African Technology Policy Studies Network

WORKING PAPER SERIES | No. 58
Estimating Land Prices andOpportunity Costs of Conservation
in a Megadiversity Country
Philip M. Osano
Mathieu RougetJane Turpie
Wilfried Thuiller
Andrew Balmford

Estimating Land Prices and Opportunity Costs ofConservation in a Megadiversity Country

Philip M. Osano
University of Cape Town,
South Africa
South African National Biodiversity Institute,
South Africa
Africa Technology Policy Studies Network,
Kenya
Mathieu RougetSouth African National Biodiversity Institute,
South Africa
University of Cape Town,
South Africa

Jane Turpie
University of Cape Town,
South Africa
Wilfried Thuiller
South African National Biodiversity Institute,
South Africa
Laboratoire D'Ecologie Alpine
Université Joseph Fourier
FranceAndrew Balmford
University of Cape Town,
South Africa
University of Cambridge,
UKThe African Technology Policy Studies Network (ATPS) is a multidisciplinary network of researchers, private sector actors andpolicy makers promoting the generation, dissemination, use andmastery of science, technology and innovation (ST&I) for African
development, environmental sustainability and global inclusion.
ATPS intends to achieve its mandate through research, capacity
building and training, science communication/dissemination andsensitization, participatory multi-stakeholder dialogue,knowledge brokerage, and policy advocacy.
Published by the African Technology Policy Studies Network
P O Box 10081, 00100 GPO Nairobi Kenya
2011

Published by the African Technology Policy Studies Network

ISBN: 978-9966-030-10-8

Table of Contents
Acknowledgement

4

Abstract

5

1.

Introduction

6

2.

Materials & Methods

8

3.

Results

12

4.

Discussions

14

References

16

Tables19

Figures

22

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 3

Acknowledgement
We obtained land purchase data from various estate agents and landowners in the Western Cape;R. Parker; WWF-SA; Western Cape Nature Conservation Board; Western Cape Department ofAgriculture; South Africa National Parks Authority; Fauna & Flora International; Cape Town
Metropolitan Council; and Cape Property Services. R. Cowling, M. Botha and two anonymous
reviewers provided critical comments on an earlier draft of the manuscript. The South African
National Biodiversity Institute provided facilities for this study. A.B was funded as Pola Pasvolsky
Visiting Chair at the Percy Fitzpatrick Institute, University of Cape Town. P.O was supported by aTropical Biology Association Scholarship, South African National Biodiversity Institute grant, aPercy Fitzpatrick Institute grant and an African Technology Policy Studies Network research
fellowship.

4 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity CountryAbstract
We used data on 174 land purchases to estimate two aspects of opportunity costs across Western
Cape Province, South Africa; (i) that of complete loss (acquisition cost), and (ii) and partial loss ofbenefits of land ownership, the latter due to biodiversity-friendly management on potentially arable
private land. Observed land prices varied by more than four orders of magnitude (from $US 15.ha-1
to 178,000.ha-1 per unit area of farmland). Mean annual precipitation, percentage of untransformed
land, property area, and topographic diversity were the most significant predictors of land price (R2=0.67). Modelled acquisition costs were highest in vegetation types previously classified (based on
biological importance and degree of conversion) as Critically Endangered. Our upper estimate of
the annual opportunity costs of retaining remaining habitat patches ranged from negative to US$
8,300.ha-1.yr-1 (mean $US 56.ha-1.yr-1, expressed per unit area of remaining natural vegetation).
They were again highest in Critically Endangered (mean of $US 90.ha-1.yr-1) and lowest in the
Least Threatened vegetation types ($US 40.ha-1.yr-1).
Keywords: land price, opportunity cost, vegetation types, biodiversity conservation, Western Cape

