -13 1961 93 370 306 7 -13 1962 95 363 311 11 -15 31 Table 3 Eastern Wisconsin Dairy--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962. Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 99 ~~~ 198 ~~- 18 1931 86 -~- 185 ~~~ 17 1932 76 ~~~ 172 ~~~ 16 1933 67 ~~~ 166 --~ 15 1931 67 --- 179 --- 13 1935 73 85 189 23 12 1936 711 87 193 53 5 1937 82 98 206 73 2 1938 83 111 206 90 ~ 2 1939 80 133 206 101 - 8 1910 77 151 211 106 - 9 1911 71 177 227 85 ~12 1912 83 228 255 86 ~16 1913 83 281 271 65 ~18 1911 91 133 273 22 -17 1915 91 392 273 - 8 -13 1916 101 170 291 ~17 ~16 1917 111 583 327 9 -27 1918 123 613 316 36 ~36 1919 123 651 331 61 -39 1950 121 631 338 89 ~10 1951 112 661 366 53 ~39 1952 118 655 370 11 ~13 1953 118 637 368 ~31 ~11 1951 113 603 372 -97 -33 1955 111 585 371 ~122 ~28 1956 116 585 383 ~155 ~16 1957 165 571 398 -227 - 6 1958 175 568 112 ~263 5 1959 189 517 110 -237 11 1960 210 511 122 ~210 21 1961 211 528 ‘ 125 ~150 22 1962 221 517 130 ~ 52 12 32 Table 11 Western Wisconsin Dairy--Estimated Market Value from Costs and Returns series, Ex Post and Ex Ante Value Series from the PIOduction Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs and Returns PrEluction Function Res idual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (g) 1930 81 ~~~ 136 ~~~ '9 1931 62 ~~~ 126 ~~~ 9 1932 53 ~~~ 116 ~~~ 10 1933 17 ~~~ 112 ~~~ 8 1931 15 ~~~ 121 ~~~ 8 1935 11 62 128 1 7 1936 16 61 130 29 2 1937 16 67 139 16 ~ 1 1938 11 77 139 59 - h 1939 12 87 139 70 - 8 1910 39 103 115 83 ~11 1911 39 117 153 65 ~13 1912 13 150 172 70 ~18 1913 15 187 183 55 ~20 1911 52 222 181 21 ~20 1915 50 261 185 ~17 ~16 1916 57 315 196 -33 ~16 1917 65 398 220 ~38 ~23 1918 72 113 232 ~36 ~27 1919 72 150 223 ~ 6 ~32 1950 67 110 225 32 -35 1951 80 162 213 31 ~37 1952 81 156 213 31 ~13 1953 85 139 213 28 ~16 1951 75 115 215 ~25 ~12 1955 70 102 211 ~53 ~39 1956 71 101 251 ~87 ~33 1957 82 391 261 ~136 ~30 1958 85 387 269 ~161 ~28 1959 96 374 267 ~116 -30 1960 99 371 273 ~111 ~28 1961 105 355 276 ~ 81 ~26 1962 111 313 280 ~ 19 ~29 33 Table 5 Dairy-Hog, Minnesota--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 "Costs—and_Returns PrSdGcEISHfliEEcE16fi_" Res idua 1 Re turn Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 96' ~~~ 170 ~~~ 67 1931 83 ~~~ 157 ~~~ 62 1932 69 ~~~ 116 ~~~ 58 1933 57 --- 111 --- 55 1931 63 ~~~ 152 ~~~ 58 1935 59 75 160 19 62 1936 63 75 161 13 57 1937 67 81 175 66 56 1938 68 96 175 85 51 1939 59 113 175 108 16 1910 62 130 182 116 13 1911 61 115 191 110 12 1912 66 182 219 158 13 1913 71 226 233 165 11 1911 76 267 235 115 10 1915 77 311 237 112 12 1916 89 393 251 120 11 1917 92 502 280 167 35 1918 101 566 296 192 ‘ 27 1919 102 571 286 235 11 1950 107 565 289 263 9 1951 121 580 313 260 7 1952 131 566 317 221 ~ 1 1953 113 539 316 188 ~ 6 1951 129 511 319 120 ~ 9 1955 112 500 318 92 ~ 10 1956 118 503 327 75 ~ 10 1957 176 193 310 37 - 11 1958 191 190 353 35 - 18 1959 199 170 351 12 - 23 1960 205 163 361 18 21 1961 198 118 361 3 ~ 16 1962 206 111 368 7 ~ 20 31 Table 6 Hog-Dairy, Corn Belt--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm.Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 95 . --- 199 --- 103 1931 82 ~~~ 181 ~~~ 98 1932 66 ~~~ 171 ~~~ 92 1933 50 --- 165 --- 89 1931 56 --- 179 --- 97 1935 61 81 189 6 105 1936 67 85 192 37 101 1937 67 95 205 57 105 1938 68 113 205 90 100 1939 68 136 201 128 91 1910 68 160 213 173 95 1911 68 176 226 162 99 1912 72 222 255 186 109 1913 76 276 270 209 110 1911 85 323 272 211 107 1915 87 373 271 202 109 1916 99 155 292 236 111 1917 112 568 329 329 116 1918 126 610 316 316 117 1919 130 653 331 385 99 1950 131 619 336 121 93 1951 118 686 36 3 111 96 1952 155 687 366 397 90 1953 159 650 361 369 85 1951 159 623 366 289 83 1955 170 609 363 271 75 1956 182 603 371 220 83 1957 190 588 389 167 89 1958 193 585 102 153 89 1959 215 558 399 175 79 1960 220 539 111 120 81 1961 209 519 115 119 90 1962 217 503 120 131 89 .. -. 35 Table 7 Hog-Beef Raising, Corn Belt-~Estimated Market Value from Costa and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm.Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex.Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 69 ~~~ 101 ~~~ 18 1931 58 --- 93 ~~~ 19 1932 50 ~~~ 87 ~~~ 17 1933 M5 --- K 84 --- 16 1931 37 --- 91 --- 18 1935 38 13 96 ~ 12 21 1936 10 13 98 10 17 1937 10 18 105 7 19 1938 10 56 105 18 16 1939 10 67 105 31 11 1910 10 76 109 63 11 1911 10 85 116 51 10 1912 15 108 131 76 9 1913 50 137 139 81 6 1911 56 166 110 71 1 1915 60 191 111 39 7 1916 67 231 150 19 9 1917 71 291 170 51 2 1948 77 335 177 30 1 1919 81 332 172 59 ~ 8 1950 83 331 171 108 ~ 11 1951 95 350 187 163 ~ 23 1952 108 351 189 137 ~ 30 1953 101 332 188 119 - 31 1951 100 323 189 80 ~ 32 1955 101 317 187 38 ~ 32 1956 107 313 193 - 19 - 29 1957 116 301 201 ~ 67 ~ 28 1958 122 291 208 ~ 88 ~ 29 1959 128 282 207 - 38 ~ 36 1 o 1 5 272 213 - 51 - 33 1321 133 261 216 ~ 16 ~ 31 1962 139 255 218 ~ 31 ~ 32 Table 8 36 Hog-Beef Fattening, Corn Belt--Estimated Market Value from Costs and Returns Series, Ex Post and Ex.Ante Value Series from the Production Function and Residual Return Models for Farm.Real Estate in Dollars per.Acre 1930-1962 Coats and Returns fiPdeuction Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 133 ~~~ 286' ~~~ 205 1931 113 ~~~ 267 ~~~ 192 1932 90 --- 219 --- 179 1933 68 ~~~ 211 ~~~ 172 1931 73 ~~~ 263 ~~~ 188 1935 75 112 278 35 203 1936 79 112 285 67 200 1937 79 126 306 70 216 1938 79 116 307 111 209 1939 79 176 308 160 201 1910 79 205 323 222 210 1911 80 233 315 221 219 1912 85 291 391 285 217 1913 90 372 119 336 251 1911 97 112 125 372 251 1915 102 501 131 101 252 1916 122 605 169 161 269 19117 136 767 532 7011 271+ 1918 155 891 561 789 273 1919 167 916 519 901 231 1950 173 941 555 955 220 1951 201 1015 598 1110 211 1952 222 1071 605 1006 199 1953 211 1018 601 907 190 1951 212 989 613 691 192 1955 230 963 611 633 180 1956 229 938 632 111 197 1957 238 913 660 326 208 1958 239 881 687 291 212 1959 255 817 688 338 199 1960 262 813 707 256 208 1961 271 811 719 255 221 1962 283 788 730 218 227 .~ -- 37 Table 9 Cash Grain, Corn Belt-~Estimated Market Value from.Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per.Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 119 ~~~ 191 ~~~ 199 1931 130 ~~~ 178 ~~~ 190 1932 107 ~~~ 166 ~~~ 180 1933 88 ~~~ 161 ~~~ 175 1934 97 --- 172 --- 190 1935 99 81 180 29 200 1936 106 ~ 85 183 71 197 1937 111 105 191 127 201 1938 120 118 195 166 200 1939 111 128 195 198 197 1910 125 116 203 216 200 1911 127 165 211 211 211 1912 111 208 210 293 229 1913 116 267 251 311 239 1941 169 319 255 393 236 1915 176 369 256 102 237 1916 181 151 270 191 217 1917 206 569 299 611 259 1918 220 669 307 716 257 1919 231 659 299 718 211 1950 236 61 301 775 231 1951 278 680 321 820 213 1952 299 669 320 749 233 1953 310 607 316 660 225 1951 301 607 315 613 221 1955 309 603 309 581 211 1956 321 598 311 513 211 1957 352 585 323 522 212 1958 370 557 332 155 221 1959 391 511 330 100 220 1960 391 186 339 319 233 1961 391 112 313 283 237 1962 106 113 318 219 236 38 Table 10 Southern Piedmont Cotton--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per.Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 26 ~~~ 75 ~~~ 1 1931 22 ~~~ 69 ~~~ 5 1932 16 --- 65 --- .. 5 1933 1’4- --- 62 ....- 5 1931 19 ~-- 67 --- 3 1935 22 39 69 13 0 1936 21 11 70 30 - 3 1937 22 18 75 15 ~ 6 1938 21 53 75 19 - 6 1939 23 57 75 50 - 9 1910 23 61 78 50 ~ 12 1911 25 67 83 18 ~ 15 1912 27 87 93 37 - 17 1913 30 108 99 32 ~ 18 1911 35 126 100 1 ~ 16 1915 38 115 101 ~ 35 ~ 13 1916 13 171 108 ~ 61 - 12 1917 52 217 121 ~ 51 ~ 18 1918 55 212 128 ~ 51 ~ 21 1919 58 237 126 ~ 23 ~ 22 1950 57 228 128 ~ 9 . - 21 1951 65 238 138 ~ 7 ~ 21 1952 68 226 110 ~ 15 ~ 25 1953 72 216 138 ~ 20 ~ 25 1951 71 211 139 ~ 35 ~ 21 1955 72 206 137 - 38 - 21 1956 76 4 201 111 ~ 26 ~ 23 1957 82 198 116 ~ 51 ~ 20 1958 83 197 151 - 73 - 17 1959 92 192 150 - 16 - 20 1960 98 191 151 - 11 ~ 19 1961 101 188 156 ~ 56 ~ 17 1962 110 186 158 ~ 31 ~ 18 \/ l 1 ~4 I. 39 Table 11 Texas Black Prairie Cotton-dEstimated Market Value from.Costs and Returns Series, Ex Post and Ex.Ante Value Series from.the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex.Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 81 ~~~ 111 ~~~ 39 1931 66 ~~~ 103 ~~~ 37 1932 17 --- 97 --- 35 1933 15 --- 93 --- 32 1931 19 ~~~ 100 ~~~ 28 1935 51 51 101 58 23 1936 52 51 107 86 16 1937 56 60 111 121 11 1938 55 68 111 136 6 1939 50 72 111 136 1 1910 51 78 119 136 ~ 5 1911 19 88 127 133 ~ 10 1912 55 111 113 127 ~ 15 1913 53 111 153 100 ~ 15 1911 59 167 155 63 ~ 16 1915 59 193 156 10 ~ 13 1916 71 231 168 - 36 - 11 1917 79 296 188 ~ 50 ~ 17 1918 91 313 198 1 - 28 1919 88 353 192 21 - 32 1950 97 363 192 79 - 10 1951 113 106 201 137 ~ 51 1952 108 110 201 101 ~ 52 1953 111 108 199 55 - 55 1951 120 117 196 16 ~ 60 1955 125 101 193 - 11 - 56 1956 136 397 196 - 62 ~ 57 1957 131 381 203 ~118 ~ 17 1958 112 351 209 ~166 ~ 12 1959 155 311 208 ~165 ~ 13 1960 157 298 213 -l63 - 10 1961 159 272 216 ~150 - 39 1962 159 261 218 ~ 89 ~ 11 u. -- 10 Table 12 Northern Plains, Wheat-Small Grain-Livestook-«Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 27 ~~~ 12 ~~~ 23 1931 25 --- 39 --- 23 1932 21 ~~~ 36 ~~~ 23 1933 19 --- 35 --- 23 1931 19 --- 38 --- 25 1935 19 19 10 ~ 15 28 1936 19 20 10 ~ 12 29 1937 19 23 7 13 - 13 33 1938 19 27 13 - 11 34 1939 17 29 43 - 10 35 1910 11 31 11 ~ 2 36 1911 11 37 17 1 38 1912 11 18 53 25 12 1913 15 56 56 11 13 1911 18 68 56 72 10 1915 19 78 57 87 39 1916 21 96 60 118 38 1917 21 121 67 155 38 1918 28 111 69 203 31 1919 30 115 67 192 27 1950 29 115 67 179 27 1951 32 153 71 176 26 1952 36 118 72 159 22 1953 37 133 71 98 23 1951 37 129 72 68 21 1955 36 123 71 52 26 1956 37 121 73 17 25 1957 10 120 76 13 22 1958 11 119 78 11 21 1959 18 110 78 51 22 1 0 1 109 79 52 26 1361 5% 102 80 38 26 1962 52 100 81 - 1 33 ’4 \/ .n-uufl ~ .~ .~ :r‘ . .. r. .1".- . . ~ 1-- v. 1 x . ~ \ 11 Table 13 Northern Plains Wheat-Corn-Livestock--Estimated Market Value from Costa and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 33 --- 51 --- 29 1931 30 ~~~ 17 ~~~ 27 1932 25 ~~~ 11 ~~~ . 26 1933 23 --- 42 --- 25 1931 21 -~- 16 ~~~ 29 1935 23 21 18 ~ 11 33 1936 21 22 19 ~ 9 33 1937 23 2h 53 - 15 38 1938 22 30 52 - 15 39 1939 20 35 52 - 1 38 1910 18 10 51 13 39 1911 18 11 58 16 11 1912 19 56 65 16 11 1913 20 66 69 78 13 1911 21 80 70 92 12 1915 27 93 70 98 11 1916 29 111 71 130 39 1917 32 116 83 175 38 1918 38 171 86 211 29 1919 11 176 83 229 22 1950 10 176 83 211 22 1951 11 188 89 200 22 1952 50 186 89 186 16 1953 50 170 88 103 19 1951 19 161 89 62 20 1955 50 158 88 56 21 1956 51 156 90 23 26 1957 51 152 93 - 22 29 1958 59 151 95 2 29 1959 61 113 95 29 21 1960 65 139 97 9 30 1961 66 129 98 32 30 1962 69 121 100 39 31 12 Table 11 Northern Plains‘WheatéRoughage-Livestock--Estimated Market Value from Costsjand Returns Series, Ex Post and Ex.Ante Value Series from.the Production Function and Residual Return Models for Farm Real Estate in Dollars per.Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 21 ~~~ 28 ~~~ 6 1931 18 ~~~ 26 ~~~ 7 1932 15 ~~~ 21 --- 8 1933 13 --- 23 --- 8 1931 13 ~~~ 25 ~~~ 10 1935 13 13 27 ~ 20 12 1936 13 11 27 ~ 17 13 1937 13 15 29 ~ 22 16 1938 13 18 28 ~ 27 18 1939 11 19 29 ~ 21. 18 1910 10 23 30 ~ 16 19 1911 10 21 31 ~ 16 20 1912 11 31 35 7 21 1913 11 36 38 31 21 1911 11 13 38 19 19 1915 15 50 38 60 18 1916 17 62 11 78 17 1917 18 79 15 99 15 1918 22 93 17 121 9 1919 21 95 16 121 5 1950 23 95 16 108 5 1951 26 102 19 107 1 1952 29 100 19 91 1 1953 29 93 19 11 3 1951 29 90 18 21 3 1955 29 87 18 17 1 1956 30 86 19 8 1 1957 32 85 51 - 15 5 1958 3h 83 52 - 3 5 1959 37 79 52 2 h 1960 38 78 53 ~ 10 8 1961 38 71 51 - 6 7 1962 10 68 51 ~ 11 12 .- u-c o 'w -. \_/ 43 Table 15 Southern Plains Winter Wheat--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Eannte Ex Post (1) (2) (3) (1) (5) 1930 12 ~~~ 55 ~~~ 51 1931 38 ~~~ 51 ~~~ 50 1932 33 --- 17 ~-- 17 1933 26 --- 16 ___ #7 1931 27 --- 19 --- 53 1935 27 27 51 2 57 1936 27 28 52 0 59 1937 29 31 55 1 63 1938 29 11 51 7 65 1939 28 11 51 17 66 1910 27 19 56 19 71 1911 27 52 59 19 77 1912 29 61 66 36 86 1913 33 75 71 73 90 1911 38 89 71 97 90 196 11 1&1 70 123 91 1916 18 127 71 165 97 1917 57 159 83 217 105 1918 66 181 86 289 98 1919 69 177 81 287 95 1950 68 173 81 282 97 1951 77 181 90 305 102 1952 81 179 91 266 105 1953 86 169 89 231 95 1951 80 166 90 200 100 1955 83 162 89 200 100 1956 85 159 91 165 107 1957 86 153 91 110 118 1958 91 116 98 70 126 1959 91 138 97 111 121 1 0 132 100 99 127 1321 33 121 101 116 129 1962 101 119 103 111 131 1 \ I, .....-- ....-_ . r- H .c. ..—..... 1 0 1 . \ 1 A \4/ 11 Table 16 Southern Plains‘Wheat-Grain-Sorghums-dEstimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm.Rea1 Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex.Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 12 ~~~ 11 -~- 29 1931 11 --- 1O --- 28 1932 39 ~~~ 38 --- 27 1933 28 ~~~ 36 ~~~ 29 1931 26 --- 39 ~-- 33 1935 25 21 11 ~ 10 36 1936 25 22 11 ~ 13 39 1937 25 25 11 - 12 12 1938 21 30 11 ~ 3 13 1939 21 32 11 6 11 1910 23 36 16 12 17 1911 23 38 19 19 51 1912 25 50 55 32 58 1913 29 57 59, 18 62 1911 31 69 59 19 65 1915 11 82 59 78 61 1916 17 100 63 101 68 1917 55 126 71 131 76 1918 65 117 71 206 70 1919 71 116 72 226 68 1950 79 111 72 219 56 1951 81 150 78 221 71 1952 85 118 79 197 79 1953 89 137 79 121 80 1951 89 131 80 75 90 1 89 ~ 121 81 26 97 1322 87 118 81 ~ 1 108 1957 87 1L2 88 ~ 27 121 1958 87 102 93 - 11 132 1959 95 98 93 21 130 1960 101 100 96 61 133 1961 106 101 97 111 133 1962 111 106 98 153; 133 .. I _.-..- —~'. g..- .H —-4. I‘M“ \_.. ,. 4 ‘4 I . ‘1 x.» ~ 1 1 ‘ 1 4 \. \v/ 15 Table 17 ‘Wheat-Fallow, Washington and Oregon-~Estimated Market Value from Costs and Returns Series, Ex Post and Ex.Ante Value Series from.the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex.Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 21 ~~~ *11 ~~~ 39 1931 18 ~~~ 11 ~~~ 39 1932 16 --- 38 --- 39 1933 18 ~~~ 36 ~~~ 10 1931 19 --- 39 --- 11 1935 17 22 10 ~ 15 17 1936 19 21 11 ~ 7 18 1937 21 29 13 10 50 1938 18 33 13 21 51 1939 20 35 13 21 52 1910 20 39 11 29 55 1911 18 11 17 27 60 1912 20 19 53 31 67 1913 21 58 56 12 72 1911 22 67 57 62 73 1915 26 71 58 79 73 1916 32 87 62 105 79 1917 39 108 70 159 81 1918 10 125 71 192 86 1919 51 128 72 213 79 1950 52 131 72 213 80 1951 62 111 77 210 81 1952 66 116 78 220 82 1953 71 112 77 208 80 1951 73 113 77 190 78 1955 75 111 75 191 75 1956 77 111 77 165 80 1957 76 110 79 110 85 1958 78 135 81 139 85 1959 85 121 81 119 81 1960 85 117 83 107 86 1961 87 109 81 111 87 1962 88 102 85 112 89 16 Table 18 Northern Plains Cattle--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from.the Production Function and Residual Return Models for Farm Real Estate in Dollars per.Acre 1930-1962 H-_._.—_._. Costs and Returns Production Funétion Residual—Return ‘Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1+) (5) 1930 6 ~~~ 8 ~~~ 2 1931 5 --- 8 --- 2 1932 1 ~~~ 7 ~~~ 2 1933 )4- --- 7 -.... 2 1931 1 ~~~ 8 ~~~ 2 1935 3 1 8 ~ 1 2 1936 3 1 8 - 3 3 1937 1 1 9 - 1 3 1938 1 5 9 ~ 6 1 1939 1 6 9 - 7 1 1910 3 8 9 ~ 5 1 19a 3 9 10 - 3 1 1912 1 11 11 2 5 1913 1 12 12 9 1 1911 5 11 12 11 1 1915 6 11 12 12 1 1916 6 16 13 11 1 1917 8 20 15 18 1 1918 9 22 16 20 3 1919 9 21 16 22 2 1950 9 25 16 20 2 1951 10 29 16 21 2 1952 11 33 16 21 1 1953 11 33 16 19 0 1951 10 31 17 12 0 1955 10 29 17 12 0 1956 10 28 17 8 1 1957 10 21 18 ~ 2 1 1958 11 22 19 ~ 5 1 1959 11 22 19 2 1 1960 11 21 19 ~ 2 1 1961 11 23 20 ~ 1 .1 1962 11 22 20 2 1 17 Table 19 Intermountain Region Cattle--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Costs and Returns Production Function Residual Return Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) (2) (3) (1) (5) 1930 12 ~~~ 25 ~~~ 20 1931 12 ~~~ 23 ~~~ 18 1932 10 ~~~ 21 ~~~ 17 1933 8 ~~~ 21 ~~~ 17 19311 9 --- 22 --- 18 1935 9 12 21 5 20 1936 9 12 21 5 20 1937 9 12 26 8 21 1938 9 13 27 11 21 1939 9 15 27 15 20 1910 9 18 28 21 21 1911 9 20 30 27 22 1912 ‘10 26 31 35 21 1913 11 32 37 39 25 1911 12 36 38 38 26 1915 13 39 39 38 27 1916 15 17 12 11 29 1917 17 56 19 15 33 1918 16 63 53 51 31 1919 16 66 53 61 32 1950 16 69 51 68 32 1951 17 80 58 79 31 1952 18 92 58 96 31 1953 18 97 57 93 30 1951 17 93 59 73 32 1955 17 91 59 62 31 1956 17 87 62 16 37 1957 17 77 66 19 10 1958 18 72 69 12 12 1959 19 75 69 30 10 1960 20 79 70 15 10 1961 21 81 71 17 13 1962 21 81 72 50 13 ‘./ 18 Table 20 Northern Plains Sheep--Estimated Market Value from Costs and Returns Series, Ex Post and Ex Ante Value Series from the Production Function and Residual Return Models for Farm Real Estate in Dollars per Acre 1930-1962 Market Value Estimates Estimates Year Estimate Ex Ante Ex Post Ex Ante Ex Post (1) .(2) (3) (1) (5) 1930 6 ~~~ 12 ~~~ 7 1931 5 --- 11 --- 7 1932 1 ~~~ 10 ~~~ 6 1933 3 --- 10 --- 6 1931 3 ~~~ 10 ~~~ 6 1935 3 7 11 3 7 1936 3 7 11 3 7 1937 3 8 11 1 7 1938 3 10 11 5 7 1939 3 10 11 1 7 1910 3 11 11 5 7 1911 3 13 12 7 8 1912 1 16 13 11 8 1913 1 18 11 16 8 1911 1 20 11 18 8 1915 5 22 11 18 8 1916 6 21 15 21 9 1917 7 27 17 21 9 1918 8 29 18 25 9 1919 8 30 18 21 9 1950 7 30 18 21 10 1951 8 31 19 25 9 1952 9 10 18 33 7 1953 9 11 18 28 8 1951 9 39 18 23 8 1955 9 37 18 21 8 1956 9 35 18 17 9 1957 9 30 19 1 10 1958 9 29 19 8 10 1959 10 28 19 13 9 1960 10 28 20 11 9 1961 10 27 20 11 9 1962 10 21 20 13 10 19 .muoaaov uamuuso cw moauom nonuo “00H u mdmanhzmfi no woman moxmwcH m: . I no . m 0 .N mm . NN NNH mm QMH N8." m1. .. 8. - 6mg- £29 3 a 69 89 ~#. I Hm.m 0min MOJN ONH mm mNH 08.“ oo. mm.m . mm.m- mn.ma mod mm mHH mmmfi mm. mm.m sm.m :m.Hm mmH mm mmH mama 0m.H :1. ido.m sm.wa moH mm mHH Emma mm. - mm. ma. - mn.ma mm mm noH mama :m. op. :o.H mo.m~ mm om mm mmma sm. - so. - Ho.H- #6.:H mm mm mm :an :m.m- mo. mm.m- mw.n~ mm om mm mama 00. :m. - 4m. - :m.>fi moH moH mm mama m4.m mm. ow.m mo.wH moH moH ooH Hmmfl an. - 0:. HH. . mm.nH pm mm mm ommH wd oMl Nm 0H I ow o+~l mm o “H mm mm mm maH mm.m om. ma.m mH.om nag NEH moH mama ~m.~ op. - Fm. mo.>~ pm mm HoH wjmfl Po.m mH.H - 1m.H 61.6H 1m mm 66H 61mg 6:. mo. m4. mm.1fl mm m» :HH nJmH ma. mm. >H.H 6H.:H om as :HH ::mH ®O.N on. .. mm; mm.NH +3 ON. 8H Tam.“ :w.~ 4m. ww.m mm.HH mm mm oHH mama om.H mm. m>.H ~6.w m: m: Hod asmfi mm. mm. mm. mm .0 0.: N: mm 0+5." OO . mm . mm . PO .6 mm mm .nm mmmH N0 . 1 mm . mo . NP . 0 mm mm mm wmma HH. H:.H mm.H mo.m mm N: NE Emma mo. Hm. mm.H ma.m mm mm mp mmma 6:. Ho. - ms. mm.m mm :m we 4mma mm. ms. - ms. - 5:.m om om mm mmma mm..n.. NH . ENSHI mm.m mm mm ow NmmH o~.H- mm. - mo.m- om.n on mm we Hmmfi mm.~ . m: on mm ommH unocommou ucoaomaoo m>z_ca m>z MovaH m>z xmvcH ooaum xmvcH mm: know oowum hua>auosvoum owauso hammow mowuom woumaom van muonvonm o=Hm> Hana no: Humowun «an ammonuuoz Huuuaoo Hm manna 50 .muuHHov uaouuao cw mowuom Honuo ”00H 1 m:manhdma no woman moxocaH mm. em.m :m.m mm.mm mm: mm 0:: mmm: Hm. o:.m- me.:- mm.om NH: Hm :m: Hem: mH.H mm.m no.n sp.wm om: om :m: com: wH.H- mm.m- Pm.:- m~.mm mm mm ma: mmm: om. mm.m mm.m mm.wm was om Hm: mam: ms. :1. m mm.m ”1.:m mo: mm 1:: 16m: on. :m. :6. 2 .8 mm mm :2 83 mp. 61. mo. - :m.om mm mm mo: mom: m:.m- NH. - nm.m- sm.om mm mm mm :nm: Hw.m- NH. m:.m- mw.mm :m mm co: mom: ms. :0. - ms. - H:.nm no: mo: mm mom: ::.: mm. mm.n :w.nm mo: mo: mm Ham: 6:. m:. - mm. m:.om mm om mm 0mm: Hm.m- H6.H- mn.w- m:.om :m mm mm m:mH ::.m 66. H:.m :w.wm m:: m:: mo: m:m: mm.H mo. 3 :m.H mm.:m aoH mm moH >:ma mm.: m:.:. mm.m mm.mm mm Hm mo: m:m: :m. m». - :m. - om.wH ms ms 0:: n:m: oo. ow.: Hm.H ::.m: om 6» :a: ::m: mw.m m:.:- 66.: mm.~: me Oh mo: m:mH m:.m mm.m mm.: 16.6: mm mm an: m:m: m6.m mm. mo.m mm.o: 6: m: :m H:mH am. my. hm.: Pm.s mm hm om o:m: pm. mm. mm. - om.m mm :m Hm mmm: mo.Hn :w. mm. a mw.m mm pm mp wmma m:. 66.: Hw.H am.» On m: mm 1mm: pm. ~:.:- om. - om.n mm m: mm 6mm: mH.H mm. m:.m om.m mm mm mm mmma :n. 66. :m. mo.: 1: 6m pm :mm: om. my. - w:. :m.m 6: mm pm mmm: mm.:- mo. om.:- «0.: N: .:m o» mmm: m:.m- so. - mm.m- mm.m mm mm op :mm: so.» mm n:nt on 0mm: ucoaomaoo unocomaoo m>z ca m>z xowaH m>z NowsH oownm NovaH was“ How? oowum muw>auoavoum owcmsu manna» magnum voumeom can muosooum o=Ho>_Hq:H no: [Huoowwn “on swmaoomws_cwoummm mm «Hana 51 .mumHHov uaouuso ca magnum Honuo “00H u m:man1:ma no woman moxovaH 6:. - no.N mm.H 1m.NH NHH mm 1NH NmmH mm. N6.H- mo.H- No.1H NoH Hm mHH HmmH 6:. Nm.H N1.H HH.NH moH mm :NH ommH mm. I mm.NI NH.MI mm.mH mm mm BAH mmma NN. oH.N NN.N mm.mH wHH om HmH NmmH :1. N:.H NN.N :m.mH ooH mm 1HH 1an 1H. mN. 6:. N:.:H 1m Nm moH mmmH Fm. OP. mo. mm.mH :w Hm 30H mmmH 1:.H- 1o. o:.H- mm.mH :N mm mm :mmH 0N.HI ma. ww.HI mm.mH mm #0 mm mmmfi Hm. - om. - HH.H- mm.wH NoH noH mm NmmH No.m mm. Mb.m OH.®H mOH OHH mm Mama Hm. mN. om. 1m.:H 1N Hm mm ommH Pm.MI mm.HI MN.MI NN.MH mm mm mm meH mm.N om. mm.H oo.mH :HH mHH NoH N:mH NN.H Ho. - HN.H :o.1H moH mm moH 1:mH wm.m m@. I mm.N mm.mH mm Hm MOH meH mm. nN. - om. om.NH N1 H1 oHH n:mH mH. - mm. :m. ow.NH 61 mm NHH ::mH 3m.H H#. I mn.H mm.HH NP mm mOH mzma :m.H :m.H NN.m m:.oH mm mm moH N:mH mm.H No. - 1N.H mH11 m: N: om H:mH NN. mm. Hm. NN.m Nm mm Hm o:mH nN. - HN. :o. - 1m.: 6N mm om mmmH mm. I mm. 0:. Hd.# EN hm 0N wmmH OH. MN.H mm.H N®.: mm m: ON Emma o:. NH.H- N1. Nn.m HN H: No mmmH NO.H ®#. mm.H JN.: 0N PM aw mmma cm. No. . NN. mm.N 6H 1N ow :mmH :N. mm. - Hm. H:.N mH :N Hm mmmH wo.H- mH. mm. No.m NH NN mm NmmH :®;HI mo. I N®.HI mmnm mm Om mp Hmma unocomaoo unocomaoo m>= ca m>z wovaH m>= novaH mowum .-.nuvww: mm: wwmw :ooHum ..-:: hua>auo=©oum owcmao hHummw 52 .mumHHov ucouuao :H mowuom nonuo mooH u m:mHu1:mH no woman moxowaH moauomrvouoawm.onm mwmnuoum.ome>.H¢cwmwmz.meoMLIWumedmfiz-:Hmmu wwwnn :w-oaan oH.H- :o.m :m.H 1:.nN NNH 1N H:H NmmH om.H ::.N- :m. . .mn.mN mHH Hm nNH HmmH mN.H- NN.N- mo.:- mm.6N mm NN ONH mmmH om.H :6.N :m.m m:.:N NHH mm NmH NmmH O1. . NN.H Nm.N Hm.0N mm :N NHH 1mmH NN. - Nm. oH. mm.1H mm Hm 1oH mmmH mo.H- no.H Ho. - mw.1H mm NN moH nmmH mN.H- mo. - Hm.H- om.1H mm 1N mm :an mm. - :0. m1. - HN.mH mm mm ooH mmmH #N.HI ha. I mm.HI 00.0N mm OOH mm Nnma m:.m :0. mm. mm.HN WOH 00H OOH Han 0N. :H. :m. Hm.1H mm mm mm ommH mJoJI .JmoHl MOoWI NN.N.H mm mm mm meH ON.H MN. I N:.H Om.mm NHH OHH NOH ®#ma mm.H w:. - 1:.H mN.HN moH NoH moH 1:mH 6N.N mN. - mm.N mm.6N mm mm moH m:mH mH.N :m. mm.N m1.1H mm ow 10H n:mH NN. I Mb. mm. 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H1813?” as... .8838 3.838- ,N 033 56 .NHoHHov uaouuso cw moHuom nonuo “OOH I meHuban so comma moxovaa mm. ::.N No.: mo.NN noH 6N HNH NNNH Nm.H NN.H- Ho. - No.HN NN N1 NHH HNNH 1n. . mNH NNH 1o.HN NN H1 :NH 009 m:.H- HN.N- NN.:- HN.NH NN m1 :HH NNNH oo. 1N.N 1N.N 1o.:N HoH N1 NNH NmmH :N.H- NN.H N:. 01.HN Hm N1 1HH 1mmH NN. - NH. mo. - HN.HN 1N NN 16H NNNH 1N.H- 1N.H 01. - om.HN mN :N NoH NNNH om.Hu Ho. I HN.H: oo.mm mm mm 00H :mmH N:. - NN. - 0N. - HN.NN 1m 1m ooH NmmH NH.N- HN. - 6:.N- Ho.:N ooH mm NoH NNNH 8N HN.H HN.: H:.mN 0: N3 N3 HmmH ON.N No. NN.N 0N.NN mm 1m om ommH NN.H- N:.H- NH.N- 1m.NH m1 NN mm N:NH Ho.m- N:. NN.N- NH.NN mm om NoH N:mH oH.N mm. - 1H.1 :N.om NNH 1NH HoH 1:NH NN.: NN. .. 1H.: 1:.mN Nm :m :2 33 om.H ::. :N.H NN.NH HN N1 16H m:mH :N . NN . 16 .H Nm .5 N1 61 :2 ::NH 1H.N NN. .. 12H HN.NH NN 8 mm 21H HN.N mm.H NN.N N1.:H NN 6N NoH N:NH 1m.N on. 1:.m om.oH N: on Hm H:mH HN. NN. NN. N:.1 Hm NN 1N o:mH 1m} :N. Hm. :m.N 1N mm NN mmmH H1. a mm. mo. 1 mw.n :m 0m m1 wmmH H1.N- No.N NN. - NN.N :N :m 01. 1NNH NN.N No.N- 0N. :m.N 1N mm on NNNH m:. NH.H 1N.H :H.N NN 1m 01 NNNH Nm. Nm. - ON. 1a.: NH :m Nm :NNH 8N 8.7 mm. 1m.: NH 6m HN mmmH 1n. - 16. on. - N:.N :H NH ON NNNH 1m.m- Ho. - NN.N- NN.N NH HN N1 HNmH om.1 Hm mm N1 onH ucocoqaoo unocomaoo m>z aH m>z wovcH m>z xowaH ooHnm wovaH mm: uwow ooHum muw>auo=coum owcono NHumow moHuom woumHom can Nuosuoum osHo> chwmmuzwanmowwnuHom auou .chuw ammo mm onMH .mHmHHov uaouuso :H mowuom Hanna 00H u m:mHI1:mH no woman moxmvcH 57 moHuom wouwHom was muwswonm uwHowszawHH z 1wwwwwuumow 86 6883 8.886-. AN .381 NN. NN. 6N.H 6N.HH :NH N6H HNH N66H NN. 66. - NH. - 6H.6H NHH m6 HNH H66H 66. N6. N6. NN.oH NNH 66 1NH 666H :1. - :H.H- NN.H- 66.6 :HH 6m NHH m6mH :1. :N.H N6.N N6.HH 6NH N6H NNH N66H 00. mm. mm. 0:.m HHH mm 0H.” Emma wfi . I NH . I mm . I on. w OOH 0m #0." mnma :6. .. :H . 6:. .. 6N.N :2 N6 66H 666H mm. no . HI NN. . 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HN ON 0.: Hmma HN.N mm wuNN NHH 6N6H unocomaoo unocomaoo m>z.cH m>z xovsH m>z_ wovaH oowum wwwaH mm: HowM onum auH>Huoavoum owamno hHumoM 58 .manHov ucouuao aH moHHom Honuo “OOH u mdeI1:mH no woman moxovaH 6H. - 16.H H .H :H.6H 66H 66 1HH N66H 6N.H N6.H- 1m. NN.NH N6 H6 N6H H66H 0H . I mp. mm. CNNMH mm mm mm." ome 6N. - HN. H6.H- 16.NH H6 :N N6H 666H :6. H6. 66.H N6.:H N6H 6N 6HH N66H 66. 