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JEL Classification No. : Q 24, Q 15

FARMLAND PRICES IN A DEVELOPING ECONOMY:

SOME STYLIZED FACTS AND DETERMINANTS*

BASANTA K. PRADHAN and A. SUBRAMANIAN

National Council of Applied Economic ResearchParisila Bhawan, 11 -Indraprastha Estate, New Delhi - 110002

Tele : 3317860-68 Fax : (91-11) 3327164E-mail: [email protected]

Farmland Prices in a Developing Economy:

Some Stylized Facts and Determinants

* * This paper is an outcome of MIMAP-INDIA project supported by the

International Development Research Centre (IDRC), Ottawa, Canada. Wethank IDRC for the financial support and P.K. Roy for the comments on anearlier version of this paper. However, the remaining errors are ourresponsibility.

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I. Introduction

Agricultural land values and factors that affect such values are of utmost interest to real

estate appraisers, economic analysts and policy makers. Land price increase are related

on one side to macro economic factors like rapid urbanisation, population growth and

economic cycles and on the other side, to the specific factors such as distress selling,

land rents etc., which influence the land markets.

Rising urban growth has created a variety of burdensome problems like increasing

contraction of agricultural land and changing crop patterns. Therefore, new land-use

policies should be instituted to provide guidelines for accommodation of continuing urban

growth with persistent agricultural land. Policy formulation, however, requires a more

complete understanding of the process of urbanisation which is engulping agricultural

land. In particular, little is known concerning land-price trends as they affect or are

affected by land use changes occurring in rural regions.

These issues in the developed countries such as USA have attracted a great deal of

attention1 though they are also important for a developing economy where more than sixty

per cent of the population depends on farmland based activities for income and

employment. The identification of regions with low farmland prices help better allocation of

industries where the marginal productivity of capital and labour will be higher. This,

incidentally, has poverty alleviating effects in terms of employment generation and also

restricts migration from rural to urban, when urban sector suffers from the limited capacity

to absorb growing labour force of rising urban unemployment and urban poverty.

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It is also interesting to study the cross section farmland price movements in a developing

economy like India, where land markets are regulated in terms of land ceiling, restrictions

on the conversion of farmland to industrial or residential purposes, constitutional

restrictions on the sale and purchase of land owned by some vulnerable groups and high

taxes on the transaction of land.

Most studies in the literature on farmland prices attribute the changes in farmland prices

to inflation, availablity of credit, real interest rates, etc. As a result the literature often fails

to differentiate between long and short term determinants (See Falk 1991, Alson 1986).

Essentially, most studies evaluate the changes in farmland prices by short term

determinants such as those mentioned above. This paper is an attempt to study the

impact of long term determinants such as density of population, location of the farmland

from the nearest town and yield from land on changes in farmland prices using statewise

Indian data.

Markets for land in India are segmented, comprising of both sale and lease markets. The

former operating purely in the domain of purchase and sale transaction of land whereas

the latter is the rental market. Almost all the studies on land markets in India are

concentrated on the rental or lease markets2, while there are seldom any studies on the

issue of variations and factors influencing these variations in farmland prices. The scarcity

of studies on these issues reflect the fact that the data on farmland prices are hard to find.

Specially designed surveys to study the variations of farmland prices and factors

influencing these variations are required.

However, there are few scattered evidences on the study of farmland prices based on

sample surveys confined to few selected villages.3 These case studies are confined to

Ahmednagar district in Maharashtra4, Nainital Tarai region in Uttar Pradesh5 and Nadia

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and Hoogly district in West Bengal6. None of these case studies have any clear theoretical

focus and pertains to a small, and often non-random sample. Though these studies offer

useful insights, it is extremely difficult to draw policy implications based on their results.

In light of these short comings, this paper differs from the existing studies on farmland

prices in India in two important ways. First, it is an aggregate analysis based on random

samples from districts and states across India. Secondly, a model of the determination of

farmland prices is explicitly outlined to discuss and develop a principal hypothesis.

The aim of this paper is two fold; first to construct a cross-section of state and district level

farmland prices by estimating the weighted average farmland prices for both irrigated and

unirrigated land based on the extensive survey of 371 villages covering 18 major states in

India.7 Second, to explore the determinants of farmland prices across states in India.

A brief description of the survey and the methodological issues involved in the calculation

of aggregate farmland prices are given in section II. In section III, we present a profile of

farmland prices across districts and states in India. This is followed by section IV which

presents a simple model for the determinantion of farmland prices in India. Section V

reports the estimated results followed by the last section which summarises the findings

and sets out the conclusions.

II. Some Methodological Issues

A comparison of land prices within the same country, given the definational problems and

data insufficiency is a difficult enough proposition. One of the main difficulty in the

collection and analysis of statistics about land prices is the diversified characteristics

across not only regions of the country, but between sites in the same district and village.

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The statistics for farmland prices were collected at the village level based on sample

survey conducted for 1994-95 during 1996 under two categories - irrigated and unirrigated

farmland. A three stage sample design was adopted to select the households with the first

and second stage units as districts and villages, respectively. For each selected district, a

random sample of four villages were selected. However, the estimates of district level

farmland prices for three states includes only three villages. The districts are Kachchh in

Gujarat; Karnal in Haryana and Phulabani in Orissa. The rest of the states include four

villages in each district. The number of districts selected in each state are as follows:

thirteen in Uttar Pradesh; ten in Madhya Pradesh; eight in Maharastra; seven in Andhra

Pradesh; six each in Bihar, Haryana, Orissa and Rajasthan; five in Gujarat; four each in

Himachal Pradesh, Karnataka, Punjab, Tamilnadu, and West Bengal; three in Kerala,

and two and one in Assam and Tripura, respectively. The list of districts selected are

given in Table I. The total number of selected districts and villages across 18 states are 94

and 371, respectively.

