MAT 240 SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Questions

Real Estate County Data for 2019
2019 Data (n=1000)
Region
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
State
County
in
grant
il
vermilion
in
henry
in
wayne
il
coles
il
macoupin
in
vigo
oh
jefferson
il
jackson
oh
marion
mi
bay
il
whiteside
oh
trumbull
in
madison
il
knox
il
stephenson
il
macon
in
delaware
il
henry
oh
seneca
oh
darke
oh
scioto
oh
belmont
oh
sandusky
il
rock island
oh
clark
oh
columbiana
in
howard
listing price
219,500
254,500
235,000
203,800
220,800
197,600
165,800
246,500
154,300
149,700
145,100
283,700
243,000
229,100
205,100
235,600
212,900
221,600
257,700
211,900
160,800
204,200
172,500
253,900
166,300
240,500
241,400
304,300
$’s per
square foot
$116
$156
$148
$141
$117
$111
$122
$136
$105
$116
$117
$136
$133
$187
$118
$140
$128
$134
$123
$168
$114
$131
$101
$146
$127
$137
$164
$152
square feet
1,898
1,632
1,588
1,441
1,893
1,783
1,362
1,814
1,463
1,296
1,239
2,087
1,827
1,224
1,740
1,682
1,659
1,651
2,087
1,263
1,416
1,562
1,710
1,738
1,305
1,752
1,469
1,996
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
oh
il
il
il
mi
in
oh
il
il
il
mi
oh
oh
oh
oh
oh
oh
il
oh
il
oh
oh
oh
mi
mi
il
in
wi
il
mi
il
mi
mi
wi
mi
wi
richland
peoria
la salle
madison
wayne
vanderburgh
mahoning
williamson
winnebago
adams
saginaw
montgomery
allen
lucas
ashtabula
lawrence
huron
tazewell
summit
sangamon
ashland
tuscarawas
ross
shiawassee
calhoun
kankakee
lawrence
manitowoc
st. clair
ingham
mclean
jackson
isabella
wood
montcalm
grant
248,900
187,900
311,100
254,500
213,800
214,100
207,500
171,600
236,700
266,100
171,800
225,300
227,600
228,300
177,000
248,300
199,700
278,700
185,800
213,500
188,000
270,700
257,200
192,400
266,200
148,700
270,600
181,400
201,400
222,500
203,800
139,200
163,000
266,500
218,300
243,200
$132
$131
$154
$156
$172
$134
$123
$141
$140
$166
$118
$151
$147
$115
$107
$156
$147
$165
$101
$130
$151
$149
$127
$129
$130
$115
$137
$140
$164
$125
$134
$116
$125
$144
$105
$121
1,880
1,434
2,015
1,628
1,243
1,596
1,688
1,218
1,692
1,599
1,452
1,493
1,550
1,978
1,658
1,587
1,359
1,693
1,847
1,643
1,246
1,815
2,018
1,494
2,042
1,293
1,978
1,294
1,225
1,777
1,526
1,201
1,307
1,853
2,081
2,014
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
oh
oh
oh
wi
mi
wi
in
mi
mi
oh
il
oh
oh
mi
oh
mi
il
oh
oh
wi
mi
mi
in
mi
oh
mi
wi
wi
il
in
in
oh
in
wi
wi
oh
cuyahoga
stark
athens
milwaukee
lenawee
fond du lac
st. joseph
ionia
genesee
muskingum
ogle
washington
wayne
muskegon
pickaway
st. joseph
champaign
knox
lorain
calumet
midland
marquette
elkhart
monroe
lake
eaton
douglas
marathon
dekalb
marion
allen
hancock
lake
portage
rock
greene
265,100
201,000
246,400
184,900
191,500
135,300
193,000
193,600
194,800
188,300
236,600
324,400
256,700
230,400
265,700
188,500
246,700
192,200
226,200
226,400
174,500
172,500
202,300
228,600
225,900
189,900
461,400
431,200
347,500
323,300
398,000
380,300
470,600
531,000
513,100
581,800
$136
$163
$158
$111
$118
$103
$111
$137
$166
$94
$208
$156
$129
$131
$143
$135
$121
$127
$126
$111
$151
$120
$182
$136
$135
$96
$129
$119
$97
$95
$113
$94
$109
$109
$104
$113
1,947
1,230
1,560
1,666
1,628
1,312
1,736
1,416
1,173
1,999
1,138
2,081
1,986
1,757
1,853
1,397
2,031
1,510
1,789
2,033
1,157
1,433
1,113
1,679
1,676
1,976
3,581
3,638
3,574
3,408
3,525
4,028
4,316
4,888
4,950
5,146
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
ar
ar
ar
ar
ar
ar
ar
ar
ar
ar
ar
ar
ar
ar
ar
la
la
la
la
la
la
la
la
la
la
la
la
la
la
la
la
la
la
la
la
la
baxter
benton
craighead
crawford
crittenden
faulkner
garland
jefferson
lonoke
pope
pulaski
saline
sebastian
washington
white
acadia
ascension
bossier
caddo
calcasieu
east baton rouge
iberia
jefferson
lafayette
lafourche
livingston
orleans
ouachita
rapides
st. charles
st. landry
st. martin
st. mary
st. tammany
tangipahoa
terrebonne
286,900
232,900
251,100
172,900
298,800
280,500
274,800
314,600
289,000
273,200
211,300
273,500
208,600
215,600
254,800
226,300
303,200
305,500
278,500
214,300
254,100
188,700
306,100
221,000
297,000
297,300
258,600
304,300
347,800
171,400
254,700
252,600
260,400
280,300
242,300
254,700
$132
$138
$110
$100
$155
$129
$134
$144
$132
$114
$121
$114
$111
$126
$145
$135
$149
$140
$158
$115
$135
$125
$131
$108
$128
$139
$152
$158
$151
$99
$118
$137
$141
$150
$110
$156
2,176
1,690
2,285
1,734
1,928
2,168
2,056
2,179
2,190
2,389
1,745
2,396
1,879
1,712
1,763
1,681
2,030
2,177
1,768
1,871
1,881
1,511
2,344
2,040
2,320
2,135
1,698
1,921
2,297
1,724
2,150
1,845
1,842
1,869
2,202
1,637
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
la
la
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
ok
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
vermilion
vernon
canadian
carter
cherokee
cleveland
comanche
creek
garfield
grady
le flore
muskogee
oklahoma
osage
payne
pottawatomie
rogers
tulsa
wagoner
washington
angelina
bastrop
bell
bexar
bowie
brazoria
brazos
cameron
collin
comal
coryell
dallas
denton
ector
el paso
ellis
270,600
255,900
218,000
263,000
302,100
263,800
252,100
362,200
220,300
229,500
292,200
206,400
219,000
194,800
272,200
204,800
183,700
295,000
209,100
167,100
270,200
179,600
323,100
260,800
223,600
246,300
242,500
269,200
374,700
249,200
184,100
279,600
254,500
151,300
249,200
163,400
$164
$113
$95
$142
$167
$117
$140
$157
$132
$158
$128
$125
$121
$129
$124
$136
$104
$130
$134
$110
$172
$103
$143
$111
$153
$130
$132
$151
$161
