# RECORD d458e3973f160efbbd5c7fd28ec076d63dc933e1 # TITLE: An Estimation of the Coastal Premium for Residential Housing Prices in San Diego County # YEAR: 2011.0 VENUE: nan DOI: 10.1007/s11146-009-9195-x # AUTHORS: Stephen J Conroy; Jennifer Milosch # PAGES: 18 CANDIDATE TABLE PAGES (1-based): [7, 9, 14, 15, 16, 17] # 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. ===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) ===== An Estimation of the Coastal Premium for Residential Housing Prices in San Diego County Stephen J. Conroy & Jennifer L. Milosch Published online: 22 July 2009 # The Author(s) 2009. This article is published with open access at Springerlink.com 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 (approxi- mately 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. Keywords Coastal premium . Hedonic model . San Diego . Housing prices . California housing J Real Estate Finan Econ (2011) 42:211–228 DOI 10.1007/s11146-009-9195-x S. J. Conroy (*) Department of Economics, School of Business Administration, University of San Diego, San Diego, CA 92110-2492, USA e-mail: sconroy@sandiego.edu J. L. Milosch Department of Economics, University of California, 2127 North Hall, Santa Barbara, CA 93106-9210, USA e-mail: jmilosch@gmail.com ===== PAGE 7 (score 22, numeric-paren 0, table-words 2, var-words 11) ===== Table 1 Descriptive statistics of the variables Variable Description Mean Std Dev Min Max Dependent variable Ln_Price Natural logarithm of sales price 13.31098 0.454904 11.513 16.30 Price Sales price of house 691887.9 556376.9 100,000 12,000,000 Structural characteristics Age Age of house (in years) 37.45743 15.61142 1 98.00 Age_2 Age of house squared 1646.751 1249.535 1 9,604.00 Bedrooms Number of bedrooms 3.306775 0.820486 1 8.00 Bathrooms Number of bathrooms 2.176845 0.825796 0.5 13.50 SqftStruc Square footage of house 1818.762 943.3803 336 16,281.00 LotSqft Square footage of lot on which house is situated 22060.86 102943.1 1210 8,455,867 Spatial and neighborhood variables DtownDist Distance (in feet) from house to downtown 85888.85 55667.39 6906.7 306,053 LnDtownDist Natural logarithm of Dtowndist 11.14917 0.664710 8.84025 12.63151 FreeDist Distance (in feet) from house to nearest freeway 11116.37 12194.96 87.611 158,716 LnFreeDist Natural logarithm of Freedist 8.857125 1.026237 4.4729 11.97487 Dist_API Elementary school district’s base Academic Perform. Index (200–1,000) 767.8436 57.2554 653 939.00 Coastal variables CoastDist Distance (in feet) from house to coast 47975.45 33182.93 73.747 299,490 LnCoastDist Natural logarithm of Coastdist 10.46281 9.65E-01 4.3006 1.26E+01 Coast_500ft Dummy: Res. within 500 ft. of coast 0.003337 0.057671 0 1.00 Coast_1000ft Dummy: Res. 500 to 1,000 ft. of coast 0.003842 0.061870 0 1.00 Coast_2000ft Dummy: Res. 1,000 to 2,000 ft. of coast 0.010111 0.10005 0 1.00 Coast_3000ft Dummy: Res. 2,000 to 3,000 ft. of coast 0.012437 0.110830 0 1.00 Coast_4000ft Dummy: Res. 3,000 to 4,000 ft. of coast 0.012134 0.109487 0 1.00 Coast_5280ft Dummy: Res. 4,000 to 5,280 ft. of coast 0.017189 0.129982 0 1.00 Coast_0to1miles Dummy: Res. within 1 mile of coast 0.059050 0.235729 0 1.00 Coast_2miles Dummy: Res. 1 to 2 miles of coast 0.047725 0.213195 0 1.00 Coast_3miles Dummy: Res. 2 to 3 miles of coast 0.044692 0.206636 0 1.00 An Estimation of the Coastal Premium for Residential Housing... 