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 farms per annum  Part of EU Farm Accountancy Data Network  A panel survey with about 7 years in sample  Data from 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

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  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

 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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