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 5

1. Introduction
Successful biodiversity conservation interventions depend on the availability of reliable information
on both their benefits and costs (Balmford et al., 2003, Naidoo and Ricketts, 2006, Naidoo et al.,2006). However, data on conservation costs are scarce. In their absence, planners are often obliged
to use poor proxies of cost, such as the area of the land under consideration. This paper considers
two particularly poorly studied aspects of opportunity cost: (I) purchase/acquisition costs (the costof complete loss of the benefits of landownership); and (ii) opportunity cost to landowners of
biodiversity-friendly land management on private land (the cost of partial loss of benefits of landownership). We illustrate how these costs can be estimated reasonably quickly and cheaply inareas with well functioning land markets.
Several authors have highlighted the substantial costs of effective biodiversity conservation, andthe wide global and regional variation in management costs. The different conservation cost
components include management, land purchase and opportunity costs. To date, few studies of
the conservation costs have included land purchase/acquisition costs, and fewer still haveconsidered opportunity costs of conservation on private land.
Two main approaches guide studies that estimate the opportunity costs of biodiversity
conservation. The first involves the use of income values, such as potential net returns (per area.yr1) from the most profitable alternative land use. The second approach involves estimating the landacquisition (purchase) costs or, the difference in the price of land under uses that are more or lessfavourable to conservation. Here, it is assumed that the sale value of a parcel of land is equal to thediscounted flow of net revenue that the parcel is expected to generate into the future i.e. its Net
Present Value.
In our study, we investigated variation in prices of farmland- a proxy for acquisition costs- acrossWestern Cape Province, South Africa. We then used these to explore variation in opportunity costsof partial loss of benefits to landowners arising from a switch in land use management in potentiallyarable land across vegetation types of varying conservation priority. The Western Cape is an area ofglobal importance for biodiversity: it contains most of one of 34 global hotspots - the Cape Floristic
Region (CFR) - and much of a second – the Succulent Karoo. For the CFR, management andtransaction costs are known; the management costs of a representative system of conservation
areas for the entire CFR is estimated at $US 45.5 million.yr-1, discounted over a 20-year horizon,
6 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

plus an additional annual maintenance cost of $US 29.6 million. These figures compare favourably
to the US $1,100 million.yr-1 estimated benefits from the CFR's terrestrial ecosystem goods andservices.
Here we focus entirely on agricultural land, and exclude other forms of land use. We seek to answer
the following questions:
1. How much do land prices vary across a representative sample of farms in the Western Cape?
2. Which factors predict this variation in land price, and how?
3. To what extent can these factors be used to predict land prices, hence estimate acquisition
costs across the Western Cape?
4. How do predicted land prices (acquisition costs) and opportunity costs (partial loss of benefitsof landownership) vary across vegetation types, which differ in conservation priority?

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 7

2.

Materials & Methods

Study area
The study area comprises the Western Cape Province, an area of 129,370 km2, which includes
most of the Cape Floristic Region (Fig. 1a). The CFR has attracted global attention due to its highfloristic endemism, and high levels of threats to its biodiversity from expansion of intensive
agriculture, urbanisation, and invasive alien vegetations .
Gathering data on land priceWe built our dataset from a variety of sources (Table 1), and included all properties for which we
knew:
1. Identity of the property (name or unique identification number);
2. Size of the property (the total area of the land parcel);
3. The value of the property estimated from the sale price (amount paid for an agricultural property
in a completed transaction) or listed price (the price requested for a property on the market). All
sales were assumed to have been undertaken in the open market;
4. Year of sale. Pre-1995 sales were excluded because of the reforms in land policies instituted by
the post-apartheid government in 1994;
5. Percentage of land transformation as estimated from a GIS layer .
We excluded properties if: (1) they could not be accurately located; (2) they were donations forconservation; (3) they were not bona fide market transactions (e.g. family exchanges ); (4) they lessthan 10 ha (farming is considered to be un-economical below 10 ha [M. Botha, ReMax Properties,
pers.comm.], or; (5) In the Estate Agents dataset (Table 1), there was considerable discrepancy
between the property area as reported by the estate agents and the area in the GIS database (>500
ha or >100% of reported area). The final dataset comprised 174 farms. All the properties were
spatially geo-referenced on a digitised 1:50,000 map (Directorate of Surveys & Mapping, SA) usingnames and/or unique property (ERF) numbers.
All datasets gave information on sale price with the exception of the estate agents', which, for 27
properties gave information only on listed price. We predicted the equivalent sale price for theseproperties based on the relationship between listed and sale price of 25 properties as recorded in
the Cape Property Services dataset:
Sale price = 0.9784 x Listed price (R2 = 0.99, n=25)
8 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

