# RECORD 2bbdabdc28e5b845377e3ce418d4b3325d2d76ec
# TITLE: Valuing Historical and Open Space Amenities with Hedonic Property Valuation Models
# YEAR: 2016.0  VENUE: Agricultural and Resource Economics Review  DOI: 10.1017/age.2016.14
# AUTHORS: R. Hicks; Bonnie M. Queen
# PAGES: 24  CANDIDATE TABLE PAGES (1-based): [13, 14, 15, 18, 20, 21]
# ABSTRACT: Impacts of historic and cultural amenities on property values and the economy have not been widely studied in part because of problems isolating statistical effects using nonmarket valuation and lack of study areas. Three jurisdictions in Virginia containing major historic sites provide a unique setting in which to isolate the effects of historical amenities on residential property values using revealed preferences and quantify their economic benefits. We find that historic areas provide both open space and historic amenities. Furthermore, being adjacent to a historical area is not a positive benefit on average, perhaps because of activity and congestion associated with tourism. Residing close to such areas is valuable to buyers.


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Valuing Historical and Open Space
Amenities with Hedonic Property
Valuation Models
Robert L. Hicks and Bonnie M. Queen
Impacts of historic and cultural amenities on property values and the economy
have not been widely studied in part because of problems isolating statistical
effects using nonmarket valuation and lack of study areas. Three jurisdictions in
Virginia containing major historic sites provide a unique setting in which to
isolate the effects of historical amenities on residential property values using
revealed preferences and quantify their economic benefits. We find that historic
areas provide both open space and historic amenities. Furthermore, being
adjacent to a historical area is not a positive benefit on average, perhaps because
of activity and congestion associated with tourism. Residing close to such areas
is valuable to buyers.
Key Words: hedonic property models, open space, historic amenities
Historic and cultural sites, like environmental resources, often provide benefits
that are nonexcludable and nonrival to varying degrees and therefore are likely
to be undervalued by traditional markets (Navrud and Ready 2002). The failure
of markets to appropriately value the services flowing from historic amenities
leads to a less-than-efficient amount of historic preservation since the cost of
preservation often falls to private property owners who thus incur private
costs when preserving amenities that provide public benefits. Even when
historical properties are publicly owned, there is potential pressure to use
the areas for additional purposes unrelated to a site’s historic importance. If
those multiple uses impair the flow of historical services, then even publicly
held resources may not be efficiently managed in terms of the economic
benefits they provide. A growing body of literature has demonstrated that
economic value arises from preservation of cultural heritage sites using
environmental valuation techniques (e.g., Garrod and Willis 2002, Riganti and
Willis 2002, Hansen 1997, Bille 2002, Chambers, Chambers, and Whitehead
1998, Morey et al. 2002, Bravi, Scarpa, and Sirchia 2002, Carson, Mitchell,
Robert L. Hicks is a professor of economics and marine science at the College of William and
Mary. Bonnie M. Queen is an economist for the Office of State Budget and Management for the
State of North Carolina. Senior authorship is not assigned. Correspondence: Robert L. Hicks ▪
Department of Economics ▪College of William and Mary ▪129 Morton Hall ▪Williamsburg, VA
23187 ▪Phone 757.221.3707 ▪Email rob.hicks@wm.edu.
The views expressed are the authors’ and do not necessarily represent the policies or views of
any sponsoring agencies.
Agricultural and Resource Economics Review 45/1 (April 2016) 44–67
© The Author(s) 2016. This is an Open Access article, distributed under the terms of the Creative
Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted re-use, distribution, and reproduction in any medium, provided the original work is
properly cited.