These data on farmland prices under the two categories, collected from the villages are

seperately aggregated to get district and state level statistics. The area under these two

types of land are used as weights in the process of aggregation. The statistics on the total

area, both for irrigated and unirrigated in hectares are available from the District Census

Handbook, Village Directory published by the respective state governments. These are

collected every 10 years. No alternative source of statistics exists. The data for 1991,

1981 and 1971 on area irrigated and unirrigated were used as weights. Here, we make

every attempt to use the closest year for which data are available. Nevertheless, due to

the non-availability of 1991 census data for some villages in few states, 1981 or 1971

census data were considered to derive weights.

For example, the district level farmland prices for irrigated land are arrived at by an

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weighted average of the village level farmland prices. This is the product of farmland

prices and the area irrigated per hectare as proportion to the total area irrigated in the

sample villages. This is represented as follows:

nPid = Piv Wiv

i =1

where Wiv = Liv/Tid

Pid is the weighted average price of irrigated land in the districts, d, P iv is the price of the

irrigated land in the village v, Liv is the total irrigated land in the village v and T id is the total

irrigated land in all the sample villages in a district, d.

The weights used here is the area irrigated for each sample village covering all the

agricultural land put together to correspond to the net area sown plus the current fallow

land in each village. Area sown more than once in the same year is counted only once.

The same procedure was adopted for the computation of district level farmland prices for

unirrigated land.

For some villages like Mallapuram in Kerela, Manila and Baster in Madhya pradesh,

Sambalpur and Koraput in Orissa, the statistics on area irrigated are not available.

Similarly, data on total unirrigated area are not available for Ludhiana in Punjab. The

districts for which data on both irrigated and unirrigated farmland are not available are

West Nimar in Madhya Pradesh, Kendujhar, Phulbani; Ganjam and Puri in Orissa, Assam

and Nagaland. For these cases, the simple averages are taken to calculate the district

and state level prices.

The states for which the 1991 census data were used are Nizamabad in Andhra Pradesh;

all the five districts in Gujarat and Haryana; Bhilwari, Jodhpur, Jhunjhunu, Dholpur,

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Bharatpur in Rajasthan, Galasin village in Bilaspur district of Himachal Pradesh. For rest

of the villages in the states, the 1981 census data are used except for Mallapuram in

Kerela; Satana in Madhya Pradesh; Koraput in Orissa; Almora and Sitapur in Uttar

Pradesh for which the 1971 census data on area irrigated and unirrigated are used as

weights.

The series for state level farmland prices are calculated as the weighted average of the

computed district level farmland prices. The estimation procedure is the same as that

followed for districts except that the weights estimated rely on the data for the sample

districts instead of the sample villages. For state level aggregates in Orissa and Assam,

simple averages are calculated due to the non-availability of data on area irrigated and

unirrigated.

III. Farmland Prices Across States: A Profile

A comparison of the total irrigated farmland prices across states show that the highest

price is in the state of Kerala and West Bengal, while the lowest is in Orissa and Bihar

(See table II). The state of Orissa also has the lowest price for unirrigated farmland

followed by Madhya Pradesh.

The most important factor among states, although with few exceptions, that influence land

prices is the density of population per sq. Km. In Kerala and West Bengal, the density of

population - both rural and urban and combined are the highest among states. In Kerela,

the population per square Km. is 743, 659 and 1983 in total, rural and urban, respectively.

This is also true for the unirrigated farmland as well. Among the states having the lowest

price for unirrigated farmland, Bihar is replaced by Madhya Pradesh.

The above discussion on the price of farmland prices across states show that the poorer

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states like Orissa, Bihar, Assam and Madhya Pradesh have tremendous potential in the

location of industries, where the marginal productivity of capital and labour would be

higher as land prices are lower. With the development of necessary infrastructure in these

states, industries would prosper.

If the focus is shifted to a more disaggregated level namely districts, the variations in

farmland prices for both irrigated and unirrigated seems to be interesting (See table I).

The higher farmland prices are in the rural regions of Palghat in Kerala with low density of

rural population, while the density of urban population is quite high. This reflects the

demand for more land from the urban areas which has an impact on the rural land prices

by the process of expansion of cities. This is prevalent in all the districts where urban

regions grow by enchroaching on rural areas.

In West Bengal, higher farmland prices are in the Birbhum regions, though the density of

population in both rural and urban are not high. In the regions of Darjeeling where

commercial crops like tea and coffee are produced and where population density is high,

the farmland prices are the second highest being next only to Birbhum. The regions of

Bardhaman has the highest population density in both rural and urban, but the land prices

are the lowest.

In Uttar Pradesh, Muzaffarnagar has the highest farmland prices while Almora the lowest.

In Muzaffarnagar, the population density is the highest among the rural regions and

lowest for Almora. The latter also has the lowest population density in urban region as

well.

In the most industrialised state of the country, Gujarat, the district level farmland prices for

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both irrigated and unirrigated farmland are the highest in Gandinagar. It is expected that

the higher level of industrialisation will lead to a greater level of urbanisation and thereby

has an impact on the rural land prices. This is true in the all-India context for the reason

that the most important factor which has brought about changes in the farmland prices is

the population pressure on land. The density of population for Gujarat is the highest for

rural areas as well as for the total. But the population density in urban areas does not

have as much pressure on farmland as in the rural areas. This is true for Kachchh which

has the lowest farmland price among the districts of Gujarat. This region also show that

the population pressure on the rural areas has influence on the farmland prices.

The next most industrialised state, reflected by the share of manufacturing in NSDP is

Maharashtra. Here, the district of Pune has the highest farmland price for both irrigated

and unirrigated farmland. The population per square Km. for both rural and urban areas

combined are high. In this state, population pressure from both urban and rural

Maharashtra have a combined effect in the higher level of farmland prices. In Solapur

which has the lowest farmland prices show that it is the population pressure in the rural

areas which seems to have brought about an increase in the farmland prices for both

irrigated and unirrigated farmlands.

For the least industrialised state of Assam, the survey includes only two districts. These

districts are Marigaon and Kokrajhar. The farmland prices are higher in Kokrajhar than in

the other district. The density of rural population is higher in Marigaon, while the

population density in urban areas are higher for Kokrajhar. The population pressure in the

urban regions seems to have a larger effect for what ever reason the irrigated land is

demanded.