217 ===== PAGE 8 (score 13, numeric-paren 0, table-words 2, var-words 9) ===== Equation (2) provides an estimate for the (natural log) coastal premium, βl. However, this is essentially an average premium measured at the mean for the entire data set. Since we expect the effect to be nonlinear, we construct a piece-wise linear spline of the distance from coast in order to estimate the effect of living within one mile of the coast, between one and two miles, etc. Thus, we adjust (2) to the following: lnPi ¼ ai þ X J j¼1 bjSij þ X K k¼1 bkTik þ X L l¼1 blDil þ X M m¼1 bmFim þ ni; for i ¼ 1; 2; . . . n ð Þ ð3Þ where Dil is a dummy variable denoting a specific distance from the coast for observation i. This corresponds to “Model 2” in our regression estimates. We can also adjust the distance dummy variables into smaller increments to capture even smaller distance effects, i.e., within 500 feet, between 500 and 1,000 feet, etc. (our “Model 3”).3 Results We present the results of the standard hedonic pricing model in Table 2. Model 1 includes the log coastal distance variable which provides the coastal premium estimation. In this model, all coefficients are significant at the 1% level, except for LotSqft, which is still significant at the 5% level and Bedrooms, which is not significant in any of the models. While the non-effect of having more bedrooms is somewhat surprising (e.g., in a meta analysis by Sirmans et al. 2006, the bedroom effect was positive and significant), there is some recent evidence for a similar, insignificant bedrooms effect in Boston (Stevenson 2004). Closer to home, the 3 These distance increments were somewhat arbitrarily selected, with the intention of capturing homes that are very close to the ocean—within 500 feet—and beyond. In California, ocean-front property owners’ property line extends only to the mean high tide (California Civil Code, Section 1009) so some typical waterfront amenities found in other parts of the country such as boat docks, launches, etc. are not feasible or relevant to this study. In addition, coastal bluffs, prevalent in San Diego County, can render direct private access to the beach impossible in certain areas. There are often limited public access points along the bluffs through which all beachgoers may pass (see Locklin 1999). Table 1 (continued) Variable Description Mean Std Dev Min Max Coast_4miles Dummy: Res. 3 to 4 miles of coast 0.052578 0.223202 0 1.00 Coast_5miles Dummy: Res. 4 to 5 miles of coast 0.062892 0.24278 0 1.00 Coast_6miles Dummy: Res 5 to 6 miles of coast 0.074722 0.26296 0 1.00 218 S.J. Conroy, J.L. Milosch ===== PAGE 9 (score 74, numeric-paren 44, table-words 6, var-words 8) ===== bedroom effect for the Orange County study by Boarnet and Chalermpong (2001) was actually negative. Perhaps buyers in some markets prefer “space” to “bed- rooms,” especially as the large Baby Boom cohort enters its peak earnings years, leaving the childbearing years behind. Indeed, in a recent demographic report of San Table 2 Semi-log fixed effectsd regression results Variable Model 1 Model 2 Model 3 Estimated Coefficient t- Statistic Estimated Coefficient t- Statistic Estimated Coefficient t- Statistic Housing and school characteristics Constant 11.7966a (43.93) 11.2239a (39.88) 11.3138a (40.59) Age −0.007867a (−12.55) −0.007734a (−12.18) −0.007778a (−12.36) Age_2 0.000086a (11.63) 0.000087a (11.65) 0.000085a (11.41) Bedrooms 0.002218 (0.63) 0.002250 (0.64) 0.003540 (1.01) Bathrooms 0.042084a (8.05) 0.045153a (8.55) 0.041481a (7.92) SqftStruc 0.000226a (47.76) 0.000225a (46.92) 0.000226a (47.68) LotSqft 4.84e–08b (2.24) 4.73e–08b (2.16) 4.87e–08b (2.24) Spatial and neighborhood variables LnDtownDist 0.175223a (7.66) 0.076875a (3.29) 0.075096a (3.25) LnFreeDist 0.010732a (3.06) 0.015182a (4.17) 0.009351a (2.57) Dist_API 0.000956a (5.87) 0.001068a (6.48) 0.001046a (6.41) Coastal variables LnCoastDist −0.146047a (−21.12) Coast_500ft 0.702698a (15.43) Coast_1000ft 0.487333a (11.11) Coast_2000ft 0.454806a (13.67) Coast_3000ft 0.438393a (13.97) Coast_4000ft 0.293654a (9.38) Coast_5280ft 0.201110a (6.77) Coast_0to1miles 0.352211a (13.40) Coast_2miles 0.200634a (7.92) 0.193321a (7.71) Coast_3miles 0.118684a (5.24) 0.115352a (5.15) Coast_4miles 0.082033a (4.13) 0.082574a (4.20) Coast_5miles 0.053282a (3.00) 0.053941a (3.07) Coast_6miles 0.035755a (2.59) 0.033207b (2.43) Model statistics Adj. R-Squared 0.7798 0.7749 0.7795 F-Statistic 301.34 280.79 276.80 Number of Obs. 9755 9755 9755 a indicate significance at the 0.01 levels b indicate significance at the 0.05 levels c indicate significance at the 0.10 levels d Fixed effects parameter coefficients for each of 106 neighborhoods omitted here for brevity An Estimation of the Coastal Premium for Residential Housing... 219 ===== PAGE 10 (score 28, numeric-paren 0, table-words 7, var-words 13) ===== Diego County, the three cities with the lowest percentage of children (Solana Beach, Coronado and Del Mar) were among the top four in terms of median household income (SANDAG 2006). The variable of interest, LnCoastDist is −0.146, suggesting that a 10% increase in distance from the coast is associated with a 1.46% decline in price.4 To state this in a more concrete way, since the median house price for the sample is $540,000 and the mean distance from the coast is 47,975.45 feet (approximately 9.09 miles), then taking a median-priced house at the mean distance from the coast, a 10% increase in distance from the coast (or 4,797.5 feet) would correspond to a $7,886.20 reduction in price. Extending this to get to a one-mile increment (11% of the mean distance, corresponding to a 1.61% decreases in price), the effect of moving one mile away would be approximately $8,680. While this is considerably lower than the finding for Orange County, there are several important considerations. First, the result from Boarnet and Chalermpong (2001) was actually for the San Joaquin Hills Transportation Corridor, which runs through the more affluent South Orange County region near Laguna Beach, Laguna Niguel and Aliso Viejo. Second, while actual mean coastal distance for this subsample was not presented in their paper, a casual inspection of the map provided suggests a much lower mean than this current investigation’s 9.09 miles and their data appear to be in a fairly narrow band around the 73 freeway. At 299,416 feet (or about 56.7 miles), the span of this data set for San Diego County is certainly much larger than the entire width (east-west range) for all of Orange County. Results presented for Model 1 also suggest that an additional bathroom is associated with a 4.3% higher sales price.5 Square feet of structure is the most significant coefficient and the estimated coefficient implies that a 100 square foot increase in structure is associated with a 2.29% higher price. Lot square footage is only significant at the 5% level and the effect is much smaller, with each 1,000 additional