All analyses were based on sale price and the data processed using Arcview GIS 3.2 software(ESRI, Redlands, California).
Standardising land price
We used the 1995 - 2002 and the 2003 - 2004 South African GDP deflator to standardize all landpurchase prices to year 2000. We chose year 2000 as the base year because it had the highest
number of property sales in our final dataset, and because it is the base year for national accounts
estimates at constant prices. We expressed land prices in South African Rand per hectare offarmland (R.ha-1) and in US $.ha-1, using the exchange rate of 1st July 2000 (US$ 1=ZAR 6.79).
Spatial predictors
We conducted a literature review, expert consultations, and interviews with commercial estateagents and identified 20 potential predictors which we thought might influence agricultural property
prices: mean annual precipitation (Fig. 1b); altitude; topographic diversity (surface area divided by
the planimetric area); proximity to coastline, major and all roads, rivers, and urban centres; mean
agricultural land capability; mean crop production potential for different crops (lupens, lucern,
wheat, oat, barley and wine); property area; percentage of untransformed land (i.e. naturalvegetation: Fig. 1b); human population density in the surrounding area (based on 1996 census);vegetation groups; and geological groups. We could not generate sufficient data on improvements
to farms (buildings and other infrastructure) for the study area.
We obtained all the spatial layers from the South African National Biodiversity Institute , with the
exception of agricultural land capability and crop production potential that were provided by the
Agricultural Research Council (ARC, Stellenbosch), and population density provided by Statistics
South Africa (StatsSA). The land capability layer classifies land based on its overall suitability for
ecologically sustainable use for crops, grazing, woodland and wildlife. For the last two predictors,
we reclassified the original 120 vegetation types into 20 groups based on a set of biogeographic
features, and also reclassified the original geology layer from 54 geological types to 15 geologicalgroups based on soil types and fertility.
Statistical analysis
We carried out a preliminary statistical analysis to quantify univariate correlations among potential
predictors and excluded mean crop production potential of oat and barley because they were 99%
correlated to mean crop production potential for wheat. We then used a stepwise Generalized
Linear Model (GLM) in S-PLUS (S-Plus 2000 version, Mathsoft Inc.) to predict land price using 18 of
the predictors listed above (Table 2). Land prices were log10-transformed to fit a normal distribution.
The model was calibrated using 121 randomly selected properties and validated on 53 properties.
From the calibration data set, an initial model was produced by selecting variables on the basis of
R2 values and Akaike's Information Criteria (AIC). The use of stepwise regression reduces
collinearity amongst the variables. We then excluded one predictor at a time (the last predictor
entered in the initial model) and tested model performance on the validation data set. Due topossible over-fitting in the initial model, models with fewer variables could lead to a higher R2 in thevalidation data set. We retained the model with the highest R2 in the validation data set (Table 3), andthen used this final model to predict land price for 30,150 cadastres across the Western Cape
Province.
Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 9

Estimating opportunity cost for landowners partial loss of benefits oflandownership
In our study area, livestock and crop production are the two main land uses that could conflict withconservation. Estimating the foregone profit from limiting livestock production involves quantifying
potential losses that could be incurred from stock reduction. Our land price model was not suitable
for that purpose, so we restricted our analysis to crop-suitable areas.
We considered potentially arable land as defined by the agricultural land capability surface. We
selected all 12,278 cadastres – with some remaining natural vegetation – and with a land capability
index between 1 (land suitable for irrigated crops) and 6 (land marginally suitable for crops). We
assumed that conservation of the remaining natural vegetation on private land could be undertaken
voluntarily by landowners, or through legal restrictions. We therefore estimated the cost of partial
loss of benefits of landownership to landowners of retaining remaining natural areas within eachcadastre. We did this by calculating the difference between the predicted current land price (i.e.
with natural vegetation retained) and the predicted land price if all remaining natural habitat were to
be fully converted to agriculture, minus the estimated costs of conversion (Sinden, 2004):