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In Andhra Pradesh, farmland prices are the highest in West Godavari which is the most

prosperous region in terms of agricultural productivity and per capita income, though, the

total area in quantity terms is the lowest. In this region, the density of population is the

highest for both rural and urban areas. The lowest farmland prices is in the district of

Medak which incidentally does not have the lowest rural population density but has the

urban population density as the lowest. These hold good for both irrigated and unirrigated

farmland prices in this state.

The explanation provided above considers the effect of one or two variables at a time,

however, they may mask true relationships or may suggest relationships which in fact

have other explanations. It is for this reason that it is necessary to turn to multi-variate

analysis to highlight more precisely the determination of farmland prices. Before doing so,

however, we consider the underlying theoritical issues in the next section to organise the

discussion and develop a principal hypothesis.

IV. Some Theoritical Considerations

Land as a resource is virtually fixed in terms of total quantity at any point in time, although

the division between numerous alternative uses varies considerably through time. In

areas where agriculture is less dominant or urban influences are strong, various

representations of urban influences needs to be considered in understanding farmland

prices. If at any point in the future the expected best and high use of land which is most

profitable will be for purposes of non-agriculture then the current market value of the land

will tend to be higher than the agricultural use value. Hence, land under such

circumstances will have a higher valuation.

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Several factors influence the changing pattern in farmland prices. Among them are the

density of population per square Km, the rate of economic growth, the increase in the

purchasing power of the population, the rate of inflation and the distance from the nearest

urban center. The urban center affect land use and values through residential

developments, recreational enterprises and other non-agricultural uses. Thus, land values

are affected by a number of forces which have varying degrees of influence in different

areas.

Farmland prices acquired for non-agricultural purposes generally must pay a premium to

bid the land away from its agricultural use. The sale of land at prices above those that had

prevailed in an area will tend to increase the value of all land, since prices convey

information and owners will therefore raise their expectation.

Investment of public and private capital in the development of irrigation facility and in

public and private services, enhance the rural environment and also increase the value of

farmland.

We consider, four elements which form the basic determinants of farmland prices in the

econometric equation: farm income or returns, location, population pressure and farmland

utilisation characteristics.

The first variable implies that higher returns from land will cause an increase in land

prices. Agriculture is one of the most important uses of land and therefore reason enough

for purchase of land, even in areas where urban influences are strong. And land put to

foodgrain and non-foodgrain production will reflect differential returns which are essential

for models that are used to explain farmland prices in rural areas. Hence, foodgrain and

non-foodgrain yield are considered seperately in the price equation.

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The location variable considered in the price equation is important in the sense that the

demand for rural land for non-agricultural purposes have become increasingly important

in the competition. In general, many of the areas with high farmland prices are located

near urban fringes. This suggests that location with respect to population is an important

determinant of farmland prices.

The population pressure on land is another determinant of farmland prices. This includes

both urban and rural density of population and also the combined effect of both. The

demand for farmland can be from the urban residents demanding land for multiple

purposes, i.e., cultivation, conversion of land to residential complexes and location of

industrial units, etc. Higher the density of population more is the demand and hence

higher will be the price of farmland. Therefore, all the three components of population

pressure namely urban, rural and combined, are considered in the econometric exercise.

The specification also includes the pattern of land utilisation. The price of irrigated

farmland will depend on the price of unirrigated farmland as, generally, a margin is added

to the price of unirrigated farmland which includes the cost of public and private

investment in developing irrigation facility in the process of conversion of unirrigated

farmland to irrigated farmland.

Based on the above theoritical considerations, the model is specified as follows:

Pf= 0 + 1Y + 2L + 3P + 4F + ef

where 1 > 0, 2 < 0, 3 > 0, 4 > 0.

The variable Y represents returns from land - foodgrain and non-foodgrain yield, L is the

distance variable, P is the variable for population pressure, F for prices of farmland under

different types of land and ef is the error term. Each of these variables can be postulated

as

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variables are not available for these two states. Numerious combinations of the above

mentioned variables were estimated with various forms of specifications including linear

and log linear equations. The results reported here are the best among the alternative

specifications estimated. These results are reported in the equation form with t values

presented in the parenthesis below each equation. The R2 values indicated below each

equation shows that all the models presented here have good fits.

In the first equation, the estimates based on D, Piu and R gives satisfactory results with

high R2 and Pi showing an inverse relationship with D and direct for the rest of the

variables considered in the model. The negative sign for the variable - distance from the

nearest town, is expected because the farther away a land is from the town, the lower is

the price of land. This may be due to various reasons including the fact that the availability

of the infrastructure facility away from town is lower. The only variable which is significant

in this model is the price of unirrigated farmland. This shows that the price of unirrigated

farmland will be higher, if the price of the irrigated farmland is high. The higher price for

the irrigated farmland is the margin over the price of unirrigated farmland due to the

investment of public and private capital in the development of irrigation facility.

While unirrigated farmland prices depends on the density of population in the rural areas;

the larger the population in the rural areas, higher is the demand for unirrigated farmland.

This relationship is expressed in the third equation. This specification also includes

variables such as foodgrain yield (FY) and the distance from the nearest town (D).

However, these variables do not show any significant relationship but are with expected

signs.

At the aggregate level (for all-India), the OLS estimates show that the price of unirrigated

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farmland prices is the only significant variable while all the other variables included in the

specification were insignificant but are with the expected signs. While among the

determinants for the price of unirrigated farmland prices, density of population in the rural

areas is the only significant variable. This essentially shows that the price of irrigated

farmland prices depends on the density of population in the rural areas. The stylized facts

presented in the previous section also points to this explanation.

The current returns from farmland, shown by FY or NFY, included in the specifications are

insignificant. This supports the view that it is probably a reflection of the existence of small

and part time operations in the farmland (Shi, Phillips and colyer, 1997). With the

developed tenancy markets in India, farms are often for investments rather than an

important source of current income.

Though, at the aggregate, these results may be true but at a disaggregated level, the

relationship may seem different. Therefore, specifications at the village level for all the

states except Nagaland and Tripura were estimated. The estimated equations for each

state are provided seperately in Appendix I and the results are summarised in Table III

and Table IV.