$165
$126
$144
$124
$102
$123
$112
1,650
2,256
2,290
1,856
1,809
2,262
1,806
2,305
1,670
1,455
2,291
1,646
1,804
1,515
2,204
1,510
1,769
2,267
1,560
1,526
1,568
1,736
2,262
2,359
1,463
1,900
1,835
1,781
2,325
1,510
1,460
1,940
2,052
1,477
2,023
1,459
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
West South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
tx
al
al
al
al
al
al
al
al
fort bend
galveston
grayson
gregg
guadalupe
hardin
harris
harrison
hays
henderson
hidalgo
hood
hunt
jefferson
johnson
kaufman
kerr
liberty
lubbock
mclennan
midland
montgomery
nacogdoches
nueces
orange
parker
potter
randall
autauga
baldwin
blount
calhoun
coffee
colbert
cullman
dale
222,800
296,100
248,700
188,200
263,800
283,500
196,200
255,300
230,000
285,700
224,600
207,200
306,900
213,500
234,900
264,300
262,900
206,400
476,700
407,300
515,000
480,300
630,800
496,200
610,600
727,900
726,800
476,700
289,800
197,100
214,200
257,100
297,200
219,900
239,500
317,500
$114
$139
$135
$112
$146
$152
$115
$107
$154
$122
$121
$91
$133
$99
$115
$134
$139
$113
$133
$104
$141
$116
$124
$115
$118
$122
$126
$90
$117
$120
$116
$148
$129
$111
$121
$123
1,958
2,127
1,837
1,679
1,804
1,867
1,700
2,396
1,490
2,343
1,853
2,278
2,299
2,154
2,040
1,977
1,888
1,822
3,583
3,920
3,648
4,142
5,107
4,310
5,196
5,962
5,777
5,284
2,468
1,646
1,846
1,738
2,295
1,987
1,978
2,579
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
al
al
al
al
al
al
al
al
al
al
al
al
al
al
al
al
al
al
al
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
ky
elmore
etowah
houston
jackson
jefferson
lauderdale
lee
limestone
madison
marshall
mobile
montgomery
morgan
russell
shelby
st. clair
talladega
tuscaloosa
walker
boone
boyd
bullitt
campbell
christian
daviess
fayette
franklin
hardin
henderson
hopkins
jefferson
jessamine
kenton
laurel
madison
mccracken
262,700
167,700
263,700
284,900
317,300
291,600
291,100
248,800
252,500
233,700
316,900
298,500
285,500
207,400
190,100
235,200
203,500
259,000
206,500
325,000
213,600
319,300
269,400
325,600
239,800
227,800
272,400
223,600
311,400
208,000
265,900
174,900
273,600
256,800
265,400
229,300
$114
$71
$103
$118
$135
$137
$162
$144
$115
$134
$128
$136
$134
$124
$89
$102
$113
$137
$115
$142
$102
$126
$145
$136
$113
$109
$112
$133
$127
$104
$117
$98
$110
$105
$135
$126
2,313
2,372
2,549
2,417
2,351
2,130
1,792
1,733
2,199
1,743
2,476
2,200
2,133
1,679
2,133
2,306
1,800
1,895
1,803
2,290
2,104
2,527
1,864
2,390
2,118
2,085
2,432
1,675
2,460
2,007
2,279
1,777
2,479
2,450
1,960
1,824
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
ky
ky
ky
ky
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
ms
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
oldham
pulaski
scott
warren
desoto
forrest
hancock
harrison
hinds
jackson
jones
lafayette
lamar
lauderdale
lee
lowndes
madison
oktibbeha
pearl river
rankin
warren
anderson
blount
bradley
carter
coffee
cumberland
davidson
dickson
greene
hamblen
hamilton
hawkins
jefferson
knox
loudon
232,500
234,200
217,500
279,300
288,300
254,800
336,800
281,200
268,600
179,400
187,200
295,100
251,400
275,800
273,100
283,400
279,400
264,400
256,100
240,500
191,100
329,700
308,500
181,300
293,000
174,700
266,600
326,400
250,300
226,000
309,000
303,200
269,000
264,900
300,500
291,600
$127
$117
$111
$158
$118
$111
$143
$130
$106
$92
$116
$119
$142
$134
$132
$155
$110
$124
$146
$119
$105
$138
$135
$92
$126
$93
$108
$126
$117
$116
$124
$136
$113
$129
$139
$133
1,834
2,003
1,964
1,773
2,438
2,295
2,357
2,158
2,532
1,947
1,607
2,482
1,770
2,063
2,066
1,827
2,538
2,135
1,759
2,019
1,826
2,389
2,278
1,973
2,328
1,887
2,466
2,585
2,140
1,950
2,482
2,232
2,385
2,048
2,162
2,200
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
East South Central
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
tn
ms
ms
al
al
al
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
madison
maury
mcminn
montgomery
putnam
roane
robertson
rutherford
sevier
sullivan
sumner
washington
williamson
wilson
madison
jones
lafayette
talladega
tuscaloosa
jackson
albany
bronx
broome
cattaraugus
cayuga
chautauqua
chemung
clinton
columbia
cortland
dutchess
erie
franklin
fulton
genesee
herkimer
292,500
275,300
234,900
250,300
169,700
217,200
305,600
159,900
229,600
207,500
544,400
505,500
532,300
476,600
564,900
630,100
605,300
675,500
633,100
715,300
297,400
314,100
310,300
372,600
265,400
336,400
282,600
351,500
266,000
343,200
298,300
275,200
302,600
379,900
329,700
306,900
$167
$110
$108
$151
$97
$129
$134
$87
$120
$104
$117
$119
$117
$114
$99
$127
$115
$104
$107
$111
$207
$207
$154
$179
$166
$177
$228
$179
$165
$181
$175
$189
$192
$193
$166
$171
1,756
2,507
2,176
1,663
1,744
1,690
2,281
1,847
1,908
1,996
4,666
4,245
4,562
4,177
5,719
4,947
5,248
6,498
5,894
6,420
1,439
1,515
2,017
2,077
1,602
1,905
1,242
1,969
1,610
1,893
1,708
1,453
1,577
1,965
1,983
1,797
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
ny
pa
pa
pa
pa
pa
pa
jefferson
kings
livingston
madison
monroe
montgomery
new york
niagara
oneida
onondaga
ontario
orange
oswego
otsego
putnam
rensselaer
richmond
rockland
saratoga
schenectady
st. lawrence
steuben
suffolk
tioga
tompkins
ulster
warren
washington
wayne
westchester
adams
allegheny
armstrong
beaver
berks
blair
287,800
247,900
353,400
311,800
318,400
259,300
314,400
261,400
308,500
307,300
295,800
329,300
266,500
225,700
272,300
347,000
313,300
302,300
304,600
331,300
256,900
270,600
244,700
257,400
238,000
