inline Part 2 - Access the data set House Price. Select two comparable college towns (DO NOT USE AMES IOWA) and: Develop a model that predicts the sale price of a house in college city 1. Develop a model that predicts the sale price of a house in college city 2. Develop a model that predicts the sale price of a house using both college city 1 and 2 data. . Compare and contrast your models and their results. Which model is the best predictor and why? Be as specific as possible. (Hint) See case study completed on page 254-255 of the text for assistance Data Set for part 2 Your analysis should be between 3 and 4 pages, not including the title page or reference page or appendices. Proper APA formatting should be followed. Your paper should be submitted to the assignment submission box. Previous 10419 1018 10620 09 1062110620 10433 10621 30623 10622 10634 10623 VENUS SENG 10624 10625 10627 10626 104201427 30629 10634 1063010629 10631 10630 10032 10631 10633 10613 10634 10613 30635 10434 1063610611 30637 1936 10638 50637 10639 1064010439 10641 10649 10442 10641 5064310642 10644 10645 10644 104 10445 10647 046 104 10647 10649 10646 10450 1049 1045110010 E 1990:201 1065330653 10454 10443 2065510454 10454 455 DAST 10615 " www 175000 245000 250000 21500 314500 250000 315000 169000 282500 200000 114000 1000 000001 100500 H 240000 172500 384000 325500 312000 172600 501000 1000 12000 120000 117700 250000 450000 289400 187000 32000 333000 400000 254000 160000 he 135000 30000 DIELE C www 4/1/14 19304 SULLE SUUS Wittit ww VI sams A MINIA 4 S 14 30/5/ 4/26/16 3/21/14 kan SOME 4/24/16 TUTUS ww/ww FUNGS FURY 90/0/2 91/09 " MANIA www/ 4/4/ 4/8/16 www 4 1 A 14 04/06 Mane 2001 www ARIE 4 4 pone 14 4/05/04 N 3 4/02/16 1 6/24/20 4 Mane s 1/23/1 4 anin 1/6/16 " 14 N 15 S 14 S 4 " " 1 " 4 4 S 1 LA L 41 1 4 D LIS 4 ACE E 2.75 25 3 A IN a 13 + 1 AS 16 3 41 . 33 35 E 10 T ST " 1 2 1.25 3 25 KO 4 1 SE 12 11 " 2 1 1514 3040 1509 1224 3544 3374 2450 2004 2428 M TE SE 1909 m 2200 7942 4 INLE Met 2301 3011 3764 1901 4000 3011 1114 THE mi 300 WHIT 25 2544 SHE 2009 3001 1911 NH 187 1119 (7810 THEY 2016 THE FIRE 200 140 LISA HINA 10012 11912 TAR THIN FRIZE Ara 4376 PH TIME 1382 www FINDE 2000 1024 204752 CHR 300 FERM FINTE Sepianty Ingletanty Mandy imperanty Ingeranty ingefany Segetaty Siperianty Singefany Ingefany indy ingefany gely ngay Wany Senty ingefany Singleandy LISA Spanje Apus sandy Seggny p Apatin p LINE 2004 1991 1999 1991 MEI AUST 3000 30 CHE THE IMI ims 2006 4560 2007 1900 2007 300 1001 1004 200 000 WH 1444 200 MUS Sighlands STAISE ress egy Singly SingleFly 24TM S PE Panty Singefany Tan Hay MU nganty 3000 1904 3000 1999 wa water 3919 WwCode Wa 200 2011 Wie W Wales wat THE Wesse westsch wenste www WCF Wal TIM 10 THE www wendy fer A wwwww 104190418 1042041 104210425 30422421 30423 0422 10424 425 10425424 104260475 5436 0427 104290424 104300429 10431 436 1043243 10433 43 10434041 30435 0434 10430435 10437414 104300437 1043046 104404 10441 440 0441 104430447 0443 2444 0445 0445 104480447 10449448 10450 445 164330 10452 433 10453 45 20454453 30455454 1044 045 104570 Ready . 