Translating invasive species science into policy

Kimberly Burnett, University of Hawaii MAX  0  

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Outline

All Miconia photo credits: K. Burnett, near Hana Coqui sushi photo credit: NWRC Hilo, HI • Miconia – how much damage? Depends on policy. – Working with nonmarket values.

• Coqui frogs – damage to property values (no policy analysis). – Market values.

•

My hope

: elucidate the VALUE of good scientific data in economic modeling.

How fast does it grow?

g(n)

600000 500000 400000 300000 200000 100000 0 0 2000000 4000000 6000000 8000000

n

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bn

  1 

n K

  Where K = 100 trees per acre above 1800 mm/yr rainfall line, b = 30%

Island

Kauai Oahu Maui Hawaii Molokai

K

15,849,057 8,713,551 14,133,791 78,216,124 3,087,479

Where is it? Tells us potential damage

The value of losing birds & water

mgd lost recharge (annual)

Island

Kauai Oahu Maui Big Island

# birds

22 13 17 20 Molokai 11

Low

3.2

3.2

3.7

18.0

N/A

High

3.9

3.9

4.6

22.0

N/A Total annual damages by island (millions)

Low

$91.6

$61.4

$77.8

$169.0

$36.9

Medium

$154.0

$98.5

$126.0

$225.0

$68.2

High

$234.0

$145.0

$187.0

$297.0

$108.0

Per tree damage

Island

Medium annual damages/K Kauai $9.74

Oahu Maui Hawaii Molokai $11.30

$8.93

$2.88

$22.09

Cost of control?

Island

Kauai Oahu Maui Big Island Molokai

Search Constant ($1000*acre) Search Coefficient (



) Treatment Coefficient

$158,490,570 $87,135,510 1.6095

1.6258

$141,337,910 $782,161,240 $30,874,790 1.6089

1.6028

1.6

$13.39

   $1, 000* potential acres

n

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Current population?

Island

Kauai Oahu Maui Hawaii Molokai

n

0 1,540 6,890 111,050 315,000 0

Optimal population

Island

Kauai Oahu Maui Hawaii Molokai

n

0 1,540 6,890 111,050 315,000 0

n

* 9,171 5,495 8,901 39,937 0

Policy comparisons

Island Policy

Do nothing Remain at current population forever Status quo spending Optimal policy of population reduction and maintenance Oahu $3.08 b $10.5 m $16.9 m $10.4 m Maui $4.6 b $73.5 m $51.7 m $17.2 m

Recap

• Population reduction optimal for most islands.

• For Oahu, close to the optimal population (just above). Spend more today to reduce population, then can spend less every year to keep it there (cut the growth every year). Strategy saves on future damages.

• Better data => better understanding of growth/cost/damage functions => better model of response of population to spending => better policy => less damage. • Difficulty with nonmarket valuation (true value of endangered birds, etc.).

Falling property prices?

Hedonic pricing theory

• Wish to explain determinants of total property price • Some things add to price, others subtract –

Structural

• Number of rooms, number of bathrooms, square footage (+) • Acreage (+) –

Neighborhood/Accessibility

• Proximity to public transportation, school districts, other amenities (+/–) • Zoning (+/–) –

Environmental

• Presence of coqui ( • Elevation (+) –

???)

–

Financial

• Mortgage rates (–) • Buyer in HI (–) • Derive implicit value of each characteristic from explicit price of property using multiple regression analysis

Study site and data

• 50,033 real estate transactions on Big Island, 1995-2005 • 9 main districts (see map) divided into 10 sub-districts each to control for neighborhood characteristics • SFLA to represent structure • Frog complaints registered to NWRC Hilo, 1997-2001 • Use GIS to identify property transactions occurring after complaint, within 500m and 800m of frog complaints • Financial variables – Prices deflated using West Urban CPI – 30 year mortgage rates from Federal Reserve – Buyer residing in HI used to control for information effects

Outlier, excluded (over 100,000 ac)

Percentage of transactions with frog complaints prior to sale Region

Puna S Hilo N Hilo Hamakua N Kohala S Kohala N Kona S Kona Kau

Average Price

$26,555 $126,141 $283,383 $154,732 $202,599 $409,796 $334,393 $773,204 $25,179

Frogs 500m

19.3% 5.7% 7.0% 10.0% 33.7% 37.3% 13.2% 23.5% 15.1%

Frogs 800m

34.1% 23.2% 7.0% 25.5% 42.9% 48.4% 33.9% 40.6% 23.8%

Average Acres

2.6

10.8

18.2

38.2

10.4

6.8

2.9

21.7

8.7

N. Obs.

24,019 4,443 633 928 1,758 3,479 6,733 2,064 5,981

Puna Close-up Frogs within 500 m Frogs within 800 m Transactions

Impact on Property Price

Variable Frogs SFLA Assessed Land Val Acres Mortgage rate (monthly) Buyer in HI Constant Number of observations Adj R 2 Frogs 500m -135,957*** (19327) 67*** (4) 0.5*** (0.2) 471** (190) -25,494*** (4043) -24,981*** (9470) 212,062*** (28833) 50,033 0.2

Frogs 800m -6,816 (7845) 64*** (4) 0.5*** (0.2) 474** (191) -17,237*** (3917) -24,969*** (9479) 139,301*** (27384) 50,033 0.2

***,** indicate statistical significance at 99% and 95% confidence respectively Huber-White Robust Standard errors in parentheses.

Recap

• Presence of frogs have a negative impact on property value • Tells us nothing about optimal policy (don’t know the response of population to spending) • Need to build model

Directions for future research

• Miconia: – Better data on: current number of trees on each island, growth, costs, locations • Coqui: – Real estate analysis: increase years of BI data, add Maui data – Calculate lost profits to horticultural industry from • Reduced revenues from lost sales if infested • Increased costs from removing frogs for certification – Model the increase in potential viability of brown treesnake and accompanying increase in potential damages (biodiversity loss, power supply and medical expenses) due to coqui prey base

Acknowledgements

Special thanks to Earl Campbell, Mindy Wilkinson, and Christy Martin for answering zillions of questions!