66.H- 66.H- 6N.HH NN NN :6 666H mm.HI HN.H NH. dm.ma mm mm NOH mmmH 0H. Pm.MI Hm.MI N:.ma :0 mm #m :mmH NN.NI NN.N mm. mm.©H QHH mm ONH mmma 6H. NN.H N:.H N6.1H NNH 6HH 66H N66H :H. mo.H mN.H oH.NH NHH 6HH N6 H66H 61.H- 66. 6H.H- N:.NH :6 66 :6H 6:6H :N. N:. 6N. N6.:H N6H N6H 66H N:6H 00. NM?" ®H .N #03:.” .JOH ®OH Wm PJQH 6N.N N6. - NN.N 61.NH 6N N6H 6N 6:6H 6N. N6. H:. N:. 6 66 N1 66 6:6H 6N. 6H. N:. 16.6 N6 61 66 ::6H 6H.H NH. - N6.H 66.N 66 N6 6N N:6H NN. Pm. mm.H N©.N mm mm om Ndma mm.N wN. I mN.N mm.m H# mm mp H£QH 66. NN. NN. N6.N 6N HN NN 6:6H NN. 66. HN. 6:.N :N HN N1 6N6H NN. - HH. - ::. - 6H.N NN 6N 61 NN6H Hm. I mn.HI Nw. mn.m nN Nm mh Nmma 1:. :6.16. . 16.N HN N: 6: 6N6H m3. PM. NH. I :O.m HN mm mm mmma NN.H mm. I mm. wH.m NN N: Nm :mmH :m. Hm. I mm. NN.N ma wN mm mmma :N. om o I 8 6 I .JNoN 0H ON wN. NmmH HH.: 1m 16m mm ommH ucosomaoo unocomaoo m>z.cH m>z xovcH m>z NovcH moHum MovcH mmz“ ummm moaum huH>Huo=vonm owamso thmow NoHuom wouuHmM 6cm Nuosvoum osHu> HuaH. .uqz Huoowwuaouuoo owunum MomHm meoH: 0 oHnu 59 .NHQHHou uaounso cH moHuom Honuo mooH u m:mHI1:mH no woman moxovaH 6H.H- .N. N. - NN.6 66 HN NHH N66H H6.H m6. .. 16:6. 16.6 66H 66 H6H H66H N6. - 66. N6. H6.: NN N1 HNH 666H NM 6 NN. 6 I 0.36 I m®6+~ Fm mm 8H mmmfi nN . I Hm . mm . mN . 0 mm R NNH wmmH HH. N:. 66. N6.: NN NN N6H 166H HH. 1H. - 66. - :N.: N1 6N 16 666H HN. - 6N. NH. 6:.: 61 N1 H6H 666H m1: 6 I mH 6 I NW 6 I mm 6 +~ mN. mm mm id 6N. - NH. :H. - 6N.: 1N H6 66 N66H HH6 0N6 I m06 I mo6m om mm #m Nan HN. NN. N:. NH.6 N6 :6 N6 H66H 6H. N6. NH. 66.: :N 66 :6 666H wm. I Pm. I mm. I vhf: Nm mm mm maH NN6HI ®O6 mH6HI Hn6n mm mm :0.“ mann N6.H 6H. N6.H 61.6 6NH 1HH N6H 1:6H 1N.H 6N. .. N6. N6.6 66 66 66H 6:6H NN. 66. NN. :6.: N1 N6 16H 6:6H 66. N6. N6. N1.N 16 :6 66H ::6H NN. 6H. - N6. 66.N 66 N6 :oH N:6H 0.: . mN . mm . mm . N mm m: 00H N:.HmH 16. 6H. N1. 6N.N H: N: N6 H:6H 66. 16. 16. 66.H NN HN H6 6:6H wH . mo 6 EN . Q: . H NN Hm mm mmmH H6. - 66. N6. - NN.H NN 1N HN NN6H mm . Nw . I N: . I OH . H ON 0.: on mmmH mm . mm . :N. . NMH ®N Hm Hm mmmH HH 6 I +~O 6 I “H 6 6.. mm 6 “H mm mm 4mm.” H:. NN. - 66. N6. NH 6N N6 NN6H mm 6 I m." 6 . mm 6 I mm 6 W." NHH a NmmH mN. I mH. I +3. I .nH.H HN MN mm HmmH 11. 66.H NN HN N6 6N6H uflmflomfiou ufichnHEOU mam HHH mam Nova—H ME N025“ 00H; MOHVGH mm: HQOM. ooHum huH>Huonvoum owaonu hHuqu -moHuom 6wuuHow vow Nwonvonm wsHu> HucwHunz Huuowuuxooumo>HHImHauu HHwamrwmosz -mamem suwnuuoz H ,aHnwa THE: 6 II I II C I O 0 II 0 I I. O 6 O n I I o . I 6 O _ I. O I I ll 0 O 6 66 I .. I6 0 . z I _ II 0 1 fl 0 II- I 6 I I \. I 6O .maoHHov acouuso :H uoHuom quuo «00H I m:mHI1:mH no woman moxovaH . 66.1 H 6N NNH N66H N6. 1N. 66. :H.6 mm :N 16H H66H 66. 66. - 6H. :6.6 NN 61 NHH 666H N6. - 61. N6. 66.6 1N :N :6H 666H :N. 6H.H- 66. - H6.6 H6H HN 6NH N66H 6N. NH. N:. N:.6 66 N1 NNH 166H NN. 66. N6. 66.6 HN :1 6HH 666H N6. NH. 6N. N6.6 N1 N1 N6H 666H N6. - 1N. 6N. - N6.6 NN NN 66H :66H H:. - N6. - N:. - 66.6 NN NN H6H N66H 6N. - No. - NN. - N:.6 :6 m6 H6H N66H 6N. - NH. - 1:. - 66.6 H6H N6 N6H H66H :6. N:. 66. :6.6 1N 66 16 666H 6N. 66. HN. N6.6 NN 6N 66 6:6H NN. - N:. - 6N.H- NN.6 66H N6 N6H N:6H 6N.H- 6H. 6H.H- N6.N 1HH 6HH H6H 1:6H 66.H NH. 16. 66.6 66 6N 66H 6:6H HN.H 1H. - :H.H N6.: N1 61 66H 6:6H 6N. 6H. 1:. 6:.: 66 66 66 ::6H 66. - NH. 6H. 66.: N6 16 66 N:6H H6.H 6N. - N1. N6.N N6 N6 N6H N:6H N6. 6:. N6.H 66.N NN N: 6N H:6H :6. 6H. 66. 11.H 6N N6 H6 6:6H 66. 66. NH. 66.H :N Hm N1 6N6H 66. 66. 66. 66.H NN H6 61 NN6H H6. - N6. 66. - 6H.N N6 6: :1 166H HN. 11. N6. 6N.H NH NN 6: 6N6H 6N. 61. - N66 - N1.H 6N N6 61 666H 66. NH. :1. 66. :H HN 66 :N6H 66. NH. 6N. 61. HH 6H H6 NN6H 66. NN. - :H. - N6. :H 1H 6N NN6H N:. - N6. 6:. - NN.H 6N 6N 11 HN6H 66. - 1H. - N1 - 6H.N HN 6N 6N 6wa ucoaomaou unocommoo m>zflaH m>z Nommm m>z xoccH ooHum wovaH mmz. Mao» ooHum huH>Huosvonm owamno NHuumw I 2 ,1 . . . ’l 6 - 1. f I . . .1. 4 . . 6 I , 6.. . /. I r 1 ll I « . r . . x / . . .v . . 61 .NHNHHov ucouuao :H ooHuom nonuo mooH u m:mHI1:mH no woman mmxmvcH N106 m.:. I :HN6 N.#.m mm +Hm HOH HWQH 006 I mm 6 W." 6 I MN6m mm PM. NHH 08H 6N. 61. - H6. - 6N.N N6 H6 N6H 666H NH . Hm. ::. 66 .N 16H 66 6NH N66H :6. - N6. H6. - 6H.N 6N 11 HHH 666H NN. .. :6. NH . .. N6.N :N NN N6H :66H mm. I :0. mm. I mm.m mm mm HoH mmmH HH 6 I . no 6 I mH 6 I an 6 m mm mm mm Nmmfi 6N. HN. 6:. :1.N N6H N6H H6H H66H HN. H6. NN. NN.N 66 66 66 666H mm. I ON. I Hm. I wo.m :w mm mm m+~mH 6:. - 16. - 66. - MW.” 66H 66 N6H WMMH mm. 60. mm. . 6HH NHH :oH H NN. NH. - H1. 6N.N N6 66 N6H 6:6H HH. :6. 6H. :6.N N1 N6 16H 6:6H HH. NH. NN. 6:.N N6 66 66H ::6H NN. NN. 6:. N6.H N6 6: N6H N:6H 66. N6. N6. N6. 1N NN :N 6:6H NH. :6. 6H. 66. 6N NN NN 6N6H mm . I :0 . I mm . I om . NN mm @1. wmmH 6H. ::. :6. 6H.H NN 6: HN 1N6H NO 6 mm 6 I m.: 6 I mm 6 wH mm 00 mmmfl mm . MN . N0 . mH .H Hm mm .15 nmmH MH 6 I mo 6 OH 6 I H n 6 :HH ON ON. #mm." 6N. 6N. - :6. - H6. 1H 6N 16 NN6H mm 6 I mo 6 w." 6 I 00 6 ad” PH “OH NmmH 6N. I :H. . 6N. . HN. NN :N N6 HN6H 6N.H .mm .I1N 66H 6N6H uaoammwoo unocomaou m2: :H m>z wovaH m>z NovcH ooHum xmvcm.mmz Hmmw ooHum muH>Huosvoum owamnu hHumow IIII'Illlllullnl’ll luI‘ll'lIIlI|oltlI\‘.|ll.|l IIIIII IIIII >HHIonanoqumoaz maHmHm anonunoz . oHnma .711? J I- 62 .manHov unmunso :H moHnom noguo “OOH I m:mHIN:mH no woman moxovca 66. HN. 1:.H 66.1 6NH 66 NNH N66H wm. mm. I mH. I ®H.w Nm om ®OH HmmH 1N. NN. H6. 1N.6 66H 6N NHH 666H 1o. 6o.H.. N6. .. 61.6 H6 6N NoH 666H :H. NH.H 6N.H :1.6 66H NN HNH N66H NH. 6N. :H. N:.6 6N 6N 66H 166H :N. N6. 6H. - :N.6 :N NN 66 666H H:. 6H. HN. . 66.6 6N N6 :6 666H NH. 6N. - N6. - HN.6 H6 66 N6 :66H on. OM. I ww. I m®.n Mm Nm mm Man H6. 3. H:. .. 61.6 60H 60H 03 N66H 6N. N6. ::. 66.6 :6 66H :6 666H HN. 1N. - N6. - 66.6 1N :6 N6 6:6H N6. 66. I 16.H- :N.6 N6 66 66 N:6H NH.H DH. I NO.H HM.N mHH NOH on .Nde 6N.H NN. - Ho.H 6N.6 66 66 6HH 6:6H 6N. 1H. - NH. NN.6 NN N1 6HH 6:6H No. mN. 0M. 0H.m Hm mm ONH ##mH 66. N:. N6. N6.N N6 :6 6HH N:6H MN. MM. 00.H OO.M NJ 0: MOH Hde 00. Ho. Ho. :m.H om :m om o:mH MN. NO. I 0H. Mm.H OM HM mm mMmH 6N. 6H. 66. - 11.H NN 6N N6 NN6H 66. 66. 66. 6:.N 6N 6: 6N 1N6H MN. Mm. I ON. I Om.H OM md NO QMQH N:. 6H. N6. 6H.N :N 6N 1N 6N6H mN. MO. mN. N®.H mN HM N® :MQH 6:. mm. I 1H. NN.H HN 6N HN NN6H 60. .. mo. .. 60. I 6H.H NH NH HoH NN6H 86HI mo6 I m06HI nmofl ON mH 3H HmmH - NN.N 6m .I:N 166H 6N6H unocomaoo uaoaomaoo m>z :H m>z NovnH m>z wovnH ooHnm MovaH mmzn Ham“ moHum muH>Huo=von owcmno thmow moHuom woumHom van muoavoum oaHm> HmaH Hus“ HuwoNIIumo£3_uoucH3. mchHm chosusom : oHan 3 6 .NHNHHov uaouuno :H NoHuom nonuo «OOH I m:mHIN:mH so comma moxovaH 6:. 6N. 6N.H 66.1 6NH 66 66H N66H 6N. NN. - H6. 6:.6 1HH 6N NNH H66H 66. . 66.N :6.H N:.6 1HH 6N 6:H 666H NH. 6N. - N6. - :6.: 6N NN H6H 666H 6N. 6N.H 66.H 66.6 66H 6N NHH N66H :N. - 6N. 6H. 66.N H1 61 H6 166H :H. HN. . 16. - HN.N .66 :N NN 666H 3 . 6H . 6N . NN . N 61 H N 1N 666H :N. - :6. . NN. . N6.N 66 61 :N :66H Mm. I MM. I mm. I 0&3: wa Nm mm MmmH mm . NO . I mm . OM . m Nm mOH mm HmmH HH. - 6N. - H:. - H6.: HN 66 H6 666H m+~6 I NC 6 H36 I mm 6+~ mm mm Fm meH 6:. - 66. - 66.H- NN.6 66 66H 66 N:6H HN.H 1N. N:.H NN.6 6HH N6H 16H 1:6H 66. HN. - N6. 66.: NN 1N N6H 6:6H 66. 66. - 66. NN.: 61 61 66H 6:6H 00. N:. N:. NN.: wN mm HHH ::mH 66. 66. 61. 61.N N6 66 N6 N:6H H6. 6H. 6N. 66.N 66 16 16 N:6H N6. NH. 61. 6N.N H: 6: 66 H:6H 66. N6. N6. 66.H NN NN 6N 6:6H NH. N6. - 6H. 1:.H 6N NN 6N 6N6H :6. 6 N6. H6. . 1N.H 6N 6N NN NN6H n: . Nm . . Nm . mm . H wM .:.: Nw NMmH mH . I ::. I MO. I Ho.H wH NM Nm WMmH :N. :N. N:. :6.H 6N 1N 6N 6N6H 6N. NH. - N6. 6H.H HN HN 16 :N6H 6N. 6H. - 6N. :H.H HN 6N 6N NN6H 6o . .. 6H . .. 6H . .. NN. 6H NH NN NN6H H6. 6 N6. NN. - NN.H 6H 6m N6 HN6H H .H N unocomaoo unocomaoo mhz :H m>z Novmmm>z NovmmmooHum xwvmmmmz_ mwmw -ooHum. NuH>Huosvonm «woman NHHNNM moHumw-mmumHom wmw uuoavoum osHm> HmaHHuwz. HuwoMIrmannuuomrchuoruoonz. wchHm mwosuaom 61 .NHNHHov uaouuoo cH moHuoN Hanuo «OOH I m:mHI1:mH no woman Noxocca NH. . 16. H6.6 6HH N6 6NH N66H HN. HN. - 6H. - ::.6 66H 66 6HH H66H 66. 66. 66. :6.6 16H 6N 6NH 666H NH. - 66. - N1. . NN.: :6 :N NHH 666H 6N. - 1N. NH. H6.6 N6H 6N 6NH N66H NH. :N. 6:. 6:.6 66H 66 NHH 166H HH. - 6H. :6. N6.6 16 NN 6HH 666H 66. - 6H. 66. - 66.: 66 66 16H 666H 66 . 66 . .. 66 . .. 6:. 6 66H H6H 66H :66H NN. - H6. HN. - 66.6 16H H6H 66H N66H HH. - 6H. H6. - 61.6 HHH 66H 66H N66H N:. HN. 66. 11.6 HHH 16H :6H H66H 6N. N6. NN. N6.6 N6 N6 66H 666H HN. - 6H. - 6N. - 6N.: N6 N6 66 6:6H N6. - NN. 6:. - 6H.6 66 16 N6H N:6H NN.H :6. - NH.H 66.6 N6H 6HH N6 1:6H N6. N6. - 66. N:.: 6N 6N 66 6:6H HN. N6. - NH. N6.N N6 N6 66H 6:6H 6H. 66. 6H. :N.N :6 :6 H6H ::6H 66. HN. - 6N. NH.N H6 H6 66H N:6H 66. 6H. 66. 6N.N 66 66 HHH N:6H :6. N6. 66. NN.N N: H: 66H H:6H 66. - :6. H6. - 16.H NN HN :6H 6:6H 1N. H6. NN. N6.H NN NN H6H 6N6H :m . I wH . MM . I OM . H MN mN OOH wmmH NN. - 6:. N6. 66.H NN 6N 6N 1N6H 6N. 6:. - :N. - N6.H 6N :: 66 6N6H MN . mN . w: . Nw . H MM mm N0 DMQH HN. 66. - 6H. :N.H 6N NN N1 :N6H N:. NN. - 6N. 6H.H NN NN NN NN6H 66. - :6. H6. - 66. 6H 6H 66H NN6H 6N. - H6. - HN. - 66.H 6H 6N 66 HN6H HN.H 6m11 :NN 16 . 6N6H ucocomfioo ufimflafioc maa HHH man ”065 NE vaGH flown—m NmHuGH mm: HNWM ooHnm NuH>Huoscoum owamno hHumoM moHuom woumHmm wad muonwonm o=Hm> HwaH. .962. HumoHIIco. one 6am sou. cHnmmz. 3oHHmm umunz m «H:6H .muwHHoc uamuuso cw mmwumm uwSuo ”OOH u mdmanh:ma co vwmmn mmxmvcH 6N. NN. N:. 66.H H:H 1HH 6NH N66H :6. NH. I :H. I N6.H H6H 66 N6H H66H 6H. I NH. :6. I NN.H :HH 66 6HH 666H H6. NH. I HH. I 6N.H NHH 66H N6H 666H 1N. NH. 6:. 1N.H 6NH N6H 6HH N66H 6H. 6H. 6N. 16. 66 6N 66H 166H 66. 66. I 66. I 11. N1 61 66 666H mo. I NO. HO. I mm. QN mw NOH mmma :6. I N6. I 66. I :N. N1 61 66 :66H 6N. I N6. I NN. I 66. :N NN H6H N66H 6N. I NN. I H6. I NH.H 6HH 16H N6H N66H 6N. NN. 16. 66.H N6H HNH 6NH H66H :H. N6. I 66. NH.H 66H 66H 66 666H ma. I mo. HH. I mOIH mm mm 00H mde :H. :6. NH. 1H.H 66H 6HH 66 N:6H HN. H6. NN. 66. N6 16 66 1:6H 1H. 66. 1H. 11. N1 61 :6 6:6H :6. N6. I N6. 66. 66 66 66 6:6H mo. I #0. NO. I mm. mm mm mm gm." mo. no. I OO. 00. mm Pm mm Mima 6H. 16. 1H. 66. 66 N6 16H N:6H 16. H6. I 66. N:. H: N: :6 H:6H H6. H6. N6. 1N. 6N 6N 16 6:6H H6. H6. N64 6N. NN :N 66 6N6H NO. I no. I NO. I mm. Hm mm mm mmmfi N6. 66. 1H. 6:. 1N 6N 66H 1N6H N6. I N6. I 66. I NN. NN 1N 6N 6N6H 6H. H6. HH. NN. 1N 6N H6 6N6H H6. N6. N6. 1H. 6H NH 6N :N6H mo. I #0. I :0. I JH. MH NH mp mmmH N6. I 66. N6. I NH. 1H 6N 6N NN6H 66. I :6. I NH. I HN. 6N NN 6N HN6H :N. Nm ‘ImN 66H 6N6H unocomaoo ucoaomaoo m>z aw m>zn xmvaH m>z xmvcH ouwnm xmvawvmmz_ umwM muwum mua>auosvoum omcmso hHummw 66 .muwHHow uamuuso cw moauwm guano mooH I mzmHdemH no woman mmxmvcH mmwuww vmuwnwm vamsmuunwoum 01Ho> Hawqwnwz wumeIIcoauomcfiauasomumudH w magma 6N. 16. NN. 66.6 6NH HHH N6H N66H 6N. N1. I N6. I 6N.: H6H 66H ::H H66H :6. I 66. 6H. N1.: 66H 66 H1H 666H :6. 16. HH. N6.: :6H HHH N:H 666H H6.H 6N. 1N.H N6.: 66H 6HH 6:H N66H Nd. mm. NN. m~.m HHH #w MMH Emma HH. I 6N. I 6N. I N:.N 6N H1 HNH 666H HH. I 66. HH. I 61.N 66 :1 NNH 666H 6H. I 6:. 6N. 66.N N6H 11 NNH :66H NN. I 6H. I ::. I 66.N 66 6N NHH N66H mm. I NH. Hm. I mm.m HJH mAH mHH Nnma N6. 1N. 6N.H 6N.: 61H 1:H 6HH H66H 66. :N. 6N. 66.N 6NH 6HH N6H 666H mm. I 00. wm. I 0N.N Nm Nm OOH mdmfi N:. H6. 6:. HH.N 6HH 6HH 66H N:6H N6. 16. 66. N6.N N6 N6 66H 1:6H 6N. 6N. I 66. N6.N H1 :1 16 6:6H NN. H6. NN. N6.H N6 N6 6HH 6:6H 66. I N6. :6. I 61.H 66 66 66H ::6H 6H. N6. I NH. :1.H N6 16 N6H N:6H 6N. ON. 6:. H6.H 16 N6 6HH N:6H NH. 66. 1N. 6H.H H: N: 66 H:6H 66. 16. I No. I NN. HN 6N 1N 6:6H 66. N6. N6. 66. NN :N N6 6N6H NO. I mH. m0. mm. mm mm Hm wmma NH. 6H. NN. N1. 6N 6N :1 1N6H 66. I 6N. I 6N. I H6. NH 6N N6 6N6H Om. MO. I NN. 0N. hm Hm Pm mmma N6. N6. I H6. 6:. 1H 6H H6 :N6H m0. I mo. I NH. I w#. NH ma mm mmmfi NH. I 16. HH. I 66. NN HN 66H NN6H NN. I 66. NN. I 61. 1N 1N 66H HN6H N6. 6m 1N :6 6N6H Hawaafiou UGQGOQBOU NE a.“ mam ”OUGH NE xwvcH Gown—N ”Own—”Ina mm: meiM mofinm huH>Huoswoum mwamno haummw 67 .mumHHov unmuuao 6H 666966 Hmsuo “00H 0 mdedemH so comma mmxovcH NN. 6N. 1:. 66.H 6NH N6 HNH N66H 6H. I NN. I NN. I N6.H NN 11 16H H66H :6. I 1H. NH. 6N.H N6H :N 6NH 666H NH. I 6H. I NN. I NN.H N6 1N NHH 666H 6H. I 6N. 6H. ::.H 6HH 66 NNH N66H 6H. 66. I 66. NN.H N6H N6H H6H 166H N6. 66. NH. 6H.H 66 66 66H 666H NH. I HH. N6. I 66.H 6N NN N6H 666H no . no . I 00 . mo . .H mm +5 mm #mma 6N. I 16. I NN. I N6.H 1N 66 66 N66H N1. I N6. I HN. I H:.H NHH HHH N6H N66H 66. 6N. 6N. NN.N N1H H1H :6H H66H 6N. 6H. I 6H. 1N.H 6HH HNH H6 666H N6. I 66. N6. I 1N.H N6H 66H N6H 6:6H 6H . N6 . NN . 6N .H N6H 66H N6H N:6H 6H. N6. NN. NH.H 66 :6 66 1:6H HH. 16. I :6. 66. N1 N1 N6 6:6H 66. I 66. H6. 6N. N6 66 N6H ::6H 16. 6H. I N6. I :N. N6 61 16 N:6H OH . no . OH . Fm . ON. :0 mod” NJQH HH. 66. 6H. N1. 16 66 N6H H:6H :6. :6. N6. 66. 6: 1: 66 6:6H 66. H6. I N6. N:. 6N :: NN 6N6H 6H. I 66. I :N. I 6:. NN 6N 66 NN6H 66. :H. NN. :6. H6 H6 66H 1N6H 66. N6. I N6. I H:. NN N: 61 6N6H N6. 66. N6. N:. 6N 1N :6 6N6H 66. N6. N6. 6:. NN :N N6 :N6H 6H. 16. I N6. NN. 6N 6N 6N NN6H 66. I N6. I HH. I 6N. NN NN 66H NN6H 1H. I 66. NH. I mm. NN 6N 6HH HM6H . N N 66 ucmcomwoo ucwcomaoo m>z aw m>z xmvawm>z xuquIMONHm NavcHHmmz. mumw CHAPTER III FARM REAL ESTATE MARKET BEHAVIOR.AND COMPOSITION In order to view the data derived from the production function and residual return models in perspective a short review of aggregate land value behavior as well as a look at the changing composition of the farm real estate market during the period will be useful. Real Estate Value fighavior 1939-196% As Table ho, column 2 shows net farm income hit its depression low in 1932. Between 1932 and 1937 it improved but did not climb as fast as prices since a large portion of the price rise was due to shorter supplies caused by widespread drought. Also contributing to the rise in prices were the recovery of the non-farm economy and the New Deal farm legislation, probably in that order. .A decrease in net farm income occurred in 1938 and 1939 due to a general recession which began in late 1937. Farm prices were down 20 percent and gross farm income declined from.$11.7 billion in 1937 to $10 billion in 1938. The recession was a result of several factors including rapid increase in costs, high inventories, stiffening of credit policies and the declining influence of certain emergency New Deal policies.1 In l9hl net farm income turned up again prior to the U.S. entering WOrld War II and then increased sharply during the war years. lMurray R. Benedict, garm Policies of the United States l190- 1220, (New York: The Twentieth Century Fund, 1953), p. 365. 68 69 H6 N6 61 :6 :6H NN :66H 66 N6 6N N6 66H NN N66H 6N N6H 61 N6 6NH NN N66H 6N :2 N1 H6 :NH 61 H66H 66 66 N6 :6 :HH 66 666H 6N N1 16 NN 66H 66 6:6H 6N 66H 16 :N 6:H N6 N:6H N1 N6 H6 N1 1NH 66 1:6H HN 6N 6: N6 6NH N6 6:6H N1 N1 1: N6 H6H 6: 6:6H 6N N6 6: N6 16 N: ::6H N1 16 N: H6 16 6N N:6H 61 66 6N 16 HN NN N:6H 61 6N NN H6 :6 HN H:6H N6 6N NN 6: 1N 6N 6:6H 66 :N II N: 1N 6N 6N6H 66 NN II 6: 6N HN NN6H N6 NN II 66 6: HN 1N6H :6 NN II N: 6N 6N 6N6H 66 1N II N: N: NN 6N6H 66 6H II 1: :N 1N :N6H N6 NH II 6: HN 6N NN6H N6 HH II N: 1H NN NN6H 66 NH II N6 1N NN HN6H 66 NN II N6 6N N:. 6N6H H66 H66 ::H a HNfi HHH pause Baum mom ”5366.5 6865 60.7.5 «835 8.5m ouu< Mom 03.; some Shah HmOHmhfim QEDOdH uuz deOHuwZ mmOHU Hgfimdou umz Hfiuon. Gaga—mm H60“ Shah I'lllll-| I I'll! EI-1II|II I' ‘IIII‘IIII I ll . It‘ll: --.6mewr.Honmoou doHumHouuoo xcwm-mwawo-.m won—6.30m Ema. 00H I:._,...HHI \.H @789 6335 63.25 Jan—H5 anon Hmoflmhsm can £26on 38362 6695 «moufium 5:56:60 .mfioocH Baum uoc kuoa Noumumu Hood 8.3m mo ouud Hum 632', owuuo>< no 63865 0: 033.. .voumHooHuo uoz oz oz 66. 66.H 66.H 66. :66H I 666H N6 . oz oz oz oz N66H I 666H oz oz 66. oz oz :66H I 6:6H oz oz oz oz NN. :66H I NN6H 66. 66. oz J66. Hm. :66H I 6N6H oomwoo show you uoovoum xovoH coaum oaooaH sham voHuom man How sham Hooqmznm «soooH uoz Hocoqomz macaw Hoaomcoo uoz kuoa 70 6am 663H6> coma ooosuom ucoHonwooo coaumHouuoo zoom coauuomm .166 oHaaa .:66H «H66 mHnaa .N66H .IOHumHuaum HausuHsuHuo< 6 .n: .m admmH hHah N¢de «mam «GowuuauHm QEooaH Sham n .mufiooflomum Honda mo ooouom Nmmoofiaom unannoo mo 66>Hom : .1m .6 H:66H szn .6de .mmm .owuoHHom o>uomom Hanovom m .ooauaouHm oaoocH sham m .mn .m «#mmH uoaouoo q¢nm= «mum .oucoamon>on uoxuqz oumumm Huom.auum «moouoom moon anon Honmznm oaoooH uoz HmoOHumz 66090 noabocoo uoz Hmuoa 71 A close relationship between land values (Table 2, col. 1) and net farm income (Table 2, col. 2) during this period has been established. The low point for land values came in 1933, one year later than the net farm income low, when the index stood at 26 (1957-1959 = 100). The index rose to 31 in 1937 and 1938, then dropped to 30 for the years 1939 and l9h0, again a one-year lag behind net farm income--then rose steadily during the war. During the early war years, land values rose slowly because of uncertainty as to how high farm income levels would climb and because of the nearness of the depression years. A post war depression did not occur as expected and continuing international tensions along with a large foreign aid program caused a generally rising price level. Farm income continued to increase causing upward pressure on farm real estate values till the index stood at 66 for the year ending march l9h9. This was a 120 percent increase in land values since l9h0. During l9n9 farm real estate values dipped due to a drop in farm product prices and general economic activity, which started late in l9h8. The l9h9.Agricultural Adjustment Act would probably have checked the downturn in land values had not the Korean war brought some inflationary factors back into the economy which did the job instead. In late 1951 farm product prices again began to slide, stopping the upward trend in land values. The index of farm real estate values for the year 1951 was 75; for 1952 was 82, for 1953 was 83 and for 195h was 82. So the trends in real estate values for the period 1930 through 1953 agree closely with changes in farm income and product prices. Then in 1954 while farm income dropped again for the fourth consecutive year real estate values turned upward. By early 1957, \/ 72 land values were 12 percent above their 1953 low despite stable farm income at a relatively low level throughout the l95h-l957 period. Between l95h and 196A farm real estate values have followed an ever upward trend with the farm real estate value index in l96h at 131. This represents a 60 percent increase in land value during the period, while total net farm income was below the 195% level for 5 of the 10 years and only slightly above that level for the other 5 years. 2 calculated for the A Spearman rank correlation coefficient indexes of land values and total net farm income in Table no for the period 1930 through 1964 is .6h. Taking only the period 1933, when land values hit bottom, to 195A, when land values rose despite the fall in net farm income, the rS = .88; and for the period 1955 through l96h, rS = .50. This indicates that the correlation between the two series was much higher before l95h than after. The seemingly odd land value behavior since 195M has called forth a host of hypotheses and explanations trying to rationalize it. Expectations concerning future income streams to farm real estate play a particularly heavy role in determining market price, and assumptions about the existence, direction, and magnitude of certain trends in the composition of the land market itself, the general economy, and the structure of agriculture production are the foundations upon which these expectations are based. 2The Spearman rank correlation coefficient is 6Zd2 r831- 1 N(N2-l) where d is the difference between the ranks of the series and N is the number of observations. For further explanation of the statistic and its use see Sidney Siegel, Nonparametric Statistics for the Behavioral Sciences, (New York: MCGraw-Hill Book Co., Inc., 1956), pp. 202-213. .N NH.1. -. . . 73 'Land Values and Other Economicigndicators Historically farm real estate prices have been highly correlated with the general price level.3 For the period 1930-1964, the Spearman rank correlation coefficient (rs) is .99 between land values and the consumer price index. From l95h to 1962 the rS is 1, indicating that the correlation is perfect throughout the period. The correlation between land values and gross national product (GNP),+ for the period l9hO-l96h, rS = .99 and for the period 1955-196M, rS = 1 indicating that this correlation is also very strong throughout the period covered. In order to expect this relationship to continue, assuming certain other relationships constant, agricultural earnings would have to move in the same direction as non-agricultural earnings. This has not been the case, for since 1953 net farm income in current dollars has leveled off while the purchasing power of farm income has declined. Hathaway has found that the agricultural business cycle is conforming more closely with the non-farm.business cycle in the later years. Large non-farm business cycles affect agriculture in the same direction as measured by net income, income per worker, and value of farm assets. The effect of milder cycles on agriculture is less pronounced and other factors tend to overwhelm their effects. Since farmers are more and more dependent on non-farm.sources of supply for 3William H. Scofield, "Dominant Forces and Emerging Trends in the Farm.Real Estate market." Paper presented at a seminar on land prices, North Central Regional Land Economics Committee, Chicago, Illinois, N0vember l2, 196M. AWilliam H. Scofield, “Prevailing Land Market Forces," Journal of Farm Economics, Vol. 39, (1957), pp. 1500-1510. 7h production items and since aggregate demand for farm products is very income inelastic, mild expansions increase farmers production costs without appreciably increasing their returns. The main benefit to agriculture of a mild business expansion is the opportunity it affords excess labor to move more easily from farm to non-farm jdbs rather than any increase in demand for farm products.5 .Even though aggregate net farm income has leveled off, out movement of labor and farm consolidation has proceeded at a rate which has caused the net income per farm to show an upward trend since the post World'war 11 low in 1950. This trend was slightly upward from 1950 through 1957 and then accelerated into the 1960’s. Therefore, in recent years there has been a larger net income per farm to distribute among the productive factors. Thus, if the total physical quantity of the so-called unpaid factors--capital, operator and family labor, and land--per farm is increasing at a slower rate than per farm net income, they will be eligible to split a larger return. The Spearman rank correlation coefficient between land values and average total net farm income per farm.for 1930-1964 is rS - .90 and for the period l955-l96h, rs . .96. If land values are primarily based on productivity returns, it appears that this splitting of larger returns has been happening over the period and further that land values may be based more on productive returns or that the land market is quicker to respond to farm income changes in the years since 1955 than formerly. This would also cause the high correlation between the land prices index, and both the con- sumer price index, and GNP even though total net farm income has been declining. 5Dale E. Hathaway, "Agriculture and the Business Cycle," Policy for Commercial %§riculture, Joint Economic Committee, (Washington: U.S. overnmen n ng ce, 1957), pp. 51-76. 75 The Real Estate Market and the Expansion Buyer The expansion buyer has become more prominent in the farm real estate market in recent years. Table 41 indicates the percent of the total farm real estate buyers who were expansion buyers. The trend has been consistently upward since 1948 from 35.5 percent to 51 percent. The slight drop in 1956 was not due to a decrease in the number of expansion buyers but to the relative increase in the number of non- farmer buyers in that year, probably to take advantage of the Soil Bank Program. ,According to Table 42 the market has dwindled in size from Table 41 Farmer Expansion Buyers as a Percentage of Total Buyers in _ the Farm Real Estate Market _l'48-l'o armer Expansion Buyers Year Percentage of;Total 1948 35.5 1949 36-5 1950 36.5 1951 37-5 1952 38.1 1953 38-3 1954 38.4 1955 38-7 1956 37-9 1957 39-9 1958 39.8 1959 41.4 1960 46.9 1961 48.1 1962 47.9 1963 51.0 Source: Compiled from data in various issues of garm.Real Estate Market Developments, ERS, U.S.D.A., 1949-1964. approximately 291 thousand farm.transfers in 1950 to about 140 thousand in 1963. So even though expansion buyers are an increasing portion of 76 the total market, the total market has declined rapidly enough that the number of farm expansion buyers has decreased through the years from about 106.2 thousand in 1950 to about 71.4 thousand in 1963. But since farm.numbers have been declining during the period expansion buyers as a percent of total farms in 1950 stood at 1.97 percent and in 1963 at 2.28 percent. The figures for the two intervening census periods are lower but the figure for 1963 is higher than for 1954 indicating an upward trend at least since 1954. The expansion buyer then is becoming more dominant in the land market and the upward pressure he exerts on price is becoming stronger. Scofield is convinced that "Land prices in commercial farming areas today are set chiefly by the expansion buyer who can compete effectively with the non-farmer investor buyer. Farmers themselves have been chiefly responsible for the upward trend 6 in land prices over the last decade." 