359540 172000 149900 472000 384246 175000 199000 58600 $3000 75000 10000 THIVY 104400 323958 THORE 348240 69500 100000 372958 275000 184900 195000 267900 10000 214300 43500 100000 281100 $12000 115500 ***** 129000 175000 181000 234000 $57500 402641 135000 16500 2/06/16 17 1/18/14 3 MM/14 2/4 Man 400 2014/06 44/ 4/4/16 4/14/16 IN 2/12/16 14 M/14 AJA/TA 5/2.04 MAINE sans 4/6/16 2/29/16 1/23/14 san M/1/14 4/22/14 14/04 E 4/24/16 6/17/18 2/09/16 5/03/16 sana MILDE sass Mens 1/31/1 M/IMG 3/A4 AVVIA Ana AGE 40204 Sheett C 4 " 11 IN N F A " " 4 4 4 4 . 1 1 E 4 4 4 1 1 # 1. # " 4 S $ 1 D 12 11 325 25 47 1 BE 14 10 E " 14 2 a E E VE 12 12 11 11 133 E L 14 4 13 12 1 E 4 3 3 F 1 2300 1896 2355 3503 1508 1959 3539 1328 1119 1316 2650 4119 1438 4100 1625 1500 2400 2014 2100 2041 21701 STTF 2400 2141 200 AS19 000 2200 1400 Fun 1404 3294 2438 1552 2350 201 140 2418 Wiza 9141 10400 B100 1547 150 ETUST www FECO 4 178604 205224 153503 117434 14884 NODE 174324 MA 1304 **** HII 100 180 15246 13:01 124114 FOR THO PRIKL *** *73200 (243) 1000 9047 WICH 1965 NIE T 15246 PRE 25744 family Engelanty Segely Single Singlefany Segely y Ungefamily gely gely Singletony Ingely Segetaty supstaty supstanty Sy Sy sperandy Ingely Singefany Sy Semily Singefany Sepert 1000 Sepety singularity jedy A 1994 THY 196 CHE 1992 1996 09061 1999 THE ME www. 190 THAT M FRE tve IME INCL THE NAL YNE ar 1905 1979 1969 1900 rear 2000 300 DO 2004 1944 2001 2004 IME www. A TA A TU Tiedona bek FA GRA www www fo SA may ben S T T N www www.m wwwppy e Ne ya kany #B 9000 - . MO RE Fed Access the data set House Price. Select two comparable college towns (DO NOT USE AMES IOWA) and: Develop a model that predicts the sale price of a house in college city 1. Develop a model that predicts the sale price of a house in college city 2. Develop a model that predicts the sale price of a house using both college city 1 and 2 data. Compare and contrast your models and their results. Which model is the best predictor and why? Be as specific as possible. (Hint) See case study completed on page 254-255 of the text for assistance Data Set for part 2 Your analysis should be between 3 and 4 pages, not including the title page or reference page or appendices. Proper APA formatting should be followed. Your paper should be submitted to the assignment submission box. B4 10283 85 10284 86 10285 87 10286 88 10287 89 10288 290 10289 291 10290 292 10291 293 10292 294 10293 #295 10294 0296 10295 0297 10296 298 10297 0299 10298 0300 10299 0301 10300 0302 10301 10303 10302 L0304 10303 10305 10304 10306 10305 10307 10306 10308 10307 10309 10308 10310 10309 10311 10310 10312 10311 10313 10312 10314 10313 10315 10314 10316 10315 10317 10316 10318 10317 10319 10318 10320 10319 10321 10320 10322 10321 Ready fx 295000 4/8/16 5/18/16 4/15/16 2/29/16 5/1/16 3/15/16 3/22/16 2/9/16 6/5/16 2/3/16 4/29/16 2/25/16 3/14/16 6/27/16 4/11/16 4/27/16 4/22/16 3/10/16 3/24/16 6/1/16 2/3/16 3/24/16 3/17/16 7/1/16 5/10/16 2/29/16 3/10/16 2/9/16 5/3/16 5/13/16 4/15/16 4/18/16 4/13/16 4/6/16 5/31/16 2/17/16 3/1/16 5/11/16 6/24/16 B 245000 458000 277000 660000 420000 385000 140000 231000 