Lachlan Morrison's Final Project Report

VICTORIAN BROAD ACRE

CROPPING FARMLAND VALUES

AND THEIR DRIVERS 2014 Project Report

1. Abstract The drivers behind the value of broad

acre cropping land are complex and

highly variable. This study aimed to

quantify the effect of a change in

expected yield, expected price,

expected interest rate, soil quality and

size & length of investment. A

spreadsheet was created in Microsoft

Excel so that all receipts and expenses

involved in a purchase of land could be

included. They could then be varied to

study the effect on the maximum price

a farmer should pay if they wish to

earn X% p.a. on their investment. It

was found that yield based on the

properties’ location had the greatest

effect on the maximum price.

Individual farmers’ ability to grow

better yields and market their grain at

better prices also had a large effect, as

well as the length of time they planned

on investing for and the interest rate

they could receive. There was also

large effects on the individuals farmers

ability to grow better yields, market

their grains at better prices, the length

of time they planned on investing and

the interest rate they could receive.

Lachie Morrison 558412 Supervised by Bill Malcolm

Lachlan Morrison 2014

1

1. Abstract ..................................................................................................................................... 0

2. Introduction .............................................................................................................................. 2

3. Method, Data and Assumptions ................................................................................................. 7

3.1 Method.................................................................................................................................... 7

3.2 Data, Assumptions and Limitations .......................................................................................... 9

4. Results ..................................................................................................................................... 10

5. Discussion ................................................................................................................................ 16

5.1 Port Prices Figure 1 ................................................................................................................ 16

5.2 Yield Figure 2 ......................................................................................................................... 17

5.3 Drought Figure 3 .................................................................................................................... 17

5.4 Return on Asset (ROA) Figure 4 .............................................................................................. 17

5.5 Soil Quality Figure 5 ............................................................................................................... 17

5.6 Interest Rate Figure 6............................................................................................................. 18

5.7 20 Year Investment Figures 4-9 .............................................................................................. 18

5.8 NPV and Cash Surplus Figure 10 ............................................................................................. 18

5.9 Risk ........................................................................................................................................ 19

6. Conclusion ............................................................................................................................... 20

7. Acknowledgements ................................................................................................................. 20

8. References ............................................................................................................................... 20

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2. Introduction

The value of a block of farmland varies from farmer to farmer due to a number of factors. The purpose

of this paper was to determine the largest factors that influence broad acre cropping farm land values.

These factors included but were not limited to: expected yield, expected price, expected interest rate,

soil quality and size of investment. It was also important to recognise that while a farm is a business

and an asset, strong significance must be placed on the farmer’s family standing. Whether or not there

is someone to take over the farm can ultimately decide whether or not to invest in expansion, hence

the length of investment was also be examined. The cropping regions of Victoria vary a great deal in

both yields and input costs so it was important to assess the differences between these regions. Ouyen

(Mallee), Murtoa (Wimmera), Willaura (South Western) and Teesdale (Central) were the sites chosen

to compare as they represent the major cropping regions of Victoria.

There has been little research done into the value of farmland compared to commercial and residential

properties (Eves 2010). However, there are a couple of methods used in past literature that attempt

to provide a guide as to how to value land, and also show how actual farmers think through a new

investment.

Makeham and Malcolm (1993)

Makeham and Malcolm preferred to think in terms of “willingness to pay” and “expected return”. In

effect it was suggested that the value of farmland was almost entirely subjective and depended on

the rate of return the individual required from the purchase.

For example if 100ha of cropping land was able to return a net gain of $1000 p.a. and

Joe Bloggs wants to earn 10% p.a. on his investment (ROI), he would be willing to pay

$10,000 for the land. Jill Farmer however only requires a ROI of 8% p.a. and so would be

willing to pay up to $12,500 for the same land.

The discounted cash flow method (DCF) is a way of calculating the true present value of one cash flow

of a project over its life according to equation 1 below:

𝑫𝑷𝑽𝑭𝑽

(𝟏 + 𝒊)𝒏

Where: DPV = discounted present value

Equation 1. Discounted Present Value


3

FV = nominal value of a cash flow amount in a future period

i = interest rate or discount rate, which reflects the cost of tying up capital and so

represents the opportunity cost

n = time in years before the cash flow occurs

The theory behind discounting a cash flow is simple; $1 today is worth more than $1 in a year’s time.