For five states Bihar, Gujarat, Haryana, Rajasthan and West Bengal, the estimates for

unirrigated farmland prices are not presented because various forms of specifications

were tried in vain.

For nine states such as Bihar, Gujarat, Haryana, Kerala, Maharashtra, Punjab, Rajasthan,

Tamilnadu and West Bengal, Piu was included in the specification to increase the overall

fit. With the inclusion of this variable, the overall fit increased and in all these specifications

Piu was significant. While inclusion of Pi, the price of irrigated farmland only for

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Maharashtra increased the R2 and turned out to be a significant variable.

In a large number of states, rural density of population, foodgrain yield and distance from

the nearest town are significant determinants for irrigated farmland prices (see Table III).

The density of population in rural areas is significant for Andhra Pradesh, Madhya

Pradesh, Tamilnadu, Maharashtra, Punjab and Uttar Pradesh, while foodgrain yield is

significant for Andhra Pradesh, Himachal Pradesh, Orissa, Tamilnadu and Uttar Pradesh.

The distance from the nearest town is significant for Haryana, Himachal Pradesh, Kerala,

Madhya Pradesh and Tamilnadu.

For the unirrigated farmland prices, rural density of population and foodgrain yield are the

determinants for six and five states, respectively (see Table VI). The density of population

in rural areas is significant for Andhra Pradesh, Himachal Pradesh, Madhya Pradesh,

Punjab, Tamilnadu and Uttar Pradesh, and foodgrain yield or returns from land is

significant in Andhra Pradesh, Himachal Pradesh, Orissa, Tamilnadu and Uttar pradesh.

The significance of foodgrain yield or returns from land have been pointed out in the

literature. These studies utilize the rent capitalisation models to show that net returns to

agriculture activities in conjunction with other explanatory variables is a significant variable

in explaining agricultural land prices (Alston 1986; Featherstone and Baker 1987 and

Runge and Halbech 1990).

Now for analytical reasons, we focus our concern to the specific states and the

determinants, there of, for both irrigated and unirrigated farmland prices.

The density of population in rural areas and foodgrain yield are the significant factors in

explaining irrigated farmland prices in Andhra Pradesh. Both the variables, distance from

the nearest town and density of population in urban areas do not show any significant

relationship with irrigated farmland prices. Certain regions in this state are highly

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prosperous in terms of agricultural production. Therefore, returns from farmland is

significant, reflected by the significant FY. The return from farmland in certain regions are

high, hence, the rural population has a higher demand for farmland prices for its returns.

The price of unirrigated farmland is the major determinant of prices for irrigated farmland

in Bihar and Haryana. While the distance from the nearest town is also significant in

explaining irrigated farmland prices in Haryana. The estimates for the unirrigated farmland

in these two states did not show any significant relationship with any of the variables in the

specification. The state of Haryana has highly commercialised and developed agricultural

sector. This region is comparable to the regions in U.S.A, where a recent study using

distance from an urban area as a linear explanatory factor indicates that proximity to an

urban area is more important in determining agricultural land values (Shi, Phipps and

Colyer, 1997).

The distance from the nearest town and the foodgrain yield are the significant factors

influencing both irrigated and unirrigated farmland prices in Himachal Pradesh. For the

prices of unirrigated farmland both rural and urban density of population also showed

significant relationship.

The only state where distance from the nearest town, density of population in the rural

areas and foodgrain yield are significant factors is in Tamilnadu for the irrigated farmland

prices. For the unirrigated farmland prices, it is the state of Himachal Pradesh.

In Tamilnadu, all the factors considered as independent variables are significant in all the

alternate specifications for the price of irrigated farmland as dependent variable. For the

price of unirrigated farmland, the combined effect of both rural and urban density of

population are significant thought only the density of population in the rural areas is

significant in one of the specifications which excludes density of population in the urban

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areas. The significance of the combined effect may be because of the highly significant

variable R.

In Tamilnadu, among the determinants for the price of unirrigated farmland, distance from

the nearest town is insignificant. Urbanisation does not seem to influence the unirrigated

farmland prices in the rural regions of Tamilnadu. Population pressure on land and the

foodgrain yield are the major determinants of unirrigated farmland prices in this state. This

conclusion is true for Uttar Pradesh as well, while the distance from the nearest town is

also insignificant in this state.

The distance from the nearest town as well as NFY and Piu are significant in Kerala. The

significance of NFY is not surprising as most regions of this state are under commercial

crops which have higher returns. This variable is also a significant factor in explaining the

price of unirrigated farmland prices apart from the combined influence of urban and rural

density of population denoted by T. Though both urban and rural density of population are

insignificant individually. The combined effect assumes importance as there lies little

difference between rural and urban areas in Kerala.

The estimates for farmland prices in Maharasthra show that the density of population in

urban areas is a significant variable in all the specifications. This result was also

highlighted in section III. While density of population in the rural areas and the combined

effect of both urban and rural density of population are significant, when included

individually. With the inclusion of Piu, the explanatory power of the specification increases.

For the unirrigated farmland prices, the inclusion of Pi increases the overall fit and the

level of significance.

The distance from the nearest town and density of population in both rural and urban

areas are significant factors across all specifications for Madhya Pradesh. While non-

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foodgrain yield is a significant variable for some specification.

The foodgrain yield is the only significant variable in explaining both irrigated and

unirrigated farmland prices in Orissa. The returns from farmland is the major determinant

for both the types of land use. In Punjab, Piu is the only variable which is significant. With

the dropping of this variable, the value of R2 falls and non of the variables were significant,

while Piu in punjab is explained by the density of population in the rural areas. Therefore,

density of population in rural punjab is the major determinant of irrigated farmland prices.

This is not surprising as punjab is highly prosperious and especially rural punjab for its

high yield.

The density of population in urban areas is a significant variable for Himachal Pradesh,

Maharashtra, Madhya Pradesh and Rajasthan. This reflects the demand for farmland

from the urban residents for various reasons including for purposes of capital gains and

location of industries. This is specially important for the relatively more industrialised state

like Maharashtra.