228,600
236,500
257,300
321,300
330,000
247,000
224,200
289,200
315,300
207,400
323,900
$191
$189
$220
$158
$206
$156
$191
$172
$168
$179
$186
$168
$237
$204
$178
$190
$168
$165
$157
$163
$148
$191
$160
$218
$142
$162
$205
$203
$204
$159
$158
$196
$186
$157
$173
$178
1,504
1,310
1,603
1,973
1,545
1,658
1,644
1,517
1,840
1,714
1,594
1,963
1,126
1,104
1,532
1,827
1,862
1,835
1,946
2,031
1,734
1,416
1,528
1,183
1,678
1,407
1,156
1,265
1,573
2,072
1,566
1,145
1,558
2,010
1,199
1,815
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
pa
ny
bradford
bucks
cambria
carbon
centre
chester
clearfield
columbia
crawford
cumberland
dauphin
delaware
erie
fayette
franklin
indiana
lackawanna
lancaster
lawrence
lebanon
lehigh
luzerne
lycoming
mifflin
monroe
montgomery
northampton
northumberland
philadelphia
pike
schuylkill
somerset
venango
washington
york
richmond
251,400
325,800
302,200
192,900
290,200
234,800
257,900
277,800
292,400
270,900
282,800
242,100
229,200
272,900
277,700
284,600
251,000
248,500
418,800
377,800
305,800
386,500
185,600
309,500
414,000
289,000
286,100
301,600
246,700
256,400
315,900
323,900
284,800
315,500
368,600
320,600
$208
$182
$176
$143
$184
$156
$170
$174
$204
$186
$140
$184
$181
$184
$204
$166
$184
$170
$203
$201
$194
$186
$145
$164
$199
$175
$218
$163
$166
$168
$188
$164
$163
$173
$178
$170
1,206
1,794
1,716
1,349
1,574
1,506
1,517
1,595
1,435
1,456
2,025
1,315
1,263
1,485
1,363
1,713
1,365
1,465
2,063
1,881
1,579
2,077
1,283
1,888
2,085
1,648
1,313
1,852
1,489
1,529
1,682
1,972
1,749
1,820
2,068
1,887
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
ny
ny
ny
ny
ny
ny
ny
ny
pa
pa
pa
pa
ia
ia
ia
ia
ia
ia
ia
ia
ia
ia
ia
ia
ia
ks
ks
ks
ks
ks
ks
ks
ks
ks
ks
mn
rockland
saratoga
schenectady
st. lawrence
cattaraugus
cayuga
chautauqua
chemung
centre
chester
northumberland
philadelphia
black hawk
cerro gordo
clinton
dallas
dubuque
johnson
linn
polk
pottawattamie
scott
story
warren
woodbury
butler
douglas
johnson
leavenworth
reno
riley
saline
sedgwick
shawnee
wyandotte
anoka
269,200
277,300
596,900
507,400
585,600
523,300
532,600
614,600
626,500
786,800
633,300
822,200
286,700
361,000
301,200
401,900
331,100
421,400
425,300
393,600
297,400
286,600
395,000
402,400
245,500
268,300
267,600
369,000
321,700
402,000
356,900
245,200
303,400
247,100
259,500
292,300
$150
$151
$163
$142
$161
$167
$144
$153
$144
$149
$134
$161
$204
$193
$214
$184
$180
$195
$188
$192
$186
$191
$170
$199
$139
$160
$179
$155
$188
$196
$158
$142
$194
$164
$159
$201
1,796
1,842
3,656
3,583
3,637
3,141
3,697
4,022
4,354
5,290
4,736
5,108
1,404
1,869
1,405
2,183
1,839
2,165
2,265
2,048
1,603
1,501
2,325
2,022
1,761
1,677
1,498
2,387
1,712
2,052
2,261
1,721
1,563
1,511
1,631
1,455
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mn
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
mo
blue earth
carver
chisago
clay
crow wing
dakota
goodhue
hennepin
olmsted
otter tail
ramsey
rice
scott
sherburne
st. louis
stearns
washington
winona
wright
boone
buchanan
cape girardeau
cass
christian
clay
cole
franklin
greene
jackson
jasper
jefferson
johnson
lincoln
newton
platte
saint louis city
315,900
290,500
309,900
350,900
263,200
262,800
340,200
353,300
211,000
315,400
344,400
263,800
426,800
361,600
337,200
362,000
282,100
270,600
420,500
418,900
266,200
292,100
291,200
351,600
294,000
408,200
310,900
388,900
409,400
319,600
324,000
371,300
260,100
322,600
269,200
273,200
$208
$156
$196
$163
$164
$144
$148
$158
$148
$173
$166
$187
$181
$177
$163
$163
$172
$149
$179
$179
$168
$162
$180
$197
$177
$187
$143
$166
$181
$176
$183
$155
$144
$146
$184
$172
1,518
1,868
1,583
2,158
1,602
1,824
2,296
2,236
1,427
1,828
2,073
1,412
2,361
2,041
2,066
2,223
1,642
1,816
2,351
2,341
1,589
1,798
1,620
1,786
1,659
2,183
2,173
2,341
2,262
1,813
1,766
2,388
1,804
2,205
1,467
1,592
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
West North Central
mo
mo
mo
mo
nd
nd
nd
nd
ne
ne
ne
ne
ne
sd
sd
sd
mo
mo
mo
mo
mo
mo
nd
nd
nd
nd
ne
ne
ne
ne
ne
sd
sd
ia
ia
ia
st. charles
st. francois
st. louis
taney
burleigh
cass
grand forks
ward
buffalo
douglas
hall
lancaster
sarpy
lincoln
minnehaha
pennington
platte
saint louis city
st. charles
st. francois
st. louis
taney
burleigh
cass
grand forks
ward
buffalo
douglas
hall
lancaster
sarpy
lincoln
minnehaha
johnson
linn
polk
309,400
325,500
387,900
351,700
345,300
259,700
390,700
402,800
471,600
324,500
276,900
354,400
273,200
461,000
414,900
233,600
289,100
253,900
430,300
355,600
440,100
264,200
428,200
389,000
394,400
370,900
360,800
278,700
324,900
300,700
510,600
543,800
623,500
643,300
745,500
720,300
$143
$167
$195
$194
$162
$179
$173
$252
$197
$162
$154
$149
$164
$195
$217
$146
$189
$156
$187
$200
$197
$187
$178
$183
$169
$175
$152
$191
$185
$191
$149
$158
$162
$160
$165
$162
2,158
1,951
1,994
1,816
2,132
1,454
2,258
1,599
2,393
2,006
1,794
2,379
1,665
2,359
1,908
1,600
1,530
1,623
2,302
1,774
2,229
1,410
2,400
2,120
2,338
2,125
2,375
1,458
1,756
1,574
3,431
3,431
3,854
4,021
4,531
4,459
West North Central
West North Central
West North Central
West North Central
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
ia
ia
ia
ia
nj
nj
nj
nj
nj
nj
nj
nj
nj
nj
nj
nj
dc
de
de
de
md
md
md
md
md
md
md
md
md
md
md
md
md
md
md
md
pottawattamie
scott
story
warren
atlantic
burlington
camden
cape may
cumberland
gloucester
hudson
hunterdon
mercer
monmouth
ocean
salem
district of columbia
kent
new castle
sussex
calvert