DCFs have major drawbacks surrounding the assumptions that are required to be made. The discount

rate and predicted cash flows can vary so much as it is virtually impossible to predict incomes and

expenses three years into the future, let alone 10. Regardless, the concept of a DCF is relevant and as

long as the assumptions made are conservative, it is a good tool for calculating net present value of

an investment.

From the first example if Joe Bloggs purchased that land for $10,000 and believed that

after 10 years (n) he would be able to sell the land for $15,000 then the nominal value

of the cash flow (FV) is $5000 ($15,000-$10,000). Joe wants to be relatively conservative

and decides that $1.12 next year is worth $1 this year and so ends up with a discount

rate of 12% (i). In doing so Joe is predicting that after 10 years he will actually have only

gained $1610 worth of today’s money which is the “discounted present value”. 𝐷𝑃𝑉 =

5000

(1+0.12)10 = 1610

The DCF rule can be applied to every single facet of a potential investment from predicted wages to

soil degradation. The sum of every individual present value is called the net present value (NPV) and

is ultimately the value Makeham and Malcolm suggest to use. They say it requires a “defined planning

horizon including the walk-in-walk-out value of land, machinery and livestock. This allows expected

inflation or capital gains over time to be considered.”

Boehlje and Eidman Estimate (1988)

Boehlje and Eidman attempted to estimate land value as shown in equation 2 below:

𝑽 =𝑹 − 𝑬 − 𝑳 − 𝑰

𝒅

Equation 2. Boehlje and Eidman land value estimate


4

Where: V = property value

R = total cash farm receipts

E = total cash farm expenses

L = unpaid family labour

I = interest on non real estate capital

d = pre-tax nominal discount rate

This has the same basic concept as the DCF that Makeham and Malcolm used where there is a form

of discounting value, however it does not reflect the fact that annual percentage capital gain is actually

a percentage of a different value each year.

Barry, Hopkin and Baker (1988)

Barry, Hopkin and Baker took the valuation a step further than Makeham and Malcolm and also

included the issue of how the purchase is financed where Makeham and Malcolm assumed equity

capital and the cost of the debt to be the same. In reality the debt can cost more than just the equity

capital as there is always the possibility of negative cash flows which can cause the debt to actually

cost more if the payments are not able to be met. So even if the end benefit is good it is not always

feasible to actually take out the loan. This addition makes the equation a lot more complex as shown

in equation 3.

𝑵𝑷𝑽 = −𝑰𝑵𝑽 − ∑𝑷𝒏 + (𝟏 − 𝒕)𝑰

(𝟏 + 𝒓)𝒏+ ∑

𝒂(𝟏 + 𝒇)𝒏

(𝟏 + 𝒓)𝒏+

𝑽𝒎 − 𝑪𝒎 − 𝑫𝒎

(𝟏 + 𝒓)𝒎

𝒎

𝒏=𝟏

𝒎

𝒏=𝟏

Where: NPV = net present value

INV = the initial investment or deposit

r = the after tax nominal discount rate

Pn = the principal repayment period in n

t = average marginal tax rate

Equation 3. Barry, Hopkin and Baker’s net present value


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f = annual inflation rate

a = annual net return from the land

In = interest repayment period in n

Vm = expected salvage value of the property at time m

Cm = capital gains tax liability at time m

Dm = the debt outstanding at time m

Summary of the Models – reproduced directly from Madden and Malcolm (1996)

“The models of land value examined so far have ranged from the simple income capitalisation method

through to the Barry, Hopkin and Baker method which separates out the costs of finance from the

cost of other capital invested. A summary of the features of the three models examine is given in Table

1. As shown in Table 1 none of the models examined include all of the thirteen key determinants of a

realistic bid price.”

This table gives a clear understanding of both the key determinants set out by Madden and Malcolm

and also which of these determinants are included in each of the three models. It is obvious from the

table that the Boehlje and Eidman model is very simplistic in comparison to the others so for an in

depth inquiry such as this paper it is not as useful. The major differences between the other two is

that the Madden and Malcolm model recognises the fact that it is usually not reasonable to purchase

Table 1. Features of the land value

models contained in the literature.


6

new land without spending money on improving the property or purchasing new stock/machinery

which will have a salvage value at the end of the planning horizon. As mentioned previously the Barry,

Hopkin and Baker model also included the debt servicing ability of the individual looking to purchase

the property.

Gregson (2008)

In his thesis Gregson analysed the effect that profit, interest rates and commodity price amongst other

variables had on the value of farmland. He conceded that profit is “notoriously difficult” to measure

in farming with unpaid family labour and management, depreciation and opportunity cost all coming

with serious consistency issues across the market. Interestingly in the three models he constructed,

net profit had no significant effect on land value. This was consistent with Melichar (1979) and Esparon

(2002) and it’s thought that while profit itself doesn’t necessarily effect land value, profit drivers like

rainfall and soil type were important.