The above mentioned observation has implications for the macro economic stability in the

shortrun, when large capital flows are invested in the land markets in relatively advanced

states such as Maharashtra to harvest capital gains. The investments in these markets

are relatively less risky than the risk prone financial markets and the rate of returns from

capital gains are also high.

If sources of these capital flows are of the short term variety and are borrowed from the

international markets then these investments in the land markets can lead to balance of

payment criss in the short run as these investments are of a long term nature. Therefore,

presence of statutary regulations in land markets becomes a binding force given the long

term nature of investment in this market.

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VI. Concluding Remarks

This study first constructs the farmland prices and then identifies their major determinants

across districts and states in India through a simple model. The specifications were

estimated for each state and for all-India, seperately.

The estimates presented in Table I and appendix gives quantitative information to urban

planners and policy makers who require to know the level of farmland prices and a

complete understanding of the effects of urbanisation and the other determinants causing

an increase in them. The estimates have good results with a high level of explanation - R 2,

statistical significance for the variables and are with expected signs.

The estimates at the aggregate level (for all-India) showed that the major determinant for

the price of irrigated farmland is the density of population in the rural areas. The distance

from the nearest town or the threat from urbanisation as well as returns from land are not

important in determining farmland prices at the aggregate level.

Nevertheless, the estimates for the states show that density of population in the rural

areas, foodgrain yield and distance from the nearest town are the major determinants for

irrigated farmland prices. This follows from the fact that large number of states in India has

these variables as significant determinants for the irrigated farmland prices. For the

unirrigated farmland prices, it is the density of population in the rural areas and foodgrain

yield.

The regions with low and high farmland prices were identified and their determinants

estimated. These results will help the policy makers to identify regions with low farmland

prices for the promotion of industries and for better allocation of industries locating them

away from the regions with high farmland prices. This will increase the marginal

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productivity of capital and labour and will help alleviate poverty.

It was also pointed out that the demand for farmland from the urban residents in a

relatively developed state like Maharashtra is important. In this state, land is demanded

either for its capital gains from the appreciating land prices or for purposes of location of

industries. It was argued that this has implications for the macro economic stability in the

short run. Therefore, presence of statutary regulations becomes a binding force.

However, certain short-comings of the analysis has to be born in mind. In the estimation

of the model at the village level for variables such as R, U, T, FY and NFY, it was

assumed that the district level figures were assumed to hold good for the villages in any

particular district. This was assumed under data constraints; due to the non-availability of

statistics for the above mentioned variables at the village level.

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Table I: Farmland Price per Acre and Population Density in Districts Across States

--------------------------------------------------------------------------------------------------

-- Weighted Weighted Total Rural Urban

Average

Average Average Populat. Populat. Populat. Distance

States District Price of Price of per Sq. per Sq. per Sq. from the

Irrigated Unirrigated Km. Km. Km. Nearest

Land Land Town

(in Rs.'000) (in Rs.'000) (in K.M.)

--------------------------------------------------------------------------------------------------

--Andhra Pradesh Chittoor 131.31 89.02 216 175 3150 15

Prakasam 94.88 69.58 157 133 1590 12

Adilabad 58.91 35.41 129 101 2384 17

Cuddapah 89.68 51.25 148 114 2320 19

West Godavari 265.82 124.98 454 367 5247 15

Medak 75.97 48.70 234 203 2369 19

Nizamabad 110.68 69.09 256 207 4320 9

Bihar Nalanda 69.34 42.58 844 751 2923 9

Purba Champaram 113.29 67.04 767 734 2921 14

Muzaffarpur 92.54 46.15 931 860 4967 26

Saharsa 91.56 63.13 602 575 1635 30

Bhagalpur 82.03 37.63 573 511 4563 22

Ranci 85.07 63.58 288 201 2249 47

Gujarat Kachchh 60.00 53.49 28 19 1110 7

Kheda 75.90 37.89 478 390 2094 7

Gandhinagar 125.42 79.37 630 419 2312 7

Bharuch 88.84 63.20 171 137 2582 10Surendranagar 66.24 42.49 115 84 939 5

Haryana Bhiwani 117.30 66.73 222 185 3821 5

Gurgaon 191.38 112.91 415 340 3136 8

Sonipat 148.57 95.00 545 429 4564 10

Kaithal 207.42 171.18 293 253 4190 11

Karnal 179.03 140.00 450 334 5572 28

Faridabad 122.70 93.68 702 397 3798 22

Himachal Pradesh Kullu 294.44 137.36 55 51 1508 23

Bilaspur 102.08 66.18 253 242 960 16

Mandi 147.59 80.32 197 184 1930 43

Shimla 75.00 41.05 120 97 2492 62

Karnataka Raichur 136.86 57.78 165 132 2393 17

Bellary 428.73 98.69 191 140 1391 28

Chitra Durga 98.78 55.01 201 148 6019 20

Shimoga 75.74 54.45 181 135 3210 11Kerala Thrissur 440.86 206.74 903 739 2391 8

Mallapuram 350.00 237.92 872 833 1647 11

Palaghat 737.49 122.25 532 471 1688 28

Maharasthra Nashik 86.51 45.34 248 165 2642 38

Dhule 71.58 41.45 193 155 4878 23

Satara 76.50 44.48 234 208 1503 20

Solapur 111.57 76.15 217 156 5442 23

Bid 61.35 42.13 170 141 3549 14

Yavatmal 87.98 55.56 153 127 5342 26

Ratnagiri 64.76 39.88 188 173 2405 34

Pune 175.21 81.96 354 181 4868 34

---------------------------------------------------------------------------------------------------

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(Continued)

--------------------------------------------------------------------------------------------------

-

Weighted Weighted Total Rural Urban Average

Average Average Populat. Populat. Populat.

