How I learned to stop worrying and love Random Forests (Part 1)

October 12, 2018

In this notebook I'll show how a non-paramtric model like Random Forests can not only fit structured data incredibly well but can also be easily interpretable! To do this I'll review step-by-step the Random Forest model training workflow covered in the Fast.ai Machine Learning course (which I highly recommend. No where else will you find such a practical view of learning and using machine learning). I'll be exploring the Ames Housing dataset which is the subject of the kaggle Housing Prices competition.

Imports

%load_ext autoreload
%autoreload 2

%matplotlib inline

from fastai.imports import *
from fastai.structured import *

from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display

from sklearn import metrics

/home/paperspace/anaconda3/envs/fastai/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/home/paperspace/anaconda3/envs/fastai/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/home/paperspace/anaconda3/envs/fastai/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/home/paperspace/anaconda3/envs/fastai/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d

PATH = "data/house-prices/"

df_raw = pd.read_csv(f'{PATH}train.csv', low_memory=False)

In any sort of data science work, it's important to look at your data, to make sure you understand the format, how it's stored, what type of values it holds, etc. Even if you've read descriptions about your data, the actual data may not be what you expect.

def display_all(df):
    with pd.option_context("display.max_rows", 1000, "display.max_columns", 1000): 
        display(df)

display_all(df_raw.T)
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Id 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 ... 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460
MSSubClass 60 20 60 70 60 50 20 60 50 190 20 60 20 20 20 45 20 90 20 20 60 45 20 120 20 20 20 20 20 30 70 20 20 20 120 60 20 20 20 90 20 20 85 20 20 120 50 20 190 20 60 50 90 20 80 20 160 60 60 20 20 75 120 70 60 60 20 20 30 50 20 20 60 20 50 180 20 50 90 50 60 120 20 20 80 60 60 160 50 20 20 20 30 190 60 60 20 20 30 20 20 60 90 20 50 60 30 20 50 20 50 80 60 20 70 160 20 20 60 60 80 50 20 120 20 190 120 45 60 20 60 60 20 20 20 20 20 90 60 60 20 20 50 20 90 160 30 60 20 50 20 20 60 20 30 50 20 60 60 60 20 60 20 45 40 190 20 60 60 20 50 20 160 20 20 20 60 50 20 30 160 70 20 50 50 75 80 50 90 120 70 60 20 160 20 160 20 75 75 20 20 20 50 120 50 20 20 20 60 20 30 20 60 20 60 20 20 70 50 120 20 60 60 20 20 160 60 160 20 120 20 60 160 20 60 160 20 60 20 50 20 30 50 160 60 20 190 20 60 50 30 120 60 80 20 60 60 20 60 20 80 60 80 50 30 20 60 75 30 20 60 20 60 20 20 50 20 20 20 60 60 20 120 20 120 160 50 20 20 70 60 190 50 60 20 80 50 60 60 20 190 60 20 20 75 20 60 50 30 20 60 20 190 20 70 60 60 60 60 80 60 60 60 20 80 45 120 20 75 70 90 20 20 120 60 190 20 20 20 20 60 20 90 120 160 50 20 20 160 60 120 120 50 30 50 20 20 120 80 60 85 50 85 160 60 70 20 80 20 20 60 50 120 20 60 30 85 60 20 60 50 20 60 45 60 120 50 80 20 60 50 60 20 30 50 20 20 60 30 60 120 20 30 60 60 20 50 70 60 60 20 190 20 30 60 20 60 70 50 20 90 20 20 60 20 60 80 20 20 20 160 50 160 60 180 60 50 45 30 50 20 90 50 120 60 20 20 60 50 50 30 20 60 60 90 20 70 20 70 50 60 70 20 70 20 120 20 70 20 60 120 60 180 20 120 20 20 60 20 30 20 20 70 120 20 20 20 20 190 180 160 50 60 20 30 30 20 50 20 20 ... 20 60 160 20 60 60 50 20 50 190 50 160 120 20 70 160 30 120 20 20 85 60 20 60 90 190 50 20 60 60 60 70 60 60 20 50 20 20 30 20 20 30 20 90 120 80 20 160 20 50 50 90 70 30 20 60 20 120 80 120 20 20 50 120 20 20 20 20 50 160 190 75 60 20 30 20 20 60 160 180 20 60 120 60 20 20 60 20 20 20 20 20 60 20 60 20 120 60 60 50 120 30 190 30 20 60 60 60 160 45 20 60 50 60 20 70 50 20 120 20 20 20 20 20 60 85 160 60 160 120 90 160 50 20 20 20 70 120 50 20 30 20 20 20 160 60 20 60 60 20 60 60 20 20 20 20 80 20 80 20 30 20 20 20 80 20 120 20 60 90 50 20 70 60 60 30 50 50 20 30 20 60 60 20 190 50 20 70 50 70 20 20 20 30 60 20 80 120 20 60 160 20 20 90 80 20 20 60 70 60 80 20 160 50 70 50 20 50 50 20 60 120 60 30 20 50 190 20 60 60 190 160 50 120 60 60 60 75 20 20 20 60 50 20 20 20 20 20 20 20 60 50 30 80 85 20 90 20 50 160 20 20 50 20 60 80 60 20 120 80 90 90 90 20 70 70 160 60 20 20 60 20 85 20 70 80 60 80 75 20 20 120 20 60 60 50 20 30 80 20 60 20 50 70 120 160 190 20 50 50 40 20 20 80 50 90 60 80 60 50 20 20 20 90 50 50 20 20 120 60 80 160 70 60 20 20 20 180 60 20 60 70 60 20 160 20 120 20 20 20 20 20 60 60 20 60 20 120 20 20 20 20 60 30 20 30 30 20 50 60 20 80 20 50 160 20 90 30 60 20 20 20 60 50 60 30 20 20 20 70 90 60 50 50 60 80 20 20 160 20 70 20 50 60 160 60 60 160 120 20 50 80 60 20 60 20 30 50 160 80 30 20 70 30 50 50 60 50 20 50 20 90 85 190 120 60 20 70 50 50 50 60 20 20 50 120 85 20 70 60 60 50 90 20 50 120 190 60 20 20 60 120 120 80 20 20 60 50 30 20 60 120 30 60 20 20 20 20 20 60 70 120 60 30 20 85 20 60 50 180 90 20 180 20 20 60 20 70 20 20
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LotArea 8450 9600 11250 9550 14260 14115 10084 10382 6120 7420 11200 11924 12968 10652 10920 6120 11241 10791 13695 7560 14215 7449 9742 4224 8246 14230 7200 11478 16321 6324 8500 8544 11049 10552 7313 13418 10859 8532 7922 6040 8658 16905 9180 9200 7945 7658 12822 11096 4456 7742 13869 6240 8472 50271 7134 10175 2645 11645 13682 7200 13072 7200 6442 10300 9375 9591 19900 10665 4608 15593 13651 7599 10141 10200 5790 1596 8475 8635 10778 10440 13000 4500 10206 8892 8530 16059 11911 3951 8470 8070 7200 8500 13360 7200 9337 9765 10264 10921 10625 9320 10603 9206 7018 10402 7758 9375 10800 6000 8500 11751 9525 7750 9965 21000 7259 3230 11616 8536 12376 8461 21453 6060 9464 7892 17043 6780 4928 4388 7590 8973 14200 12224 7388 6853 10335 10400 10355 11070 9066 15426 10500 11645 8520 10335 9100 2522 6120 9505 7500 6240 10356 13891 14803 13500 11340 9600 7200 12003 12552 19378 11120 13688 12182 5500 5400 10106 10708 10562 8244 16669 12358 31770 5306 10197 12416 12615 10029 13650 17423 8520 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LotShape Reg Reg IR1 IR1 IR1 IR1 Reg IR1 Reg Reg Reg IR1 IR2 IR1 IR1 Reg IR1 Reg Reg Reg IR1 Reg Reg Reg IR1 Reg Reg Reg IR1 IR1 Reg IR1 Reg IR1 Reg Reg Reg Reg Reg Reg Reg Reg IR1 IR1 Reg Reg IR1 Reg Reg Reg IR2 Reg IR2 IR1 Reg IR1 Reg IR1 IR2 Reg Reg Reg IR1 IR1 Reg Reg Reg IR1 Reg Reg IR1 Reg IR1 Reg Reg Reg IR1 Reg Reg Reg Reg Reg Reg IR1 IR1 Reg IR2 Reg IR1 Reg Reg Reg IR1 Reg IR1 IR2 IR1 Reg Reg IR1 IR1 Reg Reg IR1 Reg Reg Reg Reg Reg IR1 Reg IR1 Reg Reg IR1 Reg Reg Reg Reg Reg IR1 Reg Reg Reg IR1 Reg IR1 IR1 Reg Reg Reg IR1 Reg IR1 IR1 Reg IR1 Reg IR1 IR1 Reg Reg Reg IR1 Reg Reg Reg IR1 Reg Reg Reg Reg IR1 Reg Reg Reg Reg Reg Reg IR1 IR1 IR1 Reg Reg Reg Reg IR1 Reg IR1 IR1 IR1 IR1 IR1 IR1 IR1 Reg IR1 Reg IR1 Reg Reg Reg Reg Reg IR1 IR2 IR1 Reg Reg Reg Reg IR1 IR1 Reg IR1 Reg Reg Reg Reg Reg Reg Reg Reg Reg Reg Reg IR1 IR1 IR1 Reg Reg Reg Reg IR2 IR1 IR1 Reg Reg IR1 Reg IR1 IR1 Reg Reg Reg Reg IR1 Reg Reg Reg Reg IR1 Reg Reg Reg Reg Reg IR1 Reg Reg Reg Reg Reg Reg IR1 Reg Reg Reg Reg IR2 IR1 IR1 IR1 Reg Reg IR1 IR1 Reg Reg Reg Reg IR1 IR1 Reg IR3 IR1 IR1 Reg Reg IR1 Reg IR1 IR1 Reg Reg Reg IR1 Reg Reg Reg Reg Reg Reg Reg Reg IR1 IR1 IR1 IR1 Reg Reg Reg Reg IR1 Reg IR1 Reg IR1 Reg Reg Reg IR3 IR1 Reg Reg Reg Reg IR1 IR1 IR1 IR1 Reg Reg IR3 Reg IR1 IR1 Reg Reg Reg Reg IR1 IR1 Reg Reg Reg IR1 Reg IR1 Reg IR1 Reg IR1 Reg IR1 IR1 IR1 Reg Reg IR1 Reg Reg Reg IR1 Reg Reg IR1 IR1 Reg IR2 IR1 IR1 Reg Reg Reg IR2 IR1 Reg IR1 IR1 IR1 Reg Reg Reg IR1 Reg IR1 IR1 Reg IR1 IR1 Reg Reg Reg Reg IR1 IR1 IR1 Reg IR1 Reg Reg IR1 Reg IR2 Reg Reg Reg IR1 Reg Reg IR1 IR1 Reg Reg Reg Reg Reg Reg Reg IR2 IR1 Reg Reg IR1 IR1 Reg Reg Reg Reg Reg Reg IR2 Reg IR2 IR1 Reg IR1 Reg Reg Reg IR1 Reg Reg Reg Reg Reg IR1 Reg IR1 Reg IR1 Reg IR1 Reg IR2 Reg Reg Reg Reg Reg Reg Reg Reg Reg Reg IR1 IR1 Reg Reg Reg IR1 IR1 Reg Reg Reg Reg IR1 Reg IR1 IR1 Reg IR1 IR1 Reg Reg Reg Reg IR1 IR1 IR1 Reg Reg IR1 Reg Reg Reg Reg Reg Reg IR1 IR1 Reg Reg IR1 Reg Reg IR1 Reg Reg Reg Reg IR1 Reg Reg Reg IR1 Reg Reg IR1 ... IR1 IR1 Reg IR1 Reg Reg IR1 IR1 Reg Reg Reg Reg Reg Reg IR1 Reg IR1 IR1 Reg Reg IR1 IR1 Reg Reg Reg Reg Reg IR1 IR1 Reg Reg Reg Reg Reg Reg IR1 IR1 IR1 Reg Reg Reg Reg IR1 IR1 Reg Reg IR3 Reg IR1 Reg Reg Reg Reg Reg Reg Reg Reg IR1 IR1 Reg Reg Reg Reg Reg IR1 Reg Reg IR1 Reg Reg Reg Reg IR1 Reg Reg IR1 IR1 Reg Reg Reg Reg Reg IR1 Reg Reg IR1 IR1 IR2 Reg Reg IR1 IR1 Reg Reg Reg Reg IR1 IR2 IR1 IR1 Reg Reg Reg Reg IR1 Reg IR1 Reg Reg Reg Reg Reg Reg IR1 Reg Reg Reg IR1 Reg Reg Reg Reg Reg Reg IR2 Reg Reg Reg Reg IR1 Reg Reg Reg Reg Reg IR1 Reg Reg Reg IR1 Reg IR1 Reg Reg Reg IR1 IR1 IR3 Reg IR1 Reg Reg IR1 Reg Reg Reg Reg IR1 Reg Reg Reg Reg IR1 Reg IR1 Reg Reg IR3 IR1 Reg Reg Reg Reg IR1 IR1 Reg Reg Reg IR1 IR1 Reg IR1 Reg Reg Reg Reg Reg Reg Reg Reg IR1 Reg IR1 Reg IR1 IR1 Reg IR1 Reg Reg Reg IR1 Reg Reg IR1 IR1 IR1 IR1 Reg IR1 Reg Reg IR1 IR1 IR1 Reg IR1 Reg IR1 Reg IR2 IR1 IR1 Reg Reg Reg IR1 Reg IR1 Reg Reg Reg Reg Reg Reg IR1 IR1 IR1 Reg Reg Reg Reg Reg Reg Reg IR1 Reg Reg Reg IR1 Reg IR1 Reg IR1 IR1 IR1 Reg Reg Reg Reg IR1 Reg Reg Reg IR1 Reg IR1 Reg Reg Reg IR1 Reg Reg IR1 Reg Reg Reg IR2 Reg IR1 Reg Reg Reg Reg IR1 Reg Reg IR1 Reg Reg Reg IR1 Reg IR1 Reg Reg IR1 Reg IR1 Reg IR3 Reg Reg Reg Reg Reg IR1 IR1 IR1 Reg Reg IR1 IR1 IR1 Reg Reg IR1 IR1 Reg Reg IR1 IR1 IR1 Reg Reg Reg Reg IR1 IR1 IR1 IR1 Reg Reg Reg Reg Reg Reg Reg IR3 Reg IR1 IR1 Reg Reg Reg IR1 IR1 Reg Reg Reg Reg Reg IR1 IR1 Reg Reg IR1 Reg IR2 Reg Reg IR1 IR1 Reg Reg Reg Reg Reg Reg IR1 Reg IR1 Reg Reg Reg Reg IR1 IR1 IR1 IR1 Reg Reg Reg Reg IR1 Reg IR2 IR1 IR3 Reg Reg IR1 Reg IR1 IR1 IR1 Reg IR1 Reg Reg Reg IR1 IR1 IR1 Reg Reg IR1 Reg Reg IR2 Reg Reg Reg Reg Reg IR1 Reg Reg Reg Reg Reg IR1 Reg Reg Reg Reg IR1 Reg IR1 Reg Reg Reg Reg Reg Reg IR1 IR1 Reg Reg Reg Reg IR1 Reg IR1 Reg IR1 Reg IR1 Reg IR1 Reg Reg Reg IR1 Reg Reg Reg IR1 Reg IR1 Reg Reg Reg IR1 Reg IR1 IR1 Reg Reg IR1 IR3 IR1 Reg IR1 Reg Reg Reg Reg Reg Reg IR1 Reg Reg Reg Reg Reg IR1 Reg Reg Reg Reg Reg Reg Reg Reg Reg Reg Reg Reg Reg
LandContour Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Low Bnk Lvl Lvl Lvl HLS Bnk Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl HLS Bnk Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl HLS HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl HLS Low Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Low Lvl Bnk Lvl Lvl Lvl HLS HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl ... Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Bnk Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk HLS Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl HLS Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl HLS Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl HLS Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Low Lvl Lvl Lvl Lvl Lvl Bnk Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl Lvl
Utilities AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub AllPub ... 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LandSlope Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Sev Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Sev Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Sev Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Sev Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Sev Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Sev Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Mod Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl ... Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Mod Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Mod Gtl Mod Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Sev Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Sev Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Mod Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl Gtl
Neighborhood CollgCr Veenker CollgCr Crawfor NoRidge Mitchel Somerst NWAmes OldTown BrkSide Sawyer NridgHt Sawyer CollgCr NAmes BrkSide NAmes Sawyer SawyerW NAmes NridgHt IDOTRR CollgCr MeadowV Sawyer NridgHt NAmes NridgHt NAmes BrkSide IDOTRR Sawyer CollgCr NAmes NridgHt NridgHt CollgCr NAmes NAmes Edwards NAmes Timber SawyerW CollgCr NAmes NridgHt Mitchel Somerst OldTown Sawyer Gilbert BrkSide IDOTRR Veenker NAmes NAmes Somerst CollgCr StoneBr CollgCr SawyerW IDOTRR NridgHt OldTown CollgCr NridgHt NAmes CollgCr OldTown ClearCr NAmes Mitchel Gilbert NAmes OldTown MeadowV NAmes BrkSide Sawyer OldTown NAmes Mitchel Somerst NAmes Gilbert NoRidge Gilbert Somerst IDOTRR CollgCr NAmes NAmes Crawfor OldTown CollgCr Gilbert CollgCr Edwards Edwards NAmes NWAmes SawyerW SawyerW CollgCr IDOTRR Somerst OldTown OldTown IDOTRR NWAmes Edwards Gilbert CollgCr Crawfor Crawfor Somerst Sawyer Edwards SawyerW CollgCr ClearCr IDOTRR NAmes SawyerW NWAmes IDOTRR NPkVill OldTown NAmes NAmes NAmes Gilbert NAmes Timber Sawyer NWAmes NAmes Mitchel CollgCr CollgCr NAmes CollgCr NAmes CollgCr Sawyer Edwards BrkSide Gilbert SawyerW BrkSide CollgCr NridgHt NWAmes ClearCr OldTown Edwards NAmes Timber Somerst Gilbert Veenker NridgHt NridgHt OldTown OldTown Edwards ClearCr NridgHt Gilbert Timber OldTown NAmes StoneBr NAmes Timber Edwards ClearCr Sawyer StoneBr OldTown Somerst Crawfor Edwards OldTown BrkSide OldTown Mitchel OldTown SawyerW StoneBr Crawfor NAmes CollgCr Edwards CollgCr NPkVill Somerst NAmes OldTown NridgHt Edwards Mitchel OldTown CollgCr OldTown Gilbert Sawyer NAmes SawyerW NAmes Edwards Edwards Somerst CollgCr CollgCr NAmes CollgCr OldTown Crawfor Blmngtn CollgCr Gilbert NWAmes NAmes NridgHt BrDale NoRidge BrDale Sawyer Blmngtn NAmes NoRidge BrDale CollgCr Gilbert BrDale CollgCr SawyerW NridgHt Edwards Somerst OldTown OldTown SawyerW SawyerW NWAmes OldTown NAmes CollgCr ClearCr BrkSide Crawfor SawyerW NAmes NAmes Gilbert Somerst CollgCr CollgCr OldTown NAmes CollgCr Sawyer OldTown OldTown NWAmes Gilbert SWISU IDOTRR Edwards Somerst ClearCr NoRidge NAmes Mitchel BrkSide Mitchel Gilbert NridgHt ClearCr SawyerW Somerst NridgHt Somerst SawyerW Somerst NAmes NAmes Sawyer BrkSide CollgCr SWISU Edwards NWAmes NAmes Mitchel IDOTRR Somerst NWAmes Crawfor Crawfor CollgCr CollgCr CollgCr OldTown CollgCr SawyerW IDOTRR Edwards NridgHt Gilbert NAmes OldTown Timber OldTown Gilbert NWAmes Somerst NoRidge NWAmes NridgHt NridgHt SawyerW OldTown NAmes IDOTRR Veenker NAmes BrkSide IDOTRR NAmes NAmes NridgHt NridgHt Gilbert Timber StoneBr CollgCr NWAmes NAmes Timber SawyerW NAmes NridgHt MeadowV BrkSide NAmes NAmes NridgHt NridgHt NridgHt ClearCr Edwards OldTown SWISU CollgCr Gilbert MeadowV ClearCr NoRidge Mitchel BrkSide Edwards BrDale NWAmes IDOTRR NAmes NAmes NAmes NAmes Gilbert ClearCr SawyerW NAmes CollgCr Edwards CollgCr Somerst StoneBr Gilbert SWISU Somerst CollgCr SawyerW ClearCr Blmngtn Edwards Edwards CollgCr NridgHt Edwards Mitchel NAmes BrkSide OldTown Edwards CollgCr NAmes IDOTRR Somerst Veenker CollgCr Sawyer NoRidge Gilbert Sawyer SWISU Crawfor NridgHt Somerst Edwards Gilbert Somerst OldTown SawyerW Gilbert Sawyer Crawfor Edwards NAmes Mitchel NWAmes Mitchel NoRidge NAmes OldTown SawyerW NAmes CollgCr Timber BrDale OldTown BrDale Gilbert MeadowV CollgCr OldTown BrkSide Crawfor Edwards NridgHt Edwards BrkSide Blmngtn CollgCr Edwards NAmes Gilbert IDOTRR OldTown OldTown ClearCr Timber Somerst Mitchel NWAmes OldTown ClearCr OldTown BrkSide Somerst SWISU Sawyer Crawfor CollgCr Blmngtn NAmes NAmes NridgHt SawyerW StoneBr NWAmes Edwards NridgHt StoneBr Sawyer CollgCr NridgHt CollgCr OldTown NridgHt NridgHt OldTown Mitchel Sawyer NAmes NAmes NWAmes OldTown MeadowV MeadowV NAmes Gilbert NAmes OldTown IDOTRR NoRidge BrkSide Sawyer NAmes ... BrkSide NWAmes NPkVill CollgCr Timber Gilbert Crawfor NAmes OldTown NAmes NAmes NridgHt SawyerW Somerst BrkSide Somerst BrkSide Somerst Edwards Sawyer NAmes NoRidge Blmngtn CollgCr Mitchel Edwards OldTown NridgHt NWAmes Somerst NoRidge OldTown NAmes CollgCr NridgHt BrkSide NAmes NWAmes IDOTRR CollgCr Edwards OldTown Somerst NWAmes Blmngtn CollgCr NAmes MeadowV Mitchel SWISU Sawyer Edwards Crawfor OldTown NAmes NWAmes CollgCr StoneBr Gilbert Blmngtn Edwards CollgCr OldTown Blmngtn Timber CollgCr NAmes Timber NAmes BrDale SWISU SWISU NoRidge CollgCr Crawfor Edwards Timber CollgCr MeadowV MeadowV Sawyer NWAmes NridgHt SawyerW NWAmes Edwards StoneBr CollgCr Mitchel Edwards Gilbert CollgCr NAmes Edwards CollgCr NWAmes NridgHt NoRidge NridgHt Crawfor StoneBr IDOTRR OldTown OldTown NAmes ClearCr Gilbert NAmes MeadowV NAmes NAmes NWAmes Edwards Edwards CollgCr Crawfor OldTown NAmes CollgCr CollgCr NWAmes Sawyer CollgCr NAmes Gilbert SawyerW MeadowV Somerst Edwards Somerst NAmes Somerst SWISU NAmes NAmes CollgCr IDOTRR StoneBr BrkSide NWAmes SWISU NAmes NAmes NAmes BrDale NoRidge SawyerW Gilbert Gilbert NridgHt Gilbert NWAmes NAmes NAmes OldTown NridgHt Gilbert Sawyer Sawyer NAmes IDOTRR CollgCr Edwards Edwards Gilbert NAmes Blmngtn Gilbert Gilbert OldTown SWISU Mitchel OldTown SawyerW Gilbert BrkSide NAmes Sawyer Mitchel BrkSide NAmes NWAmes CollgCr Sawyer Edwards BrkSide SawyerW Crawfor OldTown OldTown NAmes Edwards Crawfor IDOTRR NAmes Veenker NAmes NridgHt Somerst NWAmes NPkVill Crawfor Sawyer Sawyer Veenker NridgHt CollgCr Gilbert Edwards NoRidge Mitchel NAmes Somerst ClearCr Crawfor NoRidge Mitchel OldTown Crawfor Edwards Timber Crawfor NoRidge OldTown Timber Edwards OldTown NoRidge CollgCr Gilbert Mitchel Somerst OldTown Mitchel Sawyer Gilbert Gilbert OldTown CollgCr Sawyer NAmes CollgCr BrkSide CollgCr NWAmes SawyerW SawyerW CollgCr NAmes Somerst SawyerW Gilbert Edwards Sawyer NAmes Sawyer Sawyer Somerst BrkSide BrDale NAmes Sawyer NAmes NAmes Gilbert NAmes Somerst NAmes NridgHt NAmes Sawyer NAmes NAmes NAmes SWISU Crawfor NridgHt CollgCr SawyerW Timber CollgCr Somerst NWAmes NridgHt Crawfor NWAmes Somerst Mitchel OldTown NAmes NAmes NridgHt Mitchel Veenker Edwards BrkSide NoRidge Edwards Gilbert NAmes Gilbert NAmes ClearCr BrkSide NAmes Somerst OldTown Timber Crawfor NAmes ClearCr NWAmes Sawyer Edwards Crawfor NAmes NWAmes NWAmes CollgCr IDOTRR CollgCr Timber CollgCr Mitchel SWISU BrkSide NWAmes ClearCr StoneBr NridgHt NAmes BrDale OldTown NWAmes Edwards NAmes NAmes Edwards Edwards NAmes Gilbert Crawfor NoRidge Somerst Edwards NridgHt NridgHt CollgCr OldTown SawyerW Crawfor CollgCr NoRidge NoRidge Edwards NAmes CollgCr Somerst CollgCr Edwards NAmes BrkSide NoRidge BrkSide Somerst IDOTRR Edwards Mitchel OldTown Gilbert Somerst CollgCr Edwards IDOTRR BrDale NWAmes NWAmes OldTown CollgCr CollgCr NAmes SawyerW CollgCr Crawfor CollgCr OldTown NWAmes Timber SawyerW OldTown NAmes NAmes BrkSide NoRidge CollgCr NWAmes NAmes NAmes Somerst NridgHt SWISU StoneBr NAmes Gilbert Somerst Somerst CollgCr MeadowV CollgCr CollgCr OldTown NAmes CollgCr NoRidge Somerst Timber SWISU Edwards BrDale Timber Edwards NAmes OldTown Sawyer Edwards IDOTRR NWAmes OldTown Gilbert BrkSide CollgCr NAmes NAmes OldTown Blmngtn Timber Timber BrkSide NAmes SWISU BrkSide Gilbert CollgCr Somerst OldTown Crawfor CollgCr Mitchel OldTown NWAmes CollgCr NAmes NAmes SawyerW BrkSide Blmngtn OldTown NoRidge NAmes Crawfor NWAmes NPkVill CollgCr Edwards NAmes NAmes NoRidge NAmes OldTown NWAmes Gilbert NPkVill OldTown Gilbert Mitchel NAmes NAmes NridgHt OldTown NWAmes Crawfor CollgCr Somerst BrkSide CollgCr Sawyer Mitchel CollgCr Edwards MeadowV NAmes Somerst Edwards Mitchel Somerst Gilbert NWAmes Crawfor NAmes Edwards
Condition1 Norm Feedr Norm Norm Norm Norm Norm PosN Artery Artery Norm Norm Norm Norm Norm Norm Norm Norm RRAe Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRNn Norm Norm Norm Norm Norm Norm Norm RRAe Norm Norm RRAn Norm Norm PosA Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr PosN Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm RRAe Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm RRAn Artery Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Artery Norm Norm Norm RRAe Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRAn Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm RRNe Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm RRAn Norm Norm Norm PosA Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Artery Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm PosN RRAe Norm Norm PosN Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Feedr Norm Norm Norm RRAn Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm RRAn Norm Norm Norm Norm Feedr Norm Feedr Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm PosN Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Feedr Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm PosN Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm PosA Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRAn Feedr Norm Norm Norm Norm Norm Artery Norm RRNe Norm PosA Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Artery Norm Norm Norm Norm Norm ... Norm PosN Norm Norm Norm RRAn Norm Norm Norm Norm Norm Norm Norm Norm RRAn Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Artery Norm Norm Norm Feedr Norm PosA Norm Norm Norm Norm RRAn Feedr Norm Norm PosN Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm PosN Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm RRAn Artery Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm RRAn Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm PosN Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm PosN Norm Feedr Feedr Norm Norm RRAn Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRNn Norm Norm Norm Norm Norm Norm RRAe Norm Norm Norm Norm Feedr Artery Norm RRAn Norm Feedr Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm RRAe Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm RRAn Norm Norm Norm Norm Norm Feedr Norm Norm Feedr Norm PosN Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm PosA Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Feedr Norm Norm Norm RRAe Norm Norm Norm Norm PosN Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Feedr Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRAn Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRAn Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm RRAn Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm
Condition2 Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRNn Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm ... Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Feedr Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRAn Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Artery Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm RRAe Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm Norm
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OverallQual 7 6 7 7 8 5 8 7 7 5 5 9 5 7 6 7 6 4 5 5 8 7 8 5 5 8 5 8 5 4 4 5 8 5 9 8 5 5 5 4 6 5 5 5 5 9 7 8 4 5 6 6 5 9 5 6 8 7 10 5 6 5 8 7 7 8 7 7 4 7 7 4 7 5 3 4 4 5 4 5 6 6 8 5 7 8 6 6 3 4 4 5 5 6 6 6 7 4 5 4 6 6 5 7 7 8 4 5 5 6 6 7 7 6 6 6 5 5 7 6 6 4 6 6 6 6 6 5 6 5 7 6 5 8 5 7 5 7 8 6 4 7 5 7 5 6 5 7 7 5 5 8 6 6 6 6 5 8 7 7 6 9 7 4 6 5 5 8 7 8 5 6 7 6 6 6 6 5 9 5 6 7 5 7 5 10 7 5 5 8 8 7 7 7 5 6 7 8 6 8 4 6 6 6 5 7 5 4 7 6 5 6 7 5 6 5 7 4 7 7 7 6 6 4 10 5 7 6 5 7 6 8 6 5 6 6 7 7 8 6 8 5 5 6 7 7 6 6 7 6 3 8 6 6 5 7 6 7 7 5 6 8 6 5 5 6 6 5 5 6 8 7 8 6 5 7 7 4 9 7 7 6 7 8 6 7 6 4 5 6 8 5 5 7 6 6 5 7 6 6 5 8 7 5 7 8 7 6 4 9 6 6 5 7 7 7 7 8 7 7 9 8 7 3 7 5 8 6 6 5 5 5 8 7 6 5 9 7 7 6 8 4 3 9 5 6 6 6 7 9 9 7 5 6 6 6 6 5 5 8 6 5 7 6 6 5 6 6 5 5 6 4 6 5 7 1 5 8 9 6 5 7 7 6 6 8 5 6 7 10 5 6 5 4 5 5 5 5 5 7 8 7 5 8 6 4 6 6 7 8 5 5 7 5 7 7 6 6 5 5 7 6 5 8 6 7 7 4 7 6 6 6 5 6 4 7 6 6 5 6 10 4 5 7 7 6 6 7 6 3 6 7 6 8 5 7 5 4 8 5 8 7 5 6 5 7 7 5 8 6 8 7 6 8 8 5 6 9 8 4 9 9 7 6 5 5 5 5 5 4 5 6 6 5 5 4 8 7 5 5 ... 5 6 6 9 7 6 5 5 3 6 4 7 6 7 7 7 4 7 4 5 7 8 7 8 5 5 6 9 6 7 8 8 6 6 10 4 5 6 3 7 3 5 8 5 7 5 6 4 7 5 5 5 6 5 6 8 7 8 7 7 4 7 5 7 8 5 5 8 5 6 5 7 8 7 5 4 9 8 4 4 5 6 6 7 8 3 9 5 5 4 7 7 6 5 8 6 7 7 9 6 8 3 5 6 5 7 6 6 6 5 5 6 5 6 7 7 5 5 6 5 6 5 7 6 6 6 4 8 7 8 3 7 6 5 5 6 6 8 4 7 2 5 5 6 5 8 7 7 6 8 6 7 5 5 5 8 8 5 5 5 6 7 4 5 7 4 7 7 7 5 4 5 6 8 6 6 6 5 6 5 5 5 8 5 4 5 6 7 7 7 6 5 6 6 7 5 5 7 8 6 6 6 5 4 5 7 8 6 6 10 6 6 7 5 6 8 5 6 5 5 7 8 10 5 5 5 3 8 7 7 4 8 5 6 6 6 7 7 7 4 4 7 5 7 5 7 4 6 5 8 6 8 4 4 5 5 6 8 4 6 5 5 6 5 7 6 6 5 9 5 5 5 4 5 5 5 7 7 6 8 7 7 7 10 8 6 7 6 6 5 7 7 5 7 7 6 8 5 7 6 6 5 4 6 6 7 7 9 8 5 7 6 5 6 5 5 6 6 8 4 7 7 5 6 6 6 6 5 8 8 5 5 5 7 5 5 5 6 10 5 7 6 8 7 7 9 7 5 5 6 7 7 8 9 4 6 8 7 8 4 6 3 7 4 8 4 3 5 6 7 8 5 4 5 5 6 5 4 7 5 4 6 8 6 7 4 7 8 7 8 5 6 6 8 7 7 5 5 6 9 5 7 4 6 7 7 7 5 6 8 4 6 7 10 7 8 6 5 6 5 3 6 7 5 6 4 7 6 9 6 7 5 5 6 7 8 5 5 5 6 6 6 7 8 3 8 5 5 4 7 7 6 4 8 6 7 4 8 5 6 6 6 6 6 5 6 7 5 5 6 5 6 4 6 5 6 4 8 6 7 6 6 10 6 7 6 5 8 4 5 5 8 5 5 7 6 6 7 5 5
OverallCond 5 8 5 5 5 5 5 6 5 6 5 5 6 5 5 8 7 5 5 6 5 7 5 7 8 5 7 5 6 6 4 6 5 5 5 5 5 6 7 5 5 6 7 6 6 5 5 5 5 7 6 6 5 5 5 5 5 5 5 7 5 7 5 6 5 5 5 5 6 4 6 6 5 7 6 5 7 5 5 6 6 5 5 5 5 5 5 5 2 5 5 3 7 6 5 8 5 5 5 5 7 5 5 5 4 5 7 5 7 6 4 5 5 5 8 5 5 5 5 5 5 5 7 5 5 8 5 7 5 7 6 5 6 5 6 6 5 5 5 5 5 5 4 5 5 5 7 5 5 4 6 5 5 7 5 5 7 5 5 5 6 5 5 6 7 7 5 5 5 6 6 5 7 5 5 7 5 5 5 6 5 6 6 5 8 9 5 7 5 5 8 9 5 5 7 6 5 4 6 5 5 6 6 5 7 5 5 5 5 7 6 5 5 5 7 6 5 4 9 5 5 5 6 6 5 5 5 6 5 5 6 5 5 6 5 3 5 7 5 4 5 9 4 6 5 5 8 5 5 7 2 5 5 7 6 5 5 5 5 5 5 5 7 7 5 6 5 8 6 7 5 7 7 6 7 7 5 5 5 5 6 5 5 5 5 5 7 4 5 7 5 6 4 7 5 6 5 5 6 8 5 5 5 7 9 5 5 7 5 5 5 6 7 5 7 5 7 5 5 5 5 5 5 8 7 6 5 5 6 5 4 6 5 5 5 6 5 5 7 7 5 4 4 5 3 5 8 5 5 5 5 5 6 8 5 5 6 5 4 5 6 5 5 8 5 7 5 5 6 7 5 4 5 6 5 1 5 5 2 5 6 5 5 3 5 5 3 6 5 5 8 5 7 5 6 6 5 5 2 5 5 5 8 5 5 4 5 7 5 5 3 5 5 6 5 5 7 6 6 6 5 7 5 5 5 8 5 6 5 5 5 7 5 5 7 6 8 7 6 8 5 4 7 5 5 5 6 5 6 7 8 5 5 5 5 6 5 6 7 4 5 9 5 7 5 5 5 7 5 5 5 6 5 5 5 6 5 5 5 7 5 5 8 5 7 7 7 6 4 8 6 7 5 6 8 5 5 6 7 7 ... 7 7 6 5 5 5 7 7 6 5 4 5 5 5 8 5 7 5 5 6 9 5 5 5 5 5 8 5 6 5 5 9 8 5 5 7 6 6 4 5 3 6 5 6 5 8 3 4 5 5 5 5 7 4 5 6 5 5 5 5 5 5 5 5 6 5 5 5 5 7 8 3 7 5 7 3 5 5 6 4 4 8 5 5 5 5 5 5 4 7 5 5 6 6 5 5 5 6 5 7 5 4 5 6 6 5 7 6 4 7 5 6 3 6 5 6 8 5 5 5 7 5 5 6 5 6 5 5 5 5 4 5 5 8 7 5 7 5 6 5 5 5 7 6 5 5 5 5 5 5 5 6 7 7 7 5 5 7 6 5 5 5 3 9 5 5 5 5 5 5 3 5 4 5 5 5 5 8 5 5 7 7 5 3 4 6 5 7 7 9 5 4 7 8 6 8 8 5 5 6 5 7 5 4 7 5 5 5 7 5 6 6 5 6 8 5 5 8 6 5 5 5 5 6 4 7 5 5 5 5 4 5 8 5 7 5 5 6 5 5 5 5 8 5 6 7 4 5 7 5 5 7 6 9 5 5 5 5 5 5 5 5 6 3 5 8 5 8 5 5 5 6 5 5 6 5 5 5 5 5 5 6 6 5 7 7 5 5 5 7 5 5 6 7 5 6 5 8 5 8 5 6 5 6 5 5 4 5 9 5 6 7 6 7 6 5 6 5 5 4 5 5 7 5 7 6 5 5 5 5 7 7 4 5 7 5 6 5 5 7 5 7 5 5 5 5 5 5 7 5 8 5 5 5 6 5 5 5 5 5 3 6 5 7 5 4 6 9 7 5 5 5 6 6 6 5 5 4 5 7 5 5 5 6 5 4 7 5 5 5 5 6 9 5 5 6 5 6 5 5 6 5 4 5 5 5 5 6 5 5 6 6 6 5 5 5 5 5 5 5 3 7 7 7 5 8 5 7 5 6 5 5 5 7 5 5 5 8 4 6 7 5 5 5 4 5 7 5 7 6 5 8 5 5 7 5 6 5 5 5 6 5 5 7 5 6 5 6 7 7 5 6 6 5 5 9 6 5 7 6 7 5 5 6 5 5 7 5 7 7 5 5 5 5 5 5 6 9 6 6
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RoofMatl CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShngl CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg Metal CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShake CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShngl CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg Membran CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShake CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg Tar&Grv CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShngl CompShg CompShg CompShg CompShg CompShg Tar&Grv CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg Tar&Grv CompShg CompShg CompShg CompShg WdShake CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg ... CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg Tar&Grv CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShake CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShngl CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg WdShake CompShg CompShg CompShg CompShg Roll CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg ClyTile CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg Tar&Grv CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg CompShg
Exterior1st VinylSd MetalSd VinylSd Wd Sdng VinylSd VinylSd VinylSd HdBoard BrkFace MetalSd HdBoard WdShing HdBoard VinylSd MetalSd Wd Sdng Wd Sdng MetalSd VinylSd BrkFace VinylSd Wd Sdng VinylSd CemntBd Plywood VinylSd Wd Sdng VinylSd MetalSd MetalSd BrkFace HdBoard VinylSd BrkFace MetalSd VinylSd VinylSd Wd Sdng VinylSd AsbShng Wd Sdng VinylSd HdBoard VinylSd BrkFace MetalSd VinylSd VinylSd MetalSd HdBoard VinylSd Wd Sdng Wd Sdng WdShing MetalSd HdBoard MetalSd VinylSd VinylSd HdBoard VinylSd MetalSd VinylSd Stucco VinylSd VinylSd Plywood VinylSd MetalSd BrkFace Plywood HdBoard VinylSd Wd Sdng VinylSd CemntBd VinylSd Wd Sdng HdBoard Wd Sdng VinylSd VinylSd VinylSd MetalSd HdBoard HdBoard VinylSd VinylSd Plywood VinylSd BrkFace HdBoard Wd Sdng MetalSd VinylSd VinylSd VinylSd HdBoard Wd Sdng Plywood Plywood HdBoard HdBoard VinylSd Stucco VinylSd VinylSd VinylSd CemntBd Plywood Wd Sdng VinylSd VinylSd Wd Sdng Wd Sdng MetalSd Wd Sdng VinylSd Plywood VinylSd Plywood AsbShng MetalSd Plywood HdBoard Wd Sdng Plywood WdShing VinylSd Plywood MetalSd VinylSd MetalSd VinylSd Plywood Plywood MetalSd VinylSd VinylSd VinylSd HdBoard VinylSd BrkFace VinylSd HdBoard VinylSd Wd Sdng VinylSd VinylSd MetalSd HdBoard VinylSd HdBoard BrkFace Wd Sdng Wd Sdng Wd Sdng VinylSd VinylSd VinylSd Plywood VinylSd VinylSd MetalSd MetalSd Wd Sdng Wd Sdng VinylSd VinylSd Plywood MetalSd BrkFace HdBoard WdShing VinylSd WdShing Plywood MetalSd VinylSd Wd Sdng MetalSd Stucco Wd Sdng VinylSd AsbShng Wd Sdng HdBoard HdBoard Plywood CemntBd BrkFace HdBoard VinylSd VinylSd HdBoard Plywood CemntBd Stucco Wd Sdng VinylSd VinylSd Plywood MetalSd VinylSd AsbShng HdBoard HdBoard Wd Sdng Plywood HdBoard Stucco VinylSd VinylSd VinylSd HdBoard HdBoard VinylSd MetalSd VinylSd VinylSd VinylSd VinylSd VinylSd HdBoard VinylSd HdBoard VinylSd HdBoard HdBoard VinylSd MetalSd VinylSd HdBoard HdBoard VinylSd HdBoard VinylSd HdBoard VinylSd VinylSd VinylSd VinylSd Wd Sdng Plywood VinylSd Wd Sdng AsbShng Wd Sdng VinylSd Wd Sdng Wd Sdng VinylSd VinylSd HdBoard MetalSd VinylSd VinylSd VinylSd VinylSd BrkFace Wd Sdng CemntBd HdBoard Wd Sdng Wd Sdng MetalSd VinylSd Wd Sdng VinylSd HdBoard VinylSd Plywood VinylSd Wd Sdng HdBoard Wd Sdng VinylSd VinylSd CemntBd Plywood Plywood VinylSd VinylSd VinylSd Plywood MetalSd Wd Sdng HdBoard MetalSd Wd Sdng VinylSd VinylSd Wd Sdng Plywood HdBoard HdBoard Wd Sdng VinylSd HdBoard HdBoard MetalSd VinylSd VinylSd VinylSd CemntBd CemntBd HdBoard MetalSd VinylSd VinylSd HdBoard VinylSd MetalSd BrkFace Wd Sdng VinylSd CemntBd VinylSd HdBoard Plywood VinylSd VinylSd Plywood VinylSd Wd Sdng MetalSd BrkFace Wd Sdng Wd Sdng Wd Sdng HdBoard Wd Sdng VinylSd VinylSd VinylSd Plywood VinylSd VinylSd Wd Sdng Wd Sdng VinylSd Wd Sdng Stucco MetalSd CemntBd MetalSd MetalSd Wd Sdng VinylSd CemntBd MetalSd Wd Sdng VinylSd VinylSd Wd Sdng VinylSd HdBoard CemntBd Wd Sdng VinylSd VinylSd MetalSd HdBoard HdBoard HdBoard VinylSd Plywood Plywood HdBoard Wd Sdng VinylSd WdShing Plywood MetalSd VinylSd Wd Sdng VinylSd CemntBd VinylSd VinylSd BrkFace VinylSd VinylSd Wd Sdng HdBoard VinylSd Wd Sdng MetalSd VinylSd VinylSd MetalSd VinylSd MetalSd WdShing MetalSd Wd Sdng MetalSd VinylSd MetalSd CemntBd BrkFace VinylSd Wd Sdng VinylSd MetalSd Plywood MetalSd Wd Sdng VinylSd VinylSd BrkComm Wd Sdng MetalSd WdShing VinylSd VinylSd HdBoard Wd Sdng MetalSd VinylSd VinylSd CemntBd HdBoard VinylSd Wd Sdng HdBoard Wd Sdng MetalSd VinylSd HdBoard HdBoard MetalSd HdBoard VinylSd CemntBd VinylSd Stucco Wd Sdng WdShing Wd Sdng VinylSd VinylSd MetalSd WdShing VinylSd Wd Sdng BrkFace VinylSd MetalSd MetalSd Wd Sdng Plywood VinylSd VinylSd Plywood HdBoard AsbShng Plywood Stucco MetalSd VinylSd Wd Sdng MetalSd Stucco HdBoard VinylSd Plywood MetalSd VinylSd HdBoard HdBoard WdShing VinylSd VinylSd CemntBd HdBoard VinylSd VinylSd VinylSd Stucco VinylSd VinylSd Stucco VinylSd HdBoard MetalSd MetalSd Plywood Wd Sdng CemntBd CemntBd Wd Sdng VinylSd BrkFace MetalSd Wd Sdng BrkFace Wd Sdng HdBoard MetalSd ... Wd Sdng HdBoard Plywood VinylSd VinylSd VinylSd MetalSd Wd Sdng VinylSd HdBoard AsbShng VinylSd Plywood CemntBd HdBoard MetalSd Wd Sdng VinylSd MetalSd VinylSd CemntBd VinylSd VinylSd CemntBd Plywood MetalSd VinylSd VinylSd Plywood VinylSd VinylSd Wd Sdng Wd Sdng VinylSd CemntBd MetalSd Wd Sdng HdBoard Wd Sdng VinylSd BrkComm Wd Sdng VinylSd VinylSd VinylSd HdBoard Plywood CemntBd VinylSd Wd Sdng Wd Sdng AsphShn Wd Sdng MetalSd MetalSd VinylSd VinylSd HdBoard HdBoard VinylSd VinylSd VinylSd Wd Sdng VinylSd Stone VinylSd MetalSd VinylSd WdShing HdBoard VinylSd Wd Sdng MetalSd VinylSd MetalSd Wd Sdng VinylSd VinylSd CemntBd CemntBd Wd Sdng HdBoard VinylSd HdBoard BrkFace BrkFace CemntBd VinylSd HdBoard MetalSd VinylSd VinylSd VinylSd HdBoard VinylSd Plywood VinylSd HdBoard VinylSd MetalSd CemntBd MetalSd Wd Sdng MetalSd Plywood Wd Sdng VinylSd HdBoard CemntBd MetalSd MetalSd MetalSd MetalSd HdBoard VinylSd Wd Sdng Wd Sdng VinylSd VinylSd VinylSd VinylSd HdBoard VinylSd HdBoard HdBoard HdBoard CemntBd VinylSd VinylSd MetalSd BrkFace MetalSd MetalSd MetalSd MetalSd VinylSd Wd Sdng HdBoard MetalSd Plywood Wd Sdng HdBoard Wd Sdng BrkFace HdBoard HdBoard HdBoard VinylSd VinylSd MetalSd HdBoard Plywood WdShing Wd Sdng MetalSd VinylSd VinylSd VinylSd MetalSd BrkFace BrkFace VinylSd AsbShng VinylSd HdBoard Plywood VinylSd VinylSd VinylSd MetalSd WdShing HdBoard Wd Sdng VinylSd VinylSd Wd Sdng VinylSd VinylSd Plywood Stucco HdBoard Plywood VinylSd Wd Sdng MetalSd MetalSd Wd Sdng MetalSd Wd Sdng VinylSd BrkFace HdBoard Stone Wd Sdng VinylSd Plywood BrkFace VinylSd VinylSd HdBoard Plywood HdBoard VinylSd Plywood Plywood VinylSd VinylSd VinylSd Stucco HdBoard Plywood MetalSd MetalSd MetalSd Wd Sdng HdBoard HdBoard MetalSd Wd Sdng Wd Sdng MetalSd CemntBd Wd Sdng Stucco MetalSd AsbShng HdBoard ImStucc VinylSd VinylSd MetalSd MetalSd VinylSd VinylSd HdBoard VinylSd VinylSd MetalSd VinylSd HdBoard MetalSd VinylSd Wd Sdng VinylSd HdBoard HdBoard HdBoard VinylSd MetalSd VinylSd Wd Sdng Wd Sdng MetalSd VinylSd MetalSd VinylSd VinylSd CemntBd AsbShng CemntBd Wd Sdng HdBoard Wd Sdng Wd Sdng VinylSd VinylSd VinylSd VinylSd MetalSd BrkFace Plywood Wd Sdng HdBoard Plywood MetalSd Wd Sdng VinylSd VinylSd VinylSd VinylSd VinylSd VinylSd Plywood VinylSd BrkFace VinylSd VinylSd HdBoard AsbShng MetalSd BrkFace VinylSd HdBoard Wd Sdng VinylSd Wd Sdng HdBoard Wd Sdng VinylSd HdBoard VinylSd MetalSd Wd Sdng Wd Sdng MetalSd MetalSd Wd Sdng Stucco Stucco MetalSd Plywood BrkFace HdBoard Plywood Wd Sdng AsbShng HdBoard HdBoard VinylSd MetalSd VinylSd HdBoard HdBoard MetalSd Wd Sdng MetalSd HdBoard Wd Sdng CemntBd VinylSd Wd Sdng CemntBd Wd Sdng HdBoard WdShing HdBoard MetalSd VinylSd Stucco BrkFace VinylSd Wd Sdng VinylSd VinylSd VinylSd VinylSd VinylSd VinylSd MetalSd HdBoard BrkFace VinylSd Wd Sdng VinylSd Wd Sdng HdBoard VinylSd VinylSd VinylSd Wd Sdng BrkFace VinylSd HdBoard VinylSd VinylSd AsbShng MetalSd VinylSd BrkFace VinylSd VinylSd MetalSd MetalSd Wd Sdng HdBoard Plywood Plywood Wd Sdng VinylSd MetalSd MetalSd VinylSd VinylSd BrkFace VinylSd MetalSd BrkFace VinylSd VinylSd MetalSd MetalSd HdBoard MetalSd VinylSd VinylSd MetalSd HdBoard VinylSd MetalSd VinylSd MetalSd VinylSd VinylSd VinylSd MetalSd VinylSd VinylSd CemntBd VinylSd VinylSd CBlock AsbShng VinylSd VinylSd CemntBd VinylSd Wd Sdng Wd Sdng HdBoard VinylSd Stucco BrkFace Wd Sdng Wd Sdng WdShing VinylSd Plywood Stucco VinylSd Wd Sdng VinylSd Plywood HdBoard Wd Sdng VinylSd VinylSd Wd Sdng MetalSd VinylSd WdShing WdShing VinylSd VinylSd VinylSd Wd Sdng CemntBd HdBoard HdBoard VinylSd Plywood VinylSd VinylSd BrkFace VinylSd Wd Sdng VinylSd VinylSd VinylSd HdBoard Plywood HdBoard Plywood VinylSd Plywood HdBoard HdBoard VinylSd MetalSd MetalSd MetalSd VinylSd Plywood Wd Sdng VinylSd BrkFace Wd Sdng HdBoard VinylSd MetalSd HdBoard MetalSd VinylSd VinylSd Wd Sdng VinylSd VinylSd HdBoard VinylSd MetalSd CemntBd VinylSd CemntBd VinylSd VinylSd VinylSd VinylSd Plywood CemntBd MetalSd HdBoard
Exterior2nd VinylSd MetalSd VinylSd Wd Shng VinylSd VinylSd VinylSd HdBoard Wd Shng MetalSd HdBoard Wd Shng Plywood VinylSd MetalSd Wd Sdng Wd Sdng MetalSd VinylSd Plywood VinylSd Wd Sdng VinylSd CmentBd Plywood VinylSd Wd Sdng VinylSd MetalSd MetalSd BrkFace HdBoard VinylSd BrkFace MetalSd VinylSd VinylSd Wd Sdng VinylSd Plywood Wd Sdng VinylSd HdBoard VinylSd Wd Sdng MetalSd VinylSd VinylSd MetalSd HdBoard VinylSd Wd Sdng Wd Sdng Wd Shng MetalSd Plywood MetalSd VinylSd VinylSd HdBoard VinylSd MetalSd VinylSd Stucco VinylSd VinylSd Plywood VinylSd MetalSd AsbShng Plywood Plywood VinylSd Wd Sdng VinylSd CmentBd VinylSd Wd Sdng HdBoard Wd Sdng VinylSd VinylSd VinylSd MetalSd HdBoard HdBoard VinylSd VinylSd Plywood VinylSd Wd Sdng HdBoard Wd Sdng MetalSd VinylSd VinylSd VinylSd HdBoard Wd Sdng Plywood Plywood HdBoard HdBoard VinylSd Stucco VinylSd VinylSd VinylSd CmentBd Plywood Wd Sdng VinylSd VinylSd Wd Sdng Wd Sdng MetalSd Wd Sdng VinylSd Plywood VinylSd Plywood AsbShng MetalSd Plywood HdBoard Wd Sdng Plywood Wd Sdng VinylSd Plywood MetalSd VinylSd MetalSd VinylSd Plywood Plywood MetalSd VinylSd VinylSd VinylSd HdBoard VinylSd Wd Sdng VinylSd HdBoard VinylSd Wd Sdng VinylSd VinylSd MetalSd HdBoard VinylSd HdBoard Plywood Wd Sdng Wd Sdng Wd Sdng VinylSd VinylSd VinylSd Plywood VinylSd VinylSd MetalSd MetalSd Wd Sdng Wd Sdng VinylSd VinylSd Plywood MetalSd Plywood HdBoard Wd Shng Plywood Wd Shng Plywood MetalSd VinylSd Wd Sdng MetalSd Stucco Wd Sdng VinylSd Plywood Wd Sdng HdBoard HdBoard Plywood CmentBd BrkFace HdBoard VinylSd VinylSd HdBoard Brk Cmn CmentBd Stucco Wd Sdng VinylSd VinylSd Plywood MetalSd VinylSd AsbShng HdBoard HdBoard Wd Sdng Plywood HdBoard Stucco VinylSd VinylSd VinylSd HdBoard HdBoard VinylSd MetalSd VinylSd VinylSd VinylSd VinylSd VinylSd HdBoard VinylSd HdBoard VinylSd HdBoard HdBoard VinylSd MetalSd VinylSd HdBoard HdBoard VinylSd HdBoard VinylSd HdBoard VinylSd VinylSd VinylSd VinylSd Wd Sdng Plywood VinylSd Wd Sdng AsbShng BrkFace VinylSd HdBoard Plywood VinylSd VinylSd HdBoard MetalSd VinylSd VinylSd VinylSd VinylSd BrkFace Wd Sdng CmentBd Plywood Wd Sdng Wd Sdng MetalSd VinylSd Wd Sdng VinylSd HdBoard VinylSd Plywood VinylSd Wd Sdng ImStucc Wd Sdng VinylSd VinylSd CmentBd Plywood Plywood VinylSd VinylSd VinylSd ImStucc MetalSd Wd Sdng HdBoard MetalSd Wd Sdng VinylSd VinylSd Wd Sdng Plywood HdBoard HdBoard Wd Sdng VinylSd AsphShn HdBoard MetalSd VinylSd VinylSd VinylSd CmentBd CmentBd Plywood MetalSd VinylSd VinylSd HdBoard VinylSd MetalSd BrkFace Wd Sdng VinylSd CmentBd VinylSd HdBoard Plywood VinylSd VinylSd Plywood VinylSd Wd Sdng MetalSd BrkFace Wd Sdng Wd Shng Wd Shng HdBoard Wd Sdng VinylSd VinylSd VinylSd Plywood VinylSd VinylSd Wd Sdng Wd Sdng VinylSd AsbShng Stucco MetalSd CmentBd MetalSd MetalSd Wd Sdng Wd Shng CmentBd MetalSd Wd Sdng VinylSd VinylSd MetalSd VinylSd HdBoard CmentBd ImStucc VinylSd VinylSd MetalSd HdBoard HdBoard HdBoard VinylSd Plywood Plywood HdBoard Wd Sdng VinylSd Plywood Plywood MetalSd VinylSd Wd Sdng VinylSd CmentBd VinylSd VinylSd Wd Sdng VinylSd VinylSd Wd Sdng HdBoard VinylSd VinylSd MetalSd VinylSd VinylSd MetalSd VinylSd MetalSd Wd Shng MetalSd Wd Sdng MetalSd VinylSd MetalSd CmentBd BrkFace VinylSd Wd Sdng VinylSd MetalSd Plywood MetalSd Plywood VinylSd VinylSd Brk Cmn Wd Sdng MetalSd Wd Shng VinylSd VinylSd HdBoard Stucco MetalSd VinylSd VinylSd CmentBd HdBoard VinylSd Wd Sdng HdBoard Wd Sdng MetalSd VinylSd HdBoard HdBoard MetalSd HdBoard VinylSd CmentBd VinylSd Stucco Wd Sdng Stucco Wd Sdng VinylSd VinylSd MetalSd Wd Shng VinylSd Wd Sdng Plywood VinylSd MetalSd MetalSd Wd Sdng Plywood VinylSd VinylSd Plywood HdBoard AsbShng Plywood Wd Shng MetalSd VinylSd Wd Sdng MetalSd Stucco HdBoard VinylSd Plywood MetalSd VinylSd HdBoard HdBoard Plywood VinylSd VinylSd CmentBd HdBoard VinylSd VinylSd VinylSd Stucco VinylSd VinylSd Stucco VinylSd Plywood MetalSd MetalSd Plywood Wd Sdng CmentBd CmentBd Wd Sdng VinylSd Wd Sdng MetalSd Wd Sdng BrkFace Wd Sdng HdBoard MetalSd ... Plywood HdBoard Brk Cmn VinylSd VinylSd VinylSd MetalSd Wd Sdng VinylSd HdBoard AsbShng Wd Shng Plywood CmentBd HdBoard MetalSd Wd Sdng VinylSd MetalSd VinylSd CmentBd VinylSd VinylSd CmentBd Plywood MetalSd VinylSd VinylSd Plywood VinylSd VinylSd Wd Sdng Wd Sdng VinylSd CmentBd MetalSd Wd Sdng HdBoard Wd Sdng VinylSd Brk Cmn Wd Sdng VinylSd VinylSd VinylSd HdBoard Plywood CmentBd VinylSd Wd Sdng Wd Sdng AsphShn Wd Sdng Stucco MetalSd VinylSd VinylSd HdBoard HdBoard VinylSd VinylSd VinylSd Wd Sdng VinylSd HdBoard VinylSd HdBoard VinylSd Wd Shng HdBoard VinylSd Plywood MetalSd VinylSd MetalSd Wd Sdng VinylSd VinylSd CmentBd CmentBd Wd Sdng HdBoard VinylSd HdBoard BrkFace Wd Sdng CmentBd VinylSd HdBoard MetalSd VinylSd VinylSd VinylSd HdBoard VinylSd Plywood VinylSd HdBoard VinylSd MetalSd CmentBd MetalSd Wd Sdng MetalSd Plywood Wd Sdng VinylSd HdBoard CmentBd MetalSd MetalSd MetalSd MetalSd Plywood VinylSd Wd Sdng Wd Sdng Plywood VinylSd VinylSd VinylSd HdBoard VinylSd HdBoard HdBoard HdBoard CmentBd VinylSd VinylSd MetalSd BrkFace MetalSd MetalSd MetalSd MetalSd VinylSd Wd Sdng HdBoard MetalSd Plywood Wd Sdng MetalSd Wd Sdng BrkFace HdBoard HdBoard HdBoard VinylSd VinylSd MetalSd HdBoard Plywood Wd Shng Wd Sdng MetalSd VinylSd VinylSd VinylSd MetalSd Wd Sdng BrkFace VinylSd AsbShng VinylSd HdBoard Plywood VinylSd VinylSd VinylSd MetalSd Plywood HdBoard Wd Sdng VinylSd VinylSd Wd Sdng VinylSd VinylSd ImStucc Stucco HdBoard Plywood VinylSd Plywood MetalSd MetalSd Wd Shng MetalSd Wd Sdng VinylSd BrkFace Plywood Stone Wd Sdng VinylSd Plywood BrkFace VinylSd VinylSd HdBoard Brk Cmn HdBoard VinylSd Plywood Plywood VinylSd VinylSd VinylSd Stucco HdBoard Plywood MetalSd MetalSd MetalSd Wd Sdng HdBoard Plywood MetalSd Wd Sdng Wd Sdng MetalSd CmentBd ImStucc Stucco MetalSd AsbShng HdBoard ImStucc VinylSd VinylSd MetalSd MetalSd VinylSd VinylSd HdBoard VinylSd VinylSd MetalSd VinylSd HdBoard MetalSd VinylSd Wd Sdng VinylSd HdBoard HdBoard HdBoard VinylSd MetalSd VinylSd Wd Sdng Wd Sdng MetalSd VinylSd MetalSd VinylSd VinylSd CmentBd AsbShng CmentBd Wd Sdng HdBoard Wd Sdng Wd Sdng VinylSd VinylSd VinylSd VinylSd MetalSd HdBoard Plywood Wd Sdng HdBoard Plywood MetalSd Wd Sdng Wd Shng VinylSd VinylSd VinylSd VinylSd VinylSd Plywood VinylSd Stucco VinylSd VinylSd HdBoard AsbShng MetalSd BrkFace Wd Shng HdBoard Wd Sdng VinylSd Wd Sdng HdBoard Wd Sdng VinylSd HdBoard VinylSd MetalSd Wd Sdng Wd Sdng MetalSd MetalSd Wd Sdng CmentBd Stucco MetalSd Plywood BrkFace Plywood Plywood Wd Shng AsbShng Plywood HdBoard VinylSd MetalSd VinylSd HdBoard HdBoard Wd Shng Wd Shng MetalSd HdBoard Wd Sdng CmentBd VinylSd HdBoard CmentBd Wd Sdng HdBoard Plywood HdBoard MetalSd VinylSd Stucco BrkFace VinylSd Wd Sdng VinylSd VinylSd VinylSd VinylSd VinylSd VinylSd MetalSd HdBoard HdBoard VinylSd Wd Sdng VinylSd Wd Sdng HdBoard VinylSd VinylSd VinylSd Wd Sdng BrkFace VinylSd HdBoard VinylSd VinylSd AsbShng MetalSd VinylSd Wd Sdng VinylSd VinylSd MetalSd MetalSd Wd Sdng HdBoard Plywood Plywood Wd Sdng VinylSd MetalSd MetalSd VinylSd VinylSd Stone VinylSd MetalSd HdBoard VinylSd VinylSd MetalSd MetalSd HdBoard MetalSd VinylSd VinylSd MetalSd HdBoard VinylSd MetalSd VinylSd Wd Sdng VinylSd VinylSd VinylSd MetalSd VinylSd VinylSd CmentBd VinylSd VinylSd CBlock AsbShng VinylSd VinylSd CmentBd VinylSd Wd Sdng Wd Sdng HdBoard VinylSd Stucco Plywood Wd Sdng Wd Sdng Wd Shng VinylSd Plywood Stucco VinylSd Wd Sdng VinylSd Plywood HdBoard Wd Sdng VinylSd VinylSd Wd Sdng MetalSd Wd Sdng Wd Shng Wd Shng VinylSd VinylSd VinylSd Wd Sdng CmentBd Wd Shng Plywood VinylSd Plywood VinylSd VinylSd Stone VinylSd Wd Sdng VinylSd AsbShng VinylSd HdBoard Plywood HdBoard Plywood VinylSd Plywood HdBoard HdBoard VinylSd MetalSd MetalSd MetalSd VinylSd Plywood Wd Sdng VinylSd BrkFace Wd Sdng HdBoard VinylSd MetalSd HdBoard MetalSd VinylSd VinylSd Wd Sdng VinylSd VinylSd HdBoard VinylSd HdBoard CmentBd VinylSd CmentBd VinylSd VinylSd VinylSd VinylSd Plywood CmentBd MetalSd HdBoard