It was found that interest rates however did have an effect on land values, where in general higher

interest rates will lead to lower asset prices and vice versa. Gregson explained that it was possible that

it was in fact the availability of credit or the banks willingness to lend that has the largest effect and

not just the interest rate itself – it should be noted that this was different to the findings of a study by

Just and Miranowski (1993).

Gregson concluded that while only one of his models showed a significant effect due to commodity

prices they are still an important factor in determining farmland value. The main issue is that there is

no true way of knowing what the price of the commodity will be in 10 years’ time and so it is not

reasonable to use in an attempt to predict a lands future value.

He also acknowledged that the size of the land being sold, family situation and whether or not the

land is expected to achieve capital gains all also affect the value of farmland however they are

“impossible to capture in the simple linear models of relationships between profit, interest rate and

commodity prices” that he used.


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Grains Research & Development Corporation 2012

Water use efficiency ($WUE) is a measure of the gross income efficiency of a farm according to the

rain it receives. It is expressed as dollars of gross income per hectare per 100mm of annual rainfall.

GRDC believe that the value of broadacre land above a threshold value is the product of:

Annual rainfall (each extra mm adds $8/ha to its value)

$WUE derived through employing a crop rotation (this adds $16/ha to land value for each unit

of $WUE)

Freight rate from local depot to port (negative in its effect at -$56/ha for each $/tonne of

freight rate increase)

GRDC claim that “the rise in land value over the last twenty years, for the most part, is a product of

gains in $WUE arising from improved prices and yields.”

This study involved the development of a model in Microsoft Excel which tallied all predicted incomes

and expenses for the purchase of a new block of land to extend the current farm. From there a

maximum “willingness” to pay could be calculated given that the farmer wanted to earn X% p.a. on

their investment. It also included the issue of being able to service the loan and allowed for losses to

be made in a year. The four cropping regions were compared based on market land values from the

Valuer-General’s Guide to Property Values (Valuer-General Victoria, 2013) in order to deduce which

was the “safest” and the most profitable region to invest in. The model was also used to assess the

effect of boom and bust years on the value of the land as well as expected yield, expected price,

expected interest rate, soil quality, the length & size of the investment and the required rate of return.

3. Method, Data and Assumptions

3.1 Method

A spreadsheet was made using Microsoft Excel so that data could be entered and then altered

depending on circumstance. It was set up so that any user can enter the data that is relevant to them

The spreadsheet will then inform them whether the investment is likely to be a good one based on

the figures they entered. Image 1 and Image 2 below show screenshots of the two sheets that require

data entry. The spreadsheet returns the net present value (NPV), equity and cash surplus as well as

yearly cash flows which all form the basis of the decision whether or not to take on the investment.


8

Image 1. The “Summary” sheet where the most important data is entered and a summary of the

results is also given.

Image 2. The “Expenses” sheet where the running costs are entered.


9

The regions were then compared assuming a $1,000,000 investment and were subjected to sensitivity

analysis. The sensitivity analysis included varying the estimated yield and price received for the crops,

and introducing a drought year into the model.

The length of the investment was then extended to 20 years using data from Teesdale, which then

underwent the same analysis. The effect of a change in interest rate and a lime & gypsum application

was also compared between the 10 and 20 year investments.

3.2 Data, Assumptions and Limitations

All locations used the same port price for the grain however a “freight” expense was included in the

spreadsheet and was calculated as the price at the Geelong port minus the price offered at the local

GrainCorp site (GrainCorp1, 2014 & GrainCorp2, 2014). Data specific to the regions was entered based

on estimates from Grains Research and Development Corporation’s (GRDC) Gross Margin Guide

(GRDC, 2014) the Valuer-Generals Guide to Property Values (2013) and farmers local to the area. The

expected prices were set at $250/t for wheat, $240/t for barley and $480/t for canola (GRDC, 2014).

