1# RECORD d458e3973f160efbbd5c7fd28ec076d63dc933e12# TITLE: An Estimation of the Coastal Premium for Residential Housing Prices in San Diego County3# YEAR: 2011.0 VENUE: nan DOI: 10.1007/s11146-009-9195-x4# AUTHORS: Stephen J Conroy; Jennifer Milosch5# PAGES: 18 CANDIDATE TABLE PAGES (1-based): [7, 9, 14, 15, 16, 17]6# ABSTRACT: The authors attempt to estimate the “coastal premium”—additional value conferred on a residence from being located near the coast—of single family homes in San Diego County, while controlling for other locational and structural characteristics. A previous investigation published in 2001 for south Orange County found that moving away from the coast by one mile was associated with a $42,000 lower housing price. Intrigued by this finding, we investigate whether (a) a similar coastal premium exists for all of San Diego County and (b) the premium varies by incremental distance from the coast (e.g., for 500-feet increments). Using data from 9,755 San Diego County home sales in 2006, results presented here suggest that for a median-priced home ($540,000) at the mean distance from the coast (approximately 9 miles—and considerably farther than the Orange County estimate) a one-mile increase in distance from the coast would reduce the sale price by approximately $8,680. Specifying by specific distance increments, we find that the coastal premium is approximately 101.9% for houses within 500 feet of the coast (i.e., their value is 101.9% higher than similar homes located beyond six miles of the coast), falling to 62.8% for homes between 500 and 1,000 feet, declining to approximately 3.3% for homes located between five and six miles of the coast, disappearing entirely beyond around six miles. Since average comparisons of the sort initially considered in this analysis can be very misleading, researchers should consider the nonlinear incremental distance effects in model specifications.789===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) =====1011An Estimation of the Coastal Premium for Residential12Housing Prices in San Diego County13Stephen J. Conroy & Jennifer L. Milosch14Published online: 22 July 200915# The Author(s) 2009. This article is published with open access at Springerlink.com16Abstract The authors attempt to estimate the “coastal premium”—additional value17conferred on a residence from being located near the coast—of single family homes18in San Diego County, while controlling for other locational and structural19characteristics. A previous investigation published in 2001 for south Orange County20found that moving away from the coast by one mile was associated with a $42,00021lower housing price. Intrigued by this finding, we investigate whether (a) a similar22coastal premium exists for all of San Diego County and (b) the premium varies by23incremental distance from the coast (e.g., for 500-feet increments). Using data from249,755 San Diego County home sales in 2006, results presented here suggest that for25a median-priced home ($540,000) at the mean distance from the coast (approxi-26mately 9 miles—and considerably farther than the Orange County estimate) a one-27mile increase in distance from the coast would reduce the sale price by28approximately $8,680. Specifying by specific distance increments, we find that the29coastal premium is approximately 101.9% for houses within 500 feet of the coast30(i.e., their value is 101.9% higher than similar homes located beyond six miles of the31coast), falling to 62.8% for homes between 500 and 1,000 feet, declining to32approximately 3.3% for homes located between five and six miles of the coast,33disappearing entirely beyond around six miles. Since average comparisons of the34sort initially considered in this analysis can be very misleading, researchers should35consider the nonlinear incremental distance effects in model