Opportunity Cost =

[ [

NPV _
Conversion Cost
Area

Where;
NPV = (LVF - LVP)
Area refers to the farmland portion consisting of natural vegetation; LVF refers to the estimated priceof the farm if it is 100% transformed, and LVP refers to the estimated price of farm at the present levelof transformation. We used a discount rate of 8% to annualise the opportunity cost figures.
Conversion cost comprises of on-farm costs of vegetation clearing, soil ripping and labour. We
estimated the maximum and minimum took the mean cost of conversion as $US 74.ha-1 ofremaining natural vegetation.
This method can generate negative opportunity cost values (where our model estimates that
unconverted land cost more to convert than it would yield following conversion); this situation arosefor one cadastre. Note that because non – converted land is in reality likely to be less productive
than already converted land (but by unknown amount), our approach probably overestimates theopportunity costs of non – conversion. No data was available at a spatial scale fine enough tocapture within-farm variations in crop suitability.
The cost of complete (acquisition costs) and partial loss of benefits of landownership in relation to biodiversity conservation priorities
To explore the relationship between opportunity cost and conservation priority, we assigned ourfarms to one of four conservation priority classes based on their predominant vegetation type(Table 4). These classes; Critically Endangered (CE), Endangered (EN), Vulnerable (VU) and Least
Threatened (LT) reflect national biodiversity targets for each vegetation type, and the extent towhich they have been transformed .
10 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity CountryWe divided the sum of the predicted land prices for all 30,102 cadastres assigned to a given
vegetation type by their total area to calculate mean land price per hectare of farmland for thatvegetation type, and then calculated the area-weighted mean price per hectare of farmland for all
the vegetation types in a given priority class. Similar calculations were done for estimating the mean
cost of partial loss of benefits of landownership (opportunity cost)/ha of remaining naturalvegetation.

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 11

3. Results
Variation in land price
Our dataset revealed very substantial variation in land prices across Western Cape, spanning morethan four orders of magnitude (from $US 15.ha-1 to 178 000.ha-1 of farmland) (Fig. 1a). This striking
variation was not the result of bias in our sample: the frequency distribution of property sales in our
final dataset was remarkably similar to that for all agricultural properties listed in a reference
dataset, the Farmer's Weekly (n=529 farms). The modal band for land prices was between $US 101
and 1000.ha-1 of farmland (Fig. 2).
Land price model
The best model for predicting land price included four predictors: mean annual precipitation;
percentage of untransformed land; property area; and topographic diversity (R2 = 0.66 and 0.67 forthe calibration and validation data sets respectively; Table 3). We checked that land prices did notvary systematically across our nine data sources (by entering source as a further term in the GLM),
but found no effect.
We then explored the partial effects of the model predictors by plotting residual land prices against
each predictor. Residual land prices were positively associated with mean annual precipitation(Fig. 3a), and negatively related to the percentage of untransformed land (Fig. 3b). After accounting
for the association with both mean annual precipitation and percentage of untransformed land,
lower prices were paid per hectare of farmland for larger land parcels (Fig. 3c). Finally, we observeda negative association between residual land prices and topographic diversity of farms; rugged
land was relatively cheap (Fig. 3d).
Estimating the opportunity cost of outright land purchase (acquisition cost)To estimate the land purchase (acquisition) cost, we extrapolated the predicted land price for eachcadastre to generate a land price map for the province (Fig. 4a).
Areas of high conservation priority have high acquisition costs. The mean predicted land price was$US 1,160.ha-1 of farmland for Critically Endangered (CE) vegetation types, $US 970.ha-1 forEndangered (EN) vegetation types, $US 490.ha-1 for Vulnerable (VU) vegetation types, and $US
220.ha-1 for Least Threatened (LT) vegetation types. Surprisingly, even within each priority class,there was considerable variation; Land prices ranged from $US 590 to 5,600.ha-1 of farmland
12 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

within CE vegetation types, from $US 260 to 9,000.ha-1 of farmland within EN vegetation types, from$US 126 to 2,300.ha-1 of farmland within VU vegetation types, and from $US 37 to 9,130.ha-1 offarmland within the LT vegetation types.
Estimating opportunity cost of retaining natural vegetation on private land
The mean estimated opportunity cost per hectare of retaining the remaining natural vegetation was$US 56.ha-1.yr-1. However, this masked very substantial variation across the province, with costs
per unit area for individual cadastres ranging from negative cost to $US 8,300.ha-1.yr-1. More than
90% of the cadastres had a predicted mean opportunity cost of less than $US 500.ha-1.yr-1 (Fig.
4b).
The highest opportunity costs are incurred in CE vegetation types (mean of $US 90.ha-1 ofremaining natural vegetation/yr). EN vegetation types incur a mean opportunity cost equivalent to$US 70.ha-1.yr-1, VU vegetation types, $US 60.ha-1.yr-1, and LT vegetation types, $US 40.ha-1 ofremaining natural vegetation/yr (Fig. 5). Considerable variation was found among vegetation typesof the same class; for example opportunity cost for CE vegetation types ranged from $US 52 to
554.ha-1.yr-1, (Fig. 5).

