Price Formation on Land Market Auctions in East Germany An Empirical Analysis

Transcription

1 Price Formation on Land Market Auctions in East Germany An Empirical Analysis Silke Hüttel, Martin Odening, Karin Kataria, Alfons Balmann Humboldt-Universität Berlin, Department of Agricultural Economics Leibniz-Institut für Agrarentwicklung in Mittel- und Osteuropa, Halle(S) IAMO Forum 21st June, / 24

2 Introduction Production Factor Land Crucial production factor but immobile and limited Land market supply Private land market: by exit of farms Public land market e.g., privatization: by institution with public tenders Demand for land Growth of surviving farms Non-agricultural investors ) How do prices emerge in the di erent market segments? 2 / 24

3 Introduction Literature analyzing farmland values Dynamic asset pricing Cross sectional hedonic pricing ) price formation process? Price formation in a competitive market ) negotiations or public auctions? 3 / 24

4 Introduction Objectives Aim Improve the understanding of agricultural land price formation in public auctions How? Empirically explore the formation of prices on land auctions Saxony-Anhalt: state-owned land sold by auctions with notable portion of traded farmland Base: auction theory and spatial econometrics 4 / 24

5 Introduction Outline The land market in Saxony-Anhalt Land price determinants and hypotheses Data and empirical model Results and discussion 5 / 24

6 The Land Market in Saxony-Anhalt Land Market in Saxony-Anhalt Privatization process with supply by institution BVVG: Bundesverwertungs- und Vermögensverwaltung Privatization of formerly state-owned land on behalf of the German federation LGSA: Landgesellschaft Sachsen-Anhalt Service for land transactions including rentals of state-owned land Both: rst price sealed bid auction with public tenders ) LGSA: tenant has option to enter at the highest bid ) LGSA cuts large plots to make them a ordable for farmers 6 / 24

7 The Land Market in Saxony-Anhalt Land prices in Saxony-Anhalt Average annual farmland sale pricesall sold plots, sold by BVVG and by LGSA *Statistisches Landesamt Sachsen-Anhalt (2010) **Meldesystem der BVVG, ab 2003 Controlling-Bericht der BVVG ***Values obtained from data provided by LGSA 7 / 24

8 Land Price Determinants and Hypotheses Land price determinants Borrow from hedonic pricing models Productivity, e.g., soil quality Neighborhood and location, e.g. local rental prices Environment, e.g., biogas plants Factors of the tendering procedure Public rst price sealed bid auction Common versus private value auction 8 / 24

9 Land Price Determinants and Hypotheses Hypotheses Based on auction theory Optimal bidding Optimal bid increases as # of bidders increases ) higher # of bids = higher price Di erent optimal bids among di erent groups of bidders: ) non-agricultural bidders: higher optimal bids.= higher price. 9 / 24

10 Land Price Determinants and Hypotheses Hypotheses Based on hedonic pricing Valuation of land w.r.t. productivity Large plots = high price High soil quality = high price Share of arable land high = high price Environment Biogas plants: high demand for land = high price Precipitation relates to productivity Value added: possible o -farm income high = high or low bid 10 / 24

11 Land Price Determinants and Hypotheses Hypotheses Based on hedonic pricing (cont d) Neighbourhood and location High neighboring prices for comparable plots: high productivity = high price Low exit rates: low additional supply of land = high price High rental rates for agricultural land: high demand = high price 11 / 24

12 Data and Empirical Model Data Tender results of the Landgesellschaft Sachsen-Anhalt prices for agricultural land: arable and grassland, forestry land and other includes horticultural land, gardens Limited information of buyers... of local market conditions... about exact coordinates of the slots... original plot size 12 / 24

13 Data and Empirical Model Explanatory variables: descriptives mean s.d. min max soil quality (0-102) % arable land slot size (ha) arable land (ha) grassland (ha) # of bids % farmers bids price (e/sq m) acceptance bid in 48% of the cases from tenant N= / 24

14 Data and Empirical Model Regional structural variables: descriptives Landkreis level unless otherwise indicated mean s.d. min max exit rate (%) land rental price (e/ha) installed kw from biogas (subdistrict, 2011) value added (1000 e) 72,601 23,153 4, ,342 precipitation (mm, subdistrict, yearly average) 14 / 24

15 Data and Empirical Model The price function: spatial issues Land prices typically vary with location through regional soil quality... rainfall and its distribution in the vegetation season... regional structure, e.g., neighbors... price expectations based on comparable slots Spatially correlated land prices expected Problem: lowest regional level: local subdistrict (Gemarkung) ) di cult to account for spatial correlation 15 / 24

16 Data and Empirical Model Spatial Distribution of Prices 2003/04 versus 2009/10 Average prices per local subdistrict (Gemarkung) in Saxony-Anhalt 16 / 24

17 Data and Empirical Model Spatial pricing model: procedure 1 BoxCox test: linear versus log-linear model ) model: ln(price) = xβ + u where x denotes the matrix of explanatory variables 2 Test residuals u for spatial error correlation: Moran s I ) use binary spatial weight matrix W 3 LM-test: spatial error versus spatial lag model ) weighted average of neighbors prices Wy on RHS ) nal model: ln(price) = ρwy + xβ + e where: ρ unknown parameter; e: error vector 17 / 24

18 Results and Discussion Results spatial lag model Variables Estimates rho (neighbor price) arable land (ha) arable land ^ % arable land grassland (ha) grassland ^ soil quality # bids % bids farmers local buyer Variables (cont d) Estimates installed kw biogas 7.47e-05 rental price land exit rate precipitation -1.32e-01 regional value added -4.04e-02 constant Note: ***, ** and * denote signi cance at the 1, 5 and 10 per cent level, respectively. 18 / 24

19 Results and Discussion Results spatial lag model (cont d) yearly dummies, reference 2003 Estimates Note: ***, ** and * denote signi cance at the 1, 5 and 10 per cent level, respectively. 19 / 24

20 Results and Discussion Summary of Results Soil quality and share of arable land: main characteristics No size e ect () censoring by LGSA Public auction format in uences the price: number of bids and share of bids of non-farmers Demand for land through biogas farms a ects prices Neighboring plots seem to be a reference for valuation 20 / 24

21 Results and Discussion Outlook Assess impact of preemption right of current tenant Account for censored plot size Prices /ha Slot size ha Potential endogeneity of number of bidders Comparison with BVVG-data 21 / 24

22 Annex Spatial Weight Matrix Binary weight matrix Queen contiguity Elements of W w ij = 0 if no common border & corner w ii = 0 (A) w ij = 1 if neighbor of rst order (B and C) Use Gemarkung, assume neighborhood within 22 / 24

23 Annex The spatial model: testing results Step 2: Moran s I regression residuals: ) spatial dependence Step 3: robust LM-test: ) reject spatial error model at 5%; ) no rejection of spatial lag model 23 / 24

24 Annex Spatial pricing model: spatial lag model y = ρwy + Xβ + e Weighted average of neighbors prices Wy on RHS ρ: unknown parameter; e: error vector Endogenous ρwy Rewrite: y = (I ρw) 1 Xβ + (I ρw) 1 e with I : identity matrix ) comprises spatial error model Estimation: maximum likelihood with ^βml = (X 0 X) 1 X 0 (y ρwy) 24 / 24