315000 296900 272400 155000 283500 274000 435000 212000 152000 330000 542000 547400 20000 532250 412000 542000 137000 130000 156000 164000 86000 179900 197000 120000 74467 38000 143000 154900 144430 197500 210000 Query4 Sheet1 + 2 3 4 7 5 4 5 4 4 4 4 3 4 4 5 4 4 3 5 5 3 4 3 4 2 3 3 4 2 3 3 4 3 3 D 2 4 3 3.5 4 2.5 2 1.5 4 2.5 2.5 1 2.5 1.5 3 3.5 2 2.5 4.5 4 1 5 2.5 4 1 1 2 1 1 3 2.5 3 1 2 2 2 1226 3429 3483 6135 3643 3122 2128 2180 2815 3216 2157 1545 2578 2358 3480 3884 2730 2724 4500 3692 768 5500 3780 3315 1168 1200 1468 1836 896 2830 1664 2813 1084 1110 1645 2028 1794 2637 2254 F G 108900 21780 32234.4 54885.6 15681.6 15246 6474 6467 17424 11325.6 12632.4 12196.8 32234.4 7069 9339 23522.4 27007.2 4500 23958 31798.8 13068 30927.6 37897.2 21780 3436 3531 2886 10476 4275 13068 12632 4 33541.2 16988.4 126324 11761.2 13503.6 14810 4 24395.6 25700.4 H Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family 1970 1979 1987 1994 1993 2001 1995 1996 1995 1992 1990 1986 1988 1992 1988 1989 1994 1999 1986 2001 1940 1956 1952 1952 1993 1995 1995 1947 1945 1963 1958 1950 1970 1950 1960 1956 1966 1966 1968 dtv 528-98 Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, Al Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, Al Tuscaloosa, AL Tuscoosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, Al Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Conditional Fe Formatting as University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama @ 10476 10475 10477 10476 10478 10477 10479 10478 10480 10479 10481 10480 10482 10481 10483 10482 10484 10483 10485 10484 10486 10485 10487 10486 10488 10487 10489 10488 10490 10489 10491 10490 10492 10491 10493 10492 10494 10493 10495 10494 10496 10495 10497 10496 10498 10497 10499 10498 10500 10499 10501 10500 10502 10501 10503 10502 10504 10503 10505 10504 10506 10505 10507 10506 10508 10507 10509 10508 10510 10509 10511 10510 10512 10511 SHEEN OLS 285000 182500 325000 158000 132500 162000 164000 147500 140000 187000 145000 138000 140000 162500 193000 156000 150000 144000 149900 45000 115000 150000 437500 149900 179000 135000 110000 170500 129210 125000 221000 272000 199500 135100 335000 171000 143000 Query4 5/16/16 5/31/16 4/1/16 6/8/16 6/24/16 5/4/16 5/31/16 5/27/16 6/2/16 5/13/16 6/15/16 5/9/16 5/19/16 4/7/16 5/23/16 4/21/16 5/27/16 5/27/16 5/10/16 5/27/16 3/25/16 4/12/16 5/31/16 6/7/16 6/9/16 1/20/16 4/27/16 5/26/16 4/4/16 2/3/26 5/18/16 4/5/16 1/25/16 5/26/16 3/31/16 2/4/16 /18/16 1/26/16 Sheet1 + 4 4 4 5 3 2 4 2 2 3 3 3 3 4 3 5 4 2 3 3 2 3 3 2 3 4 2 2 3 2 3 4 4 5 4 5 3 3 2 3.5 2 S 2 1 2 2 1.75 1 2.5 2 2 2 1.5 2 2 1.5 2 25 1 1 1 3.5 1 2 1 1 1.75 1 2 2.5 4 12 12 13 1.5 15 4758 1436 2318 2054 1368 1638 1718 2116 1660 1584 1263 1792 1530 1643 2231 2009 2100 2123 1134 2016 2226 660 1443 1180 2156 1488 2349 1818 936 1280 880 1618 2128 3304 2268 1760 3120 1109 1514 143748 148104 17424 13068 15246 117612 6272 5663 148104 12196.8 10454 9583 8160 8973 8581 9060 6970 7361 7841 10800 139392 10454 7405 GODE 40075.2 9104 24829.2 213444 22215.6 213444 21780 22651.2 121968 11325.6 10019 113256 10019 12632.4 Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single family Single Family Single Family 1549 1966 2006 1971 1989 1930 1939 1955 1949 1957 1951 1954 1956 1965 1966 1963 1967 1967 1961 1958 1949 1945 1959 1941 1962 1999 1945 1952 1549 1955 1952 1946 1988 1997 1976 1966 2001 1944 1956 m Waterloo-Cedar Tall University of Northere wa Watertoo-Cedar Fall University of Northere w Waterloo-Cedar Fall University of Northern lows Waterloo Cetar full University of Northern wa Waterloo Cadar Fall University of Northern low Waterloo Cetar Fall University of Northernows Waterloo-Cedar Fall University of Northern lowa Waterloo-Cedar Fall University of Northern lowa Waterloo-Cedar Fall University of Northern law Waterloo Cedar Fall University of Northern w Waterloo-Cedar Fall University of Northern lows Waterloo-Cedar Fall University of Northern lowa Waterloo-Cedar Fall University of Northern w Waterloo-Cedar Fall University of North Waterloo Cedar Fall University of Nork Watefoo Cedar Fall University of Northe Watele Cedar Fall University of No Waterloo Cedar Fall University of Ne low Waterloo-Cedar Fall University of Northermowa Waterloo-Cedar Fall University of North Waterloo Cadar Fat University of Northern tows Waterloo-Cedar Fall University of Northermos Watefoo-Cedar Fat University of Northern Iows Watefo-Cedar Fall University of Northe Waggloo Cedar Fall University of Northern lows Wartoo-Cedar Fall University of N Waterloo Cedar Fall University of Northern low Wateise-Cedar Fall University of Northern lows Waterloo Cedar fall University of Northenows Waterlos Cedar Fall University of Northern tow Waterloo-Cedar Fall University of Northern towa Waterloo Cedar Fall University of Northern wa Waterloo Cedar Fall University of Northern lowa Waterloo-Cedar Full Uversity of Norther Waterloo-Cedar fall University of Northern low Waterloo Cedar Fat University of Northern lows Waterloo Cedar Fal University of Northern lows Waterloo Cetar Fall University of Norw Waterloo-Cadar fall University of Northern lowa inline Part 2 - Access the data set House Price. Select two comparable college towns (DO NOT USE AMES IOWA) and: Develop a model that predicts the sale price of a house in college city 1. Develop a model that predicts the sale price of a house in college city 2. Develop a model that predicts the sale price of a house using both college city 1 and 2 data. . Compare and contrast your models and their results. Which model is the best predictor and why? Be as specific as possible. (Hint) See case study completed on page 254-255 of the text for assistance Data Set for part 2 Your analysis should be between 3 and 4 pages, not including the title page or reference page or appendices. Proper APA formatting should be followed. Your paper should be submitted to the assignment submission box. Previous 10419 1018 10620 09 1062110620 10433 10621 30623 10622 10634 10623 VENUS SENG 10624 10625 10627 10626 104201427 30629 10634 1063010629 10631 10630 10032 10631 10633 10613 10634 10613 30635 10434 1063610611 30637 1936 10638 50637 10639 1064010439 10641 10649 10442 10641 5064310642 10644 10645 10644 