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 13

4. Discussion
Variation in land prices and opportunity costs of vegetation retention on
private land
Our analyses have yielded two main results. Firstly, we found enormous variation – of more thanfour orders of magnitude – in observed land prices (from $US 15 to 178 000.ha-1 of farm land: Fig1a) and predicted land prices (acquisition costs: Fig.4a) across the Western Cape. Similar variation
applied to our estimated opportunity cost of retaining natural vegetation of private land (fromnegative cost to 8 300.ha-1 of remaining natural vegetation/yr: Fig 4b). Secondly, both acquisitionand opportunity cost of vegetation retention on private land co-varied positively with conservation
priority; both were highest within Critically Endangered, and lowest within Least Threatened
vegetation types.
Three important sets of caveats should be considered. Firstly, while our model of land price wasbased on intuitively sensible predictors and had reasonable predictive power (similar to thatreported by, its application for conservation planning effectively assumes that the current price ofland captures the expected NPV of future benefits from that land. While this is true in wellfunctioning land markets, rigidities in the South African land market may make this assumption
untenable; compulsory land acquisition may inflate land prices beyond our current estimates.
Secondly, our model does not take into account the dynamics in the land markets, and other factors
such as the expansion of markets e.g. for wine, creating pressure to convert land to viticulture; andthe enactment of new land use legislations e.g. National Environmental Management: Biodiversity
Act, that could impact on conservation outcomes.
Third, our method assumes that non-converted land has the potential upon conversion to be as
profitable as converted land (under similar environmental conditions). This assumption may be
wrong; hence our estimates of absolute cost should be treated with caution. Nevertheless, we
believe the extent and pattern of variation we found still hold.
Given the wide variations in management costs, our findings are not entirely surprising, but they arenevertheless striking; opportunity costs (like other costs) are highest in areas of high conservation
priority because these areas are characterised by multiple socio-economic pressures. Thisvariation has crucial implications for conservation planning and practice.

14 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

Application to conservation planning and policy
In most conservation planning exercises, decisions on the allocation of resources are made purely
on biological criteria, ignoring socio-economic constraints. However, variability in economic
factors can be just as important as ecological variability for conserving biodiversity efficiently andeffectively. That is certainly true in the Western Cape, where we found that the variations inopportunity costs are enormous (Fig. 4), where costs co-vary positively with conservation priority
identified on the basis of biological criteria (Fig. 5). Most surprisingly, even within high conservation
priority areas (such as CE vegetation types), within-class variations in opportunity costs are
considerable. These findings suggest that explicitly incorporating (rather than ignoring) such costvariation could contribute substantially to refining of conservation priorities as a first step towards
efficiency. The second step involves investigating the extent to which addressing costs changes
priorities i.e. determining the efficiency gains on no-cost and cost accounted scenarios, for which
studies are now underway in the CFR.
Lastly, moving from planning to implementation, our results can be useful in two ways. First, theland acquisition costs surface (Fig 4a) is useful in identifying opportunities for strategic expansionof reserves between and within-conservation priority classes given a limited budget. Second, ourestimation of opportunity cost for landowners of retaining natural vegetation on private land (Fig.
4b) can inform the development of proposed off-reserve conservation mechanisms, including
conservation easements and payments for biodiversity services. Our results can be used todetermine land owners payment level that is at par with the opportunity costs of compliance in apayment for biodiversity scheme, with the variations in costs, which we uncovered, reflected on
variable levels of payments. These mechanisms have already been initiated to encourage
landowners to protect Critically Endangered vegetation types (M. Botha, Botanical Society ofSouth Africa. Pers. comm.). Our study forms an initial first step towards the integration of economic
costs into conservation planning in the study area, with a potential for replication in other Africancountries to help increase cost-effectiveness of conservation interventions.