specifications.36Keywords Coastal premium . Hedonic model . San Diego . Housing prices .37California housing38J Real Estate Finan Econ (2011) 42:211–22839DOI 10.1007/s11146-009-9195-x40S. J. Conroy (*)41Department of Economics, School of Business Administration, University of San Diego, San Diego,42CA 92110-2492, USA43e-mail: sconroy@sandiego.edu44J. L. Milosch45Department of Economics, University of California, 2127 North Hall, Santa Barbara,46CA 93106-9210, USA47e-mail: jmilosch@gmail.com484950===== PAGE 7 (score 22, numeric-paren 0, table-words 2, var-words 11) =====5152Table 1 Descriptive statistics of the variables53Variable54Description55Mean56Std Dev57Min58Max59Dependent variable60Ln_Price61Natural logarithm of sales62price6313.31098640.4549046511.5136616.3067Price68Sales price of house69691887.970556376.971100,0007212,000,00073Structural characteristics74Age75Age of house (in years)7637.457437715.611427817998.0080Age_281Age of house squared821646.751831249.535841859,604.0086Bedrooms87Number of bedrooms883.306775890.820486901918.0092Bathrooms93Number of bathrooms942.176845950.825796960.59713.5098SqftStruc99Square footage of house1001818.762101943.380310233610316,281.00104LotSqft105Square footage of lot on106which house is situated10722060.86108102943.110912101108,455,867111Spatial and neighborhood variables112DtownDist113Distance (in feet) from114house to downtown11585888.8511655667.391176906.7118306,053119LnDtownDist120Natural logarithm of121Dtowndist12211.149171230.6647101248.8402512512.63151126FreeDist127Distance (in feet) from128house to nearest freeway12911116.3713012194.9613187.611132158,716133LnFreeDist134Natural logarithm of Freedist1358.8571251361.0262371374.472913811.97487139Dist_API140Elementary school district’s141base Academic Perform.142Index (200–1,000)143767.843614457.2554145653146939.00147Coastal variables148CoastDist149Distance (in feet) from150house to coast15147975.4515233182.9315373.747154299,490155LnCoastDist156Natural logarithm of157Coastdist15810.462811599.65E-011604.30061611.26E+01162Coast_500ft163Dummy: Res. within164500 ft. of coast1650.0033371660.05767116701681.00169Coast_1000ft170Dummy: Res. 500 to1711,000 ft. of coast1720.0038421730.06187017401751.00176Coast_2000ft177Dummy: Res. 1,000 to1782,000 ft. of coast1790.0101111800.1000518101821.00183Coast_3000ft184Dummy: Res. 2,000 to1853,000 ft. of coast1860.0124371870.11083018801891.00190Coast_4000ft191Dummy: Res. 3,000 to1924,000 ft. of coast1930.0121341940.10948719501961.00197Coast_5280ft198Dummy: Res. 4,000 to1995,280 ft. of coast2000.0171892010.12998220202031.00204Coast_0to1miles205Dummy: Res. within 1 mile206of coast2070.0590502080.23572920902101.00211Coast_2miles212Dummy: Res. 1 to 2 miles213of coast2140.0477252150.21319521602171.00218Coast_3miles219Dummy: Res. 2 to 3 miles220of coast2210.0446922220.20663622302241.00225An Estimation of the Coastal Premium for Residential Housing...226217227228229===== PAGE 8 (score 13, numeric-paren 0, table-words 2, var-words 9) =====230231Equation (2) provides an estimate for the (natural log) coastal premium, βl.232However, this is essentially an average premium measured at the mean for the entire233data set. Since we expect the effect to be nonlinear, we construct a piece-wise linear234spline of the distance from coast in order to estimate the effect of living within one235mile of the coast, between one and two miles, etc. Thus, we adjust (2) to the236following:237lnPi ¼ ai þ238X239J240j¼1241bjSij þ242X243K244k¼1245bkTik þ246X247L248l¼1249blDil þ250X251M252m¼1253bmFim þ ni; for i ¼ 1; 2; . . . n254ð255Þ ð3Þ256where Dil is a dummy variable denoting a specific distance from the coast for257observation i. This corresponds to “Model 2” in our regression estimates. We can258also adjust the distance dummy variables into smaller increments to capture even259smaller distance effects, i.e., within 