Distance

States District Price of Price of per Sq. per Sq. per Sq. from the

Irrigated Unirrigated Km. Km. Km. Nearest

Land Land Town

(in Rs.'000) (in Rs.'000) (in K.M)

--------------------------------------------------------------------------------------------------

--

Madhya Pradesh Datia 60.45 48.36 194 153 3554 30

Satana 96.28 44.23 195 163 965 19

Ratlam 110.70 53.47 200 139 3103 17West Nimar 72.50 43.75 151 129 3777 10

Batul 71.26 35.02 118 96 4275 34

Hosangabad 50.00 24.27 126 93 1956 19

Mandla 42.50 21.35 97 90 1153 28

Bilaspur 81.31 47.99 191 161 1685 9

Durg 55.38 36.72 281 189 2659 23

Bastar 22.50 13.51 58 54 2292 27

Orissa Sambalpur 63.75 35.00 154 130 1429 13

Kendujhar 37.50 15.00 161 144 910 33

Phulabani 56.67 33.33 78 73 1023 9

ganjam 113.75 63.75 252 219 1678 9

Puri 71.25 46.75 353 292 2398 14

Korapui 41.25 20.54 112 100 1172 34

Punjab Gurdaspur 139.71 99.91 493 393 5056 9

Ludhiana 259.57 132.50 641 336 6302 8Sangpur 101.31 67.33 335 257 4939 8

Hoshiarpur 165.00 87.54 375 325 2716 11

Rajasthan Udaipur 94.68 67.40 167 141 2127 39

Bhilwara 124.32 78.63 152 127 881 30

Jodhpur 60.07 52.68 94 61 3665 65

Jhunjhunu 117.53 71.36 267 217 2294 14

Dholpur 176.18 116.18 247 209 2003 18

Bharatpur 93.64 47.25 326 270 2457 11

Tamil Nadu Dharampur 93.53 41.14 252 230 3228 8

Coimbatore 132.36 102.70 470 255 1975 11

Tiruchirapalli 141.19 78.81 373 283 2892 14

Kanniyakumari 198.50 154.76 950 812 5874 7

Uttar Pradesh Sultanpur 83.70 69.46 577 555 3985 39

Almora 38.00 23.79 155 146 1421 22

Banda 47.49 30.81 244 214 5620 20Baharaich 91.95 72.66 402 372 6699 7

Saharanpur 122.82 78.91 626 473 11414 11

Muzaffarnagar 166.52 106.55 709 544 10302 14

Rampur 97.89 85.44 635 482 6231 14

Bijnor 72.99 50.51 538 409 9047 9

Fatehpur 95.01 68.82 457 420 2378 7

Sitapur 89.13 70.95 497 444 4288 21

Nainital 87.86 75.56 227 156 4032 9

Moradabad 138.51 107.95 691 512 7712 15

Hardwar 107.50 80.00 476 338 5391 7

West Bengal Birbhum 244.88 145.00 562 519 3801 25

Maldah 173.62 145.94 706 660 8985 38

Bardhaman 187.10 135.88 861 618 3179 29

Darjeeling 197.64 205.07 413 293 5717 11

Assam Marigaon 77.50 57.50 410 393 1841 11

Kokrajhar 91.25 65.00 256 241 2949 8

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Nagaland Kohima 95.00 68.75 96 68 1999 52

Tripura West Tripura 107.79 60.96 427 333 3198 32

--------------------------------------------------------------------------------------------------

--

Table II: Farmland Prices for Irrigated and Unirrigated land

per Acre and Density of Population by state

------------------------------------------------------------------------------------

Weighted Average Farmland Price Total Rural Urban

Irrigated Unirrigated Population Population Population

States (in Rs.'000) (in Rs.'000) per Sq. Km. per Sq. Km. per Sq. Km.

-------------------------------------------------------------------------------------

(Col. 1) (Col. 2) (Col. 3) (Col. 4) (Col. 5) (Col. 6)-------------------------------------------------------------------------------------

Andhra Pradesh 168.31 58.24 203 165 2765

Bihar 73.45 52.46 590 529 2784

Gujarat 103.65 45.51 108 81 1550

Haryana 166.36 96.97 385 288 3964

Himachal Pradesh 180.78 99.85 126 113 1981

Karnataka 187.09 75.98 183 138 2482

Kerala 572.21 140.67 743 659 1983

Maharasthra 97.42 53.43 225 161 3760

Madhya Pradesh 75.43 37.68 132 108 2081

Orissa 64.03 24.82 169 146 1546

Punjab 140.18 90.35 451 321 5157

Rajasthan 123.78 59.69 164 130 2073

Tamil Nadu 153.36 96.70 391 289 2406

Uttar Pradesh 109.68 55.52 448 368 6129West Bengal 211.55 168.48 679 545 3587

Assam 84.38 61.25 307 291 2384

Nagaland 95.00 68.75 96 68 1999

Tripura 107.79 60.96 427 333 3198

-------------------------------------------------------------------------------------

Table III: Summary Results from OLS Estimates for

Irrigated Farmland Prices

------------------------------------------------------------------

Significant Names of States Number of

Variables States

------------------------------------------------------------------

D Haryana, Himachal Pradesh, Kerala, 5

Madhya Pradesh, Tamilnadu

R Andhra Pradesh, Maharashtra,

Madhya Pradesh, Tamilnadu,

Uttar Pradesh 5

U Maharashtra, Madhya Pradesh, 3

Rajasthan

T Tamilnadu, Uttar Pradesh 2

FY Andhra Pradesh, Himachal Pradesh

Orissa, Tamilnadu, Uttar Pradesh 5

NFY Kerala, Madhya Pradesh, Tamilnadu 3

Piu Bihar, Gujarat, Haryana, Kerala,

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Maharashtra, Punjab, Rajasthan,

Tamilnadu, West Bengal 9

------------------------------------------------------------------

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Table IV: Summary Results from OLS Estimates for

Unirrigated Farmland Prices

------------------------------------------------------------------

Significant Names of States Number of

Variables States

------------------------------------------------------------------D Himachal Pradesh, Madhya Pradesh 2

R Andhra Pradesh, Himachal Pradesh,

Madhya Pradesh, Punjab, Tamilnadu,

Uttar Pradesh 6

U Himachal Pradesh, Madhya Pradesh 2

T Tamilnadu 1

FY Andhra Pradesh, Himachal Pradesh,

Orissa, Tamilnadu, Uttar Pradesh 5

NFY Madhya Pradesh 1

Piu Maharashtra 1

------------------------------------------------------------------

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APPENDIX

-----------------------------------------------------------------------------------------------------------Estimates for all India based on State level statistics