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 15

References
ARMSWORTH, P. R., DAILY, G. C., KAREIVA, P. & SANCHIRICO, J. N. (2006) Land market feedbacks
can undermine biodiversity conservation. Proc. of the Nat. Acad. of Sciences, 103, 5403-5408.
BALMFORD, A., GASTON, K. J., BLYTH, S., JAMES, A. & KAPOS, V. (2003) Global variation interrestrial conservation costs, conservation benefits, and unmet conservation needs. Proc. of theNat. Acad. of Sciences, 100, 1046-1050.
BLOM, A. (2004) An estimate of the costs of an effective system of protected areas in the Niger Delta– Congo Basin Forest Region. Biod. and Conserv. 13, 2661-2678.
BRUNER, A. G., HANKS, J. & HANNAH, L. (2003) How Much Will Effective Protected Areas Systems
Cost? . Vth World Parks Congress, 8-17 September, 2003. Durban, South Africa.
CHOMITZ, K. M., ALGER, K., THOMAS, T. S., ORLANDO, H. & NOVA, P. V. (2005) Opportunity costsof conservation in a biodiversity hotspot: the case of southern Bahia. Envir. and Dev. Econ. 10, 293312.
COWLING, R. M., PRESSEY, R. L., ROUGET, M. & LOMBARD, A. T. (2003) A conservation plan for a
global biodiversity hotspot--the Cape Floristic Region, South Africa. Biol. Conserv. 112, 191-216.
DEPARTMENT OF AGRICULTURE AND ENVIRONMENTAL AFFAIRS (2004) Guide to Machinery
Costs. Pretoria, Directorate of Agricultural Economics.
DIDIZA, T. T. (2006) Land and Agrarian Reform in South Africa 1994-2006: Presentation by Minister
for Agriculture and Land Affairs, Republic of South Africa. International Conference on Agrarian
Reform and Rural Development, March 2006, Brazil. Brazil, Ministry of Agriculture and Land Affairs,
South Africa.
DRIVER, A., MAZE, K., ROUGET, M., LOMBARD, A. T., NEL, J., TURPIE, J. K., COWLING, R. M.,
DESMET, P., GOODMAN, P., HARRIS, J., JONAS, Z., REYERS, B., SINK, K. & STRAUSS, T. (2005)
National Spatial Biodiversity Assessment 2004: priorities for biodiversity conservation in South
Africa. Pretoria, South African National Biodiversity Institute.
EMERTON, L. (1999) Balancing Opportunity Costs of Wildlife Conservation for Communities
Around Lake Mburo National Park, Uganda. Evaluating Eden Series. London, International Institutefor Environment and Development.
FAIRBANKS, D. H. K., HUGHES, C. J. & TURPIE, J. K. (2004) Potential impact of viticulture
expansion on habitat types in the Cape Floristic Region, South Africa. Biod. and Conserv. 13, 10751100.