https://doi.org/10.1017/age.2016.14 Published online by Cambridge University Press


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Table 1. Log Linear Models with Contiguity-based Spatial Weight Matrices
Strict Contiguity
200-meter Contiguity
400-meter Contiguity
600-meter Contiguity
Variable
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Constant
5.8423
44.9440
6.2835
61.4222
6.1086
52.0860
5.7542
43.5599
Age
0.0616
15.2607
0.0657
16.5206
0.0530
13.5754
0.0478
12.0331
Bathrooms
0.0286
1.9343
0.0169
1.1437
0.0101
0.6857
0.0134
0.9073
Bedrooms
0.0860
5.1746
0.1049
5.4593
0.1200
6.3163
0.1305
7.4568
Dock
0.2154
5.5903
0.1814
4.8653
0.2231
5.9027
0.2198
5.6961
Square feet
0.7462
41.7834
0.6810
39.3776
0.7053
38.7594
0.7546
42.7783
Central air
0.1035
6.4610
0.1171
7.1324
0.0919
5.6053
0.0926
5.6566
Lot size
0.0046
1.5806
0.0060
1.7832
0.0120
3.4345
0.0121
3.6175
Adjacent golf
0.0163
0.6336
0.0703
2.4266
0.0395
1.3148
0.0061
0.2161
Distance fixed
0.0316
16.7966
0.0283
15.1693
0.0289
16.0611
0.0291
16.4908
Adjacent fixed
0.1379
2.0083
0.0995
1.3958
0.1498
2.3457
0.1727
2.6857
Distance historic
0.0489
6.4415
0.0444
5.1727
0.0265
2.5745
0.0095
0.9130
Adjacent historic
0.5175
5.3227
0.4934
4.8451
0.2972
2.6345
0.1366
1.1847
Adjacent adjusted
0.3929
25.4133
0.3664
23.9456
0.3657
24.6169
0.3649
24.8259
Distance railroad
0.0568
7.3370
0.0589
6.7463
0.0483
4.5809
0.0394
3.6151
Distance water
0.0240
4.2566
0.0199
3.2290
0.0185
2.6480
0.0274
3.9995
Adjacent water
0.2740
4.5128
0.2564
3.9774
0.2650
3.8140
0.3246
4.7136
λ
0.4550
427.1031
0.5510
262.8649
0.6970
138.3261
0.7439
108.4002
σ2
0.12
0.12
0.12
0.13
R2
0.56
0.57
0.57
0.55
N
12,337
12,337
12,337
12,337
Log likelihood
603.08
391.58
335.87
523.48
Agricultural and Resource Economics Review
56 April 2016
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Table 2. Semi-log Models with Contiguity-based Spatial Weight Matrices
Strict Contiguity
200-meter Contiguity
400-meter Contiguity
600-meter Contiguity
Variable
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Constant
11.1270
379.6046
11.1744
364.9903
11.1345
338.8904
11.1345
338.8904
Age
0.0017
4.5662
0.0015
3.7826
0.0005
1.4055
0.0005
1.4055
Bathrooms
0.0052
0.7222
0.0048
0.6573
0.0034
0.4631
0.0034
0.4631
Bedrooms
0.0336
13.0655
0.0296
11.4472
0.0354
13.0245
0.0354
13.0245
Dock
0.2700
6.7807
0.2326
6.0702
0.2801
7.1615
0.2801
7.1615
Square feet
0.0002
35.8411
0.0002
29.6874
0.0002
29.8074
0.0002
29.8074
Central air
0.1409
7.9052
0.1412
8.0795
0.1097
6.1635
0.1097
6.1635
Lot size
0.0164
1.4767
0.0247
2.2378
0.0164
1.4318
0.0164
1.4318
Adjacent golf
0.1628
6.1477
0.2005
6.5199
0.1573
5.0233
0.1573
5.0233
Distance fixed
1.3682
11.7357
1.0616
9.1511
1.0559
9.8384
1.0559
9.8384
Adjacent fixed
0.0374
0.5251
0.0028
0.0381
0.0427
0.6496
0.0427
0.6496
Distance historic
0.0364
5.8955
0.0390
5.3752
0.0320
3.2372
0.0320
3.2372
Adjacent historic
0.0170
0.2389
0.0202
0.2998
0.0374
0.5740
0.0374
0.5740
Adjacent adjusted
0.2399
25.4139
0.2266