carroll
cecil
charles
frederick
harford
howard
montgomery
prince george’s
queen anne’s
st. mary’s
washington
wicomico
worcester
allegany
anne arundel
815,200
869,200
930,400
745,100
214,600
309,600
238,700
351,300
314,800
253,300
290,300
352,500
446,400
278,800
316,000
237,000
286,600
370,000
343,700
323,300
302,200
361,100
357,700
309,700
263,600
243,000
310,200
317,000
379,600
273,400
295,900
282,100
353,900
274,400
265,100
355,200
$157
$151
$178
$140
$173
$169
$208
$199
$260
$209
$194
$249
$220
$241
$156
$201
$212
$224
$179
$164
$198
$180
$201
$154
$212
$178
$182
$170
$276
$190
$196
$152
$265
$226
$221
$183
5,182
5,774
5,223
5,315
1,239
1,833
1,148
1,763
1,209
1,211
1,498
1,415
2,032
1,159
2,028
1,182
1,349
1,653
1,921
1,972
1,529
2,007
1,779
2,012
1,246
1,364
1,703
1,866
1,377
1,436
1,510
1,851
1,335
1,215
1,201
1,942
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
md
md
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
va
baltimore
baltimore city
albemarle
alexandria city
arlington
augusta
bedford
campbell
charlottesville city
chesapeake city
chesterfield
danville city
fairfax
fauquier
franklin
frederick
hampton city
hanover
henrico
henry
james city
loudoun
lynchburg city
montgomery
newport news city
norfolk city
pittsylvania
portsmouth city
prince william
richmond city
roanoke
roanoke city
rockingham
spotsylvania
stafford
suffolk city
311,800
351,000
431,000
357,200
238,100
336,700
242,600
372,000
310,800
343,400
235,700
272,400
390,100
254,100
303,100
274,900
267,200
273,300
248,100
361,200
271,300
259,000
346,400
277,500
296,900
359,900
347,600
407,500
294,100
339,800
348,200
248,900
396,600
340,500
306,800
291,400
$162
$189
$228
$214
$145
$206
$198
$218
$187
$221
$160
$192
$214
$219
$205
$189
$156
$138
$171
$260
$209
$179
$180
$247
$178
$177
$210
$231
$144
$177
$170
$195
$205
$199
$153
$183
1,921
1,861
1,893
1,673
1,646
1,638
1,224
1,703
1,662
1,555
1,476
1,417
1,826
1,162
1,475
1,453
1,716
1,979
1,451
1,390
1,296
1,450
1,922
1,122
1,667
2,030
1,656
1,765
2,036
1,915
2,054
1,277
1,939
1,708
2,007
1,594
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
Mid Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
va
va
va
md
md
md
md
md
md
md
md
md
md
va
va
va
va
va
va
va
va
va
va
nj
nj
nj
nj
nj
nj
dc
de
de
nc
nc
nc
nc
virginia beach city
washington
york
prince george’s
queen anne’s
st. mary’s
washington
wicomico
worcester
allegany
anne arundel
baltimore
baltimore city
albemarle
alexandria city
arlington
portsmouth city
prince william
richmond city
roanoke
roanoke city
rockingham
spotsylvania
hudson
hunterdon
mercer
monmouth
ocean
salem
district of columbia
kent
new castle
alamance
beaufort
brunswick
buncombe
322,500
373,200
320,900
317,100
239,100
292,400
222,100
339,100
355,800
329,100
258,800
354,100
241,400
381,800
296,300
323,700
279,900
281,400
235,800
286,900
265,400
313,400
574,800
444,400
459,300
456,100
524,900
547,400
535,300
690,000
580,600
580,200
315,700
411,900
281,000
446,000
$167
$182
$171
$166
$142
$150
$164
$172
$185
$192
$184
$210
$170
$207
$262
$245
$165
$179
$160
$220
$228
$210
$166
$140
$142
$139
$119
$131
$133
$132
$116
$130
$187
$190
$155
$204
1,927
2,050
1,878
1,906
1,683
1,948
1,356
1,973
1,923
1,716
1,405
1,686
1,419
1,845
1,130
1,322
1,696
1,573
1,472
1,307
1,163
1,490
3,463
3,168
3,237
3,288
4,428
4,178
4,020
5,227
4,985
4,479
1,684
2,163
1,818
2,190
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
nc
ga
ga
ga
burke
cabarrus
caldwell
carteret
catawba
chatham
cleveland
craven
cumberland
davidson
durham
edgecombe
forsyth
franklin
gaston
granville
guilford
halifax
harnett
haywood
henderson
hoke
iredell
johnston
lee
lenoir
lincoln
mecklenburg
moore
nash
new hanover
onslow
orange
henry
houston
jackson
431,700
430,300
255,900
313,200
329,700
322,200
250,700
312,500
327,500
504,500
335,300
349,400
411,500
329,700
441,500
380,700
256,700
360,800
447,900
298,000
413,400
305,700
413,600
390,700
348,600
399,900
395,700
363,300
339,300
333,200
306,300
313,800
488,300
328,100
355,300
368,900
$207
$179
$160
$146
$149
$200
$149
$192
$155
$196
$153
$170
$190
$176
$231
$189
$147
$147
$225
$169
$162
$152
$203
$172
$208
$173
$186
$158
$182
$190
$165
$187
$227
$156
$154
$172
2,085
2,410
1,604
2,147
2,215
1,611
1,681
1,626
2,115
2,571
2,191
2,061
2,166
1,871
1,908
2,019
1,745
2,447
1,992
1,760
2,555
2,011
2,034
2,276
1,675
2,309
2,131
2,304
1,869
1,758
1,855
1,676
2,151
2,109
2,306
2,148
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
ga
ga
ga
ga
ga
ga
ga
nc
nc
nc
nc
nc
nc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
sc
fl
fl
fl
fl
laurens
liberty
lowndes
muscogee
newton
paulding
richmond
union
wake
watauga
wayne
wilkes
wilson
aiken
beaufort
berkeley
charleston
cherokee
darlington
dorchester
florence
georgetown
horry
kershaw
lancaster
lexington
oconee
orangeburg
richland
spartanburg
sumter
york
alachua
bay
brevard
broward
275,900
325,100
318,200
339,700
339,800
348,200
387,700
297,500
399,800
438,700
357,100
346,300
453,900
378,800
314,400
351,900
408,000
374,100
362,900
403,900
404,900
371,300
443,300
268,700
439,000
378,600
331,000
305,400
396,300
375,100
291,900
355,600
265,400
309,700
413,700
405,400
$163
$187
$134
$164
$189
$154
$209
$175
$169
$182
$195
$152
$179
$175
$149
$184
$197
$183
$205
$164
$187
$185
$206
$162
$180
$199
$150
$172
$206
$165
$143
$195
$158
$169
$170
$225
1,694
1,734
2,380
2,076
1,800
2,263
1,859
1,696
2,368
2,405
1,831
2,274
2,539
2,164