'Land as an Envestment Another influence in the real estate market whose role has changed during the period under study is the non-farmer investor. There appears to be a widely held belief both among farmers and many non-farm investors that land offers safety and protection of capital from.loss of purchasing power during periods of inflation. Boyne found that real capital gains accruing to farm Operators due to farm real estate invest- ment in the United States between 1940 and 1960 amounted to $26.5 billion in 1960 dollars.7 He found further that the real capital gain has 6Scofield, loc cit. 7David H. Boyne, "Changes in the Real Wealth Position of Farm Operators 1940-1960," Technical Bulletin 294, (Agricultural Experiment Station, Michigan State University, 19647, p. 38. 77 .mmm oHomH «Hm: .m «:NNH «moHuNHumum HonouHooHuw< .HH oHnNB Scum .6m .m «:66H noooooo .A66Ioov 666 NH .6 ~666H 662.HH66IooV NonmemoH6>oo uqxagz monomm Hmmm spam AmV naoHoo HNV casHoo HHV caoHoo «OOH—HOW .NoomsHuNm * NN.N :.H1 6.H6 6:H N.:: *6NH.N N66H :6.H 6.N1 :.H: :1H H.1: :61.N 666H 61.H 6.6N .:.NN NNN 6.6: NN1.: :66H 16.H N.66H 6.6N H6N 6.:6 NNN.6 666H Aucmwwomv Amocmmwoaav AHNMwW «6 Amwammwoeoo ANV Amocmwwonav HHV Hoo\A6v Hoo A:V Hoo xHNV Hoo wwmwmmmv “NV Hoo xA666H\AHV Hoov Nahum mo .62 mo mommnousm 6H mnohom wouuommomua Nahum oooH yam Nahum mo .oz “66» ucoouom no 606656 Iuom cowmaomxm «wouooouom 6 mm ooawoomxm mo .02 coamomoxm Nahum mo .02 mumow wouooHom Mom mahom.mo nonapz mo oomwnousm GOHmammxm New moomnouom oonoomxm mo Hoeaoz «muommcoua.summ mo Honsoz m: oHoNH muommcoua_auom iii m a--- I I 78 increased at a rate through time which has approximately offset the decline in rate of return from production in the later years. Five- year averages of total return on investment which includes both real capital gain and productive returns are 1940-1944, 10.6 percent; 1945- 1949, 7.1 percent; 1950-1954, 8.3 percent and 1955-1959, 7.8 percent.8 Behind the argument that land is a "safé'investment lies a deep-rooted value based in our agricultural history that land ownership carries with it a certain prestige or status. Further, land is a tangible asset which can be seen, walked on, felt, surveyed, and identified with, qualities which are important in a rural-oriented society and which few other assets possess. As the U.S. becomes increasingly urbanized, succeeding generations will more and more lose their identification with rural life and rural values. These non-economic rationales for investment in farm real estate will then diminish and economic factors being equal non-farmers can be expected to show less interest as buyers of farm real estate. This trend is already in evidence when we look at the percentage distribution of non-farmer buyers and sellers in the land market through time. Table 43 shows the percentage of non-farmer buyers of farm real estate to be quite constant since 1950 except for the 1956-1959 period when nOn-farmer buyers of farm real estate increased as a percentage of the total. The increase during this period may be attributed in part to the investment benefits derivable from real estate ownership due to the Soil Bank and Conservation Reserve programs. 82.111.- p. 43. 79 Table 43 Non-Farmer Buyers and Sellers in the Farm.Real Estate Market as a Percentage of the Total Market Year Non-Farmer Buyers Non-Farmer Sellers Net Buyers Over Sellers (1) (2) (3) 1948 28.2 17.0 11.2 1949 27.9 11.9 16.0 1950 28.4 15.3 13.1 1951 32.1 15.5 16.6 1952 30.3 14.5 15.8 1953 33.4 14.6 18.8 1954 33.9 15.9 18.0 1955 32.8 14.6 18.2 1956 35.5 14.2 21.3 1957 35.9 15.1 20.8 1958 35.0 15.2 19.8 1959 36.2 26.1 10.1 1960 33.8 26.0 7.8 1961 32. 1 24.1 8.0 1962 32.2 25.4 6.8 1963 31.0 24.9 6.1 Source: Data compiled from.various issues of germ Real Estate Market Developments, ERS, U.S.D.A., 1949-1964. 80 On the seller side of the market the percentage of non-farmers remains quite constant until 1959 when the market experiences a sub- stantial jump in the percentage of non-farmer sellers from 15.2 to 26.1 percent. From 1959 to 1964 the percentage of non-farmer sellers has remained relatively constant at this higher level. This jump may be attributed to a combination of forces including lower rates of rental returns from real estate investment, profit taking of accrued capital gains, higher rates of return from opportunity cost investments and expiration of Soil Bank contracts. The difference between the non-farmer percentage of total buyers and the non-farmer percentage of total sellers in a given year yields the net percentage of transfers from farmer sellers to non- . farmer buyers since the percentage of non-farmer buyers during the period is always greater than the percentage of non-farmer sellers. (See Table 43, col. 3). High net farm incomes in the late 1940's and early 1950’s probably caused a rising net percentage of non-farmer buyers in the farm real estate market. The jump in 1956 is, as stated above, probably due substantially to the Soil Bank and Conservation Reserve programs. Between 1958 and 1960 the trend was sharply downward, leveling off between 1960 and 1964. Economic factors being equal and assuming no change in the institutional structure of agriculture we would expect the net non-farmer buyer prOportion of the farm real estate market to slowly decrease in the future. If, however, the institutional structure of asset ownership in agriculture were to change, say toward large corporate farms, this trend may very well reverse and move substantially upward in the future. 81 Land Investment and Taxes Investors considering farm real estate as an alternative often mention tax advantages as a reason for favoring land as an investment. Just what they mean is not always clear since ownership of land draws with it several different kinds of tax considerations. First there is the capital gains tax which applies to land. Capital appreciation of land through time will increase the net worth position of the owner. If he sells the real estate at some future time for more than he paid for it the difference between the price he originally paid and his selling price is a capital gain. Income from capital gains of productive assets is eligible to be taxed at a much lower rate than income derived from normal business operations for profit. While investments in farm real estate are eligible for this capital gain tax rate so are many other investment alternatives, the most notable of which are corporate stockS. So the capital gains tax provisions would neither hinder nor enhance the position of farm real estate as one of many alternative investment sources. Probably the tax advantages which farm real estate investors are concerned with are those arising through the ability to convert income from.production to income from capital gains under present tax laws. Persons in high personal income tax brackets can purchase a farm which can be operated in such a way as to show net losses, thus lowering their income tax bracket. Part of the loss from the farming operation can arise from heavy investment in items which improve the value of the real estate such as fertilizer, drainage, leveling, imr provement and modernization of buildings and fences. In effect the 82 investor can add to net worth in the form of capital appreciation of the real estate investment which will be taxed sometime in the future at the capital gains tax rate while at the same time he can show a net 1099 from farming operations for which he can receive tax credit allowing him to be taxed in a lower personal income tax bracket. This type of tax advantage associated with farm real estate will make it a more desirable alternative than other sources of investment to some investors. This advantage will continue as long as existing tax laws stay in effect. Investors, both farmer and non-farmer, must also take into account the property tax when making investment decisions regarding farm real estate. Table 44 shows the movement in the effective farm real estate tax rate and the average per acre tax on farm real estate through time. The effective tax rate is found by dividing the total current value of farm real estate into the total amount of farm real estate tax collected. It is not the rate of the tax assessor which is then applied to the assessed valuation to determine the tax bill but rather the percentage of current market value of farm real estate that is collected as real property taxes for any given year.9 The average 9The per acre tax figures in Table 44 do not agree with those published in.Agricultural Statistics. In computing that per acre tax series the value of public and Indian lands is deducted from the total value of farm real estate. Public and Indian land values are calculated by assuming their value to be comparable to similar privately held land. Since public and Indian lands are generally of poorer quality than pri- vately held land they are likely over valued by this method. For this reason the.égricultural Statistics estimates are upward biased. 0n the other hand the estimates in Table 44 are downward biased due to not ex- cluding public and Indian land from.total value of farm real estate. The two series fonm the upper and lower boundaries of the estimates of average tax per acre. The absolute spread between the two estimates becomes larger as the tax per acre increases. This is because there is an acreage difference in calculating the two estimates amounting to approximately 8 percent. That is, the Table 44 estimates are approximately 8 percent below the ricultural Statistics estimates. See Agricultural Statistics (1962, Tagfie 703: and I963, TaBIe 703). 83 61 . 66 .NN N6 . 61N NN6 .6 :6H N1 . :N .NN 1N. 6NN 6N6 .66 N66H 61. N6.NN 6N. 66N N16.66 N66H 16. :1.:1 6N. N11 6N6..6N H66H :6. 66.:6 N6. 31 66N.61 666H H6. NN .66 N6 . 661 NN6 .61 6:6H 16. 66 .N6 66. 666 16661 N:6H N6. N6.66 NN. 666 N6:.N6 1:6H 6:. HN.N6 6N. 6H6 6:6.H6 6:6H H:. 6N.1: 6N. 66: :NN.N6 6:6H 1N . NN .N: 1N. 6H: 66N.N: ::6H 6N. 66.1N 66. 66: :66.H: N:6H 1N . 6N . :N 16 . H 66: 1:6 .1N N:6H NN . :6 .HN NH .H 16: 66:.:N H:6H NN. H1 .HN 6H .H H6: 6N6 .NN 6:6H NN. 1H .NN 6H .H 16: 6N6 .:N 6N6H NN . NN .NN :H .H 66: 61H .6N NN6H NN . HN .NN 6H .H 66: NHN.6N 1N6H 1N . 6:.NN 6H . H :6N 66N.:N 6N6H 1N . :6 . HN NH .H N6N :6N .NN 6N6H 1N . N6 .6N 6H .H NNN H6N .NN :N6H 6N . N6.6N 6N.H N6N N6N.6N NN6H 6: . 16 . 6N :N . H 6: 6NH .1N NN6H N6 . N1.N: 6N.H 6N6 6N1 .N: HN6H 66. N6.N: NH .H 166 N1N.1: 6N6H 5 3 5 NV 3 “6.3.303 AoumHHonv AuooouomV “6.3.30: ”6.“:sz AmumHHon :3:sz H:V Hoo.HNV Hoo HHV Hoo\HNV Hoo Quwumm HNOM 59mm £0 mufiuafl HQOM Ewh madam NN.H. mflundH. Quwumu HQOM mudumfl Ham“ Eflh unfin— oumm oguoommu can «698“. 333.— Hmmm Baum 93¢ Mom owouo>< .6989 oumumu Hood Baum HmuoH +3 6HH—Nu. 84 .H: .6 «N66H 66:63 .3663 36863656 687.8: 3366 H666 866 Hi H66 .661 «H666 ::66H “:61 «Hana .N66H .IoHHmHHaHm HauauHsoHuN<.HNv Hoo .H: .6 «mmmH umowa< NAHNIQUV 666056OH6>09 uoxumz_oumomm Hoax anon AHV Hoo “mouoom NN.H 61.6NH N6.H N6:.H NN6.N:H N66H 1N.H 6H.:NH N6.H N6N.H 6N:.1NH N66H 6H.H NN.NHH Ho.H 6NN.H N6N.HNH H66H NH.H N:.6HH 66. H6N.H 6N6.6NH 666H 16.H :6.HHH 66. NNH.H N6N.:NH 666H N6. 6N.N6H 66. N6H.H :N6.6HH N66H N6. 6N.16 66. ::6.H HN:.6HH 166H 6N. 66.66 66 . 116 :N6.N6H 666H HN . N .66 66 . NN6 N1H .N6 666H H66 H:V ANN HNH HHV AouuHHonV AmuuHHoav -Auooouomv AwuoHHon ooHHHfizv AmuuHHoa ooHHHHZQ H:6Hoo . ANN Hoo HHV Hoo \ANV Hoo Ouwumm wam Hannah £0 Ouwumm HQOMH Shah mad“ NGH. ocundfi madam” HQOHH Ouwumfl Hmmm Hannah was 686. 93¢ .36 owmuo>< mo 032, 6664 666 05606636 EH66 H366. mo 03.; .2366. II III'IIII " III 1 .Illl It'll I III! 1111! '1‘ .ooaaHucooII:d oHan ". H \~fi/ 85 per acre tax in 1963 of $1.32 represented the twentieth consecutive yearly rise since the lowest per acre tax during the period of $.36 in 1943. The rise has been due to rising land values throughout the 20-year period and since 1952 a slight rising trend in the tax rate. The per acre tax rose 267 percent between 1943 and 1963 while the per acre value of farm real estate rose 246 percent during the same period. Thus we can see that the tax burden on farm real estate is tending to become greater. In order for the tax burden in percentage terms to be the same as it was in 1943, tax collections would have been $1,384 million instead of $1,468 million in 1963. The combination of increasing tax rates and increasing land values has resulted in an average per acre tax increase per year of 4.8 cents over the 20-year period. If a buyer of farm real estate in 1943 had been able to look into the future and had determined that the average increase in taxes would be 4.8 cents per year for the next 20 years he could have dis- counted this increase in his future costs and lowered his valuation of the property accordingly. Suppose for example that our buyer has somehow determined the net annual return he expects this property to yield without assuming any increase in property taxes and has settled upon an acceptable capitalization rate. To determine the present value of the future income streams accruing to the property he divides the net average annual return by the capitalization rate. The result is the price he can afford to pay for the property under his assumptions. But since our 1943 buyer is omnicient with respect to the average annual increase in real estate taxes he can determine the effect of this in- creasing cost on the present value of his property. By dividing the 86 average annual increase in the tax by the square of his acceptable capitalization rate and subtracting this figure from the value he calculated previously, he finds the 1943 present value of the property considering the tax increase.10 This assumes the increase in costs. will be maintained at the constant rate in perpetuity. Assuming a constant capitalization rate of 5 percent and the above 4.8 cent yearly incremental increase in cost of ownership due to taxes the decrease in value per acre due to the tax increase is $19.80. (.048/.0025). This takes the inflationary trend of the past 20 years into account. If only the tax rate change is taken into account exclusive of the inflationary trend in land values the change in value due to the tax would be much less and may in this case even be positive since the effective rate for 14 of the 20 years is below the effective rate in 1943. To take a hypothetical example, if we expect the tax rate to in- crease one cent per acre per year in perpetuity, all other things constant, and we assume a 5 percent capitalization rate the decrease in present value due to the tax increase is $4.00 per acre (.01/.0025). we can most likely expect real estate tax rates to continue their upward trend in the future. Improvement and maintenance of county roads will continue along with the increased expense of school consolidation, school bus routes, higher classroom education costs, and expanded county services. The rural to urban movement leaves rural counties with narrower personal property tax bases and the urban counties with the added expense of providing services for the rural 19A constant increment of change in the net annual income stream can be accounted for by an addition to the general capitalization formula. The formula then becomes V = a/r fi/r2 where i_is the annual increment of change in the income stream and can be either positive or negative. RIR. Renne, Land Economics, (New York: Harper R.Bros., 1947), p. 216. 87 immigrants. The rate of tax increases per acre per year has been very slightly upward since the early 1950's. Five-year averages of increases over the past 20-year period are 1943-1948, 4.2 cents per year; 1948- 1953, 3.2 cents per year; 1953-1958, 5 cents per year; and 1958-1963, 6.8 cents per year. We can expect at least the same rate of rise in the future. Land Values and Government Programs Any government program.is a policy means toward realization of certain values which society deems important and which it otherwise may not be able to achieve. Most government agricultural programs of the past 35 years have had as their goal stabilizing and/or increasing farm incomes. Three general approaches have been used. First we have the programs affecting the price of agricultural products. In this category fall the commodity price support programs usually with some type of restriction on the quantity of land which can be used or on the quantity of the product which can be marketed, govern- ment disposal programs such as school lunch, food stamp, and PL 480, and tariffs or other trade restrictions at the international level. When any of these programs are successful in raising the price of an agricultural product or keeping it above market clearing levels the returns available for distribution to agricultural production factors including land will be larger. Depending on the share of total revenue accruing to land, a portion of the added returns due to the program will be capitalized into land values. Since these programs are com- modity oriented they have a differential impact on land values in different areas of the country. 88 When marketing quotas in the form of acreage allotments are used in conjunction with a price support program.awnership of land with an acreage allotment is tantamount to having a license to produce a specific commodity. .As with any license which tends to restrict entry the allotment itself gains a value imputed to it by the difference between returns to holders of the allotment and returns to those with- out it. Since in this case the license is in the form of land with an acreage allotment, in areas where the allotments have value, this value is capitalized into land values. The allotment would have no value in areas where an unrestricted alternative provided returns equal to returns from the restricted crop. The second category of government programs are those which attempt to increase productivity. Included here are the Agricultural Conservation Programs with cost sharing arrangements for approved practices and improvements, technical assistance provided by the Soil Conservation Service, research and development by agricultural experiment stations and research and dissemination of information by land-grant colleges and the Agricultural Extension Service. .Activities under the Agricultural Conservation Program.most directly affect the value of land through practices designed to improve the productive quality of that input. Effects of this program then to a greater extent than effects of others in this category tend to be directly capitalized into land values. The third category of programs also directly affect land values and the welfare of landholders. Here we are speaking of the Soil Bank.Acreage Reserve and conservation reserve type programs which 89 allow payments to landholders for taking land out of agricultural production and diverting its use through various forms of conservation practices. Control of the land is necessary for participation in this type of program. Thus, at least some of the benefits become attached to the land and are capitalized into market prices. The acreage reserve portion of the Soil Bank Program operated on a year-to-year basis and was voluntary, so a participant must have decided that his returns would be greater by participating in the pro- gram. The effect of the acreage reserve on land prices then would have been much the same as any program designed to raise farm income such as a price support program. The Conservation Reserve program was a longer run approach where participants contracted with the program for up to 10 years. To the extent that higher and more stable income streams were realized both demand for land and incentive for present owners to retain possession would increase. Higher land prices should have resulted, although Table 40 shows no sharp increase in 1956 in the index of land values. An important effect of past government programs on land values is the mere fact that even though most were called "temporary" they did persist. The expectations that government programs of some type will continue to exist is an important land market factor due to their general stabilizing influence on farm income. Lower capitalization rates can be used for land values when a smaller allowance can be made for risk and uncertainty arising from instability. 90 Summary The non-farmer investor is becoming a smaller part of the farm real estate market. With each succeeding generation fewer and fewer urban investors have strong actual or sentimental ties with agriculture and thus will have fewer non-economic motives for owning farm real estate. Farm real estate investments involve certain costs not associated with other types of investments. The property tax is a direct cost to the farmland investors when his net return is calculated which means that the gross land rental return must be higher than returns from comparable investments in which the property tax has already been deducted or where it doesn?t play as important a role as an expense item. And the property tax cost is increasing over time due to both the rate and base increasing. .Also farm real estate hold- ings involve a cost in terms of the investor's time for managing the investment or in terms of money for hiring this management service. These costs are normally much less in alternative investments such as corporate stocks or securities. The non-farmer investor then is looking more at the economic aspect of farm real estate investment as opposed to alternative forms of investment, and except for the period 1956-1959 when the Soil Bank provided both high returns and investment security, has tended to become a smaller component of the farm real estate market. (See Table 43, col. 3). This has caused the expansion buyer to become an increasingly dominant force in the farm real estate market. Even though net farm income per farm declined from its 1948 high and fluctuated between 1949 and 1955 at relatively low levels before beginning its present 91 rising trend in 1956, farm real estate prices rose steadily throughout the entire period with only two minor setbacks occurring in 1950 and 1954. Thus, it appears the expansion buyer has been willing to bid up the price of farm real estate over a rather long period partly on the basis of factors other than his current net farm income. One of these factors is the expectation that some type of government agri- cultural program will persist. As we shall find and discuss in greater detail in Chapter V the expansion buyer generally appears willing to pay more for farm real estate to add to his existing land base at present than may be warranted by capitalization of expected income flows accruing to that input calculated from the residual return model but less than warranted by the series derived from the production function model. CHAPTER IV THEORETICAL FRAMEWORK FOR FARM REAL ESTATE VALUES Before proceeding to an analysis of the data derived from our two models a conceptual framework from which we may view the results must be developed. Expected Behavior of Land Marginal Value Products over_lime Land price rises can be rationalized on a purely theoretical basis. Picture a production function analysis, using a Cobb-Douglas type of statistical function on time series data, where the variables are land, labor, and capital measured in physical terms--acres, man- hours, and constant dollars. And for the sake of argument let us assume the coefficients to be one-third for each input so that the function is Y = lxiXQXB where X1, X2, and X3 are land, capital and labor respectively. Suppose that in time period one the input values are such that the function is Y = 1(27)5(8)%(42.9)%' so solving for Y we get 1 x 3 x 2 x 3.5 = 21. Now time passes and with it come some very important changes in the inputs. Land substitutes are developed which allow one acre of land to yield almost twice its capability in time period one, but the same amount of land is used as before. Labor substitutes are developed which would allow reduction to about one-third the labor used in the first time period but less labor leaves than would be possible. The use of capital increases by about two and one-half times. 