1# RECORD 8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b22# TITLE: Spatial Heterogeneity in Hedonic Price Effects for Lake Water Quality3# YEAR: 2023.0 VENUE: Land Economics DOI: 10.3368/le.100.1.102122-0086r4# AUTHORS: Kristen Swedberg; Diego S. Cardoso; Adriana Castillo-Castillo; Saleh Mamun; K. Boyle; C. Nolte; M. Papenfus; S. Polasky5# PAGES: 42 CANDIDATE TABLE PAGES (1-based): [33, 34]6# ABSTRACT: This study uses Zillow’s Transaction and Assessment Database to investigate variation in hedonic price effects of water clarity on single-family houses throughout the United States. We consider five spatial scales and estimate models using different sample selection criteria and model specifications. The results indicate considerable spatial heterogeneity within and across the four U.S. census regions. However, we also find heterogeneity resulting from different types of investigator decisions, including sample selection and modeling choices. Thus, it is necessary to use practical knowledge to consider the limits of market areas and to investigate the robustness of estimation results to investigator choices.789===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) =====10111 12 13Spatial Heterogeneity in Hedonic Price Effects for Lake Water Quality 14Kristen Swedberg1,2, Diego S. Cardoso3, Adriana Castillo-Castillo4, Saleh Mamun4,5,6, 15Kevin J. Boyle1,7, Christoph Nolte8,9, Michael Papenfus10, Stephen Polasky4,5,11 16 171 Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, VA 182 ORISE Fellow in Office of Water, Environmental Protection Agency, Washington, DC 193 Department of Agricultural Economics, Purdue University, West Lafayette, IN 204 Department of Applied Economics, University of Minnesota, St. Paul, MN 215 The Natural Capital Project, University of Minnesota, St. Paul, MN 226 Natural Resources Research Institute, University of Minnesota – Duluth, Duluth, MN 237 Blackwood Department of Real Estate, Virginia Tech, Blacksburg, VA 248 Department of Earth & Environment, Boston University, Boston, MA 259 Faculty of Computing & Data Sciences, Boston University, Boston, MA 2610 Environmental Protection Agency, Corvallis, OR 2711 Department of Ecology, Evolution & Behavior, University of Minnesota, St. Paul, MN 28 29Abstract 30This study uses Zillow’s ZTRAX property transaction database to investigate variation in hedonic 31price effects of water clarity on single-family houses throughout the United States. We consider 32five spatial scales and estimate models using different sample selection criteria and model 33specifications. Our results indicate considerable spatial heterogeneity both within and across the 34four U.S. Census regions. However, we also find heterogeneity resulting from different types of 35investigator decisions, including sample selection and modelling choices. Thus, it is necessary to 36use practical knowledge to consider the limits of market areas and to investigate the robustness of 37estimation results to investigator choices. (JEL Q51) 38 39 40doi:10.3368/le.100.1.102122-0086R41by guest on October 20, 2026. Copyright 202342Downloaded from 434445===== PAGE 33 (score 57, numeric-paren 36, table-words 1, var-words 12) =====464733 48 49Table 2 Descriptive Statistics by Region 50 51Full Sample 52Northeast 53Midwest 54South 55West 56Sale Price (2020 $) 57450,060 