2,107
1,913
2,067
2,046
1,769
2,460
2,166
2,006
2,154
1,662
2,439
1,903
2,210
1,780
1,924
2,271
2,035
1,822
1,684
1,830
2,437
1,798
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
South Atlantic
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
fl
az
az
az
az
az
az
az
az
az
az
co
co
charlotte
citrus
clay
collier
columbia
duval
escambia
flagler
hernando
highlands
hillsborough
indian river
lake
lee
leon
manatee
marion
martin
miami-dade
monroe
nassau
okaloosa
orange
osceola
cochise
coconino
gila
maricopa
mohave
navajo
pima
pinal
yavapai
yuma
adams
arapahoe
474,900
284,200
425,700
413,800
376,300
320,400
387,900
290,900
362,600
274,600
362,900
371,300
348,200
391,900
619,200
707,400
560,200
592,000
792,900
854,400
736,100
879,200
987,600
865,400
381,000
332,300
312,000
352,700
471,000
358,200
359,300
314,800
319,000
363,000
365,600
257,500
$188
$177
$177
$178
$180
$170
$170
$156
$184
$166
$182
$198
$180
$160
$147
$178
$151
$151
$165
$173
$133
$142
$153
$146
$189
$233
$205
$158
$225
$222
$203
$223
$140
$157
$220
$170
2,520
1,606
2,408
2,322
2,095
1,885
2,285
1,860
1,967
1,650
1,994
1,873
1,931
2,443
4,213
3,985
3,717
3,919
4,810
4,929
5,517
6,196
6,461
5,923
2,018
1,428
1,525
2,237
2,097
1,611
1,768
1,413
2,280
2,311
1,661
1,513
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
co
co
co
co
co
co
co
co
co
co
co
co
co
id
id
id
id
id
id
mt
mt
mt
mt
mt
mt
nm
nm
nm
nm
nm
nm
nm
nm
nm
nm
nm
boulder
broomfield
denver
douglas
eagle
el paso
garfield
jefferson
la plata
larimer
mesa
pueblo
weld
ada
bannock
bonneville
canyon
kootenai
twin falls
cascade
flathead
gallatin
lewis and clark
missoula
yellowstone
bernalillo
chaves
curry
dona ana
eddy
lea
mckinley
otero
san juan
sandoval
santa fe
295,300
292,800
336,400
387,000
361,600
270,500
364,900
354,800
434,700
348,400
300,700
305,700
382,600
402,600
317,800
412,100
295,600
308,700
319,000
296,000
309,300
390,200
287,300
370,300
326,000
378,000
390,200
307,900
319,400
383,900
394,500
386,500
468,500
332,500
434,600
381,200
$207
$146
$181
$162
$178
$158
$167
$156
$202
$183
$168
$167
$178
$174
$227
$184
$155
$184
$169
$126
$155
$203
$187
$177
$198
$196
$204
$158
$148
$185
$186
$169
$211
$174
$190
$171
1,424
2,005
1,859
2,383
2,035
1,713
2,186
2,274
2,151
1,904
1,792
1,830
2,151
2,309
1,403
2,245
1,903
1,675
1,892
2,341
1,998
1,924
1,533
2,091
1,643
1,926
1,915
1,953
2,160
2,071
2,125
2,290
2,220
1,911
2,283
2,223
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
nm
nv
nv
nv
nv
nv
nv
ut
ut
ut
ut
ut
ut
ut
wy
wy
co
co
co
id
id
id
id
id
id
mt
mt
mt
mt
mt
mt
az
az
az
az
az
valencia
carson city
clark
douglas
elko
lyon
washoe
cache
davis
salt lake
tooele
utah
washington
weber
laramie
natrona
mesa
pueblo
weld
ada
bannock
bonneville
canyon
kootenai
twin falls
cascade
flathead
gallatin
lewis and clark
missoula
yellowstone
maricopa
mohave
navajo
pima
pinal
298,100
396,700
274,100
414,000
365,300
355,700
357,500
278,900
486,700
304,300
311,300
343,900
370,500
354,600
292,500
334,700
308,200
225,200
299,200
315,500
367,600
328,300
279,800
351,300
415,500
309,800
336,000
351,100
299,500
355,600
311,200
389,700
342,100
339,000
393,900
336,000
$208
$205
$193
$174
$180
$206
$153
$185
$207
$185
$199
$220
$209
$188
$202
$142
$163
$154
$212
$142
$206
$218
$178
$229
$177
$194
$185
$158
$176
$187
$199
$172
$219
$177
$225
$196
1,430
1,936
1,419
2,375
2,032
1,725
2,336
1,509
2,351
1,645
1,568
1,564
1,770
1,890
1,448
2,356
1,890
1,462
1,412
2,222
1,781
1,503
1,572
1,536
2,349
1,598
1,813
2,225
1,702
1,901
1,564
2,270
1,561
1,916
1,751
1,717
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
Mountain
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
az
mt
mt
mt
mt
nm
nm
nm
nm
nm
nm
nm
co
co
co
az
ct
ct
ct
ct
ct
ct
ct
ct
ma
ma
ma
ma
ma
ma
ma
ma
ma
ma
ma
ma
yavapai
gallatin
lewis and clark
missoula
yellowstone
bernalillo
chaves
curry
dona ana
eddy
lea
mckinley
el paso
garfield
jefferson
gila
fairfield
hartford
litchfield
middlesex
new haven
new london
tolland
windham
barnstable
berkshire
bristol
essex
franklin
hampden
hampshire
middlesex
norfolk
plymouth
suffolk
worcester
389,700
348,800
320,100
310,100
335,100
280,100
493,600
528,000
534,400
599,300
588,700
731,500
539,600
748,100
777,600
751,800
364,100
342,500
324,800
347,000
329,000
388,700
378,100
415,600
337,000
422,800
362,400
393,300
385,200
357,300
258,800
379,000
313,400
316,800
358,600
395,800
$169
$244
$162
$196
$152
$189
$137
$142
$156
$165
$118
$143
$126
$129
$137
$142
$149
$175
$194
$157
$163
$172
$187
$161
$197
$168
$214
$175
$171
$143
$153
$167
$174
$178
$153
$176
2,300
1,431
1,970
1,586
2,199
1,484
3,608
3,720
3,431
3,636
4,995
5,099
4,273
5,817
5,694
5,284
2,444
1,956
1,673
2,214
2,013
2,265
2,018
2,584
1,711
2,511
1,695
2,245
2,247
2,507
1,686
2,265
1,806
1,775
2,346
2,246
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
me
me
me
me
me
nh
nh
nh
nh
nh
nh
nh
ri
ri
ri
ri
ri
vt
vt
vt
vt
vt
ct
ct
ct
ct
ct
ct
ct
ct
ma
ma
ma
ma
ma
ma
androscoggin
cumberland
kennebec
penobscot
york
belknap
cheshire
grafton
hillsborough
merrimack
rockingham
strafford
bristol
kent
newport
providence
washington
chittenden
franklin
rutland
washington
windsor
fairfield
hartford
litchfield
middlesex
new haven
new london
tolland
windham
barnstable
berkshire
bristol
essex
franklin
hampden
333,600
347,900
294,700
367,200
306,100
357,600
341,300
358,200
372,300
340,300
401,400
462,600
355,100
375,800
369,600
386,100
360,700
340,300
330,300
