L EXINGTON -F AYETTE C OUNTY AS A S TUDY A REA FOR E XAMINING U RBAN G ROWTH M ANAGEMENT P OLICIES...
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Transcript of L EXINGTON -F AYETTE C OUNTY AS A S TUDY A REA FOR E XAMINING U RBAN G ROWTH M ANAGEMENT P OLICIES...
LEXINGTON-FAYETTE COUNTYAS A STUDY AREA FOREXAMINING URBAN GROWTH MANAGEMENT POLICIES
Meaghan Mroz-BarrettFaculty Advisor: Dr. Brian Lee
University of Kentucky
Department of Landscape Architecture
OVERVIEW Introduction
Urban Growth Management Policies Urban Growth Boundaries
State of Current Research Sample Papers Current Questions and Issues
How does Lexington Fit? Location History Size Growth Rate
Findings Procedure Affect on Housing Price
Conclusions
INTRODUCTION:BACKGROUND
URBAN GROWTH MANAGEMENT POLICIES Rules that govern:
When When How
Variety Density Limits Building Standards Cost Shifting Land Withdrawal Direct or Indirect Growth
Controls (adapted from Quigley, J.M. and
Rosenthal, L.A. (2004))
URBAN GROWTH BOUNDARIES Type of Urban Growth
Management Policy Direct Growth Control Delineates Urban from Rural
Development in Urban Very Low Density in Rural
Often used with other policies
Critics often argue that this type of policy raises housing prices due to a reduction in the available supply
Nelson, et al, 2002
Bengston, et al, 2004
Landis, 2006
Jun, 2004, 2006Ihlanfeldt, 2007
Nelson, et al, 2002
Bengston, et al, 2004
Landis, 2006
Jun, 2004, 2006
Ihlanfeldt, 2007
Nelson, et al, 2002Bengston, et al, 2004
Landis, 2006
Jun, 2004, 2006
Ihlanfeldt, 2007
Empirical Study Land Use Regulation’s
affects on Housing Price Examined Florida
municipalities due to range of policies
Found Regulation: Increases housing price Decreases land prices Increases house size
Nelson, et al, 2002
Bengston, et al, 2004Landis, 2006
Jun, 2004, 2006
Ihlanfeldt, 2007
Nelson, et al, 2002
Bengston, et al, 2004
Landis, 2006Jun, 2004, 2006
Ihlanfeldt, 2007
STATE OF CURRENT RESEARCH:SAMPLE PAPERS
Literature Review Examined housing price
effects Supply and demand too
simple to determine housing price affects
Housing price is determined by market demands not land constraints
Literature Review Examined policies and
implementation Found:
Lack of empirical studies Importance of
administration Need for complimentary
policies Coordination is a key
component in effectiveness
Significance of stakeholder participation
Re-examination of Growth Policies
Used California cities due to their diversity of Growth Management methods
Found: Growth management
can limit population growth
If they constrain growth to below their demand, housing prices are affected
Increase the chance of infill development
Empirical Studies on Portland, OR’s Urban Growth Boundary
Used a regression model to test affects on housing prices
Determined that housing price was affected by:
Household Income Vacancy Rates Density Professional Workers Households with
Children Commute Time
STATE OF CURRENT RESEARCH:ISSUES
Majority of studies done in Portland or in California
Not representative of most cities in terms of: Size Growth Rate State Mandated Growth
Management
Portland is further complicated by a state border
California has a wide range of unique constraints Earthquakes Wildfires Habitat
Current Studies Lack: Empirical Studies Standard Protocol Clear Consensus
Complications to Research: Lack of counterfactual
knowledge Lag time in affects Separation of effects of
overlapping policies Unclear policy goals
HOW DOES LEXINGTON-FAYETTE FIT:LOCATION
HOW DOES LEXINGTON-FAYETTE FIT:HISTORY