square foot of lot size associated with about a 0.005% higher price. The coefficients for age and age-squared imply that price declines with age, though the effect becomes less as age increases. In other words, doubling the age would not double the negative “age effect.” Perhaps the “new home” amenity (whether perceived or due to actual structural characteristics) depreciates rapidly and then any negative “age effects” quickly stabilize. The effect of higher Academic Performance Index (API) is also in the expected direction, with house price increasing by 10.03% for a 100-point increase in the local elementary school’s mean API. While not reported in Table 2, fixed effects by neighborhood or ZIP code are presented in the Appendix, Table 3. University City, a somewhat upscale neighborhood northeast of downtown was omitted as the reference category. The largest fixed effect is for Coronado, certainly a well-known landmark and upscale neighborhood, whose coefficient of 0.795 translates to a 121.4% higher price. 5 Following Thornton and Innes (1989) and Halvorsen and Palmquist (1980), interpretation of semilogarithmic regression coefficients follows: g ¼ 100  exp bΔX ð Þ  1 ½ , where ΔX is a (non- infinitesimal) change in variable X, b is the regression coefficient and g can be interpreted as the percent change in the dependent variable as a result of ΔX. 4 Following Ramanathan (1993) and others, we interpret log-log coefficients as elasticities. 220 S.J. Conroy, J.L. Milosch ===== PAGE 14 (score 32, numeric-paren 0, table-words 4, var-words 7) ===== Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. Appendix Table 3 Fixed effects coefficients for each area in sample Dummy Variable Number Neighborhood/ Area Name Model 1 Model 2 Model 3 Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic 1 Adams North 0.28927 4.59 0.18119 2.71 0.182391 2.76 2 Allied Gardens 0.04901 1.53 −0.05775 −1.57 −0.055051 −1.52 3 Alta Vista −0.09120 −1.47 −0.17225 −2.66 −0.167230 −2.61 4 Bay Ho −0.05230 −1.65 −0.07698 −2.39 −0.073174 −2.29 5 Bay Park 0.12506 3.91 0.07106 2.18 0.069225 2.15 6 Bay Terraces −0.16586 −5.46 −0.23021 −6.39 −0.224297 −6.29 7 Birdland −0.01704 −0.26 −0.10723 −1.61 −0.106122 −1.61 8 Carmel Mountain −0.18879 −4.52 −0.22915 −5.08 −0.228515 −5.12 9 Carmel Valley −0.22690 −5.11 −0.19074 −4.24 −0.182397 −4.1 10 Chollas Creek −0.01958 −0.30 −0.12251 −1.77 −0.11643 −1.7 11 Clairemont Mesa East −0.04282 −1.45 −0.09474 −2.86 −0.094954 −2.9 12 Clairemont Mesa West −0.03336 −1.03 −0.08537 −2.41 −0.081550 −2.32 13 College East 0.04669 1.25 −0.05524 −1.32 −0.054744 −1.32 14 College West 0.14982 4.20 0.04538 1.11 0.047855 1.19 15 Del Cerro 0.20273 5.80 0.09975 2.52 0.097364 2.49 16 Del Mar Heights 0.01915 0.37 0.04904 0.92 0.07717 1.46 17 Egger Highlands −0.31759 −8.28 −0.31390 −7.98 −0.297656 −7.64 18 El Cerrito −0.02035 −0.44 −0.12419 −2.45 −0.116716 −2.33 19 Emerald Hills −0.05906 −0.91 −0.14561 −2.12 −0.139839 −2.06 20 Encanto −0.16363 −4.70 −0.24374 −6.09 −0.238071 −6.01 21 Fairmont Park 0.09880 1.82 −0.03441 −0.61 −0.043679 −0.78 22 Jamacha Lomita −0.11405 −3.26 −0.19814 −4.97 −0.188044 −4.77 23 Kensington 0.49584 11.61 0.38714 8.16 0.390514 8.32 24 La Jolla 0.48319 17.16 0.53742 18.10 0.500030 16.89 25 La Playa 0.59002 11.97 0.49965 9.60 0.566019 10.94 26 Lake Murray −0.01612 −0.51 −0.12774 −3.56 −0.122028 −3.44 27 Lincoln Park −0.23100 −3.42 −0.32725 −4.69 −0.329624 −4.78 28 Linda Vista 0.03438 0.79 −0.05841 −1.27 −0.053966 −1.18 29 Loma Portal 0.26730 5.87 0.16731 3.56 0.180524 3.88 30 Midtown 0.49938 7.10 0.34434 4.79 0.346354 4.87 224 S.J. Conroy, J.L. Milosch ===== PAGE 15 (score 33, numeric-paren 0, table-words 4, var-words 7) ===== Table 3 (continued) Dummy Variable Number Neighborhood/ Area Name Model 1 Model 2 Model 3 Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic 31 Mira Mesa −0.17876 −6.91 −0.17060 −5.61 −0.166830 −5.54 32 Miramar Ranch North −0.06266 −1.25 −0.08657 −1.63 −0.087594 −1.67 33 Mission Hills 0.60679 12.50 0.46618 9.40 0.468856 9.55 34 Mountain View −0.11201 −2.06 −0.21336 −3.75 −0.217225 −3.85 35 Nestor −0.30624 −8.24 −0.28427 −7.53 −0.290085 −7.76 36 North Clairmont −0.06973 −2.28 −0.09314 −2.92 −0.090670 −2.87 37 North Park 0.37026 6.10 0.20721 3.31 0.2102822 3.39 38 Oak Park −0.02968 −0.81 −0.12551 −2.99 −0.119818 −2.88 39 Otay Mesa West −0.25950 −8.65 −0.26194 −8.10 −0.263984 −8.25 40 Pacific Beach 0.19852 6.42 0.17414 5.31 0.1955586 6.01 41 Paradise Hills −0.07725 −2.21 −0.14286 −3.68 −0.136876 −3.56 42 Point Loma Heights 0.10439 2.89 0.01869 0.48 0.061810 1.59 43 Rancho Bernardo −0.25249 −6.95 −0.29109 −7.32 −0.288715 −7.33 44 Rancho Penasquitos −0.24791 −7.21 −0.25299 −6.56 −0.248629 −6.52 45 Redwood Village/ Rolando Park −0.04673 −1.30 −0.14748 −3.61 −0.139700 −3.46 46 Ridgeview/ Webster −0.01280 −0.25 −0.12444 −2.32 −0.123291 −2.32 47 Rolando 0.02701 0.67 −0.07682 −1.72 −0.070189 −1.58 48 Roseville/Fleet Ridge 0.32390 6.53 0.24412 4.68 0.285232 5.51 49 Sabre Springs −0.17211 −3.28 −0.21201 −3.82 −0.211167 −3.85 50 San Carlos 0.07392 2.21 −0.03514 −0.93 −0.030838 −0.83 51 San Ysidro −0.23873 −5.05 −0.21912 −4.49 −0.227604 −4.71 52 Scripps Ranch −0.02174 −0.72 −0.06663 −1.95 −0.062204 −1.84 53 Serra Mesa 0.01262 0.38 −0.06808 −1.77 −0.067867 −1.78 54 Skyline −0.15035 −4.18 −0.22852 −5.59 −0.220226 −5.44 55 Sunset Cliffs 0.15960 3.31 0.18815 3.75 0.102672 2.04 56 Talmadge 0.25699 6.08 0.14829 3.15 0.155251 3.33 57 Tierrasanta 0.07548 2.52 −0.01000 −0.29 −0.008803 −0.25 58 University City Reference Reference Reference Reference Reference Reference 59 University Heights 0.30387 4.00 0.16772 2.15 0.1699697 2.2 60 Valencia Park −0.14738 −3.46 −0.24302 −5.31 −0.239873 −5.29 61 Wooded Area 0.32130 5.94 0.25579 4.49 0.237969 4.19 62 Alpine −0.06862 −1.70 −0.20178 −4.77 −0.200639 −4.79 An Estimation of the Coastal Premium for Residential Housing... 225 ===== PAGE 16 (score 32, numeric-paren 0, table-words 4, var-words 2) ===== Table 3 (continued) Dummy Variable Number Neighborhood/ Area Name Model 1 Model 2 Model 3 Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic 63 Bonita 0.09817 2.25 0.03829 0.81 0.0445891 0.95 64 Chula Vista −0.12586 −4.38 −0.16545 −5.37 −0.163104 −5.35 65 Chula Vista −0.14830 −5.38 −0.17702 −5.68 −0.177358 −5.75 66 Chula Vista −0.04784 −0.92 −0.10239 −1.85 −0.094617 −1.72 67 Imperial Beach −0.35680 −10.07 −0.36678 −9.76 −0.327387 −8.77 68 Jamul 0.00308 0.07 −0.10917 −2.42 −0.097273 −2.18 69 La Mesa −0.01517 −0.53 −0.12031 −3.57 −0.114246 −3.43 70 La Mesa −0.01353 −0.43 −0.12279 −3.44 −0.120666 −3.42 71 Lemon Grove −0.08566 −2.73 −0.17847 −4.90 −0.168174 −4.66 72 National City −0.06829 −1.87 −0.15213 −3.93 −0.150348 −3.93 73 Spring Valley −0.13428 −4.66 −0.22682 −6.69 −0.216723 −6.46 74 Spring Valley −0.14937 −3.56 −0.25453 −5.62 −0.243374 −5.43 75 Bonsall −0.20346 −3.51 −0.17813 −2.96 −0.168412 −2.83 76 Borrego Springs −0.48494 −7.28 −0.63586 −9.43 −0.616979 −9.24 77 Cardiff-By- The-Sea −0.37486 −7.28 −0.30723 −5.86 −0.256439 −4.92 78 Carlsbad −0.42648 −9.68 −0.31961 −7.11 −0.300920 −6.75 79 Carlsbad −0.20215 −3.68 −0.13294 −2.34 −0.122106 −2.17 80 Del Mar 0.14282 2.40 0.20353 3.36 0.176549 2.93 81 El Cajon −0.06339 −2.01 −0.17889 −5.16 −0.174909 −5.1 82 El Cajon −0.06980 −2.39 −0.18120 −5.46 −0.178580 −5.44 83 El Cajon −0.09807 −2.94 −0.21435 −5.90 −0.213630 −5.94 84 Encinitas −0.25771 −6.07 −0.19005 −4.43 −0.181308 −4.26 85 Escondido −0.15033 −4.10 −0.17411 −4.43 −0.170918 −4.39 86 Escondido −0.24079 −6.39 −0.24816 −6.15 −0.245108 −6.14 87 Escondido −0.21075 −5.89 −0.24460 −6.38 −0.235583 −6.21 88 Fallbrook −0.23597 −5.56 −0.22067 −4.94 −0.210999 −4.77 89 Escondido −0.06510 −1.66 −0.06864 −1.64 −0.067351 −1.62 90 Julian −0.53007 −8.98 −0.69398 −11.55 −0.674622 −11.34 91 Lakeside −0.11258 −3.26 −0.23749 −6.37 −0.227898 −6.18 92 Oceanside −0.51933 −12.71 −0.39524 −9.64 −0.383747 −9.44 93 Oceanside −0.46783 −11.42 −0.36782 −8.54 −0.356750 −8.37 94 Oceanside −0.46307 −10.62 −0.36157 −7.84 −0.347510 −7.61 95 Poway −0.09670 −2.67 −0.16638 −4.21 −0.155905 −3.98 96 Ramona −0.18611 −5.04 −0.30823 −7.91 −0.292458 −7.57 97 Rancho Santa Fe 0.13079 2.77 0.16003 3.25 0.175013 3.59 98 San Marcos −0.29376 −7.32 −0.27956 −6.51 −0.273430 −6.43 99 Santee −0.20511 −6.09 −0.32879 −8.86 −0.316647 −8.62 100 Solana Beach 0.04162 0.79 0.15963 3.01 0.090114 1.71 226 S.J. Conroy, J.L. Milosch ===== PAGE 17 (score 24, numeric-paren 0, table-words 4, var-words 13) ===== References Benson, E. D., Hansen, J. L., Schwartz, A. L., Jr., & Smersh, G. T. (1998). Pricing residential amenities: the value of a view. Journal of Real Estate Finance and Economics, 16(1), 55–73. Blackley, P., Follain, J. R., Jr., & Ondrich, J. (1984). Box-cox estimation of hedonic models: how serious is the iterative OLS variance bias? Review of Economics and Statistics, 66(2), 348–353. Boarnet, M. G., & Chalermpong, S. (2001). New highways, house prices, and urban development: a case study of toll roads in Orange County, CA. Housing Policy Debate, 12(3), 575–605. Brasington, D. M. (1999). Which measures of school quality does the housing market value? Journal of Real Estate Research, 18(3), 395–413. Cropper, M. L., Deck, L. B., & McConnell, K. E. (1988). On the choice of functional form for hedonic price functions. Review of Economics and Statistics, 70(4), 668–675. Freeman, A. M., III. (2003). The measurement of environmental and resource values: theory and methods (2nd ed.). Washington, D.C.: Resources for the Future. Giuliano, G. (1989). New directions for understanding transportation and land use. Environment and Planning, 21, 145–159. Halvorsen, R., & Palmquist, R. (1980). The interpretation of dummy variables in semilogarithmic equations. American Economic Review, 70, 474–475. Irwin, E. G. (2002). The effects of open space on residential property values. Land Economics, 78(4), 465–480. Kilpatrick, J. A., Throupe, R. L., Carruthers, J. I., & Krause, A. (2007). The impact of transit corridors on residential property values. Journal of Real Estate Research, 29(3), 303–320. Langley, J. C. (1976). Adverse impacts of the Washington beltway on residential property values. Land Economics, 52(1), 54–65. Locklin, L. (1999). Public Access Action Plan. California Coastal Commission. World Wide Web page (accessed May 11, 2009). Mahan, B. L., Polasky, S., & Adams, R. M. (2000). Valuing