Hedonic Estimation of Southeastern Oklahoma Forestland Prices

Transcription

1 Hedonc Estmaton of Southeastern Oklahoma Forestland Prces Stephen A. Kng Assstant Professor San Dego State Unversty Imperal Valley Campus 720 Heber Ave. Calexco, CA Phone: 760/ Fax: 760/ Dean F. Schrener Professor Emertus Department of Agrcultural Economcs Oklahoma State Unversty Stllwater, OK Phone: 405/ Selected Paper prepared for presentaton at the Southern Agrcultural Economcs Assocaton Annual Meetng, Tulsa, Oklahoma, February 14-18, 2004 The authors would lke to thank Davd Lews, Darrell Kletke, Jmmy Wood, and Allen Fnchum for supplyng data used n ths study. The authors also apprecate the helpful comments of Note Lansford, Davd Lews, Arthur Stoecker, and two anonymous revewers. Copyrght 2004 by Stephen A. Kng and Dean F. Schrener. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 Abstract Forestland s a composte good, the prce of whch vares wth ts characterstcs, such as ts ablty to produce tmber and ts proxmty to markets. Sales of predomnately forested land n southeastern Oklahoma were examned to better understand and quantfy the nfluences of physcal and spatal characterstcs on sales prces. Key words: Hedonc Models, Forestland Prces JEL Classfcaton: Q23, Q24

3 Introducton The prce of forestland s nfluenced by many attrbutes such as ts physcal attrbutes, locaton attrbutes, and other attrbutes such as taxaton and regulaton polces. The purpose of ths study was to determne whch attrbutes have the greatest nfluence on the sales prce of forestland. Emprcally a hedonc prcng model (HPM) was used to estmate the prce of unmproved forestland n two countes of southeast Oklahoma. The HPM can be traced to Court. However, the use of HPM dd not become wdely used untl Rosen publshed a theoretcal model that could serve as a bass for emprcal technques. Applyng Rosen s logc, the many factors that nfluence the prce of land render t a composte good and the value at whch t s exchanged a hedonc prce. Once these attrbutes of the composte good are known, we can then decompose the prce of the good nto the margnal value of each attrbute. Goods wth dfferent combnatons of the attrbutes wll trade for dfferent prces n the market. Emprcally we regress prces for the composte good on the level for each attrbute. From the regresson coeffcents, we obtan a value for an attrbute on the composte good. Most of the applcatons usng Rosen s model have dealt wth dfferentated consumer products. A semnal paper by Palmqust n 1989 adapted Rosen s model to form a theoretcal hedonc model for land as a factor or producton. Followng Palmqust s paper, there has been an expanson of the HPM to land markets. The purpose of Palmqust s paper was to estmate the derved demand for agrcultural land as a dfferentated factor of producton and to develop welfare measurement technques that could be appled to varous land and agrcultural polcy questons. 1

4 Applcatons of the HPM to Forestland Markets The majorty of the land market applcatons have been n the analyss of farm and urbanfrnge land. The applcaton of HPM to the forestland market has been less common. A revew of lterature has revealed three papers applyng HPM n the analyss of forestland. These are: Turner, Newton, and Denns; Roos (1995); and Roos (1996). Turner, Newton, and Denns Economc Relatonshps between Parcel Characterstcs and Prce n the Market for Vermont Forestland The data used by Turner, Newton, and Denns conssted of 139 sales of unmproved predomnately forested parcels 100 to 500 acres n sze, wth sales that occurred between January 1986 and Aprl The dependent varable was the real sales prce per acre. The ndependent varables were grouped nto physcal characterstcs, locaton characterstcs, and other characterstcs. They used a transcendental functonal form converted to log-log form and performed the estmaton usng ordnary least squares (OLS) regresson. The physcal characterstc ncluded: the number of acres n the parcel; percentage of non-forested area; a bnary varable ndcatng frontage on a publc road; a bnary varable ndcatng whether frontage road s paved; and percentage of parcel area wth a slope steeper than 15-percent. The locaton characterstcs ncluded: populaton per square mle n the town where the parcel s located; rate of populaton growth for the town; rate of populaton growth for the county; road dstance to hghway; and road dstance to nearest commercal sk area. The other explanatory varables are equalzed town real estate tax rate and a trend varable ndcatng the month of sale. The results ndcate the area of non-forested area, presence of a publc road on the frontage of the parcel, and the percentage of land area wth a slope greater than 15% were statstcally sgnfcant. Of the physcal characterstcs the percentage of non-forested land and the presence of a publc road made a postve contrbuton to explanng prce, whereas the others had a 2