Source: LFUCG Planning Department, http://www.lexingtonky.gov/index.aspx?page=328
HOW DOES LEXINGTON-FAYETTE FIT:SIZE
Rank Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1New York--Northern New Jersey--Long
Island, NY--NJ--CT--PA 21,199,865 19,549,649 1,650,216 8.4%
2Los Angeles--Riverside--Orange County,
CA 16,373,645 14,531,529 1,842,116 12.7%
3 Chicago--Gary--Kenosha, IL--IN--WI 9,157,540 8,239,820 917,720 11.1%
4 Washington--Baltimore, DC--MD--VA--WV 7,608,070 6,727,050 881,020 13.1%
5 San Francisco--Oakland--San Jose, CA 7,039,362 6,253,311 786,051 12.6%
6Philadelphia--Wilmington--Atlantic City,
PA--NJ--DE--MD 6,188,463 5,892,937 295,526 5.0%
7Boston--Worcester--Lawrence, MA--NH--
ME--CT 5,819,100 5,455,403 363,697 6.7%
8 Detroit--Ann Arbor--Flint, MI 5,456,428 5,187,171 269,257 5.2%
9 Dallas--Fort Worth, TX 5,221,801 4,037,282 1,184,519 29.3%
10 Houston--Galveston--Brazoria, TX 4,669,571 3,731,131 938,440 25.2%
23 Portland--Salem, OR--WA 2,265,223 1,793,476 471,747 26.3%
86 Lexington, KY 479,198 405,936 73,262 18.0%
263 Missoula, MT 95,802 78,687 17,115 21.8%
Rank Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1New York--Northern New Jersey--Long
Island, NY--NJ--CT--PA 21,199,865 19,549,649 1,650,216 8.4%
2Los Angeles--Riverside--Orange County,
CA 16,373,645 14,531,529 1,842,116 12.7%
3 Chicago--Gary--Kenosha, IL--IN--WI 9,157,540 8,239,820 917,720 11.1%
4 Washington--Baltimore, DC--MD--VA--WV 7,608,070 6,727,050 881,020 13.1%
5 San Francisco--Oakland--San Jose, CA 7,039,362 6,253,311 786,051 12.6%
6Philadelphia--Wilmington--Atlantic City,
PA--NJ--DE--MD 6,188,463 5,892,937 295,526 5.0%
7Boston--Worcester--Lawrence, MA--NH--
ME--CT 5,819,100 5,455,403 363,697 6.7%
8 Detroit--Ann Arbor--Flint, MI 5,456,428 5,187,171 269,257 5.2%
9 Dallas--Fort Worth, TX 5,221,801 4,037,282 1,184,519 29.3%
10 Houston--Galveston--Brazoria, TX 4,669,571 3,731,131 938,440 25.2%
23 Portland--Salem, OR--WA 2,265,223 1,793,476 471,747 26.3%
86 Lexington, KY 479,198 405,936 73,262 18.0%
263 Missoula, MT 95,802 78,687 17,115 21.8%
Rank Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1New York--Northern New Jersey--Long
Island, NY--NJ--CT--PA 21,199,865 19,549,649 1,650,216 8.4%
2Los Angeles--Riverside--Orange County,
CA 16,373,645 14,531,529 1,842,116 12.7%
3 Chicago--Gary--Kenosha, IL--IN--WI 9,157,540 8,239,820 917,720 11.1%
4 Washington--Baltimore, DC--MD--VA--WV 7,608,070 6,727,050 881,020 13.1%
5 San Francisco--Oakland--San Jose, CA 7,039,362 6,253,311 786,051 12.6%
6Philadelphia--Wilmington--Atlantic City,
PA--NJ--DE--MD 6,188,463 5,892,937 295,526 5.0%
7Boston--Worcester--Lawrence, MA--NH--
ME--CT 5,819,100 5,455,403 363,697 6.7%
8 Detroit--Ann Arbor--Flint, MI 5,456,428 5,187,171 269,257 5.2%
9 Dallas--Fort Worth, TX 5,221,801 4,037,282 1,184,519 29.3%
10 Houston--Galveston--Brazoria, TX 4,669,571 3,731,131 938,440 25.2%
23 Portland--Salem, OR--WA 2,265,223 1,793,476 471,747 26.3%
86 Lexington, KY 479,198 405,936 73,262 18.0%
263 Missoula, MT 95,802 78,687 17,115 21.8%
Rank Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1New York--Northern New Jersey--Long
Island, NY--NJ--CT--PA 21,199,865 19,549,649 1,650,216 8.4%
2Los Angeles--Riverside--Orange County,
CA 16,373,645 14,531,529 1,842,116 12.7%
3 Chicago--Gary--Kenosha, IL--IN--WI 9,157,540 8,239,820 917,720 11.1%
4 Washington--Baltimore, DC--MD--VA--WV 7,608,070 6,727,050 881,020 13.1%
5 San Francisco--Oakland--San Jose, CA 7,039,362 6,253,311 786,051 12.6%
6Philadelphia--Wilmington--Atlantic City,