MasVnrType BrkFace None BrkFace None BrkFace None Stone Stone None None None Stone None Stone BrkFace None BrkFace None None None BrkFace None BrkFace None None Stone None Stone None None None None None None BrkFace Stone None BrkFace None None BrkFace None None None None BrkFace None None None None None None None None None BrkFace BrkFace None BrkFace None None None Stone None BrkFace BrkFace BrkFace BrkFace None None BrkFace None BrkFace BrkFace None None None None None None BrkFace BrkFace Stone BrkCmn BrkFace BrkFace None Stone None None None BrkCmn None None None BrkFace BrkFace BrkFace None None BrkFace BrkFace None None BrkFace BrkFace None None None BrkFace None None Stone BrkFace None BrkFace BrkFace None None None None None BrkFace None None None None None BrkFace BrkFace BrkFace BrkFace None BrkFace None BrkFace BrkFace BrkFace BrkFace None None None None BrkFace BrkFace Stone None BrkFace BrkFace None None Stone BrkFace None None None None BrkFace None BrkFace None BrkFace BrkFace None None None None Stone None BrkFace None Stone None BrkCmn Stone None BrkFace None Stone None BrkFace None BrkFace None None None None None Stone None None BrkFace None Stone None None Stone None None BrkFace None None None BrkFace None None None None BrkFace Stone None None None None BrkFace BrkFace BrkFace None BrkFace BrkFace None None None None BrkFace BrkFace BrkFace BrkFace None BrkFace BrkFace BrkFace BrkFace None NaN BrkFace BrkFace None BrkFace None Stone None None None None BrkFace None None BrkFace BrkCmn None Stone None BrkFace None BrkFace None Stone BrkFace None BrkFace Stone BrkFace None None BrkFace None None None BrkFace None None BrkFace BrkCmn None None None None BrkFace BrkFace BrkFace Stone Stone Stone None None None None BrkFace None None None None BrkFace Stone None None BrkFace BrkFace None BrkFace None BrkFace None None Stone BrkFace None None None BrkFace None None None None None BrkFace None BrkFace BrkFace Stone BrkFace BrkFace None BrkFace None None BrkFace None None BrkFace None BrkFace Stone None None Stone BrkFace None BrkFace None None BrkFace BrkFace None None None BrkFace Stone BrkFace BrkFace None None None None None BrkFace None BrkFace BrkFace None None BrkFace BrkFace BrkFace None BrkFace BrkFace BrkFace None None None None None None None None None Stone BrkFace None None None None None BrkFace None BrkFace BrkFace Stone None None None None None None None BrkFace None Stone None None None BrkFace None None None None BrkFace Stone None None Stone None None None BrkFace None None None BrkFace Stone None BrkFace BrkFace None BrkFace None BrkFace None BrkFace None BrkFace None None None None None None None Stone BrkFace None BrkFace None None None None None None None None BrkFace None None BrkFace None None None BrkCmn Stone None None None BrkFace BrkFace None Stone Stone BrkFace None None BrkFace BrkFace None None BrkFace BrkFace Stone BrkCmn BrkFace BrkFace None BrkFace None None BrkFace None None None None None None None None None None None BrkFace None ... None BrkFace None None BrkFace None None BrkFace None BrkFace None Stone None NaN None None None NaN None None Stone BrkFace BrkFace None None None None Stone BrkFace None BrkFace None BrkFace None Stone None None BrkFace None Stone None None BrkFace BrkFace BrkFace BrkFace None None Stone None None None None None BrkFace None BrkFace None BrkFace BrkFace None Stone None BrkFace None None BrkFace Stone None BrkFace None None None Stone None None Stone BrkFace None None BrkCmn BrkFace Stone BrkFace None None Stone None BrkFace None None None BrkCmn Stone BrkFace BrkFace Stone None Stone BrkFace None None None None BrkFace None None BrkFace None None BrkFace None None BrkFace None None None None BrkFace None BrkFace None BrkFace BrkFace None None None None Stone None None BrkFace None BrkFace None None None None None BrkFace None None BrkFace None BrkFace BrkFace None None None Stone None BrkFace None None None Stone None None None None None BrkFace None None BrkFace None BrkFace None None BrkFace None None None None None None None None None None None BrkFace Stone None None None BrkFace None None None None BrkFace None None Stone None None Stone Stone BrkFace None BrkFace None None None Stone Stone None None BrkFace None None None None None BrkFace None None None None None Stone None None BrkFace None None None BrkFace None Stone BrkFace None BrkFace Stone None None None None BrkFace None None None BrkFace None BrkFace None BrkFace None Stone None None None None None None None Stone None BrkFace None None BrkFace BrkFace None BrkFace Stone BrkFace BrkFace None None BrkFace None BrkFace None None Stone None None BrkFace BrkFace Stone BrkFace NaN None BrkFace None BrkFace None None None Stone None None Stone None BrkFace None Stone None None None None None None BrkFace None None BrkFace BrkFace None None BrkFace BrkFace None Stone None BrkFace NaN None BrkFace BrkFace None None None None BrkFace BrkCmn None Stone BrkFace BrkFace None None None BrkFace BrkFace BrkFace Stone None None None BrkFace BrkFace Stone Stone Stone None None BrkFace None BrkFace BrkFace BrkFace None None Stone None BrkFace BrkFace None None None None BrkFace None None None None None Stone None None None None BrkFace BrkFace None None None None None BrkFace None None None None Stone None None BrkFace BrkFace None BrkFace None BrkFace BrkFace None BrkFace BrkFace None Stone None None None None BrkFace None BrkFace BrkFace None None None BrkFace None BrkFace None None BrkFace None None None None None None None BrkFace None Stone None BrkFace None None None BrkFace BrkFace None None None None None None None Stone None Stone None None None BrkFace None None None Stone None BrkFace None BrkFace None BrkFace BrkFace None BrkFace None None Stone BrkFace None Stone BrkFace None None None BrkFace None BrkFace None Stone None BrkFace None BrkFace Stone None BrkFace None BrkFace BrkFace None None None Stone BrkFace None None None Stone None None None
MasVnrArea 196 0 162 0 350 0 186 240 0 0 0 286 0 306 212 0 180 0 0 0 380 0 281 0 0 640 0 200 0 0 0 0 0 0 246 132 0 650 0 0 101 0 0 0 0 412 0 0 0 0 0 0 0 0 0 272 456 0 1031 0 0 0 178 0 573 344 287 167 0 0 1115 0 40 104 0 0 0 0 0 0 576 443 468 66 22 284 0 76 0 0 0 203 0 0 0 68 183 48 0 0 28 336 0 0 600 768 0 0 0 480 0 0 220 184 0 1129 116 0 0 0 0 0 135 0 0 0 0 0 266 85 309 40 0 136 0 288 196 70 320 0 0 0 0 183 336 50 0 180 120 0 0 436 252 0 0 0 0 84 0 456 0 664 226 0 0 0 0 300 0 653 0 112 0 491 132 0 268 0 748 0 456 0 98 0 0 0 0 0 275 0 0 138 0 50 0 0 205 0 0 262 0 0 0 205 0 0 0 0 128 260 0 0 0 0 153 64 266 0 312 16 0 0 0 0 922 142 290 127 0 16 220 506 297 0 NaN 604 98 0 254 0 36 0 0 0 0 102 0 0 101 472 0 481 0 108 0 302 0 180 172 0 399 270 46 0 0 210 0 0 0 174 0 0 348 183 0 0 0 0 315 299 340 68 166 72 0 0 0 0 31 0 0 0 0 34 238 0 0 1600 365 0 56 0 150 0 0 246 246 0 0 0 112 0 0 0 0 0 278 0 256 225 370 388 172 0 300 0 0 175 0 0 84 0 296 146 0 0 200 113 0 176 0 0 340 616 0 0 0 30 106 870 362 0 0 0 0 0 106 0 120 530 0 0 500 510 120 0 247 305 200 0 0 0 0 0 0 0 0 0 350 16 0 0 0 0 0 16 0 255 125 272 0 0 0 0 0 0 0 288 0 100 0 0 0 650 0 0 0 0 350 100 0 0 432 0 0 0 203 0 0 0 200 126 0 473 74 0 145 0 108 0 232 0 376 0 0 0 0 0 0 0 200 270 0 72 0 0 0 0 0 0 0 0 42 0 0 320 0 0 0 161 110 0 0 0 136 18 0 224 248 120 0 0 80 304 0 0 215 772 336 435 378 562 0 116 0 0 168 0 0 0 0 0 0 0 0 0 0 0 89 0 ... 0 424 0 0 44 0 0 151 0 105 0 106 0 NaN 0 0 0 NaN 0 0 210 975 16 0 0 0 0 450 298 0 423 0 340 0 230 0 0 571 0 24 0 0 53 164 16 220 0 0 108 0 0 0 0 0 206 0 196 0 76 145 0 84 0 14 0 0 324 338 0 281 0 0 0 295 0 0 70 396 0 0 67 252 135 99 0 0 208 0 75 0 0 0 272 145 210 160 240 0 154 480 0 0 0 0 200 0 0 360 0 0 120 0 0 140 0 0 0 0 169 0 144 0 100 425 0 0 0 0 50 0 0 212 0 166 0 0 0 0 0 206 0 0 45 0 304 362 0 0 0 42 0 660 0 0 0 328 0 0 0 0 0 196 0 0 170 0 130 0 0 180 0 0 0 0 0 0 0 0 0 0 0 44 340 0 0 0 85 0 0 0 0 132 0 0 288 0 0 166 186 270 0 72 0 0 0 268 72 0 0 1378 0 0 0 0 0 337 0 0 0 0 0 186 0 0 226 0 0 0 95 0 149 456 0 425 143 0 0 0 0 51 0 0 0 171 0 234 0 120 0 420 0 0 0 0 0 0 0 72 0 236 0 0 320 44 0 63 74 300 766 0 1 120 0 180 0 0 106 0 0 32 252 0 81 NaN 0 74 0 335 0 0 0 163 0 0 92 0 554 0 182 0 0 0 0 0 0 218 0 0 632 180 0 0 114 84 0 567 0 359 NaN 0 110 54 0 0 0 0 451 621 0 788 86 268 0 0 0 168 148 82 796 0 344 0 391 228 117 300 94 0 0 88 0 80 336 165 0 0 270 0 178 132 0 0 0 0 428 0 0 0 0 0 410 0 0 0 312 360 564 0 0 0 0 0 149 0 0 0 0 100 0 0 368 318 0 579 0 143 65 0 216 66 0 16 0 0 0 0 158 0 170 171 0 0 0 705 0 80 0 0 408 0 0 0 0 0 0 0 184 0 160 0 244 0 0 0 45 245 0 0 0 0 0 0 0 84 0 174 0 0 0 123 0 0 0 366 0 130 0 731 0 312 420 0 170 0 0 243 448 0 294 310 0 0 0 318 0 237 0 426 0 96 0 147 160 0 106 0 189 438 0 0 0 194 80 0 0 0 119 0 0 0
ExterQual Gd TA Gd TA Gd TA Gd TA TA TA TA Ex TA Gd TA TA TA TA TA TA Gd TA Gd TA TA Gd TA Gd TA TA TA TA Gd TA Ex Gd TA TA TA TA TA TA TA TA TA Ex Gd Gd TA TA TA TA Fa Gd TA TA Gd Gd Ex TA TA TA Gd TA TA Gd TA Gd TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA Gd Gd Gd Fa TA TA TA TA TA TA Ex Gd TA TA TA TA Gd TA TA TA Gd TA TA TA TA TA TA Gd TA TA TA TA TA TA Gd TA TA TA Gd TA TA TA TA TA TA TA Gd TA Gd TA TA TA TA Gd TA TA Gd TA Gd TA Gd TA Gd TA TA TA Gd TA TA TA TA TA Gd Gd Gd TA Gd Gd TA TA TA Gd Gd Gd Gd TA TA Gd TA TA TA Gd Gd Ex Gd Gd TA TA TA TA Gd TA TA TA Gd Gd TA Gd Gd TA TA Ex Gd TA Gd TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA Gd TA Gd Gd Gd Gd TA TA Ex TA Gd TA TA Gd TA Gd TA TA Gd TA Gd Gd Ex TA Gd TA TA TA Gd TA TA TA Gd Gd TA Gd Gd TA TA Gd Gd Gd Gd TA TA Gd TA TA TA TA TA TA TA TA Gd TA Gd TA TA Gd Gd TA Ex TA Gd Gd Gd Gd Gd Gd TA TA TA TA Gd TA TA TA TA TA TA Gd Gd TA TA Gd Gd TA TA Gd Gd TA TA Gd TA TA TA TA TA Gd Gd Gd Gd TA TA Gd Gd TA Gd TA Gd TA TA TA TA TA Gd Gd TA TA Gd Gd Gd TA Gd Fa TA Ex TA TA TA TA Gd Ex Ex TA TA TA TA Gd TA TA TA Gd TA TA Gd TA TA TA TA TA TA TA TA TA TA TA Gd Fa Gd Gd Gd TA TA Gd Gd TA Gd Gd TA TA Gd Ex TA TA TA TA TA TA TA TA TA Gd Gd Gd TA Gd TA TA TA TA Gd Gd TA TA Ex TA Gd Gd TA TA TA TA TA Gd TA Gd TA TA Gd TA Gd TA TA TA TA TA TA Gd TA Gd TA TA Ex TA TA Gd Gd TA Gd Gd TA TA TA TA Gd Gd TA TA TA TA TA TA Gd Gd TA TA TA Gd TA TA Gd Gd Gd TA TA Gd Gd TA TA Ex Gd TA Gd Gd Gd TA TA Gd TA TA TA TA TA TA Gd TA TA TA Gd TA TA TA ... TA TA TA Gd Gd Gd Gd TA TA TA TA Gd TA Gd TA Gd TA Gd TA TA Ex Gd Gd Gd TA TA TA Ex TA Gd Gd Gd TA TA Ex TA TA TA TA Gd TA TA Gd TA Gd Gd TA TA Gd TA TA TA TA TA TA Gd Gd Gd Gd Gd TA Gd TA Gd Gd TA TA Gd TA TA TA TA Gd Gd TA TA Gd Gd TA TA TA TA Gd TA Gd TA Gd TA TA TA Gd Gd TA TA Gd TA Gd Gd Ex TA Gd Fa TA TA TA Gd Gd TA TA TA TA TA TA TA Gd TA TA TA Gd TA Gd TA Gd TA TA TA TA Gd Gd Gd TA Gd TA TA TA Gd TA Gd TA TA TA TA TA TA TA Gd Gd Gd TA Gd TA TA TA TA TA Gd Gd TA TA TA TA Gd TA TA TA TA Gd Gd Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA Gd TA TA TA Gd TA TA TA TA TA TA Gd TA Gd TA Gd Gd Gd TA Gd TA TA TA Gd Gd TA TA Gd TA TA Gd TA TA Gd TA TA TA TA TA Ex Gd TA TA TA TA Gd Gd TA TA Gd TA TA TA TA Gd TA Gd TA TA TA TA Gd TA Gd TA Gd TA Gd Gd Gd TA TA TA TA TA Gd TA TA TA TA TA TA Gd TA Gd TA Ex TA TA TA TA TA TA TA Gd Gd Gd Gd Gd Gd TA Ex TA TA Gd TA TA TA Gd Gd TA Gd Gd TA Gd TA Gd TA TA TA TA TA Gd Gd Fa Ex TA TA Gd TA TA TA TA TA TA TA Gd TA Gd TA TA TA TA TA TA TA Gd Gd TA TA TA TA TA TA TA TA Ex TA Gd TA Gd Gd Gd Gd Gd TA TA TA Gd Gd Gd Gd TA TA Gd Gd Gd TA TA TA Gd Fa Gd TA TA Gd Gd TA Gd TA TA TA TA TA TA TA Gd TA TA TA Gd TA Gd TA TA Gd Gd TA TA TA Gd Gd Gd TA TA TA Gd Gd TA Gd TA TA Gd Gd Gd TA Gd Gd Fa TA TA Ex Gd Gd TA TA TA TA TA TA TA TA TA TA TA TA Ex TA Gd TA TA TA Gd Gd TA TA TA TA TA TA Gd Gd TA Gd TA TA TA TA Gd TA TA Gd TA Gd TA Gd TA Gd TA TA Gd Gd TA Gd Gd TA TA Gd Gd TA TA TA TA Gd TA Ex TA TA TA Gd Ex TA Gd TA TA Gd TA TA TA Gd TA TA Gd TA TA Ex TA Gd
ExterCond TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA Fa TA TA TA TA TA TA TA Gd TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA Gd TA TA TA Gd TA TA TA TA TA Gd Gd TA TA TA TA TA TA Fa TA TA TA Gd TA Gd Gd TA TA TA TA TA TA Fa TA Fa TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA Gd TA Gd Fa TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd Gd TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Fa TA TA TA Po TA TA TA Gd TA TA TA TA TA TA TA TA TA TA Gd TA TA TA Gd TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA Fa TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA Fa TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA Gd Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA Gd TA TA TA TA TA TA Gd TA Gd TA TA TA TA TA TA TA TA TA Gd TA TA TA Gd TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA Gd TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA Fa Gd TA TA TA TA TA TA TA Gd TA TA ... Gd Gd TA TA TA TA Gd TA TA Fa TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA Gd Gd TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA Gd TA TA TA TA Ex TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA Fa TA Gd TA TA Fa TA TA TA TA Gd TA TA TA Gd TA Gd Gd TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA Fa TA Gd TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA Gd TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd Gd TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA Gd TA Gd TA Gd TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA Gd TA TA TA TA TA TA TA Gd TA TA TA Gd TA TA TA TA Gd Gd TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA
Foundation PConc CBlock PConc BrkTil PConc Wood PConc CBlock BrkTil BrkTil CBlock PConc CBlock PConc CBlock BrkTil CBlock Slab PConc CBlock PConc PConc PConc PConc CBlock PConc CBlock PConc CBlock BrkTil BrkTil CBlock PConc CBlock PConc PConc PConc CBlock CBlock PConc CBlock CBlock CBlock CBlock CBlock PConc PConc PConc BrkTil CBlock PConc PConc CBlock CBlock CBlock CBlock PConc PConc PConc CBlock PConc BrkTil PConc BrkTil PConc PConc CBlock PConc CBlock CBlock CBlock CBlock PConc CBlock CBlock CBlock CBlock CBlock CBlock PConc CBlock PConc PConc CBlock PConc CBlock PConc PConc CBlock PConc Slab CBlock BrkTil BrkTil PConc PConc PConc CBlock BrkTil CBlock PConc CBlock Slab PConc PConc PConc BrkTil CBlock CBlock CBlock CBlock PConc PConc CBlock CBlock PConc CBlock PConc PConc PConc CBlock PConc CBlock PConc CBlock CBlock CBlock BrkTil CBlock CBlock CBlock PConc CBlock PConc CBlock PConc CBlock CBlock PConc PConc CBlock PConc CBlock PConc CBlock PConc BrkTil PConc PConc BrkTil CBlock PConc CBlock CBlock BrkTil BrkTil CBlock PConc PConc PConc PConc PConc PConc CBlock BrkTil BrkTil CBlock PConc PConc CBlock CBlock CBlock PConc CBlock CBlock CBlock PConc CBlock PConc CBlock PConc BrkTil PConc PConc PConc BrkTil PConc CBlock CBlock PConc CBlock CBlock PConc PConc CBlock CBlock PConc PConc CBlock PConc PConc CBlock BrkTil PConc CBlock PConc CBlock PConc CBlock CBlock CBlock PConc PConc PConc CBlock CBlock PConc CBlock CBlock PConc PConc PConc CBlock CBlock PConc CBlock PConc CBlock CBlock PConc CBlock PConc CBlock CBlock PConc CBlock PConc PConc PConc CBlock PConc CBlock BrkTil CBlock PConc CBlock Stone CBlock PConc CBlock CBlock PConc PConc CBlock CBlock PConc PConc PConc PConc PConc CBlock PConc CBlock BrkTil BrkTil CBlock PConc PConc CBlock CBlock PConc CBlock PConc CBlock CBlock BrkTil PConc CBlock PConc CBlock CBlock PConc PConc PConc PConc PConc CBlock CBlock CBlock BrkTil PConc PConc CBlock CBlock CBlock CBlock CBlock PConc CBlock CBlock CBlock PConc PConc PConc BrkTil PConc CBlock CBlock CBlock PConc PConc CBlock CBlock CBlock BrkTil PConc CBlock PConc PConc CBlock PConc PConc CBlock CBlock CBlock CBlock PConc CBlock BrkTil PConc CBlock CBlock PConc PConc PConc CBlock PConc PConc CBlock CBlock PConc CBlock Slab PConc CBlock BrkTil CBlock CBlock PConc PConc PConc PConc CBlock BrkTil CBlock PConc PConc PConc CBlock PConc CBlock CBlock Slab CBlock PConc CBlock CBlock CBlock PConc CBlock PConc CBlock CBlock PConc PConc BrkTil PConc PConc PConc PConc BrkTil PConc PConc BrkTil PConc PConc PConc CBlock PConc PConc BrkTil PConc Slab CBlock CBlock CBlock CBlock CBlock BrkTil PConc PConc PConc PConc PConc PConc CBlock BrkTil BrkTil PConc PConc CBlock CBlock PConc CBlock PConc PConc CBlock BrkTil BrkTil CBlock PConc CBlock CBlock PConc CBlock CBlock PConc CBlock PConc CBlock CBlock BrkTil CBlock PConc CBlock PConc BrkTil PConc PConc BrkTil PConc CBlock PConc PConc PConc CBlock CBlock PConc BrkTil CBlock BrkTil CBlock PConc PConc CBlock CBlock BrkTil CBlock PConc CBlock PConc PConc CBlock CBlock CBlock PConc CBlock CBlock PConc PConc PConc CBlock PConc PConc PConc CBlock PConc PConc PConc BrkTil PConc PConc PConc PConc CBlock CBlock CBlock CBlock CBlock CBlock PConc CBlock PConc CBlock BrkTil CBlock PConc PConc PConc CBlock ... CBlock CBlock CBlock PConc PConc PConc BrkTil CBlock BrkTil CBlock CBlock PConc CBlock PConc BrkTil PConc PConc PConc CBlock PConc CBlock PConc PConc PConc CBlock CBlock CBlock PConc CBlock PConc PConc BrkTil CBlock PConc PConc CBlock CBlock CBlock BrkTil PConc Slab BrkTil PConc CBlock PConc CBlock CBlock CBlock PConc BrkTil CBlock CBlock PConc BrkTil CBlock PConc PConc CBlock PConc PConc PConc PConc CBlock PConc CBlock CBlock CBlock PConc CBlock CBlock PConc BrkTil PConc PConc PConc Slab PConc PConc CBlock CBlock CBlock CBlock PConc PConc PConc Slab PConc PConc Slab CBlock PConc PConc CBlock CBlock PConc CBlock PConc PConc PConc CBlock PConc CBlock BrkTil BrkTil CBlock PConc PConc CBlock CBlock CBlock CBlock CBlock CBlock CBlock PConc CBlock CBlock CBlock PConc PConc CBlock CBlock PConc CBlock PConc PConc CBlock PConc PConc PConc Slab PConc PConc CBlock CBlock PConc PConc CBlock BrkTil CBlock CBlock CBlock CBlock CBlock CBlock PConc PConc PConc PConc PConc PConc CBlock CBlock CBlock CBlock PConc PConc CBlock CBlock CBlock BrkTil PConc CBlock CBlock PConc CBlock PConc PConc PConc CBlock BrkTil PConc BrkTil PConc PConc BrkTil CBlock CBlock PConc BrkTil CBlock CBlock PConc CBlock BrkTil BrkTil CBlock CBlock PConc PConc CBlock CBlock PConc BrkTil CBlock CBlock PConc PConc PConc CBlock CBlock CBlock CBlock CBlock PConc PConc PConc PConc CBlock PConc CBlock CBlock PConc CBlock BrkTil PConc CBlock CBlock BrkTil Slab Wood PConc PConc BrkTil CBlock BrkTil CBlock PConc PConc PConc CBlock PConc PConc PConc CBlock PConc PConc BrkTil PConc CBlock CBlock PConc BrkTil PConc CBlock CBlock CBlock PConc CBlock PConc PConc Wood CBlock CBlock CBlock CBlock Slab PConc Slab CBlock CBlock CBlock CBlock CBlock PConc CBlock PConc CBlock PConc CBlock CBlock CBlock Slab CBlock PConc BrkTil PConc PConc PConc PConc PConc PConc CBlock PConc PConc CBlock PConc CBlock BrkTil CBlock CBlock PConc CBlock CBlock PConc BrkTil PConc PConc PConc CBlock PConc CBlock CBlock PConc PConc PConc CBlock PConc CBlock CBlock PConc CBlock CBlock CBlock PConc CBlock CBlock CBlock PConc CBlock PConc CBlock CBlock CBlock PConc CBlock CBlock CBlock PConc PConc PConc CBlock Stone PConc CBlock CBlock CBlock PConc PConc CBlock PConc CBlock PConc PConc PConc PConc PConc PConc CBlock CBlock PConc PConc PConc PConc CBlock CBlock PConc PConc PConc PConc CBlock CBlock PConc CBlock PConc BrkTil BrkTil CBlock BrkTil PConc PConc CBlock CBlock CBlock CBlock CBlock CBlock CBlock PConc CBlock CBlock PConc PConc BrkTil PConc PConc CBlock PConc PConc BrkTil CBlock CBlock BrkTil PConc PConc CBlock CBlock CBlock PConc PConc BrkTil PConc BrkTil PConc PConc PConc PConc PConc PConc PConc PConc CBlock PConc PConc PConc PConc BrkTil CBlock CBlock PConc BrkTil CBlock BrkTil BrkTil BrkTil BrkTil CBlock BrkTil PConc BrkTil PConc CBlock CBlock BrkTil PConc PConc CBlock BrkTil CBlock BrkTil BrkTil PConc PConc PConc PConc PConc CBlock CBlock CBlock CBlock PConc CBlock Slab PConc BrkTil PConc PConc PConc CBlock CBlock CBlock CBlock PConc CBlock CBlock CBlock PConc CBlock CBlock CBlock PConc CBlock BrkTil PConc CBlock CBlock PConc PConc CBlock CBlock BrkTil PConc PConc BrkTil PConc CBlock CBlock PConc CBlock CBlock CBlock PConc PConc PConc PConc PConc CBlock Stone CBlock CBlock
BsmtQual Gd Gd Gd TA Gd Gd Ex Gd TA TA TA Ex TA Gd TA TA TA NaN TA TA Ex TA Gd Gd TA Gd TA Ex TA TA TA TA Ex TA Ex Ex Gd TA TA NaN TA TA Gd Gd TA Ex Ex Gd TA TA Gd TA Gd Ex TA TA Gd Gd Ex TA Gd TA Gd TA Gd Ex Gd Gd TA TA Gd TA Gd TA Fa Gd TA TA TA TA Gd Ex Gd TA Gd Gd Gd Gd TA Gd NaN TA Gd TA Gd Gd Gd TA TA TA TA Gd NaN Gd TA Ex Fa TA TA Gd TA Gd Ex Gd TA Gd TA Gd Gd Gd TA TA TA Gd Gd TA Gd TA TA TA TA Gd TA Ex TA TA TA TA Gd Gd TA Gd TA Gd TA Gd TA Gd Gd Gd TA Ex TA Gd TA TA NaN Ex Gd Gd Gd Ex Gd TA TA TA TA Ex Gd Gd TA TA Gd TA Gd TA Gd TA Ex TA Gd Fa NaN Gd Fa TA Gd Fa Gd Ex Gd TA Gd Gd TA Gd Ex TA TA Ex Gd TA Fa Gd TA Gd TA TA Gd Gd TA Gd Gd Gd Gd TA Gd TA TA Gd Gd Gd Gd TA Ex TA Gd TA TA Gd TA Gd TA TA Gd TA Gd Gd Ex TA Gd TA TA Gd Gd Gd Fa TA Gd Gd TA Gd Gd Gd TA Gd Gd Gd Gd NaN TA Ex Gd TA Fa Gd Gd TA TA TA Gd Gd Gd TA TA TA Ex TA Ex Gd Gd Gd Gd Gd Gd Gd TA TA TA TA Gd TA TA TA TA TA TA Gd TA TA TA Gd Gd TA TA Gd Gd TA TA Ex Gd TA Gd Gd TA Gd Gd Gd Gd Gd Ex Ex TA TA Gd TA Gd TA TA TA TA TA Gd Gd Gd TA Ex Gd Gd TA Gd TA NaN Ex Gd TA TA TA Gd Ex Ex Gd TA TA TA Gd Gd Gd TA Gd Gd TA NaN TA Gd TA Gd Gd TA TA Gd NaN Gd TA Gd Fa Gd Gd Ex Gd TA Gd Gd Fa Gd Gd TA TA Gd Ex TA Ex NaN TA TA TA TA TA Fa Gd Gd Gd TA Gd Gd TA TA Gd Ex Ex TA TA Ex TA Gd Gd TA Gd TA TA Gd Gd TA Gd Gd TA Gd TA Gd Gd TA TA TA Gd Gd Gd Fa TA TA TA Ex TA TA Ex Gd TA TA Gd TA TA TA Gd Ex Gd TA TA TA Gd TA TA Gd Gd TA TA Gd Gd TA TA Gd Gd Gd Gd Gd Ex Gd TA Gd Gd Ex Fa Ex Ex TA Ex TA TA TA Gd TA Gd Gd TA Gd TA Fa TA Gd TA TA TA ... TA Gd Gd Ex Gd Gd TA TA TA TA TA Gd Gd Gd Gd Gd Gd Gd TA TA TA Gd Gd Gd NaN TA TA Ex TA Gd Gd TA TA Gd Ex TA TA TA TA Gd NaN Fa Gd TA Gd Gd Gd TA Ex TA TA NaN TA TA TA Gd Gd Gd Gd Gd Gd Gd TA Gd Gd TA TA Gd TA TA TA TA Gd Gd Fa NaN Ex Gd TA Gd TA TA Gd Gd Gd NaN Ex Gd NaN NaN Gd Gd TA TA Gd Gd Gd Gd Ex TA Gd TA TA TA TA Ex Gd TA Gd TA TA TA Fa TA Gd TA Fa TA Gd Gd TA TA Gd TA Gd Gd TA Gd Gd Gd NaN Gd TA TA TA Gd TA Gd TA Gd TA TA TA TA TA Ex Gd Ex Gd Ex Gd TA TA TA TA Ex Gd TA TA TA TA Gd TA TA Gd TA Gd Gd Gd Gd TA Gd TA Gd Gd TA TA TA TA TA TA TA Ex TA Gd TA Gd TA TA TA TA TA TA TA TA Gd TA Gd Gd TA Gd TA Gd Gd Gd Gd Gd Gd TA Ex TA TA Gd TA Gd Gd TA TA TA NaN Gd Ex Ex TA TA TA Fa Gd Gd TA TA Gd TA Ex TA Gd Gd TA Gd TA TA Gd TA Gd Gd Gd TA Gd TA Ex Gd Gd TA TA TA TA NaN Gd NaN TA TA TA TA TA Gd TA Gd TA Ex TA Gd TA NaN TA TA Gd Gd Gd Gd Gd Gd Gd Gd Ex Gd Gd Gd TA Gd TA TA Gd TA TA Gd TA Gd Fa Gd Gd Gd TA TA TA Gd Gd TA Ex TA TA Gd TA TA TA TA TA TA Gd Gd TA Gd Gd Gd TA TA TA TA TA Gd Gd TA TA TA Gd TA TA TA Gd Ex TA Gd TA Gd Gd Gd Ex Gd Gd TA Gd Gd Gd Ex Gd TA Fa Ex Ex Gd TA TA NaN Gd TA Ex TA TA TA Gd Gd Gd TA TA TA TA Gd TA TA Gd TA TA Gd Gd TA Gd TA TA Ex Gd TA TA TA TA Gd Gd TA TA TA Gd Ex TA Ex TA Gd Gd Gd Gd TA Gd Gd TA TA Gd Ex Gd Gd Gd TA TA Gd TA TA Fa TA TA TA Gd TA Ex TA Gd TA TA Fa Gd Gd TA TA TA TA TA TA Gd Gd TA Ex Gd TA Fa Gd Gd TA NaN Gd TA Gd TA Gd TA TA TA Gd Gd Gd TA TA Gd TA TA Gd Gd Gd TA Gd TA TA TA Ex TA TA Ex Gd Ex TA Gd TA TA Gd Fa Gd Gd Gd Gd Gd Gd Gd Gd TA TA TA