393,600
401,500
187,800
299,700
338,000
339,000
368,400
387,100
268,100
317,200
381,900
351,800
346,200
441,000
391,300
356,800
351,700
$166
$145
$156
$191
$174
$178
$154
$144
$185
$198
$178
$243
$206
$175
$165
$154
$183
$183
$183
$154
$157
$102
$167
$147
$190
$194
$181
$157
$186
$154
$143
$150
$199
$179
$171
$180
2,015
2,403
1,884
1,927
1,762
2,013
2,210
2,491
2,014
1,715
2,252
1,901
1,726
2,145
2,236
2,511
1,971
1,855
1,807
2,552
2,562
1,847
1,798
2,293
1,780
1,900
2,142
1,708
1,706
2,475
2,455
2,310
2,214
2,183
2,088
1,952
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
New England
ma
ma
ma
ma
ma
ma
me
me
me
me
me
nh
nh
nh
nh
nh
nh
nh
ri
ri
ri
ri
ri
vt
vt
vt
vt
vt
nh
ri
ri
ri
ri
ri
vt
vt
hampshire
middlesex
norfolk
plymouth
suffolk
worcester
androscoggin
cumberland
kennebec
penobscot
york
belknap
cheshire
grafton
hillsborough
merrimack
rockingham
strafford
bristol
kent
newport
providence
washington
chittenden
franklin
rutland
washington
windsor
strafford
bristol
kent
newport
providence
washington
chittenden
franklin
316,500
346,700
394,600
311,000
423,600
346,000
361,400
370,700
331,100
307,400
370,000
397,300
331,400
373,800
331,300
305,500
395,000
363,700
377,100
397,900
338,300
344,100
416,200
257,600
341,300
344,200
353,200
293,800
349,800
305,000
283,700
402,200
379,100
357,800
560,900
489,900
$180
$176
$202
$183
$170
$213
$162
$213
$165
$177
$186
$181
$128
$175
$190
$166
$168
$185
$181
$165
$199
$208
$162
$130
$180
$151
$167
$182
$171
$174
$164
$157
$148
$181
$136
$131
1,755
1,966
1,949
1,703
2,491
1,622
2,226
1,744
2,005
1,740
1,991
2,196
2,588
2,131
1,744
1,843
2,358
1,969
2,079
2,405
1,697
1,654
2,565
1,981
1,892
2,280
2,120
1,614
2,045
1,748
1,732
2,556
2,566
1,975
4,128
3,747
New England
New England
New England
New England
New England
New England
New England
New England
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
vt
vt
vt
ma
ma
ma
ma
ma
ak
ak
ak
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
rutland
washington
windsor
middlesex
norfolk
plymouth
suffolk
worcester
anchorage
fairbanks north star
matanuska-susitna
alameda
butte
contra costa
el dorado
fresno
humboldt
imperial
kern
kings
lake
los angeles
madera
marin
mendocino
merced
monterey
napa
nevada
orange
placer
riverside
sacramento
san bernardino
san diego
san francisco
523,600
534,800
731,000
791,100
803,400
814,300
853,700
844,500
450,000
330,600
431,500
472,100
406,200
366,500
388,400
389,500
349,500
343,900
364,800
408,100
335,700
393,100
439,800
420,800
478,200
339,600
417,200
404,100
377,400
354,800
314,600
329,500
291,500
465,500
421,400
413,100
$126
$124
$137
$151
$142
$125
$143
$136
$259
$231
$273
$229
$217
$225
$211
$279
$234
$233
$236
$246
$235
$219
$275
$227
$237
$197
$344
$228
$234
$322
$266
$259
$176
$249
$202
$259
4,162
4,327
5,319
5,230
5,660
6,516
5,987
6,204
1,739
1,430
1,583
2,065
1,875
1,626
1,839
1,395
1,494
1,478
1,546
1,656
1,430
1,793
1,597
1,856
2,020
1,726
1,213
1,769
1,614
1,101
1,182
1,274
1,655
1,873
2,081
1,595
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
ca
hi
hi
hi
hi
or
or
or
or
or
or
or
or
or
or
or
or
or
or
or
san joaquin
san luis obispo
san mateo
santa barbara
santa clara
santa cruz
shasta
solano
sonoma
stanislaus
sutter
tehama
tulare
tuolumne
ventura
yolo
yuba
hawaii
honolulu
kauai
maui
benton
clackamas
columbia
coos
deschutes
douglas
jackson
josephine
klamath
lane
lincoln
linn
marion
multnomah
polk
468,200
370,000
314,300
355,600
329,000
405,100
344,900
343,700
381,400
389,300
354,600
371,200
403,900
367,700
373,300
516,300
424,000
437,000
273,300
512,300
342,000
351,000
401,700
367,800
399,800
351,500
357,000
301,200
491,900
387,200
404,600
371,200
392,200
419,300
385,500
298,400
$225
$297
$210
$269
$261
$207
$298
$277
$229
$272
$255
$232
$258
$227
$244
$270
$216
$222
$224
$252
$298
$294
$226
$244
$259
$240
$270
$191
$266
$254
$219
$278
$283
$217
$223
$235
2,083
1,246
1,500
1,323
1,262
1,955
1,157
1,240
1,663
1,431
1,390
1,597
1,566
1,619
1,532
1,914
1,959
1,970
1,220
2,030
1,149
1,192
1,780
1,509
1,542
1,467
1,324
1,581
1,847
1,526
1,845
1,334
1,386
1,936
1,728
1,271
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
Pacific
or
or
or
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
wa
or
or
or
wa
wa
wa
ca
ca
ca
umatilla
washington
yamhill
benton
chelan
clallam
clark
cowlitz
franklin
grant
grays harbor
island
king
kitsap
lewis
mason
pierce
skagit
snohomish
spokane
thurston
walla walla
whatcom
whitman
yakima
umatilla
washington
yamhill
benton
chelan
clallam
tuolumne
ventura
yolo
467,100
296,800
458,800
443,000
367,500
427,000
460,700
420,400
436,000
370,100
395,300
269,300
405,700
362,900
402,300
332,900
500,200
330,100
356,400
422,800
309,400
420,400
430,400
362,200
442,800
345,500
764,900
676,100
615,800
611,400
887,800
789,800
654,300
859,000
$254
$223
$227
$247
$231
$204
$240
$227
$250
$274
$284
$213
$225
$249
$219
$291
$251
$277
$257
$289
$239
$214
$256
$293
$259
$313
$218
$185
$174
$197
$201
$182
$182
$171
1,841
1,329
2,020
1,790
1,591
2,096
1,922
1,856
1,744
1,351
1,393
1,263
1,800
1,459
1,841
1,145
1,993
1,193
1,385
1,462
1,296
1,963
1,680
1,238
1,709
1,104
3,504
3,656
3,530
3,099
4,422
4,334
3,597
5,019
Pacific
Pacific
ca
hi
yuba
hawaii
777,900
856,600
$168
$174
4,635
4,927
Summary Statistics for MAT 240 Real Estate Data (for dataset in Modules 2, 3, and 4)
Listing
price ($)
Cost per
square
foot ($)
Square
feet
n
1,000
Mean
342,365
Median
318,000
Std. Dev.