104 10445 10647 046 104 10647 10649 10646 10450 1049 1045110010 E 1990:201 1065330653 10454 10443 2065510454 10454 455 DAST 10615 " www 175000 245000 250000 21500 314500 250000 315000 169000 282500 200000 114000 1000 000001 100500 H 240000 172500 384000 325500 312000 172600 501000 1000 12000 120000 117700 250000 450000 289400 187000 32000 333000 400000 254000 160000 he 135000 30000 DIELE C www 4/1/14 19304 SULLE SUUS Wittit ww VI sams A MINIA 4 S 14 30/5/ 4/26/16 3/21/14 kan SOME 4/24/16 TUTUS ww/ww FUNGS FURY 90/0/2 91/09 " MANIA www/ 4/4/ 4/8/16 www 4 1 A 14 04/06 Mane 2001 www ARIE 4 4 pone 14 4/05/04 N 3 4/02/16 1 6/24/20 4 Mane s 1/23/1 4 anin 1/6/16 " 14 N 15 S 14 S 4 " " 1 " 4 4 S 1 LA L 41 1 4 D LIS 4 ACE E 2.75 25 3 A IN a 13 + 1 AS 16 3 41 . 33 35 E 10 T ST " 1 2 1.25 3 25 KO 4 1 SE 12 11 " 2 1 1514 3040 1509 1224 3544 3374 2450 2004 2428 M TE SE 1909 m 2200 7942 4 INLE Met 2301 3011 3764 1901 4000 3011 1114 THE mi 300 WHIT 25 2544 SHE 2009 3001 1911 NH 187 1119 (7810 THEY 2016 THE FIRE 200 140 LISA HINA 10012 11912 TAR THIN FRIZE Ara 4376 PH TIME 1382 www FINDE 2000 1024 204752 CHR 300 FERM FINTE Sepianty Ingletanty Mandy imperanty Ingeranty ingefany Segetaty Siperianty Singefany Ingefany indy ingefany gely ngay Wany Senty ingefany Singleandy LISA Spanje Apus sandy Seggny p Apatin p LINE 2004 1991 1999 1991 MEI AUST 3000 30 CHE THE IMI ims 2006 4560 2007 1900 2007 300 1001 1004 200 000 WH 1444 200 MUS Sighlands STAISE ress egy Singly SingleFly 24TM S PE Panty Singefany Tan Hay MU nganty 3000 1904 3000 1999 wa water 3919 WwCode Wa 200 2011 Wie W Wales wat THE Wesse westsch wenste www WCF Wal TIM 10 THE www wendy fer A wwwww 104190418 1042041 104210425 30422421 30423 0422 10424 425 10425424 104260475 5436 0427 104290424 104300429 10431 436 1043243 10433 43 10434041 30435 0434 10430435 10437414 104300437 1043046 104404 10441 440 0441 104430447 0443 2444 0445 0445 104480447 10449448 10450 445 164330 10452 433 10453 45 20454453 30455454 1044 045 104570 Ready . 359540 172000 149900 472000 384246 175000 199000 58600 $3000 75000 10000 THIVY 104400 323958 THORE 348240 69500 100000 372958 275000 184900 195000 267900 10000 214300 43500 100000 281100 $12000 115500 ***** 129000 175000 181000 234000 $57500 402641 135000 16500 2/06/16 17 1/18/14 3 MM/14 2/4 Man 400 2014/06 44/ 4/4/16 4/14/16 IN 2/12/16 14 M/14 AJA/TA 5/2.04 MAINE sans 4/6/16 2/29/16 1/23/14 san M/1/14 4/22/14 14/04 E 4/24/16 6/17/18 2/09/16 5/03/16 sana MILDE sass Mens 1/31/1 M/IMG 3/A4 AVVIA Ana AGE 40204 Sheett C 4 " 11 IN N F A " " 4 4 4 4 . 1 1 E 4 4 4 1 1 # 1. # " 4 S $ 1 D 12 11 325 25 47 1 BE 14 10 E " 14 2 a E E VE 12 12 11 11 133 E L 14 4 13 12 1 E 4 3 3 F 1 2300 1896 2355 3503 1508 1959 3539 1328 1119 1316 2650 4119 1438 4100 1625 1500 2400 2014 2100 2041 21701 STTF 2400 2141 200 AS19 000 2200 1400 Fun 1404 3294 2438 1552 2350 201 140 2418 Wiza 9141 10400 B100 1547 150 ETUST www FECO 4 178604 205224 153503 117434 14884 NODE 174324 MA 1304 **** HII 100 180 15246 13:01 124114 FOR THO PRIKL *** *73200 (243) 1000 9047 WICH 1965 NIE T 15246 PRE 25744 family Engelanty Segely Single Singlefany