500 feet, between 500 and 1,000 feet, etc. (our260“Model 3”).3261Results262We present the results of the standard hedonic pricing model in Table 2. Model 1263includes the log coastal distance variable which provides the coastal premium264estimation. In this model, all coefficients are significant at the 1% level, except for265LotSqft, which is still significant at the 5% level and Bedrooms, which is not266significant in any of the models. While the non-effect of having more bedrooms is267somewhat surprising (e.g., in a meta analysis by Sirmans et al. 2006, the bedroom268effect was positive and significant), there is some recent evidence for a similar,269insignificant bedrooms effect in Boston (Stevenson 2004). Closer to home, the2703 These distance increments were somewhat arbitrarily selected, with the intention of capturing homes that271are very close to the ocean—within 500 feet—and beyond. In California, ocean-front property owners’272property line extends only to the mean high tide (California Civil Code, Section 1009) so some typical273waterfront amenities found in other parts of the country such as boat docks, launches, etc. are not feasible274or relevant to this study. In addition, coastal bluffs, prevalent in San Diego County, can render direct275private access to the beach impossible in certain areas. There are often limited public access points along276the bluffs through which all beachgoers may pass (see Locklin 1999).277Table 1 (continued)278Variable279Description280Mean281Std Dev282Min283Max284Coast_4miles285Dummy: Res. 3 to 4 miles286of coast2870.0525782880.22320228902901.00291Coast_5miles292Dummy: Res. 4 to 5 miles293of coast2940.0628922950.2427829602971.00298Coast_6miles299Dummy: Res 5 to 6 miles300of coast3010.0747223020.2629630303041.00305218306S.J. Conroy, J.L. Milosch307308309===== PAGE 9 (score 74, numeric-paren 44, table-words 6, var-words 8) =====310311bedroom effect for the Orange County study by Boarnet and Chalermpong (2001)312was actually negative. Perhaps buyers in some markets prefer “space” to “bed-313rooms,” especially as the large Baby Boom cohort enters its peak earnings years,314leaving the childbearing years behind. Indeed, in a recent demographic report of San315Table 2 Semi-log fixed effectsd regression results316Variable317Model 1318Model 2319Model 3320Estimated321Coefficient322t-323Statistic324Estimated325Coefficient326t-327Statistic328Estimated329Coefficient330t-331Statistic332Housing and school characteristics333Constant33411.7966a335(43.93)33611.2239a337(39.88)33811.3138a339(40.59)340Age341−0.007867a342(−12.55) −0.007734a343(−12.18) −0.007778a344(−12.36)345Age_23460.000086a347(11.63)3480.000087a349(11.65)3500.000085a351(11.41)352Bedrooms3530.002218354(0.63)3550.002250356(0.64)3570.003540358(1.01)359Bathrooms3600.042084a361(8.05)3620.045153a363(8.55)3640.041481a365(7.92)366SqftStruc3670.000226a368(47.76)3690.000225a370(46.92)3710.000226a372(47.68)373LotSqft3744.84e–08b375(2.24)3764.73e–08b377(2.16)3784.87e–08b379(2.24)380Spatial and neighborhood variables381LnDtownDist3820.175223a383(7.66)3840.076875a385(3.29)3860.075096a387(3.25)388LnFreeDist3890.010732a390(3.06)3910.015182a392(4.17)3930.009351a394(2.57)395Dist_API3960.000956a397(5.87)3980.001068a399(6.48)4000.001046a401(6.41)402Coastal variables403LnCoastDist404−0.146047a405(−21.12)406Coast_500ft4070.702698a408(15.43)409Coast_1000ft4100.487333a411(11.11)412Coast_2000ft4130.454806a414(13.67)415Coast_3000ft4160.438393a417(13.97)418Coast_4000ft4190.293654a420(9.38)421Coast_5280ft4220.201110a423(6.77)424Coast_0to1miles4250.352211a426(13.40)427Coast_2miles4280.200634a429(7.92)4300.193321a431(7.71)432Coast_3miles4330.118684a434(5.24)4350.115352a436(5.15)437Coast_4miles4380.082033a439(4.13)4400.082574a441(4.20)442Coast_5miles4430.053282a444(3.00)4450.053941a446(3.07)447Coast_6miles4480.035755a449(2.59)4500.033207b451(2.43)452Model