58344,023 59356,748 60642,456 61(426,614) 62(250,921) 63(282,424) 64(377,061) 65Secchi Depth (m) 662.49 671.67 681.49 692.93 70(1.61) 71(1.22) 72(0.87) 73(1.85) 74Lake Distance (m) 751,016 76924 771,016 781,325 79(734) 80(687) 81(708) 82(695) 83Lake Distance <150 m (1/0) 840.13 850.14 860.11 870.04 88(0.34) 89(0.34) 90(0.32) 91(0.19) 92Lake Distance 150-300 m (1/0) 930.09 940.10 950.09 960.05 97(0.29) 98(0.30) 99(0.28) 100(0.21) 101Lake Area (ha) 1028,944 103967 1045,334 1052,451 106(28,427) 107(5,083) 108(17,441) 109(7,227) 110Urbanized Area (1/0) 1110.60 1120.67 1130.67 1140.76 115(0.49) 116(0.47) 117(0.47) 118(0.43) 119Median Income (2016 $) 12083,769 12178,565 12266,228 12382,623 124(35,013) 125(32,392) 126(30,839) 127(37,863) 128Slope (deg) 1293.89 1302.69 1313.41 1323.93 133(3.27) 134(2.22) 135(3.41) 136(3.88) 137Elevation (m) 138148.95 139291.20 140136.82 141885.74 142(130.50) 143(59.09) 144(142.42) 145(791.58) 146N 147396,988 1481,241,194 1491,388,818 150344,394 151Lakes 1521,112 1534,012 154830 155308 156States 1579 15812 15916 16010 161Ecoregions 1625 16310 1647 1658 166 167Samples Restricted by Property Attribute Availability 168Northeast 169Midwest 170South 171West 172Lot Size (sqft) 17343,190 17430,916 17531,008 17617,659 177(78,291) 178(64,055) 179(55,912) 180(40,489) 181n = 380,289 n = 1,057,113 n = 1,291,411 n = 323,733 182Building Age (years) 18348.60 18443.19 18527.23 18632.70 187(29.12) 188(30.09) 189(21.85) 190(27.29) 191n = 318,287 192n = 870,459 193n = 1,069,065 n = 286,611 194Building Size (sqft) 1951,924 1961,820 1972,057 1981,969 199(871) 200(972) 201(1,024) 202(846) 203n = 345,396 204n = 860,124 205n = 684,586 206n = 271,974 207Number of Bathrooms 2082.07 2092.39 2102.71 2112.24 212(0.92) 213(1.33) 214(1.30) 215(0.84) 216n = 258,968 217n = 725,467 218n = 473,398 219n = 170,154 220Number of Bedrooms 2213.20 2223.59 2233.61 2243.27 225 226(0.98) 227(1.46) 228(1.36) 229(0.97) 230n = 258,968 231n = 725,467 232n = 473,398 233n = 170,154 234Means reported with standard deviations in parentheses. 235 236by guest on October 20, 2026. Copyright 2023237Downloaded from 238239240===== PAGE 34 (score 123, numeric-paren 97, table-words 3, var-words 6) =====24124234 243 244Table 3 National Regression Results 245Parameter 246Estimate 247Std. Error 248Parameter 249Estimate 250Std. Error 251const 2527.123*** 253(0.207) 254Secchi * MD 255-0.259** 256(0.085) 257Secchii,ii,iii 2580.231*** 259(0.034) 260Secchi* ME 2612.428*** 262(0.077) 263Secchi * Lake Area 264Demeaned3 2650.009* 266(0.004) 267Secchi * MI 268-0.0680 269(0.048) 270Secchi * <150 m 2710.025** 272(0.009) 273Secchi * MO 2740.6730 275(0.739) 276Secchi * Lake Distance 277-0.034*** 278(0.005) 279Secchi * MT 280-2.021*** 281(0.465) 282Secchi * Urbaniv 283-0.0150 284(0.014) 285Secchi * NC 286-0.1830 287(0.123) 288Secchi * Ecoregion 5.2iv 2890.075* 290(0.036) 291Secchi * ND 292-0.2230 293(0.292) 294Secchi * Ecoregion 5.3 2950.0430 296(0.033) 297Secchi * NE 298-0.1790 299(0.101) 300Secchi * Ecoregion 6.2 3010.5940 302(0.462) 303Secchi * NH 304-0.0650 305(0.047) 306Secchi * Ecoregion 7.1 3070.9910 308(0.596) 309Secchi * NJ 310-0.0030 311(0.052) 312Secchi * Ecoregion 8.2 3130.0570 314(0.077) 315Secchi * NY 316-0.0740 317(0.04) 318Secchi * Ecoregion 8.3 3190.0550 320(0.048) 321Secchi * OH 322-0.1980 323(0.16) 324Secchi * Ecoregion 8.4 