The expected loan interest rate set at 5.5% based on

Table 1. The data used for the locations

Location Average Cereal Yield (t/ha) Average Canola Yield (t/ha) Market Land Value ($/ha)

Willaura 5 2.5 7,000

Teesdale 4 2 5,000

Murtoa 3 1.5 3,100

Ouyen 2.2 1.1 1,100

The spreadsheet assumes that the purchase of the new block of land is entirely funded by a loan from

the bank. The farmers existing land is excluded from the study other than to ensure there is enough

equity to service the loan and so any figures are based on the new land only. For simplicity

improvements and water rights weren’t included and every year the block is dived in three equal parts

growing wheat, barley and canola. There is no option to run livestock and contract rates were used

for everything. A transaction cost of 5.5% of the purchase price was included to cover stamp duty.

All incomes and costs occur in the same time period i.e. sowing expenses were discounted the same

as harvest profit. This timing issue also means that the “overdraft interest” expense was only indicative

and was calculated as: overdraft interest = overdraft interest rate x sum of expenses for the year x 0.5.

It assumed that each expense was in the overdraft account for an average of 6 months (hence the ‘x


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0.5’) and while it is clearly inaccurate it is a small enough expense to not have a major effect on the

outcome. Any cash surplus at the end of each year is then used to pay off some of the loan principal.

All expenses were inflated at 2.6% every year however the receipts were inflated at 2% because

historically farm costs have been rising faster than the prices received for the goods (Australian Bureau

of Agricultural and Resource Economics and Science, 2010).

These assumptions and limitations mean that while the results from the spreadsheet will give an

indication of land value, it will not be very accurate. However, because all of the circumstances studied

had the same limitations and assumptions the results are still comparable.

4. Results

The results showed that in nearly every way Willaura was the best place to invest in followed by

Teesdale, Murtoa and Ouyen. The “maximum price” referred to in many of the results is based on the

maximum price that can be paid per hectare given that the farmer wishes to earn 2% p.a. return on

asset (ROA). Unless otherwise stated it is assumed that the investment will run for 10 years. The effect

of average price received for the grains is shown in figure 1 where the more fertile locations had a

greater response. Cash surplus based on yield was then compared in figure 2 assuming a $1m

investment at market land value. Ouyen showed a huge response to a change yield and was clearly

the most profitable at 10% greater than expected yields but also showed the greatest loss if yields

were 10% lower than expected. Yield was halved in one year at a time in figure 3 and only Teesdale

and Willaura could still post a net gain regardless of when the drought occurred. Figure 4 shows the

effect of a farmer’s requirement for ROA on maximum price and again it is the more fertile regions

that show the most elasticity. The effect of soil quality is shown in figure 5 where it was found that

regardless of the year lime and gypsum are applied a block of land that only needs half the application

will be worth 5% more.

Figures 5-9 focus solely on a property at Teesdale and to a large extent analyse the effect of investing

for 20 years instead of 10. In all situations investing for double the time results in being able to afford

to pay extra for the land. Figure 9 is a culmination of all the variables and shows that if each is only

slightly more favourable the maximum price payable can increase dramatically ($2229/ha in this case).

All of the results are based on the assumptions set out in section 3.2. Figure 9 also shows that the

assumptions don’t need to be very far out for a large change in results to occur.

Figure 10 examines the difference results in net present value (NPV) and cash surplus according to the

assumptions made.


11

Figure 1. Compares the four locations value response to a $20 increase in the price received for a

tonne of wheat & barley and a $40 increase in the price received for canola. Willaura was the most

elastic showing a $1372.3 increase in land value for every price increase.

Figure 2. Shows the cash surplus at the end of the 10 year investment at varying yields if $1m of land

is purchased at market value for the region. When everything ran as expected Willaura ($212,582)

made the most money followed by Teesdale ($157,063), Murtoa ($132,521) and Ouyen ($102,288).

Ouyen showed the greatest response to yield with a $221,436 increase in final cash surplus for every

5% increase in yield from the expected yield of 2.2 t/ha.

y = 1372.3x + 2821.7

y = 1097.7x + 1299.3

y = 824.9x + 274.1

y = 611x - 894.6

($2,000)

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

210, 200, 400 230, 220, 440 250, 240, 480 270, 260, 520 290, 280, 560

Max

imu

m V

alu

e ($

/ha)

Port Price for Wheat, Barley & Canola Respectively ($/tonne)

Maximum Value at Varying Average Port Prices

Willaura Teesdale Murtoa Ouyen

y = 79191x - 21422 y = 91121x - 114584 y = 113384x - 207308 y = 221436x - 558283

($400,000)

($300,000)

($200,000)

($100,000)

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

-10% -5% Expected Yield +5% +10%

Surp

lus

($)

Cash Surplus Response to Average Yield at Market Land Value



12

Figure 3. When the drought occurs in the later years it has slightly less of an effect on cash surplus.

