Craig E. Landry East Carolina University

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Amenity Valuation in Simultaneous Hedonic Property Markets: An Exploration of Rental and Sales Markets in the Coastal Zone. Craig E. Landry East Carolina University. Hedonic Property Price Method. Revealed preference method of non-market valuation - PowerPoint PPT Presentation

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  • Amenity Valuation in Simultaneous Hedonic Property Markets: An Exploration of Rental and Sales Markets in the Coastal ZoneCraig E. LandryEast Carolina University

  • Hedonic Property Price MethodRevealed preference method of non-market valuationUse property transaction prices as signal of economic value of environmental goods and services: P = P(a)Rosen (JPE 1974) showed how we can relate marginal implicit prices to homebuyer preferencesPa = Ua/Uq

  • Applications of Hedonic Price MethodEnvironmental values in exotic locationsSki chalets, Lake retreats, Alpine villasBeach homesBeach erosion and beach qualityFlood & wind hazardsCoastal amenities ViewProximity to beachOpen spaceWater quality

  • Land Markets in Exotic LocationsLimited land supplyCompetitive bidding for landSales prices adjust to reflect heterogeneity of parcels and structuresSome properties also traded in rental marketRental prices will reflect heterogeneityRental income can be important source of funds for mortgage, taxes, insurance

  • Second Homes in Exotic LocationsOwner often does not occupy house year-roundMay see same property traded in 2 marketsSales market capital assetRental market pure consumptionImplications for theory of hedonic prices, statistical estimation, and welfare analysis?

  • Preview of ResultsSimultaneous markets alter hedonic theory and interpretation of marginal implicit pricesImplications depend upon purpose of analysis/analytical approach utilizedEstimation of a simultaneous system of hedonic price equations improves efficiency

  • Agents in Simultaneous MarketsSuppliershomebuilders and redevelopersHomeowners buyers in the sales market and suppliers in the rental marketVacationersbuyers in the rental market

  • AssumptionsAll agents take hedonic price schedules as givenIgnore seasonal variation in rental priceAsset risk factors (forest fire, avalanche, flood, erosion) will not affect rental ratesBuyers consider rental market when forming property bidsUsage for any period of time is a reasonable representation of usage patterns

  • Homeowners Max Ui(a,n,m,q) a vector of housing attributesn personal consumption of vacation propertym rental supply of vacation propertyq numeraire subject to y + r(a)m P(a) + (m) + (n) + qy annual incomer(a) weekly hedonic rental price functionP(a) annualized hedonic sales price function(m) rental cost function (increasing and convex)(n) consumption cost function (e.g. travel cost) subject to T m + n

  • OptimizationFirst-order conditions:Uq = [1]

    Ua = (Pa ram)[2]

    Un '(n) 0, n 0,[3][Un ' (n) ]n = 0Um + [r(a) '(m)] 0, m 0, [4][Um + [r(a) '(m)] ]m = 0y + r(a)m = P(a) + (m) + (n)+ q[5]

    T m + n 0,[6] [T m n] = 0

  • Optimal Housing Attributes [2]Conventional hedonic model marginal price equals marginal rate of substitutionPa = Ua/ = Ua/Uq Maintained result if m = 0If 0 < m < T: Pa=Ua/ + ram = Ua/Uq + ram If m = T: Pa= raT

  • Optimal Consumption [3]Consumption depends upon the balance of marginal benefits and costs MB = UnMC(n) = '(n) + For n = 0: MC(1) > MB For 0 < n < T: MC(n*) = MB For n = T:MC(T) < MB

  • Optimal Rental Supply [4]Supply depends upon the balance of marginal benefits and costs MB = r(a)MC(m) = m Um / + /

    For m = 0: MC(1) > MB For 0 < m < T: MC(m*) = MB For m = T:MC(T) < MB

  • VacationersMax subject to y r(a)v + q where v is number of rental weeksFirst-order conditions implyra= /(v) Interpretation for vacationers marginal WTP / =rav

  • Hedonic Price EquationsP= P(a,)[5]r=r(a)[6]Homeowners preferences play a role in both price schedulesSelection of rental supply (m) induces differences across the two marketsDistribution of property characteristics Distribution of homeowners preferences

  • Data425 observations on properties in Dare and Brunswick counties, NCSales: 1979-1997 (expressed as annual expense)Rental rates: 1998Observe rental supply29% not rented (m = 0)36% rented fulltime (n = 0)Remaining 35% rented/consumed part of the yearOnly 12% occupied year-round (renter or owner)Observe housing and household attributes