5 negatve nfluence on prce. All of the locaton characterstcs were statstcally sgnfcant, excludng populaton densty. Populaton densty and populaton growth rates had a postve mpact on prce and the dstance to hghway and commercal sk area had a negatve nfluence on prce. The property tax varable was sgnfcant and ndcated that ncreases n property tax lead to decreases n forestland prces. The trend varable was postve, but not statstcally sgnfcant. Roos (1995) The Prce of Forest Land on Combned Forest Estates In 1995, Roos publshed a paper that appled Palmqust s adaptaton of Rosen s hedonc prce model n a study of combned forest estate land n Sweden, whch contan forestland, agrcultural land, and a resdence. The statstcal analyss was based on 198 sales durng The estates n the sample had to have a mnmum productve forest area of 20 hectares. There were 10 ndvdual explanatory varables n the model: nhabtants per square klometer n the county (INH); area of forestland n the parcel (AFOR); percentage of productve forestland of total forestland n the parcel (PROD); average ste ndex on productve forestland (SI); average standng volume per hectare of productve forestland (VOL); area of agrcultural land n the parcel (AGR); ponts for farmland productvty (FER); value ponts for resdence (VH); value ponts for outbuldngs (VB); and a trend varable ndcatng the month of sale (TREND). A lnear functonal form wth quadratc and nteracton terms was chosen. The man results and nterpretatons of the study were: the mplct prce for forestland on combned forest estates was a postve functon of populaton densty, the percentage of productve forestland compared wth the total forest area, ste ndex, and standng volume per hectare of productve forestland; forestland prces had negatve relatonshps wth the area of agrcultural land, suggestng negatve economes of scope between agrcultural and forestland; and the estmatons suggested economes of scale n agrculture and not n forestry. 3

6 Roos (1996) A Hedonc Prce Functon for Forest Land n Sweden Roos s 1996 paper focused on forestland n Sweden prncpally used for tmber producton. The parcels of land could not be more than 10% agrcultural; have at least 20 hectares of commercally productve forestland; and could not have any houses. The data conssted of 143 observatons from sales n The estmates were performed usng a lnear Box-Cox functonal form and lkelhood rato tests. The dependent varable was the prce per hectare deflated wth the monthly consumer prce ndex. There were eght ndependent varables: the number of hectares n the parcel; percentage of productve forestland of total area of forestland n the parcel; cubc meters per hectare of forestland; ste productvty; populaton per square klometer of forestland n the county; month of sale; a bnary varable ndcatng the presence of agrcultural land; and a bnary varable ndcatng buyer restrctons. Excludng the two bnary varables, all other ndependent varables were sgnfcant. The results ndcated a postve relatonshp between the per-hectare prce of forestland and the proporton of productve forestland n relaton to the total forest area on the estate, the mean standng volume, the mean ste productvty of the forestland, and the populaton densty n relaton to the area of forestland n the county. The parcel area has a negatve effect on per-hectare prces. The Theoretcal Model Sherwn Rosen explans that hedonc prces are defned as the mplct prces of attrbutes and are revealed to economc agents from observed prces of dfferentated products and the specfc amounts of characterstcs assocated wth them. Econometrcally, mplct prces are estmated by the frst-step regresson analyss (product prce regressed on characterstcs) n the constructon of hedonc prce ndexes. 4