statistics453Adj. R-Squared4540.77984550.77494560.7795457F-Statistic458301.34459280.79460276.80461Number of Obs.462975546397554649755465a indicate significance at the 0.01 levels466b indicate significance at the 0.05 levels467c indicate significance at the 0.10 levels468d Fixed effects parameter coefficients for each of 106 neighborhoods omitted here for brevity469An Estimation of the Coastal Premium for Residential Housing...470219471472473===== PAGE 10 (score 28, numeric-paren 0, table-words 7, var-words 13) =====474475Diego County, the three cities with the lowest percentage of children (Solana Beach,476Coronado and Del Mar) were among the top four in terms of median household477income (SANDAG 2006).478The variable of interest, LnCoastDist is −0.146, suggesting that a 10% increase in479distance from the coast is associated with a 1.46% decline in price.4 To state this in a480more concrete way, since the median house price for the sample is $540,000 and the481mean distance from the coast is 47,975.45 feet (approximately 9.09 miles), then482taking a median-priced house at the mean distance from the coast, a 10% increase in483distance from the coast (or 4,797.5 feet) would correspond to a $7,886.20 reduction484in price. Extending this to get to a one-mile increment (11% of the mean distance,485corresponding to a 1.61% decreases in price), the effect of moving one mile away486would be approximately $8,680. While this is considerably lower than the finding487for Orange County, there are several important considerations. First, the result from488Boarnet and Chalermpong (2001) was actually for the San Joaquin Hills489Transportation Corridor, which runs through the more affluent South Orange County490region near Laguna Beach, Laguna Niguel and Aliso Viejo. Second, while actual491mean coastal distance for this subsample was not presented in their paper, a casual492inspection of the map provided suggests a much lower mean than this current493investigation’s 9.09 miles and their data appear to be in a fairly narrow band around494the 73 freeway. At 299,416 feet (or about 56.7 miles), the span of this data set for495San Diego County is certainly much larger than the entire width (east-west range) for496all of Orange County.497Results presented for Model 1 also suggest that an additional bathroom is498associated with a 4.3% higher sales price.5 Square feet of structure is the most499significant coefficient and the estimated coefficient implies that a 100 square foot500increase in structure is associated with a 2.29% higher price. Lot square footage is501only significant at the 5% level and the effect is much smaller, with each 1,000502additional square foot of lot size associated with about a 0.005% higher price. The503coefficients for age and age-squared imply that price declines with age, though the504effect becomes less as age increases. In other words, doubling the age would not505double the negative “age effect.” Perhaps the “new home” amenity (whether506perceived or due to actual structural characteristics) depreciates rapidly and then any507negative “age effects” quickly stabilize. The effect of higher Academic Performance508Index (API) is also in the expected direction, with house price increasing by 10.03%509for a 100-point increase in the local elementary school’s mean API. While not510reported in Table 2, fixed effects by neighborhood or ZIP code are presented in the511Appendix, Table 3. University City, a somewhat upscale neighborhood northeast of512downtown was omitted as the reference category. The largest fixed effect is for513Coronado, certainly a well-known landmark and upscale neighborhood, whose514coefficient of 0.795 translates to a 121.4% higher price.5155 Following Thornton and Innes (1989) and Halvorsen and Palmquist (1980), interpretation of516semilogarithmic