BsmtCond TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA Fa TA NaN TA TA Fa TA Gd TA TA TA TA TA TA NaN TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd Gd TA Fa Gd TA TA TA TA TA TA NaN TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA Gd TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA Fa NaN TA TA TA TA TA TA TA TA TA Fa TA TA Gd TA TA TA TA TA TA NaN TA TA Fa TA TA TA TA TA NaN TA TA TA Po TA TA TA TA TA Gd TA Fa TA TA TA TA TA TA Gd TA NaN TA TA TA TA TA Po TA TA TA TA TA TA TA TA TA Gd TA Fa TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA ... TA Gd TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA Fa TA TA Gd TA TA TA TA TA TA TA TA Fa TA NaN TA TA TA TA Gd TA TA TA TA TA NaN Fa TA Fa TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA Gd TA Fa NaN TA TA TA TA TA TA TA TA TA NaN TA TA NaN NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA NaN TA NaN TA TA TA TA Fa TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA Gd TA TA TA NaN TA TA TA Fa TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA Fa TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA Fa TA TA TA TA TA TA TA TA Gd TA TA TA TA TA Gd TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA NaN Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA
BsmtExposure No Gd Mn No Av No Av Mn No No No No No Av No No No NaN No No Av No No No Mn No Mn No Gd No No No Av No No Av No No No NaN No Gd Av Av No No No Av No No Av No Gd Gd No No No No Gd Av No No Mn No No Av Gd Av No No Gd No No No No Gd No No No No No No No No No No Av Av No No NaN No No Mn No No Av No No No Mn No NaN No No No No No No No No No Av Mn No No No No Mn No Gd No No No No Av No No No No No No No No No No No No Mn No No Av No Gd No No No No No No Av Gd No Gd No No NaN No No Mn No Av Mn No Mn No No No No No No Gd No No No Av No No No No No No NaN No No Mn Av No Av Av No No Av No Av No No No No Av No No No Av No No No No Gd No No Mn No No No No Mn No No Av Mn No No No Gd No No No No Av No No No Av No No Av No No No Av No No No No Av No No No Gd No Gd No Gd No No No No No NaN Gd No Av No No No Av No No No Mn Gd No No Gd No No No Gd No Av No No No No No No No No No Av No No No No Av No No No No Mn No No No No No No No No Gd No No Mn Gd No No No Av Gd Av Av Av Gd No No Av Gd No No Mn No No No Av Gd Gd Gd Av Mn No No No NaN No No No Mn No No No No Gd Mn No No No No No Av Av Av No NaN No Mn No No Gd No No No NaN No No No Gd Av No Av No No No Av No Av No No Av No Av No No NaN No No Av Av No No No No Av No No No No No No No No No Mn Av No No Av No No No No Gd No No No No No Gd No No Mn No No No No Av Av No No No Mn Gd No No Av No No No No No No No Gd No No No No No Gd No No No No No No No No Gd No No No Av No Gd Gd Av No Gd Av Gd No Gd Gd No No No No No Av No Av Mn No No No No No No No No No ... Gd No No No No No No No No Gd No No No No No No No No No No Av No Av Mn NaN No No Av No No No No Gd No Gd No No No No Av NaN No No No No Av No No Mn No No NaN No No No No Gd Av No Gd No Av No No Av No No Gd No No Mn No No No No NaN Gd No No Av No No Av No No NaN Av No NaN NaN Av Av No Av Mn No Av No Av Mn Mn No No No Mn Gd No Gd No No No No Mn No Av No No Mn Mn No No No No No No Av No Mn No No NaN No No Mn No No No No No No No No No Mn No Av Mn No No Mn No No No No No No No No No No No Av No No No No No Av Av Gd No Mn Mn No Av No No No Gd No Mn No Gd No No No No No No No No No No Av Gd Gd Gd No No No No No No Av Gd No Av No No Gd Av No No No No No No No No NaN No Gd Av No Gd No Mn No No No Av No Mn No No No Av No No No No Av No No No Mn No Av No Mn No Av No Av Av No NaN Mn NaN No No No Mn No Av Av Mn No No No No Av NaN No Av Mn No No No Av No Av Gd Gd No No No Av No No No No No Gd No No Gd No Gd No No No Av No No No No Gd Mn No Gd No No Av No No No Av No No No No Mn Av No No No Av No Mn Av No No No No Av Mn Gd Gd No No No No Av No No No No No No Av No No No No Mn Gd No Gd No No NaN No No Av No No No No No Av Av Mn No No No No No No Mn No No No No No No No Gd Gd No No No No Av No No No No No Av No Av No No No No No No Av Av No Av Av Gd No Gd Mn No No No No Mn No No Mn No No No Gd No Av No Av No Av Gd Av Mn No No No No Av Gd No Gd Av No No No No No NaN No No No No Mn No Av No No Av Av No No No No No No Av No No No No No No Av No No No Av Av No Av Gd No No No Av No No Gd No No No No No Mn No
BsmtFinType1 GLQ ALQ GLQ ALQ GLQ GLQ GLQ ALQ Unf GLQ Rec GLQ ALQ Unf BLQ Unf ALQ NaN GLQ LwQ Unf Unf Unf GLQ Rec Unf BLQ GLQ BLQ Unf Unf Unf Unf Rec GLQ Unf Unf Rec GLQ NaN Rec BLQ ALQ LwQ ALQ GLQ GLQ GLQ Unf BLQ GLQ Unf LwQ GLQ ALQ BLQ GLQ Unf Unf ALQ ALQ Unf GLQ Unf GLQ Unf GLQ GLQ Unf BLQ ALQ ALQ Unf ALQ Unf GLQ ALQ BLQ Unf Unf Rec GLQ GLQ Unf Unf Unf Unf Unf Unf GLQ NaN Rec ALQ Rec GLQ ALQ ALQ Rec ALQ ALQ ALQ Unf NaN Unf LwQ Unf Unf ALQ Unf BLQ Rec GLQ GLQ ALQ ALQ GLQ LwQ Unf GLQ Unf ALQ Unf BLQ GLQ Unf GLQ ALQ LwQ BLQ Rec Rec GLQ Rec GLQ Rec Unf BLQ Unf GLQ GLQ ALQ GLQ Rec GLQ Rec Unf BLQ Unf GLQ Unf BLQ GLQ Rec BLQ Unf Unf NaN Unf GLQ GLQ BLQ GLQ BLQ Unf LwQ ALQ LwQ GLQ Unf Unf Rec BLQ GLQ ALQ ALQ ALQ GLQ ALQ GLQ Unf GLQ LwQ NaN Unf Unf Unf GLQ Unf GLQ GLQ Rec ALQ GLQ Unf ALQ ALQ GLQ GLQ Unf GLQ Unf ALQ LwQ GLQ LwQ Unf Unf ALQ GLQ Rec Rec GLQ GLQ ALQ GLQ BLQ GLQ Unf BLQ GLQ Unf Unf ALQ ALQ GLQ Unf GLQ Rec ALQ GLQ Unf GLQ Unf LwQ GLQ ALQ GLQ BLQ Unf LwQ GLQ ALQ Unf Unf GLQ GLQ Unf Unf Unf Rec Unf GLQ Unf LwQ Rec Unf Rec GLQ GLQ NaN Rec Unf ALQ LwQ Unf GLQ GLQ LwQ ALQ BLQ Unf LwQ GLQ Rec ALQ Unf Unf LwQ Unf BLQ GLQ GLQ GLQ Unf GLQ Unf Rec BLQ BLQ Unf Unf Rec LwQ ALQ GLQ GLQ BLQ BLQ ALQ Unf BLQ GLQ Unf ALQ Unf GLQ ALQ Unf BLQ GLQ ALQ ALQ Rec ALQ LwQ GLQ GLQ Unf GLQ GLQ Unf GLQ LwQ ALQ Unf BLQ GLQ Rec Unf Unf GLQ Rec GLQ GLQ GLQ ALQ GLQ GLQ ALQ Rec Unf Unf NaN GLQ Rec Unf BLQ BLQ GLQ GLQ Unf GLQ LwQ Unf LwQ ALQ GLQ ALQ BLQ GLQ GLQ Rec NaN ALQ GLQ Rec BLQ GLQ LwQ ALQ Unf NaN ALQ BLQ Unf BLQ GLQ Unf GLQ Unf LwQ Unf Unf Unf GLQ GLQ Unf ALQ Unf GLQ Rec ALQ NaN Rec Unf BLQ Rec ALQ Unf GLQ GLQ GLQ Unf Unf Unf BLQ Unf Unf Unf GLQ Unf Rec GLQ Unf BLQ GLQ ALQ BLQ ALQ BLQ GLQ ALQ Unf GLQ LwQ Unf GLQ Rec GLQ GLQ ALQ Unf ALQ Unf ALQ GLQ Unf Unf LwQ Unf GLQ ALQ Unf Unf GLQ Unf ALQ Unf Unf ALQ Unf ALQ ALQ Unf ALQ ALQ Unf BLQ Unf LwQ GLQ ALQ Rec LwQ Rec Unf GLQ LwQ Unf GLQ GLQ Unf GLQ GLQ GLQ GLQ GLQ Unf GLQ Rec GLQ GLQ ALQ GLQ ALQ ALQ ALQ ALQ BLQ GLQ Unf BLQ Unf BLQ Unf Rec GLQ Rec ALQ BLQ ... BLQ ALQ ALQ Unf GLQ Unf Rec ALQ Unf ALQ Unf GLQ Unf Unf Unf GLQ Unf GLQ LwQ Rec ALQ GLQ Unf Unf NaN ALQ Unf GLQ LwQ Unf GLQ BLQ BLQ Unf GLQ ALQ Rec Unf Unf GLQ NaN Unf GLQ Unf GLQ GLQ LwQ BLQ Unf Unf Unf NaN Unf ALQ BLQ GLQ GLQ GLQ Unf GLQ GLQ GLQ LwQ GLQ ALQ LwQ Rec GLQ Rec BLQ Unf GLQ GLQ GLQ Unf NaN GLQ Unf Unf GLQ Rec GLQ ALQ GLQ ALQ NaN GLQ GLQ NaN NaN Unf Unf Rec Rec GLQ ALQ GLQ GLQ GLQ Rec GLQ Unf Unf BLQ BLQ GLQ Unf GLQ ALQ ALQ BLQ Rec Unf BLQ Unf BLQ ALQ BLQ GLQ GLQ ALQ ALQ Unf BLQ ALQ GLQ BLQ Unf Unf GLQ NaN BLQ Rec GLQ BLQ GLQ Unf Unf BLQ ALQ Rec BLQ Rec ALQ Unf GLQ GLQ Unf Unf GLQ GLQ ALQ GLQ BLQ Rec GLQ GLQ BLQ Unf LwQ Unf GLQ Unf Unf Unf Unf Unf GLQ Unf GLQ BLQ BLQ Unf GLQ Unf Unf BLQ Unf ALQ BLQ ALQ ALQ GLQ GLQ BLQ Unf GLQ Rec Unf ALQ Unf BLQ ALQ ALQ ALQ ALQ ALQ GLQ Unf ALQ ALQ BLQ BLQ GLQ ALQ Unf Unf GLQ Rec GLQ ALQ ALQ Unf Rec Rec GLQ ALQ Rec Unf NaN LwQ GLQ GLQ Rec Rec BLQ BLQ GLQ Unf Unf BLQ Unf Unf GLQ ALQ Unf Unf Unf Unf Rec Unf Unf Unf Unf ALQ ALQ Rec GLQ Rec GLQ Unf GLQ Rec GLQ BLQ Rec NaN GLQ NaN Unf Rec BLQ Rec LwQ GLQ GLQ Unf ALQ GLQ ALQ GLQ ALQ NaN Rec Unf Unf Unf Unf Unf GLQ GLQ Unf GLQ GLQ Unf Unf Unf GLQ Rec BLQ ALQ Unf BLQ LwQ Unf LwQ GLQ Unf GLQ ALQ Unf Rec Unf Unf GLQ GLQ Unf Unf Rec BLQ GLQ Unf BLQ ALQ Unf Rec BLQ ALQ GLQ Unf GLQ ALQ LwQ Unf Unf Unf ALQ Rec GLQ Unf GLQ ALQ Unf GLQ Rec BLQ ALQ GLQ GLQ LwQ GLQ BLQ GLQ Unf Unf GLQ Unf ALQ GLQ GLQ GLQ GLQ GLQ Unf Rec ALQ Unf Unf Unf ALQ ALQ NaN GLQ LwQ Unf Unf Rec ALQ LwQ Unf Unf ALQ ALQ Unf LwQ ALQ Unf Unf GLQ Rec Unf GLQ Unf Unf Unf ALQ BLQ GLQ GLQ LwQ LwQ Rec Unf GLQ GLQ Unf Rec GLQ GLQ GLQ Unf ALQ Unf Unf Unf GLQ GLQ ALQ GLQ BLQ ALQ BLQ GLQ GLQ Unf Unf Rec LwQ BLQ Unf Rec BLQ Unf Unf Rec BLQ BLQ Rec GLQ ALQ GLQ Unf ALQ Unf GLQ Unf BLQ Unf Rec BLQ Unf GLQ Unf GLQ Unf GLQ GLQ ALQ Unf Unf GLQ BLQ NaN GLQ Unf ALQ Unf GLQ BLQ Rec ALQ ALQ GLQ Unf ALQ Unf GLQ BLQ BLQ BLQ Unf LwQ Unf Unf ALQ Unf ALQ GLQ GLQ GLQ Unf GLQ GLQ Unf Unf LwQ Rec GLQ Unf GLQ Unf Unf GLQ Unf GLQ Unf ALQ GLQ GLQ BLQ
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BsmtFinSF2 0 0 0 0 0 0 0 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 668 0 486 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93 491 506 0 0 0 0 0 0 0 712 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 362 0 0 0 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 169 0 0 0 0 0 869 150 0 670 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1080 0 0 0 0 0 0 0 0 0 0 0 181 768 0 0 0 0 0 215 374 208 0 0 441 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 184 0 0 0 279 0 0 0 0 0 306 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 180 0 0 0 0 0 0 0 0 0 712 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 580 0 0 0 0 0 0 690 0 0 692 0 228 0 0 125 0 0 1063 0 620 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 175 0 0 0 0 0 0 0 0 0 0 0 0 0 0 820 0 0 0 0 0 0 0 0 1474 0 0 0 0 0 0 0 264 0 479 0 0 147 0 0 0 0 0 0 0 0 232 0 0 0 0 0 0 0 380 0 0 544 0 0 294 0 0 0 0 0 0 0 0 0 0 258 0 0 121 180 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 391 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 531 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 344 0 0 0 0 539 0 0 0 0 0 713 0 0 0 0 0 0 0 0 0 0 0 0 0 0 210 311 0 0 0 0 0 0 0 1120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 165 0 532 0 0 0 0 0 279 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 273 0 0 0 0 0 465 0 0 0 0 0 0 0 0 0 0 400 468 0 0 41 0 0 0 0 0 0 0 0 0 0 0 682 64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 557 0 0 0 0 230 0 0 106 0 0 0 0 0 0 0 0 0 791 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 240 0 0 0 0 0 0 287 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 547 0 0 0 0 0 0 0 0 0 0 0 391 0 0 0 0 0 0 0 0 469 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 177 0 0 108 0 0 0 0 0 0 374 0 0 0 0 600 492 0 0 0 0 0 0 0 0 211 0 0 0 0 0 0 0 0 0 0 0 168 0 0 0 0 0 0 96 0 0 0 1031 0 0 0 0 0 438 0 0 0 0 0 0 0 0 0 0 0 0 375 0 0 0 0 0 0 0 0 0 144 0 0 0 81 0 0 0 0 0 0 0 0 0 0 0 0 906 0 0 0 0 0 0 0 0 608 0 0 0 0 0 0 276 0 0 0 0 661 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 173 0 972 105 0 0 0 0 0 0 420 0 0 0 0 0 0 0 0 469 546 0 0 0 0 0 0 0 0 334 0 352 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 872 0 0 0 0 0 374 0 0 0 0 0 0 0 0 0 0 0 0 0 0 110 0 0 0 0 0 627 0 0 0 0 0 0 0 0 0 0 163 0 1029 290
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TotalBsmtSF 856 1262 920 756 1145 796 1686 1107 952 991 1040 1175 912 1494 1253 832 1004 0 1114 1029 1158 637 1777 1040 1060 1566 900 1704 1484 520 649 1228 1234 1398 1561 1117 1097 1297 1057 0 1088 1350 840 938 1150 1752 1434 1656 736 955 794 816 816 1842 384 1425 970 860 1410 780 1158 530 1370 576 1057 1143 1947 1453 747 1304 2223 845 832 1086 840 462 952 672 1768 440 896 1237 1563 1065 384 1288 684 612 1013 990 0 1235 876 1214 824 680 1588 960 458 950 1610 741 0 1226 1040 1053 641 789 793 1844 994 384 1264 1809 1028 729 1092 1125 1673 728 938 732 1080 1199 1362 520 1078 672 660 1008 924 992 1063 1267 1461 1304 1214 1907 1004 928 864 1734 910 1490 1728 970 715 884 1080 896 969 1710 825 1602 1200 572 0 774 991 1392 1232 1572 1541 882 1149 644 1617 1582 840 1686 720 1080 1064 1362 1606 1202 1151 1052 2216 968 756 793 0 1362 504 1107 1188 660 1086 1593 853 725 1431 970 864 855 1726 1360 755 1713 1121 1196 617 848 720 1424 1140 1100 1157 1092 864 1212 900 990 689 1070 1436 686 798 1248 1498 1010 713 864 2392 630 1203 483 912 1373 1194 1462 483 894 860 483 1414 996 1694 735 1566 686 540 626 948 1845 1020 1367 840 1444 728 1573 798 1302 1314 975 864 1604 963 0 1362 1482 506 926 680 1422 802 720 740 1143 1095 1385 1152 1240 816 952 1560 864 2121 1160 807 1262 1314 1468 1575 625 912 858 882 698 1079 780 768 795 1416 1003 910 975 702 1092 1165 1028 1541 894 1470 2000 700 319 861 1896 697 972 793 2136 728 716 845 1088 1347 1372 1249 1136 1502 1162 710 720 1719 1383 844 596 1728 1056 3206 1358 943 1499 1922 1536 1208 1215 967 721 0 1684 536 972 958 1478 764 1848 1869 1453 616 624 940 1200 1158 1142 1062 1086 888 883 0 483 796 672 1394 1099 1268 1063 953 0 744 608 847 683 870 1580 1856 982 1026 1293 939 784 1580 1256 658 1041 1468 1682 861 804 0 788 735 1144 894 864 961 1092 1260 1310 672 1141 806 1281 1064 840 1063 1034 1276 1056 1470 1008 1080 1340 672 1370 756 1056 1344 1602 988 1470 1196 651 1518 907 1208 1392 483 901 765 926 630 799 648 884 440 684 3094 1440 1078 1258 915 1436 1517 930 780 649 813 1533 872 768 1728 1242 624 1364 588 709 832 560 864 715 1040 1375 1277 728 1626 832 1488 808 547 1976 1494 970 1478 2153 1705 907 1833 1792 910 1216 999 1113 1073 1484 954 630 264 806 728 1269 190 720 3200 1026 864 912 ... 858 1330 804 1800 817 783 728 1098 600 588 720 764 918 1428 728 673 440 1241 894 1121 944 1225 1266 1128 0 1164 485 1930 848 770 1396 916 822 750 1700 747 1050 1442 1007 1187 0 691 1574 1680 1346 985 1657 546 1710 1008 720 0 602 1022 1082 810 1504 1220 384 1362 1132 1199 912 1346 1565 882 1268 1638 768 672 686 824 1338 1654 920 0 1620 1055 546 630 1134 800 1306 1475 2524 0 1992 990 0 0 1302 1316 816 1216 1065 1193 1364 973 1104 854 1338 894 662 1103 1154 1306 799 780 942 845 1048 727 810 690 1240 800 796 1096 848 990 1258 1040 1459 1251 691 936 546 1082 970 1247 0 600 1181 864 936 1314 684 1074 672 1271 290 950 1010 655 630 1463 910 868 924 1836 773 803 816 1008 833 1734 408 894 533 1040 1012 1552 672 698 384 1005 1373 1530 847 936 1122 974 1008 1128 916 960 1032 780 1567 915 952 780 1466 1006 672 1042 1298 704 572 650 932 1466 1073 816 864 1437 1219 1314 1580 901 855 1296 894 1198 1360 1502 1694 959 1127 1930 1096 1261 625 1598 952 1683 876 818 731 0 1216 1600 2396 1120 1572 784 978 1624 831 994 1249 776 702 1224 663 728 879 815 1212 1051 864 866 884 1630 1056 2158 1056 1682 931 1660 1055 559 672 648 925 894 0 1300 0 672 912 952 1040 2136 788 588 894 912 1702 1075 1361 1106 0 1188 940 747 764 847 1141 1476 884 1689 1053 2076 792 585 756 1012 735 876 2110 1405 864 1192 746 884 1986 864 856 1054 841 1050 1104 764 1405 691 925 2002 728 874 1332 1489 935 1019 661 928 723 1680 1128 698 1573 1309 1040 912 804 780 1328 1624 1501 1085 1152 630 994 832 864 1052 1120 547 6110 1246 978 771 1165 1616 976 1652 1368 990 924 1278 1902 1274 1453 1393 948 952 1622 1352 1753 864 1478 0 750 420 1795 796 544 816 1510 935 1588 911 816 803 765 1350 1656 693 916 864 858 1114 1284 896 728 960 1568 1732 1482 684 1248 858 698 2033 992 570 864 1078 756 1980 612 1530 715 616 600 814 873 757 848 1657 840 992 1108 2633 1026 1571 768 984 483 384 864 1205 596 816 560 796 1392 714 1746 735 1525 1584 864 482 1356 1094 747 939 1208 976 862 839 1286 1485 672 1594 768 833 622 791 944 856 0 1844 833 1386 777 1284 1144 1844 708 1069 848 697 1024 1252 1223 913 788 1440 732 958 656 936 1126 1319 864 1932 912 539 588 848 1017 952 1422 814 1188 1220 560 630 896 1573 547 1140 1221 953 1542 1152 1078 1256
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SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseF FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseF SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA FuseA SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA FuseF SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA FuseA SBrkr SBrkr FuseF SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr FuseF SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr NaN FuseF SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseF SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr SBrkr SBrkr SBrkr FuseF SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr SBrkr FuseA SBrkr
1stFlrSF 856 1262 920 961 1145 796 1694 1107 1022 1077 1040 1182 912 1494 1253 854 1004 1296 1114 1339 1158 1108 1795 1060 1060 1600 900 1704 1600 520 649 1228 1234 1700 1561 1132 1097 1297 1057 1152 1324 1328 884 938 1150 1752 1518 1656 736 955 794 816 816 1842 1360 1425 983 860 1426 780 1158 581 1370 902 1057 1143 2207 1479 747 1304 2223 845 885 1086 840 526 952 1072 1768 682 1182 1337 1563 1065 804 1301 684 612 1013 990 1040 1235 964 1260 905 680 1588 960 835 1225 1610 977 1535 1226 1226 1053 1047 789 997 1844 1216 774 1282 2259 1436 729 1092 1125 1699 728 988 772 1080 1199 1586 520 958 840 660 1053 1216 1022 1327 1296 1721 1682 1214 1959 1004 928 864 1734 910 1501 1728 970 875 884 1080 896 969 1710 1097 1252 1200 572 1040 774 991 1392 1232 1572 1541 882 1149 808 1867 1610 840 1707 854 1656 1064 1362 1651 2158 1164 1252 2234 968 769 901 1340 1362 936 1518 1217 808 1224 1593 1549 725 1431 970 864 855 1726 1360 929 1713 1121 1279 865 848 720 1442 1696 1100 1180 1092 864 1212 932 990 689 1236 1436 810 1137 1248 1498 1010 811 864 2392 630 1214 483 912 1555 1194 1490 483 894 860 483 1414 1014 1694 798 1566 866 889 626 1222 1872 908 1375 840 1444 1306 1625 798 1302 1314 1005 864 1604 963 882 1382 1482 1212 926 764 1422 802 1052 778 1113 1095 1363 1164 1632 816 952 1560 864 2121 1156 1175 1262 1314 1468 1575 625 1085 858 900 698 1079 936 1148 1468 1644 1003 910 975 1041 1152 1336 1210 1541 894 1675 2000 1122 1035 861 1944 697 972 793 2036 832 716 1153 1088 1372 1472 1249 1136 1553 1163 1898 803 1719 1383 1445 596 1728 1056 1629 1358 943 1619 1922 1536 1621 1215 993 841 1040 1684 536 972 958 1478 764 1848 1869 1453 616 720 1192 1200 1167 1142 1352 1086 912 988 495 483 790 672 1394 1431 1268 1287 953 1120 752 1319 847 904 914 1580 1856 1007 1026 1301 939 784 1079 1269 658 1125 1479 1742 961 804 882 788 735 1144 894 876 1077 1112 1288 1310 672 1165 806 1620 1166 840 1071 1050 1276 1056 1478 1028 1080 1340 672 1370 756 1056 1344 1602 988 1470 1196 707 1644 907 1208 1412 483 1088 765 926 630 827 734 904 694 684 2402 1440 1128 1258 933 1689 1888 956 780 679 813 1533 888 786 1728 1242 624 1663 833 979 832 575 864 849 1040 1414 1277 888 1634 832 1502 1161 1072 1976 1652 970 1493 2069 1718 1131 1850 1792 916 1216 999 1113 1073 1484 1766 630 616 958 728 1269 886 720 3228 1133 899 912 ... 858 1542 804 1800 824 783 976 1098 600 1095 720 764 918 1428 1136 673 869 1241 894 1121 999 1276 1266 1149 1302 1164 1001 1940 1118 778 1407 916 1020 750 1718 774 1050 1442 1077 1208 944 691 1574 1680 1504 985 1657 546 1710 1008 720 1664 900 1022 1082 810 1504 1360 802 1506 1132 1220 912 1504 2898 882 1264 1646 968 672 948 1687 1352 1654 954 845 1620 1055 798 630 1803 800 1306 1532 2524 1733 1992 990 1771 930 1302 1316 1127 1526 1091 1523 1364 979 1130 1096 1338 894 1422 1103 1154 1306 799 798 1291 893 1048 829 1002 698 1240 960 1096 1096 848 990 1258 1040 1459 1251 691 996 546 1082 970 1247 1040 624 1390 1200 936 1314 773 1088 757 1601 438 950 1134 1194 630 1500 1442 887 948 1836 773 1098 816 1008 833 1734 779 894 1021 1040 1012 1552 960 698 812 1005 1555 1530 847 936 1328 974 1178 1142 916 986 1032 780 1567 1167 952 1088 1466 1006 672 1042 1298 860 572 832 932 1466 1811 816 902 1437 1265 1314 1580 943 855 1640 894 1258 1432 1502 1694 959 1236 1831 1118 1261 625 1636 1170 2129 923 818 820 1124 1298 1652 2411 1130 1572 949 1014 1624 831 1028 1622 764 842 1224 663 728 879 815 1212 1382 864 866 884 1630 1074 2196 1056 1700 1283 1660 1055 1080 672 960 999 894 1318 1314 672 672 912 1211 1168 2136 788 1138 894 912 1702 1507 1361 1190 1224 1188 1024 892 764 847 1141 1484 884 1689 1173 2076 792 1140 756 1034 1134 988 2110 1405 874 1516 760 959 1987 864 1166 1054 892 1050 1104 1060 1337 713 964 2018 1968 874 1332 1489 935 1357 661 928 735 1724 1128 698 1573 1339 1040 912 1699 825 1328 1582 1659 1120 1152 630 1378 832 864 1052 1128 1072 4692 1246 1005 753 1203 1616 976 1652 1368 990 1122 1294 1902 1274 1453 1422 948 1092 1630 1352 1787 948 1478 720 1061 708 1795 796 774 816 1584 955 1588 954 816 803 765 1334 1656 693 920 864 872 1114 1284 1172 728 960 2156 1776 1494 938 1338 858 786 2053 992 1222 892 1078 769 1980 990 1530 1281 616 520 814 882 925 848 1668 840 1661 1108 2633 1026 1571 790 984 483 754 864 2117 998 1416 698 796 1392 1664 1746 869 1525 1584 900 1221 1500 1133 1687 939 1136 1160 950 864 1294 1464 694 1646 768 833 741 1236 944 1112 1040 1844 1053 1569 1246 1310 1144 1844 708 1069 848 1575 1344 1252 1223 1048 804 1440 734 958 968 962 1126 1537 864 1932 1236 1040 1423 848 1026 952 1422 913 1188 1220 796 630 896 1578 1072 1140 1221 953 2073 1188 1078 1256