PA--NJ--DE--MD 6,188,463 5,892,937 295,526 5.0%
7Boston--Worcester--Lawrence, MA--NH--
ME--CT 5,819,100 5,455,403 363,697 6.7%
8 Detroit--Ann Arbor--Flint, MI 5,456,428 5,187,171 269,257 5.2%
9 Dallas--Fort Worth, TX 5,221,801 4,037,282 1,184,519 29.3%
10 Houston--Galveston--Brazoria, TX 4,669,571 3,731,131 938,440 25.2%
23 Portland--Salem, OR--WA 2,265,223 1,793,476 471,747 26.3%
86 Lexington, KY 479,198 405,936 73,262 18.0%
263 Missoula, MT 95,802 78,687 17,115 21.8%
HOW DOES LEXINGTON-FAYETTE FIT:SIZE
Source: 2000 Census Data File 3: http://www.census.gov/population/www/cen2000/briefs/phc-t3/index.html
Rank Metropolitan Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1 Las Vegas, NV--AZ 1,563,282 852,737 710,545 83.3%
2 Naples, FL 251,377 152,099 99,278 65.3%
3 Yuma, AZ 160,026 106,895 53,131 49.7%
4 McAllen--Edinburg--Mission, TX 569,463 383,545 185,918 48.5%
5 Austin--San Marcos, TX 1,249,763 846,227 403,536 47.7%
6 Fayetteville--Springdale--Rogers, AR 311,121 210,908 100,213 47.5%
7 Boise City, ID 432,345 295,851 136,494 46.1%
8 Phoenix--Mesa, AZ 3,251,876 2,238,480 1,013,396 45.3%
9 Laredo, TX 193,117 133,239 59,878 44.9%
10 Provo--Orem, UT 368,536 263,590 104,946 39.8%
33 Portland--Salem, OR--WA 2,265,223 1,793,476 471,747 26.3%
50 Missoula, MT 95,802 78,687 17,115 21.8%
74 Lexington, KY 479,198 405,936 73,262 18.0%
Rank Metropolitan Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1 Las Vegas, NV--AZ 1,563,282 852,737 710,545 83.3%
2 Naples, FL 251,377 152,099 99,278 65.3%
3 Yuma, AZ 160,026 106,895 53,131 49.7%
4 McAllen--Edinburg--Mission, TX 569,463 383,545 185,918 48.5%
5 Austin--San Marcos, TX 1,249,763 846,227 403,536 47.7%
6 Fayetteville--Springdale--Rogers, AR 311,121 210,908 100,213 47.5%
7 Boise City, ID 432,345 295,851 136,494 46.1%
8 Phoenix--Mesa, AZ 3,251,876 2,238,480 1,013,396 45.3%
9 Laredo, TX 193,117 133,239 59,878 44.9%
10 Provo--Orem, UT 368,536 263,590 104,946 39.8%
33 Portland--Salem, OR--WA 2,265,223 1,793,476 471,747 26.3%
50 Missoula, MT 95,802 78,687 17,115 21.8%
74 Lexington, KY 479,198 405,936 73,262 18.0%
Rank Metropolitan Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1 Las Vegas, NV--AZ 1,563,282 852,737 710,545 83.3%
2 Naples, FL 251,377 152,099 99,278 65.3%
3 Yuma, AZ 160,026 106,895 53,131 49.7%
4 McAllen--Edinburg--Mission, TX 569,463 383,545 185,918 48.5%
5 Austin--San Marcos, TX 1,249,763 846,227 403,536 47.7%
6 Fayetteville--Springdale--Rogers, AR 311,121 210,908 100,213 47.5%
7 Boise City, ID 432,345 295,851 136,494 46.1%
8 Phoenix--Mesa, AZ 3,251,876 2,238,480 1,013,396 45.3%
9 Laredo, TX 193,117 133,239 59,878 44.9%
10 Provo--Orem, UT 368,536 263,590 104,946 39.8%
33 Portland--Salem, OR—WA 2,265,223 1,793,476 471,747 26.3%
50 Missoula, MT 95,802 78,687 17,115 21.8%
74 Lexington, KY 479,198 405,936 73,262 18.0%
Rank Metropolitan Area NameCensus Population
Change, 1990 to 2000
April 1, 2000 April 1, 1990 Number Percent
1 Las Vegas, NV--AZ 1,563,282 852,737 710,545 83.3%
2 Naples, FL 251,377 152,099 99,278 65.3%
3 Yuma, AZ 160,026 106,895 53,131 49.7%
4 McAllen--Edinburg--Mission, TX 569,463 383,545 185,918 48.5%
5 Austin--San Marcos, TX 1,249,763 846,227 403,536 47.7%
6 Fayetteville--Springdale--Rogers, AR 311,121 210,908 100,213 47.5%
7 Boise City, ID 432,345 295,851 136,494 46.1%
8 Phoenix--Mesa, AZ 3,251,876 2,238,480 1,013,396 45.3%
9 Laredo, TX 193,117 133,239 59,878 44.9%
10 Provo--Orem, UT 368,536 263,590 104,946 39.8%
33 Portland--Salem, OR—WA 2,265,223 1,793,476 471,747 26.3%
50 Missoula, MT 95,802 78,687 17,115 21.8%
