Determinants of Land Use Change on the Southern

Cumberland Plateau

Matthew Lane, Kevin Willis

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Robert Gottfried, Douglass Williams

R - 8 2 9 8 0 2 0 1 -

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� drivers of this land use change in our study region

The purposes of our STAR grant

�

impacts �

to understand the socioeconomic

to explore possible future trends in land use and their potential environmental

to examine the effects of possible policy responses

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� l

� ili

� l

;

� icy responses.

Deve oping a spatial socioeconomic model of change in land use for the period 1980-2003;

We are doing this by:

Integrating this model w th our research on bird diversity as a function of land cover and a water qua ty proxy to understand the socioeconomic processes bringing about environmental change in the region;

Using this understanding and the model to assess potentia future impacts on birds and water quality of likely socioeconomic events or trends

Exploring the impacts of possible pol

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Overview of presentation

• • • • • • •

•

Focus on homebuilding and LULC change Review of the literature Theoretical framework The case study area The data Logit analysis of housing Does homebuilding lead to conversion of native forest - Discussion Conclusion

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•

•

•

• landowner strategy

•

Urban areas are affecting the Plateau via:

Exurban expansion/permanent homes

Second home growth

Increasing demand for paper

These interact with changes in corporate

Political process

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Why Are Homes Being Built?

• retirement and second homes

•

•

• Crossville

•

•

Growing demand by baby boomers for

Growing population in the Southeast

Florida rebound

Escaping Chattanooga, McMinnville,

Stock market of the 90’s

Lower interest rates

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Review of literature

• – –

explicit

• –

•

•

Noneconomic models can incorporate economic variables fail to make underlying socioeconomic processes

Economic models lack of fine scale data led to larger scale analyses inappropriate for supporting many ecological models

Share of land uses at some aggregate level such as county or state; e.g, Alig and Healy, 1987; Hardie and Parks, 1997; Hardie, et al., 2000; Miller and Platinga, 1999; Platinga,1996; Stavins and Jaffe, 1990; Wu and Segerson, 1995

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•

•

•

Finer scale economic analyses

Location of deforestation – parcel level (Cropper, Puri and Griffiths, 2001)

Land cover transitions in two watersheds – cell-based (Turner, Wear and Flamm, 1996)

Probability of land use change for San Francisco Bay and Sacramento – cell-based (Landis, 1995 and Landis and Zhang, 1998)

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Most to this study

•

residential

•

relevant

Probability of parcel conversion in Patuxent watershed near Washington, DC and Baltimore for one period of time (Bockstael. 1996; Costanza et al, 1996; Geogehegan, Wainger and Bockstael, 1997)

- Uses probit to obtain transition probability of conversion to

- Cannot provide overall quantity of parcels converted, only their location

Optimal timing of land use change of parcels in Patuxent watershed for one period (Irwin and Bockstael, 2000; Irwin and Bockstael, 2002)

-Uses hazard model that provides overall quantity of conversion - Cannot examine economic drivers of conversion over time

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This presentation:

• of parcels ten acres and over (4,752 parcels)

• the first home on a parcel

• • •

characteristics, and economic drivers

Uses logit to examine residential conversion

Residential conversion defined by building

Examines a long period, 1980-2003 Large rural study area: 616,000 acres Combines site characteristics, owner

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Theoretical Framework

Ctij t t , Ot, It, S*et t , S*Ot, S*It t t, 97*Ot

where: et c NtOtIt

Land moves to its most highly valued use Land market is imperfect – lack of information on new land uses

In reduced form:

= m (e , c, N , S*c, S*N , 97*e , 97*c, 97*N, 97*I),

= exogenous economic variables at time t = parcel characteristics = land use in time t of neighboring parcels = owner characteristics in time t = cost of inputs to conversion process in time t

S = large parcel (0,1) 97= period 1997-2003 (0,1)

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Effect of neighboring parcels

•

• changing land market

Externalities, both positive and negative

Information flows in an imperfect and

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The Case Study Area

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Geographic characteristics:

� plateau surface of 7 southern counties of central Tennessee

� 616,000 acres

�

� i

�

� l

�

contains some of the largest remaining privately owned, contiguous temperate deciduous forest in North America