Ouyen never recovers from the drought and money can only be made within 10 years at Murtoa if the

drought occurs in year 8 or later. Note that there is a dip in year 5 because that is the year that lime

and gypsum were applied in the spreadsheet.

Figure 4. The rate of ROA required by the farmer has an increasing effect on maximum price as the

land itself becomes more profitable. At Teesdale the farmer who is investing over 20 years can afford

to pay an extra ~5% than one who is investing for 10 years. The green “Market Value” data points

refer to the ROA received when the market rate is paid for the block of land.

($250,000)

($200,000)

($150,000)

($100,000)

($50,000)

$0

$50,000

$100,000

$150,000

1 2 3 4 5 6 7 8 9 10

Cas

h S

urp

lus

($)

Year of Drought

Effect of a 50% Drought in Different Years


0.978

1.469 1.827

1.944

1.250

y = -1225.2x + 9421.2 y = -806x + 6225.2 y = -841.2x + 6554.4 y = -475.4x + 3712.3 y = -157.4x + 1257.9

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

$9,000

$10,000

0.00 0.50 1.00 1.50 2.00 2.50 3.00

Max

imu

m P

rice

($/h

a)

ROA Required (%)

Effect of Required ROA on Maximum Price

Willaura Teesdale Teesdale 20 Years Murtoa Ouyen Market Value


13

Figure 5. A 20 year investor can always pay $266/ha more than a 10 year investor regardless of the

amount or timing of lime and gypsum application. Land that only requires 1t/ha is worth 5% more

than the land that requires 2t/ha. Note that all of these assume that lime and gypsum are applied

every 5 years starting from the first application.

Figure 6. The relationship between the average interest rate on the loan and the maximum price is

actually a ln(x) relationship. However given the range of interest rates examined a linear relationship

provides a very close estimate that an increase in interest rate offered by the financial institution of

1% equates to a drop in maximum price of around $475/ha.

$4,000

$4,100

$4,200

$4,300

$4,400

$4,500

$4,600

$4,700

$4,800

$4,900

Teesdale 10 years1t/ha



Teesdale 20 years2 t/ha

Max

imu

m P

rice

($/h

a)

Effect of Soil Quality on Maximum Price at Teesdale

Year 1 Year 3 Year 5

y = -2909ln(x) + 9564.4R² = 0.9992

y = -2936ln(x) + 9870.6R² = 0.9997

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50

Max

imu

m P

rice

($/h

a)

Average Interest Rate (%)

Interest Rate's Effect on Maximum Price at Teesdale

Teesdale 10 years Teesdale 20 years


14

Figure 7. If the investment is planned to last 20 years instead of 10 years then the farmer can afford

to pay more for the land and still make a 2% p.a. ROA. If everything runs as expected they can afford

to pay an extra $266/ha which jumped to $429/ha more if yield is actually 10% higher.

Figure 8. The 10 year investment had 50% yield in year 1 and the 20 year investment had 50% yield in

years 1 and 11. This resulted in the investor being able to pay an extra $115/ha or 3% more when

investing for 20 years as opposed to 10 years.

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

-10% Expected Yield +10%

Max

imu

m P

rice

($/h

a)

Maximum Price Depending on Yield at Teesdale

Teesdale 10 years Teesdale 20 years

$3,851

$3,966

$3,780

$3,800

$3,820

$3,840

$3,860

$3,880

$3,900

$3,920

$3,940

$3,960

$3,980

Teesdale 10 Years Teesdale 20 Years

Max

imu

m P

rice

($/h

a)

Effect of a 1 in 10 Year 50% Drought on Maximum Price


15

Figure 9. “Farmer 1” had the average assumptions set out in section 3.2. “Farmer 2” on the other hand

was investing for 20 years, received a 5% interest rate, could yield 5% higher, could attain $5/t extra

on cereals and $10/t extra on canola and only required a 1.5% ROA. With these minor adjustments

“Farmer 2” could afford to pay 49% more for the same block of land than “Farmer 1”.

Figure 10. Ouyen had the greatest net present value at a discount rate of 8% and capital gains of 2%

yet had the lowest cash surplus after the 10 year investment.