74 Lexington, KY 479,198 405,936 73,262 18.0%
HOW DOES LEXINGTON-FAYETTE FIT:GROWTH RATE
Source: 2000 Census Data File 3: http://www.census.gov/population/www/cen2000/briefs/phc-t3/index.html
FINDINGS:PROCEDURE Duplicate Jun, 2006 procedure from Portland, Oregon in Lexington, KY Regression model using a hedonic price framework Housing Price as a function of:
Structure Housing Market Accessibility
Model predicts independent variable (Housing Price) from a series of independent variables Structural Variables
Number of bedrooms Percentage owner occupied
Housing Market Variables Median Household Income Vacancy Rate Housing Density
Sociodemographics Percentage Managerial or Professional Workers Percentage Households with Children Mean Commuting Time
Dummy Variables Urban Growth Boundary Three County Specific (Different in this study)
FINDINGS:RESULTS Similar to Jun’s findings:
Urban Growth Boundary had no affect on housing price Main difference is commute time had no affect on housing
price Housing price is positively affected by increased:
Bedrooms Owner Occupied Units Increased Median Income Managerial and Professional Workers
Housing price is negatively affected by increased: Vacancy Rates Density Children
No Affect: Commute Time Urban Growth Boundary
CONCLUSIONS:FURTHER RESEARCH Urban Growth Boundaries do not affect housing price Housing price is a function of a more complex set of
variables than simple supply and demand Lexington can help fill in the research gaps for Urban
Growth Boundaries
Further studies should be conducted to gain a wider understanding of the affects of Urban Growth Boundaries in different areas and situations.
SOURCES: Bengston, David N., Jennifer O. Fletcher, and Kristen C. Nelson. 2004. Public Policies for
Managing Urban Growth and Protecting Open Space: Policy Instruments and Lessons Learned in the United States. Landscaper and Urban Planning 69(2-3):271–286.
Gabaix, X. (1999) Zipf’s law for cities: An explanation. The Quarterly Journal of Economics, 3, 739-767.
Ihlanfeldt, K.R. (2007) The effect of land use regulation on housing and land prices. Journal of Urban Economics, 61(3), 420-435.
Nelson, Arthur C., Rolf Pendall, Casey J. Dawkins, and Gerrit J. Knaap. 2002. The Link Between Growth Management and Housing Affordability: The Academic Evidence. A Discussion Paper Prepared for The Brookings Institution - Center on Urban and Metropolitan Policy. Retrieved March 31, 2009 from http://www.brookings.edu/es/urban/publications/growthmanagexsum.htm
Quigley, J.M., and Rosenthal, L.A. (2004). The Effects of Land-Use Regulation on the Price of Housing: What Do We Know? What Can We Learn?. UC Berkeley: Berkeley Program on Housing and Urban Policy. Retrieved March 14, 2010, from: http://escholarship.org/uc/item/90m9g90w
Torrens, P. (2000). CASA Working Paper 28: How cellular models of urban systems work. Centre for Advanced Spatial Analysis, University College London — Retrieved January 11, 2010, from http://www.casa.ucl.ac.uk/working_papers/paper28.pdf
U.S. Department of the Interior. United States National Atlas. – Retrieved April 15, 2010 from http://www.nationalatlas.gov/atlasftp.html
U.S. Census Bureau. Ranking Tables for Metropolitan Areas. – Retrieved April 15, 2010 from http://www.census.gov/population/www/cen2000/briefs/phc-t3/index.html
Lexington—Fayette Urban County Government Department of Planning. 2007 Comprehensive Plan. Retrieved April 15, 2010 from http://www.lexingtonky.gov/index.aspx?page=333
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