7 The prce of the commodty P( Z ) s descrbed by n objectvely measured attrbutes or = ( 1 K n ) characterstcs, Z z, z, th 2, z, wth each z measurng the characterstc contaned n that good. The good Z s heterogeneous, yet each separate z can be consdered homogeneous, and the demand for the good can be analyzed n terms of the demand for each of ts homogeneous components. Each homogeneous characterstc s assumed to have a dstnct market equlbrum prce and thus the prce of the heterogeneous good s a functon of the prces of ts homogeneous components. The resultng hedonc prce functon s defned as: (1) P ( Z ) p( z, z, 2, ) 1 K z n =. Ths functon relates prces and characterstcs and s the buyer s (and seller s) equvalent of a hedonc prce regresson, obtaned from shoppng around and comparng prces of products wth dfferent characterstcs. The functon gves the mnmum prce of any package of characterstcs. If two products offer the same bundle of characterstcs, but sell for dfferent prces, consumers only consder the less expensve one. The mplct prce for a characterstc can be wrtten as the followng partal dervatve of equaton (1): (2) P z ( Z ) P =. z Buyers and sellers can be thought of as facng a margnal mplct prce schedule for each characterstc. A buyer maxmzes utlty by movng along these prce schedules untl the consumer s margnal wllngness to pay s equal to the margnal mplct prce of the characterstc. 5

8 Varables and Data Two data sets were developed for ths study, a forestland sales transactons dataset and spatal characterstcs dataset. The forestland sales transactons data comes from a dataset developed by Darrell Kletke and Davd Lews of the Department of Agrcultural Economcs and Department of Forestry, respectvely, at Oklahoma State Unversty. The prmary source of ther data was the State of Oklahoma Tax Commsson. Ths data set ncludes: the sales prce of forestland parcels, the sze (acres) of each parcel sold, ts locaton, the classfcaton of land uses wthn each parcel, and the expected annual per acre tmber producton for each parcel. Jmmy Wood and Allen Fnchum of the Department of Geography at Oklahoma State Unversty developed the spatal characterstcs data set. The data set ncludes nformaton on the dstance to the nearest cty havng a populaton of 2,000 or more, the populaton growth of that cty, the dstance to the nearest communty contanng two or more wood-processng mlls, the dstance to the nearest natural resource attracton, the dstance to major roadways, and ndcaton of whether the parcel fronts a road of any type. Forestland sales transactons data set The data set was drawn from 109 sales of land parcels n two countes of southeast Oklahoma n 1999, 40 transactons n Pushmataha County and 69 transactons n McCurtan County. The dependent varable was prce per acre ( PRICE ), whch was converted to logarthmc form ( ln Prce). The land area n the parcels was classfed nto cropland, mproved pasture, natve pasture, and tmberland. In order to be ncluded n the study, the parcel could not nclude any buldngs and had to nclude tmber-producng sol. Once ths adjustment was made, the total land transactons nvolvng unmproved tmberland numbered 81, 36 n Pushmataha County and 45 n McCurtan County. Of the 81 transactons, none ncluded cropland, one 6

9 ncluded mproved pasture, and 12 ncluded natve pasture. The mproved and natve pasture uses were combned to form the varable OPEN, whch denotes the percentage of the parcel classfed as open land. The explanatory varable ACRES s the sze of the parcel n acres, whch was converted to logarthmc form ( ln Acres). The varable TmProd denotes the expected annual per acre tmber productvty n cubc feet per acre per year. Spatal characterstcs data set The spatal data set descrbes relatonshps between the forestland parcels and the larger regonal economy, whch ncludes most of the countes n southeastern Oklahoma and borderng countes n Arkansas and Texas. The creaton of the data set nvolved the use of the ArcVew geographc nformaton systems (GIS) software. The spatal data set provdes the followng categores of nformaton: 1. Dstance to urban areas and populaton growth of those areas. 2. Dstance to wood processng communtes. 3. Dstance to land areas classfed as natural resource attractons. 4. Dstance to major roadways. 5. A bnary varable ndcatng whether the forestland parcel fronts a road of any type. The varable DstCty s the dstance n roadway mles to the closest communty havng a populaton of at least 2,000. The varable PopGro s the populaton growth of that cty measured as the change n populaton between the 1990 and 2000 census perods. There are thrteen communtes wthn close proxmty of the forestland parcels that contan at least two wood-processng facltes. These communtes were determned from use of the Arkansas Wood Usng Industres Drectory, Oklahoma Wood Manufactures Drectory, and the Texas Forest Servce Drectory of Forest Products Industres. Ths data was combned wth data on the 7