$4,592

$6,821

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

Farmer 1 Farmer 2

Max

imu

m P

rice

Comparing Two Farmers With Different Views on the One Block of Land at Teesdale

$0

$50,000

$100,000

$150,000

$200,000

$250,000

$300,000

$350,000

$400,000

$450,000

Willaura Teesdale Teesdale 20Years

Murtoa Ouyen

Difference Between NPV and Cash Surplus

NPV Cash Surplus


16

5. Discussion

Of the four locations examined Willaura is certainly deemed to be the safest to invest in given the

current land values. As seen in figure 2 if a 10 year period played out according to plan then an

investment in a farm in Willaura would yield a cash surplus 35% higher than the same investment at

Teesdale, 60% higher than Murtoa and 108% higher than Ouyen. Therefore if the assumptions made

in this study are correct then land at Ouyen is quite overpriced while land at Willaura is under-priced

by comparison.

5.1 Port Prices Figure 1

The price available for the goods produced had a large and varying effect on the value of the land. An

increase of $20 in cereals and $40 in canola added anywhere from $611/ha in Ouyen to $1372/ha in

Willaura. This difference in response was solely due to the different yields of the areas. In fact it was

found that a linear relationship exists between an increase in land value due to a change in price

received and expected yield:

𝑰𝒏𝒄𝒓𝒆𝒂𝒔𝒆 𝑰𝒏 𝑳𝒂𝒏𝒅 𝑽𝒂𝒍𝒖𝒆 = 𝟏𝟑. 𝟔𝟎𝟑𝟓 ∗ 𝒀𝒊𝒆𝒍𝒅 + 𝟎. 𝟓𝟑𝟏𝟗

Where:

Increase in land value = Increase in value of land ($/ha) for every $1 increase in cereal price

and corresponding $0.50 increase in canola price.

Yield = Cereal yield and corresponding canola yield

For example if John was looking to buy in an area that had a wheat yield of 3.5 t/ha and thought that

he could market their wheat $5 better than Dave who is also looking to purchase the same block. John

would be able to pay an extra $240.7/ha:

13.6035 x 3.5t/ha + 0.5319 = 48.144 per $ increase in sale price

48.144 x $5 = $240.7/ha

This is based on the current assumptions and also assumes that expenses will increase at a rate

according to yield.


17

5.2 Yield Figure 2

Willaura was the least responsive to a change in yield which means two things; starting with the same

dollar investment a technology/practice that increases yield is going to be less effective in Willaura

than Ouyen (the most yield sensitive). Secondly if average yields aren’t as good as it was first hoped

then it is less of an issue at Willaura because at market land value Willaura is the only location that

can still post a profit if yield is 10% lower than expected. Ouyen on the other hand is high risk but high

reward if the farmer thinks they can improve the yield beyond expected.

5.3 Drought Figure 3

When an equal dollar investment was undertaken in the four locations and given the assumptions

made Teesdale and Willaura were the only ones to still be able to make any money when a drought

occurred in any of the ten years of the investment. While a boom year would likely recover a portion

of the losses experienced during a drought year, a conservative farmer would always assume that they

would endure more drought years than booms. The results in figure 3 suggest that current land values

at Ouyen and Murtoa are likely overpriced due to the losses experienced with a single drought.

5.4 Return on Asset (ROA) Figure 4

When the actual current market rate for land in the areas is concerned Willaura again proves to be

the best to invest in as far as percentage return on the value of the land (ROA). It showed an ROA of

1.9% at market land value whereas if the assumptions are correct only 0.98% could be earned at

Ouyen. Teesdale and Murtoa as always are somewhere in between at 1.47% and 1.25% respectively.

A 1% drop in ROA proved to cause a very similar percentage increase in all the regions between 16%-

18%.

5.5 Soil Quality Figure 5

When 1t/ha of both lime and gypsum needs to be applied to the soil it is only worth an extra $13/ha

to be able to wait until year 5 for the first application. If double the lime and gypsum (2t/ha) needed

to be applied then the farmer could afford to pay $38/ha more if they could wait until year 5 for the

first application. The farmer could always pay 5% more if they only had to apply 1t/ha instead of 2t/ha.

If the assumptions are correct this means that at Teesdale even if the soil quality isn’t as good, the

timing of lime and gypsum application still doesn’t have a huge effect on the maximum price payable.


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5.6 Interest Rate Figure 6

Interest rates offered by the financial institutions appeared to have a large effect on the maximum

price payable. According to the assumptions made maximum price would drop roughly $475 for every

unit of interest rate increase. This has significant implications when it comes to purchasing land

because every farmer has different financial situations and varying levels of “risk” according to the

banks, and so will be offered very different interest rates on their loans. Interest rates also vary a great

deal due to external market forces out of the control of the farmer. Assuming a low or even current

interest rate to be the average across a 10 year investment puts that farmer at great risk of losing

money if interest rates were to rise.