92 IIIIIIIII|I111 93 We can enter these changes into the function. The land input remains the same at 27 but added to it is a bundle of land substitutes which increases the effective amount of land by almost twice. We will therefore add 23 units of land substitute giving the land input a value of 50. Capital increases by two and one-half times--from 8 to 20. Labor decreases from.42.9 to 29 but labor substitutes in the magnitude of (say) 96 increase the effective labor input by slightly over two and one-half times its former level of 42.9 to 125. The function for time period two is O I 1(11 + X1 substitute? X23 (X8 + X3 substitute)7 or 1( 27 +~23)§ (20)% (29 +-96)% which reduces to Y = 1(50)k(20fk(125)?1 Y Y Solving for Y we get 1 x 3.68 x 2.72 x 5 = 50. The marginal physical product for land in time period one is €¥§21) = .259 and for time period two if we can measure the land 27 substitute “15159): .333. If we cannot measure the substitute in the 50 land variable the land MPP appears to be 5350)= .617. The point is 27 that it is theoretically possible to get increasing marginal physical products through time for land under the situation described above which approximates what has happened in U.S. agriculture over the past 4 decades.1 Now since marginal physical product times product price equals marginal value product we can postulate a price of one in the first time period to yield a marginal value product of (.259) (1) or .259. LThis concept and the basis for the example was developed by G. L. Johnson who discussed it with the author. 94 In the second time period, even if the price decreases as much as to 80 percent of its former level and we can measure the land substitutes the MVP is (.333)(.8) or .266 -- an increase over the MVP in time period one. Thus land MVP?s may rise due to productivity increases even though product prices decline. If we cannot measure the land substitutes in the land input the MVP in the second time period will rise relative to MVP in the first time period even if prices fall to less than half their former levels (MVP = (.617)(.5) = .309). .A problem arises when we attempt to measure these substitutes empirically for inclusion in a production function. First there are the new techniques and cultural practices which increase production without substantially affecting the physical amount of inputs used but which do increase output. In this category are included such items as stubble mulching practices, hybrid seed, more productive breeds of livestock or higher educational levels of farm operators leading to greater management capacity. In another category are the technical innovations which change the input mix within the aggregated input called capital without chang- ing the amount of constant dollars worth of capital employed. An example is the disinvestment in horses and mules which approximately offset the investment in motor vehicles and tractors at least up to 1948. This was a tremendous labor saving change but was neither picked up as change in the capital input nor as an addition to labor in the form of a labor substitute. Thus, if it is theoretically possible to get increasing‘MPP‘s for land by including the land and labor substitutes it is certainly possible and probable that land MPP's will increase 95 when many of these output increasing substitutes are not measured in the conventional input categories in a production function. Therefore it is possible and highly probable to have increasing marginal value products for land even though product prices fall, if the increase in MPP more than offsets the fall in product prices. Table 45 presents average yearly rates of change in marginal physical product of real estate in 1947-1949 constant dollars, and marginal value product of real estate in current dollars for the 19 areas studied over selected time periods. ‘With very few exceptions the rate of change in MPP‘s for all areas in all time spans is positive. Of the exceptions those occurring in the 1940-1949 period are negative because of substantial jumps in MPP in the early part of the period due to generally favorable weather in 1942 and 1943 and dips in 1949.2 Any time the table shows a lower rate of increase for MVP than for‘MPP, it means the average change in prices for the period was negative. This was true with five exceptions for the period 1933-1939. In the five exception areas, Corn Belt beef raising, Northern Plains wheat-corn-livestock and Northern Plains cattle prices did not change, while Intermountain cattle and Northern Plains sheep prices increased during the period. The 1950-1954 period was characterized by falling product prices which started in late 1948, and even though MPP changes in most areas were positive the effect of the price decline overwhelmed the effect of the rising MPP's, and MVP's trended downward except for the Southern Piedmont cotton and the Washington and Oregon wheat-fallow areas. 2The Stallings weather index shows 1942 and 1943 to have had favorable weather. James Stallings, "Weather Indexes," Journal of_§arm Economics, Vol. 42, (1960) pp. 180-186. .HH 9666650 :6 666H96666 H6608 GOHuooom cowooooo96 6:6 mo 6uHo669 so 6666a 6coHumuo6ao0 u OOHHHOm 696HHon 6669920 6: [I'll 1"" 'll ll'llllll'll' l$1!|nll |ll||l.|v ‘I‘ll mo. :0. NH. I H0.I mo. I0I m0. H0. 666£N 6oHon 296:9962 6N. 6H. 6N. I NH. NN. H6. 16. No. 6Huumo :H6uaoos9mocH N6. N6. :H. I H6.I N6. I6- N6. N6. mHoumo 66H6H6 aumauuoz HH. HH. 60. 60. 0:. :0.I :0. NH. oow69o 066 couwcH£663.soHammI666:3 6m. 06. mm. I 0H.I m:. 10. 60. no. maoaw9omuowm90Ium6nz 6:H6Hm :96nuoom 66. NN. 6H. I 66.I 1:. No. 66. OH. 66623 966663 6=H6H6 69626666 :6. IoI 66. I 66. ON. N6. 66. 16. xooo66>HHI66626666I66623_6:H6H6 .oz 6H. :0. 6H. I m0. mm. 0H. 6H. 6H. 260666>HAI2960I666:3.6oH6H6 296:6962 6H. HH. NH. I :6.I ::. H6. 66. :H. :uoumm>HHIaHmuo HH686I66626.66H6H6 .oz HN. HN. 6N. I H:. 6N.H HN. 6H. N:. 666666 6H6H666 xuaHm 66266 mm. mm. 6H. H:. 06. N0. I No. 0:. uooao6Hm :96Suoom oouuoo 66. 66. 6N. I 6H. N1.H 6N. 6N. 6N. HHNN cuoo aHuuoIamao :N.m 6H.m m0.HI no. :H.m mm. :N. 60.H 9H6m =9o0 NGH266966 M66meom 16. N6. 66. I NH. N6. NH. 6N. 6N. HHNN auoo NchHNz NNNNINoz HH.H 6N. 66. I 66. 66.H 6N. 66. 1N. HHNN auoo zuHunINom N6.H 66. 6H. I 6H. :6.H 1N. 1:. N6. «Homoaaa2 NozIzuHua mm. 0:. PM. I NH. mm.H N0. mm. :n. N9H69 6966o6663.o966663 MH.H :0.H :m. I Hm. N6.H N0. N6. mm. 6966: camcoo663.o96666m mm. :N. mm. I NH.I mH.H N0.I ::. MN. 69660 6666nu9oz H69uo60 662 662 662 662 612 662 662. 662“ 6696 .1. N.6HI666H 66HI666H 6N6HINN6H 66666696 6=H6> H66Hw9mz 666666 H666 ca 666 696HH66 m:mHI>:mH 6:696:60 6H 66626696 H66H6626 H66HN962 666666 H666 :6 6609966 66666H6m $63963 6N26a0 mo 66966 6H966w 6N696>< n: 6Homa 97 In the 1955-1962 period‘MPP?s tended to rise in all areas except for the Northern Plains wheat-roughage-livestock area where there was no change. MVPfs are generally somewhat higher than‘MPP‘s indicating a slight upward trend in prices for the period. The real estate MPP has generally trended upward in all areas in all time periods. Thus the influence of the productivity component of real estate MVPQS is upward and will exert upward pressure on real estate values. If the product price component of MVP is neutral or increases an upward pressure will be exerted on land values. If the price component is negative but not enough to offset the positive MPP component the upward pressure still exists but is weaker. But if the price component is negative and larger than the positive MPP component downward pressure is exerted on land values. ‘We are, of course, assuming that a potential buyer is cognizant of the past behavior of real estate MVP's. Empirical evidence indicates that in American agriculture ability to effectively innovate existing and developing technology tends to be highly correlated with size. Census figures reveal a large decline in the number of farms over the studied period and in- creases in the average size of farms. But the decrease in the number of farms is not uniform over the different size groups. Table 46 tells most of the story as to what has happened over the studied period using 1950-1959 as representative. The over $10,000 class farms increased by 64 percent while the largest class-those selling less than $2,500--decreased by 50 percent. The $2,500-$10,000 98 farm category lost 24 percent of its farms in the lO-year period but increased as a percent of total farms from 29.8 in 1954 to 34.3 in 1959. In terms of value of marketings the over $10,000 class farm increased their share of total farm.marketings from 50.7 percent in 1950 to 71.7 percent in 1959 or an increase in actual value of sales of 93 percent. Both lower sales classes lost in share of market and in actual value of sales during the period. Table 46 Changes in Number and Total Dollar Sales of Farms by Gross Sales Categories 1950-1959, Gross Sales NUmber of Farms Percent Distribu- Percent Change Farm Class Thousands [tion of Farm Nos.‘ in each Class , 1950 1959 7 1950 1959 , 19§Qr1959 Over $10,000 484 794 9.0 21.5 +64 $2,500-$10,000 1603 1270 29.8 34.3 -24 Under $2,500 3291 1637 61.2 44.2 -50 Cross Sales Total Value of Products Percent of Total Market Farm Class Sold - Million Dollars 1950 1959 1950 1959 Over $10,000 11,303 21,860 50.7 71.7 $2,500-$10,000 8,268 6,989 37.1 23.0 Under $2,500 2,340 1.775 12.2 5.3 Source: Edward Higbee, Farms and Farmers in an Urban Age, (New York: Twentieth Century Fund, 1963), p. 156 Thus we find the already larger than average farms becoming larger and the smaller than average farms disappearing. The explanation for this occurrence lies largely in G. L. Johnson‘s fixed asset theory. gealgstate and Fixed Asset theory In classical economic theory the equilibrium amount to use of any factor is determined by equating marginal factor cost (MFG) or assuming a perfect factor market, its price, with its marginal value product (MVP). If the marginal value product is greater than the 99 price, it pays to increase the use of the resource; if less, it pays to decrease its use. N0w if with classical theory we assume that agricul- tural inputs are completely divisible and further that they can be bought and sold for the same price we are essentially assuming that there are no fixed factors. Then if output increases faster than demand and product prices fall, the MVP’S of the inputs will fall and a move- ment of resources out of agriculture into other uses will occur. Further, the equilibrium after adjustment will, according to the theory, yield equal returns to the resources left in agriculture with those in the non-farm economy. Empirical evidence in the form of low returns to factors of production in agriculture in relation to comparable factors in the non- farm economy and the inability of the market mechanism to correct the situation indicates the explanatory power of the classical theory to be less than perfect. 3. L. Johnson, in attempting to extend the theory for better explanatory power started by changing the concept of a fixed asset. He defines a fixed asset simply as one that it does not pay to vary. In other words, it does not pay to acquire any more of the asset nor does it pay to dispose of any of the asset presently on hand. The key to Johnson§s theory lies in recognizing two prices for a productive factor: an acquisition price or the price a farmer must pay to acquire additional units of an asset and a salvage price or the price a farmer could receive if he wanted to dispose of some of the factor. If acquisition price and salvage prices diverge with salvage price less than acquisition price the factor is fixed in the productive 100 range defined by the condition Px acq, 2 MVP: Z Px sal. where Px acq. is acquisition price of the factor (x), P: sal. is its salvage price, and MVP; is the marginal value product of the factor (x) in the production of an output (y). The asset is variable upward if'MVP: is greater than Px acq. and variable downward if MVP; is less than Pk sal.3 Under the fixed asset theory assumptions, movement of land out of agriculture is accomplished only when the MVP of that land in agri- cultural uses is below its salvage price. The salvage price for agricultural land is at or near the zero level, so disregarding the relatively small amount of land which is moving or is in the ripening process to move from agriculture to a higher and better use very little ‘land once in, moves out of agriculture.)+ The acquisition price is the price that would have to be paid in order to draw land into agriculture from.its (formerly) higher and better use in the non-farm sector. Some very minor instances of this occur as in the case of old school grounds reverting back to agriculture after a school consolidation or an old highway right-ofdway being 3For a more complete exposition of fixed asset theory and its applications see Glenn L. Johnson, "The State of Agricultural Supply Analy- sis," Journal of Farm Economics, Vol. 42, (1960) pp. 435-452; and Dale E. Hathaway, Government andAgricultuEg, (New York: Macmillan Co., 1963), pp. 110-126. .Also see Bob L. Jones, "FarmrNon-Farm.Labor Flows, 1917- 1962," (unpublished Ph.D. dissertation, Michigan State University, 1964); and Clark Edwards, "Resource Fixity, Credit Availability and Agricultural Organization," (unpublished Ph.D. dissertation, Michigan State University, 1958). 1+Barlowe estimates that approximately 10 percent of the total U.S. land area was used for non-agricultural purposes in 1958. Further, he estimates it will take approximately 12 percent of the total U.S. land area in non-farm uses to sustain a population of 300 million. The overall impact of the additional 2 percent taken out of agricultural use during the time which it will take this country!s population to reach 300 million will be slight. Raleigh Barlowe, "Our Future Needs for Non- Farm Lands," Land, 1958Yearbook of.Agriculture, (Washington: U.S. 101 plowed up after a new super highway was built on a nearby site. But in general the value of land moving to the non-farm higher and better use is so high as to preclude the land from ever being bid back into agriculture. Acquisition may also be accomplished through draining, clearing, or otherwise reclaiming land not presently used for agricul- ture purposes and the cost of these operations is the acquisition price of such land to agriculture. In recent years only a small amount of land has entered agriculture in this manner. Therefore, in general we can say that for land as between agricultural and non-agricultural uses the acquisition price is infinite and the salvage price is zero. Thus, for all practical purposes the supply of agricultural land measured in acres is fixed or in other words the supply curve for agricultural land has an elasticity near zero at any given point in time. .Any changes then in the demand for agricultural land in the aggregate will be reflected almost entirely as a change in land prices. ‘While supply elasticity of land measured in acres is near zero, capital investments in land and land substitutes add to total productivity of land, and supply elasticity is more elastic when land is measured in some type of constant productive units. Since the ability to increase the effective supply of land by these means depends on technological im: provement in the various types of reproducible capital or techniques applied to land this concept applies over time as the new methods, techniques and capital improvements become available and are innovated. This does not change the above static supply elasticity argument. Government Printing Office, 1958), pp. 474-479. Localized impacts around established metropolitan areas will be great but for purposes of this study agricultural land will be valued for agricultural uses and the speculation effect with regard to moving land to a higher and better use will not be considered. 102 While land is fixed in agricultural uses it is not fixed generally to the specific agricultural enterprise. The type of agricultural use to which any given parcel of land will be put depends upon where its particular comparative advantage lies. Comparative advantage dictates that a given piece of land will be used in the production of that product or combination of products from which it will receive its greatest return. .At any given point in time a specific demand, supply, and price structure exists for agricultural products. This structure, along with the productivity level or state of technology which then exists, determines the marginal value product of each of the productive factors. The acquisition price then for land in producing a specific agricultural commodity or product is the price which must be paid for agricultural land by the producers of that commodity to bid land away from its present agricultural higher and better use. Thus it is the MVP of land in that higher and better use. The salvage value is the price at which the land will change from its present use to its opportunity cost or next best alternative use--that price being the MVP of land in the next best alternative use. The acquisition and salvage prices then define the limits of the range of comparative advantage for land in the production of a specific product. With a differential change in the product price structure or productivity level of land between different types of enterprises, land at the margin of transference, that is, land where MVP in its present productive use shifts outside the comparative advantage range defined by the acquisition and salvage prices, will shift toward the more profitable use whether it be a partial shifting 103 of some land between enterprises on individual farms or complete shifts of land use on whole farms or what is more likely, a combination of both. The comparative advantage range can shift through time as various economic and technical characteristics which determine compara-I tive advantage change differentially between areas or between types of production. Contributing to a shift in the comparative advantage range for a given area or type of production are changes in land productivity (MPP) or product prices in other areas while land productivity and product price in the given area remain constant. This is an externally generated change in comparative advantage for the given area. Shifts in land MVPgs within a given area or type of production are caused by changes in land productivity or product prices in that area while land productivity and product prices outside the given area remain constant. This is an internally generated change in comparative advantage for the given area. Thus, land use and land value in a given area will change when its MVP in its present use falls outside the comparative advantage range. This can be caused by (l) movement of the comparative advantage range, (2) movement of the land MVP in that area, or (3) a combination of both movements in opposite directions or at different rates in the same direction. To the extent that both forces move in the same direction land values will change in the same direction but land use patterns will not change (i.e., land will tend to remain fixed in its present agricultural use). 99mparative.Advantage and Agricultural Production In order for the comparative advantage concept to apply an interdependence must exist between different agricultural areas. Tolley \J 104 and Hartman suggest four characteristics of American agriculture which contribute to this interdependence. First, various agricultural areas compete with each other because they produce for a common aggregate market. Second, different areas produce various common crops so they are competing in the supply of the same product. Third, agricultural areas are different enough from each other to react differently to production or demand changes occurring through time. Fourth, changes in production and demand variables normally occur in such a way that various areas are affected differently.5 According to Barlowe, four categories of economic and technical 6 characteristics combine to determine comparative advantage. They are (1) natural advantages, (2) favorable production combinations, (3) transportation advantages, and (4) institutional advantages. Over time the characteristics included in these four categories can change differentially between areas. These changes may occur sometimes by an act expressly for that purpose such as drainage, fertilizer application, irrigation, or recombination of inputs made possible through technological innovation which may favor one area relatively more than another. In some in- stances no express act on the part of the agricultural participants is required in an area to change comparative advantage patterns. Non- 5G.S. Tolley and L.M. Hartman, "IntereArea Relationships in Agricultural Supply," Journal of Farm.Economig§, Vol. 42, (1960), PP- 453-473- Raleigh Barlowe, Land Resource Economics, (Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1958), pp. 246F248. 105 farm population and industry shifts give some areas a location advantage and technological improvement in transportation facilities work to the advantage of some areas more than others. Almost any government agricul- tural program will work to change comparative advantage patterns through favoring some areas relatively more or to the detriment of others. Over time the differential impacts of changes in the economic and technical characteristics of comparative advantage will be reflected in changes in relative marginal value products of the land input between and within the different agricultural areas of the country. Or, in other words, the comparative advantage range and/or the land.MVP in a given area may shift to the point where the MVP lies outside the comparative advantage range, causing a disequilibrium.situation and pressures for corrective adjustment. The effect of changes in variables constituting shifts in comparative advantage may be very difficult or impossible to pinpoint in the time period in which they occur or even in the next several years since there is usually a lag between shift and adjustment. Or the shift may not be great enough to cause the land MVP to lie outside the comparative advantage range in the short run. However, the net effect of many shifts in variables constituting comparative advantage occurring through time and affecting areas differently will show up in the changing relationships of different area’s land marginal value products and hence in land prices. Labor and Fixed.Asset Theory Labor also has a diverging acquisition and salvage price for the farm sector. .According to Jones, the acquisition value for a person entering the farm sector is the present value of his expected future 106 net income streams from.the best non-farm job he could hold given his age, educational background, alternative jobs available and the unemploy- ment rate. The person should enter agriculture only if at that point in time his expected earnings in agriculture are equal to or greater than the expected earnings in the best available non-farm job. The salvage value of a person already in agriculture and con- templating leaving is the present value of expected future income streams from the best non-farm.job he could hold given his age and other factors mentioned above at the time of transferring out. The divergence between acquisition and salvage prices widen as age increases due to the difference in kinds of jobs available and transfer costs. Unlike the case for land the salvage price for agriculture labor is greater than zero and the acquisition price is less than infinity. Now applying the comparative advantage range concept developed above for land use within agriculture to labor use between agriculture and the non-farm sector we see that labor is fixed in agriculture if its marginal value product in agriculture falls within the borders of the comparative advantage range delineated by the acquisition and salvage prices for farm labor. The comparative advantage range may shift over time as well as the actual MVP of farm labor. The growth rate of the general economy and the changing labor requirements toward higher skill levels has tended to shift the acquisition price upward and the salvage price downward. Any time agricultural labor MVPfs do not keep pace with the movement in the acquisition price it means that expecta- tions in an earlier period were wrong and labor in agriculture is receiving lower returns than labor in the non-farmer economy which was comparable labor at the time the expectations were formed. Empirical 107 evidence indicates that farm labor‘MVPPS have not kept pace with acquisition prices; that the MVP's of a substantial amount of farm labor have dropped below salvage prices and out movements from agriculture have resulted; but a substantial portion of the labor fixed in agriculture is not receiving returns comparable to non-farm counterparts. Implications of Fixed Asset Thgg£y_ Given this situation the labor fixed in agriculture will attempt to push their'MVP's toward the labor acquisition price. One way for the individual farmer to increase his MVP is to organize his farm as efficiently as possible and to use all relevant existing technology. The efficient use of much of the new technology available in recent years requires large operating units. Many commercial farms are still too small to efficiently use available labor and new or existing technology. This can take two forms. The new technology may be such that the operating unit can become more efficient only by increasing and re-combining the quantities of all the major factors of production (land, labor, and capital). The payoff in this case is a lower cost per unit of production. Or the farmer may be forced to adopt the new technology in order to survive as Cochrane points out in what he calls the "Agricultural Treadmill" effect.7 If this new technology is labor or both labor and capital saving it means the farmer on his existing unit will have excess or under-employed resources in the 7Willard W. Cochrane, Farm Prices, Myth or Reality, (Minneapolis: University of Minnesota Press, 1958), chap. 5. 