residential property valuation in philadephia
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Residential Property Residential Property ValuationValuation
in Philadephiain Philadephia
Kevin Gillen, PhD StudentKevin Gillen, PhD Student
Real Estate Dept., The Wharton SchoolReal Estate Dept., The Wharton School
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Density of Transactions: # Sales/SqMile
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Univariate Distribution of Univariate Distribution of PricePrice
5 0 0 0 0 01 0 0 0 0 0 01 5 0 0 0 0 0
P R IC E
P R IC E
0 6 2 5 0 0 01 2 5 0 0 0 0
P R IC E
0
5 E -6
0 .0 0 0 0 1
Density
M o m e n tsN 9 9 9 1 5 .0 0 0 0M e a n5 5 6 4 3 .8 4 6 4S td D e v4 5 8 3 7 .9 1 2 1S k e w n e s s 4 .8 2 8 8U S S 5 .1 9 3 E + 1 4C V 8 2 .3 7 7 3
S u m W g ts9 9 9 1 5 .0 0 0 0S u m 5 .5 6 0 E + 0 9V a ria n c e 2 .1 0 1 E + 0 9K u rto s is 6 3 .6 1 8 9C S S 2 .0 9 9 E + 1 4S td M e a n 1 4 5 .0 1 3 8
Q u a n tile s
1 0 0 % M a x 1 5 2 0 0 0 0 .0 0 7 5 % Q 3 7 3 0 0 0 .0 0 0 0 5 0 % M e d 4 8 0 0 0 .0 0 0 0 2 5 % Q 1 2 7 0 0 0 .0 0 0 0 0 % M in 5 0 0 0 .0 0 0 0 R a n g e1 5 1 5 0 0 0 .0 0 Q 3 -Q 14 6 0 0 0 .0 0 0 0 M o d e 3 5 0 0 0 .0 0 0 0
9 9 .0 %2 1 0 0 0 0 .0 0 0 9 7 .5 %1 5 3 0 0 0 .0 0 0 9 5 .0 %1 2 4 8 0 0 .0 0 0 9 0 .0 %1 0 0 0 0 0 .0 0 0 1 0 .0 %1 3 0 0 0 .0 0 0 0 5 .0 % 8 5 1 0 .0 0 0 0 2 .5 % 6 5 0 0 .0 0 0 0 1 .0 % 5 0 0 0 .0 0 0 0
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2 0 0 4 0 0 6 0 0
P R C P S Q F T
P R C P S Q F T
0 1 2 02 4 03 6 04 8 06 0 0
P R C P S Q F T
0
0 .0 1
0 .0 2
Density
M o m e n tsN 9 9 9 1 5 .0 0 0 0M e a n 3 4 .9 0 3 0S td D e v 3 0 .4 2 5 4S k e w n e s s 4 .8 8 4 0U S S 2 1 4 2 0 8 9 8 2C V 8 7 .1 7 1 4
S u m W g ts9 9 9 1 5 .0 0 0 0S u m3 4 8 7 3 2 8 .8 3V a ria n c e 9 2 5 .7 0 5 1K u rto s is 3 9 .6 6 4 2C S S 9 2 4 9 0 8 9 8 .2S td M e a n 0 .0 9 6 3
Q u a n tile s1 0 0 % M a x 6 0 6 .0 6 0 6 7 5 % Q 3 4 1 .3 6 2 1 5 0 % M e d 3 0 .3 4 4 8 2 5 % Q 1 1 9 .2 7 2 7 0 % M in 1 .0 1 4 9 R a n g e 6 0 5 .0 4 5 7 Q 3 -Q 1 2 2 .0 8 9 5 M o d e 3 3 .3 3 3 3
9 9 .0 % 1 7 7 .7 5 3 5 9 7 .5 % 1 0 7 .0 8 2 6 9 5 .0 % 7 4 .4 1 3 9 9 0 .0 % 5 4 .3 9 1 6 1 0 .0 % 1 0 .7 5 2 7 5 .0 % 7 .5 3 0 9 2 .5 % 5 .6 9 6 2 1 .0 % 4 .1 1 5 2
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Quantifying Spatial Dependence Quantifying Spatial Dependence in Property Valuesin Property Values
Range = 4 km
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Quantifying Spatial Dependence Quantifying Spatial Dependence in Property Valuesin Property Values
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(9) D
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I
WUWYX
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Spatial Autoregression Spatial Autoregression ModelModel
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Spatial Autoregression of House Prices in PhiladelphiaR-Squared = .84
INTERCEPT -12275 -3.936TAX ASSESSMENT 3.51382 253.263FRONTAGE 24.3439 5.553LN(LOT SQ. FOOTAGE) 5216.052 31.342# STORIES 1019.408 5.789IS HOUSE ON CORNER? 818.1268 3.489EXTERIOR = MASONRY/OTHER 1506.198 4.771EXTERIOR=STONE 4907.206 11.1EXTERIOR=FRAME -1842.86 -4.279MEDIAN AGE 48.55923 3.638PCT OF TRACT RENTAL PROPERTIES -80.6843 -15.345PCT OF TRACT WHITE COLLAR 51.4149 8.311LN(TRACT HOUSEHOLD INCOME) 2014.04 7.22LATITUDE -17497 -8.136LONGITUDE 5342.651 4.287RHO: 1/4 MILE 0.351004 45.993YEAR1990 Dummy 1811.193 3.768YEAR1991 Dummy 545.459 1.129YEAR1992 Dummy 267.9447 0.557YEAR1993 Dummy 446.0964 0.931YEAR1994 Dummy 507.6398 1.076YEAR1995 Dummy 349.5008 0.739YEAR1996 Dummy 760.7217 1.62YEAR1997 Dummy -13.625 -0.029
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