  • Chart1

    146164105

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    181818

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    351136

    42747

    29524

    313114

    34342

    20325

    11128

    9415

    44442

    44817

    563623

    Rental Supply

    Occupancy

    Vacancy

    Weeks per Year

    Frequency

    Figure 1: Property Usage

    Sheet1

    m = rentalfrequency

    0146

    42

    98

    1337

    1726

    2234

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    2642

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    n=occupancyfrequency

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    vacantfrequency

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    Sheet1

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    8

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    26

    34

    1

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    29

    20

    11

    9

    4

    56

    Frequency

    Weeks per Year

    Frequency

    Figure 1: Rental Supply

    Sheet1 (2)

    164

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    34

    26

    6

    10

    1

    7

    5

    3

    1

    4

    36

    Weeks per Year

    Frequency

    Figure 2: Occupancy Count

    Sheet2

    105

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    17

    21

    17

    18

    5

    36

    47

    24

    14

    2

    25

    28

    15

    2

    17

    23

    Weeks per Year

    Frequency

    Figure 3: Vacancy Count

    Sheet3

    164105164

    1284128

    34334

    26126

    616

    101710

    1211

    7177

    5185

    353

    1361

    4474

    362436

    311431

    34234

    352535

    392839

    431543

    44244

    481748

    522352

    Rental Supply

    Vacancy

    Occupancy

    Weeks per Year

    Frequency

    Figure 1: Rental Supply, Occupancy, and Vacancy Counts

    weeksvacantmn

    0105146164

    442128

    53

    61

    81

    917834

    13213726

    1717266

    1818

    215

    22363511

    2647427

    3024295

    3114

    342

    3525203

    3928111

    431594

    442

    48174

    52235636

    425425425

    Rental Supply

    Occupancy

    Vacancy

    Weeks per Year

    Frequency

    Figure 1: Property Usage

  • Econometric ModelPi(a,) = xip + pi [5]ri (a)= zir + ri[6]

    Estimate likelihood function as a Bivariate Normal Box-Cox transformation of dependent variableModel selection in first-stage probit model

  • PROBIT SELECTION EQUATION (Pr(m>0))

    Probit regression Number of obs = 690 LR chi2(8) = 63.71 Prob > chi2 = 0.0000Log likelihood = -397.06653 Pseudo R2 = 0.0743----------------------------------------------------------------------------------- rental | Coef. Std. Err. z P>|z| -------------+-------------------------------------------------------------------- gradsch | -0.0958244 0.115899 -0.83 0.408 hschool | -0.2626746 0.153001 -1.72 0.086 retire | -0.6391054 0.1069345 -5.98 0.000 incom98 | -0.0002414 0.0006404 -0.38 0.706 nodare | -0.0632154 0.1286555 -0.49 0.623 cendare | 0.4560292 0.1576279 2.89 0.004 sodare | 0.6586108 0.351817 1.87 0.061 nobrun | -0.233207 0.1881866 -1.24 0.215 _cons | 0.7978059 0.1494016 5.34 0.000 ------------------------------------------------------------------------------------

  • Results for BVN ModelBox-Cox parameter different from zero for sales model (p
  • Results of BVN ModelFor significance level of 10%:10/14 significant coefficients in sales model Lotsize, bedrooms, air, fireplace, multistory, age, ocean-frontage, distance from shore, distance from CBD, elevationYearly dummies generally statistically significant increasing trend11/13 significant coefficients in rental modelSquare-footage, lotsize, bedrooms, air, fireplace, garage, multistory, age, ocean-frontage, distance from shore, distance from CBDRisk variables have no explanatory powerCoefficient on Hazard Ratio not significant (p=0.168)

  • Selected Results: BVN Model Number of obs = 425 LR chi2(45) = 2310.85 Log likelihood = -6268.3611 Prob > chi2 = 0.0000 ---------------------------------------------------------------------------------------------- | Coef. Std. Err. z P>|z| ----------------------------------------------------------------------------------------------- sales | sqft | 2.28e-06 1.83e-06 1.25 0.213 lotsize | 1.15e-06 3.88e-07 2.96 0.003 air1 | 0.017921 0.0074215 2.41 0.016 pur_age | -0.0008969 0.0003158 -2.84 0.005 ocean | 0.0243136 0.0085695 2.84 0.005 distance | -0.0000364 0.0000148 -2.46 0.014 elev | 0.0009411 0.0004239 2.22 0.026 -------------+--------------------------------------------------------------------------------rental | sqft | 0.0000224 0.0000128 1.75 0.07