regression coefficients follows: g ¼ 100 exp bΔX517ð518Þ 1519½520, where ΔX is a (non-521infinitesimal) change in variable X, b is the regression coefficient and g can be interpreted as the percent522change in the dependent variable as a result of ΔX.5234 Following Ramanathan (1993) and others, we interpret log-log coefficients as elasticities.524220525S.J. Conroy, J.L. Milosch526527528===== PAGE 14 (score 32, numeric-paren 0, table-words 4, var-words 7) =====529530Open Access531This article is distributed under the terms of the Creative Commons Attribution532Noncommercial License which permits any noncommercial use, distribution, and reproduction in any533medium, provided the original author(s) and source are credited.534Appendix535Table 3 Fixed effects coefficients for each area in sample536Dummy537Variable538Number539Neighborhood/540Area Name541Model 1542Model 2543Model 3544Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic5451546Adams North5470.289275484.595490.181195502.715510.1823915522.765532554Allied Gardens5550.049015561.53557−0.05775558−1.57559−0.055051560−1.525613562Alta Vista563−0.09120564−1.47565−0.17225566−2.66567−0.167230568−2.615694570Bay Ho571−0.05230572−1.65573−0.07698574−2.39575−0.073174576−2.295775578Bay Park5790.125065803.915810.071065822.185830.0692255842.155856586Bay Terraces587−0.16586588−5.46589−0.23021590−6.39591−0.224297592−6.295937594Birdland595−0.01704596−0.26597−0.10723598−1.61599−0.106122600−1.616018602Carmel603Mountain604−0.18879605−4.52606−0.22915607−5.08608−0.228515609−5.126109611Carmel Valley612−0.22690613−5.11614−0.19074615−4.24616−0.182397617−4.161810619Chollas Creek620−0.01958621−0.30622−0.12251623−1.77624−0.11643625−1.762611627Clairemont628Mesa East629−0.04282630−1.45631−0.09474632−2.86633−0.094954634−2.963512636Clairemont637Mesa West638−0.03336639−1.03640−0.08537641−2.41642−0.081550643−2.3264413645College East6460.046696471.25648−0.05524649−1.32650−0.054744651−1.3265214653College West6540.149826554.206560.045386571.116580.0478556591.1966015661Del Cerro6620.202736635.806640.099756652.526660.0973646672.4966816669Del Mar670Heights6710.019156720.376730.049046740.926750.077176761.4667717678Egger679Highlands680−0.31759681−8.28682−0.31390683−7.98684−0.297656685−7.6468618687El Cerrito688−0.02035689−0.44690−0.12419691−2.45692−0.116716693−2.3369419695Emerald Hills696−0.05906697−0.91698−0.14561699−2.12700−0.139839701−2.0670220703Encanto704−0.16363705−4.70706−0.24374707−6.09708−0.238071709−6.0171021711Fairmont Park7120.098807131.82714−0.03441715−0.61716−0.043679717−0.7871822719Jamacha720Lomita721−0.11405722−3.26723−0.19814724−4.97725−0.188044726−4.7772723728Kensington7290.4958473011.617310.387147328.167330.3905147348.3273524736La Jolla7370.4831973817.167390.5374274018.107410.50003074216.8974325744La Playa7450.5900274611.977470.499657489.607490.56601975010.9475126752Lake Murray753−0.01612754−0.51755−0.12774756−3.56757−0.122028758−3.4475927760Lincoln Park761−0.23100762−3.42763−0.32725764−4.69765−0.329624766−4.7876728768Linda Vista7690.034387700.79771−0.05841772−1.27773−0.053966774−1.1877529776Loma Portal7770.267307785.877790.167317803.567810.1805247823.8878330784Midtown7850.499387867.107870.344347884.797890.3463547904.87791224792S.J. Conroy, J.L. Milosch793794795===== PAGE 15 (score 33, numeric-paren 0, table-words 4, var-words 7) =====796797Table 3 (continued)798Dummy799Variable800Number801Neighborhood/802Area Name803Model 1804Model 2805Model 3806Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic80731808Mira Mesa809−0.17876810−6.91811−0.17060812−5.61813−0.166830814−5.5481532816Miramar Ranch817North818−0.06266819−1.25820−0.08657821−1.63822−0.087594823−1.6782433825Mission Hills8260.6067982712.508280.466188299.408300.4688568319.5583234833Mountain