2ndFlrSF 854 0 866 756 1053 566 0 983 752 0 0 1142 0 0 0 0 0 0 0 0 1218 0 0 0 0 0 0 0 0 0 668 0 0 0 0 1320 0 0 0 0 0 0 0 0 0 0 631 0 716 0 676 0 0 0 0 0 756 860 1519 0 0 530 0 808 977 1330 0 0 0 983 0 0 833 0 765 462 0 213 0 548 960 0 0 0 670 1116 876 612 0 0 0 0 0 1031 881 790 0 0 0 0 0 755 0 0 592 939 0 0 520 0 639 656 1414 0 884 729 0 0 1523 728 0 351 0 0 0 0 0 0 688 0 941 1032 0 0 0 0 0 0 848 836 0 0 475 0 0 739 0 1151 0 448 0 0 896 0 0 524 0 1194 956 1070 0 1096 0 0 467 547 0 551 880 0 0 0 703 0 0 0 896 668 0 0 756 901 0 720 316 1518 0 704 0 0 1178 754 0 739 0 601 0 1360 929 0 0 0 445 0 564 0 0 0 882 0 0 0 920 0 703 0 0 518 817 0 0 1257 741 0 0 672 1306 504 0 0 0 1304 504 0 1100 504 0 730 0 689 0 0 551 591 888 0 1020 0 828 700 0 0 842 0 0 1286 864 0 829 0 0 1092 0 0 0 0 709 720 0 0 844 0 1106 0 0 596 0 0 0 866 807 0 0 0 0 625 649 0 0 698 840 780 568 795 0 0 648 975 702 0 0 1242 0 0 1818 0 1121 371 0 0 804 0 325 0 809 716 1200 871 1274 0 1347 1332 1177 0 1080 0 0 0 689 596 0 0 0 0 695 167 0 0 0 0 915 0 0 0 576 605 0 0 862 880 0 0 495 0 403 0 0 0 0 838 0 517 1427 504 784 672 0 0 0 0 711 468 0 0 1081 0 0 886 0 793 665 0 858 0 874 0 526 0 0 590 406 1157 0 0 299 0 0 936 0 438 0 0 0 1098 766 0 0 840 1101 1028 0 0 0 0 1017 0 728 1254 378 0 0 0 0 1160 0 682 0 0 0 0 504 110 600 678 0 834 384 0 0 512 0 0 445 0 975 0 0 930 596 504 0 0 868 804 0 0 720 0 833 224 1103 560 0 811 0 0 0 756 0 878 0 808 0 0 0 0 0 574 0 0 0 0 910 0 0 0 0 0 648 0 688 620 728 0 0 0 0 687 0 0 ... 0 1330 744 0 1070 701 332 0 368 0 472 862 0 0 883 709 0 0 0 0 0 1336 0 1141 432 0 634 0 912 798 985 826 831 750 0 456 0 0 0 0 0 0 0 0 0 0 0 546 0 0 551 0 602 0 0 855 0 0 670 0 0 0 336 0 0 0 0 0 408 546 980 998 1168 0 0 0 0 1208 546 0 0 832 0 797 0 0 876 0 0 0 0 0 850 0 898 0 0 871 1054 895 0 0 915 0 0 954 772 813 1230 0 0 727 454 728 0 780 370 0 0 0 0 0 0 0 807 0 546 871 739 0 0 628 304 0 0 0 582 0 567 0 0 0 0 0 672 1122 0 1134 742 0 885 866 0 0 0 0 640 0 580 0 0 0 0 0 670 0 0 0 1112 0 653 0 1032 878 684 0 220 240 0 0 0 780 1362 0 252 534 0 704 539 650 0 0 0 0 918 0 0 0 0 933 601 0 0 0 0 0 0 712 872 1796 0 0 625 971 1175 743 0 406 523 0 1216 0 2065 0 0 272 685 0 829 776 0 677 630 0 689 728 984 875 0 0 0 913 464 0 0 0 0 0 0 0 790 672 0 0 0 0 584 0 240 546 0 0 678 0 702 0 1039 0 0 0 1259 0 0 0 940 892 862 1101 0 0 884 0 0 0 725 728 797 0 924 0 0 0 0 651 896 408 0 0 0 0 783 0 684 764 0 739 925 0 1479 650 192 0 0 0 589 992 660 0 903 430 0 0 0 912 748 587 0 0 0 850 0 672 994 832 0 0 0 0 950 0 978 741 1323 0 732 0 0 0 0 0 0 0 1357 1177 0 1020 0 0 0 0 0 0 862 0 0 0 0 0 1208 677 0 0 0 557 600 0 0 0 941 0 0 0 885 741 728 0 0 0 0 1215 1296 858 390 1185 873 698 0 0 804 0 1611 0 457 796 600 860 908 550 0 0 534 0 989 0 932 0 0 620 504 640 0 0 764 0 560 358 1392 862 0 349 0 0 0 691 0 1349 0 574 768 448 208 729 0 0 520 0 0 0 622 857 896 556 0 0 795 0 1044 1140 0 0 708 0 0 626 0 0 904 510 0 0 1104 0 0 830 0 0 0 0 0 685 748 0 981 0 0 0 0 870 550 0 896 0 0 0 0 694 0 1152 0 0
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BedroomAbvGr 3 3 3 3 4 1 3 3 2 2 3 4 2 3 2 2 2 2 3 3 4 3 3 3 3 3 3 3 2 1 3 3 3 4 2 4 3 3 3 2 3 2 2 3 3 2 1 3 2 3 3 3 2 0 3 3 3 3 3 2 3 3 2 3 3 4 3 3 2 3 3 2 3 3 3 2 2 2 4 2 4 2 3 3 3 4 3 2 2 3 2 2 2 4 3 3 3 3 2 3 3 3 4 3 4 3 2 2 3 3 4 3 4 3 3 2 3 2 5 3 1 3 3 2 3 2 2 3 3 3 4 3 3 2 3 3 3 5 3 3 3 3 4 3 6 3 2 3 3 3 3 2 4 1 4 2 2 4 3 4 3 3 3 1 3 4 2 3 3 2 2 3 2 3 3 4 4 4 1 2 3 4 3 3 3 4 3 3 2 0 3 4 3 3 3 3 3 4 5 3 2 3 2 1 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 2 4 3 2 3 3 4 2 3 2 3 4 2 3 4 2 3 3 3 3 3 2 3 3 3 3 4 2 3 4 3 2 3 3 3 4 3 3 3 2 3 3 3 3 2 3 3 4 2 3 3 2 4 3 2 3 3 2 3 4 3 2 2 3 2 2 3 3 3 3 3 6 3 3 3 3 4 3 3 3 2 4 3 3 3 3 4 3 1 3 3 2 3 3 3 3 4 3 4 3 4 4 4 3 5 2 1 3 5 3 6 3 3 2 3 3 3 3 3 3 4 2 2 2 3 3 2 3 2 4 2 2 3 2 2 3 3 3 3 3 2 3 4 2 3 3 3 3 2 3 3 4 2 3 4 1 2 4 1 3 3 2 3 2 3 2 5 3 3 3 4 3 3 2 2 3 2 4 2 2 1 3 2 4 3 3 3 4 3 3 3 3 2 2 3 3 3 4 3 3 2 3 2 4 2 3 2 3 2 3 2 4 2 3 1 3 2 2 2 3 2 4 3 2 4 3 2 4 3 2 2 2 3 3 4 3 4 2 3 3 3 3 3 3 3 2 2 3 3 3 1 3 2 2 2 2 3 3 3 2 3 2 4 2 3 3 3 3 3 1 3 3 3 3 2 2 4 4 3 2 ... 2 4 3 2 4 3 2 3 2 2 4 2 2 3 3 3 2 1 3 3 3 4 2 4 4 3 2 3 4 3 3 4 3 3 3 3 3 2 3 2 2 2 3 4 1 3 3 3 3 4 4 4 3 2 2 3 3 1 3 2 2 2 2 2 2 3 3 3 3 3 5 5 5 3 2 3 2 3 3 1 3 4 1 4 4 4 4 3 3 2 3 3 4 4 3 3 2 3 3 3 2 2 5 2 3 3 3 4 5 2 3 4 4 3 2 3 3 3 1 3 3 3 3 3 3 2 3 3 3 1 2 2 4 1 2 3 3 2 3 3 1 3 2 3 3 3 3 3 3 3 3 4 2 2 2 3 3 3 3 3 2 3 3 2 3 2 2 3 4 2 4 3 5 3 3 2 3 2 2 3 2 4 4 3 2 3 3 3 2 3 2 3 2 2 4 3 3 2 3 4 3 3 3 0 2 3 3 3 4 4 3 3 2 3 4 4 3 3 3 3 4 2 4 2 3 4 3 2 3 3 3 2 3 2 4 3 4 3 3 3 3 3 3 3 3 3 2 3 3 3 3 4 2 0 3 3 4 3 2 3 2 3 3 4 3 3 4 2 1 4 4 3 2 3 4 4 2 4 3 2 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 2 2 2 3 3 2 5 3 2 3 4 3 4 3 0 3 3 2 3 4 3 3 3 2 3 2 3 4 4 4 3 4 2 3 3 3 4 4 2 3 3 2 3 3 3 3 4 3 3 2 2 3 2 3 3 3 4 4 3 4 3 2 3 3 3 2 3 2 2 2 3 3 5 3 3 3 2 2 3 2 4 2 3 3 3 3 3 3 3 2 3 3 3 4 6 4 2 4 3 4 3 3 3 3 4 3 4 3 2 3 3 4 1 3 2 3 3 2 3 3 2 3 2 3 2 4 4 3 2 3 5 5 3 3 3 4 3 3 2 4 3 4 3 3 3 3 2 3 3 2 2 3 3 3 3 3 2 2 4 1 4 3 3 3 3 2 1 4 2 3 3 3 2 3 4 2 4 3 3 3 3 2 2 3 3 1 3 2 3 3 3 3 2 1 4 3 2 3 2 3 3 4 2 3
KitchenAbvGr 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1
KitchenQual Gd TA Gd Gd Gd TA Gd TA TA TA TA Ex TA Gd TA TA TA TA Gd TA Gd Gd Gd TA Gd Gd Gd Gd TA Fa TA Gd Gd Gd Ex Gd TA TA Gd Fa TA TA Gd TA TA Ex Gd Gd TA TA TA TA TA Gd TA TA Gd Gd Gd TA Gd Fa Gd TA Gd Gd TA Gd TA TA TA TA TA TA TA TA TA TA TA TA Gd TA Gd TA TA Gd Gd Gd Fa TA TA TA TA TA Gd TA Gd TA TA TA Gd Gd TA Gd TA Gd TA TA Fa TA TA TA Ex Gd Gd TA TA TA Gd Gd TA TA TA Gd TA TA TA TA TA Ex Gd Gd Gd Gd TA TA TA TA Gd Gd TA Gd TA Gd TA Gd TA Gd Gd TA TA Gd TA TA TA TA TA Ex Gd Gd TA Ex Gd TA Gd TA TA Ex Gd TA TA TA Gd TA TA Gd TA Gd Ex TA Gd TA TA Gd TA Ex Gd TA TA Ex Gd Gd Gd Gd TA TA Ex Gd TA Ex TA TA TA Gd TA TA TA TA TA TA TA Gd Gd TA TA Gd Gd TA Gd Gd Gd Gd TA TA Ex TA Gd TA TA Gd TA Ex TA TA Gd TA Gd Gd Gd TA Gd Gd TA TA Gd TA Fa TA Gd Gd Fa Gd Gd Gd TA Gd Gd Gd Gd TA TA Ex TA TA TA Gd TA Gd TA TA Gd TA Gd TA TA Ex Gd TA Ex TA Gd Gd Gd Gd Gd Gd Gd TA TA TA Gd Fa TA Gd TA TA TA Gd TA Gd TA Gd Gd TA Gd Gd Gd Fa TA Ex Gd TA TA TA Ex Gd Ex Gd Gd TA Gd Gd Gd TA Gd TA Gd TA Gd TA TA TA Gd Gd TA TA Gd Gd Gd TA Gd TA TA Ex TA Fa TA TA Gd Ex Ex Gd TA TA TA Gd Gd TA Gd Gd TA TA Gd Gd TA TA TA TA TA Gd TA TA TA TA Gd Fa TA Gd Ex TA Gd Gd Gd TA Gd Gd TA TA Gd Ex TA Gd TA TA TA TA TA TA TA Gd Ex Gd TA Gd TA TA TA TA Gd Ex TA TA Gd TA Gd Gd TA TA TA TA TA Gd TA Gd TA TA Gd TA Gd Gd TA TA TA TA TA Gd TA TA Gd Gd Ex Fa TA Gd Gd TA Gd Gd TA TA Gd Gd TA Gd TA TA TA Gd Gd Gd TA Gd TA TA TA Gd TA Gd Gd Gd Gd TA TA Gd Ex TA Gd Ex Gd TA Gd Ex Ex TA Gd Gd TA TA TA Gd TA Fa TA TA TA TA Gd TA Gd TA ... TA TA Gd Ex Gd Gd TA TA TA TA TA Gd TA Gd Gd Gd Fa Gd TA TA Ex Gd Gd Gd Gd TA Gd Ex Gd Gd Gd Gd Gd Gd Ex TA TA TA TA Gd Fa Ex Gd TA Gd TA TA TA Gd TA TA TA Gd TA TA Gd Gd Gd Gd Gd Gd Gd TA Gd Gd TA TA Gd TA TA TA TA Gd Gd Fa TA Ex Gd TA TA TA Gd Gd Gd Gd TA Ex TA TA Gd Gd Gd TA TA Gd TA Gd Gd Ex TA Gd TA TA Gd TA Gd TA TA TA Gd TA TA TA TA Gd TA Gd TA Gd TA Gd TA Gd TA TA Gd TA Gd Gd Gd TA Gd TA Gd TA Gd Gd Gd TA TA Fa TA TA TA TA Gd Gd Gd TA Gd TA TA TA Gd Gd Ex Gd Gd TA TA TA Gd TA TA Gd Fa Gd Gd Gd TA Gd TA Fa Gd TA TA TA TA Gd TA TA Gd Gd TA Fa TA Gd Fa TA TA Gd Fa Ex TA Gd Gd Gd Gd Gd Gd TA Gd TA TA Gd Gd Gd TA Gd Gd TA TA Gd Ex TA Gd TA TA TA TA TA Gd Ex TA TA TA TA Gd Gd Gd TA Gd Gd TA TA Gd Gd TA Gd TA TA Gd TA Gd TA Gd TA Gd TA Gd Gd TA TA TA TA TA TA Gd TA TA TA TA TA TA Gd TA Gd Gd Ex TA TA TA TA TA TA TA Gd Gd TA Ex Gd Gd Gd Ex Gd TA Gd TA TA TA Ex Gd TA Gd Gd TA Gd TA Gd TA TA TA TA TA Gd Gd TA Ex Gd TA Gd Gd TA Ex TA TA TA TA Gd TA Gd TA Gd TA Gd TA TA TA Ex Ex TA TA TA TA TA TA TA TA Ex Gd Gd Gd Gd Gd Gd Ex Gd TA Gd Gd Ex Gd Gd Gd TA TA Ex Gd Gd TA TA TA Gd Fa Gd TA Gd Gd TA TA Gd TA TA Gd TA TA TA Fa Gd TA TA Gd Gd TA Gd Fa TA Gd Gd TA TA TA TA Gd Gd Gd TA TA Gd Ex TA Gd TA Gd Gd Gd Gd TA Gd Gd TA Gd Gd Ex Gd Gd TA TA TA Gd TA TA Gd Gd TA Gd Gd Gd Ex TA Gd TA TA TA Gd Gd TA TA TA Gd TA TA Gd Gd TA Gd TA TA TA TA Gd TA TA Gd Gd Gd TA Gd TA Gd TA TA Gd Gd TA Gd Gd TA Gd Gd TA TA TA TA TA Gd TA Ex TA TA TA Gd Ex Fa Gd TA TA Gd TA Ex TA Ex TA TA Gd TA TA Gd Gd TA
TotRmsAbvGrd 8 6 6 7 9 5 7 7 8 5 5 11 4 7 5 5 5 6 6 6 9 6 7 6 6 7 5 7 6 4 6 6 7 6 6 9 6 5 5 6 6 5 5 5 6 6 6 7 8 6 6 6 5 5 6 7 7 7 10 4 5 6 6 9 8 9 7 7 4 7 8 4 7 6 8 5 4 6 8 5 8 5 6 6 7 9 6 4 6 5 4 6 5 9 7 6 6 6 5 6 6 7 8 6 7 9 6 5 7 7 7 7 10 7 9 5 6 5 11 8 4 4 5 5 7 5 5 5 6 6 8 7 7 6 7 7 5 9 7 7 5 7 6 6 10 7 5 8 6 7 5 6 8 4 7 5 5 8 8 9 6 10 7 4 5 6 7 8 7 6 7 7 5 6 7 7 8 8 9 5 5 9 7 6 5 12 6 8 6 5 10 7 6 7 5 7 8 8 8 7 5 6 6 3 5 5 8 6 7 6 5 6 7 5 6 6 8 8 8 5 5 8 6 4 8 6 9 5 5 7 6 9 5 5 8 5 6 7 7 7 7 4 6 6 8 6 9 5 8 7 6 5 6 7 5 8 7 8 7 4 6 10 6 6 4 7 8 8 4 6 8 5 9 6 5 5 7 4 8 8 7 5 6 6 5 5 7 5 5 7 8 9 8 9 7 6 6 7 7 6 5 9 6 5 10 8 8 6 4 8 6 5 5 8 6 6 10 8 9 6 9 10 8 6 11 5 6 7 10 6 10 6 7 6 7 7 8 7 8 6 9 4 6 6 4 6 5 6 6 10 6 6 5 5 6 6 6 6 6 7 5 8 7 5 6 6 6 6 7 7 7 7 4 5 8 4 5 8 8 7 6 5 8 5 9 6 8 6 6 9 7 7 5 4 5 6 6 8 6 7 4 6 4 10 7 8 6 8 9 8 5 5 7 5 9 6 6 10 7 6 8 8 4 8 6 6 5 5 6 6 4 7 6 7 3 6 6 4 4 7 10 8 8 6 8 7 6 10 7 6 5 5 7 6 8 6 7 6 7 5 8 6 5 6 5 6 5 7 7 7 4 8 5 7 6 5 7 9 7 7 8 6 7 5 6 5 6 7 10 3 4 5 8 6 4 4 10 8 5 5 ... 4 11 7 7 8 8 7 6 6 6 6 6 5 6 8 6 4 4 5 5 6 8 6 9 8 5 5 8 8 6 7 8 7 6 7 5 6 6 6 6 4 4 7 8 7 6 7 6 7 7 7 8 7 4 5 6 6 4 7 6 5 6 6 7 10 5 6 7 6 7 10 12 10 6 5 5 6 7 6 3 8 7 5 10 9 8 11 5 9 6 6 6 9 7 7 7 6 7 10 7 6 6 10 5 6 7 7 7 10 4 6 8 7 6 5 6 7 6 3 5 5 5 6 6 6 5 6 9 7 4 6 4 7 6 4 6 7 5 6 7 3 5 6 6 6 9 6 9 7 7 8 8 5 6 4 7 7 5 6 6 6 7 5 4 7 5 7 7 8 4 7 5 8 8 7 5 6 6 5 6 4 9 11 5 5 8 5 7 5 7 4 6 6 5 8 6 6 6 7 8 7 7 5 6 5 7 7 7 7 10 6 6 5 12 9 9 5 5 7 5 8 5 10 5 5 7 7 5 7 7 7 5 6 5 7 8 9 7 6 6 5 6 5 6 6 7 5 7 6 8 8 8 4 3 6 5 8 6 3 7 5 5 7 7 8 6 9 5 7 7 12 6 6 6 7 9 6 8 6 6 8 7 6 7 7 7 6 6 8 6 8 6 5 9 7 6 7 4 5 6 7 5 8 8 5 6 9 10 11 7 4 7 5 5 8 10 6 7 7 6 6 6 5 8 10 6 6 7 5 8 6 6 11 8 5 5 6 5 12 6 9 7 8 7 7 6 6 5 6 6 7 6 9 10 5 9 8 5 7 5 6 4 8 5 7 5 6 5 8 8 7 6 3 6 7 6 8 4 8 5 5 6 7 9 8 5 9 7 5 8 12 8 4 9 7 8 5 6 5 8 8 7 7 6 4 7 7 6 4 8 6 8 8 8 9 7 4 6 5 7 5 7 8 7 6 7 12 10 7 6 6 8 6 7 6 9 7 8 7 7 5 8 6 6 6 5 5 5 6 7 6 6 6 7 8 7 11 7 6 7 7 4 3 8 6 7 5 6 4 7 7 5 5 8 5 7 5 7 6 6 11 3 10 4 7 6 6 8 6 3 8 7 5 6 6 7 7 9 5 6
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GarageYrBlt 2003 1976 2001 1998 2000 1993 2004 1973 1931 1939 1965 2005 1962 2006 1960 1991 1970 1967 2004 1958 2005 1930 2002 1976 1968 2007 2005 2008 1957 1920 1920 1966 2007 1959 2005 2004 1995 1954 1953 NaN 1965 1959 1983 1977 1959 2005 2003 2006 NaN 1966 1997 1985 1963 1981 1962 1964 1999 2004 2006 1973 2006 1935 2006 1990 1998 2004 1970 2003 1945 1953 1973 1987 1998 1989 1915 1973 1956 1948 NaN 1966 1968 1998 2007 1974 1995 1991 2005 2009 NaN NaN 1950 1961 1921 1900 1997 1993 1999 1965 1920 NaN 1977 1985 1979 2009 1951 2003 1954 1948 NaN 1977 1954 2000 2007 1953 1945 1999 1962 2007 1990 2005 1969 1979 1958 1993 1979 NaN 1977 NaN 1966 1998 1966 2000 1974 2001 1968 1970 1967 1989 1999 1997 NaN 2005 2000 1999 1963 2004 1931 2001 NaN 1936 1975 2007 1971 1960 1923 NaN 1950 2009 2004 2006 1984 2003 2005 NaN 1926 NaN 1955 2007 2004 1981 1991 1960 1987 1961 1986 1950 1988 1958 2009 1935 2000 1920 1957 2003 1986 1993 1990 1916 1979 2001 1932 1972 1999 2004 1989 1976 2007 1918 NaN 2004 2003 1980 1924 2004 1948 1990 1962 1960 1988 1964 NaN 2009 2009 1996 1977 1957 2004 1940 1939 2005 2006 2002 1975 1989 2003 1991 1995 1987 1974 2005 1959 1995 1972 1976 2002 1971 2004 1993 2007 1949 2008 NaN 1940 1980 1994 1988 1910 1954 2003 1958 NaN 2006 2004 1964 1957 1999 2003 2006 2001 1956 1991 2007 1978 1974 1965 1981 1997 1939 1966 1987 2006 1954 1999 1958 1982 1978 2003 1951 2007 1977 1989 2006 2008 2008 1992 2006 1962 NaN 1970 2003 2006 NaN 1949 1977 1953 1984 1950 1997 1968 1950 1953 1998 2001 1975 2003 2004 1990 NaN 1961 2003 1993 1948 1939 1965 1925 2004 1983 2006 1993 1980 2006 2004 1987 1955 1961 1941 1993 1960 1930 1920 2002 1958 2003 2004 1998 1965 2005 2002 1984 1958 2002 1950 1949 2005 1976 1939 1960 1960 2003 2006 2007 1986 1941 2005 1940 1995 1992 1976 1958 1998 1978 1940 2003 1972 1976 1964 1963 1962 1954 1997 2000 1991 1984 1953 2003 NaN 1998 2004 2010 2000 1924 2006 2006 1950 1992 2004 NaN 1977 1999 2008 1978 2001 1959 NaN 1940 1956 1985 1962 1920 2007 1996 2005 1940 1998 1995 1993 1936 1960 2006 2008 1958 1955 2009 1927 1993 2007 1978 1918 1940 1968 1997 1977 1954 1998 1956 1947 1989 1964 2007 1988 1971 NaN 1971 1997 NaN 1996 1990 1926 1990 2005 2008 NaN 1930 2007 1994 1956 1966 1998 1937 1981 1932 1975 1996 2008 1976 1973 1916 1954 1925 1950 2009 1971 1965 1939 NaN 2004 1970 1942 2006 1993 1985 1977 2005 2006 2000 1996 1997 2006 2007 1995 2004 2003 1915 1998 1963 1950 1965 1971 1970 1970 1976 1941 2006 1964 1938 NaN 1992 1925 1967 1958 ... NaN 1977 1976 2007 2002 2005 1940 1955 NaN 1958 NaN 2003 1979 2007 1997 2000 NaN 2007 1999 1963 1961 1998 2007 2002 1977 1950 1950 2010 1976 2006 1997 1925 1964 2005 2008 1946 1961 1970 1922 2006 1956 1920 2006 1976 2005 1977 1970 1970 2004 NaN 1948 NaN 1923 1956 1948 2001 1996 1984 1991 2005 2005 2006 1957 2005 1976 1980 1960 2008 1941 1972 NaN 1970 1993 2002 1938 1957 2008 2001 NaN 1970 1957 1966 2005 1990 1981 1955 2005 1996 1960 1946 2007 2007 1964 1957 2002 1976 2005 1994 2008 1977 2001 1994 1945 1976 1966 1996 1993 1964 1973 1985 1956 1968 1954 1977 2006 1940 1950 1969 2004 1996 1971 1963 2002 1964 1995 1992 1973 2005 2004 2005 1987 1999 1925 1977 1956 2006 NaN 1987 1936 1978 1930 1981 1960 1954 1970 1994 1990 2006 2000 2004 1995 1976 1957 1953 1955 2007 2002 1967 1958 1959 1920 2005 1956 NaN 1992 1977 2007 2004 2004 1980 1981 NaN 1930 1995 1997 1926 1950 NaN 1977 1972 1988 1976 2006 NaN 1941 1928 1985 1941 1982 1930 1950 1959 1956 2002 1965 1976 1965 2008 2008 1974 1978 1993 1968 1969 1978 2009 2008 2000 1935 1995 1977 1958 2006 NaN 1932 1992 1984 1926 1921 NaN 1990 2008 1996 1970 1963 1965 1920 1994 2002 1999 1975 1999 1925 1999 1969 2005 2006 1916 2001 1974 1972 1998 1960 2000 1975 1990 1966 2003 1980 2006 1992 1988 1941 1965 1962 1966 1978 2009 NaN NaN 1964 1968 1949 1951 2004 1958 2007 1992 2008 1960 1977 1962 1962 1959 NaN 1914 2003 2004 2005 2006 2003 2007 1974 2006 1931 1984 2005 1976 1950 1950 1968 2003 1968 1974 2003 1978 1994 NaN 2005 1969 1999 1956 1957 1940 1998 1999 1960 2009 1982 1958 1979 1968 1965 1959 1985 1948 1972 1967 2002 1980 2002 1990 1977 NaN 1969 1939 1963 1964 2000 2006 1964 1972 1985 1976 1957 1968 1963 2005 2008 1959 1999 1942 1994 2005 2004 2006 2005 2000 1948 1991 1959 2005 1990 1999 1956 1969 2008 2006 2001 1954 1957 1955 1992 NaN 2007 NaN NaN 1982 1920 1998 2006 1976 1958 1951 1970 1977 1973 NaN 2002 1972 1974 2004 2002 1929 2006 1997 1968 2006 1998 NaN 1969 1962 1999 1996 2000 1968 1966 1971 2000 2004 1998 2005 1920 2007 2005 2000 1999 1977 2003 2003 1967 1955 1998 2001 2005 2007 1925 1977 1973 2007 1938 1970 1989 2007 1939 1922 1978 1916 2006 2003 2000 1967 1967 2003 2006 2005 1966 1933 1950 1925 1929 2004 2006 2007 1998 2004 1988 NaN 1966 1986 2001 1950 1956 2005 1922 2007 1971 1998 1962 1969 1968 1977 2003 1966 1970 1960 1994 1962 1940 1981 2005 1976 1928 2000 1977 1962 1974 2008 1957 1979 1993 2004 2008 1916 2004 1990 1962 1995 1950 NaN NaN 2008 2005 NaN 2004 1999 1978 1941 1950 1965
GarageFinish RFn RFn RFn Unf RFn Unf RFn RFn Unf RFn Unf Fin Unf RFn RFn Unf Fin Unf Unf Unf RFn Unf RFn Unf Unf RFn Unf RFn RFn Unf Unf Unf RFn RFn Fin Fin Unf Fin Unf NaN RFn RFn RFn Unf RFn RFn RFn RFn NaN Unf Fin Unf Unf Fin Unf RFn Fin RFn Fin Unf Unf Unf RFn Unf RFn RFn RFn RFn Unf Fin Fin Unf Fin Unf Unf Unf Unf Unf NaN Unf Fin Fin RFn Unf Fin Unf Fin RFn NaN NaN Unf Unf Unf Unf RFn Fin RFn Fin Unf NaN RFn Fin Unf RFn Unf RFn Unf Unf NaN RFn Unf Fin Fin Unf Unf Unf Unf Unf Unf Fin Unf Unf Unf RFn Unf NaN RFn NaN RFn RFn Fin RFn Unf Fin RFn Unf RFn Unf Fin RFn NaN Fin Unf RFn Unf Unf Unf Fin NaN Unf Unf RFn RFn RFn Unf NaN Unf Fin RFn RFn Unf Fin RFn NaN Unf NaN Fin Fin Fin RFn Unf Fin RFn Unf Fin Unf Unf Unf Fin Unf Unf Unf RFn Unf Unf Unf Unf Unf Unf Fin Unf Fin Fin Unf Unf Unf Fin Unf NaN Fin RFn Unf Unf RFn Unf RFn RFn RFn Fin RFn NaN RFn RFn Unf Fin Unf Unf Unf Unf Fin RFn RFn RFn Unf Fin Unf RFn Unf Unf Fin RFn Fin Unf Unf Fin Unf RFn RFn Fin Unf RFn NaN Unf RFn RFn Fin Unf Unf RFn Fin NaN Fin RFn RFn RFn Fin RFn RFn RFn Unf Unf Fin Unf Unf Unf Fin Fin Unf Fin RFn RFn Unf Fin RFn Unf Unf Fin Unf Fin Fin Fin Fin RFn Fin RFn RFn RFn NaN Unf Unf RFn NaN Unf Fin Fin Unf Unf RFn Unf Unf Unf Fin RFn Unf Unf Fin RFn NaN Unf Fin Fin Unf Unf RFn Unf Fin RFn RFn RFn Unf RFn Fin Fin Unf Fin Unf Fin RFn Unf Unf Unf RFn RFn RFn Fin Fin Fin RFn RFn Unf Fin Unf Unf RFn Unf Unf RFn Unf RFn Fin RFn RFn Unf Unf Unf RFn RFn Fin Unf RFn RFn Unf RFn Unf Fin Unf RFn RFn Fin Fin RFn Fin Unf Unf Fin NaN Unf RFn Fin Fin Unf RFn RFn Unf Fin Fin NaN Unf RFn Fin Unf Fin RFn NaN Unf Unf RFn RFn Unf Fin Fin Fin Unf Fin Fin Unf Unf Unf RFn Fin Unf Fin Fin Unf Unf Fin Fin Unf Unf Unf Fin Fin RFn Fin RFn Unf Fin Unf RFn Unf Unf NaN Unf Fin NaN RFn Unf Unf Unf Unf Fin NaN Unf Fin Unf Unf Fin Fin Unf Unf Unf Unf Fin RFn Unf Unf Unf Fin Unf Unf Fin RFn Unf Unf NaN Fin Unf Unf RFn RFn RFn RFn RFn RFn RFn Unf Fin Fin RFn Unf Fin Fin Unf Unf Unf Unf RFn Unf Unf Unf Fin Unf RFn Unf Unf NaN RFn Unf Fin Fin ... NaN Fin Unf Fin Fin Fin Unf Unf NaN RFn NaN RFn Unf Fin Unf Unf NaN Fin Unf Unf RFn Fin Fin Unf Unf Unf Unf Fin Fin RFn Fin Unf RFn RFn Fin Unf Unf RFn Unf RFn Unf Unf RFn Unf Fin Unf Unf RFn Fin NaN Unf NaN Unf Unf Unf Fin Fin RFn RFn Fin Fin RFn Unf Fin Fin Unf Unf RFn Unf Unf NaN Fin RFn Unf Unf Unf Fin Fin NaN Unf RFn Unf RFn Unf Fin Unf Fin Unf Unf Unf Fin Fin RFn Unf Fin Fin RFn Fin Fin Unf Fin RFn Unf Unf RFn RFn RFn RFn Fin Unf Unf Unf Unf Fin RFn Unf Unf Fin RFn Unf RFn Fin RFn RFn Fin Unf RFn RFn Unf Fin Unf Unf Unf Unf Fin RFn NaN RFn Unf RFn Unf Unf RFn Fin Unf RFn Fin RFn RFn Fin Fin RFn Unf Unf Unf RFn Fin RFn RFn RFn Unf RFn Unf NaN Fin Unf Fin Fin Fin Unf Unf NaN Unf RFn Fin Unf Unf NaN RFn Unf Unf Unf RFn NaN Unf Unf Unf Unf Unf Unf Unf Fin Fin Unf Unf RFn RFn RFn RFn RFn Unf Unf Fin Unf Unf RFn RFn Fin Unf Fin Fin RFn Fin NaN Unf Fin Unf Unf Unf NaN Fin Fin Fin Unf RFn Unf Unf RFn RFn Fin Fin Unf Unf Fin Fin Fin Fin Unf RFn Unf Unf RFn Unf Unf RFn RFn Unf RFn Unf RFn RFn RFn Unf Unf Unf RFn Unf RFn NaN NaN Unf Unf Unf Unf Fin RFn Fin Unf Fin Unf RFn Unf Unf RFn NaN Unf RFn Fin Unf RFn RFn RFn RFn Fin Unf Fin RFn Unf Unf Unf Fin RFn RFn RFn Fin Unf Fin NaN Fin Unf Fin Unf Unf Unf Fin Unf Unf Fin Unf Unf Fin RFn Unf RFn Unf Unf Unf RFn RFn RFn RFn Fin Unf NaN Unf Unf Unf Unf Fin Fin RFn Unf RFn RFn Unf RFn RFn Fin Fin RFn Fin Unf RFn RFn Unf Fin RFn Unf Unf RFn Fin RFn RFn Fin Unf Unf RFn RFn RFn Unf RFn Unf RFn NaN RFn NaN NaN Unf Unf Fin RFn Unf Unf Unf Unf RFn Unf NaN RFn RFn Unf Unf RFn Unf Fin Unf RFn Fin RFn NaN Unf Fin Unf Fin RFn RFn RFn Fin Unf Fin RFn Fin Unf Fin RFn RFn RFn RFn Fin Fin Fin RFn RFn RFn Fin Fin Unf Unf Unf Fin Unf Fin Unf Unf Unf Unf RFn Unf Fin Unf RFn Unf RFn Unf Fin Fin Unf Unf Unf Unf RFn Fin RFn Unf Unf Fin Unf NaN Unf Fin RFn Unf Unf Fin Unf RFn Unf Fin Unf RFn RFn RFn Fin Unf Unf Unf RFn Unf Unf Fin Fin RFn Unf Fin RFn RFn Unf Fin Unf RFn Fin RFn Fin Unf RFn Unf Unf RFn Unf NaN NaN Fin Fin NaN RFn RFn Unf RFn Unf Fin
GarageCars 2 2 2 3 3 2 2 2 2 1 1 3 1 3 1 2 2 2 2 1 3 1 2 2 1 3 2 3 1 1 1 1 2 2 2 3 2 2 1 0 2 1 2 1 1 2 2 3 0 1 2 2 2 3 2 2 2 2 3 1 2 1 2 2 2 3 2 2 1 2 2 2 2 2 1 1 1 1 0 2 1 2 3 2 2 2 2 2 0 0 2 2 2 2 2 2 2 1 1 0 2 2 2 3 1 2 1 1 0 2 1 2 3 2 1 2 1 2 3 2 2 1 1 2 2 0 2 0 2 2 2 2 2 2 2 2 1 3 3 2 0 2 2 2 2 2 1 2 0 1 2 3 2 2 1 0 2 3 2 2 2 3 2 0 1 0 1 3 2 2 2 2 2 2 2 2 2 2 3 2 2 1 1 2 2 3 2 1 2 2 2 2 2 2 1 2 3 2 0 3 2 2 1 2 1 2 1 1 2 2 0 2 2 2 1 1 2 1 2 2 2 2 2 2 3 1 3 1 1 2 1 3 1 1 2 1 2 2 3 1 2 0 1 1 2 2 1 2 2 2 0 2 2 1 1 2 2 2 2 1 2 3 2 2 2 2 2 1 1 1 3 2 3 1 1 2 2 2 3 2 2 2 2 3 2 2 2 0 1 1 2 0 1 2 2 2 1 2 2 1 1 2 3 2 3 3 3 0 2 3 2 1 2 2 2 2 2 3 3 2 3 3 2 1 2 2 2 1 2 1 1 1 3 2 2 2 3 2 2 1 2 1 2 2 1 1 1 2 2 3 2 2 1 2 1 2 2 2 1 2 2 1 2 1 2 1 2 1 1 2 2 2 1 1 2 0 2 2 3 2 1 2 2 2 2 2 0 1 2 3 1 2 1 0 1 1 2 1 1 2 2 2 1 2 2 2 1 1 3 3 1 2 2 2 3 2 2 3 1 1 4 2 2 3 1 1 2 1 2 2 1 0 2 2 0 2 2 1 1 2 3 0 2 3 2 2 2 2 1 1 1 2 2 2 2 2 3 2 1 1 2 2 1 1 0 2 2 1 3 2 2 2 2 3 2 2 2 3 3 2 3 3 1 2 1 1 2 2 2 1 1 1 2 1 1 0 2 1 1 1 ... 0 2 2 2 2 2 1 1 0 1 0 2 1 2 2 2 0 2 2 2 2 3 2 2 2 1 1 3 2 2 3 2 2 2 3 1 1 2 1 2 2 1 3 2 2 1 2 1 2 0 1 0 1 1 1 2 2 2 2 2 2 2 1 2 2 2 2 3 1 1 0 2 3 3 1 1 3 2 0 1 2 2 2 2 2 2 3 2 2 1 2 2 2 1 2 2 2 2 3 2 2 3 2 2 2 2 2 2 2 2 1 2 1 2 3 1 2 1 2 1 2 1 2 1 2 2 1 3 2 2 2 2 2 2 1 2 0 2 1 2 1 1 1 2 2 2 2 2 2 3 2 2 1 1 1 3 2 2 1 1 1 3 1 0 2 2 3 3 2 2 2 0 1 2 2 1 1 0 2 2 2 2 3 0 1 1 2 1 1 2 1 2 2 1 2 2 2 2 3 2 2 2 2 2 2 2 3 2 2 3 1 2 2 0 2 2 1 1 1 0 2 2 3 2 3 1 2 3 2 2 4 2 1 2 1 2 3 1 2 2 1 2 1 2 2 3 1 2 2 2 2 2 1 1 1 1 2 2 0 0 1 1 1 2 2 1 3 2 3 1 2 2 2 2 0 1 2 2 2 2 2 3 2 3 2 2 2 3 2 1 2 2 1 2 2 1 2 0 2 2 2 1 1 2 2 2 1 3 3 1 2 2 1 1 2 2 2 2 2 2 2 2 2 0 1 1 2 2 2 3 2 1 1 2 2 1 2 2 2 1 2 1 3 3 2 2 2 2 2 2 2 2 2 3 1 2 3 2 3 1 2 1 2 0 3 0 0 1 2 2 3 2 1 1 2 2 2 0 2 1 4 2 2 2 2 1 2 3 2 0 4 2 2 3 3 2 1 2 2 3 2 2 1 2 2 2 2 1 2 2 1 1 2 3 3 3 1 2 1 2 1 2 2 2 1 1 2 1 3 2 2 3 1 2 3 3 2 1 1 1 1 2 2 3 3 2 2 0 2 2 2 1 2 2 2 3 2 3 1 2 2 2 2 2 1 2 2 1 1 2 2 2 1 2 2 2 2 3 2 2 2 2 3 1 2 1 1 2 1 0 0 3 2 0 2 2 2 1 1 1
GarageArea 548 460 608 642 836 480 636 484 468 205 384 736 352 840 352 576 480 516 576 294 853 280 534 572 270 890 576 772 319 240 250 271 484 447 556 691 672 498 246 0 440 308 504 308 300 576 670 826 0 386 388 528 516 894 572 576 480 565 641 352 576 288 484 480 645 852 576 558 220 667 516 360 427 490 379 297 283 240 0 440 509 405 758 461 400 462 400 528 0 0 420 480 432 506 684 420 472 432 366 0 480 476 410 740 240 648 273 250 0 546 325 400 792 450 180 440 288 430 594 390 540 264 288 530 435 0 440 0 453 750 487 390 624 471 440 530 318 766 660 470 0 660 720 577 504 380 180 434 0 240 440 866 495 564 312 0 625 680 678 576 516 726 532 0 216 0 303 789 440 511 660 528 504 504 616 576 521 451 1166 480 440 216 252 484 576 840 497 180 528 682 440 484 666 380 352 440 786 795 0 856 440 473 398 420 240 500 349 312 454 504 0 460 644 576 299 447 484 210 431 438 675 390 434 576 968 280 721 280 336 430 312 810 288 308 440 264 494 457 818 220 750 0 352 288 463 604 440 451 500 389 0 538 520 309 294 429 673 660 564 308 884 868 492 484 504 576 413 240 924 504 1053 439 671 338 264 672 573 400 732 505 575 572 626 898 529 528 440 0 280 384 685 0 281 539 418 588 282 576 539 300 375 683 843 552 870 888 746 0 539 708 420 240 410 513 546 432 484 1025 656 588 840 872 576 220 564 360 473 292 441 189 352 308 880 484 472 529 676 532 440 297 431 294 400 564 336 312 301 498 474 706 617 445 200 484 240 521 400 528 288 592 470 240 672 264 566 468 514 296 244 576 460 680 264 270 434 0 576 610 834 463 308 572 639 360 501 430 0 352 577 846 384 560 294 0 240 596 600 264 338 438 500 400 240 420 373 490 240 308 947 836 350 572 484 360 678 396 440 864 240 304 784 529 520 696 297 240 569 352 628 576 264 0 440 470 0 550 440 180 352 528 672 0 360 648 493 480 578 431 198 308 270 576 422 676 560 528 513 529 228 352 552 576 360 240 0 398 526 312 866 506 528 534 525 908 499 624 508 694 826 672 772 874 164 402 264 264 515 487 520 286 336 240 429 308 273 0 546 240 288 297 ... 0 619 440 702 510 393 256 260 0 264 0 474 264 480 532 490 0 569 400 480 588 676 388 779 539 240 255 606 551 614 870 424 440 564 786 305 368 615 210 632 528 216 824 528 457 328 484 286 550 0 312 0 180 280 240 528 478 565 402 440 451 632 160 437 665 461 461 800 240 264 0 672 796 900 240 290 912 905 0 286 484 484 624 514 542 452 716 672 336 308 436 440 540 364 586 478 484 467 836 432 582 1248 560 440 480 533 380 442 576 576 286 441 280 440 826 240 566 299 420 299 528 308 527 461 409 564 286 1043 380 550 400 462 576 884 308 440 0 461 240 478 246 280 254 539 440 712 719 422 463 862 431 483 308 240 326 928 527 450 300 286 308 782 288 0 392 672 660 630 434 672 576 0 205 466 460 180 288 0 714 495 840 484 1052 0 280 225 403 234 288 324 306 528 470 432 492 528 502 626 830 540 440 924 450 400 588 644 776 472 540 807 358 433 625 0 360 541 264 210 186 0 693 482 813 720 995 392 420 757 493 442 1356 492 250 402 299 400 660 225 573 459 280 546 216 451 495 701 384 544 506 500 462 492 234 364 300 384 539 552 0 0 288 322 315 528 388 264 668 576 1052 404 600 540 462 531 0 180 474 434 484 472 543 954 528 850 400 477 615 888 396 276 522 478 288 518 397 560 691 0 400 460 502 338 304 520 511 506 308 746 1014 315 586 462 288 312 552 400 497 480 577 528 544 484 484 0 336 280 528 390 499 753 484 264 432 528 572 288 525 525 1418 305 490 213 844 834 380 840 474 480 528 496 567 508 750 779 280 576 860 466 748 248 442 287 564 0 895 0 0 264 520 462 825 576 288 297 440 630 506 0 492 288 480 576 647 342 440 308 508 712 514 0 968 490 624 666 839 487 264 500 440 770 621 430 368 432 480 663 588 336 420 502 338 377 583 804 936 722 160 660 264 400 200 550 576 576 280 240 564 216 758 440 541 792 288 672 648 642 572 180 240 216 208 398 662 754 936 482 396 0 528 542 622 271 420 620 370 660 560 1069 336 540 776 440 420 432 484 528 525 288 240 467 372 440 216 451 484 462 528 774 923 550 672 420 812 192 626 240 312 556 384 0 0 840 525 0 400 460 500 252 240 276