108 form of labor, capital, or both. Since many commercial farmers have found themselves in this position in recent years--too little land to efficiently use available labor and new or existing technology--they have continued to bid actively for the relatively small proportion of 8 farmland which becomes available for sale each year. Since much of the new technology available is of the labor and land saving type and in light of the earlier discussion about the introduction of input substitutes into the production function we would expect both labor and land MVPfs to increase as the farmer attempts to improve his position. The labor MVP even though higher may still be far below the labor acquisition price. But the MVP of land has increased along with that of labor and he can now afford to pay a higher price for additional increments of land on which he can apply his excess labor and capital. Thus it is theoretically possible and empirically quite probable for land prices to rise and for expansion buyers to be willing to pay these higher prices even though labor and/or capital may not be receiving returns comparable to their non-farm counterparts.9 Summary and Implications One reason for the expansion buyer’s rationale in bidding up land values is connected with the fixity of labor and capital and the advancement of capital and labor saving technology on the individual 8Williamn. Scofield, "Dominant Forces and Emerging Trends in the Farm Real Estate Market;" paper presented at the Seminar on Land Prices, North Central Regional Land Economics Committee, Chicago, November 21, 1964. 90. L. Quance is presently working on a Ph.D. dissertation at Michigan State University on Capital flows in U.S. agriculture under the same Resources for the Future, Inc. project of which this study is also a part. He contemplates explaining capital?s role in agriculture within the general framework of fixed asset theory. 109 farm unit. Labor once committed to agriculture tends to become fixed there even though its returns drop below returns to labor in the non- agriculture economy which was comparable to the agricultural labor at the time of commitment to the respective occupations. An agricultural laborer having worked in that industry for say 15 years cannot expect to move to a non-agricultural occupation at the same job or rate of return as the non-agricultural laborer who has been working in his chosen industry for 15 years is presently enjoying. The agricultural laborer must plan on starting in the non-agricultural job at approxi- mately the same job and rate of return as anyone else just starting in that occupation. For this reason, even though the farmer is not earning as much as he expected to be when he choose the agricultural occupation, he stays in agriculture because he is still earning more than he could if he moved to a non-agricultural job after having worked in agriculture for several years. Capital items can also become fixed to a farm, in that at any point in time they may be earning less than expected when they were purchased but more in their present use than could be realized from their sale. Thus, both capital and labor may be earning less than their non-farm counterparts and yet be fixed in agriculture. With the innovation of labor and/or capital saving technology the available capital and labor becomes under employed. One way out of this dilemma for many farmers is to enter the land market to expand the farm land base and thus use to capacity the fixed labor and capital assets and in doing so attempt to increase net farm income. These expansion buyers could conceivably bid the price of expansion land purchases up to a maximum where the return on the land 110 investment plus the out-of-the-pocket costs for operating expenses exhausts the additional total product derived from the expansion unit. The rationale is that labor and capital are fixed and any under employed portions are essentially free goods for use on the expansion purchase. 80 up to nearly the full net income from.the expansion purchase may be capitalized into the purchase price of land bought for expansion purposes. The expected effect of government programs on the value of farm real estate, under the assumptions of fixity of labor and capital and rapid technological advance resulting in excess capacity with regard to these inputs on individual farms, is quite different than would be expected if agricultural firms were in equilibrium in the classical economic sense. According to traditional theory, programs which hold commodity prices above market clearing levels will cause the marginal value products of all inputs to increase and more of all to be used. The amount of increased use of each would be determined by the relative elasticities of their supply curves and the elasticities of substitution. But where labor and/or capital over capacity already exists, the in- crease in their MVP’s may not be enough to make it profitable to add more of these inputs. That is, the input MVP may not rise enough to equal the cost of additional units of the input. In this case the increase in returns from the government program will be allocated to the land input and capitalized into a higher price for land. .As long as laborfs MVP in agriculture is equal to or greater than its MVP would be in its non-farm alternative it is fixed in its present use. The farmer then is willing to accept a return to his labor equal to or near the level of returns he could presently receive from a non-farm lll source. He is also willing to accept a return from his capital in- vestment approximately equal to the return he could receive by selling this input and investing the money in an alternative use. Thus he is willing to allocate a greater share of net farm income to the land input and any increase in net farm income tends to be totally allocated to land. This means that even though the expansion buyer's labor and capital may not be earning returns comparable to labor and capital elsewhere he will pay a higher price for the expansion land purchase. His actions are justified both by the fixed asset argument and because land MVP?s have risen. CHAPTER.V AN ANALYSIS OF RESULTS IN HISTORICAL PERSPECTIVE In this chapter the models used are appraised as to their use- fulness and relevance and the data is analysed in its historical perspective. Appraisal of the Residual Return Mpdgl_ The capitalized ex post and ex ante series from.the data yielded results which allow an appraisal of the assumptions and usefulness of the model. The assumption regarding the imputed return to labor was that the current factory workerfs wage in the specific area, adjusted by the national non-farm unemployment rate reflected the farm labor salvage value--that is, the minimum farm labor wage bEIOW‘WhiCh out movement of labor from the farm would occur. Both the ex pest and ex ante series generally show a closer relationship with market value estimates from Costs and Returns data in the pre WOrld War 11 years than in the post'Wbrld War 11 period. While the Costs and Returns estimates trend upward with only an occasional dip since the early 1930's the ex pgst series generally build to a peak in the early post war years and then decline through 1962, with the series in some areas exhibiting negative land values for some years. Both the Egg£§_ and Returns market value estimates and the ex pgst estimates from the production function model exhibit generally rising land value trends throughout the period. This fact pinpoints the cause for the different 112 113 behavior exhibited by the ex pgst series from.the residual return model to our assumption about the imputed salvage value of labor. By a rather back door route the results indicate that during the 1930’s the assumed salvage value was near that which farmers them- selves appeared to consider proper since the residual model series generally approximate the Costs and Returns market estimates. Begin- ning in the mid 1940‘s factory workers wages began an increase which carried through the remainder of the period. .As these higher wages were imputed into the residual calculations lower residuals were left for land with the resulting decrease in the residual series and the wider and wider divergence between them.and the Costs and Returns estimates. Thus, it appears that the unemployment rate adjusted factory wage is becoming less and less accurate as a proxy for what farmers themselves believe to be their true salvage value. It is true that out movement of labor from agriculture has been great over the period. For those who moved out the salvage value which they recognized for themselves was obviously higher than the one assumed in our model. But for those farmers still in agriculture and willing to pay the Costs and Returns estimated price for land our assumed labor salvage price is too high. One explanation for the change in recognized labor salvage values is that those most willing to move out of agriculture are the first to leave. .As the farm labor income falls relative to non-farm wages the farmers who are left are probably those who are more the agricultural fundamentalist types who put a greater value on rural life for a variety of reasons and are willing to accept a lower labor income in order to stay on the farm. 114 Another explanation is that a large proportion of the decline in number of farmers is due to non-entry of young farmers as opposed to out movement of established farmers. It follows then that the average age of active farmers is increasing. Due to the work rules and customs established with regard to employment in the non-farm economy which favor employment of younger initial entrants into this job market, the unemployment rate adjustment applied to the factory wage is not enough to reflect the plight of the older farmer searching for a non-farm job. His age might exclude him from consideration for a non-farm job with wages comparable tothose of a factory worker as we have assumed. Preliminary results of a Ph.D. thesis presently being completed by Chennareddy support these findings. Chennareddy developed a model for estimating the present value of future income streams for a 25-year old and a 45-year old worker in the farm sector and in four different occupations in the non-farm sector. He found the present value of future income streams for a 25-year old farm worker to be most highly correlated with those of a factory worker while for a 45-year old farm worker the highest correlation was with workers in laundrys and retail trades. Thus when first entering farming the workerfs relevant salvage value appears to be a wage comparable to that he could receive as a factory worker but after having been engaged in farming for approximately 20 years, the farm.worker!s salvage value has declined to a level comparable to what he could receive in the relatively low paying non-farm jobs such as laundry worker or employee in the retail trades.1 lVenkareddy Chennareddy, Present Values of the Expected Future 115 Certain specific areas, however, deviate enough from the general pattern to warrant further comment. The ex post series in the Southern Plains wheat and wheat-grain-sorghums areas and the Washington and Oregon wheat-fallow area exhibit upward trends throughout the period at a higher level than the Costs and Returns market price estimates series. This means that farm labor incomes have surpassed factory workers incomes in these areas. Factory wage rates were consistently lower in these areas than in the Midwest and East accounting for part of the reason for the relatively high farm.labor incomes. A similar though less apparent situation exists in the Northern Plains wheat areas. Here the ex pgst series generally trends upward although not as fast and the whole trend in each area lies below the trend in the Costs and Returns market price estimate. Since all of the wheat areas exhibit similar trends 8 plausable explanation is that the price stabilizing influence of the government wheat price support programs plus relatively low factory wages have caused this effect. Two other areas whose ex post series only level off without any appreciable drop in the post war years are the cash grain and hog- beef fattening areas of the Corn Belt. .A main enterprise of these farms is corn. With the combination of mechanization, increased size, and increased use of fertilizer contributing to large productivity increases particularly since 1955 (See Tables 27 and 28) and government supported corn prices, net farm incomes have increased enough that farm labor income has increased at approximately the same rate as lgcome Streams and their Relevance to the Mobility of FarmlWorkeps, Ph.D. dissertation in progress, Michigan State University. 116 factory workers wages if_the price of land remained relatively constant. Since the ex post series lies below the Costs and Returns series land prices have risen and labor income has declined relative to factory workers incomes. Further analysis along these lines are beyond the scope of this study but the evidence presented becomes very significant with regard to the usefulness of the residual model in light of earlier discussion on fixed asset theory and expected behavior of land marginal value products. The analysis indicates that farmers may rationally pay higher prices for land if the land.MVP's support this action even though they may not be receiving returns for their labor comparable to labor returns in the non-farm economy. In order for the residual model to estimate land values equal to the production function land value es- timates, the yearly residual return estimates would need to equal the yearly MVP estimates. Thus, the residual model would estimate land values equal to values estimated from the production function model only if the production function model exhibited constant returns to scale, and the imputed capital and labor returns in the residual model equaled the returns to these inputs yielded by the production function model. This means that farm labor and capital returns would have to equal those in the non-farm occupation chosen as representing the labor and capital salvage values. Obviously this has not been the case, at least for labor, since farm labor returns have declined relative to labor returns in the non-farm economy. The Costs and Returns market price series is gen- erally bracketed by the ex pgst series from the residual model on the 117 low side and the same series from the production function model on the high side. Thus when there is a spread between the residual and production function series the relative position of the Costs and Returns series to the other two may give some indication as to the relative strengths of declining farm labor income in relation to non- farm labor income on one hand and rising land MVP?S on the other as farmers? criteria in evaluating the price to pay for more land. The residual model is useful as a tool for analysis and com: parison. But due to the problems in arriving at a salvage value for' farm labor and capital it should be used with extreme care in estimating market values of land and then only on a case by case basis. Indi- vidual farmers who have their own criteria with regard to their specific labor salvage value and the minimum return they will accept on non- land capital may use the model to estimate what they could afford to pay for additional land. But these imputed values certainly cannot be generalized when using the model for this purpose. Appraisal of the Production Function Model Reder states that since it is not a "production function" in the economic theory sense and since the difference is one of theoretical importance the Cobb-Douglas function is useless in making empirical estimates of input marginal physical products and in determining the demand curve for these inputs.2 Bronfenbrenner answers Reder?s criticism.and the following draws heavily on his comments.3 The 2Melvin W. Reder, "An Alternative Interpretation of the Cobb- Douglas Function," Econometrica, Vol. 11, (1943), pp. 259-264. 3Martin Bronfenbrenner, "Production Functions: Cobb-Douglas, Interfirm, Intrafirm” Econometrica, Vol. 12, (1944), pp. 35-44. 118 theoretical function is an intrafirm.function which holds at one moment in time for a specific firm. The Cobb-DOuglas function is fit to different observations on the same firm.over time (time-series) to observations on different firms at one moment in time (cross-section), or in our case both simultaneously. Thus according to the argument it is an interfirm function fit to observations where each observation lies at one point on different intrafirm functions. .At any one point in time for any single firm its actual location on its intrafirm function is the only one which is relevant--all other points are hypothetical. The interfirm (Cobb-Douglas) function is a locus of all these "actual" locations of each firm on its intrafirm function. If we assume a long run competitive equilibrium the interfirm function must be a straight line running through the origin or a number of parallel straight lines running through their respective origins and tangent to each of the relevant intrafirm functions at its point of maximum.average product. This means that the sum of the coefficients for the physical inputs must equal one, thus yielding constant returns to scale. .At the points of tangency between the inter-and intrafirm functions the slopes of the two must be equal and since they are at the same coordinates, beyond which the intrafirm function lies below the interfirm function and declines with respect to the interfirm function the elasticity of production computed for the intrafirm function where the other inputs are fixed will be less than one while the elasticity computed for the interfirm.function where all inputs are varied in proportion will be one by definition. This yields decreasing marginal physical products for the variable factor with 119 the others fixed and constant returns to scale when all are varied in proportion. Due to imperfections in the factor markets or to long run disequilibrium, the interfirm.production function may not be tangential to the intrafirm curves but rather cut them from.above or below. The sum of the elasticities of the physical inputs then may be either greater or less than one and the resulting marginal physical products for the individual inputs will allocate either more or less than the total product in returns to the factors. Bronfenbrenner indicates an impressive list of some 15 studies, both time series and cross sectional, in which the Cobb-Douglas technique was used and for which it presented results bearing out the marginal productivity theory which it was designed to verify. Unsatisfactory results, where they have occurred, have been due to statistical instability of the data according to Bronfenbrenner. One which yielded partially unsatisfactory results was a time series study by Leonard Felsenthal, "Studies in the Cobb-Douglas Production Ennction for‘Mining and Manufacturing in Germany, 1925-1936," (un- published M.A. thesis, University of Chicago, 1940). The sum.of the coefficients significantly exceeded one but the ratio of the labor coefficient to the sum of the coefficients corresponded closely to the actual proportionate share of labor in German national income during the studied period.h Since 1944 when Bronfenbrenner wrote his article, the Cobb- Douglas technique has been adapted for use in numerous production thido pp. 112—113. 