5.7 20 Year Investment Figures 4-9

According to the assumptions a 20 year investment in comparison to a 10 year investment brought

more money when land was purchased at market value or alternatively allowed the farmer to pay

extra for the land and still earn the same ROA. At the assumed rates of everything a block of land at

Teesdale was worth $266/ha (5.8%) more to a 20 year investor. No matter the timing of lime and

gypsum (figure 5) it was still worth an extra $266/ha. However, if 2t/ha needed to be applied then a

20 year investor could only pay and extra $226/ha, which is still a significant improvement.

Figure 5 showed that 20 year investors have the greatest advantage at an interest rate of 5.5%

($266/ha). Their advantage gradually depletes either side of 5.5%, however at an interest rate of

8.05% there was still a significant advantage of $241/ha. A 20 year investor was also more drought

tolerant as seen in figure 8. When faced with the equivalent level of droughts the long term investor

could afford to pay $115/ha (3%) more.

This is all basically occurs because over time the size of the loan decreases and so therefore the

average interest expense is lower in a longer term investment.

5.8 NPV and Cash Surplus Figure 10

Most of the previous studies mentioned in section 2 used a discounted or net present value (NPV)

equation to compare investments with a set discount rate. This study did not use any of the equations

used in the past literature because none of them truly reflected the nature of a farm loan. Farm loans

are typically “interest only” loans with the expectation for the principal to be paid back as cash

becomes available. This also means that if a loss is made then the loan will be renegotiated and

increased, which also raises the interest payments each year. Makeham & Malcolm’s (1993) model

doesn’t take into account a loan and instead assumes that the money would be invested anyway and

the investor would just use it as a tool to compare potential investments to a base discount rate. The


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true effect of a variable loan was shown at Ouyen which had the highest NPV of the $1m 10 year

investments yet the lowest cash surplus. This was caused by massive losses in years 5 and 10 due to

lime and gypsum application, thus raising the debt figure and therefore the loan interest after those

years. This shows that just because an investment has a better NPV than another, it’s not necessarily

a better investment because it must be financed. This is a view shared by Makeham & Malcolm and

while the ability to service the loan wasn’t included in their model, they certainly discussed its effect

to a great extent about it in their paper.

It is important to remember that this study assumed that the potential purchase is actually just an

extension on the current property. It analysed whether the new land can support itself and in reality

a loss on the new block could very well be supported by the existing farm and no new loan is required.

It also didn’t take into account family factors such as number of kids wanting to farm nor did it consider

proximity to the current farm and/or towns & cities like other studies did.

5.9 Risk

One of the biggest factors in any investment is its level of risk. The number one question farmers have

when faced with the data presented in this study is, “I know that if I do “x” better I can afford to pay

“$y” more for the land but does that mean I should?” The answer is no, and it’s because of risk. For

example as discussed previously in section 5.7 a 20 year investor in land at Teesdale can afford to pay

$266/ha more and still make the same level of return if the assumptions made are correct. The fact is

that a lot can happen in 20 years and figure 9 shows that if the assumptions are only marginally wrong

then that can have big consequences on the maximum price payable. The data from this study should

instead be used to say that a 20 year investor can’t afford to pay any more than $266/ha more than

the same 10 year investor. As shown in figure 4 a farmer investing for 20 years in Teesdale at market

land values can make 24% more ROA. The conservative farmer would take that to be a bit of a bonus

leeway knowing that things aren’t likely to pan out as planned. So instead of offering the full $266/ha

more that they could afford to pay according to the results they may look at figure 7 and decide that

they would only offer $120/ha. This would allow them some breathing space knowing that they can

still make the return even if their average yields are actually 10% lower. The same can be applied to a

farmer who believes they can get a slightly better interest rate or market their grain $20 better etc.

“Risk” can vary even between regions with the more fertile regions being more risk adverse as seen in

figures 1, 2 & 3. In all cases Willaura is the only location that consistently makes some money under

all the stresses applied to it in the spreadsheet. Ouyen is definitely the riskiest investment as a drop

in expected average yield by only 10% resulted in a nearly $340,000 loss over 10 years from a $1m

investment. Conversely if average yields are 10% higher than expected then according to the


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assumptions upwards of $550,000 could be made over the 10 years. This is a hugely risky investment

keeping in mind that a 10% drop in average yield is only a drop from 2.2t/ha to 2t/ha.