View834−0.11201835−2.06836−0.21336837−3.75838−0.217225839−3.8584035841Nestor842−0.30624843−8.24844−0.28427845−7.53846−0.290085847−7.7684836849North850Clairmont851−0.06973852−2.28853−0.09314854−2.92855−0.090670856−2.8785737858North Park8590.370268606.108610.207218623.318630.21028228643.3986538866Oak Park867−0.02968868−0.81869−0.12551870−2.99871−0.119818872−2.8887339874Otay Mesa875West876−0.25950877−8.65878−0.26194879−8.10880−0.263984881−8.2588240883Pacific Beach8840.198528856.428860.174148875.318880.19555868896.0189041891Paradise Hills892−0.07725893−2.21894−0.14286895−3.68896−0.136876897−3.5689842899Point Loma900Heights9010.104399022.899030.018699040.489050.0618109061.5990743908Rancho909Bernardo910−0.25249911−6.95912−0.29109913−7.32914−0.288715915−7.3391644917Rancho918Penasquitos919−0.24791920−7.21921−0.25299922−6.56923−0.248629924−6.5292545926Redwood927Village/928Rolando Park929−0.04673930−1.30931−0.14748932−3.61933−0.139700934−3.4693546936Ridgeview/937Webster938−0.01280939−0.25940−0.12444941−2.32942−0.123291943−2.3294447945Rolando9460.027019470.67948−0.07682949−1.72950−0.070189951−1.5895248953Roseville/Fleet954Ridge9550.323909566.539570.244129584.689590.2852329605.5196149962Sabre Springs963−0.17211964−3.28965−0.21201966−3.82967−0.211167968−3.8596950970San Carlos9710.073929722.21973−0.03514974−0.93975−0.030838976−0.8397751978San Ysidro979−0.23873980−5.05981−0.21912982−4.49983−0.227604984−4.7198552986Scripps Ranch987−0.02174988−0.72989−0.06663990−1.95991−0.062204992−1.8499353994Serra Mesa9950.012629960.38997−0.06808998−1.77999−0.0678671000−1.781001541002Skyline1003−0.150351004−4.181005−0.228521006−5.591007−0.2202261008−5.441009551010Sunset Cliffs10110.1596010123.3110130.1881510143.7510150.10267210162.041017561018Talmadge10190.2569910206.0810210.1482910223.1510230.15525110243.331025571026Tierrasanta10270.0754810282.521029−0.010001030−0.291031−0.0088031032−0.251033581034University City1035Reference1036Reference Reference1037Reference Reference1038Reference1039591040University1041Heights10420.3038710434.0010440.1677210452.1510460.169969710472.21048601049Valencia Park1050−0.147381051−3.461052−0.243021053−5.311054−0.2398731055−5.291056611057Wooded Area10580.3213010595.9410600.2557910614.4910620.23796910634.191064621065Alpine1066−0.068621067−1.701068−0.201781069−4.771070−0.2006391071−4.791072An Estimation of the Coastal Premium for Residential Housing...1073225107410751076===== PAGE 16 (score 32, numeric-paren 0, table-words 4, var-words 2) =====10771078Table 3 (continued)1079Dummy1080Variable1081Number1082Neighborhood/1083Area Name1084Model 11085Model 21086Model 31087Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic1088631089Bonita10900.0981710912.2510920.0382910930.8110940.044589110950.951096641097Chula Vista1098−0.125861099−4.381100−0.165451101−5.371102−0.1631041103−5.351104651105Chula Vista1106−0.148301107−5.381108−0.177021109−5.681110−0.1773581111−5.751112661113Chula Vista1114−0.047841115−0.921116−0.102391117−1.851118−0.0946171119−1.721120671121Imperial Beach1122−0.356801123−10.071124−0.366781125−9.761126−0.3273871127−8.771128681129Jamul11300.0030811310.071132−0.109171133−2.421134−0.0972731135−2.181136691137La Mesa1138−0.015171139−0.531140−0.120311141−3.571142−0.1142461143−3.431144701145La Mesa1146−0.013531147−0.431148−0.122791149−3.441150−0.1206661151−3.421152711153Lemon Grove1154−0.085661155−2.731156−0.178471157−4.901158−0.1681741159−4.661160721161National City1162−0.068291163−1.871164−0.152131165−3.931166−0.1503481167−3.931168731169Spring Valley1170−0.134281171−4.661172−0.226821173−6.691174−0.2167231175−6.461176741177Spring Valley1178−0.149371179−3.561180−0.254531181−5.621182−0.2433741183−5.431184751185Bonsall1186−0.203461187−3.511188−0.178131189−2.961190−0.1684121191−2.831192761193Borrego1194Springs1195−0.484941196−7.281197−0.635861198−9.431199−0.6169791200−9.241201771202Cardiff-By-1203The-Sea1204−0.374861205−7.281206−0.307231207−5.861208−0.2564391209−4.921210781211Carlsbad1212−0.426481213−9.681214−0.319611215−7.111216−0.3009201217−6.751218791219Carlsbad1220−0.202151221−3.681222−0.132941223−2.341224−0.1221061225−2.171226801227Del