120 function studies and a great deal more has been learned about its properties. The "statistical instabilities" which he alludes to as the reason for unsatisfactory results have been more Clearly defined. When the sum of the coefficients is less than one the most probable cause is omission or under estimation of relevant inputs. Sums of coefficients greater than one usually occur because the unit of measurement and mode of within category aggregation used in entering the inputs does not properly reflect the effective quantities of inputs actually used. It comes back to the fact that acres, man-hours, and constant dollars do not in many cases do an adequate job of reflecting the actual changes which take place in the input categories over time or between firms. The within input category mix changes, both in terms of relative quantities of individual items and in terms of quality, cannot be reflected by the commonly employed methods of measurement. An alternative interpretation-~the one used in this study--is that the sum of the coefficients is not an indication of returns to scale but rather of returns to size, in which the within input category mix changes as the size of the firm increases allowing the use of certain available technology not readily adaptable to the smaller size firms. If we choose to assume, contrary to the argument presented in Appendix B for the use of a restricted function that no relevant input variables have been left out of the function, and that the sum.of the elasticities is an indication of returns to scale, and we further believe constant returns to scale to hold, then according to Bronfen- brenner an adjustment of the coefficients scaling them.down in propor- tion till their sum equals one (that is géz, for each b1)is a 121 reasonable method of determining proportionate shares of total product to allocate to each factor. Incorporating this technique for the original unrestricted function where the land coefficient was .519 and the sum of coefficients was 1.62 we find the share of total product allocated to land to be (12%2) or .320. This would scale down the‘MPPés derived from.the original function to about 62 percent of their non-adjusted levels, which were not calculated in this study. Using this technique on the land coefficient from.the restricted function, the scaled down coefficient would be (:32?) or .267. Thus the MPP's derived from the restricted function £6t3uging the adjusted coefficient would be about 75 percent of the level at which they were in fact estimated and presented in Tables 21-39. This would also fix a lower limit on the two land value series derived from the production function model at 75 percent of their tabled values in Tables 2-20 (columns 2 and 3). Reduction of these series to a constant 75 percent of their present levels would not change either the within series or between production function derived series relationships. It would, of course, change the within year relationships of these two series with the other three in the table. Decreasing the magnitude of the production function derived ex ante series will decrease the pressure for increases in land prices. The areas where this adjustment will reverse the pressure, that is, where before the adjustment the ex ante series lay above the Costs and Returns market estimates but lay below after the adjustment include Eastern Wisconsin Dairy, 1935-1940; Western Wisconsin Dairy, 1935-1936; 122 Hog-Beef Raising, Corn Belt, 1935-1937; Cash Grain Corn Belt, 1939- 1941 and 1959-1962; Texas Black Prairie Cotton, 1935-1938; all wheat areas, 1935-1937 or 1938; and in addition, Southern Plains Winter Wheat, 1960-1962; Wheat-Grain-Sorghums, 1954-1962; and‘Wheat-Fallow, 1961-1962. .Adjustment affecting the relationship between the ex post series and market estimates series in the same way includes Southern Plains Winter Wheat, 1948-1961; Wheat-Grain-Sorghums, 1945-1962; and'Wheat-Fallow, 1951-1962; and Cash Grain, Corn Belt, 1949-1962. These adjustments should be kept in mind with a view toward possible alternative interpretations of the data throughout the latter analysis sections of this chapter. Pr0posed use of the Production Function as a basis for Allocation of Net Farm Income. The coefficient scaling technique allows the production function model to be used in determining proportionate shares for distribution of net farm.income to the unpaid factors--land, operator and family labor, and capital. In a sense then, used in this way, the production function becomes a simultaneously determining residual return allocation model. The problem.with most residual return models, including the one used in this study, is the necessity of assuming a rate of return for all factors except the one to which the residual is to be allocated. Then when net income fluctuates widely from one period to another, the residual factor assumes the total of either the windfall gain or loss from these fluctuations. A more reasonable approach would be for all factors to share these windfall gains or losses in proportion to the contribution of each to net farm income. .Ill " sli... .1 i 123 Hurd has developed a simultaneous net farm income allocation procedure in which he converts physical quantities of land, labor, and capital to a common denominator using current market prices of each in a base period in order to determine the proportionate share of net farm income to allocate to each factor.5 Iden extended the Hurd procedure to using current market prices in each year for the factors thus allowing the proportionate shares to change from year to year.6 The difficulty with both procedures is that an implicit assumption must be made that the market is pricing the factors perfectly in accordance with their actual relative worths in producing net farm income. Thus to the extent that the factor markets deviate from perfection due to institutional barriers and/or imperfect knowledge and foresight, the proportionate shares calculated for allocative purposes will be biased. Use of the scaled coefficients from a production function yields a proportionate share for each input which can be used to allo- cate net farm income without the necessity of introducing current market prices of the inputs in the process. This will allow the results from.the production function simultaneous allocation model to be analyzed in terms of current market prices for the inputs to answer questions of over or under investment in specific factors and the appropriateness of the given combination of factors in view of their current market prices. Although the technique was not employed in 5Edgar B. Hurd, "Allocation of Net Farm Income," Agricultural Eponomics Research, Vol. 9, (1957), pp. 10-19. 6George Iden, "Farmland Values Re-explored," Agricultural Economics Research, Vol. 16, (1964), pp. 41-50. 124 this study it has promise for proving a useful tool in future investi- gations of this general type. Analysis of the Land MVP Series over the Studied Period Across all areas we find both price and productivity declining in the early 1930's. The Depression coupled with widespread drought conditions were primarily responsible. The first New Deal agricultural legislation, the Agricultural Adjustment Act of 1933, appears to have slightly reversed the downward trend during 1933 but its greatest impact came in 1934 and 1935 when generally larger price increases were evident. This coupled with more favorable weather in 1935 caused large jumps in land‘MVP3s in these years. In 1936 parts of the 1933 Agricultural Adjustment.Act were declared unconstitutional while other emergency provisions had run their course. Thus prices generally slowed their advance in the wheat, cattle, and sheep areas, and declined in the dairy and Corn Belt areas in 1936. Productivity increases in the dairy and Corn Belt areas generally increased thus softening the effect of price declines on land MVPfs. In 1937 the Agricultural Marketing Agreement Act was passed. It was designed to boost milk producers incomes through establishing minimum prices which processors could pay producers for milk in local market areas where producers agreed to the production control terms of the marketing agreement. Prices still declined in 1937 and 1938 although productivity increases caused land MVPfs to decline only slightly in the dairy areas. The most important New Deal legislation in agriculture was the Agricultural Adjustment Act of 1938. It established the basic 125 price support and production control provisions for the storable agricultural commodities and the basic provisions in this act with amendments are still in effect today. When it appeared that a crop covered by the act was going to be in surplus causing severe price declines the Secretary of Agriculture could use price support and marketing quotas to keep the price above the market clearing levels and/or bring production in line with consumption. This legislation did not receive a realistic test of effectiveness until much later in the period due to the beginning of‘WOrld War II. From the beginning of the studied period till 1940 agriculture was in a depressed state. During this period so was the rest of the economy, so relatively the farmer was about as well off as his urban neighbor. ‘With World War II came sharply increased demand for agricul- tural products across the board. These increases were due to great demand by the armed forces where consumption levels per capita were generally higher than in civilian occupations, need for agricultural products by allied nations, wastes and losses due to the war, and increased domestic demand due to higher income levels and low unemploy- ment rates. To cope with the increased demand the government agri- cultural policy changed from production restriction to encouragement to expand production. .Acreage allotments were dropped and price support levels increased in an effort to decrease some of the uncer- tainty about future demand for expanded output. Both the basic crops and the Steagall Commodities were to be supported at or above 90 percent of parity for at least two years after the end of hostilities.7 7The basic crops include cotton, corn, wheat, tobacco, rice and peanuts. The Steagall commodities are hogs, eggs, chickens, turkeys, milk, butterfat, dry peas, dry edible beans, soybeans, flax seed and 126 As anticipated, prices increased enough during the war that price support provisions were not actually used. In fact, shortages of many commodities persisted throughout the war and demand remained high after the war due partly to the needs of both allies and former enemies in the warfs aftermath and beginning of reconstruction. The price break came in the wheat and cotton areas in 1947 and carried through 1948 while for all other areas it arrived a year later in 1948. Land productivity increased throughout the war years up to 1948 or 1949 in all areas but in general the increase was at a faster rate in the earlier years of the war than in the latter part of the period. This was probably due partly to pressing into production more and more land resources which under ordinary circumstances would have been considered sub marginal for these uses. Also many improved capital inputs were difficult if not impossible to obtain due to the war effort. Finally Stallings weather index shows very favorable weather for wheat, corn and cotton in the early 194033. The net effect was a tremendous increase in land marginal value products between 1940 and 1948 in all areas. In both the dairy and cotton areas MVPfs increased on the average 255 percent, in the wheat areas they averaged a 248 percent increase, in the Corn Belt areas they averaged an increase of 241 percent and in the Western cattle and sheep areas the increase averaged 203 percent. Thus, wartime demands were relatively more favorable to marginal value products of land in the dairy, cotton, and wheat areas than in the Corn Belt and‘Western livestock areas. peanuts for oil, potatoes, sweet potatoes, and American-Egyptian cotton (upland cotton is a basic crop). , .I'v 127 The mandatory supports at 90 percent of parity for the basic crops and the Steagall Commodities ended December 31, l948--two years after the end of WOrld War II. Congress decided to extend support for the basic commodities and the Steagall Commodities, hogs, chickens, eggs and milk at 90 percent of parity and for the other Steagall com- modities and all other cr0ps at 60-90 percent of parity the exact level to be based on a formula which considered carryover, estimated production, and estimated disappearance. Thereafter a sliding support scale was to go into effect but this provision was superseded by a series of amendments preventing the formula from operating until 1955 for the basics. Price declines to support levels coupled with declines in productivity in the wheat, cotton, and corn areas due to relatively unfavorable weather caused declines in land MVP?s in this year. After a particularly heavy battle over the Brannan Plan, Congress decided instead in favor of frozen supports for basics at 90 percent of parity in 1950. The Steagall commodities and all other crops started on the sliding scale support in 1950. Marketing quotas in the form of acreage allotments were put into effect. Slight produc- tivity and price increases in 1950 caused land MVP?s to increase in most areas. Then with the outbreak of the Korean war government policy again turned toward encouraging production and the price support levels were maintained for the basic crops at 90 percent of parity through 1954. Prices during 1951 and 1952 held firm due to increased war demands but fell back to support levels in 1953 and 1954. Productivity increases were not great enough to offset the price declines and land MVP‘s generally declined. 128 The Eisenhower administration was characterized by a determina- tion to and rigid, high price supports which tended to encourage continued over production of crops already in surplus. The 1954 Agri- cultural Act provided for sliding support levels which finally were put into effect causing prices to be generally lower and again land MVP's declined because productivity increases were not enough to offset price declines. Price support levels were raised slightly in April 1956 from levels at the beginning of the year for corn, wheat, rice, dairy products, oats, barley, rye, and grain sorghums in response to the forthcoming presidential election and the dip in net farm income in 1955, but Eisenhower still resisted Democratic pressures to return to high, rigid price supports. A bill passed by Congress returning the basic cr0ps to rigid price supports at 90 percent of parity was vetoed by the President. Eisenhower then pr0posed the Soil Bank.Acreage and Conservation Reserve Program, which was passed by Congress. The Acreage Reserve provision lasted four years while the Conservation Reserve portion was in effect for five years. Stable to moderately higher prices in the 1955-1958 period coupled with substantial increases in productivity, particularly in the Corn Belt areas, caused land MVPfs to increase considerably during the period. No specific change in land MVP?s or productivity can be attri- buted to the Soil Bank program on the basis of the data in this study. In the 1960 election campaigns the Republican farm program stressed movement toward the free market and fewer governmental restric- tions on the farmer?s freedom of action, while the Democrat farm proposal was to increase farm incomes by strict government administered production control programs. The change of administrations 129 did in fact change the emphasis toward stricter production control and included direct payments and two price systems. Prices remained approximately the same in the early 196033 as they were in the late 195038. But during the decade of the 1950’s large increases in productivity took place which showed no sign of abating in the 196033. The productivity gains were large enough to more than compensate for price declines in the latter part of the 1950's and land MVP?s trended upward throughout the period and into the 1960's. These trends were different in different areas. Between 1955, generally the low point for land MVP’s in most areas during the 1950's and 1962, land MVP?s in the'Western livestock areas increased on the average 64 percent, in the Corn Belt areas the increases averaged 55 percent, in the dairy areas the average increase was 42 percent, in the wheat areas the increase averaged 37 percent with the Southern Plains wheat areas increasing more substantially than the NOrthern Plains and Washington-Oregon wheat areas, and in the cotton areas the increases averaged 21 percent. Part of the reason for the almost complete reversal of gains in land MVPfs during this period as compared to the war period is that consumer demand has shifted away from cereal grain products and toward meat and livestock products while synthetics have replaced cotton to a large extent in the con- sumerfs market basket. Nevertheless, land marginal value products have trended steadily upward over the period. While it is difficult to attribute specific changes in land MVPfs to specific government programs we can be certain from the above analysis that the land MVP's most directly 130 and strongly affected are those in the areas most heavily dependent on government price support programs. If productivity increases continue at a rate similar to that in evidence in the study, drops in prices of farm products of politically acceptable magnitudes, assuming some type of farm program will be with us into the future, will probably not be great enough to overwhelm the productivity trend and land MVPSS'will continue their rising trend into the future. This means that the question is not whether landowners will gain or lose from.amall changes in government programs but rather how much will they gain when we assume that land prices are based totally on capi- talized marginal value products. Apalysis of the Land Value Series The ex ante land value estimates by both the residual returns model and the production function model indicate that in the wheat areas, the Western livestock areas, the Central Northeast dairy area, the Minnesota dairy-hog area, and all Corn Belt areas except the hog-beef raising area, great Pressure for land price increases should have been built up during the war years. While land values as estimated in the Costs and Returns series did in fact increase during the war in these areas, their rise was much less spectacular than appears to have been warranted by these estimates. The most obvious reasons for the slow reaction of the land market during this period are that farmers had their hands full during the early war years at- tempting to pay off debts incurred during the Depression; that the Depression had made buyers cautious about making long-term investments which may be very difficult to pay off if another depression were to Tallul- 131 occur after the war; and that regardless of whether another Depression occurred or not, the heavy demand for farm products would no doubt slacken at warSS end as had happened after World War I and the govern- ment programs gave no guarantees beyond two years after the end of hostilities so the expected lower income streams would not support large land price increases.8 The residual model ex pgst series for these same areas also indicate that even with the adjusted factory workers yearly wage imputed as the salvage value for farm labor much higher prices for land could in fact have been paid while leaving the farmer relatively as well off as his factory worker cousin. In the post war period, however, the residual return ex pgst series declines rapidly in all areas except the Southern Plains wheat areas, the Washington-Oregon wheat area, the Intermountain cattle area, and the Northern Plains sheep area. This indicates that farm incomes did not keep pace with non-farm incomes and in order for farm labor income to be equal to factory workers incomes the residual return to land had to drop severely--and in some cases become negative. In the exception areas the residual model ex pgst series indicates that residual returns to land even in the post war period would have supported higher land prices than were actually paid according to the Costs and Returns estimates. In the exception wheat areas this can be partly explained by the fact that under the 8Lerohl found lO-year average expected prices of 13 farm come modities to be below actual prices for the 1942-1951. Since price expectations play a particularly heavy role in determining land prices this is significant in explaining a slow reaction of land price during the war. Milburn L. Lerohl, "Expected Prices for U.S. Agricultural Commodities, 1917-1962," (unpublished Ph.D. dissertation, Michigan State University, 1965), p. 69. 132 assumptions of the residual model the full amount of any government program.payments are added to the land residual and capitalized into land values. The wheat program supporting wheat prices at high levels coupled with relatively large payments for conservation practices through the Agricultural Conservation Program increased the residual to land substantially in these areas. In part, however, the high residual values can be explained by the relatively low factory wages imputed in these areas. The wool program no doubt contributed to the high residuals in the N0rthern Plains sheep area. No further explanation is evident for the high residuals in the Intermountain cattle area. In the remaining areas the residual model shows that if farm labor returns are to be comparable to factory workers wages the return to land had to be low or negative throughout the entire studied period. The relationship between the production function ex post series and the Costs and Returns market value estimates appears to depend somewhat on the level of the residual return ex pgst series particularly in the latter years. Generally, the nearer the residual ex post series comes to equaling the Costs and Returns market estimates the nearer the market estimates come to the price which the production function model estimates can be paid for land on the basis of its marginal value product. This relationship shows that farmers pur- chasing land evidently consider some minimal return to their labor which they are willing to accept and this figure is influenced by the level of non-farm.wages in their respective areas. 133 The production function ex post series indicate that in all areas except the Southern Plains wheat areas and Washington and Oregon wheat area the market price is below--some places substantially below-~the price which could be paid for land based on the actual future income streams accruing to land under the assumptions of the production function model. Admittedly the ex post estimates for the latter years are based on the assumption that the income streams in the last 5 years of the study are a reasonable basis for expectations of the level of future income streams. And the ex ante series which is the predicted price which the potential buyer thinks he can afford to pay for land based on the past 5 years throughout the time period is consistently higher than the ex post series. But the difference in value levels between the ex pest and Costs and Returns series is large enough in most areas to warrant the conclusion that the trend in land prices in the future will be generally upward. CHAPTER.VI SUMMARY AND CONCLUSIONS The primary objective of this study was to delineate the factors in the farm real estate market which have affected the price of farm real estate between 1930 and 1962. As an aid in analysis of these factors, a production function model and a residual return model were postulated to estimate the income streams accruing to farm real estate over the period under two different sets of assumptions for 19 different type-of-farming areas in the United States. The estimated annual income streams from both models were capitalized to yield for each area and each year an ex ante or expected price which could be paid for real estate based on the income streams of the past five years and an ex post or actual price which could have been paid based on actual income streams accruing to farm real estate under the assumptions of the models. Further the year-to-year changes in the estimated marginal value products or yearly income streams from the production function model were partitioned into price and pro- ductivity components to further aid in the analysis. Theoretical arguments are employed which indicate that over the period the marginal physical product of farm.real estate should have increased primarily due to the technological revolution going on in agriculture during the period which has allowed large increases in agricultural production without the use of increased quantities of land and with the use of much less labor. Fixed asset theory was 134 135 employed to argue that it is economically sound for farmers to bid up the price of farm.real estate even though the returns to their labor may not be comparable to labor returns in the non-farm economy. Both of these arguments were verified by the data although it was found that farmers are influenced by the non-farm wage rate in deter- mining what price they are willing to pay for farm real estate. The net percentage of non-farmer buyers over sellers in the farm real estate market is decreasing due to urbanization and time breaking many of the strong ties a multitude of urban people once had with the rural sector and the increased costs of property taxes and management services involved in farm real estate investments. While non-farmer investor interest is declining the farmer ex- pansion buyer is rapidly becoming more dominant in the farm real estate market. .As labor and capital saving technology becomes innovated excess capacity in these inputs develops and the answer for many farmers is to expand the size of the existing farm unit to make effi- cient use of the available capital and labor. Many farms are too small to make use of available technology and we find these farm units disappearing and being absorbed in the form of expansion purchases by the already larger than average farms. Government programs are found, as expected, to have a greater impact in those areas where farm income levels depend directly and heavily on these programs. Although specific changes in land values were not traceable to specific programs, in general the impact of government programs appeared to be twofold. First, the reduction in uncertainty in the post war period due to price support programs 136 appeared to have some influence in raising land prices in the wheat areas but no influence was detectable elsewhere. Second, through raising farm incomes either by price support or various direct payments farmers! incomes are higher relative to non-farm incomes and they seem more willing to bid land prices up if their labor incomes are more comparable to non-farm wages in their area. Further, the data indicate that the productivity component of income streams to land is rising at a rate which suggests that changes in government programs within the limits of political acceptability in the immediate future will probably not cause land MVP?s to fall but rather will only affect the rate of increase. Finally, the data suggests that current land prices are below what expansion buyers could afford to pay for farm real estate to add to their existing units. Thus the cautious conclusion that farm real estate market prices will continue their upward trend is advanced. BIBLIOGRAPHY Books Barlowe, Raleigh. LandvAesource Economics. Englewood Cliffs, N.J.: Prentice-Hall, Inc., 1958. Benedict, Murray R. Farm Policies of the United States 1790-1950. New York: Twentieth Century Fund, 1953. Clawson, Marion, and Held, Burnell. The Federal Lands: Their Use and Management. Baltimore: John Hopkins Press, 1957. Congress and the Nation. Washington: Congressional Quarterly Service, Inc., 1965. Cochrane, Willard W. Farm Prices, Myth or Reality. Minneapolis: University of Minnesota Press, 1958. Grouse, Earl F., and Everett, Charles H. Apral Appraisals. Englewood Cliffs, N. J.: Prentice-Hall, Inc., 1956. Hathaway, Dale E. §pvernment and Agriculture. New York: Macmillan Co., 1963. Heady, Earl 0., and Dillon, John L. {Agricultural Production Functions. Ames: Iowa State University Press, 1961. Heady, Earl 0., Johnson, Glen L., and Hardin, Lowell 8., Editors. Resource Productivity, Returns to Scale and Farm Size. Ames: Iowa State University Press, 1956. Heady, Earl 0., and Tweeten, Luther G. Resource Demand and Structure of the Agricultural Industry. Ames: Iowa State University Press, 1963. Higbee, Edward. Egrms and Farmers in an UrbanAgg, New York: Twentieth Century Fund, 1963. Murray, William G. Fprm Appraisal. Second Edition. .Ames: Iowa State University Press, 1947. Renne, Roland R. Land Economics. New York: Harper and Brothers, 1947. 