Segely y Ungefamily gely gely Singletony Ingely Segetaty supstaty supstanty Sy Sy sperandy Ingely Singefany Sy Semily Singefany Sepert 1000 Sepety singularity jedy A 1994 THY 196 CHE 1992 1996 09061 1999 THE ME www. 190 THAT M FRE tve IME INCL THE NAL YNE ar 1905 1979 1969 1900 rear 2000 300 DO 2004 1944 2001 2004 IME www. A TA A TU Tiedona bek FA GRA www www fo SA may ben S T T N www www.m wwwppy e Ne ya kany #B 9000 - . MO RE Fed Access the data set House Price. Select two comparable college towns (DO NOT USE AMES IOWA) and: Develop a model that predicts the sale price of a house in college city 1. Develop a model that predicts the sale price of a house in college city 2. Develop a model that predicts the sale price of a house using both college city 1 and 2 data. Compare and contrast your models and their results. Which model is the best predictor and why? Be as specific as possible. (Hint) See case study completed on page 254-255 of the text for assistance Data Set for part 2 Your analysis should be between 3 and 4 pages, not including the title page or reference page or appendices. Proper APA formatting should be followed. Your paper should be submitted to the assignment submission box. B4 10283 85 10284 86 10285 87 10286 88 10287 89 10288 290 10289 291 10290 292 10291 293 10292 294 10293 #295 10294 0296 10295 0297 10296 298 10297 0299 10298 0300 10299 0301 10300 0302 10301 10303 10302 L0304 10303 10305 10304 10306 10305 10307 10306 10308 10307 10309 10308 10310 10309 10311 10310 10312 10311 10313 10312 10314 10313 10315 10314 10316 10315 10317 10316 10318 10317 10319 10318 10320 10319 10321 10320 10322 10321 Ready fx 295000 4/8/16 5/18/16 4/15/16 2/29/16 5/1/16 3/15/16 3/22/16 2/9/16 6/5/16 2/3/16 4/29/16 2/25/16 3/14/16 6/27/16 4/11/16 4/27/16 4/22/16 3/10/16 3/24/16 6/1/16 2/3/16 3/24/16 3/17/16 7/1/16 5/10/16 2/29/16 3/10/16 2/9/16 5/3/16 5/13/16 4/15/16 4/18/16 4/13/16 4/6/16 5/31/16 2/17/16 3/1/16 5/11/16 6/24/16 B 245000 458000 277000 660000 420000 385000 140000 231000 315000 296900 272400 155000 283500 274000 435000 212000 152000 330000 542000 547400 20000 532250 412000 542000 137000 130000 156000 164000 86000 179900 197000 120000 74467 38000 143000 154900 144430 197500 210000 Query4 Sheet1 + 2 3 4 7 5 4 5 4 4 4 4 3 4 4 5 4 4 3 5 5 3 4 3 4 2 3 3 4 2 3 3 4 3 3 D 2 4 3 3.5 4 2.5 2 1.5 4 2.5 2.5 1 2.5 1.5 3 3.5 2 2.5 4.5 4 1 5 2.5 4 1 1 2 1 1 3 2.5 3 1 2 2 2 1226 3429 3483 6135 3643 3122 2128 2180 2815 3216 2157 1545 2578 2358 3480 3884 2730 2724 4500 3692 768 5500 3780 3315 1168 1200 1468 1836 896 2830 1664 2813 1084 1110 1645 2028 1794 2637 2254 F G 108900 21780 32234.4 54885.6 15681.6 15246 6474 6467 17424 11325.6 12632.4 12196.8 32234.4 7069 9339 23522.4 27007.2 4500 23958 31798.8 13068 30927.6 37897.2 21780 3436 3531 2886 10476 4275 13068 12632 4 33541.2 16988.4 126324 11761.2 13503.6 14810 4 24395.6 25700.4 H Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family 1970 1979 1987 1994 1993 2001 1995 1996 1995 1992 1990 1986 1988 1992 1988 1989 1994 1999 1986 2001 1940 1956 1952 1952 1993 1995 1995 1947 1945 1963 1958 1950 1970 1950 1960 1956 