16 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity CountryFAIRBANKS, D. H. K., THOMPSON, M. W., VINK, D. E., NEWBY, T. S., VAN DEN BERG, H. M. &EVERARD, D. A. (2000) The South African Land-cover Characteristics Database: a synopsis of thelandscape. S. Afri. J. of Sci. 96, 69-82.
FERRARO, P. J. (2002) The local costs of establishing protected areas in low-income nations:
Ranomafana National Park, Madagascar. Ecol. Econ. 43, 261-275.
FERRARO, P. J. (2003) Assigning priority to environmental policy interventions in a heterogeneous
world. Journ. of Pol. Anal and Manage. 22, 27.
FRAZEE, S. R., COWLING, R. M., PRESSEY, R. L., TURPIE, J. K. & LINDENBERG, N. (2003)
Estimating the costs of conserving a biodiversity hotspot: a case-study of the Cape Floristic Region,
South Africa. Biol. Conserv. 112, 275-290.
INTERNATIONAL MONETARY FUND (2004) Staff Report for 2004-Article IV Consultation with South
Africa. Pretoria, IMF.
JAMES, A., GASTON, K. J. & BALMFORD, A. (2001) Can We Afford to Conserve Biodiversity?
Bioscience, 51, 43-52.
JOHNSON, J. B. & OMLAND, K. S. (2004) Model selection in ecology and evolution. Trends in Ecol.
& Evol. 19, 101-108.
MOORE, J., BALMFORD, A., ALLNUTT, T. & BURGESS, N. (2004) Integrating costs into
conservation planning across Africa. Biol. Conserv. 117, 343-350.
MYERS, N., MITTERMEIER, R. A., MITTERMEIER, C. G., DA FONSECA, G. A. B. & KENT, J. (2000)
Biodiversity hotspots for conservation priorities. Nature, 403, 853-858.
NAIDOO, R. & ADAMOWICZ, W. L. (2006) Modeling Opportunity Costs of Conservation inTransitional Landscapes. Conserv. Biol. 20, 490-500.
NAIDOO, R., BALMFORD, A., FERRARO, P. J., POLASKY, S., RICKETTS, T. H. & ROUGET, M. (2006)
Integrating economic costs into conservation planning. Trends in Ecol. & Evol. 21, 681-687.
NAIDOO, R. & RICKETTS, T. H. (2006) Mapping the Economic Costs and Benefits of Conservation.
PLoS Biology, 4, e360.
NORTON-GRIFFITHS, M. & SOUTHEY, C. (1995) The opportunity costs of biodiversity conservationin Kenya. Ecol. Econ. 12, 125-139.
PAGIOLA, S., AGOSTINI, P., GOBBI, J., DE HAAN, C., IBRAHIM, M., MURGUEITIO, E., RAMIREZ, E.,
ROSALES, M. & RUIZ, J. P. (2004) Paying for Biodiversity Conservation Services in AgriculturalLandscapes. Environmental Economics Series Paper. Washington D.C., Environment Department,
World Bank.
Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 17

REPUBLIC OF SOUTH AFRICA (2004) National Environmental Management: Biodiversity Act,
2004. South Africa.
ROUGET, M., RICHARDSON, D. M., COWLING, R. M., LLOYD, J. W. & LOMBARD, A. T. (2003)
Current patterns of habitat transformation and future threats to biodiversity in terrestrial ecosystemsof the Cape Floristic Region, South Africa. Biol. Conserv. 112, 63-85.
SINDEN, J. A. (2004) Estimating the opportunity costs of biodiversity protection in the Brigalow
Belt, New South Wales. Journ. of Environ. Manage. 70, 351-362.
STATISTICS SOUTH AFRICA (2003) Statistical Release P0441: Gross Domestic Product, Third
Quarter 2003. Pretoria, Statistics South Africa.
STATISTICS SOUTH AFRICA (2004) Statistical Release P0441: Gross Domestic Product, Third
Quarter 2004. Pretoria, Statistics South Africa.
TURPIE, J. K., HEYDENRYCH, B. J. & LAMBERTH, S. J. (2003) Economic value of terrestrial andmarine biodiversity in the Cape Floristic Region: implications for defining effective and socially
optimal conservation strategies. Biol. Conserv. 112, 233-251.
WEERSINK, A., CLARK, S., TURVEY, C. G. & SARKER, R. (1999) The Effect of Agricultural Policy on
Farmland Values. Land Econ. 75, 425-439.

18 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

Tables
Table 1. List of datasets on land purchases.
SourceSpatial Locality

PeriodOriginal Size No Used

1. Farmer’s Weekly Journal
2. SANParks aFarm Name

2003-2004

529

61

Farm Name & ERF No

1998-2003

34

17

3. WWF-SA

Farm Name & ERF No

1968-2002

94

19

1. Estate Agents

Farm Name

2004

50

35

2. Western Cape Department of Agriculture
3. Cape Property Services b

Farm Name & ERF No

2001-2004

68

18

Farm Name & ERF No

1995-2004

408

1

Local Dataset
1. FFI c

Farm Name

1997-2002

16

10

2. Parker, R MSc Project

Farm Name40

9

3. Landowners

Farm Name & ERF No

4

4National Dataset

Provincial Dataset

Total sample size (n)174aSouth Africa National Parks Authority
Most of the CPS data were excluded because of the reasons explained in page 7. The dataset also contained
information on non-agricultural properties (commercial, residential and other property types).
c
Fauna and Flora International.
b

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Countryr | 19-

Table 2. Variables used in the model to predict land prices in Western Cape
Province, South Africa.
aVariable