Pi = -1.29 - 0.88 D + 1.77 P iu + 0.14 RUR(0.30) (2.1 2) (0.80)

R-Squared = 0.52 Adj. R-Squared = 0.40

Pi = 12.90 - 1.35 D + 2.21 P iu(0.48) (3.56)

R-Squared = 0.49 Adj.R-Squared = 0.41

Piu = 28.39 + 0.11 R + 0.01 FY - 0.23 D(2.43) (1.17) (0.29)


-----------------------------------------------------------------------------------------------------------Estimates based on village level statistics across States

Andhra Pradesh

Pi = - 37.39 - 0.31 D + 0.51 R + 0.03 FY(0.63) (5.71) (2.83)


Pi = -38.84 - 0.36 D + 0.03 FY + 0.005 U + 0.45 R(0.70) (2.62) (0.56) (3.39)


Piu = -12.55 - 0.37 D + 0.20 R + 0.001 U + 0.02 FY(1.21) (2.47) (0.36) (3.32)


Bihar

LPi = 0.04 - 0.008 LD + 0.74 LP iu + 0.06 LFY + 0.12 LU(0.18) (7.29) (0.42) (1.7)


Pi = - 4.06 - 0.02 D + 1.26 P iu + 0.01 FY + 0.004 U(0.11) (6.38) (0.81) (1.8)


Gujarat

LPi = 0.99 - 0.05 LD + 0.03 LNFY + 0.80 LP iu + 0.03 LR(0.73) (1.12) (11.03) (1.12)


Haryana

Pi = - 7.90 - 0.39 D + 1.56 P iu + 0.004 U(2.07) (23.00) (0.92)


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Himachal Pradesh

LPi = - 94.11 - 0.31 LD + 1.16 LR + 0.53 LU + 12.16 LFY(2.84) (1.47) (1.08) (2.33)


LPiu = - 141.34 - 0.35 LD + 2.09 LR + 1.03 LU + 17.38 LFY(3.36) (2.71) (2.14) (3.41)


Kerela

LPi = - 17.01 - 0.11 LD + 1.15 LNFY + 1.21 LP iu + 0.91 LU(0.66) (4.55) (5.27) (1.47)


Pi = - 292.08 - 12.97 D + 0.08 NFY + 2.13 P iu

(2.50) (4.28) (3.59)R-Squared = 0.75 Adj.R-Squared = 0.65

LPiu = - 212.32 - 0.24 LD + 9.94 LNFY + 20.02 LT(1.04) (1.80) (1.89)


Maharasthra

LPi = -2.24 + 0.88 LR + 0.17 LU + 0.01 LNFY + 0.01 P iu(4.19) (2.34) (0.57) (9.84)


Pi = - 147.55 + 0.001 NFY + 0.01 U + 0.86 R + 0.01 FY(1.09) (4.05) (2.75) (0.64)R-Squared = 0.47 Adj.R-Squared = 0.40

Pi = - 56.49 + 0.0001 NFY + 0.01 U + 0.13 R + 0.006 FY + 0.36 T(0.14) (2.01 ) (0.37) (0.49) (2.93)


Pi = - 11.26 + 0.0003 NFY + 0.09 R - 0.04 D + 0.007 U + 0.06 T + 0.004 FY(0.53) (0.37) (0.26) (2.07) (0.78) (0.41)


Piu = 8.82 - 0.13 D + 0.002 U + 0.001 FY + 0.43 P i

(1.3) (1.7) (0.35) (8.39)R-Squared = 0.78 Adj.R-Squared = 0.75

Madhya Pradesh

Pi = 25.33 - 0.93 D + 0.26 R + 0.009 U + 0.002 FY(3.62) (3.00) (2.61) (0.15)


Pi = 10.74 - 0.79 D + 0.30 R + 0.006 U + 0.02 NFY


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LPi = - 2.76 - 0.19 LD + 0.80 LR + 0.15 LU + 0.36 LNFY(3.18) (7.12) (1.72) (4.19)


Piu = 5.92 - 0.48 D + 0.17 R + 0.005 U + 0.007 FY


LPiu = - 3.94 - 0.17 LD + 0.89 LR + 0.20 LU + 0.33 LNFY(2.65) (7.32) (2.15) (3.53)

R-Squared = 0.75 Adj.R-Squared = 0.72Orissa

LPi = - 8.56 + 0.18 LR - 0.18 LD + 1.71 LFY(1.30) (1.34) (4.12)


Piu = - 31.99 - 0.13 D + 0.04 FY + 0.05 R + 0.0006 NFY(0.93) (3.45) (0.65) (0.26)


LPiu = - 8.06 - 0.007 D + 1.48 LFY + 0.001 R + 0.09 LNFY(1.93) (2.63) (0.75) (0.29)


Punjab

LPi = 2.88 - 0.07 LD + 0.10 LNFY + 0.01 P iu + 9.68 R + 2.84 U

(0.73) (0.04) (4.87) (0.005) (0.66)R-Squared = 0.84 Adj.R-Squared = 0.77

Pi = - 11.62 - 1.31 D + 1.70 P iu + 0.03 R + 0.002 U(0.71) (8.06) (0.23) (0.36)


LPiu = - 9.63 + 1.45 LR + 0.70 LFY(2.10) (1.12)

R-Squared =0.25 Adj.R-Squared = 0.13

Rajasthan

Pi = - 27.75 + 0.01 FY + 0.01 U + 1.17 P iu + 0.03 R(1.74) (2.42) (10.49) (0.65)


Tamilnadu

Pi = - 15.96 + 0.15 R + 0.005 NFY(3.05) (1.31)


Pi = - 27.88 + 0.07 R + 0.002 NFY + 1.07 P iu(3.43) (1.30) (8.14)


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Pi = - 29.06 + 0.04 R + 0.003 NFY + 1.16 P iu - 2.15 D(2.00) (2.21) (9.36) (2.10)