137 138 Siegel, Sidney. Nonparametric Statistics for the Behavioral Sciences. New York: McGraw-Hill Book Co., Inc., 1956. Weast, Robert C. (Ed.). Standard Mathematical Tables. Thirteenth Student Edition. Cleveland: Chemical Rubber Co., 1964. Articles and Bulletins Barlowe, Raleigh. "Our Future Needs for Non-Farm Lands," Land, 1958 Yparbook of Agriculture. ‘Washington: U.S. Government Printing Office, 1958, pp. 474-479. Blase, Melvin G. "Farm.Enlargement and Entry Factors in the Land Market," Farm Real Estate Market Developments. ERS, U.S. Department of Agriculture, Oct. 1960. Boyne, David H. Changes in the Real Wealth Position of Farm_Qperator§, 19A0-196 . Technical Bulletin 294. Michigan State University, Agricultural Experiment Station, 1964. Bronfenbrenner, Martin. "Production Functions: Cobb-Deuglas, Interfirm, Intrafirm," Econometricp, Vol. 12 (1944), pp. 35-44. Hathaway, Dale E. “Agriculture and the Business Cycle," Policy for Commercial Agriculture, Joint Economic Committee, Washington: U.S. Government Printing Office, 1957, pp. 51-76. Hock, Irving. "Estimation of Production Function Parameters Combining Time-Series and Cross Section Data," Egonometrica, Vol. 30 (1962) PP- 34-53- Paul L. and Scofield, William H. "The Market for Farm Real Estate," Land, 1958 Yearbook oprgriculture. Washington: U.S. Government Printing Office, 1958. Holm V Hurd, Edgar. “Allocation of Net Farm Income," Agricultural Economics Research, Vol. 9 (1957), pp. 10-19. Iden, George. "Farmland Values Reexplored," Agricultural Economics Research, Vol. 16 (1964), pp. 41-50. Johnson, D. Gale. "Allocation of Agricultural Income," Journal of Farm Economics, Vol. 30 (1948), pp. 720-734. Johnson, Glenn L. "The State of Agricultural Supply Analysis," Journal of Farm.Economics, Vol. 42, (1960), pp. 435-452. Larsen, Harald C. "Relationship of Land Values to Warranted Values, 1910-1948," Journal of Farm Economics, Vol. 30 (1948), pp- 579-588- 139 Reder, Melvin'W. "An Alternative Interpretation of the Cobb-Douglas Function," Econometrica, Vol. 11 (1943), pp. 259-264. Scofield, William H. "How do you Put a Value on Land?" Land, 1258 Yearbook of Agriculture. 'Washington: U.S. Government Printing Office, 1958. "Land Prices and Farm Earnings," Farm Real Estate Market Developments. ERS, U.S. Department of Agriculture, Oct. 1964. . "Prevailing Land Market Forces," Journal of Farm Economics, Vol. 39 (1957), pp. 1500-1510. . "Returns to Productive Capital in Agriculture," Farm Real Estate Market Developments. ERS, U.S. Department of Agriculture, Feb. 1960. Stallings, James. "Weather Indexes," qurnal of Farm Economics, Tolley, G.S. and Hartman, LNM. "Inter-Area Relationships in Agricultural Supply," Journal of Farm Economics, Vol. 42 (1960), pp. 453- 473. Tomek, William.G. "Using Zero-One Variables with Time Series Data in Regression Equations," Journal of Farm.Economics, Vol. 45 (1963), pp. 814-822. Government Publications Chambers, Clyde R. Relation of Land Income to Land Value. Dept. Bulletin 1224, U.S. Department of Agriculture, 1924. Foote, Richard F. Analytical 19013 for Studyin Demand and Price Structures. Agricultural Handbook No. l . ERS, U.S. Depart- ment of Agriculture, Washington: U.S. Government Printing Office, 1958. U.S. Department of Agriculture. "Agricultural Production and Efficiency," Major Statistical Series of the U.S.DNA. Vol. 2, Agricultural Handbook No. 118, Washington: U.S. Government Printing Office, 1957. . Agricultural Statistics. 1937, 1952, 1960, 1963, 1964. . ERS. Costs and Returns on Commercial Farms, 1930-1957. Statistical Bulletin No. 297, Washington: U.S. Government Printing Office, 1961. 140 . ERS. Farm Costs and Returns. Agricultural Information Bulletin No. 230 Series, washington: U.S. Government Printing Office, Series 1959, 1960, 1961, 1962, 1963. . ERS. Farm Real Estate Market Developments. Series CD-24 through CD-66, 1949-1964. Unppblished Sources Chennareddy, Venkareddy. "Present Values of the Expected Future Income Streams and their Relevance to the Mobility of Farm Workers." Ph.D. dissertation in progress, Michigan State University. Edwards, Clark. "Resource Fixity, Credit Availability and Agricultural Organization." Unpublished Ph.D. dissertation, Michigan State University, 1958. Jones, Bob F. "Farm-Non-Farm Labor Flows, 1917-1962." Unpublished Ph.D. dissertation, Michigan State University, 1964. Lerohl, Milburn L. "Expected Prices for U.S. Agricultural Commodities, 1917-1962." Unpublished Ph.D. dissertation, Michigan State university, 1965. Scofield, William H. "Dominant Forces and Emerging Trends in the Farm Real Estate Market." Paper presented at a seminar on land prices, North Central Regional Land Economics Committee, Chicago, Illinois. November 12, 1964. APPENDIX A Locati on of the 19 Type-of-Farming Areas in the Study D ,,,,I III/till V . . ,,//Il//IIIIII ‘ [’1’] . I’ll/[III Ir ,, IIII”’ ‘ CEITIAL IOITHEMT I9 IIEAT SIMI HALL DO .‘ec ’0 '. O C O O Q. ‘0‘. 141 APPENDIX B The Production Function Model The statistical function fit to the sample data is of the form Y’= axlal x252 . . . Xth e1 where Y is the dependent variable, a is a constant, X . x,n are the independent variables, 81 . . . an are 1 . parameters measuring the elasticity of Y with respect to the correspond- ing‘xi, and the log of 6i is an independent random variable assumed to have a normal distribution with a mean of zero and a homoscedastic variance for all observations. Further the xi are assumed to be independent and measured without error. The function is assumed to be linear in logs. The first economic assumption required for use of this par- ticular statistical model is that the elasticities of production are constant over all ranges of output while the marginal physical products of the inputs change. This assumption may or may not hold true but it is probably a more logical assumption than that which must be made with the use of a straight multiple linear regression model where the elasticities of production change but the marginal physical products are constant. Another economic assumption is that the total product curve for any one variable input with the others fixed at a given level is in Stage 11 throughout its range, increasing at a decreasing rate, thus 142 .143 marginal physical product declines throughout but cannot be negative since total product never reaches a maximum. A third assumption is that all inputs are complimentary in some combination; that is, some of each input must be present in order for any production to take place. A rather strong additional economic assumption is necessary in order to fit this type of function and test hypotheses beyond the normal statistical and economic assumptions commonly required of a production function. Combining cross-sectional and time-series data in the produc- tion function requires the assumption that the elasticities of production with respect to each of the inputs remain constant over both areas and time.1 In a time-series production function of the Cobb-DOuglas type for a given area constant elasticities of production must be assumed through time. In a cross-sectional function of the same type for a given time period constant elasticities must be assumed across observations or areas. When cross-sectional and time series data are combined both assumptions must be made simultaneously. This means that for a one percent change in the input magnitude (X1), output will change by a percentage equal to the corresponding regression coefficient (b1) regardless of time period or area. Admittedly this assumption may be difficult to defend for the length of time and the heterogeneity of the areas involved but if the model yields reasonable results the assumption is justified. Separate 1For another application of the model see Irving Hock, "Estima- tion of Production Function Parameters Combining Time-Series and Cross- Section Data," Econometriga, Vol. 30, (1962), pp. 34-53. 144 time series equations for each area were considered and rejected because of the very high intercorrelations found between the independent variables. Separate cross-sectional functions for each year were also considered and rejected both for high intercorrelation and low explanatory power reasons. The combined model then was adopted because it yields a reasona- bly good fit to the data while at the same time it holds the intercorrela- tion problem to a very low level. The Variables in the Productioppgpnction The function was fitted to the data using the following variables. X, The dependent variable--total output of each representative area-type farm in each year--is defined as total cash receipts from sale of crops, livestock, and livestock products, government program payments plus value of perquisites, and change in inventory of crOps and livestock during the year valued at current prices, all deflated by the specific area's prices received for products sold index to convert total output to constant dollar values. The prices received index is a Paasche type which uses current year quantities of products sold as weights. That is, the indes is EEASA_ where Q1 is current year quanti- ties, P1 is current year prices,2:gglPo is 1947-1949 base year prices. The independent variables in the production function for the representative farm in each area are as follows. X1. Real Estate--defined as total acres of land in the farm unit including crop, idle, fallow, failure, abandoned, pasture, woodland, wasteland, farmstead yards, barnyards, feed lots, roads, lanes, fences, and land in Soil Bank or other government programs. .Also included are 145 buildings and structures, wells, irrigation systems, tile or other drainage systems, and any other permanent fixtures and improvements generally classified under the heading real estate. The rationale for including these capital improvements in real estate even though the variable is measured in acres is found in the income capitalization approach to valuation of farm real estate. The income capitalization approach in farmland appraisal determines a net return accruing to the farm real estate.and then assuming this return to represent the flow of income streams from the real estate input capitalizes it by an appropriate interest rate to determine the present value of the real estate. The capitalized value is then ad- justed up or down if the capital improvements are better or not as good as typically found on other farms in the surrounding area. So assuming the representative farms in the respective areas to have a typical set of capital improvements for the area and further that the productive contribution of these improvements will appear in total product no ad- justment is made for capital improvements in the real estate variable.2 x2. ‘LEEBEf’in man-hour units. The man-hours of labor input estimated for the area-type farms includes total hours of operator and family labor including management plus hours of hired labor. It is an artificial series built from estimates of man-hours required under average rates of performance with existing technology levels and with the types of power and equipment normally used in crap and livestock 2For a further discussion of the income approach in farm real estate appraisal and valuation of buildings and improvements see William G. Murray, Farm Appraisal, Iowa State College Press, Second Edition 1947, particularily pages 181-184. 146 production, maintenance and repairs and management on the number of acres and size of enterprises found on the given area-type farm.3 No attempt is made to separate out management. The labor input for the area-type farms appears to be over estimated from two sources. The distribution of farms of a given area type tends to be skewed toward the right thus causing the arithmetic mean to be greater than the mode. To the extent that use of the geometric mean does not correct for the skewness, more small farms fall into the omitted extremes than do large farms when the extremes are defined as beyond plus or minus three standard deviations from the geometric mean. Now if the larger farms are able to use labor saving capital and techni- ques to a greater extent than the smaller farms, basing labor requirements per acre of crop or unit of livestock on the average of all farms will tend to over estimate the labor requirement on the farms used in the sample of area-type farms to build the representative farm unit. No attempt has been made here to correct for this possible source of bias in the labor input data but we must recognize that it exists and may affect the production function coefficient for labor. The other source of bias which increases the labor requirement portion in the Costs and Returns series is found in the estimates of labor used for repair and maintenance of machinery and buildings and in management of the operating unit. Impossible to divorce in the estimates for these items is labor time spent in certain endeavors which in the 3For a fuller discussion of derivation of labor requirements see "Agricultural Production and Efficiency," Majpr Statistical Series of the U.S.D.A., Vol. 2, Agricultural Handbook No. 118, (Washington: U.S. Government Printing Office, 1957). 147 non-farm economy would be considered personal business. For example, repair and maintenance on that portion of the family car used for personal rather than farm business, repair and maintenance of the family dwelling, and that portion of trips to town or time spent on records which are personal rather than farm business. To at least partially correct for this bias a constant number of labor hours is subtracted from the family and Operator hours series in the Cost and Returns data for each year and each area type farm amounting to approximately one and one-half hours per day or 550 hours per year. ‘x3. Operating Expenses--in constant dollar values. Included is the tgzal cash paid for goods and services and personal and real estate property taxes during the year excluding hired labor expense, land purchase, and purchase of depreciable capital items. A capital item depreciation figure is included for machinery improvements and other depreciable capital representing the flow of services in a given year from the capital stock. Operating expenses are deflated to constant dollars by the prices paid index for each area. This is a current ‘weighted, Paasche type index of the form. E3131, where Q1 is current ZPle year quantities, P1 is current year prices and Po is base 1947-1949 year prices. Dummy Variables Two sets of dummy variables are used in the function for area and time. Zero-one dummy variables can be used in a regression model if the data can logically be divided into mutually exclusive groups and the effect of differences in these groups is to change the level of 148 the function without changing its slope.h The first condition in this case is met for both areas and time because each area has a different set of characteristics which contribute to different levels of technical efficiency in production while production conditions as influenced by weather, technology, and size of the farm unit change from year to year. The second condition is not so easily rationalized. Under the general assumption, which had to be made in order to use the model, production elasticities of the inputs are assumed constant through time and over areas. If the fit can be improved by introducing the time and area dummy variables without significantly changing the coefficients of the physical input variables in which we are interested they are a valuable addition to the function. A statistical test for a significant difference between the regression coefficients for the physical inputs in separate cross-sectional functions for each year and separate time-series functions for each area without dummy variables and the combined function with the dummy variables included would determine if they should be included. Due to the high intercorrelation in the separate functions between the independent variables the estimated standard errors would be very large and the likelihood of detecting significant differences would be greatly reduced. Therefore, the dummies are included in the function on the assumption that they are appropriate as follows: Eu . . . Xél Area dummies. Data from 19 areas are included in the function. Thus 18 variables are added to the function, for each hSee‘William G. Tomek, "Using Zero-One Variables with Time Series Data in Regression Equations," qurnal of Farm Economics, Vol. 45, (1963), pp. 814-822. 149 area except one which is used as the base from.which the 18 others deviate. The variables are entered as l in logs (10 in natural numbers) if the observation came from the area represented by K1 and 0 in logs (1 in natural numbers) otherwise. §§§,. . . 323. Time dummies. Data for 33 years are included in the function. Therefore, using the same procedure as for the area dummies, 32 time dummy variables are entered in the function. Thus for the base year and base area all time and area dummy variables take on the value of zero. Table 47 presents the results of the production function. The multiple coefficient of determination adjusted by the degrees of freedom (R2) is .8096. That is, approximately 81 percent of the variance in output is "explained" by the variance of the independent variables.‘ The sum of the elasticities of production of the physical inputs is 1.62 and is significantly different from 1 at the .01 level of signifi- cance. According to classical economic theory increasing returns to scale are thus indicated. In this case if all physical inputs included in the function were increased by 1 percent, output would increase by 1.62 percent. If we hold strictly to the economic theory assumptions about re- turns to scale only constant returns to scale are possible. The theory assumes strictly homogeneous inputs and states that if All_inputs are increased proportionately, in order to have constant returns to scale, output must also increase proportionately. For increasing or decreasing returns to scale output must increase more than or less than proportionately NWO. :wo. m 66H . . .30. mo. .H6>6H OH. 663 #6 moHHNOHMHHHmHm 66 Ono 6:0. W10. 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H 60. . lumdSEHH669< :mo ***H0H.I : w ***H>m momnommm :mo. mH0.I Hmma 66H ***ma6. moopmm DNA Ammdaesm maaa ImeomaH HoHuom . . 0am I0I 90% ommmv OMQH H:0. \H . mm . 1mm668650 6269 9669660 9MM9WSa69m 96666696600 uc 6HQNH96> 9099m .opm 966 0 0 p .666 6696669N6m H H66600 6 66668666 6696669666 369.9661 60696656 606965609m moawoonInooo 669669966990 62p 809% 6H6>QH 60960 N : HMHGNH 92666696600 626 66909966 69666690 ompdaflpmm .mp26H0HMM6oo 2066669N6m m 6 6 0 HD- B 151 respectively. But if all inputs are homogeneous within categories and proportional increases are accomplished also with homogeneous inputs then no recombination of inputs is possible and only proportional increases in output can result. Then one or more of the theoretical assumptions was not met in order for the function to yield a sum of the input coefficients different from one. Several discrepancies are possible. The function may not contain all the relevant inputs. But if this were the case we would expect the sum of the coefficients to be less than one if the omitted input were limiting and unaffected if not. If the omitted input were limiting and did not increase proportionally with the included inputs decreasing returns to size may result.5 The large sum may be attributed to changes in the quality of the inputs or changes in the input mix within a category. In this case the theoretical assumption of input homogeneity is not met. One of the main criticisms of measuring the inputs in acres, man-hours, and constant dollars is that it is impossible to account for quality differences and changes. For example, operating expenses are measured in constant dollar values. The addition of a constant dollars worth of operating expenses today may contribute more output than a constant dollars worth which was on hand due to quality changes resulting from a technological innovation or due to changes in the within category input mix. 5The word "sizé’is used here in preference to "scale" since scale is reserved for the theoretical situation described above where only constant returns can result. I, ..3. 3:3. 152 D. Gale Johnson observed in l9h8 that in the period 1913 to 1948 investments made in tractors and other motor vehicles were more than offset by declines in investment in horses and mules while the invest- ment in other machinery remained about the same.6 Thus, increasing returns to size may occur through a more productive recombination of inputs the magnitude of which is not totally reflected in the method of measurement of the variables in the function. The dummy variables for area are intended to at least partially account for differences in the quality of inputs between areas, and the time dummies are intended to account for changes in quality through time. To the extent that they fail to account for the total differences and changes we would expect a sum of the coefficients different from one. We could further expect this sum to be larger than one instead of smaller because the rate of technological innovation has proceeded at an accelerating pace through the studied period. If along with Heady and Dillon7 we assume that at least part of the reason for the high sum.of the coefficients is due to irregularities in the method of aggregation and measurement within input categories; if we further assume that these irregularities affect all categories equally (the irregularities are randomly distributed between categories); and finally if we believe constant returns to scale to hold at any one place and time, then the closer this sum is to one the more economically 6D. Gale Johnson, "Allocation of Agricultural Income," Journal of Farm Economics, Vol. 30, (l9h8), p. 729. 7Earl O. Heady and John L. Dillon, ggricultural Production Functions, (Ames: Iowa State University Press, 1961), p. 589. t 61:; _ 153 reliable we can consider the results from the model provided the explanatory power of the model does not significantly deteriorate. With this in mind a restriction was placed on the sum of the coefficients of the physical inputs-~real estate, labor, and operating expenses--to bring the sum down to a minimum level without significantly changing the explanatory power of the model at the .01 level of signi- ficance. In other words, the restricted model minimizes the sum.of squares subject to the restriction and an F test on the error sum of squares between the restricted and unrestricted models was used to deter- mine the level of restriction which was possible without significantly changing the error sum of squares at the .01 level of significance.8 The restricted sum of the coefficients fulfilling the criteria is 1.3% which is still significantly different from 1 at the .01 level. The R? for the restricted function is .8093 only .0003 less than for the unrestricted function. The restricted function yields results which are not significantly different from the original in a statistical sense but at the same time yields physical input coefficients which are more reasonable in terms of economic theory. 8The form of the hypothesis is that 5 +»B +-a = X where g , 1 2 . 