Units

Group

Entire Study Area (n=30150)
Mean

Std Dev

Min

Max

MAP

mm/yr

climate349.08196.8333.90

2036.8

ALT

meterstopography351.13309.911.10

2096.60

topography106.048.75

100.00244.88ROUGHNESSDCOAST

metersspatial

512.34451.140.00

2652.0

DROAD1

metersspatial

98.69

120.390.00

1126.0

DROAD12

metersspatial

10.82

18.78

0.00

204.00DRIVERS

metersspatial

11.93

19.76

0.00

213.00

DURBAN

metersspatial101.77

98.71

0.00

716.00LANDCAPscore (1-9)

production

5.72

1.49

3.00

8.00

LUPENS

tons/haproduction

0.95

0.84

0.00

3.7

LUCERN

tons/haproduction

13.47

5.64

0.00

22.80

WHEAT

tons/haproduction

2.27

1.56

0.00

6.00

WINE

tons/haproduction

3.93

3.29

0.00

15.90

AREA

ha

farm size

421.51

921.771040072.50

NATVEG

percentage

land use

54

41

0.00

100

POPDENlog (people/QDS)

3.72

0.85

1.40

6.10

VEGGROUP

20 categories

vegetation

GEOL.FERT

15 categories

geologya MAP = mean annual precipitation; ALT = altitude above sea level; ROUGHNESS = topographic diversity; DCOAST = minimum distancefrom the coastline; DROAD1 = distance to the nearest major road; DROAD12 = distance to the nearest minor or major road; DRIVERS =distance to the nearest river; DURBAN = distance to the nearest urban center; LANDCAP = land potential based on geo-physical andclimatic variables; LUPENS = mean potential lupens production; LUCERN = mean potential lucern production; WHEAT = mean potential
wheat production; WINE = mean potential dry wine production; AREA = total area of farm; POPDEN = mean population density;
VEGGROUP = dominant vegetation group; GEOGROUP = dominant geological group.

Table 3. Stepwise Generalised Linear Model showing the steps, co-efficientvalues to reflect the statistical effects for the predictors, residual devianceand Akaike's Information Criteria (AIC)abModel + StepDF

Resid.Dev

Co-efficient Values

AIC

1

MAP

1

105.4565.93

0.001923-19.26-75.97

2+ NATVEG

1

50.33-0.546204-107.70

3+ AREA1

43.98-0.348359-122.57

4+ ROUGHNESS

1

41.12

-0.019553-128.99

a variables were mean annual precipitation (MAP); percentage of untransformed land (NATVEG); area of property (AREA);
and topographic diversity (ROUGHNESS)bAkaike’s Information Criteria
20 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

Priority Class for Vegetation Conservation

No. Of Vegetation Types

Critically Endangered (CE)13

Endangered (EN)23

Vulnerable (VU)

8

Least Threatened (LT)

12

Total

56

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 21

Figures

Figure 1: Study area showing a) district boundaries and spatial variation in
observed land prices ($US.ha-1); and b) land transformation and mean annual
precipitation.

22 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

Figure 2. Frequency distribution of property sale price ($US.ha-1) for study
dataset and Farmer's Weekly dataset (used as a Reference dataset).

Figure 3a. Residual land prices ($US.ha-1) in relation to mean annual
precipitation.

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 23

Figure 3b. Residual land prices ($US.ha-1) in relation to the percentage ofuntransformed land

Figure 3c. Residual land prices ($US.ha-1) in relation to property area

24 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

Figure 3d. Residual land prices ($US.ha-1) in relation to topographic diversity

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 25

Figure 4. Predicted surface of a) land prices in ($US.ha-1) as a proxy for
acquisition costs (total loss of benefits of landownership), and b) opportunity
costs of retaining natural vegetation on private land in ($US.ha-1.yr-1) (partial
loss of benefits of land ownership)

26 | Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country

Figure 5. Opportunity cost ($US.ha-1 of remaining natural vegetation per year)
showing upper 90 percentile, lower 10 percentile and mean per priority classof the four vegetation types (CE-Critically Endangered; EN-Endangered; VUVulnerable; and LT-Least Threatened) in Western Cape Province.

Estimating Land Prices and Opportunity Costs of Conservation in a Megadiversity Country | 27

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