Lexington-Fayette County as a Study Area for Examining Urban Growth Management Policies

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


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


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%





Island, NY--NJ--CT--PA 21,199,865 19,549,649 1,650,216 8.4%


CA 16,373,645 14,531,529 1,842,116 12.7%





PA--NJ--DE--MD 6,188,463 5,892,937 295,526 5.0%


ME--CT 5,819,100 5,455,403 363,697 6.7%





86 Lexington, KY 479,198 405,936 73,262 18.0%

263 Missoula, MT 95,802 78,687 17,115 21.8%





Island, NY--NJ--CT--PA 21,199,865 19,549,649 1,650,216 8.4%


CA 16,373,645 14,531,529 1,842,116 12.7%





PA--NJ--DE--MD 6,188,463 5,892,937 295,526 5.0%


ME--CT 5,819,100 5,455,403 363,697 6.7%





86 Lexington, KY 479,198 405,936 73,262 18.0%

263 Missoula, MT 95,802 78,687 17,115 21.8%





Island, NY--NJ--CT--PA 21,199,865 19,549,649 1,650,216 8.4%


CA 16,373,645 14,531,529 1,842,116 12.7%





PA--NJ--DE--MD 6,188,463 5,892,937 295,526 5.0%


ME--CT 5,819,100 5,455,403 363,697 6.7%





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



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%


50 Missoula, MT 95,802 78,687 17,115 21.8%

74 Lexington, KY 479,198 405,936 73,262 18.0%




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%




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%


50 Missoula, MT 95,802 78,687 17,115 21.8%

74 Lexington, KY 479,198 405,936 73,262 18.0%




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%




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%




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%




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

http://www.brookings.edu/es/urban/publications/growthmanagexsum.htm



http://escholarship.org/uc/item/90m9g90w

http://www.casa.ucl.ac.uk/working_papers/paper28.pdf

http://www.nationalatlas.gov/atlasftp.html

http://www.nationalatlas.gov/atlasftp.html

http://www.census.gov/population/www/cen2000/briefs/phc-t3/index.html

http://www.census.gov/population/www/cen2000/briefs/phc-t3/index.html

http://www.lexingtonky.gov/index.aspx?page=333

http://www.lexingtonky.gov/index.aspx?page=333

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Lexington-Fayette County as a Study Area for Examining Urban Growth Management Policies