1 52 and B are the real estate, labor and operating expefises coeffic1ents respectiéely and X is to be determined such that = (ESSr - ESSu)/P where ESSu is the error ESSu/N-K-l sum of squares in the unrestricted function, ESS is the error sum of squares in the restricted model, P is the number of degrees of freedom for the numerator and is equal to the number of restrictions (in this case 1), N-K-l is the denominator degrees of freedom with N the number of observations, and K the number of independent variables, and F is the tabled F statistic for the .01 level of significance with P and N-K-l degrees of freedom. F.01(P,N-K-l df) 151+ The restriction partially corrects for some of the aggregation and measurement problems in the independent variables but the restricted sum is still high enough that further analysis is warranted. Empirical evidence indicates that average farm size is increasing primarily via the smaller than average farms being absorbed by the larger than average farm units. As farm units increase in size they become more flexible with regard to recombination of productive factors and the ability to innovate new and existing technology which the smaller size farms are unable to use efficiently. The sum of the coefficients in this case can be interpreted as an indicator of returns to size where the term "size" rather than "scale" denotes a relaxing of the assumption of proportional increases and homogeneity of all inputs. To sum up, the functional form chosen as the model from which to derive real estate marginal value products is a restricted Cobb-Douglas linear in logarithms production function. In using this model several strong assumptions must be made which necessarily abstract from.reality. After weighing the consequences of these abstractions and possible alternative interpretations the conclusion was reached that the model would yield results approximating reality closely enough to be useful. APPENDIX C Capitalization Rate One of the big questions arising when attempting to determine the present value of future income streams accruing to any productive input is what capitalization rate should be used. Even in an ex post sense the decision is difficult because of the wide array of rates of return on different types of investment and the subjectiveness in evaluat- ing the factors determining the interest rate. Since we live in a world of differential risks and uncertainties attached to different types of investment the interest rate chosen for any given type of investment reflects the subjective evaluation of investors of the relative risk involved in the initial investment and uncertainty about the stability and magnitude of the future income streams accruing to it. Crouse and Everett indicate three factors beyond the general money market which influence capitalization rates for farm real estate. They are physical and economic risk as it affects regularity of income streams, marketability or liquidity of investment, and competition with other forms of investment.1 Although the capitalization rate for farm.teal estate has tended to be greatly influenced by the current farm mortgage rate many rural appraisers and others connected with farm real estate argue that 1Earl F. Crouse and Charles H. Everett, gpral Appraisals, (Englewood Cliffs, N.J.: Prentice-Hall, Inc., 1956;, pp. 35-35: 155 156 ownership is a higher risk venture than mortgage lending and should assume a higher rate of return.2 Larsen argues the inappropriateness of the farm mortgage rate for use in capitalization on the basis that the former reflects trends in returns from riskless investments while the latter should take into account the opportunity cost of alternative investments with risk features similar to land. He finally comes to the position that since the mortgage rate reflects actual interest paid by farm buyers for their long-term credit, this rate plus an additional risk of ownership factor should approximate a reasonable capitalization rate. The ownership risk factor must be adjusted through time because ownership risk has decreased. Technology has increased efficiency, allowed greater timeliness of operations, introduced more hardy varieties of craps and livestock and allowed soil and water conserving practices and techniques thus causing supply to be more stable. Price stabiliz- ing government programs have cut the risk factor on the demand side. Thus both production and income risks have been declining, leading to the position that the ownership risk factor should also decline through time.3 But how much of the risk from price fluctuations is simply trans- ferred to risk from legislative change in government programs? And how much has technology allowed use of land which would formerly have been sub-marginal thus possibly even increasing the production risk in certain areas? 2Raleigh Barlowe, Land Resource Economics, (Englewood Cliffs, N.J.: Prentice-Hall, Inc., 1958), pp. 191-193. 3Harald C. Larsen, "Relationship of Land Values to Warranted Values, l9lO-l9h8," Journal of farm Economics, Vol.30 (l9h8), pp. 579-588. 157 Another factor which may have tended to increase the risk of ownership factor through time is the institutionalization and imperson- alization of sources of credit. .As credit facilities have become larger and many have become affiliated nationally, more hard and fast rules have replaced discretionary authority at the local level where the loans are serviced. This has meant less liberal treatment as far as the credit source "riding along" with even the better managers when they have found themselves in trouble due to unforeseen difficulties such as several years of unfavorable weather. Finally, Murray points out that farm real estate ownership is for not only production but also Consumption ends in that it provides a home for the operator and his family. Since the consumption portion should not be expected to yield a monetary return the expected rate of return on the total investment should be adjusted downward.)+ The amount to add to the capitalization rate for the risk of ownership factor then appears to be impossible to establish empirically and at best could only be a subjective estimate. Another way to approach the problem is to look at the market for farm.real estate in terms of the interest rate. According to Chambers in a land value and income study done in 192A, . . . "it is difficult to see how the anticipated rate of return on farm land, that is, the rate of capitalization, can get very far away from the mortgage rate of interest when farm.mortgages are readily available to a large class of potential sellers of land. If buyers bid up the price of land because 1*William G. Murray, Farm.Appraisal, Second Edition, (Ames: Iowa State College Press, l9h7), p. 162. 158 they are willing to accept a low rate of return on their investments, some of these retired or retiring farmers will decide to sell rather than lease their farms. This will increase the supply of land for sale and thus hold down its price. If, on the other hand, farm land tends to offer a better return than farm mortgages, fewer farms will be offered for sale, which will increase the price.5 Of course, the land market is highly imperfect in that only a very small portion of the land in any one area is for sale at any one time and then the interested buyers come from.a limited surrounding area. And the capitalization rate is only one of several factors which determine price. ‘While a wide gap between the mortgage rate and capi- talization rate will motivate buyers and sellers to act to narrow the gap, slight discrepancies may not provide this motivation so the two rates will not always coincide. The important thing, however, is that there is a tendency for the capitalization rate to move toward the mortgage rate. Thus the best objective indication of the capitalization rate without any subjective adjustments is the farm mortgage rate. In obtaining the capitalized land value series from both the production function and the residual calculations, the farm mortgage rate on new loans charged by the Federal Land Bank in the respective areas is used. 5Clyde R. Chambers, Relation of Land;;ncome to Land Value, U.S.D.A, Dept. Bulletin 122h, (Washington: U.S. Government Printing Office, l92h), p. Rh. ' .APPENDIX D Supplemental Data The following table presents for each of the 19 areas in the study, (1) the imputed salvage value for farm labor based on factory workers wages weighted by the non-farm unemployment rate used in the residual return model, (2) the interest rate charged for new loans on January 1 by the Federal Land Bank and used to compute the imputed salvage return for capital in the residual model and for derivation of the ex post and ex ante land value series from both the residual return and production function models, and (3) the per acre return to land calculated from the residual return model and used in deriving the residual return ex post and ex ante land value series. Table #8 Farm Labor Salvage Value, Interest Rate, and Per Acre Residual Return Series for 19 Farming Areas in the United States 1° Year ALabOr Salvage Value Interest Rate Per Acre Residual Return 3(Dollarsjg (Percent) (Dollars) Central Northeast Dairy 1930 682TH0 5.5 1.23 1931 21h.87 5.5 2.09 1932 -0- 5-5 -99 1933 -o- 5.0 1.68 1939 -0- 5.0 1.61 1935 -0- 5-0 3.90 1936 179.56 h.0 2.26 1937 3h7.16 h.0 2.11 1938 57.uu u.0 3.15 1939 172.27 h.0 1.52 19h0 350.79 h.o 3.09 19111 767.25 11.0 1.66 19h2 1h51.05 6.0 2.66 159 160 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars) (Percent) (Dollars) 1963 2017.69 6.0 .060 1966 2236.05 6.0 .11 1965 2070.63 6.0 6.66 1966 1803.91 6.0 9.09 1967 2067.52 6.0 6.63 1968 2239.65 6.0 10.70 1969 2012.56 6.5 3.23 1950 2261.86 6.5 3.62 ' 1951 2812.56 6.5 5.16 1952 2926 . 12 6 .5 1 . 26 1953 3170.20 6.5 - 3-57 1956 2660.27 6.5 .79 1955 3066.63 6.5 1.39 1956 3260.11 6.5 - .11 1957 3330.38 5.0 1.22 1958 2838.61 6.0 2.35 1959 3296-35 5-5 - ~32 1960 3353.88 6.0 - 1.26 1961 3151.26 6.0 .86 1962 3572-15 5-8 - 5-33 Eastern'Wisconsin Dairy 1930 725-35 5-5 -40 1931 228-39 5-5 - .53 1932 -0- 5.5 1.05 1933 -0- 5.5 3.00 1936 -0- 5.0 2.00 1935 -0- 5.0 8.16 1936 190.86 6.0 2.99 1937 369.01 6.0 6.61 1938 61.05 6.0 5.67 1939 183.11 6.0 .95 1960 372.87 6.0 2.61 1961 815.56 6.0 1.60 1962 1562.37 6.0 .03 '1963 2166.68 6.0 - 1.66 1966 2376.78 6.0 - 6.76 1965 2200.95 6.0 1.61 1966 1917.65 6.0 6.85 1967 2150.30 6.0 5.02 1968 2371.78 6.0 2.79 1969 2052.60 6.0 - .92 1950 2376.60 6.0 - 6.00 1951 3003.87 6.0 1-39 1952 3136.91 6.0 - 3.16 1953 3361 93 6.0 - 9-80 1956 2800.16 6.0 - 6.36 1955 3269.56 6.0 -12.53 1956 3660.99 6.0 -11.52 161 Table #8--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars) (Percent) (Dollars), 1957 3692.22 6. -10.37 1958 3006.03 5.5 - 8.69 1959 3539-95 5-0 - 9-00 1960 3606.22 6.0 6.28 1961 3351.69 5.5 11.16 1962 3888.52 5.5 5.23 Eastern giscons in Dairy 1930 725.35 5.5 - 1.10 1931 228.39 5.5 - 1.06 1932 -0- 5.5 .82 1933 -0- 5.5 1.63 1936 -0- 5.0 .55 1935 -0- 5.0 5.76 1936 190.86 6.0 2.69 1937 369.01 6.0 3.28 1938 61.05 6.0 3.62 1939 183.11 6.0 2.19 1960 372.87 6.0 .96 1961 815.56 6.0 2.11 1962 1562.37 6.0 - .06 1963 2166.68 6.0 - 1.66 1966 2376.78 6.0 - 5.36 1965 2200.95 6.0 - 1.56 1966 1917.65 6.0 2.79 1967 2150.30 6.0 .83 1968 2371.78 6.0 3.75 1969 2052.60 6.0 .33 1950 2376.60 6.0 - 2.11 1951 3003.87 6.0 2.96 1952 3136.91 6.0 .34 1953 3361.93 6.0 - 6.38 1956 2800.16 6.0 - 5.17 1955 3269.56 6.0 - 8.95 1956 3660.99 6.0 - 5.85 - 1957 3692.22 6.5 - 6.61 1958 3006.03 5.5 .66 1959 3539-95 5.0 - 6-95 1960 3606.22 6.0 ~6.52 1961 3351.69 5.5 1.06 1962 3888.52 5.5 - 1.35 Dairy-Hog, Minnesota 1930 707.63 5.5 1.62 1931 222.81 5.5 -57 1932 -0- 5.5 1-72 1933 -o- 5.5 1.83 1936 -0- 5-0 ' -35 Table 68--Continued 162 Year Labor Salvage Value Interest Rate Per.Acre Residual Return (Dollarg) (Percent) (Dollars) 1935 -O- 5-0 7-69 1936 186.20 6.0 6.80 1937 . 352.66 6.0 5.82 1938 59.56 6.0 6.29 1939 178.66 6.0 5.76 1960 363.75 6.0 6.08 1961 795.61 6.0 5.36 1962 1506.68 6.0 5.90 1963 2092.28 6.0 3.35 1966 2318.70 6.0 * .68 1965 2167.17 6.0 5.17 1966 1870.59 6.0 12.18 1967 2022.18 6.0 10.63 1968 2267.10 6.0 13.77 1969 2007.66 6.0 5.66 1950 2272.63 6.0 2.21 1951 2821.29 6.0 8.09 1952 3029.21 6.0 5.39 1953 3266.88 6.0 1.32 1956 2771.68 6.0 .96 1955 3175.85 6.0 - 1.19 1956 3327.89 6.0 .53 1957 2608.26 6.5 5.37 1958 3000.96 5.5 3.38 1959 3666.85 5.0 - 6.20 1960 3559.62 6.0 - 6.10 1961 3613.67 5.5 3.19 1962 3831.98 5.5 - 2.67 Hog-Dairy13Corn Belt - 1930 737.58 5.6 1.55 1931 232.26 5.5 .99 1932 -0- 5.5 .67 1933 ‘0' 5.5 d 18 1936 --o- 5.0 - 1.29 1935 -0- 5.0 9.38 1936 196.09 6.0 6.76 1937 375.26 6.0 7.91 1938 62.09 6.0 7.87 1939 186.21 6.0 6.13 1960 379.17 6.0 6.33 1961 829.33 6.0 6.03 196% 1568.66 6.0 10.92 1963 2180.96 6.0 9.96 196? 2616.98 6.0 5.03 1966 2238.18 6.0 8.75 1966 1969.89 6.0 18.25 1967 2213.20 6.0 13.30 1963. 2620.88 6.0 22.69 Table 68--Continued 163 Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars) _(Percent) (Dollars) 1969 2113.02 6.0 12.95 1950 2388.07 6.0 9.57 1951 2996.65 6.0 13.16 1952 3128.36 6.0 10.66 1953 3369.65 6.0 8. 18 1956 2826.35 6.0 12.18 1955 3296.72 6.0 - 1.07 1956 3692.21 6.0 2.10 1957 3569.00 6.6 8.23 1958 3259-71 5-5 15-99 1959 3587-13 5-0 1.67 1960 3630.56 6.0 .72 1961 3626.51 5.8 8.13 1962 3912.68 5.5 3.05 gpg-ggef Raisingl Corn gelt 1930 719.21 5.8 - 1.68 1931 226.66 5.5 .16 1932 -0- 5.5 .70 1933 -0- 5.5 - .60 1936 -0- 5.0 2.26 1935 -0- 5.0 6.56 1936 189.26 6.0 - .75 1937 365-90 6.0 3-13 1938 60.56 6.0 3.00 1939 181.56 6.0 3.19 1960 369.71 6.0 1.81 1961 808.66 6.0 2.06 1962 1529.32 6.0 3.66 1963 2126.56 6.0 2.10 1966 2356.67 6.0 - 2.88 1965 2182.33 6.0 - 2.02 1966 1802.11 6.0 7.79 1967 2157.98 6.0 .08 1968 2360.67 6.0 8.80 1969 2069.10 6.0 5.62 1950 2367.59 6.0 5.86 1951 2915.66 6.0 6.70 1952 3057.16 6.0 2.66 1953 3300.78 6.0 - 6.20 1956 2768.69 6.0 - 1.92 1955 3217.22 6.0 - 6.81 1956 3617.86 6.0 - 6.32 1957 3681.26 6.8 - 1.70 1958 3006.72 5.5 5.11 1959 3511.87 5.0 - 5.08 1960 3559.62 6.0 - 5.13 1961 3372.28 5.8 - 1.26 1962 3836.85 5.5 - 3.58 liale Ill 166 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return i(pollars)3 3(Percent) _(Dollars) Hog:§eef Fattening Corn gelt 1930 717.85 5.8 6.96 1931 226.03 5.5 3.91 1932 -0- 5.5 3.68 1933 -0- 5-5 -51 1936 -0- 5.0 - 2.36 1935 -0- 5.0 12.67 1936 188.89 '6.0 2.39 1937 365.20 6.0 13.11 1938 60.62 6.0 10.39 1939 181.22 6.0 8.02 1960 369.01 6.0 8.68 1961 807.11 6.0 9.26 1962 1526.62 6.0 20.72 1963 2122.51 6.0 19.07 1966 2352.21 6.0 15.35 1965 2178.20 6.0 15.83 1966 1897.62 6.0 62.87 1967 2153.88 6.0 36.11 1968 2356.00 6.0 50.73 1969 2065.18 6.0 29.05 1950 2363.09 6.0 35.07 1951 2906.66 6.0 32.30 1952 3067.99 6.0 20.83 1953 3288.26 6.0 11.63 1956 2771.31 6.0 22.85 1955 3210.32 6.0 - 1.01 1956 3609.66 6.0 8.20 1957 3676.80 6.8 15.56 1958 3006.03 5.5 27.16 1959 3500.27 5.0 7.82 1960 3550.06 6.0 3.05 1961 3365.76 5.8 11.00 1962 3831.61 5.5 22.12' Cash Grain, Corn Belt . 1930 765.80 6.0 .61 1931 236.83 5.5 - .03 1932 -o- 5.5 1.86 1933 -0- 5-5 2-15 1936 -0- 5.0 3.26 1935 -0- 5.0 12.06 1936 196.26 6.0 10.99 1937 379.62 6.0 . 9.96 1938 62.78 6.0 7.86 1939 188.28 6.0 10.31 1960 383.38 6.0 7.03 1961 838.53 6.0 15.75 1962 1585.85 6.0 17.22 165 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars)3 (Percent) (Dollars) 1963 2205.15 *6.0 18.13 1966 2663.19 6.0 16.01 1965 2263.01 6.0 20.10 1966 1971.51 6.0 36.68 1967 2237.76 6.0 33.29 1968 2657.70 6.0 27.29 1969 2150.15 6.0 22.28 1950 2633.09 6.0 23.10 1951 3031.83 6.0 28.76 1952 3158.96 6.0 21.96 1953 3626.66 6.0 19.62 1956 2855.92 6.0 20.77 1955 3353.10 6.0 16.99 1956 3565.61 6.0 22.60 1957 3621.20 5.0 11.55 1958 3107-33 5-5 15-51 1959 3663.50 5.0 5.81 1960 3692.33 6.0 13.15 1961 3686.23 5.5 19.11 1962 3978-75 5.5 20-76 Southern Plains Cotton 1930 511.29 6.0 - 1.87 1931 160.99 6.0 - .63 1932 -0- 6.0 .38 1933 -0- 5.0 2.11 1936 -0- 5.0 2.80 1935 -0- 5.0 2.91 1936 136.53 6.0 2.66 1937 260.11 6.0 .55 1938 63.06 6.0 2.06 1939 129.07 6.0 2.33 1960 262.83 6.0 1.68 1961 576.86 6.0 - .35 1962 1087.20 6.0 - .73 1963 1511.76 6.0 - 3.71 1966 1675.36 6.0 - 3.93 1965 1551.62 6.0 - 2.22 1966 1351.58 6.0 2.31 1967 1536.10 6.0 .31 1968 1678.06 6.0 .63 1969 1675.66 6.5 - 2.65 1950 1730.06 6.5 - 2.68 1951 2097.08 6.5 1.63 1952 2152. 11 5.0 - 1.02 1953 2307.10 5.0 - 3.18 1956 1928.91 5.0 - 3.37 1955 2257-57 5-0 - -15 1956 2615.09 5.0 - 5.19 166 Table 68--Continued Year Labor Salvage Value Interest Rate Per.Acre Residual Return (Dollars) (Percent), ((Dollars) 1957 2503.77 5.0 - 5.26 1958 2136.36 6.0 2.72 1959 2695-38 5.5 - 2.96 1960 2535.66 6.0 - 3.99 1961 2601.03 6.0 .25 1962 2760.65 6.0 - 1.97 Texas Black Prairie Cotton 1930 686.69 6.0 - .11 1931 216.16 5.5 .31 1932 -0- 5.5 2.63 1933 '0' 505 6.01.- 1936 -o- 5.0 6.62 1935 -0- 5.0 6.95 1936 180.66 6.0 6.51 1937 369.25 6.0 6.72 1938 57.78 6.0 5.32 1939 173.30 6.0 6.86 1960 352.89 6.0 6.09 1961 771.85 6.0 3.02 1962 1659.76 6.0 - 1.33 1963 2029.79 6.0 - .99 1966 2269.65 6.0 - 6.51 19,-}5 2083001} ll'oo ‘ 3.1l-0 1966 1816.73 6.0 2.58 1967 2059,79 6-0 8930 1968 2253.08 6.0 2.83 1969 1961.58 6.0 5.28 1950 2197.21 6.0 5.06 1951 2760.92 6.0 - 3.65 1952 2907.78 6.0 .16 1953 3129.95 6.0 1.20 1956 2697.18 6.0 - 6.07 1955 3073.66 6.0 - 3.78 1956 3299.55 6.0 -16.30 1957 3625.70 5.0 -10.06 1958 2919.26 5.5 - .96 1959 3332-91 5-0 j 6-91 1960 3339.27 6.0 - 3.76 1961 3173.91 5.5 - 1.23 1962 3596.11 5.5 - .87 Egrthern Plains, Wheat-Small Grain-Livestock 1930 660.82 5.5 - .89 1931 201.78 5.5 - 1.60 1932 -0- 5-5 - ~77 1933 -0- 5-5 - -09 1936 -0- 5.0 - 1.17 1935 -0- 5.0 .16 167 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return 4(Dollars) ((Percent) (Dollars), 1936’ 168.62 6.0 - 1.23 1937 326.01 6.0 - .08 1938 53.96 6.0 .06 1939 161.77 6.0 .70 1960 329.61 6.0 .86 1961 720.69 6.0 2.78 1962 1362.62 6.0 3.16 1943 1896-73 6.0 5.53 1946 2099~79 6-0 3-55 1965 1966.65 6.0 5.65 1966 1693.98 6.0 7.18 1967 1922.76 6.0 10.99 1968 2103.17 6.0 5.73 1969 1863.56 6.0 1.73 1950 2109.50 6.0 6.33 1951 2600.69 6.0 5.56 1952 2757.56 6.0 .78 1953 2962-33 6-0 -33 1956 2680.80 6.0 - 1.23 1955 2886.17 6.0 3.66 1956 3102.77 6.0 6.78 1957 3193-69 6-5 -25 1958 2733-93 5-5 3-98 1959 3056.98 5.0 - 1.56 1960 3066.66 6.0 1.65 1961 3002.31 5.5 - 5.25 1962 3301.66 5.5 9.71 Northern Plains Wheat-Corn-Livestock 1930 569-65 5-5 .76 1931 210.79 5.5 - .65 1932 -0- 5.5 - .08 1933 -0- 5.5 - 1.56 1936 -0- 5.0 - 1.60 1935 -0- 5.0 1.27 1936 176.15 6.0 - 1.81 1937 360.58 6.0 .11 1938 56.35 6.0 .99 1939 169.00 6.0 2.18 1960 366.13 6.0 1.68 1961 752.69 6.0 3.03 1962 1623.50 6.0 5.58 1963 1979.60 6.0 3.63 1966 2193.60 6.0 3.65 1965 2031.33 6.0 6.80 1966 1769.67 6.0 8.68 1967 2008.66 6.0 12.35 1968 2197.16 6.0 8.67 168 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars) 3(Percent) (Dollars) 1969 1925.93 ‘6.0 1.39 1950 2203.76 6.0 3.31 1951 2716.89 6.0 7.66 1952 2880. 75 6 . 0 1 . 79 1953 3096.69 6.0 .99 1956 2591.66 6.0 .68 1955 3015.12 6.0 - 3.01 1956 3113.86 6.0 - .96 1957 3189.66 6.5 3.02 1958 2806.72 5.5 7.08 1959 3271.51 5.0 - 3.87 1960 3290.98 6.0 2.61 1961 3183.87 5.9 1.56 1962 3569.31 5.5 2.66 Northern Plains, Hheat-Roughage-Livestock 1930 686.33 5.5 - 1.15 1931 222.81 5.5 - 1.50 1932 ‘0‘ 5.5 " .50 1933 -0- 5.5 - 1.13 1936 -0- 5.0 - 1.66 1935 -0- 5.0 .01 1936 186.20 6.0 - 2.08 1937 360.00 6.0 - 1.66 1938 59.56 6.0 .30 1939 178.66 6.0 .65 1960 363.75 6.0 .37 1961 795.61 6.0 2.13 1962 1506.68 6.0 2.72 1963 2092.28 6.0 3.16 1966 2318.70 6.0 2.59 1965 2167. 17 6.0 2.86 1966 1870.59 6.0 6.56 1967 2123.20 6.0 6. 98 1968 2322.66 6.0 6.02 1969 2035.76 6.0 .62 1950 -2329.63 6.0 2.01 1951 . 2871.83 6.0 3.03 1952 3065.03 6.0 - 1.60 1953 3271.17 6.0 .27 1956 2739.63 6.0 - .70 1955 3187.06 6.0 .67 1956 3668.67 6.0 - 1.60 1957 3353.10 6.5 .80 1958 2970.76 5.5 1. 38 1959 3603.67 5.0 - 3.51 1960 3666.35 6.0 1.37 1961 3356.86 5.6 - 3.60 1962 3713-30 5-5 6-39 ‘J‘I 'l I‘ lij 169 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (lelars) )(Percent) (Dollars), Southern Plains Winter Wheat 1930 715.81 5.5 1.66 1931 225-39 5-5 -91 1932 -0- 5.5 - .68 1933 -0- 5.5 - 1.10 1936 -0- 5.0 .19 1935 -0- 5-0 ~55 1936 188.35 6.0 1.76 1937 366.16 6.0 .35 1938 60.25 6.0 .98 1939 180.70 6.0 .21 1960 367.96 6.0 .38 1961 806.81 6.0 6.50 1962 1522.07 6.0 6.87 1963 2116.66 6.0" 5.61 1966 2365.51 6.0 5.02 1965 2171.99 6.0 6.56 1966 1892.22 6.0 10.76 1967 2167.76 6.0 19.63 1968 2369.28 6.0 8.16 1969 2059.30 6.0 5.02 1950 2356.36 6.0 8.67 1951 2881.56 6.0 5.38 1952 3035.32 6.0 16.60 1953 3203.76 6.0 2.60 1956 2800.51 6.0 6.16 1955 3155.16 6.0 1.86 1956 3362.60 6.0 - .13 1957 3680.05 6.5 3.11 1958 3062.12 5.5 12.78 1959 3653.09 5-0 6-52 1960 3539-95 6.0 7-85 1961 3391-50 5-5 7-99 1962 3856.32 5.5 8.85 Southern Plains, Wheat-Grain-Sorghums 1930 696106 5.8 - .86 1931 219.16 5.5 - .69 1932 -0- 5.5 - 1.56 1933 -O- 5-5 - 1-17 1936 -0- 5.0 - .07 1935 '0' 500 "' ell-5 1936 183.15 6.0 .52 1937 356.10 6.0 .71 1938 58.59 6.0 .71 1939 175.71 h-0 -95 1960 357.80 6.0 .83 1961 782.58 6.0 2.31 1962 1680.03 6.0 3.38 170 Table 68--Continued Year Labor Salvage Value Interest Rate ‘Per Acre Residual Return (Dollars) (Percent) (Dollars) 1963 2058.00 6.0 1.06 1966 2280.72 6.0 6.80 1965 2112.00 6.0 6.07 1966 1839.96 6.0 6.35 1967 2088.62 6.0 16:02 1968 2286.60 6.0 6.09 1969 2002.62 6.0 7.15 1950 1535.76 6.0 2.99 1951 2786.35 6.0 2.22 1952 2967.09 6.0 6.53 1953 3172.66 6.0 - 3.36 1956 2766.22 6.0 - 1.53 1955 3106.66 6.0 - 2.82 1956 3326.66 6.0 - 2.03 1957 3663.95 6.8 1.38 1958 2960.66 5.5 10.22 1959 3372.22 5.0 8.19 1960 3398.80 6.0 10.65 1961 3236-03 5-5 9.35 1962 3669.87 5.5 5.66 Wheat-Fallow‘Washington and Oregon 1930 773.07 6.0 - 1.61 1931 263.62 5.5 - 1.98 1932 -0- 5.5 - 1.16 1933 -0- 5.5 - .08 1936 -0- 5.0 .21 1935 -0- 5.0 1.07 1936 203.62 6.0 2.27 1937 393.29 6.0 1.36 1938 65.07 6.0 .65 1939 195.16 6.0 .86 1960 397.60 6.0 .28 1961 869.19 6.0 2.50 1962 1663.83 6.0 3.17 1963 2285.78 6.0 6.65 1966 2533.15 6.0 6.20 1965 2365.75 6.0 6.01 1966 2063.69 6.0 9.99 1967 2319.57 6.0 8.19 1968 2537.23 6.0 10.86 1969 2226.06 6.0 5.22 1950 2662.06 6.0 7.03 1951 3226.66 6.0 8.39 1952 3390.66 6.0 7.63 1953 3590-12 6-0 7-93 1956 3083.18 6.0 7.08 1955 3696.65 6.0 1.33 1956 3667.21 6.0 2.90 1957 3666.32 5.0 8.13 171 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars) (Percent) (Dollars) 1958 3212.35 5.5 6.20 1959 3663.50 5.0 5.63 1960 3739.51 6.0 6.96 1961 3566.13 6.0 6.66 1962 6038.65 5.5 7.19 Northern Plains Cattle 1930 680.36 5.7 .23 1931 216.22 5.5 .23 1932 -0- 5.5 .03 1933 -0- 5.5 .10 1936 -0- 5-0 - -39 1935 -0- 5.0 - .23 1936 179.02 6.0 - .63 1937 366.12 6.0 - .51 1938 57.27 6.0 .07 1939 171.75 6.0 .13 1960 369.76 6.0 .19 1961 766.95 6.0 .39 1962 1666.70 6.0 .81 1963 2011.66 6.0 .65 1966 2229.35 6.0 .35 1965 2066 . 62 6 . 0 . 66 1966 1798.50 6.0 .73 1967 2061.38 6.0 1.28 1968 2232.96 6.0 1.18 1969 1957.31 6.0 - .06 1950 2196.90 6.0 .67 1951 2722.16 6.0 1.68 1952 2868 . 37 6. 0 . 67 1953 3081.66 6.0 - .03 1956 2653.75 6.0 .00 1955 3062.28 6.0 .32 1956 3271.61 6.0 .52 1957 3278.66 6.7 .07 1958 2876.36 5.5 .69 1959 3293.60 5.0 .03 1960 3371.85 6.0 - .08 1961 3232.96 6.0 .23 1962 3616.70 5.5 - .27 Intermountain Region Cattle 1930 767.17 5.9 .91 1931 235.26 5.5 .38 1932 -o- 5.5 .65 1933 -O- 5-5 -36 1936 -0- 5.0 - .65 1935 -0- 5.0 .82 1936 196.60 6.0 .98 172 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars) (Percent) (Dollars) 1937 380.11 6.0 1.08 1938 62.89 6.0 1.20 1939 188.62 6.2 .86 1960 386.08 6.0 1.15 1961 860.06 6.0 1.76 1962 1588.76 6.0 1.59 1963 2209.19 6.0 1.18 1966 2668.26 6.0 .92 1965 2267. 16 6. 0 1.66 1966 1975.08 6.0 2.11 1967 2261.86 6.0 3.06 1968 2652.21 6.0 3.86 1969 2169.51 6.0 1.83 1950 2659.59 6.0 3.03 1951 3092.98 6.0 5.69 1952 3251.98 6.0 3.17 1953 3672.51 6.0 .06 1956 2968.62 6.0 .10 1955 3379.66 6.0 .13 1956 3555.88 6.0 .67 1957 3575.77 6-6 1-85 1958 3133-07 5.5 6-35 1959 3576-66 5-0 3-50 1960 3639.56 6.0 1.21 1961 3667.69 6.0 2.06 1962 3891.51 5.5 2.95 Northern Plains Sheep 1930 680.36 5.7 .32 1931 216.22 5.5 .03 1932 -0- 5-5 .09 1933 -0- 5-5 .67 1936 -O- 5.0 .11 1935 -0- 5.0 .20 1936 179.02 6.0 .06 1937 366.12 6.0 .23 1938 57.27 6.0 .21 1939 171.75 6.0 .66 1960 369.76 6.0 .60 1961 766.95 6.0 .72 1962 1666.70 6.0 .90 1963 2011.66 6.0 .71 1966 2229.35 6.0 .51 1965 2066.62 6.0 .73 1966 1798.50 6.0 .92 1967 2061.38 6.0 1.08 1968 2232.96 6.0 .98 1969 1957.31 6.0 .06 1950 2196.90 6.0 1.21 173 Table 68--Continued Year Labor Salvage Value Interest Rate Per Acre Residual Return (Dollars) (Percent) (Dollars) 1951 2722.16 6.0 2.86 1952 2868.37 6.0 .20 1953 3081.66 6.0 .16 1956 2653.75 6.0 .11 1955 3062.28 6.0 .02 1956 3271.61 6.0 .25 1957 3278.66 6.7 1.02 1958 2876.36 5.5 1.35 1959 3293-60 5-0 ~67 1960 3371.85 6.0 .37 1961 3232.96 6.0 .21 1962 3616.70 5.5 .78 '— h-u‘ .. TE U IV. L 1111;111an ()1 HICHIGRN STR IB \HllillllllWll‘lllilll 11 31293 2 RARIES WWI 03E3