10 expected annual per acre tmber output from each parcel and the cost per cubc foot mle to transport tmber to form the varable TranCost, whch measures the annualzed cost of transportng tmber per acre from parcel to the nearest communty havng two or more woodprocessng mlls. Land classfed as a natural resource attracton ncluded major lakes, State Parks, or the Ouachta Natonal Forest. The varable DstNat s a measure of the road mle dstance to the nearest natural resource attracton for parcel. The varable DstHwy s the lnear dstance n mles to the nearest major roadway, such as a State or U.S. Hghway. FRONT s a bnary varable that ndcates whether the parcel fronts (or s near) any type of road, ths may nclude non-paved roadways such as secton lne roads. To determne whether the land fronts (near) a road, a ¼ mle buffer was placed around each forestland parcel f the buffer ntersected a road of any type a number one was assgned (1 = yes, 0 = no). Table-1 provdes a descrpton of the varables of the hedonc prce model, the explanatory varables expected effect on prce, and the source of data The Emprcal Model The sales prce of land per acre often vares nversely wth the sze of the parcel beng sold. Due to ths characterstc, varous researchers (Chcone; Hushak and Sadr; and Turner, Newton and Denns) have chosen a transcendental functon to model the relatonshp between land prce per acre and the relevant attrbutes that nfluence the prce per acre. In transcendental form, we have: = 2Ln β1 (3) PRICE = β 0 ACRES EXP( β X ) where PRICE s the purchase prce per acre, ACRES s the total acres of the parcel beng sold,, the X are the other parcel attrbutes, and the β are the estmated coeffcents. Convertng ths 8

11 equaton to logarthmc form allows estmaton usng ordnary least squares. Thus the statstcal model s: (4) ( ) ln β 0 + β1 ln( ACRES) + ( β X ) + ln PRICE = = 2L µ, where µ s the error term. For β 1, for a unt change n any sngle. e β1 ACRES n 1 provdes the percentage change n PRICE β for a unt change n ACRES. For β, > 1, ( e 1) provdes the percentage change n PRICE X The emprcal model used to estmate forestland prces for McCurtan and Pushmataha Countes was a transcendental logarthmc functon specfed as follows: (5) ln Prce = ln β + β ln Acres β TmProd 5 + β DstCty 2 + β Front + β OPEN 6 + β DstHwy 3 + µ, where Prce s the prce per acre for observaton, Acres s the parcel sze n acres for observaton, DstCty s the dstance n roadway mles to the nearest town havng a populaton of 2,000 or more for observaton, DstHwy s the lnear dstance to the nearest major roadway for observaton, TmProd s the expected annual tmber productvty for observaton, Front s a bnary varable ndcatng whether the parcel fronts on (or near) a road of any type for observaton, and OPEN s the percentage of the parcel that s open land for observaton. For McCurtan County = 45, and for Pushmataha County = 36. The varables PopGro, TranCost, and DstNat were not ncluded n the fnal model. Summary statstcs for the varables used to estmate (5) are reported n Tables 2 and 3 for McCurtan and Pushmataha Countes, respectvely. 9

12 Results McCurtan county regresson results Due to heteroskedastcty the estmated generalzed least squares procedure was used to estmate equaton (5) for the McCurtan County data. Of the sx explanatory varables n the model, four were sgnfcant. These were ln Acres, DstHwy, TmProd, and OPEN. The varables DstCty and FRONT were not statstcally sgnfcant. The coeffcent on ln Acres was sgnfcant at the 5% level and the sgn was consstent wth expectatons. The results are consstent wth the declnng margnal relatonshp between parcel sze and per acre sales prce found n most studes. The results ndcate that as parcel sze ncreases by one acre that per acre sales prce declnes by $4.80. The coeffcent on DstCty was not sgnfcant. The sgn on the coeffcent agrees wth expectatons, whch ndcates that sales prce per acre declnes as dstance to cty ncreases. The coeffcent on DstHwy was sgnfcant at the 5% level and the sgn on the coeffcent was consstent wth expectatons. It ndcates that for every addtonal lnear mle the per acre sales prce of forestland declnes by $90. The varable FRONT was not statstcally sgnfcant and the sgn does not agree wth expectatons. The varable TmProd was sgnfcant at the 5% level and the varable OPEN was sgnfcant at the 10% level. The sgn on TmProd does not conform to the expectaton that hgher tmber productvty ncreases per acre sales prce. The results ndcate that for each addtonal cubc foot of annual growth per acre, the sales prce per acre declnes by $1.70. The sgn on OPEN conforms to expectatons and ndcates that per acre sales prce declnes by $7.50 for each 1-percent ncrease n open space for the parcel beng sold. The overall F-statstc was 7.45 and sgnfcant wth a p-value of less than , the R- square was 0.54, and the adjusted R-square was A summary of the overall regresson results for McCurtan County appears n Table-4. 10

