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A Multilevel Property Hedonic Approach to Valuing Parks and Open Space

A Multilevel Property Hedonic Approach to Valuing Parks and Open Space. Treg Christopher Dissertation Seminar Oct. 15, 2009. Outline. Ecosystem Goods and Services and Valuation Property Hedonic Model Spatial Issues Multilevel Modeling Methods Research Results Baltimore City Parks

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A Multilevel Property Hedonic Approach to Valuing Parks and Open Space

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  1. A Multilevel Property Hedonic Approach to Valuing Parks and Open Space Treg Christopher Dissertation Seminar Oct. 15, 2009

  2. Outline • Ecosystem Goods and Services and Valuation • Property Hedonic Model • Spatial Issues • Multilevel Modeling Methods • Research Results • Baltimore City Parks • Baltimore County Open Space

  3. Benefits of Parks and Open Spaces • Protects water quality • Provides wildlife habitat • Improves physical & mental health • Educational opportunities • Flood regulation • Offsets urban heat sink

  4. Valuing the Environment • If we can assign a quantitative monetary value on ecosystem goods and services, we can: • justify the use if public funds for restoration or protection projects; • make objective decision on the most efficient use of limited resources; • through cost-benefit analysis, set priorities for programs, policies or actions.

  5. Valuation Methods • Stated Preference: • Uses surveys to ask people what they would be willing to pay. • How much would you be willing to pay to improve water quality in a nearby lake? • Revealed Preference: • Uses market prices to estimate how much people are willing to pay. • How much does proximity to a park contribute to the value of a property?

  6. Types of Revealed Preferences • Market price method • Estimates economic value based on the price of marketable goods. • Productivity method • Estimates economic value based on the cost of producing marketable goods. • Hedonic price method • Estimates economic value based on effects on the price of other marketable goods. • Travel cost method • Estimates economic value of a site based on how much people are willing to pay to travel to visit the site.

  7. Property Hedonic Approach • Assumes that a home buyer implicitly prices characteristics embodied in the property: • Structural (house size, age, # of baths, lot size) • Neighborhood (ethnicity, income, unemployment, crime) • Locational/Environmental (access/proximity to parks, schools,shopping) • To live in a location with higher levels of an amenity • Households pay higher house prices • Cost differences reflect households’ implicit valuation

  8. Property Hedonic Approach • Hedonic prices are identified through a comparison of similar goods that differ for the quality of one characteristic Proximity to a park Proximity to a park Distance 100m 500m

  9. Property Hedonics & Environmental Externalities • Negative externality • air pollution (Batalhone 2002) • proximity to hazardous waste sites (McCluskey 2003) • Positive externality • open space (Bastian 2002; Bolitzer 2000; Geoghegan 1997; Irwin 2002) • Improvements to water or air quality (Cho 2006; Kim 2003; Leggett 2000)

  10. Previous Studies of Parks • Positive value to preserving most types of open space in urban-suburban areas • Close proximity to parks in urban areas has a significant positive impact on home value but depends on context: • Neighborhood Effects: • Parks in urban areas and more densely populated suburbs tend to show greater benefits than those in more sparsely-populated areas • Park Characteristics: • Natural, wooded areas have greater beneficial impacts • Larger parks have greater beneficial impacts • High crime in the areas surrounding parks will reduce the value of parks

  11. Property Hedonic Limitations • Need large data sets and detailed information on all aspects that affect prices • We assume that buyers choose houses that maximize their utility • requires information that the buyer may not perceive • Assuming a single housing market for all consumers • Only measures “use” values of the environment • recreation and aesthetics • Numerous statistical assumptions

  12. The Pitfalls of Spatial Analyses • Spatial dependencies (autocorrelation) • Data from location near to each other are more likely to be similar than data from location remote from each other • Causes bias in coefficients and standard errors • Heterogeneity of relationships (Non-stationarity) • Phenomena is not distributed evenly in space • Scale and the Modifiable areal unit problem (MAUP) • Results may depend on the areal unit used • Block Groups vs. Tracts (scale issue) • Block Groups vs. ‘Neighborhoods’ or Zipcodes (zoning)