Ecological characteristics:

predominantly oak and hickory – mast of mature oak canopy is a keystone resource w thin the food web of the ecosystem

headwaters of some of the most biologically diverse freshwater stream systems in the world

important neotropicamigratory bird habitat

one of the most diverse woody plant ecosystems in the eastern United States

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�

Social

� l

�

� l

land is largely privately held

characteristics:

relatively high levels of unemp oyment and poverty

inmigration to study area counties has exceeded outmigration since 1983-84

ow levels of education

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Land Cover, 1980-2003All Plateau

Land Cover 1980-2003

600000

500000

400000

300000

200000

100000

0

i

Logged

Pi l i

Mi ine

Ac

res

Nat ve Forest

Grass

ne P antat on

xed P

1980 1990 1997 2000 2003

Year

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1980 2003

Within our study area, 20% of the forest being managed for hardwoods was converted to other uses.

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19

0

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Sal

Sales of Raw Land by Sales Size

2000

4000

6000

8000

10000

12000

14000

16000

18000

Year

Acr

es Sales less than 500 acres

es 500 acres of more

Sales of Raw Land by Sales Size

20

lin

Houses per year in each time period

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

1980-1989 1990-1996 1997-1999 2000-2004

Periods

Ra

te

Bledsoe Frank Grundy Marion Sequatchie Van Buren Warren

Houses per year in each time period

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Homebuilding Over Time by 2000 Owner TypeHomebuilding over time by 2000 owner

800

H o

me

s b

uilt

pe

r y

ea

r 700

600

500

400

300

200

100

0

ls

Absentees

Loca

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

22

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Percentage of new homes owned by absentees

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Percent of New Homes Owned by Absentees

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Pe

rce

nt

0

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Sal

Sales of Raw Land by Sales Size

2000

4000

6000

8000

10000

12000

14000

16000

18000

Year

Ac

res Sales less than 500 acres

es 500 acres of more

Sales of Raw Land by Sales Size

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19781980

19821984

19861988

19901992

19941996

19982000

2002

I

AVERAGE RAW LAND PRICE BY SALE SIZE

0.0000

200.0000

400.0000

600.0000

800.0000

1000.0000

1200.0000

1400.0000

1600.0000

1800.0000

YEAR

A V

ER

A G

E R

E A

L P

R C

E P

ER

A C

R E

200+Acres

0-199 Acres

All Raw Sales

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Annual Rate of Change of Land Cover, All plateau

0

Ag/

l

-15000

-10000

-5000

5000

10000

1980 to 1990 1990 to 1997 1997 to 2000 2000 to 2003

Periods

Ac

res

pe

r y

ea

r

Native Forest

Logged

res

Pine P antation

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Annual Rates of Conversion Parcels>10 acres

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1997-00

i

f i

f /

Pi

1980-90 1990-97 2000-03

Per od

A c

r es

p e

r ye

ar

Native orest to p ne

Native orest to ag res

ne to ag/res

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Native Forest Conversion to:

35% Pine Plantation

42% Agricultural / Residential

22% Cleared / Unclassified

1980 - 2003

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Summary

• while continuing (despite the pine beetle!), has slowed

• grass) has surpassed pine and increased rapidly

• are on the landscape

The conversion of native forest to pine,

Conversion to “ag/res” (predominantly

Why? May need to know who big players

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0

20

40

60

80

1 2 3 4 5 6 7 8 9

LAND DISTRIBUTION BY DECILES

8.0444

4.3643 2.8504 1.9694 1.4239 1.0924 0.8441 0.6740 0.5145

78.2227

100

10

DECILE

% O

F T

OT

AL

LA

ND

HE

LD

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TN

38%

LAND DISTRIBUTION BY OWNER LOCATION (INCLUDES GOVERNMENT)

19%

LOCAL 43%

OUT SIDE

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3%

7%

DISTRIBUTION OF ACRES BY OWNER TYPE

Other

Individual 53%

Gov ernment

Timber 18%

Business 19%

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0

20000

40000

60000

80000

100000

120000

Bowater In

c

State

of TN

USXCorp

.