125,914
Min
135,300
Q1
265,250
Q3
381,600
Max
987,600
1,000
169
166
41
71
139
191
344
1,000
2,111
1,881
921
1,101
1,626
2,215
6,516
This graph shows the frequency for listing price.
This graph shows the frequency for square feet.
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
MAT-240-H6464 Applied Statistics 23EW6
DB
Project One Guidelines and Rubric
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
1/11
7/30/23, 7:26 AM

Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6

MAT 240 Project One Guidelines and Rubric
Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techniques to address research problems
Perform regression analysis to address an authentic problem
Overview
The purpose of this project is to have you complete all of the steps of a real-world linear regression research project starting with develop
comprehensive statistical analysis, and ending with summarizing your research conclusions.
Scenario
You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict housing prices for homes sold in 2019.
help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide
footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and gra
Directions
Using the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if
what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate Dat
Supporting Materials section) for your statistical analysis.
Note: Present your data in a clearly labeled table and using clearly labeled graphs.
Specifically, include the following in your report:
Introduction
A. Describe the report: Give a brief description of the purpose of your report.
a. Define the question your report is trying to answer.
b. Explain when using linear regression is most appropriate.
i. When using linear regression, what would you expect the scatterplot to look like?
c. Explain the difference between predictor (x) and response (y) variables in a linear regression to justify the selection of variable
Data Collection
A. Sampling the data: Select a random sample of 50 houses. Describe how you obtained your sample data (provide Excel formulas as ap
a. Identify your predictor and response variables.
B. Scatterplot: Create a scatterplot of your predictor and response variables to ensure they are appropriate for developing a linear mo
Data Analysis
A. Histogram: Create a histogram for each of the two variables.
B. Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.
C. Interpret the graphs and statistics:
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
2/11
7/30/23, 7:26 AM
p
g p
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
a. Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, e
b. Compare and contrast the center, shape, spread, and any unusual characteristic for your sample of house sales with the nation
National Summary Statistics and Graphs House Listing Price by Region PDF). Determine whether your sample is representativ
Develop Your Regression Model
A. Scatterplot: Provide a scatterplot of the variables with a line of best fit and regression equation.
a. Based on your scatterplot, explain if a regression model is appropriate.
B. Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.
a. Identify any possible outliers or influential points and discuss their effect on the correlation.
b. Discuss keeping or removing outlier data points and what impact your decision would have on your model.
C. Calculate r: Calculate the correlation coefficient (r).
a. Explain how the r value you calculated supports what you noticed in your scatterplot.
Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model
A. Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.
B. Interpret regression equation: Interpret the slope and intercept in context. For example, answer the questions: what does the slope
represent? Revisit the Scenario above.
C. Strength of the equation: Provide and interpret R-squared.
a. Determine the strength of the linear regression equation you developed.
D. Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for base
1500 square feet.
Conclusions
A. Summarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summ
a. Did you see the results you expected, or was anything different from your expectations or experiences?
b. What changes could support different results, or help to solve a different problem?
c. Provide at least one question that would be interesting for follow-up research.
You can use the following tutorial that is specifically about this assignment. Make sure to check the assignment prompt for specific numbe
different national statistics. You should use the national statistics posted with this assignment.
MAT-240 Module 4 Project One Video
What to Submit
To complete this project, you must submit the following:
Project One Template Word Document: Use this template to structure your report, and submit the finished version as a Word document.
Supporting Materials
The following resources may help support your work on the project:
Document: National Summary Statistics and Graphs Real Estate Data PDF
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
3/11
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
Document: National Summary Statistics and Graphs Real Estate Data PDF
Use this data for input in your project report.
Spreadsheet: Real Estate Data Spreadsheet
Use this data for input in your project report.
Tutorial: Downloading Office 365 Programs PDF
Use this tutorial for support with Office 365 programs.
Use these tutorials for support with the Excel functions you will use in the project:
Tutorial: Random Sampling in Excel PDF
Tutorial: Scatterplots in Excel PDF
Tutorial: Descriptive Statistics in Excel PDF
Tutorial: Creating Histograms in Excel PDF
Project One Rubric
Criteria
Introduction: Describe the
Report
Exemplary
Proficient
Needs Improvement
Exceeds proficiency in an
exceptionally clear manner
Defines the question the report
is trying to answer, and explains
Shows progress toward
proficiency, but with errors or
(100%)
when using linear regression is
omissions; areas for
most appropriate, what the
improvement may include
scatterplot will look like, and
inaccurately defining the
the difference between
question, the appropriateness
response and predictor
and justification of the linear
variables in a linear regression
to justify the selection of
regression model or the
selection of variables, or
variables (85%)
introduction lacking essential
detail and clarity (55%)
Data Collection: Sampling
N/A
the Data
Data Collection: Scatterplot
N/A
Selects a random sample of 50
Shows progress toward
houses and describes how the
data was obtained. Identifies
proficiency, but with errors or
omissions; areas for
the response and predictor
variables (100%)
improvement may include
inaccurate selection of random
sample or inaccurate or unclear
selection of response and
predictor values (55%)
Creates a scatterplot of the
predictor and response
variables to ensure they are
Shows progress toward
proficiency, but with errors or
omissions; areas for
appropriate for developing a
linear model (100%)
improvement may include
inaccurate scatterplot
representation of the
information or inaccurate or
unclear determination of
response and predictor
variables (55%)
Data Analysis: Histogram
N/A
Creates histograms for the two
variables (100%)
Shows progress toward
proficiency, but with errors or
omissions; areas for
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
4/11
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
improvement may include
histograms that are created
incorrectly or are inaccurate
(55%)
Data Analysis: Summary
Statistics
N/A
Creates a table to show the
mean, median, and standard
deviation for two variables
Shows progress toward
proficiency, but with errors or
omissions; areas for
(100%)
improvement may include table
showing mean, median and
standard deviation that are
inaccurate or created
incorrectly (55%)
Data Analysis: Interpret
Graphs and Statistics
Develop Regression Model:
Scatterplot
N/A
N/A
Interprets the graphs and
statistics center, spread, shape,
Shows progress toward
proficiency, but with errors or
and any unusual characteristic
(outliers, gaps, etc.) for the two
omissions; areas for
improvement may include
variables based on the graphs
and sample statistics, and
compares and contrasts with
inaccurate or cursory
interpretation of the
characteristics of the graph and
national housing market sales
and determines if their sample
statistics or inaccurate or
cursory comparison or contrast
is representative (100%)
with the national market (55%)
Provides a scatterplot with a
line of best fit; explains if a
Shows progress toward
proficiency, but with errors or