GarageQual TA TA TA TA TA TA TA TA Fa Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA NaN NaN TA TA TA TA TA TA TA TA Fa NaN TA TA TA TA TA TA Fa TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA Fa TA NaN Fa TA TA TA TA Fa NaN TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA TA Gd TA TA TA TA TA TA Fa TA Fa TA TA TA Ex TA Fa TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN Fa TA TA TA Po TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Ex TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA Gd NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA Fa TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA Fa TA TA NaN TA TA TA TA TA TA NaN TA TA TA TA Po TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA NaN TA TA TA Gd TA TA NaN TA TA TA TA TA TA TA TA Fa TA TA TA TA TA Fa TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA ... NaN TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA NaN TA TA TA Fa TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA Fa TA TA TA TA TA NaN Fa TA TA TA TA NaN TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA Fa TA TA TA TA TA TA TA TA TA NaN TA TA TA TA Fa NaN TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN Fa TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA TA TA NaN TA NaN NaN TA Fa TA TA TA Fa TA TA TA TA NaN TA TA TA TA TA Fa TA TA Gd TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA Fa TA Fa TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA Fa NaN NaN TA TA NaN TA TA TA TA TA TA
GarageCond TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA NaN TA TA Gd TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA NaN NaN TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA Fa TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA NaN TA TA TA TA TA Fa NaN TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA Gd NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA Po TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Ex Gd TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA Fa TA TA NaN TA TA TA TA TA TA NaN TA TA TA TA Po TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA NaN TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA Fa TA TA TA TA TA Fa TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA Fa TA TA TA TA TA TA NaN TA TA TA TA ... NaN TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA Fa TA TA TA TA TA TA TA TA NaN TA NaN TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA Fa TA TA TA TA TA NaN TA TA TA TA TA NaN TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA NaN TA TA TA TA TA TA Fa TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA NaN Fa TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA Gd TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA Fa TA NaN TA NaN NaN TA TA TA TA TA Fa TA TA TA TA NaN TA TA TA TA TA Fa TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA NaN TA TA TA TA TA TA TA TA TA TA TA TA TA TA TA Gd TA TA TA TA TA TA TA TA Fa TA TA TA TA TA TA TA TA TA TA Po TA TA TA TA TA NaN NaN TA TA NaN TA TA TA TA TA TA
PavedDrive Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y N Y P Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y P Y Y Y Y Y Y Y Y N Y N Y Y Y Y Y Y Y Y Y Y Y Y P Y Y Y N Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y P Y Y Y Y Y Y Y N Y Y Y N Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y N Y Y N Y Y Y Y Y Y Y Y Y Y Y N Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y N P Y Y Y N Y Y P Y Y Y Y Y N Y Y Y Y Y Y P Y Y Y P Y Y Y Y Y N Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y N Y N Y Y ... Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y P Y Y Y Y Y Y P Y Y N Y Y Y Y Y Y Y P Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y P Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y Y Y Y Y Y Y Y Y Y Y Y P Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y N Y Y Y Y Y N N Y Y Y Y Y Y Y Y Y Y N Y Y Y N N Y Y Y Y Y Y Y Y Y Y Y Y P Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y N Y Y Y Y Y Y P Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N N P N Y Y Y Y Y Y Y Y Y Y N N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y P Y Y Y Y Y Y Y Y P Y Y P Y Y Y Y Y Y Y Y Y Y Y Y Y
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MiscFeature NaN NaN NaN NaN NaN Shed NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN Shed Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN Shed Shed NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN Gar2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Gar2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN TenC NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Shed NaN NaN
MiscVal 0 0 0 0 0 700 0 350 0 0 0 0 0 0 0 0 700 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 700 0 0 0 0 0 0 0 0 0 0 480 0 0 400 400 0 0 0 0 0 0 450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 700 0 0 400 0 0 0 0 0 0 0 15500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 800 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 480 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 560 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 0 0 0 0 0 0 700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2500 0 0
MoSold 2 5 9 2 12 10 8 11 4 1 2 7 9 8 5 7 3 10 6 5 11 6 9 6 5 7 5 5 12 5 7 6 1 4 8 9 6 10 1 6 12 7 12 7 5 2 8 7 6 1 7 9 5 11 2 7 8 8 10 1 5 3 10 4 2 10 7 6 6 7 2 6 12 5 5 11 4 1 4 5 6 3 10 7 5 4 3 6 10 8 7 12 8 11 5 4 8 5 5 1 2 6 6 5 6 8 8 4 8 1 10 4 10 10 7 6 9 4 5 7 10 6 6 3 1 6 2 6 7 7 5 7 7 6 7 5 7 7 12 8 4 1 6 6 11 4 11 5 4 4 1 1 6 3 3 4 6 5 5 3 6 3 5 4 10 9 11 11 5 1 5 5 6 6 11 6 9 7 7 7 6 7 6 6 6 6 6 8 6 8 12 6 9 5 5 7 9 3 7 1 3 2 5 1 4 5 3 4 4 7 4 3 7 7 3 5 4 9 5 3 4 12 2 3 6 5 6 9 5 5 4 7 6 2 5 8 5 2 12 4 4 8 4 4 5 6 4 6 9 6 5 12 12 10 6 2 5 3 5 12 5 11 7 4 6 6 6 7 2 5 8 4 4 4 6 10 4 6 5 3 1 5 4 12 7 6 6 6 2 7 7 3 1 3 10 3 6 6 6 8 6 5 1 7 5 7 6 3 3 11 5 5 5 6 8 7 8 12 4 6 9 6 8 7 6 12 5 4 7 7 11 8 10 7 7 8 7 12 5 6 4 9 5 7 4 10 4 12 10 4 12 12 3 5 6 8 7 8 7 6 6 7 7 5 7 7 7 12 3 3 1 7 2 11 7 3 7 12 6 6 5 8 4 10 6 4 5 10 7 8 4 6 7 4 7 6 9 7 11 10 12 7 8 7 5 6 3 3 8 4 6 1 6 3 12 8 3 11 4 7 11 6 2 6 5 9 8 7 9 3 7 9 8 7 6 4 10 1 3 8 4 9 6 6 7 11 6 7 6 6 6 12 7 6 7 9 5 3 6 7 12 4 12 8 8 5 4 6 5 6 6 11 6 7 8 7 4 3 9 3 3 8 6 5 3 7 6 2 5 5 6 8 5 6 12 11 5 6 6 6 ... 2 7 7 5 2 7 6 7 5 3 12 7 4 12 10 4 8 11 5 6 7 7 3 5 8 8 6 4 6 8 6 11 7 8 7 8 1 2 5 2 7 1 7 6 5 11 3 8 4 6 8 5 8 6 11 11 6 8 5 4 6 7 3 5 5 3 4 4 4 5 7 8 11 2 6 1 9 9 5 4 1 7 8 5 7 6 7 2 11 4 8 10 6 5 11 5 6 1 7 3 4 8 9 7 11 4 5 6 6 5 6 5 8 7 5 7 4 3 5 4 10 6 9 3 7 4 5 5 4 4 6 3 6 10 5 3 3 10 7 4 1 7 4 7 4 4 5 9 11 3 6 9 7 5 8 11 3 5 12 7 5 7 10 6 7 11 6 11 7 8 12 9 5 6 8 5 4 5 5 5 6 10 4 7 7 8 5 7 8 7 11 11 7 6 5 7 10 7 6 7 7 11 5 1 6 9 2 2 7 7 7 11 3 3 7 4 10 12 7 4 4 11 7 6 5 3 8 7 7 6 3 11 7 6 3 7 11 7 6 10 7 3 5 10 7 6 11 5 10 5 5 6 8 5 5 5 4 9 7 4 11 8 1 10 2 6 1 12 10 8 8 5 3 5 11 7 6 9 3 12 7 6 1 9 6 1 3 5 4 5 4 3 11 7 7 11 8 7 7 7 6 6 11 7 8 3 1 5 5 7 4 8 11 5 11 7 12 6 3 4 3 7 9 4 3 5 6 6 10 10 4 2 12 3 4 11 12 6 1 5 5 11 12 4 4 11 9 5 5 6 5 1 6 5 10 6 1 4 8 2 6 7 6 12 2 1 5 10 6 6 12 7 5 7 5 4 11 3 6 6 6 7 8 3 7 7 6 5 8 12 8 3 7 3 6 9 10 4 6 9 5 6 6 3 4 1 6 7 6 5 10 10 6 3 7 12 4 7 6 5 6 5 10 8 10 5 7 8 10 3 9 4 12 4 10 2 6 5 5 10 7 4 7 8 1 1 3 3 6 11 6 9 6 9 6 5 4 5 8 12 5 7 3 8 6 10 8 4 2 4 7 10 8 5 5 7 5 11 4 11 9 5 4 5 11 5 4 12 5 8 9 5 5 7 10 8 2 5 4 6
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2010 2010 2008
SaleType WD WD WD WD WD WD WD WD WD WD WD New WD New WD WD WD WD WD COD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD New WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD COD WD WD WD New ConLD WD WD WD WD WD WD WD WD WD COD WD WD WD WD WD WD WD WD WD WD COD WD WD New COD WD WD WD New WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD ConLI WD WD WD ConLI WD WD WD WD WD WD New WD WD WD WD WD New WD New WD WD New WD WD WD COD New WD WD WD WD WD COD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD COD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD New New ConLI WD WD WD COD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD CWD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD New WD WD New ConLw New WD New WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD Con WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New New WD ConLw WD WD WD WD WD WD WD WD WD ConLD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD New WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD New New COD WD New WD WD New WD WD WD WD WD WD WD WD WD WD WD WD New WD COD ConLD WD WD WD ConLw WD WD WD ConLI WD WD WD New WD COD WD WD WD WD WD WD WD WD WD WD COD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD ConLD WD WD WD New WD WD WD WD WD WD WD ... WD WD WD WD WD New WD WD WD ConLD WD WD WD New WD WD WD New WD WD WD WD WD WD COD ConLD WD New WD New WD WD WD New WD WD COD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD COD WD WD WD New WD WD COD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD New New WD WD WD COD WD WD WD WD WD ConLD WD WD WD WD WD WD WD WD WD WD WD WD WD CWD WD WD WD WD COD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD COD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD New WD WD WD WD WD New COD WD WD WD WD WD WD WD WD Oth WD WD WD WD WD WD WD WD COD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD ConLD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD New ConLw WD WD Oth WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD New WD WD WD WD COD WD WD WD WD WD WD WD New WD New WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD COD WD WD WD WD WD WD WD WD WD WD WD WD WD WD COD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD New New WD WD WD WD WD WD WD New WD WD WD WD New WD WD WD WD WD New WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD WD CWD WD WD WD WD WD WD New WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD WD WD COD WD WD WD WD COD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD WD COD WD New WD WD WD WD WD WD WD WD WD WD WD WD WD New WD WD WD WD WD WD WD WD
SaleCondition Normal Normal Normal Abnorml Normal Normal Normal Normal Abnorml Normal Normal Partial Normal Partial Normal Normal Normal Normal Normal Abnorml Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml AdjLand Abnorml Normal Normal Normal Normal Normal Abnorml Normal Partial Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Partial Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Abnorml Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Alloca Normal Normal Normal Normal Partial Normal Normal Normal Normal Partial Abnorml Normal Normal Normal Partial Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Family Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Partial Normal Normal Family Normal Normal Partial Normal Partial Normal Normal Partial Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Alloca Normal Normal Normal Normal Normal Normal Normal Partial Abnorml Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Family Normal Partial Partial Normal Normal Abnorml Normal Abnorml Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Family Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Partial Normal Partial Normal Partial Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Family Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Partial Abnorml Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Partial Normal Normal Normal Normal AdjLand Abnorml Normal Partial Normal Normal Normal Abnorml Normal Normal Normal Normal Abnorml Normal Normal Partial Normal Abnorml Normal Normal Normal Normal Partial Partial Abnorml Normal Partial Normal Normal Partial Normal Normal AdjLand Normal Alloca Normal Normal Normal Normal Normal Normal Normal Partial Normal Abnorml Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Family Normal Abnorml Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Alloca Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Abnorml Normal Normal Normal Normal ... Normal Normal Normal Normal Normal Partial Normal Normal Abnorml Normal Abnorml Normal Normal Partial Normal Normal Normal Partial Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Partial Normal Normal Normal Partial Normal Abnorml Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Partial Normal Normal Abnorml Normal Normal Partial Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Abnorml Partial Partial Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Family Normal Normal Normal Normal Partial Abnorml Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Partial Abnorml Normal Normal Normal Normal Family Normal Normal Normal Abnorml Normal Normal Normal Normal Abnorml Normal Normal Normal Abnorml Normal Partial Normal Normal Family Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Alloca Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Abnorml Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Partial Normal Abnorml Abnorml Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Abnorml Abnorml Normal Normal Normal Abnorml Normal Normal Partial Family Partial Normal Abnorml Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Partial Partial Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Partial Abnorml Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Family Normal Normal Normal Normal Normal Normal Partial Normal Normal Normal Normal Normal Normal Normal Partial Normal Family Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Normal Normal Normal Alloca Normal Normal Normal Normal Abnorml Normal Normal Normal Normal Normal Normal Abnorml Normal Partial Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Abnorml Normal Partial Normal Abnorml Normal Normal Normal Normal Normal Normal
SalePrice 208500 181500 223500 140000 250000 143000 307000 200000 129900 118000 129500 345000 144000 279500 157000 132000 149000 90000 159000 139000 325300 139400 230000 129900 154000 256300 134800 306000 207500 68500 40000 149350 179900 165500 277500 309000 145000 153000 109000 82000 160000 170000 144000 130250 141000 319900 239686 249700 113000 127000 177000 114500 110000 385000 130000 180500 172500 196500 438780 124900 158000 101000 202500 140000 219500 317000 180000 226000 80000 225000 244000 129500 185000 144900 107400 91000 135750 127000 136500 110000 193500 153500 245000 126500 168500 260000 174000 164500 85000 123600 109900 98600 163500 133900 204750 185000 214000 94750 83000 128950 205000 178000 118964 198900 169500 250000 100000 115000 115000 190000 136900 180000 383970 217000 259500 176000 139000 155000 320000 163990 180000 100000 136000 153900 181000 84500 128000 87000 155000 150000 226000 244000 150750 220000 180000 174000 143000 171000 230000 231500 115000 260000 166000 204000 125000 130000 105000 222500 141000 115000 122000 372402 190000 235000 125000 79000 109500 269500 254900 320000 162500 412500 220000 103200 152000 127500 190000 325624 183500 228000 128500 215000 239000 163000 184000 243000 211000 172500 501837 100000 177000 200100 120000 200000 127000 475000 173000 135000 153337 286000 315000 184000 192000 130000 127000 148500 311872 235000 104000 274900 140000 171500 112000 149000 110000 180500 143900 141000 277000 145000 98000 186000 252678 156000 161750 134450 210000 107000 311500 167240 204900 200000 179900 97000 386250 112000 290000 106000 125000 192500 148000 403000 94500 128200 216500 89500 185500 194500 318000 113000 262500 110500 79000 120000 205000 241500 137000 140000 180000 277000 76500 235000 173000 158000 145000 230000 207500 220000 231500 97000 176000 276000 151000 130000 73000 175500 185000 179500 120500 148000 266000 241500 290000 139000 124500 205000 201000 141000 415298 192000 228500 185000 207500 244600 179200 164700 159000 88000 122000 153575 233230 135900 131000 235000 167000 142500 152000 239000 175000 158500 157000 267000 205000 149900 295000 305900 225000 89500 82500 360000 165600 132000 119900 375000 178000 188500 260000 270000 260000 187500 342643 354000 301000 126175 242000 87000 324000 145250 214500 78000 119000 139000 284000 207000 192000 228950 377426 214000 202500 155000 202900 82000 87500 266000 85000 140200 151500 157500 154000 437154 318061 190000 95000 105900 140000 177500 173000 134000 130000 280000 156000 145000 198500 118000 190000 147000 159000 165000 132000 162000 172400 134432 125000 123000 219500 61000 148000 340000 394432 179000 127000 187750 213500 76000 240000 192000 81000 125000 191000 426000 119000 215000 106500 100000 109000 129000 123000 169500 67000 241000 245500 164990 108000 258000 168000 150000 115000 177000 280000 339750 60000 145000 222000 115000 228000 181134 149500 239000 126000 142000 206300 215000 113000 315000 139000 135000 275000 109008 195400 175000 85400 79900 122500 181000 81000 212000 116000 119000 90350 110000 555000 118000 162900 172500 210000 127500 190000 199900 119500 120000 110000 280000 204000 210000 188000 175500 98000 256000 161000 110000 263435 155000 62383 188700 124000 178740 167000 146500 250000 187000 212000 190000 148000 440000 251000 132500 208900 380000 297000 89471 326000 374000 155000 164000 132500 147000 156000 175000 160000 86000 115000 133000 172785 155000 91300 34900 430000 184000 130000 120000 ... 116500 272000 155000 239000 214900 178900 160000 135000 37900 140000 135000 173000 99500 182000 167500 165000 85500 199900 110000 139000 178400 336000 159895 255900 126000 125000 117000 395192 195000 197000 348000 168000 187000 173900 337500 121600 136500 185000 91000 206000 82000 86000 232000 136905 181000 149900 163500 88000 240000 102000 135000 100000 165000 85000 119200 227000 203000 187500 160000 213490 176000 194000 87000 191000 287000 112500 167500 293077 105000 118000 160000 197000 310000 230000 119750 84000 315500 287000 97000 80000 155000 173000 196000 262280 278000 139600 556581 145000 115000 84900 176485 200141 165000 144500 255000 180000 185850 248000 335000 220000 213500 81000 90000 110500 154000 328000 178000 167900 151400 135000 135000 154000 91500 159500 194000 219500 170000 138800 155900 126000 145000 133000 192000 160000 187500 147000 83500 252000 137500 197000 92900 160000 136500 146000 129000 176432 127000 170000 128000 157000 60000 119500 135000 159500 106000 325000 179900 274725 181000 280000 188000 205000 129900 134500 117000 318000 184100 130000 140000 133700 118400 212900 112000 118000 163900 115000 174000 259000 215000 140000 135000 93500 117500 239500 169000 102000 119000 94000 196000 144000 139000 197500 424870 80000 80000 149000 180000 174500 116900 143000 124000 149900 230000 120500 201800 218000 179900 230000 235128 185000 146000 224000 129000 108959 194000 233170 245350 173000 235000 625000 171000 163000 171900 200500 239000 285000 119500 115000 154900 93000 250000 392500 745000 120000 186700 104900 95000 262000 195000 189000 168000 174000 125000 165000 158000 176000 219210 144000 178000 148000 116050 197900 117000 213000 153500 271900 107000 200000 140000 290000 189000 164000 113000 145000 134500 125000 112000 229456 80500 91500 115000 134000 143000 137900 184000 145000 214000 147000 367294 127000 190000 132500 101800 142000 130000 138887 175500 195000 142500 265900 224900 248328 170000 465000 230000 178000 186500 169900 129500 119000 244000 171750 130000 294000 165400 127500 301500 99900 190000 151000 181000 128900 161500 180500 181000 183900 122000 378500 381000 144000 260000 185750 137000 177000 139000 137000 162000 197900 237000 68400 227000 180000 150500 139000 169000 132500 143000 190000 278000 281000 180500 119500 107500 162900 115000 138500 155000 140000 160000 154000 225000 177500 290000 232000 130000 325000 202500 138000 147000 179200 335000 203000 302000 333168 119000 206900 295493 208900 275000 111000 156500 72500 190000 82500 147000 55000 79000 130500 256000 176500 227000 132500 100000 125500 125000 167900 135000 52500 200000 128500 123000 155000 228500 177000 155835 108500 262500 283463 215000 122000 200000 171000 134900 410000 235000 170000 110000 149900 177500 315000 189000 260000 104900 156932 144152 216000 193000 127000 144000 232000 105000 165500 274300 466500 250000 239000 91000 117000 83000 167500 58500 237500 157000 112000 105000 125500 250000 136000 377500 131000 235000 124000 123000 163000 246578 281213 160000 137500 138000 137450 120000 193000 193879 282922 105000 275000 133000 112000 125500 215000 230000 140000 90000 257000 207000 175900 122500 340000 124000 223000 179900 127500 136500 274970 144000 142000 271000 140000 119000 182900 192140 143750 64500 186500 160000 174000 120500 394617 149700 197000 191000 149300 310000 121000 179600 129000 157900 240000 112000 92000 136000 287090 145000 84500 185000 175000 210000 266500 142125 147500