13 Pushmataha county regresson results The ordnary least squares procedure was used to estmate equaton (5) for the Pushmataha County data. Of the sx explanatory varables n the model, three were sgnfcant. These were DstHwy, TmProd, and FRONT. The varables ln Acres, DstCty, and OPEN were not statstcally sgnfcant. Also, the sgn on ln Acres does not agree wth expectatons. The results do not agree wth the declnng margnal relatonshp between parcel sze and per acre sales prce, as found n most studes. The sgn on DstCty agrees wth the expectaton that sales prce per acre declnes as dstance to cty ncreases, and the sgn on OPEN also conforms to the expectaton that per acre sales prce declnes as the amount of open space ncreases. The coeffcent on DstHwy was sgnfcant at the 5% level and the sgn on the coeffcent conforms to expectatons. It ndcates that for every addtonal lnear mle of dstance between a forestland parcel and a major roadway that the per acre sales prce of forestland declnes by $52. The varable FRONT was also statstcally sgnfcant at the 5% level, and the sgn agrees wth expectatons. The results ndcate that per acre sales prce of forestland ncreases by $210 f the parcel fronts on (or near) a road of any type. The varable TmProd was sgnfcant at the 10% level and the sgn on the coeffcent conforms to the expectaton that hgher tmber productvty ncreases per acre sales prce. The results ndcate that for each addtonal cubc foot of annual growth per acre, the sales prce per acre ncreases by $1.42. The overall F-statstc was 3.67 and sgnfcant wth a p-value of , the R-square was 0.43, and the adjusted R-square was A summary of the overall regresson results for Pushmataha County appears n Table-5. Msspecfcaton Tests Tests were conducted for heteroskedastc and normally dstrbuted resduals, as well as collnearty and nfluental observatons. The test results for McCurtan County ndcated the 11

14 presence of heteroskedastcty, and thus the estmated generalzed least squares procedure was used for the McCurtan County data. For Pushmataha County, the results dd not ndcate the presence of heteroskedastcty. The test results to evaluate whether the resduals are normally dstrbuted, dd not ndcate the presence on non-normal resduals. To methods used to detect multcollnearty dd ndcate the presence of multcollnearty among some of the varables n the full data set. Due to multcollnearty TranCost and PopGro were dropped from the model. For reasons other than collnearty, the varable DstNat was dropped. Ths measurement used most lkely was not capturng the prce nfluence of natural resource and recreatonal areas. Furthermore, the varable had lttle explanatory power. A better alternatve measure mght be the percentage of land area wthn a certan radus of the forestland parcel classfed as a natural resource attracton, such as the Rec varable n Nvens et al. To detect nfluental observatons, frst an nformal analyss was conducted by examnng each of the data seres and ther summary statstcs. Observatons havng extremely large and small values were nspected to determne whether there were any errors, no errors were noted. Formal testng methods dd ndcate the presence of nfluental observatons. However, no observatons were dropped snce there was no known nformaton that would justfy ther removal. Summary and Conclusons The objectve of ths study was to examne the sales of predomnately forested land n southeastern Oklahoma n order to better understand and quantfy the nfluences of physcal and spatal characterstcs on sales prces. Ths study has provded nsght nto factors affectng forestland prces and the purchasng behavor of forestland owners. The fnal model conssted of a seres of explanatory varables descrbng the sze of the forestland parcel, dstance to the nearest cty havng a populaton of 2000 or more, dstance to the 12