Huber JM

Mea

dConta

inerb

oard

Inc

Souther

nPin

ePlan

tatio

ns

Wern

erLum

berCo

Flatb

ushLLC

Holland

War

e

Univer

sity

of the

South

Pine Pl i l

Top 10 Forest owners in SAA study area

Forest antat on Logged/C eared Other

Acres

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Land Company Divestments, 1997-2004 (15% of landscape) Possible Bowater Divestments in Blue (13% of landscape)

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The Data

• 1980, 1990, 1997, 2000 and 2003

•

• • •

Remotely sensed land use/land cover data for

Tax map for 2000/2001 for parcels 10 acres or greater; “holes” in tax map Associated tax data for 1999-2003 Large sample of sales data for 1980-2003 Sales data for all transactions of timber companies and other holdings of 1000 or more acres from 2000-2004, including Huber sales since 1998

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Tax map and “holes”

The Logit Analysis

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Variables in the Model

? ?

-

/+

+ -? -? ? +

+ + + + + +

? +

j ?

+ --+ -

--

Variable Description Expected Sign HBUILT House built on parcel during the year Na “COUNTY” Dummy for counties BUFFER%MR Percent of land within a radius of 0.1 or 1km in

mixed pine or reservoir BUFFER%PINE Percent of land within a radius of 0.1 or 1km in

pine plantation BUFFER%AR Percent of land within a radius of 0.1 or 1km in

agriculture or residential (grassy shrub) BLUFF_FRONTAGE Parcel located on bluff (0,1) SIZELT90 Size of parcel less than 90 acres (acres) PERC_NF % of parcel in native forest PERC_PP Percentage of parcel in pine plantation PERC_OT Percentage of parcel in grass (“other”) PAVED Parcel on a paved road NEIGHB_AVG Average value of houses on parcels within 1km

of a parcel WATER Parcel has public water (0,1) SEWER Parcel has public sewer (0,1) GAS Parcel has natural gas (0,1) ELEC Parcel has electricity (0,1) NEARBY_HOUSES Number of houses on parcels within 1km DISTWHOLES Distance to nearest parcel with a house, counting

areas with parcels <10 acres as having houses LNDISTCITY Log of distance to nearest city (meters) ADJ_PROT_AREA Parcel adjacent to a protect area (0,1) DIST_ROAD Straight line distance to nearest ma or road

(meters) BUSOWNER Parcel owner is a business (0,1) TIMBEROWNER Parcel owner is a timber company (0,1) MORTGAGE_RATE Mortgage rate on a home POP_SE_CHANGE Change in population in SE United States MS_CONST_COST Marshall Swift Construction Cost Index (deflated

by CPI) WILSHIRE Wilshire stock index UNEMPLOYRATE_TN Unemployment rate in Tennessee Interaction terms *GE90: parcels 90 acres or more

*1997: for period 1997 or later

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l it = =

i2 = = 0.0786

l icient i

i

i

Logit Results Margina effects from log Number of obs 83603

chi2(47) 678.58 Prob > ch 0.0000

Log Likelihood = -3722.6869 Pseudo R2

Dependent Variable: HBUILT Independent Variab es Coeff Std. Error P>z Independent Variables Coeff cient Std. Error P>z