325-0.0170 326(0.069) 327Secchi * OK 328-0.1240 329(0.266) 330Secchi * Ecoregion 8.5 3310.0260 332(0.031) 333Secchi * OR 334-0.8670 335(0.525) 336Secchi * Ecoregion 9.2 3370.150 338(0.1) 339Secchi * PA 3400.154*** 341(0.036) 342Secchi * Ecoregion 9.3 3430.2230 344(0.201) 345Secchi * RI 3460.102* 347(0.047) 348Secchi * Ecoregion 9.4 3490.2030 350(0.149) 351Secchi * SC 352-0.0530 353(0.084) 354Secchi * Ecoregion 10.2 3550.3060 356(0.462) 357Secchi * SD 358-0.1270 359(0.287) 360Secchi * Ecoregion 11.1 3610.6620 362(0.462) 363Secchi * TN 364-0.1130 365(0.078) 366Secchi * Ecoregion 13.1 3670.3660 368(0.475) 369Secchi * TX 370-0.3930 371(0.233) 372Secchi * Ecoregion 15.4 3730.0320 374(0.048) 375Secchi * VA 376-0.1340 377(0.083) 378Secchi * ALiv 3790.0030 380(0.202) 381Secchi * VT 382-0.0780 383(0.129) 384Secchi * AR 3850.1210 386(0.15) 387Secchi * WA 388-0.9890 389(0.597) 390Secchi * AZ 391-0.3960 392(0.475) 393Secchi * WI 394-0.0330 395(0.08) 396Secchi * CA 397-0.7280 398(0.465) 399<150 m 4000.124*** 401(0.009) 402Secchi * CO 403-0.4980 404(0.461) 405Lake 406Distance 407-0.095*** 408(0.005) 409Secchi * CT 410-0.266* 411(0.12) 412Lake Areav 4130.013* 414(0.005) 415Secchi * DE 416-0.0860 417(0.402) 418Median 419Income 4200.131*** 421(0.014) 422Secchi * FL 423-0.0190 424(0.037) 425Slope 4260.019*** 427(0.002) 428Secchi * GA 4290.0330 430(0.128) 431Elevation 432-0.0230 433(0.023) 434Secchi * IA 435-0.221* 436(0.11) 437Lot Size 4380.101*** 439(0.004) 440Secchi * IL 441-0.1070 442(0.08) 443Building Age 444-0.125*** 445(0.003) 446Secchi * IN 4470.20 448(0.515) 449Building Size 4500.547*** 451(0.01) 452Secchi * KY 453-1.956*** 454(0.058) 455Quarter 2 4560.044*** 457(0.002) 458Secchi * LA 4590.0840 460(0.211) 461Quarter 3 4620.05*** 463(0.002) 464Secchi * MA 465-0.0270 466(0.024) 467Quarter 4 4680.015*** 469(0.002) 470Notes: N = 485,860 Standard Errors Clustered at Tract Level. *p<.05 **p<.01 ***p<.001 471iInteraction terms for MN and Ecoregion 8.1 were pre-selected as the excluded categories, 472because the area intersection contains the most observations in the sample. 473iiDuring estimation, interaction terms for KS, NM, WV, Ecoregion 10.1, Ecoregion 12.1 were 474fully absorbed by fixed effects and dropped from model. Observations for Ecoregion 12.1 all 475by guest on October 20, 2026. Copyright 2023476Downloaded from 477478479===== PAGE 35 (score 7, numeric-paren 0, table-words 2, var-words 3) =====48048135 482 483fall within AZ. Variation in estimates for KS, NM, and WV can be explained by the 484corresponding ecoregion interactions. 485iiiThe log of lake area is demeaned in the interaction term with Secchi. Thus, the Secchi 486coefficient alone can be interpreted as the effect of water quality for a lake with the mean log-487lake area. Secchi * Lake Area Demeaned can be interpreted as the effect of an increase in lake 488size over the sample mean. 489ivBecause this model is estimated with census tract by year fixed effects, it is not necessary to 490include Urban, Ecoregion, and State constituents in the model as control variables. 491vLake area when not interacted with Secchi is not demeaned, as it only serves as a control 492variable in the model rather than a coefficient of interest. 493 494 495 496 497 498 499 500 501 502 503 504 505by guest on October 20, 2026. Copyright 2023506Downloaded from 507