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A Hedonic Price Model of Self-Assessed Agricultural Land Values
Jeremey Lopez***, Stephen O’Neill, Cathal O'Donoghue*, Mary Ryan*
* Teagasc Rural Economy and Development Programme
** National University of Ireland Galway
*** Agrosup, Dijon
Presentation Structure
Context Drivers of Land Values Data – Dependent Variable Data – Geo Referencing FADN Methodology – Hedonic Prices Results Conclusions
Context
Irish Agriculture Growing
Lack of land access and mobility major issue
Understanding land markets important Focus here on land values
Drivers of Land Values
The environmental and agronomic drivers of land productivity, The availability of alternative land uses Local land markets The impact of agricultural policy
The environmental and agronomic drivers of land productivity
Irish agriculture is mainly land based, grass based, pastoral systems
Grass based system – highly influenced by agronomic drivers Higher share of better soils on better land
Local land markets
Local Land Markets Different Broad price growth scenario Consistent with the Property
Boom and Bust Areas near cities higher
peak Influence of non-Agricultural
Land Markets
The availability of alternative land uses
Very significant differences in farm income per hectare between Dairy and Drystock (Cattle and Sheep)
Milk Quota has limited movement between sectors over time More dairy cows on higher value land
The impact of agricultural policy
The relatively inelastic supply of inputs such as land, Combined with production and/or demand pressures
resulting from farm subsidies Can result in upward pressure on input prices
EU farm supports have gradually moved from Price supports to Payments coupled to production increasing the income from
factors associated with production, whether it be animals or land
More recently, support payments were decoupled from production potentially increasing the capitalisation of such supports into land values
The impact of agricultural policy
Many studies have focused on lease values However in Ireland where
most land is rented for short periods of time con-acre system and
it is possible to consolidate farm subsidy entitlements onto existing non-rented land
rental values are less likely to capitalise the subsidy value than in other EU countries
Given this land values may more appropriately capture this capitalisation
Data: Irish FADN
FADN: The Irish National Farm Survey Detailed survey of about 1000-1200 farms per annum Part of EU Farm Accountancy Data Network A panel survey with about 7 years in sample Data from 1984-2013 used
Choice of Dependent Variable
Many Studies Use Land Sales Data The NFS includes three potential measures of land values:
Average land sales value per hectare Average purchase value per hectare Self-reported land value per hectare
Challenge with Land Transaction Data Less than 0.25% transacted annually Since the NFS contains primarily active farmers, there are relatively few
sales data points, with more purchase data points. However all farms contain self-reported land values
Land Value Variables – Purchases and Self-Reporting
Land Value Variables - Sales
Land Value Variables - Sales
Data – Geo Referencing FADN
Agronomic Drivers for Pastoral Grass based Systems Soil (Soil Information System) Weather (Local Met Office Data) Altitude (GIS) Grass Cover and Growth (Remote Sensing)
Historically FADN not geo-referenced Geo-referenced past 2-3 years
Need temporal and spatial variability Geo-reference historical addresses to get
Data – Geo Referencing FADN
Challenges No post codes Non-unique addresses Data confidentiality
Got an extract of addresses 1995-2007 Addresses and farm code not identifiable
Developed algorithm to link Postal Service Geo-Directory Only about a third of addresses match Irish names County boundaries Different spellings Big data cleaning Many to one However spatial data more accurate to district than farm
Methodology
Utilising Panel Data Random Effects Models Due to time invariant agronomic characteristics such as soils
Next steps Quantile Regression Incorporate lagged values - GMM
Results
Results
Cross-sectional model R2 62%
Planting Forestry – negative ~ marginal significance (depends upon functional form) Positive Signif relationship with soil quality Positive Signif relationship with type of system
Inclusion of spatial agronomic characteristics improve R2 from 54% to 61%
Regional temporal differentiation in land markets significant but not as important as trend and spatial variation
Policy factors Direct Payments positive and significant Increased coefficient after decoupling Potential for exploiting
Conclusions and Next Steps
Preliminary study focusing on drivers of land values
Focus so far has been on assembling data Sales data in a poorly functioning market may not be appropriate – may over
state actual land value and over estimate results in hedonic price models
Simplistic econometrics find plausible results with expected significance and signs Next steps
Understand heterogeneity of preferences – Quantile regression Incorporate Lags using GMM
Exploit natural experiment in Less Favoured Areas Decoupling of LFA payments from 2001 prior to decoupling of pillar 1
Thank Youwww.teagasc.ie