  13. Testing for SA:Moran’s Index • positive when attributes of nearby objects are more similar than expected • 0 when arrangements are random • negative when attributes of nearby objects are less similar than expected Close in space Repulsion of attributes Attributes independent of location Close in space Affinity of attributes

  14. Spatial Non-Stationarity • Global models (e.g. OLS): Assume relationships are stationary and as such are location independent • Non-Stationarity: a different relationship in different parts of the study region • Local models: spatial decompositions of global models, the results of local models are location dependent

  15. Spatial Scales • Conclusions about processes and relationships determined at one scale should not expected to be similar at other scales • Atomistic Fallacy • Inferences about broad scale/group level/aggregated data and relationships are based on small scale/individual level analyses • Ecological Fallacy • Inferences of fine-scale/individual level data and relationships are based on broad scale or aggregated data

  16. “Tree scale”: Spatial relationship betw/ trees (the regular distribution) determined by competition for light, water, nutrients which prevents the trees from growing to closely to one another Negative association Small/ Fine “Stand or Community Scale”: Spatial relationship determined by same species having common needs (light, water nutrients) which are heterogenous across space SCALE “Forest Scale”: Spatial relationship determined by disturbances such forest fires, pest & diseases Large/Broad

  17. Baltimore Data • Property data • MD PropertyView: Structural characteristics & Sale info • Sales between 1998-2002, $ converted to Yr2000 • Property records removed if: • Sale price <$50,000 • House size <500ft2 • Lot size <500ft2 • Number of bathrooms > 6 • Not townhouse or individual residence • Approx. 14,000 records • Census data • Block groups from 2000 Census • over 400 groups with at least 5 property sales

  18. Baltimore Data

  19. Non-Spatial Statistical Assumptions • Linearity - the relationships between the predictors and the outcome variable should be linear • Normality - the errors should be normally distributed • Homoscedasticity- the variance of errors should be stable across space • Model specification - the model should be properly specified (including all relevant variables, excluding irrelevant variables ) • Multicollinearity – independent variables should not be highly correlated with each other

  20. Multilevel Models • Early history: Educational research • effects of context such as classroom and schools on individual scholastic achievement (Goldstein 1993; Raudenbush 1991). • Individuals that are members of a group cannot be modeled as independent observations • Statistical violation leads to erroneously small Standard Errors • spurious statistical significance of coefficients • Examine potential Non-stationarity • Avoiding the atomistic and ecological fallacies • Data is modeled at the appropriate scale • Allowing cross-level interactions between individual level factors and group level (contextual) factors.

  21. y1 r12 u2 0 γ0 u1 Do Groups Matter? House Price Null Multilevel Model OLS Model $1M+ y1 r1 02 $110,000 01 $50,000

  22. Null/Baseline/Unconditional Model Price for any individual (i) within Block Group (j) is a function of the group mean and the individual-level error term • Combined model Each Block Group mean is a function of the grand mean and a group-level error term Individual level variance Group level variance

  23. Intraclass Correlation Coefficient (ICC) • The ICC is the proportion of variance in property price between block groups to the total variance Individual level variance: σ2 Τ00/(Τ00 + σ2 ) 65.5% of the variance in property price is between block groups Group level variance: Τ00 0.17189 / (0.17189+0.0904)=0.655

  24. Level 1 Model Fixed intercept, Fixed slope: OLS Model House Price House Size

  25. Level 1 Model Random intercept, Fixed slope: Multilevel, L1 Model House Price House Size

  26. Spatial Error Autocorrelation • Moran’s Index OLS (SPSS): Multilevel (HLM): Moran's Index: 0.227255 p-value: 0.000000 Moran's Index: 0.014214 p-value: 0.115993

  27. Modeling Random Effects Random intercept, Random slope: Multilevel, L1 Model Random intercept, Fixed slope: Multilevel, L1 Model House Price House Size