urban Wetlands: a property price approach. Land Economics, 76(1), 100–113. Major, C., & Lusht, K. M. (2004). Beach proximity and the distribution of property values in shore communities. The Appraisal Journal, 72, 333–338. Muth, R. (1969). Cities and housing. Chicago, IL: University of Chicago Press. Obenski, S., Farber, J., & Chappell, G. (2006). Research to improve census imputation methods: Item results and conclusions. Working Paper: U.S. Census Bureau. World Wide Web page (accessed on March 17, 2009). Plattner, R. H., & Campbell, T. J. (1978). A study of the effect of water view on site value. The Appraisal Journal, 78, 20–25. Ramanathan, R. (1993). Statistical methods in econometrics. San Diego: Academic. Ridker, R. G. (1967). Economic costs of air pollution: Studies in measurement. New York: Praeger. Ridker, R. G., & Henning, J. A. (1967). The determinants of residential property values with special reference to air pollution. Review of Economics and Statistics, 49(2), 246–257. Table 3 (continued) Dummy Variable Number Neighborhood/ Area Name Model 1 Model 2 Model 3 Coefficient t-Statistic Coefficient t-Statistic Coefficient t-Statistic 101 San Marcos −0.27519 −6.36 −0.21263 −4.62 −0.199022 −4.36 102 Vista −0.26917 −5.18 −0.19086 −3.53 −0.176459 −3.29 103 Valley Center −0.20051 −4.54 −0.25197 −5.46 −0.239539 −5.24 104 Vista −0.34522 −8.21 −0.25938 −5.81 −0.243949 −5.52 105 Vista −0.26089 −6.78 −0.19973 −4.84 −0.187505 −4.59 106 Coronado 0.79548 10.35 0.60568 7.55 0.6212371 7.76 An Estimation of the Coastal Premium for Residential Housing... 227 ===== PAGE 18 (score 5, numeric-paren 0, table-words 0, var-words 5) ===== Rinehart, J. R., & Pompe, J. J. (1994). Adjusting the market value of coastal property for beach quality. The Appraisal Journal, LXII, 604–608. Rodriguez, M., & Sirmans, C. F. (1994). Quantifying the value of a view in single-family housing markets. The Appraisal Journal, 62, 600–603. Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55. SANDAG. (2006). San Diego region demographic and economic characteristics. (Monograph) San Diego: San Diego Association of Governments. June (1). World Wide Web page (accessed on June 20, 2007). Sirmans, G. S., MacDonald, L., Macpherson, D. A., & Zietz, E. N. (2006). The value of housing characteristics: a meta analysis. Journal of Real Estate Finance and Economics, 33, 215–240. Stevenson, S. (2004). New empirical evidence on heteroskedasticity in hedonic housing models. Journal of Housing Economics, 13, 136–153. Taylor, L. O., & Smith, V. K. (2000). Environmental amenities as a source of market power. Land Economics, 76(4), 550–568. Thornton, R. J., & Innes, J. T. (1989). Interpreting semilogarithmic regression coefficients in labor research. Journal of Labor Research, X(4), 443–447. Tiebout, C. M. (1956). A pure theory of local expenditures. Journal of Political Economy, 64(5), 416–424. Tyravainen, L., & Miettinen, A. (2000). Property prices and urban forest amenities. Journal of Environmental Economics and Management, 39(2), 205–223. Wooldridge, J. (2003). Cluster-sample methods in applied econometrics. American Economic Review, 93 (2), 133–138. Zajac, K. J. (2003). Analysis of imputation rates for the 100 percent person and housing unit data items from census 2000. (Monograph), U.S. Department of Commerce, Bureau of the Census. September 25. 103 pp. World Wide Web page (accessed on June 18, 2007). 228 S.J. Conroy, J.L. Milosch