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L EXINGTON -F AYETTE C OUNTY AS A S TUDY A REA FOR E XAMINING U RBAN G ROWTH M ANAGEMENT P OLICIES Meaghan Mroz-Barrett Faculty Advisor: Dr. Brian Lee.

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Presentation on theme: "L EXINGTON -F AYETTE C OUNTY AS A S TUDY A REA FOR E XAMINING U RBAN G ROWTH M ANAGEMENT P OLICIES Meaghan Mroz-Barrett Faculty Advisor: Dr. Brian Lee."— Presentation transcript:

1 L EXINGTON -F AYETTE C OUNTY AS A S TUDY A REA FOR E XAMINING U RBAN G ROWTH M ANAGEMENT P OLICIES Meaghan Mroz-Barrett Faculty Advisor: Dr. Brian Lee University of Kentucky Department of Landscape Architecture

2 O VERVIEW 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

3 I NTRODUCTION : B ACKGROUND URBAN GROWTH MANAGEMENT POLICIES Rules that govern: 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

4 Nelson, et al, 2002 Bengston, et al, 2004 Landis, 2006 Jun, 2004, 2006 Ihlanfeldt, 2007 Nelson, et al, 2002 Bengston, et al, 2004 Landis, 2006 Jun, 2004, 2006 Ihlanfeldt, 2007 Nelson, et al, 2002 Bengston, et al, 2004 Landis, 2006 Jun, 2004, 2006 Ihlanfeldt, 2007 Empirical Study Land Use Regulations 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, 2004 Landis, 2006 Jun, 2004, 2006 Ihlanfeldt, 2007 Nelson, et al, 2002 Bengston, et al, 2004 Landis, 2006 Jun, 2004, 2006 Ihlanfeldt, 2007 S TATE OF C URRENT R ESEARCH : S AMPLE P APERS 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, ORs 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

5 S TATE OF C URRENT R ESEARCH : I SSUES 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

6 H OW DOES L EXINGTON -F AYETTE F IT : L OCATION

7 H OW DOES L EXINGTON -F AYETTE F IT : H ISTORY Source: LFUCG Planning Department,

8 H OW DOES L EXINGTON -F AYETTE F IT : S IZE

9 RankArea Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1 New York--Northern New Jersey--Long Island, NY--NJ--CT--PA21,199,86519,549,6491,650,2168.4% 2Los Angeles--Riverside--Orange County, CA16,373,64514,531,5291,842, % 3Chicago--Gary--Kenosha, IL--IN--WI9,157,5408,239,820917, % 4Washington--Baltimore, DC--MD--VA--WV7,608,0706,727,050881, % 5San Francisco--Oakland--San Jose, CA7,039,3626,253,311786, % 6 Philadelphia--Wilmington--Atlantic City, PA-- NJ--DE--MD6,188,4635,892,937295,5265.0% 7 Boston--Worcester--Lawrence, MA--NH--ME-- CT5,819,1005,455,403363,6976.7% 8Detroit--Ann Arbor--Flint, MI5,456,4285,187,171269,2575.2% 9Dallas--Fort Worth, TX5,221,8014,037,2821,184, % 10Houston--Galveston--Brazoria, TX4,669,5713,731,131938, % 23Portland--Salem, OR--WA2,265,2231,793,476471, % 86Lexington, KY479,198405,93673, % 263Missoula, MT95,80278,68717, % RankArea Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1 New York--Northern New Jersey--Long Island, NY--NJ--CT--PA21,199,86519,549,6491,650,2168.4% 2Los Angeles--Riverside--Orange County, CA16,373,64514,531,5291,842, % 3Chicago--Gary--Kenosha, IL--IN--WI9,157,5408,239,820917, % 4Washington--Baltimore, DC--MD--VA--WV7,608,0706,727,050881, % 5San Francisco--Oakland--San Jose, CA7,039,3626,253,311786, % 6 Philadelphia--Wilmington--Atlantic City, PA-- NJ--DE--MD6,188,4635,892,937295,5265.0% 7 Boston--Worcester--Lawrence, MA--NH--ME-- CT5,819,1005,455,403363,6976.7% 8Detroit--Ann Arbor--Flint, MI5,456,4285,187,171269,2575.2% 9Dallas--Fort Worth, TX5,221,8014,037,2821,184, % 10Houston--Galveston--Brazoria, TX4,669,5713,731,131938, % 23Portland--Salem, OR--WA2,265,2231,793,476471, % 86Lexington, KY479,198405,93673, % 263Missoula, MT95,80278,68717, % RankArea Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1 New York--Northern New Jersey--Long Island, NY--NJ--CT--PA21,199,86519,549,6491,650,2168.4% 2Los Angeles--Riverside--Orange County, CA16,373,64514,531,5291,842, % 3Chicago--Gary--Kenosha, IL--IN--WI9,157,5408,239,820917, % 4Washington--Baltimore, DC--MD--VA--WV7,608,0706,727,050881, % 5San Francisco--Oakland--San Jose, CA7,039,3626,253,311786, % 6 Philadelphia--Wilmington--Atlantic City, PA-- NJ--DE--MD6,188,4635,892,937295,5265.0% 7 Boston--Worcester--Lawrence, MA--NH--ME-- CT5,819,1005,455,403363,6976.7% 8Detroit--Ann Arbor--Flint, MI5,456,4285,187,171269,2575.2% 9Dallas--Fort Worth, TX5,221,8014,037,2821,184, % 10Houston--Galveston--Brazoria, TX4,669,5713,731,131938, % 23Portland--Salem, OR--WA2,265,2231,793,476471, % 86Lexington, KY479,198405,93673, % 263Missoula, MT95,80278,68717, % RankArea Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1 New York--Northern New Jersey--Long Island, NY--NJ--CT--PA21,199,86519,549,6491,650,2168.4% 2Los Angeles--Riverside--Orange County, CA16,373,64514,531,5291,842, % 3Chicago--Gary--Kenosha, IL--IN--WI9,157,5408,239,820917, % 4Washington--Baltimore, DC--MD--VA--WV7,608,0706,727,050881, % 5San Francisco--Oakland--San Jose, CA7,039,3626,253,311786, % 6 Philadelphia--Wilmington--Atlantic City, PA-- NJ--DE--MD6,188,4635,892,937295,5265.0% 7 Boston--Worcester--Lawrence, MA--NH--ME-- CT5,819,1005,455,403363,6976.7% 8Detroit--Ann Arbor--Flint, MI5,456,4285,187,171269,2575.2% 9Dallas--Fort Worth, TX5,221,8014,037,2821,184, % 10Houston--Galveston--Brazoria, TX4,669,5713,731,131938, % 23Portland--Salem, OR--WA2,265,2231,793,476471, % 86Lexington, KY479,198405,93673, % 263Missoula, MT95,80278,68717, % H OW DOES L EXINGTON -F AYETTE F IT : S IZE Source: 2000 Census Data File 3:

10 RankMetropolitan Area Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1Las Vegas, NV--AZ1,563,282852,737710, % 2Naples, FL251,377152,09999, % 3Yuma, AZ160,026106,89553, % 4McAllen--Edinburg--Mission, TX569,463383,545185, % 5Austin--San Marcos, TX1,249,763846,227403, % 6Fayetteville--Springdale--Rogers, AR311,121210,908100, % 7Boise City, ID432,345295,851136, % 8Phoenix--Mesa, AZ3,251,8762,238,4801,013, % 9Laredo, TX193,117133,23959, % 10Provo--Orem, UT368,536263,590104, % 33Portland--Salem, OR--WA2,265,2231,793,476471, % 50Missoula, MT95,80278,68717, % 74Lexington, KY479,198405,93673, % RankMetropolitan Area Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1Las Vegas, NV--AZ1,563,282852,737710, % 2Naples, FL251,377152,09999, % 3Yuma, AZ160,026106,89553, % 4McAllen--Edinburg--Mission, TX569,463383,545185, % 5Austin--San Marcos, TX1,249,763846,227403, % 6Fayetteville--Springdale--Rogers, AR311,121210,908100, % 7Boise City, ID432,345295,851136, % 8Phoenix--Mesa, AZ3,251,8762,238,4801,013, % 9Laredo, TX193,117133,23959, % 10Provo--Orem, UT368,536263,590104, % 33Portland--Salem, OR--WA2,265,2231,793,476471, % 50Missoula, MT95,80278,68717, % 74Lexington, KY479,198405,93673, % RankMetropolitan Area Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1Las Vegas, NV--AZ1,563,282852,737710, % 2Naples, FL251,377152,09999, % 3Yuma, AZ160,026106,89553, % 4McAllen--Edinburg--Mission, TX569,463383,545185, % 5Austin--San Marcos, TX1,249,763846,227403, % 6Fayetteville--Springdale--Rogers, AR311,121210,908100, % 7Boise City, ID432,345295,851136, % 8Phoenix--Mesa, AZ3,251,8762,238,4801,013, % 9Laredo, TX193,117133,23959, % 10Provo--Orem, UT368,536263,590104, % 33Portland--Salem, ORWA2,265,2231,793,476471, % 50Missoula, MT95,80278,68717, % 74Lexington, KY479,198405,93673, % RankMetropolitan Area Name Census Population Change, 1990 to 2000 April 1, 2000April 1, 1990NumberPercent 1Las Vegas, NV--AZ1,563,282852,737710, % 2Naples, FL251,377152,09999, % 3Yuma, AZ160,026106,89553, % 4McAllen--Edinburg--Mission, TX569,463383,545185, % 5Austin--San Marcos, TX1,249,763846,227403, % 6Fayetteville--Springdale--Rogers, AR311,121210,908100, % 7Boise City, ID432,345295,851136, % 8Phoenix--Mesa, AZ3,251,8762,238,4801,013, % 9Laredo, TX193,117133,23959, % 10Provo--Orem, UT368,536263,590104, % 33Portland--Salem, ORWA2,265,2231,793,476471, % 50Missoula, MT95,80278,68717, % 74Lexington, KY479,198405,93673, % H OW DOES L EXINGTON -F AYETTE F IT : G ROWTH R ATE Source: 2000 Census Data File 3:

11 F INDINGS : P ROCEDURE 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)

12 F INDINGS : R ESULTS Similar to Juns 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

13 C ONCLUSIONS : F URTHER R ESEARCH 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.

14 S OURCES : Bengston, David N., Jennifer O. Fletcher, and Kristen C. Nelson 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) Zipfs law for cities: An explanation. The Quarterly Journal of Economics, 3, Ihlanfeldt, K.R. (2007) The effect of land use regulation on housing and land prices. Journal of Urban Economics, 61(3), Nelson, Arthur C., Rolf Pendall, Casey J. Dawkins, and Gerrit J. Knaap 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 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: 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 U.S. Department of the Interior. United States National Atlas. – Retrieved April 15, 2010 from U.S. Census Bureau. Ranking Tables for Metropolitan Areas. – Retrieved April 15, 2010 from LexingtonFayette Urban County Government Department of Planning Comprehensive Plan. Retrieved April 15, 2010 from

15 ANY QUESTIONS?


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