regression model is appropriate
based on the scatterplot
omissions; areas for
improvement may include
(100%)
Inaccurate scatterplot or line of
best fit or explanation of
regression model
appropriateness that is
inaccurate or cursory (55%)
Develop Regression Model:
Discuss Associations
Exceeds proficiency in an
exceptionally clear manner
(100%)
Discusses the association in the
context of the model based on
scatterplot (direction, strength,
Shows progress toward
proficiency, but with errors or
omissions; areas for
form), includes possible outliers
or influential points, discusses
improvement may include
discussion of association in the
effect on correlation, and
discusses impact of keeping or
context of the scatterplot,
possible outliers, influential
removing outliers (85%)
points and impact on
correlation, or impacts of
keeping or removing outliers
that is inaccurate or cursory
(55%)
Develop Regression Model:
Calculate r
Exceeds proficiency in an
exceptionally clear manner
(100%)
Calculates the correlation
coefficient (r) and explains how
the calculated r value supports
Shows progress toward
proficiency, but with errors or
omissions; areas for
what was noticed in the
scatterplot (85%)
improvement may include
inaccurate calculation for r or
explanation of how the r value
supports the scatterplot that is
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
5/11
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
inaccurate or cursory (55%)
Determine Line of Best Fit:
Regression Equation
N/A
Writes the regression equation
and clearly defines variables
Shows progress toward
proficiency, but with errors or
(100%)
omissions; areas for
improvement may include
regression equation that is
written inaccurately or
variables that are not clearly
defined (55%)
Determine Line of Best Fit:
Exceeds proficiency in an
Interprets the slope and
Shows progress toward
Interpret Regression
Equation
exceptionally clear manner
(100%)
intercept in context (85%)
proficiency, but with errors or
omissions; areas for
improvement may include
inaccurate interpretation of the
slope and intercept (55%)
Determine Line of Best Fit:
Strength of the Equation
N/A
Provides and interprets Rsquared, determining the
strength of the linear
Shows progress toward
proficiency, but with errors or
omissions; areas for
regression equation (100%)
improvement may include
inaccuracies in interpretation
of R-squared or the determined
strength of the regression
equation (55%)
Determine Line of Best Fit:
Use Regression Equation to
N/A
Uses a regression equation to
predict how much you should
Shows progress toward
proficiency, but with errors or
list your home for based on the
square footage of your home
omissions; areas for
improvement may include
(100%)
misuse of regression equation
or inaccurate prediction based
on provided information (55%)
Exceeds proficiency in an
exceptionally clear manner
Summarizes findings and
results in clear and concise
Shows progress toward
proficiency, but with errors or
(100%)
plain language, includes
whether the results were
expected, changes that could
omissions; areas for
improvement may include
inaccurately summarizing
support different results or that
would help to solve a different
findings or results or summary
that is cursory or missing
problem; Includes a question
for follow-up research (85%)
required elements (55%)
Exceeds proficiency in an
Clearly conveys meaning with
Shows progress toward
exceptionally clear, insightful,
sophisticated, or creative
correct grammar, sentence
structure, and spelling,
proficiency, but with errors in
grammar, sentence structure,
manner (100%)
demonstrating an
understanding of audience and
purpose (85%)
and spelling, negatively
impacting readability (55%)
Make Predictions
Conclusion: Summarize
Findings
Articulation of Response
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
6/11
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
7/11
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
8/11
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
9/11
7/30/23, 7:26 AM
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
10/11
7/30/23, 7:26 AM
Reflect in ePortfolio
Project One Guidelines and Rubric – MAT-240-H6464 Applied Statistics 23EW6
Download
Print
Open with docReader
Activity Details
Task: View this topic
https://learn.snhu.edu/d2l/le/content/1351916/viewContent/25502738/View
11/11
Median Housing Price Prediction Model for D. M. Pan National Real Estate Company
1
[Note: To complete this template, replace the bracketed text with your own content. Remove this
note before you submit your outline.]
Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company
[Your Name]
Southern New Hampshire University
Median Housing Price Model for D. M. Pan National Real Estate Company
2
Introduction
[Describe the report: Define the question your report is trying to answer.]
[Describe the report: Explain when using linear regression is the most appropriate.]
[Describe the report: Explain when using linear regression what you would expect the
scatterplot to look like.]
[Describe the report: Explain the difference between predictor (x) and response (y) variables in
a linear regression to justify the selection of variables.]
Data Collection
[Sampling the data: Select a random sample of 50 houses. Describe how you obtained your
sample data (provide Excel formulas as appropriate).]
[Sampling the data: Identify your predictor and response variables.]
[Scatterplot: Create and insert a correctly labeled scatterplot of your predictor and response
variables to ensure they are appropriate for developing a linear model.]
Data Analysis
[Histogram: Create and insert a histogram for the first variable. Be sure to include
appropriate labels.]
[Histogram: Create and insert a histogram for the second variable. Be sure to include
appropriate labels.]
[Summary statistics: Create and insert a table to show the summary statistics (mean,
median, standard deviation) for both variables.]
[Interpret the graphs and statistics: Interpret the center, spread, shape, and any unusual
characteristic (outliers, gaps, etc.) for house sales and square footage.]
Median Housing Price Model for D. M. Pan National Real Estate Company
3
[Interpret the graphs and statistics: Compare and contrast center, spread, shape, and
any unusual characteristic for your sample of house sales with the national population. Also,
determine whether your sample is representative of the national housing market sales. Note: In
the learning management system, under Supporting Materials, see National Summary Statistics
and Graphs Real Estate Data PDF.]
Develop Regression Model
[Scatterplot: Create and insert the scatterplot of the variables with a line of best fit and
the regression equation. [Based on your scatterplot, explain whether a regression model is
appropriate.]
[Discuss associations: Discuss the associations in the scatterplot, including the direction,
strength, and form, in the context of your model.]
[Discuss associations: Identify any possible outliers or influential points and discuss
their effect on correlation.]
[Discuss associations: Discuss keeping or removing outlier data points and what impact
your decision would have on your model.]
[Calculate r: Calculate the correlation coefficient and explain how the calculated r value
supports what was noticed in your scatterplot.]
Determine the Line of Best Fit
[Regression equation: Write the regression equation (i.e., line of best fit) and clearly
define your variables.]
[Interpret regression equation: Interpret the slope and intercept in context. For
example, answer the questions: What does the slope represent in this situation? What does the
intercept represent? Revisit the Scenario section in the learning management system.]
Median Housing Price Model for D. M. Pan National Real Estate Company
4
[Strength of the equation: Provide and interpret R-squared. Determine the strength of
the linear regression equation you developed.]
[Use regression equation to make predictions: Use the regression equation to predict
how much you should list your home for based on the assumed square footage of your home at
1500 square feet.]
Conclusions
[Summarize findings: Summarize your findings in clear and concise plain language for
the CEO to understand.]
[Summarize findings: Did you see the results you expected, or was anything different
from your expectations or experiences?]
[Summarize findings: What changes could support different results, or help to solve a
different problem?]
[Summarize findings: Provide at least one question that would be interesting for followup research.]