1966 1966 1968 dtv 528-98 Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, Al Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, Al Tuscaloosa, AL Tuscoosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, Al Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Tuscaloosa, AL Conditional Fe Formatting as University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama University of Alabama @ 10476 10475 10477 10476 10478 10477 10479 10478 10480 10479 10481 10480 10482 10481 10483 10482 10484 10483 10485 10484 10486 10485 10487 10486 10488 10487 10489 10488 10490 10489 10491 10490 10492 10491 10493 10492 10494 10493 10495 10494 10496 10495 10497 10496 10498 10497 10499 10498 10500 10499 10501 10500 10502 10501 10503 10502 10504 10503 10505 10504 10506 10505 10507 10506 10508 10507 10509 10508 10510 10509 10511 10510 10512 10511 SHEEN OLS 285000 182500 325000 158000 132500 162000 164000 147500 140000 187000 145000 138000 140000 162500 193000 156000 150000 144000 149900 45000 115000 150000 437500 149900 179000 135000 110000 170500 129210 125000 221000 272000 199500 135100 335000 171000 143000 Query4 5/16/16 5/31/16 4/1/16 6/8/16 6/24/16 5/4/16 5/31/16 5/27/16 6/2/16 5/13/16 6/15/16 5/9/16 5/19/16 4/7/16 5/23/16 4/21/16 5/27/16 5/27/16 5/10/16 5/27/16 3/25/16 4/12/16 5/31/16 6/7/16 6/9/16 1/20/16 4/27/16 5/26/16 4/4/16 2/3/26 5/18/16 4/5/16 1/25/16 5/26/16 3/31/16 2/4/16 /18/16 1/26/16 Sheet1 + 4 4 4 5 3 2 4 2 2 3 3 3 3 4 3 5 4 2 3 3 2 3 3 2 3 4 2 2 3 2 3 4 4 5 4 5 3 3 2 3.5 2 S 2 1 2 2 1.75 1 2.5 2 2 2 1.5 2 2 1.5 2 25 1 1 1 3.5 1 2 1 1 1.75 1 2 2.5 4 12 12 13 1.5 15 4758 1436 2318 2054 1368 1638 1718 2116 1660 1584 1263 1792 1530 1643 2231 2009 2100 2123 1134 2016 2226 660 1443 1180 2156 1488 2349 1818 936 1280 880 1618 2128 3304 2268 1760 3120 1109 1514 143748 148104 17424 13068 15246 117612 6272 5663 148104 12196.8 10454 9583 8160 8973 8581 9060 6970 7361 7841 10800 139392 10454 7405 GODE 40075.2 9104 24829.2 213444 22215.6 213444 21780 22651.2 121968 11325.6 10019 113256 10019 12632.4 Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single Family Single family Single Family Single Family 1549 1966 2006 1971 1989 1930 1939 1955 1949 1957 1951 1954 1956 1965 1966 1963 1967 1967 1961 1958 1949 1945 1959 1941 1962 1999 1945 1952 1549 1955 1952 1946 1988 1997 1976 1966 2001 1944 1956 m Waterloo-Cedar Tall University of Northere wa Watertoo-Cedar Fall University of Northere w Waterloo-Cedar Fall University of Northern lows Waterloo Cetar full University of Northern wa Waterloo Cadar Fall University of Northern low Waterloo Cetar Fall University of Northernows Waterloo-Cedar Fall University of Northern lowa Waterloo-Cedar Fall University of Northern lowa Waterloo-Cedar Fall University of Northern law Waterloo Cedar Fall University of Northern w Waterloo-Cedar Fall University of Northern lows Waterloo-Cedar Fall University of Northern lowa Waterloo-Cedar Fall University of Northern w Waterloo-Cedar Fall University of North Waterloo Cedar Fall University of Nork Watefoo Cedar Fall University of Northe 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