Pi = - 45.48 + 0.01 R + 0.003 NFY + 1.22 P iu - 2.09 D + 0.01 FY(0.30) (2.12) (8.81) (2.03) (0.91)


LPi = - 3.30 - 0.04 LD + 0.37 LR + 0.75 LFY(0.93) (4.29) (6.01)


LPi = - 2.55 - 0.04 LD + 0.42 LT + 0.59 LFY(0.93) (4.51) (4.28)


LPiu = - 7.29 + 0.85 LNFY + 0.54 LT(1.05) (1.96)


LPiu = - 2.92 - 0.003 D + 0.62 LT + 0.44 LFY(1.22) (6.09) (2.91)


LPiu = - 3.90 - 0.05 LD + 0.54 LR + 0.65 LFY + 0.003 LNFY(1.14) (5.27) (2.33) (0.02)


Uttar Pradesh

Pi = - 5.10 + 0.10 R + 0.03 FY - 0.27 D(4.00) (4.72) (1.09)


LPi = - 2.55 + 0.42 LT + 0.59 LFY - 0.04 LD(4.51) (4.28) (0.93)


Piu = - 2.44 + 0.10 R + 0.01 FY + 2.11 NFY - 0.23 D(5.12) (2.30) (0.09) (1.27)


LPiu = - 3.90 + 0.54 LR + 0.65 LFY + 0.003 LNFY - 0.05 LD(5.27) (2.33) (0.02) (1.14)


West Bengal

Pi = - 42.49 - 0.26 D + 0.07 R + 1.27 P iu + 0.01 FY(1.01) (1.97) (14.31) (0.86)

R-Squared = 0.95 Adj.R-Squared = 0.94------------------------------------------------------------------------------------------------------------

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References

Alston, J.M. (1986), "An Analysis of Growth of U.S. Farmland Prices, 1963-82", AmericanJournal of Agricultural Economics, 68 (February).

Bardhan, P.K. (1976), "Variation in Extent and Form of Agricultural Tenancy: Analysis of

Indian Data Across Regions and Over Time", Economic and Political Weekly, Vol. 11, No.37, (September 11).

Bardhan, P.K. (1984), Land, Labour, and Rural Poverty, Oxford University Press, Delhi.

Basu, K. (1992), "Limited Liability and the existence of share tenancy", Journal ofDevelopment Economics, 39.

Basu, K. (1998), The Less Developed Economy Revisited, Oxford University Press, Delhi.

Census of India (1994), General Population Tables, Series I, Part II-A(i), Office of theRegistarar General & Census Commissioner, New Delhi.

Centre for Monitoring Indian Economy (1996), India's Agricultural Sector: A Compendiumof Statistics, (July).

Chicoine, L. (1981), "Farmland Values at the Urban Fringe: An Analysis of Sales Prices",Land Economics, 57 (August).Dadibhavi R.V. and S.B. Samannavar (1991), "Trends in Land prices, Class of Buyersand Sellers of Land: An Empirical Study of Some Villages in Karnataka State", Paperpresented in the Indian society of Agricultural Economics Conference, Summariespresented in the Indian Journal of Agricultural Economics, Vol. 46, No. 3, (July-September).

Darin-Drabkin, H. (1977), Land Policy and Urban Growth,Pergamon Press, Oxford.

Featherstone, Allen. M, Timothy G. Baker (1987) "An Examination of Farm Sector RealAsset Dynamics", American Journal of Agricultural Economics, 69 (August).

Just, R. E. and John A. Miranowski (1993), "Understanding Farmland Price Changes",

American Journal of Agricultural Economics, 75 (February).Marothia, D.K., A.K. Gauraha and V.K. Choudhary (1991), "Agricultural Land Market: AMicro Economic Analysis", Paper presented in the Indian society of AgriculturalEconomics Conference, Summaries presented in the Indian Journal of AgriculturalEconomics, Vol. 46, No. 3, (July-September).

Pandey,V.K., G. Mani and S.K. Tewari (1991), "Market Segmentation and Determinantsof Land Prices: A Case Study of Nainital Terai Region", Paper presented in the Indiansociety of Agricultural Economics Conference, Summaries presented in the Indian Journalof Agricultural Economics, Vol. 46, No. 3, (July-September).

Runge, Carlisle F., Danial Halbach (1990), "Export Demand, U.S. Farm Income and LandPrices", Land Economics, 66 (May).

Shi, Y., T.Phipps and D. Colyer (1997), "Agricultural Land Values under UrbanizingInfluences", Land Economics, 73(1), (February).

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Notes

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1. For example see Alston (1986), Just and Miranowski (1993) and Shi, Phipps and Colyer

(1997).

2.See for example Bardhan (1976) and Bardhan (1984), Basu (1992) and for a general

treatment of the Tenancy markets see Basu(1998).

3.Some of these studies are summarised in the Indian Journal of Agricultural Economics (1991).

4. For the study in Maharashtra see Suryawanshi, Pagire, Pawar and Sale (1991). This study is

based on both primary and secondary data from five distinct areas of Ahmednagar district.

5.This study is based on the purposively drawn sample from the Kichha tehsil of Nainital Tarai

region. See Pandey, Mani and Tewari (1991).

6. This study examines the variation of land prices on different size of farms and highlights the

factors influencing land market prices in different size-groups of farms in the Nadia and Hooghlydistricts of West Bengal (Ray, 1991).

7.This data is from MIMAP-India survey conducted by the NCAER.

8.The district wise crop production included in FY are Rice, Jowar, Bajra, Maize, Ragi, Small

Millets, Wheat, Barley, Gram, Arhar and Other Pulses. Under NFDY the crops included areGroundnut, Sesamum, Rapeseed & Mustard, Linseed, Castorseed, Safflower, Nigerseed,Soyabean, Sunflower, Sannhemp, Tea, Coffee, Rubber, Chillies, Ginger, Turmeric, Pepper,Arecanuts, Coriander, Cardamom, Garlic, Potato, Sweet Potato, Tapioca, Banana, Onion,Sugarcane, Tobacco and Guarseed.

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