  28. Why Allow Slopes to Vary? 1. Helps reduce problem of heteroscedasticity from level 1 residual Fixed Slope Random Slope Test of homogeneity of level-1 variance ---------------------------------------- Chi-square statistic = 2291.81500 Number of degrees of freedom = 404 P-value = 0.000 Test of homogeneity of level-1 variance ---------------------------------------- Chi-square statistic = 2095.47234 Number of degrees of freedom = 387 P-value = 0.000

  29. Why Allow Slopes to Vary? 2. Mapping non-stationarity

  30. Level 2 Model: “Means as Outcomes”(MAO) The mean price for each group is an outcome to be predicted by group characteristics • Combined Equation

  31. Census Variables & Spatial Autocorrelation

  32. Compositional Effects of Structure • Price is determined by a property’s structural attributes & the structural attributes of neighboring houses Individual house attributes: Age, House Size, Lot Size Neighborhood averages of individual attributes • House Size and Age are significant at the neighborhood level Conclusions about processes and relationships determined at one scale should not expected to be similar at other scales

  33. Cross-Level Interactions House Size Results MedHsInc w/ Cross-level interaction Cross-level interpretation: Not only does house size have a positive effect on price but the slope increases with increasing neighborhood income

  34. Valuing Parks in the City of Baltimore • Include “official” city parks & other, undeveloped open spaces • n=81 • use parcels boundaries to verify and edit park boundaries • only parks > 2ha

  35. Distance to Parks Euclidean Network Combined

  36. Result of Park Coefficient Interpretation: a 0.003% (0.015% in OLS) decrease in price with every 1% increase in distance • Why Non-Significant in HLM Models vs OLS? • Spatial Autocorrelation? • Non-stationarity?

  37. Spatial Non-Stationarity of Park Coef.

  38. Explaining Non-Stationarity of Park Coef. • Interaction variables • Structural: House Size, Age, Lot Size • Park: Crime, Size of Park (ha), % Open • Neighborhood (cross-level): MedHsInc, %Unemployment, Crime, PopDensity • Results: Lack of significant interaction • What if Block Groups are divided?

  39. Conclusions: Valuing Parks in the City of Baltimore • The effect of park proximity on property price varies across space • Previous studies using Global Models are unable to examine such variation • For neighborhoods that do positively value proximity to parks: • larger and more open parks tend to be more highly valued • neighborhoods with high population densities tend to place higher value on park proximity • Unknown interactions for neighborhoods that negatively value proximity to parks

  40. Valuing Open Space in Baltimore County • Proportion of different land uses that surround a home: • commercial, residential, open space • Are their differences between open space types? • Difference in value between different spatial scales?

  41. Valuing Open Space in Baltimore County • Open space types: • Private, Conserved • easements and riparian buffers • Private, Developable • “Public” • publicly accessible • golf courses, cemeteries, school fields, campuses • About 75% wooded and 25% open: grass or farms

  42. Land Use at Multiple Scales • Do the effects of proportions of open space and other land uses vary with the scale at which they are examined? • Block group, 1km, • 500m,100m 1 km 500m 100m

  43. Results

  44. Conclusions • Private, Conserved > Private, Developable > Public • Preference for visual amenities over direct use (recreation) • Preference for absence of negative externalities vs presence of positive amenities such as recreation • Results do change with scale

  45. Future Research • Include a 3rd level • group block groups by tracts, PRIZM (consumer behavior) classes, school districts • Cross-classified design • use a non-nested grouping where properties “belong” to a socio-economic group (neighborhood) and the park to which they are closest Property Block Group Tract Park Block Groups Park

  46. Multilevel Weaknesses • Need for bounded (discrete) L2 groups may result in artificial boundaries being formed whereas processes and associations may be more diffuse • Block Group (Census units) boundaries are created to minimize the heterogeneity within groups • Requires large datasets (min 30 groups & 5 observations/group) • Shrinkage of unreliable estimates of random effects is towards the grand mean rather than neighboring groups

  47. Multilevel Valuation of ES • Valuation of Ecosystem Service is dependent on context • supply of services & demand of consumers • Context is scale dependent • variables should be measured at appropriate scale(s) • interactions can occur across scales

  48. Thanks for Listening! Questions?

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