81 rows × 1460 columns

display_all(df_raw.describe(include='all').T)
count unique top freq mean std min 25% 50% 75% max
Id 1460 NaN NaN NaN 730.5 421.61 1 365.75 730.5 1095.25 1460
MSSubClass 1460 NaN NaN NaN 56.8973 42.3006 20 20 50 70 190
MSZoning 1460 5 RL 1151 NaN NaN NaN NaN NaN NaN NaN
LotFrontage 1201 NaN NaN NaN 70.05 24.2848 21 59 69 80 313
LotArea 1460 NaN NaN NaN 10516.8 9981.26 1300 7553.5 9478.5 11601.5 215245
Street 1460 2 Pave 1454 NaN NaN NaN NaN NaN NaN NaN
Alley 91 2 Grvl 50 NaN NaN NaN NaN NaN NaN NaN
LotShape 1460 4 Reg 925 NaN NaN NaN NaN NaN NaN NaN
LandContour 1460 4 Lvl 1311 NaN NaN NaN NaN NaN NaN NaN
Utilities 1460 2 AllPub 1459 NaN NaN NaN NaN NaN NaN NaN
LotConfig 1460 5 Inside 1052 NaN NaN NaN NaN NaN NaN NaN
LandSlope 1460 3 Gtl 1382 NaN NaN NaN NaN NaN NaN NaN
Neighborhood 1460 25 NAmes 225 NaN NaN NaN NaN NaN NaN NaN
Condition1 1460 9 Norm 1260 NaN NaN NaN NaN NaN NaN NaN
Condition2 1460 8 Norm 1445 NaN NaN NaN NaN NaN NaN NaN
BldgType 1460 5 1Fam 1220 NaN NaN NaN NaN NaN NaN NaN
HouseStyle 1460 8 1Story 726 NaN NaN NaN NaN NaN NaN NaN
OverallQual 1460 NaN NaN NaN 6.09932 1.383 1 5 6 7 10
OverallCond 1460 NaN NaN NaN 5.57534 1.1128 1 5 5 6 9
YearBuilt 1460 NaN NaN NaN 1971.27 30.2029 1872 1954 1973 2000 2010
YearRemodAdd 1460 NaN NaN NaN 1984.87 20.6454 1950 1967 1994 2004 2010
RoofStyle 1460 6 Gable 1141 NaN NaN NaN NaN NaN NaN NaN
RoofMatl 1460 8 CompShg 1434 NaN NaN NaN NaN NaN NaN NaN
Exterior1st 1460 15 VinylSd 515 NaN NaN NaN NaN NaN NaN NaN
Exterior2nd 1460 16 VinylSd 504 NaN NaN NaN NaN NaN NaN NaN
MasVnrType 1452 4 None 864 NaN NaN NaN NaN NaN NaN NaN
MasVnrArea 1452 NaN NaN NaN 103.685 181.066 0 0 0 166 1600
ExterQual 1460 4 TA 906 NaN NaN NaN NaN NaN NaN NaN
ExterCond 1460 5 TA 1282 NaN NaN NaN NaN NaN NaN NaN
Foundation 1460 6 PConc 647 NaN NaN NaN NaN NaN NaN NaN
BsmtQual 1423 4 TA 649 NaN NaN NaN NaN NaN NaN NaN
BsmtCond 1423 4 TA 1311 NaN NaN NaN NaN NaN NaN NaN
BsmtExposure 1422 4 No 953 NaN NaN NaN NaN NaN NaN NaN
BsmtFinType1 1423 6 Unf 430 NaN NaN NaN NaN NaN NaN NaN
BsmtFinSF1 1460 NaN NaN NaN 443.64 456.098 0 0 383.5 712.25 5644
BsmtFinType2 1422 6 Unf 1256 NaN NaN NaN NaN NaN NaN NaN
BsmtFinSF2 1460 NaN NaN NaN 46.5493 161.319 0 0 0 0 1474
BsmtUnfSF 1460 NaN NaN NaN 567.24 441.867 0 223 477.5 808 2336
TotalBsmtSF 1460 NaN NaN NaN 1057.43 438.705 0 795.75 991.5 1298.25 6110
Heating 1460 6 GasA 1428 NaN NaN NaN NaN NaN NaN NaN
HeatingQC 1460 5 Ex 741 NaN NaN NaN NaN NaN NaN NaN
CentralAir 1460 2 Y 1365 NaN NaN NaN NaN NaN NaN NaN
Electrical 1459 5 SBrkr 1334 NaN NaN NaN NaN NaN NaN NaN
1stFlrSF 1460 NaN NaN NaN 1162.63 386.588 334 882 1087 1391.25 4692
2ndFlrSF 1460 NaN NaN NaN 346.992 436.528 0 0 0 728 2065
LowQualFinSF 1460 NaN NaN NaN 5.84452 48.6231 0 0 0 0 572
GrLivArea 1460 NaN NaN NaN 1515.46 525.48 334 1129.5 1464 1776.75 5642
BsmtFullBath 1460 NaN NaN NaN 0.425342 0.518911 0 0 0 1 3
BsmtHalfBath 1460 NaN NaN NaN 0.0575342 0.238753 0 0 0 0 2
FullBath 1460 NaN NaN NaN 1.56507 0.550916 0 1 2 2 3
HalfBath 1460 NaN NaN NaN 0.382877 0.502885 0 0 0 1 2
BedroomAbvGr 1460 NaN NaN NaN 2.86644 0.815778 0 2 3 3 8
KitchenAbvGr 1460 NaN NaN NaN 1.04658 0.220338 0 1 1 1 3
KitchenQual 1460 4 TA 735 NaN NaN NaN NaN NaN NaN NaN
TotRmsAbvGrd 1460 NaN NaN NaN 6.51781 1.62539 2 5 6 7 14
Functional 1460 7 Typ 1360 NaN NaN NaN NaN NaN NaN NaN
Fireplaces 1460 NaN NaN NaN 0.613014 0.644666 0 0 1 1 3
FireplaceQu 770 5 Gd 380 NaN NaN NaN NaN NaN NaN NaN
GarageType 1379 6 Attchd 870 NaN NaN NaN NaN NaN NaN NaN
GarageYrBlt 1379 NaN NaN NaN 1978.51 24.6897 1900 1961 1980 2002 2010
GarageFinish 1379 3 Unf 605 NaN NaN NaN NaN NaN NaN NaN
GarageCars 1460 NaN NaN NaN 1.76712 0.747315 0 1 2 2 4
GarageArea 1460 NaN NaN NaN 472.98 213.805 0 334.5 480 576 1418
GarageQual 1379 5 TA 1311 NaN NaN NaN NaN NaN NaN NaN
GarageCond 1379 5 TA 1326 NaN NaN NaN NaN NaN NaN NaN
PavedDrive 1460 3 Y 1340 NaN NaN NaN NaN NaN NaN NaN
WoodDeckSF 1460 NaN NaN NaN 94.2445 125.339 0 0 0 168 857
OpenPorchSF 1460 NaN NaN NaN 46.6603 66.256 0 0 25 68 547
EnclosedPorch 1460 NaN NaN NaN 21.9541 61.1191 0 0 0 0 552
3SsnPorch 1460 NaN NaN NaN 3.40959 29.3173 0 0 0 0 508
ScreenPorch 1460 NaN NaN NaN 15.061 55.7574 0 0 0 0 480
PoolArea 1460 NaN NaN NaN 2.7589 40.1773 0 0 0 0 738
PoolQC 7 3 Gd 3 NaN NaN NaN NaN NaN NaN NaN
Fence 281 4 MnPrv 157 NaN NaN NaN NaN NaN NaN NaN
MiscFeature 54 4 Shed 49 NaN NaN NaN NaN NaN NaN NaN
MiscVal 1460 NaN NaN NaN 43.489 496.123 0 0 0 0 15500
MoSold 1460 NaN NaN NaN 6.32192 2.70363 1 5 6 8 12
YrSold 1460 NaN NaN NaN 2007.82 1.3281 2006 2007 2008 2009 2010
SaleType 1460 9 WD 1267 NaN NaN NaN NaN NaN NaN NaN
SaleCondition 1460 6 Normal 1198 NaN NaN NaN NaN NaN NaN NaN
SalePrice 1460 NaN NaN NaN 180921 79442.5 34900 129975 163000 214000 755000

It's important to note what metric is being used for a project. Generally, selecting the metric(s) is an important part of the project setup. However, in this case Kaggle tells us what metric to use: RMSLE (root mean squared log error) between the actual and predicted auction prices. Therefore we take the log of the prices, so that RMSE will give us what we need.

df_raw.SalePrice = np.log(df_raw.SalePrice)
df_raw.GrLivArea = np.log(df_raw.GrLivArea)

Initial Processing

Before we can pass the dataset to the random forest we need to deal with two issues: 1. Get rid of missing values 2. Transform strings into something the forest can recognize (ints)

os.makedirs('tmp', exist_ok=True)
df_raw.to_feather('tmp/house-prices-raw')

For point 2. the categorical variables are currently stored as strings, which is inefficient, and doesn't provide the numeric coding required for a random forest. Therefore we call train_cats to convert strings to pandas categories.

df_raw.LotConfig.dtype

dtype('O')

train_cats(df_raw)

Before we used train_cats, we can see that the feature LotConfig was treated as a pandas Object ('O'). This is pandas most general datatype, and hence is slow and not useful. We can turn each of the strings into a CategoricalDtype with specific categories to make the data faster to access and ready to be turned into an int.

df_raw.LotConfig.dtype

CategoricalDtype(categories=['Corner', 'CulDSac', 'FR2', 'FR3', 'Inside'], ordered=True)

By using the prod_df function from the fastai lib, we'll replace categories with their numeric codes, handle missing continuous values, and split the dependent variable into a separate variable.