15 nearest major roadway, expected tmber productvty, a bnary varable ndcatng whether the parcel fronts on a road of any type, and the proporton of the parcel havng open land. The results for McCurtan County ndcated that four of the sx coeffcents were sgnfcant: parcel sze, dstance to a major roadway, expected tmber productvty, and the proporton of open space n the parcel. Of the statstcally sgnfcant varables wth the expected algebrac sgn, the varable havng the greatest nfluence on prce was DstHwy. It was sgnfcant at the 1% level and explaned 15% of the prce varaton. The results for Pushmataha County ndcated that three of the sx coeffcents were sgnfcant: dstance to a major roadway, expected tmber productvty, and the bnary varable ndcatng whether the parcel fronts on a road. Of the statstcally sgnfcant varables wth the expected algebrac sgn, the varables havng the greatest nfluence on prce were FRONT and DstHwy. FRONT was sgnfcant at the 2% level and explaned 65% of the prce varaton, and DstHwy was sgnfcant at the 3% level and explaned 16% of the prce varaton. These results are smlar to other studes, such as Turner, Newton, and Denns, n that the presence of roadway access has the greatest percentage effect on the sales prce of forestlands and that urban effects (dstance to cty) were nsgnfcant. Ths study was lmted by the avalablty of data. Subsequent studes should nvolve a tme seres of data and a larger number of observatons. Furthermore due to lmted avalablty of data and resources, some potentally valuable varables were not ncluded n ths study. These may nclude buyer and seller characterstcs and relatonshps, seasonalty effects, topography, and the presence of ste specfc amentes such as ponds, streams, and scenc vews. The addton of data on such varables would greatly enhance our knowledge about the factors affectng forestland prces and the behavor of forestland owners. 13

16 References Arkansas Wood Usng Drectory. Internet ste: (Accessed June 2003) Bera, A. K., and C. M. Jarque. Effcent Tests for Normalty, Heteroscedastcty and Seral Independence of Regresson Resduals: Monte Carlo Evdence. Economc Letters, 7(1981): Chcone, D. L. Farmland Values at the Urban Frnge: An Analyss of Sale prces. Land Economcs, 57(1981): Court, A. T. Hedonc Prce Indexes wth Automotve Examples. In The Dynamcs of Automoble Demand. New York: General Motors Corporaton, Hushak, L. J., and K. Sadr. A Spatal Model of Land Market Behavor. Amercan Journal of Agrcultural Economcs, 61(4)(1979): Kletke, Darrel D. and Davd K. Lews. Unpublshed dataset on forestland transactons n McCurtan and Pushmataha Countes. Stllwater, OK: Departments of Agrcultural Economcs and Forestry, Oklahoma State Unversty, Koenker, R. A Note on Studentzng a Test for Heteroskedastcty. Journal of Econometrcs, 17(1981): Nvens, Heather D.; Terry L. Kastens; Kevn C. Dhuyvetter; and Allen M. Featherstone. Usng Satellte Imagery n Predctng Kansas Farmland Values. Journal of Agrcultural and Resource Economcs 27(2)(2002): Oklahoma Wood Manufactures Drectory. Stllwater, OK: Oklahoma State Unversty, Cooperatve Extenson Servce, Department of Forestry. Palmqust, Raymond B. Land as a Dfferentated Factor of Producton: A Hedonc Model and ts Implcatons for Welfare Measurement. Land Economcs, 65(1)(1989): Ramsey, J. B. Tests for Specfcaton Errors n Classcal Lnear Least Squares Regresson Analyss. Journal of the Amercan Statstcal Assocaton, 71(1969): Roos, Anders. The Prce for Forest Land on Combned Forest Estates. Scandnavan Journal of Forest Resources. 10(1995): Roos, Anders. A Hedonc Prce Functon for Forest Land n Sweden. Canadan Journal of Forest Research, 26(1996): Rosen, Sherwn. Hedonc Prces and Implct Markets: Product Dfferentaton n Pure Competton. The Journal of Poltcal Economy, 82(1)(1974): Shapro, S. S., and M. B. Wlk. "An analyss of varance test for normalty (complete samples)." Bometrka, 52(3-4)(1965): Texas Forest Servce, Drectory of Forest Products Industres. Internet ste: (Accessed June 2003). Turner, Robert; Carlton M. Newton; and Donald F. Denns. Economc Relatonshps Between Parcel Characterstcs and Prce n the Market for Vermont Forestland. Forest Scence. 37(4)(1991): U.S. Census Bureau Census. Internet ste: (Accessed June 2003). U.S. Census Bureau Census. Internet ste: (Accessed June 2003). 14