6. Conclusion

There are many factors that affect the value of broad acre cropping land, but the most important

factor is fertility. If the assumptions made in this study are correct, a property that can yield 5t/ha of

wheat could be worth seven times as much as a property that can only yield 2.2 t/ha. It was found

that by comparison land at Willaura could be under-priced and land at Ouyen could be over-priced.

So perhaps land at Teesdale and Murtoa are therefore relatively close to the correct price in

comparison. However, not one of the locations studied showed a ROA of over 2% when average sale

price was paid for the land. If one drought year was introduced it also became very hard to turn over

any profit during a ten year investment. Both ROA and drought susceptibility suggested that land

everywhere in Victoria is over-priced. This could be because many agricultural purchases aren’t made

for the sole purpose of making short term money, but are instead for long term asset investment.

Lime and gypsum requirement only had a small effect on the price at Teesdale. Factors that were

found to have a significant effect on the price a farmer can pay for land included interest rate received,

required return, length of investment and their ability to market the grain for better prices.

7. Acknowledgements

There are two people in particular who without their help this study could never have been completed.

Bill Malcolm took time out of his very busy schedule to edit the spreadsheet and answer my many

questions so that everything actually made sense. Many hours were spent talking to Andrew Morrison

nutting out numbers and just exactly what they all mean, this provided some much needed clarity and

another angle on the findings. Greg Cracknell must also be thanked for his help with the spreadsheet

and for explaining just what a bank wants from a potential agricultural investment. Lastly there would

be no project without Graham Brodie who was in charge of the “Industry Project” subject. His lectures

and general advice was very valuable.

8. References

Australian Bureau of Agricultural and Resource Economics and Science 2010 ’Australian Commodity

Statistics 2010’ Accessed on 14/8/14, Available at

http://data.daff.gov.au/brs/data/warehouse/pe_abares99001762/ACS_2010.pdf

Barry, P.J, Hopkin, J.A. and Baker, C.B 1988. Financial Management in Agriculture, The Interstate

Printers and Publishers, Danville, Illinois.

http://data.daff.gov.au/brs/data/warehouse/pe_abares99001762/ACS_2010.pdf


21

Boehlje, M.D and Eidman, V.R 1988. Farm Management, John Wiley & Sons, New York.

Esparon, N. 2002. ‘The determinants of prices of farmland in Victoria 1988-1997: Regional, activity and

farm perspectives’ Unpublished Phd Thesis, Institute of Land and Food Resources, The University of

Melbourne.

Eves, C 2010 ‘NSW Rural Land Performance 1990-2008.’ Australasian Agribusiness Review, 18:85-102

GrainCorp1 2014. ‘GrainCorp Daily Contract Prices – Victorian Mallee and Wimmera’ Accessed on

16/8/14, Available at http://www.graincorp.com.au/daily-contract-prices/Northern%20VIC%20-

%20Wheat.pdf

GrainCorp2 2014 ‘GrainCorp Daily Contract Prices – Eastern Victoria’ Accessed on 16/8/14, Available

at http://www.graincorp.com.au/daily-contract-prices/Eastern%20VIC%20-%20Wheat.pdf

Grains Research & Development Corporation 2014 ‘Farm Gross Margin & Enterprise Planning Guide’

Accessed on 14/8/14, Available at http://www.grdc.com.au/FarmGrossMarginGuide

Grains Research & Development Corporation 2012 ‘Capitalising on rising land values: long term trend

is our friend’ Farm Business Update, 7:1-2

Just R, and Miranowski J. 1993. ‘Understanding farmland price changes’ American Journal of

Agricultural Economics, 75:156-168

Makeham, J.P. and Malcolm L.R. 1993. The Farming Game Now. Cambridge University Press,

Melbourne.

Melichar E. 1979. ‘Capital gains versus current income in the farming sector’, American Journal of

Agricultural Economics. 61:1085-1092

Valuer General Victoria 2013, ‘A Guide to Property Values 2013’ Accessed on 14/8/14, Available at

http://www.dtpli.vic.gov.au/property-and-land-titles/property-information/property-prices

http://www.graincorp.com.au/daily-contract-prices/Northern%20VIC%20-%20Wheat.pdf

http://www.graincorp.com.au/daily-contract-prices/Northern%20VIC%20-%20Wheat.pdf

http://www.graincorp.com.au/daily-contract-prices/Eastern%20VIC%20-%20Wheat.pdf

http://www.grdc.com.au/FarmGrossMarginGuide

http://www.dtpli.vic.gov.au/property-and-land-titles/property-information/property-prices