Franklin County 0.000121 0.000864 0.889 NEIGHB_AVG 0.000000 0.000000 0.051 Grundy County -0.000515 0.000640 0.421 WATER 0.001030 0.000423 0.015 Mar on County -0.000778 0.000758 0.305 SEWER 0.001008 0.000942 0.285 Sequatchie County 0.001233 0.000719 0.086 SEWER1997 0.006281 0.001364 0.000 Van Buren -0.001002 0.000663 0.131 GAS 0.002663 0.001150 0.021 Warren County -0.000556 0.001293 0.667 ELEC 0.003151 0.000491 0.000 Mar on1997 -0.005347 0.001219 0.000 ELECGE90 0.002413 0.000951 0.011 Warren1997 -0.005179 0.002912 0.075 NEARBY_HOUSES 0.000159 0.000030 0.000 BUFFER%PP, 0.1km -0.002569 0.003307 0.437 DISTWHOLES -0.000004 0.000001 0.000 BUFFER%AR, 0.1km -0.004524 0.001335 0.001 DISTWHOLESGE90 0.000004 0.000001 0.000 BUFFER%MR, 0.1km 0.009134 0.004756 0.055 LNDISTCITY 0.000082 0.000052 0.117 BLUFF_FRONTAGE 0.001218 0.000558 0.029 ADJ_PROT_AREA -0.001533 0.001070 0.152 BLUFF_FRONTAGEGE90 -0.002546 0.001270 0.045 DIST_ROAD 0.000002 0.000000 0.001 SIZELT90 0.003314 0.001030 0.001 DIST_ROADGE90 -0.000007 0.000002 0.001 PERC_PP 0.004418 0.003932 0.261 TIMBEROWNER -0.008699 0.004080 0.033 PERC_PP1997 -0.025573 0.011881 0.031 BUSOWNER -0.005015 0.001646 0.002 PERC_PPGE90 -0.037976 0.018229 0.037 BUSOWNER1997 0.004477 0.002094 0.033 PERC_OT 0.006117 0.001042 0.000 MORTGAGE_RATE -0.000027 0.000125 0.832 PERC_OT1997 -0.004692 0.001173 0.000 POP_SE_ 0.000000 0.000000 0.908 PERC_OTGE90 -0.008936 0.009235 0.333 MS_CONST_COST -0.002824 0.007049 0.689 PERC_PM -0.013242 0.011720 0.258 WILSHIRE 0.000000 0.000000 0.000 PERC_PM1997 0.004166 0.012936 0.747 UNEMPLOYRATE_TN -0.000215 0.000176 0.222 PERC_PMGE90 0.015426 0.011932 0.196 CONSTANT -0.027481 0.005116 0.000 PAVED -0.000868 0.000402 0.031

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Factors influencing probability of home construction Signifi

l

l l

l

cant variables with positive effect

BUFFER%MR, 0.1km (relative to native forest) BLUFF_FRONTAGE, SMALL PARCELS SIZELT90 PERC_AR>PERC_AR1997 (relative to native forest) NEIGHB_AVG WATER SEWER, after 1997 GAS ELEC, particular y after 1997 NEARBY_HOUSES LNDISTCITY DIST_ROAD, small parcels WILSHIRE

Significant variables with negative effect

BUFFER%AR, 0.1km >BUFFER%PP, 0.1KM (relative to native forest) BLUFF_FRONTAGE, LARGE PARCELS PERC_PP1997 PERC_PP, LARGE PARCELS PAVED DISTWHOLES for smal parcels DIST_ROAD, arge parcels TIMBEROWNER (less like y than business owner) BUSOWNER, particularly before 1997

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Homebuilding and Forest Conversion

• forest

•

•

• acres

All new homes on parcels<100 acres have

Average size 20 acres total of forest

Majority also have some grass/shrub

Average grass/shrub per parcel is about 10

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• 1990-2000 cleared native forest for grass/shrub

•

• 1997-2000 no home construction caused logging or grass/shrub

• not be causing conversion to grass/shrub. Either developers bought cleared land or sold it cleared to people who then built.

About 20% of home construction 1980-90 and

Average grass/shrubclearing was 9 and 13 acres in the two periods

CONCLUSION: Home construction itself may

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Anecdotal Evidence

• build

• and start over

•

•

• Buren County

Many people clear some land when they

People “don’t know what they have”, clear

People putting in hobby horse farms/farms

Cultural attitudes – Florida, NY, urban

Growth of grass for pasture and hay – Van

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Conclusions

•

•

•

• some of these hypotheses

Conversion of native forest to pine is slowing; may stop – Bowater’s agreement (?), pine beetle Divestment may be allowing pent up demand for pasture/hay to be supplied – may be the primary cause of conversion to grass/shrub Growing demand for second and retirement homes by baby boomers may cause some, but less, clearing Multinomial probit analysis will explore at least

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Conclusions and Issues

suggested by the theoretical framework •Homebuilding is following the pattern

•It appears to be due to permanent home growth (commuters and retirees) and second homes •Homebuilding may not be causing forest clearing as much as agricultural expansion •Problem of “splinters” needs to be resolved

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Any comments or questions?

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