17 Table 1: Descrpton of Data for the Hedonc Prce Model Varable Descrpton Expected effect on prce Data source(s) PRICE Parcel sale prce per acre N/A Kletke and Lews (2002) ACRES Sze of the parcel n acres - Kletke and Lews (2002) OPEN Percentage of parcel not forested (open land) - Kletke and Lews (2002) TmProd Expected annual per acre tmber producton on tmber sols (ft 3 ) + Kletke and Lews (2002) FRONT Dummy varable whether parcel fronts on a road + Wood and Fnchum (2003) DstHwy The lnear dstance to the nearest major roadway or hghway (mles) - Wood and Fnchum (2003) DstCty Dstance n road mles to the nearest cty wth a populaton > 2,000 - Wood and Fnchum (2003) PopGro Populaton growth of the nearest cty wth populaton > 2,000 + US Census 1990 and 2000 TranCost The annualzed cost per acre to delver tmber to market - Multple DstNat Dstance n road mles to the nearest natural resource attracton - Wood and Fnchum (2003) lnprce Log of PRICE N/A Kletke and Lews (2002) lnacres Log of Acres - Kletke and Lews (2002)

18 Table 2: Summary Statstcs of the Varables for the Hedonc Forestland Prce Model (McCurtan County) Varable Unt Mean Mnmum Maxmum Standard Number of Devaton Observatons lnprce $ PRICE $ 1, , , lnacres acres ACRES acres DstCty mles DstHwy mles TmProd ft 3 /acre FRONT proporton OPEN % Table 3: Summary Statstcs of the Varables for the Hedonc Forestland Prce Model (Pushmataha County) Varable Unt Mean Mnmum Maxmum Standard Number of Devaton Observatons LPrce $ PRICE $ , lnacres acres ACRES acres DstCty mles DstHwy mles TmProd ft 3 /acre FRONT proporton OPEN %

19 Table 4: Regresson Results for the Hedonc Forestland Prcng Model (McCurtan County) Independent Varable Coeffcent Standard Devaton T-value Prob > t Percentage Effect 1 Margnal Implct Prce 2 lnacres DstCty DstHwy TmProd FRONT OPEN Intercept R-squared Adjusted R-squared F-statstc Number of observatons: 45 Predcted prce ($) per acre: (Based on Mean Values) Mean parcel sze (acres): For B 1, (e B1/sze -1)*100 provdes the percentage change n PRICE for a unt change n ACRES. For B, where >1, (e B - 1)*100 provdes the percentage change n PRICE for a unt change n any sngle X. 2 The margnal mplct prce s the estmated percentage change tmes the predcted prce per acre, and s equvalent to the partal dervatve.

20 Table 5: Regresson Results for the Hedonc Forestland Prcng Model (Pushmataha County) Independent Varable Coeffcent Standard Devaton T-value Prob > t Percentage Effect 1 Margnal Implct Prce 2 lnacres DstCty DstHwy TmProd FRONT OPEN Intercept R-squared Adjusted R-squared F-statstc Number of observatons: 36 Predcted prce ($) per acre: (Based on Mean Values) Mean parcel sze (acres): For B 1, (e B1/sze -1)*100 provdes the percentage change n PRICE for a unt change n ACRES. For B, where >1, (e B - 1)*100 provdes the percentage change n PRICE for a unt change n any sngle X. 2 The margnal mplct prce s the estmated percentage change tmes the predcted prce per acre, and s equvalent to the partal dervatve.