Land prices as proxy for
opportunity cost of PES in CR
Presented by Ina Porras, IIED
Ongoing research funded by DFID and POLICYMIX led by Ina Porras (IIED, UK), David
Barton (NINA, Norway), Adriana Chacon (CATIE-CR)
Outline
• Background of the study
• The dilemma of PES policy planners in Costa
Rica
• Our main questions
• Where we are so far
The dilemma: Green and Fair?
• Effectiveness: protect where it is important
• Value for money: maximize impact given
limited budget
• Concern about fairness of access and
distribution of benefits and costs
All this with very little information, and under
the scrutiny of budget controllers, civil society
and pesky academics
Policy planners must consider
• Who to pay?
• How much is considered “fair”?
• How to allocate payments?
Who should be paid?
Only those with
special forest?
Pay everyone who has
forest?
Only those who need
encouraging to grow
forest?
Those whose forest is at
higher risk of change?
Only the most
vulnerable/ and forest
dependent?
How much is ‘fair’?
Flat rate,
according
to budget
Regressive/ in proportion to
property size
Regressive/ in proportion
to means
According to cost of provision,
or to level of services provided
Need measurable, practical indicators
for:
• Relative wealth to help understand access and
distribution
• Opportunity costs as a tool for low-cost spatial
targeting of PES/ and for understanding incentive levels
– opportunity costs based on landuse capacity indicators
used in many site-priority-setting models (Marxan etc.) do
not capture access and market effects on land value.
– Choice experiments? Isolated/ but too expensive at
national level
• Auctions? Not until now
We propose real estate value maps as first-step
For the Costa Rica context
• Estudio de Zonas Homogeneas, Ministerio de
Hacienda, Dirección General de Tributación
• Between 2006 – 2008 surveys on land values
across ALL the country, rural and urban areas
• Triangulating GIS data with other information
Objectives:
• How land prices/values change throughout the
country, in particular in those areas of interest for
conservation.
• What factors affect this price variability, especially with
relation to environmental variables
• Explore the use of adjusted land prices (for example,
predicted price with/without cover) as proxies for
opportunity costs for conservation.
• Use this adjusted land value predictor as an indicator
for wealth and from here the social impacts of the PES
Programme.
Data at the farm level
Province, canton, distrito, zona
Commercial, residential,
agriculture, ranching, forestry
Area (m2), front, regularity, access,
slope, land use capacity, hydrology,
type, available services
GIS point
Reported value by farmer ¢/m2
Using GIS information we have added:
• Biophysical: precipitation, elevation, land use capacity;
• Distances to: areas with flooding risk, protected areas,
sawmills, banks, schools, hospitals, airports,
international airport, San Jose, registered hotels,
volcanoes, beach, roads (and description of road),
• Environmental : forest cover, indigenous territory,
biological corridor, protected area, PES within 1km;
number of PES contracts in district;
• Socio-economic information (SDI, population),
cadastral inconsistencies (as a possible “tenure
insecurity” predictor of property value)
Stage
• Nearly 10500 clean observations in rural
areas (over 35,000 rural/ urban)
• Beginning the hedonic model (testing a partial
one in Hojancha, Osa and in Limon)
• Move on to National level
• Other Ideas? Potential combination of results
from real estate values as double-checking for
auction-based experiment.