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

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