Mar12280.1428212292.4012300.2035312313.3612320.17654912332.931234811235El Cajon1236−0.063391237−2.011238−0.178891239−5.161240−0.1749091241−5.11242821243El Cajon1244−0.069801245−2.391246−0.181201247−5.461248−0.1785801249−5.441250831251El Cajon1252−0.098071253−2.941254−0.214351255−5.901256−0.2136301257−5.941258841259Encinitas1260−0.257711261−6.071262−0.190051263−4.431264−0.1813081265−4.261266851267Escondido1268−0.150331269−4.101270−0.174111271−4.431272−0.1709181273−4.391274861275Escondido1276−0.240791277−6.391278−0.248161279−6.151280−0.2451081281−6.141282871283Escondido1284−0.210751285−5.891286−0.244601287−6.381288−0.2355831289−6.211290881291Fallbrook1292−0.235971293−5.561294−0.220671295−4.941296−0.2109991297−4.771298891299Escondido1300−0.065101301−1.661302−0.068641303−1.641304−0.0673511305−1.621306901307Julian1308−0.530071309−8.981310−0.693981311−11.551312−0.6746221313−11.341314911315Lakeside1316−0.112581317−3.261318−0.237491319−6.371320−0.2278981321−6.181322921323Oceanside1324−0.519331325−12.711326−0.395241327−9.641328−0.3837471329−9.441330931331Oceanside1332−0.467831333−11.421334−0.367821335−8.541336−0.3567501337−8.371338941339Oceanside1340−0.463071341−10.621342−0.361571343−7.841344−0.3475101345−7.611346951347Poway1348−0.096701349−2.671350−0.166381351−4.211352−0.1559051353−3.981354961355Ramona1356−0.186111357−5.041358−0.308231359−7.911360−0.2924581361−7.571362971363Rancho Santa1364Fe13650.1307913662.7713670.1600313683.2513690.17501313703.591371981372San Marcos1373−0.293761374−7.321375−0.279561376−6.511377−0.2734301378−6.431379991380Santee1381−0.205111382−6.091383−0.328791384−8.861385−0.3166471386−8.6213871001388Solana Beach13890.0416213900.7913910.1596313923.0113930.09011413941.7113952261396S.J. Conroy, J.L. Milosch139713981399===== PAGE 17 (score 24, numeric-paren 0, table-words 4, var-words 13) =====14001401References1402Benson, E. D., Hansen, J. L., Schwartz, A. L., Jr., & Smersh, G. T. (1998). Pricing residential amenities:1403the value of a view. Journal of Real Estate Finance and Economics, 16(1), 55–73.1404Blackley, P., Follain, J. R., Jr., & Ondrich, J. (1984). Box-cox estimation of hedonic models: how serious1405is the iterative OLS variance bias? Review of Economics and Statistics, 66(2), 348–353.1406Boarnet, M. G., & Chalermpong, S. (2001). New highways, house prices, and urban development: a case1407study of toll roads in Orange County, CA. Housing Policy Debate, 12(3), 575–605.1408Brasington, D. M. (1999). Which measures of school quality does the housing market value? Journal of1409Real Estate Research, 18(3), 395–413.1410Cropper, M. L., Deck, L. B., & McConnell, K. E. (1988). On the choice of functional form for hedonic1411price functions. 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Review of Economics and Statistics, 49(2), 246–257.1440Table 3 (continued)1441Dummy1442Variable1443Number1444Neighborhood/1445Area Name1446Model 11447Model 21448Model 31449Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic14501011451San Marcos1452−0.275191453−6.361454−0.212631455−4.621456−0.1990221457−4.3614581021459Vista1460−0.269171461−5.181462−0.190861463−3.531464−0.1764591465−3.2914661031467Valley Center1468−0.200511469−4.541470−0.251971471−5.461472−0.2395391473−5.2414741041475Vista1476−0.345221477−8.211478−0.259381479−5.811480−0.2439491481−5.5214821051483Vista1484−0.260891485−6.781486−0.199731487−4.841488−0.1875051489−4.5914901061491Coronado14920.79548149310.3514940.6056814957.5514960.621237114977.761498An Estimation of the Coastal Premium for Residential Housing...1499227150015011502===== PAGE 18 (score 5, numeric-paren 0, table-words 0, var-words 5) =====15031504Rinehart, J. R., & Pompe, J. J. (1994). 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