ASSUMPTION #1 Might need to return here and make sure all catgorical features have correct 'order' (i.e. would want high>medium>low). First run through doesn't look as though there are any clear orders but this might change

df, y, nas = proc_df(df_raw, 'SalePrice')

ASSUMPTION #2 It looks like both the training and test set have 1460 entries. Hence we won't be able to have a validation set that is exactly like our test set in form. Let's try just spliting them in half for the time being. 

def split_vals(a,n): return a[:n].copy(), a[n:].copy()

n_valid = 730
n_trn = len(df)-n_valid
raw_train, raw_valid = split_vals(df_raw, n_trn)
X_train, X_valid = split_vals(df, n_trn)
y_train, y_valid = split_vals(y, n_trn)

X_train.shape, y_train.shape, X_valid.shape

((730, 83), (730,), (730, 83))

Random Forests

Base Model

def rmse(x,y): return math.sqrt(((x-y)**2).mean())

def print_score(m):
    res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid),
                m.score(X_train, y_train), m.score(X_valid, y_valid)]
    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)
    print(res)

m = RandomForestRegressor(n_jobs=-1)
%time m.fit(X_train, y_train)
print_score(m)

CPU times: user 112 ms, sys: 0 ns, total: 112 ms
Wall time: 122 ms
[0.0674886561883825, 0.16050651187668158, 0.9737271770878262, 0.8229040152934082]

An r^2 in the low-80's is ok (and the RMSLE puts us around rank 2900 of 3999 on the Kaggle leaderboard, better than the sample submission rank of 3788), but we can see from the validation set score that we're over-fitting badly. Let's look into how many trees our forest REALLY needs.

Bagging

We'll grab the predictions for each individual tree, and look at one example.

preds = np.stack([t.predict(X_valid) for t in m.estimators_])
preds[:,0], np.mean(preds[:,0]), y_valid[0]

(array([12.87902, 12.47801, 12.52431, 12.40738, 12.36734, 12.87902, 12.52416, 12.47801, 12.79918, 12.52453]),
 12.58609401697524,
 12.373703486914124)

plt.plot([metrics.r2_score(y_valid, np.mean(preds[:i+1], axis=0)) for i in range(10)]);

png

Looks like our predictions level out after 7 trees. Let's double check:

mean1 = np.array([])
for j in [40,60]:
    for i in range(10):
        m = RandomForestRegressor(n_estimators = j, n_jobs=-1)
        m.fit(X_train, y_train) 
        _,_,_,x = print_score(m)
        mean1 = np.append(mean1,x)
    print(j, np.mean(mean1))

[0.06078329813378017, 0.14808315902614078, 0.9786885156246768, 0.8492578371922459]
[0.06213490549850165, 0.14589252607586442, 0.9777301925807435, 0.8536847852664445]
[0.06302084391573325, 0.14706985539698428, 0.977090605744397, 0.8513137761791155]
[0.06027380459364819, 0.14954243569422046, 0.9790442895658233, 0.8462722392965614]
[0.06082009789933832, 0.1486788260223704, 0.9786627027761602, 0.8480426723200848]
[0.061113176306752955, 0.14622019787877566, 0.9784565680143026, 0.8530268048826786]
[0.06047037052529823, 0.1465435860715457, 0.9789073844694971, 0.8523759788560876]
[0.06185057620139629, 0.1468201766952419, 0.9779335395117341, 0.8518181932254656]
[0.06091708696853102, 0.14452055879267559, 0.9785945958588975, 0.8564237304223032]
[0.06177429029468868, 0.14959291137501463, 0.9779879390639937, 0.8461684450405316]
40 0.850838446268152
[0.060685356810043035, 0.14571348958363445, 0.9787571395204159, 0.8540436753349449]
[0.05897937860285067, 0.1449348853358894, 0.9799347043906005, 0.8555993116354961]
[0.0584051621724703, 0.14773061246849337, 0.9803235092618487, 0.8499747366766278]
[0.06039026856204301, 0.14735582022834576, 0.9789632280458908, 0.8507349985890194]
[0.06172652205146036, 0.14643113091599635, 0.978021968461577, 0.8526024604511632]
[0.06001240109611416, 0.148245300490276, 0.979225662465763, 0.8489275506680399]
[0.059987500190169636, 0.14667239107437977, 0.9792428986534214, 0.8521163554976565]
[0.059652304605401135, 0.14609498777683766, 0.9794742218378987, 0.8532784069165569]
[0.059050159965790834, 0.14447297565811298, 0.9798865146257155, 0.856518259316074]
[0.059668463134298515, 0.1468496950270607, 0.9794631003463681, 0.851758603058137]
60 0.8516969410412617

Turns out 40 trees is a touch better and 60 trees doesn't really add much. So let's go with 40 trees.

m = RandomForestRegressor(n_estimators = 40, n_jobs=-1)
m.fit(X_train, y_train)
print_score(m)

[0.059744300976425024, 0.1448840502664744, 0.9794108629051286, 0.8557005892769667]





[0.059744300976425024,
 0.1448840502664744,
 0.9794108629051286,
 0.8557005892769667]

Out-of-bag (OOB) Score

Is our validation set worse than our training set because we're over-fitting, or because the validation set is for a different time period, or a bit of both? With the existing information we've shown, we can't tell. However, random forests have a very clever trick called out-of-bag (OOB) error which can handle this (and more!)

The idea is to calculate error on the training set, but only include the trees in the calculation of a row's error where that row was not included in training that tree. This allows us to see whether the model is over-fitting, without needing a separate validation set.

This also has the benefit of allowing us to see whether our model generalizes, even if we only have a small amount of data so want to avoid separating some out to create a validation set.

This is as simple as adding one more parameter to our model constructor. We print the OOB error last in our print_score function below.

m = RandomForestRegressor(n_estimators=40, n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)

[0.06338487870078498, 0.14699885287917344, 0.9768251728168189, 0.8514573072629579, 0.8463285582096834]





[0.06338487870078498,
 0.14699885287917344,
 0.9768251728168189,
 0.8514573072629579,
 0.8463285582096834]

Since our OOB score is similar to the validation set score, we can see that we are indeed overfititng our model.

Reducing over-fitting

Subsampling

It turns out that one of the easiest ways to avoid over-fitting is also one of the best ways to speed up analysis: subsampling. The basic idea is this: rather than limit the total amount of data that our model can access, let's instead limit it to a different random subset per tree. That way, given enough trees, the model can still see all the data, but for each individual tree it'll be just as fast as if we had cut down our dataset as before.

set_rf_samples(550)

m = RandomForestRegressor(n_estimators=40, n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)

[0.0761398583191239, 0.1478502137216824, 0.9665597661502742, 0.8497317203846535, 0.8462671636950128]





[0.0761398583191239,
 0.1478502137216824,
 0.9665597661502742,
 0.8497317203846535,
 0.8462671636950128]

This doesn't seem to help as much as it did in the fastai ML1 course, and this is likely due to the fact that we have such a small amount of data in this competition compared to the Bulldozer one (1460 vs 400k). Let's reset the samples and try limiting the minimum size of each leaf.

reset_rf_samples()

Another way to reduce over-fitting is to grow our trees less deeply. We do this by specifying (with min_samples_leaf) that we require some minimum number of rows in every leaf node. This has two benefits:

  • There are less decision rules for each leaf node; simpler models should generalize better
  • The predictions are made by averaging more rows in the leaf node, resulting in less volatility
m = RandomForestRegressor(n_estimators=40, min_samples_leaf = 2, n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)

[0.07165163355765716, 0.1457630727605496, 0.970385979720592, 0.853944326817759, 0.8486229872011994]





[0.07165163355765716,
 0.1457630727605496,
 0.970385979720592,
 0.853944326817759,
 0.8486229872011994]

mean1 = np.array([])
for j in [2,3,5,10]:
    for i in range(10):
        m = RandomForestRegressor(n_estimators = 40, min_samples_leaf = j,n_jobs=-1, oob_score=True)
        m.fit(X_train, y_train) 
        _,_,_,x,_ = print_score(m)
        mean1 = np.append(mean1,x)
    print(j, np.mean(mean1))

[0.07643867126338157, 0.15028797691881882, 0.9662967768926763, 0.8447356042073116, 0.8451319891529633]
[0.07423679207323304, 0.1500757796764915, 0.968210509249015, 0.8451737419475143, 0.8410564955891954]
[0.07125352275831892, 0.1476433983002825, 0.9707141483287273, 0.8501518220633878, 0.8430375862646077]
[0.07420392994013983, 0.14950225985430468, 0.9682386472982653, 0.8463548287266397, 0.8468855838506207]
[0.07562331991287108, 0.14496726987319872, 0.9670119491268498, 0.8555347740755376, 0.8459573023078613]
[0.071687640554977, 0.14751991486140112, 0.9703562084558764, 0.85040237211308, 0.8443676581846999]
[0.07216587539251824, 0.1481732413128234, 0.9699593762896418, 0.8490743817732884, 0.846756364986289]
[0.07108951766326037, 0.14814583199605705, 0.9708488083737794, 0.8491302135203511, 0.8412377611899386]
[0.0727516181963191, 0.14729525422351622, 0.9694697407275275, 0.8508576748009058, 0.8490842246999957]
[0.07386934453913648, 0.1486589294666671, 0.9685244253328013, 0.8480833401840226, 0.8448723204973069]
2 0.8489498753412038
[0.08372677369886106, 0.14744849274643387, 0.9595634690619481, 0.8505471931361361, 0.8472886736694734]
[0.08467245607053818, 0.14775835278114846, 0.9586448603256305, 0.8499183889991893, 0.8424679087499822]
[0.08423499102188407, 0.14880994618355117, 0.9590710837714125, 0.8477745311780726, 0.8461819267125071]
[0.08431328953287588, 0.1459308332505095, 0.9589949595249474, 0.8536079388579214, 0.8395407869117226]
[0.08472870629707353, 0.14905929444440646, 0.9585898953754177, 0.8472639610490809, 0.8464834949814529]
[0.0857754227771889, 0.14967218355728182, 0.9575604361737308, 0.8460053651991313, 0.8422463678411435]
[0.08335943147468013, 0.14804617507169066, 0.9599175125606864, 0.8493331238775502, 0.8463530265883873]
[0.08438729442277189, 0.1460741064222862, 0.9589229446551444, 0.8533203457496625, 0.8458894060874952]
[0.08413261506117152, 0.1458732957763408, 0.959170510169005, 0.8537233547537507, 0.8496719868905793]
[0.08406833736254357, 0.14541208649443577, 0.9592328741667019, 0.8546468616241468, 0.8459666420052352]
3 0.8497819908918339
[0.10331376259496355, 0.15157467336360986, 0.9384310802159215, 0.8420656188309315, 0.8390004137665992]
[0.10182994266414126, 0.149149286814104, 0.9401869187372994, 0.8470794810764681, 0.847931290979794]
[0.10401196683575493, 0.15264252771292924, 0.9375960910096381, 0.8398324621837361, 0.8334832625640296]
[0.10196385403596056, 0.1512224997135588, 0.9400295010250332, 0.8427986662614857, 0.8419389461696782]
[0.10149134552734931, 0.1483931950999812, 0.9405840291830881, 0.8486259701682806, 0.8428715667457082]
[0.10446731811325574, 0.1511001038760731, 0.9370485020980637, 0.8430530332031563, 0.8379910497205123]
[0.10147442430588509, 0.14884337403448794, 0.94060383987696, 0.847706133306219, 0.8419007379216997]
[0.10276381730314678, 0.14957210229658027, 0.9390848056934379, 0.8462112394513078, 0.842943122752188]
[0.10343260491172414, 0.15346605747987394, 0.9382893527002487, 0.8380995433860782, 0.8461572160649532]
[0.10254065591451306, 0.14934401440102152, 0.9393490846870229, 0.8466799178860425, 0.8357006124823286]
5 0.8479263961196797
[0.1309949301229438, 0.15829819212165033, 0.9010184723638762, 0.827743622105935, 0.8284083658742114]
[0.12853151267716192, 0.1572679340253372, 0.9047062506599285, 0.8299785309578669, 0.8311179700879857]
[0.13252916057793684, 0.15830912290764648, 0.898686324404997, 0.8277198320349708, 0.8257641446178873]
[0.13062912567365004, 0.15460263126064688, 0.9015705140003478, 0.8356925849308149, 0.8318205775026332]
[0.12866551272878476, 0.1534669441839245, 0.9045074507953184, 0.8380976725071654, 0.8355239117739588]
[0.13006257974926222, 0.15681243227438602, 0.9024224510586989, 0.8309619853868434, 0.8281864490599928]
[0.1310628275261289, 0.15950110856368674, 0.9009158373932845, 0.8251157041684929, 0.8288024845721816]
[0.12807934507697444, 0.15584865647770793, 0.9053755488421636, 0.8330334296519026, 0.8330227511007289]
[0.1299394563729615, 0.15470902680626006, 0.9026071066414758, 0.8354663586081399, 0.8246641889413727]
[0.1294807219428799, 0.15599441193879585, 0.9032935587885832, 0.8327209769160336, 0.8321308827044649]
10 0.843858064521464

Looks like we start to lose information after cutting off at 3, so lets stick with that.

We can also increase the amount of variation amongst the trees by not only use a sample of rows for each tree, but to also using a sample of columns for each split. We do this by specifying max_features, which is the proportion of features to randomly select from at each split.

mean1 = np.array([])
for j in [None,0.5,'log2','sqrt']:
    for i in range(10):
        m = RandomForestRegressor(n_estimators = 40, min_samples_leaf = 3, max_features = j,n_jobs=-1,oob_score=True)
        m.fit(X_train, y_train) 
        _,_,_,x,_ = print_score(m)
        mean1 = np.append(mean1,x)
    print(j, np.mean(mean1))

[0.08629596205331087, 0.14673354463634367, 0.9570437732052489, 0.8519930126262112]
[0.08526393739403948, 0.14931101276600306, 0.9580650678029383, 0.8467476709106188]
[0.08536557715712673, 0.1482144128894658, 0.9579650302691914, 0.8489904974106866]
[0.08540786380060128, 0.14467121012964865, 0.9579233751174834, 0.8561242404270206]
[0.08474377140447349, 0.14832322063672573, 0.9585751682973778, 0.8487686966469464]
[0.08342298736843955, 0.1464327718833762, 0.9598563689226445, 0.8525991568379488]
[0.08414412145782846, 0.14776669366836437, 0.9591593413153787, 0.8499014444529198]
[0.08292075848254406, 0.1445954250021376, 0.9603382649453241, 0.8562749378169432]
[0.08435782404655212, 0.14715693412853, 0.9589516301321095, 0.8511376525165848]
[0.0842426810018441, 0.145163164780802, 0.9590636104658777, 0.8551440772918071]
None 0.8517681386937689
[0.08663876990493713, 0.14271045847972663, 0.9567018109578527, 0.8599977537335022]
[0.08580602113182943, 0.14073902742073532, 0.957530152150599, 0.863839075339644]
[0.08549153420184985, 0.14228831656345084, 0.9578408934254062, 0.860824790604323]
[0.08608561671492831, 0.14039541707685504, 0.9572529285287024, 0.8645031297854807]
[0.08486683883290765, 0.14391335429789398, 0.9584547641852026, 0.8576276700255374]
[0.08788647302249189, 0.14213593182916184, 0.9554457385124384, 0.861122732435212]
[0.08508117521802107, 0.14143676444128053, 0.9582446491293874, 0.862485648078665]
[0.08693315683564486, 0.1416543609019095, 0.9564070681699165, 0.8620621987167294]
[0.0848818210809669, 0.14461158470496566, 0.9584400942392127, 0.856242811156285]
[0.08622447624382454, 0.14301392420772555, 0.957114911847063, 0.8594017069412279]
0.5 0.8562894451877148
[0.10975908865159559, 0.14993735742557923, 0.9305093839443451, 0.8454592179326592]
[0.11090689370089395, 0.15056194105155243, 0.9290483890576091, 0.8441690166833151]
[0.10989564726079742, 0.15209566653667894, 0.9303363605476669, 0.8409780473540908]
[0.11192857868655273, 0.15194976922659684, 0.927735141765045, 0.8412829836876272]
[0.11014177892864697, 0.15213852582225373, 0.9300239618404201, 0.8408884126166754]
[0.1124158571480795, 0.15307072235019614, 0.9271045653906768, 0.8389325940206062]
[0.11315189633463493, 0.15428449104119218, 0.926146879414086, 0.8363681104812299]
[0.11000352129980562, 0.15100102317878242, 0.9301995291685307, 0.8432587950270236]
[0.11467159008067201, 0.15436525770003937, 0.9241497797114232, 0.83619674574921]
[0.10952351707351915, 0.15117798906806945, 0.9308073537458565, 0.8428911935386567]
log2 0.8512071340281794
[0.10201857552596781, 0.146724007166574, 0.939965114371772, 0.8520122524843482]
[0.10369403108967805, 0.14749959286906064, 0.9379770108328764, 0.8504435857004247]
[0.10262774345985261, 0.14560512772992731, 0.9392460195708581, 0.8542606794986096]
[0.10142364440630638, 0.1492861625152381, 0.9406632711356216, 0.8467986790957449]
[0.10637334626578436, 0.14886180339333624, 0.9347304197270296, 0.8476684177935824]
[0.1060222947234497, 0.15023107011026943, 0.9351605120217069, 0.8448531642226768]
[0.10446203394847849, 0.147799410564309, 0.9370548703609987, 0.8498349707118514]
[0.10148018411887823, 0.14707111604136913, 0.940597096887552, 0.8513112271692451]
[0.10572217225822964, 0.14921675151203914, 0.9355270810370959, 0.8469411087097953]
[0.10351542316271169, 0.1499299431426715, 0.9381904899872553, 0.8454745013919521]
sqrt 0.8506453151905905

Looks like 0.5 works the best so lets go with that.

That's all there is to notebook #1 from the ML1 course. We're now at 2320 on the leaderboard, almost in the top 50%! But it looks like we're still heavily overfitting our training data. One bit might be trying to vary the validation set size. Let's try that before we move on to interpreting our results.

n_valid = 1
n_trn = len(df)-n_valid
raw_train, raw_valid = split_vals(df_raw, n_trn)
X_train, X_valid = split_vals(df, n_trn)
y_train, y_valid = split_vals(y, n_trn)

X_train.shape, y_train.shape, X_valid.shape

((1459, 83), (1459,), (1, 83))

m = RandomForestRegressor(n_estimators=40, min_samples_leaf = 3, max_features=0.5, n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)

[0.07836851268932105, 0.02672034952550817, 0.9615069525119146, 0.0, 0.8719304610791863]





[0.07836851268932105,
 0.02672034952550817,
 0.9615069525119146,
 0.0,
 0.8719304610791863]

Since this is such a small dataset, we might just want to use the OOB score moving forward as including all of the data in the training set does push our OOB score up to 87.2%! But it seems like a 50/50 split with the train and valid still might be the way to go. Don't see much improvement between using half the dataset for training and the whole dataset.

Now the above process worked fine but lets optimize our workflow with the use of parfit! parfit is a hyperparameter optimization package meant to complement sklearn's own GridSearchCV. Why use parfit over sklearn's in-house function? Cross-validation using GridSearchCV optimizes on the training data! As mentioned in Rachel Thomas’ post, this is dangerous and not applicable to most real-world modeling problems (especially time series data). Instead, we want to optimize our hyper-parameters on the validation set.

from parfit import bestFit
from sklearn.model_selection import ParameterGrid
from sklearn.metrics import mean_squared_error, r2_score

grid = {
    'min_samples_leaf': [1, 5, 10, 15, 20, 25],
    'max_features': ['sqrt', 'log2', 0.5, 0.6, 0.7],
    'n_estimators': [5,10,20,40,60],
    'n_jobs': [-1],
    'random_state': [42]
}
paramGrid = ParameterGrid(grid)

best_model, best_score, all_models, all_scores = bestFit(RandomForestRegressor(), paramGrid,
                                                    X_train, y_train, X_valid, y_valid, # nfolds=5 [optional, instead of validation set]
                                                    metric=r2_score, greater_is_better=True, 
                                                    scoreLabel='R2')

print(best_model, best_score)

-------------FITTING MODELS-------------


[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Batch computation too fast (0.1289s.) Setting batch_size=2.
[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    0.8s
[Parallel(n_jobs=-1)]: Done  52 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done  74 tasks      | elapsed:    1.5s
[Parallel(n_jobs=-1)]: Done  96 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    3.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.


-------------SCORING MODELS-------------


[Parallel(n_jobs=-1)]: Done  52 tasks      | elapsed:    0.9s
[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    2.2s finished

png

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features=0.6, max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=40, n_jobs=-1,
           oob_score=False, random_state=42, verbose=0, warm_start=False) 0.8632511992772915

Cool! So it looks like with a 50/50 split with the validation and training data, it reccommends the above model. Pretty close to what we had found but nice that it did it automatically.

Let's move forward with some more exploratory data analysis (EDA) and interpreting what our ramdom forest is producing.

Feature Importance

We can gain a better understanding of the features in our model by looking at each individual feature's importance. We can do this by randomizing the data for one feature at a time and seeing how randomizing that individual feature affects the accuracy of our already trained Random Forest model.

For example:

We take our housing data set and we have a column Price we are trying to predict (dependent variable). We have 79 independent variables and one of them is OverallQual. How do we figure out how important OverallQual is? We have a whole random forest and we can find out our predictive accuracy. So we will put all these rows through our random forest, and it will spit out some predictions. We will then compare them to the actual price (in this case, we get our root mean squared error and \(R^2\)). This is our starting point.

Let’s do exactly the same thing, but this time, take the OverallQual column and randomly shuffle it (i.e. randomly permute just that column). Now OverallQual has exactly the same distribution as before (same mean, same standard deviation). But it has no relationships with our dependent variable at all because we totally randomly reordered it.

Before, we might have found our r² was .89. After we shuffle OverallQual, we check again, and now r² is .52. The score got much worse when we destroyed that variable.

Okay, let’s try again. We put OverallQual back to how it was, and this time let’s take GrLivArea and shuffle that. This time, \(R^2\) is .73 and we can say the amount of decrease in our score for OverallQual was .37 and the amount of decrease for GrLivArea was .16. And this is going to give us our feature importances for each column.

??rf_feat_importance

fi = rf_feat_importance(m, df); fi[:10]
cols imp
17 OverallQual 0.368787
46 GrLivArea 0.157587
19 YearBuilt 0.071442
62 GarageArea 0.043432
38 TotalBsmtSF 0.041441
61 GarageCars 0.037355
43 1stFlrSF 0.029089
27 ExterQual 0.024814
20 YearRemodAdd 0.019673
49 FullBath 0.015176

Ok so rather unexpectedly, the most important feature is the Overall Quality of the House.

fi.plot('cols', 'imp', figsize=(10,6), legend=False);

png

def plot_fi(fi): return fi.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False)

plot_fi(fi[:30]);

png

Let's remove the unimportant features of our data and see if that helps things.

to_keep = fi[fi.imp>0.005].cols; len(to_keep)

23

df_keep = df[to_keep].copy()
n_trn = len(df)-730
X_train, X_valid = split_vals(df_keep, n_trn)
y_train, y_valid = split_vals(y, n_trn)

m = RandomForestRegressor(n_estimators=40, min_samples_leaf=3, max_features=0.5,
                          n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)

[0.09188471011366682, 0.1413076968995768, 0.9512996955548244, 0.8627365098853159, 0.8535087492166215]





[0.09188471011366682,
 0.1413076968995768,
 0.9512996955548244,
 0.8627365098853159,
 0.8535087492166215]

fi = rf_feat_importance(m, df_keep)
plot_fi(fi);

png

In the above plot we see the benefit of removing all the unimportant features. Now that the model is unburdened by a bunch of useless information, we see that OverallQual is not the sole predictor and GrLivArea and YearBuilt now have more importance.

One-hot encoding

proc_df's optional max_n_cat argument will turn some categorical variables into new columns.

For example, the column ProductSize which has 6 categories:

  • Large
  • Large / Medium
  • Medium
  • Compact
  • Small
  • Mini

gets turned into 6 new columns:

  • ProductSize_Large
  • ProductSize_Large / Medium
  • ProductSize_Medium
  • ProductSize_Compact
  • ProductSize_Small
  • ProductSize_Mini

and the column ProductSize gets removed.

It will only happen to columns whose number of categories is no bigger than the value of the max_n_cat argument.

Now some of these new columns may prove to have more important features than in the earlier situation, where all categories were in one column.

df_trn2, y_trn, nas = proc_df(df_raw, 'SalePrice', max_n_cat=7)
X_train, X_valid = split_vals(df_trn2, n_trn)

m = RandomForestRegressor(n_estimators=40, min_samples_leaf=1, max_features=0.6, n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)

[0.0602183375316198, 0.14035561713475747, 0.9790828408691752, 0.8645799414552325, 0.8455428032223401]

fi = rf_feat_importance(m, df_trn2)
plot_fi(fi[:25]);

png

So by using one-hot encoding we see a few new features at the top of the list and the ordering has changed slightly. GarageArea is no longer the 4th in terms of importance and we see new features like ExterQual_TA. More specifically, it looks like only the TA category of ExterQual and GarageQual, the Fa category of ExterCond, the Ex category of BsmtQual, the N category of PavedDrive and the Y category of CentralAir matters in their respective features.

to_keep2 = fi[fi.imp>0.005].cols; len(to_keep2)

20

df_keep2 = df_trn2[to_keep2].copy()
X_train, X_valid = split_vals(df_keep2, n_trn)
m2 = RandomForestRegressor(n_estimators=40, min_samples_leaf=1, max_features=0.6,
                          n_jobs=-1, oob_score=True)
m2.fit(X_train, y_train)
print_score(m2)

[0.05760854659268134, 0.1397712393626707, 0.9808566026347679, 0.8657052505714764, 0.8566857347554159]

fi2 = rf_feat_importance(m2, df_keep2)
plot_fi(fi2[:20]);

png

Not clear whether our trimmed one-hot encoding model is better than just the regular trimmed model but they at least have different orderings of the features. Will be useful to continue to test both versions until a winner really sticks out.

Conclusions

There's still a number of techniques i'd like to try out including: removing redundant features, partial dependence plots and tree interpretation with waterfall charts. Come back next week to see these tools in action!

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