24.5871
0.2217
24.6576
0.2217
24.6576
Distance railroad
0.0479
7.5602
0.0520
7.0782
0.0486
4.8212
0.0486
4.8212
Distance water
0.0005
0.0355
0.0019
0.1194
0.0030
0.1373
0.0030
0.1373
Adjacent water
0.1557
4.6742
0.1670
5.1277
0.1930
5.9676
0.1930
5.9676
λ
0.4910
137.3639
0.5850
199.8723
0.7250
1813.4647
0.7250
1813.4647
σ2
0.1327
0.1276
0.1289
0.1352
R2
0.5236
0.5418
0.5372
0.5145
N
12,337
12,337
12,337
12,337
Log likelihood
1073.6017
818.2522
785.1849
1016.8986
Hicks and Queen
Valuing Historical and Open Space Amenities 57
https://doi.org/10.1017/age.2016.14 Published online by Cambridge University Press


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Table 3. Models with Distance-based Spatial Weight Matrices
Log Linear Models
Semi-log Models
600 Meter (1/distance)
600 Meter (1/distance2)
600 Meter (1/distance)
600 Meter (1/distance2)
Variable
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Constant
6.3457
70.8280
6.2756
58.3744
11.1617
327.0431
11.2079
361.9085
Age
0.0603
14.9488
0.0679
16.6913
0.0008
1.9952
0.0015
3.7181
Bathrooms
0.0142
0.9694
0.0213
1.4479
0.0002
0.0288
0.0047
0.6598
Bedrooms
0.1059
5.3971
0.0883
4.6190
0.0299
10.6970
0.0262
10.1092
Dock
0.2008
5.3418
0.1933
5.1092
0.2597
6.6802
0.2494
6.3897
Square feet
0.6713
38.6585
0.6849
39.1968
0.0002
26.4267
0.0002
30.1076
Central air
0.1066
6.4215
0.1141
6.9647
0.1196
6.7155
0.1340
7.7014
Lot size
0.0160
3.8439
0.0082
2.2801
0.0186
1.5700
0.0157
1.3638
Adjacent golf
0.0330
1.0085
0.0278
0.9556
0.1443
4.2511
0.1643
5.3385
Distance fixed
0.0294
15.9305
0.0312
16.2618
1.0972
9.5646
1.1832
9.7271
Adjacent fixed
0.1800
2.7616
0.1650
2.4259
0.0883
1.3155
0.0725
1.0310
Distance historic
0.0076
0.6301
0.0426
4.7608
0.0260
2.1335
0.0357
4.6262
Adjacent historic
0.1041
0.8300
0.4410
4.1709
0.0642
0.9276
0.0506
0.7207
Adjacent adjusted
0.3739
24.8995
0.3900
25.1650
0.2304
25.0725
0.2365
25.0276
Distance railroad
0.0524
4.0430
0.0641
6.9113
0.0543
4.2643
0.0516
6.6416
Distance water
0.0285
3.6211
0.0291
4.4791
0.0185
0.6811
0.0026
0.1546
Adjacent water
0.3598
4.7185
0.3419
5.0645
0.1963
5.8833
0.1810
5.3169
λ
0.7880
124.7644
0.5670
246.5828
0.8070
366.7409
0.6020
230.5507
σ2
0.1153
0.1159
0.1234
0.1236
R2
0.5682
0.5840
0.5571
0.5563
N
12,337
12,337
12,337
12,337
Log Likelihood
123.7629
285.1293
562.5197
734.8791
Agricultural and Resource Economics Review
58 April 2016
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Table 4. Estimated Price Changes for Log Linear Models
Variable
Strict
Contiguity
600-meter
Contiguity
400-meter
Contiguity
200-meter
Contiguity
(1/d)
Distance
(1/d2)
Distance
Age
$762.71
$611.43
$647.59
$792.53
$732.31
$830.65
Bathrooms
$1,842.84
$887.58
$641.11
$1,058.20
$897.41
$1,352.58
Bedrooms
$1,835.69
$2,885.71
$2,532.02
$2,184.94
$2,218.88
$1,861.18
Dock
$40,789.34
$43,137.81
$41,852.55
$32,896.39
$37,020.48
$35,729.86
Square feet
$11,393.97
$11,918.29
$10,609.24
$10,109.61
$10,021.70
$10,301.99
Central air
$18,511.52
$17,030.37
$16,112.46
$20,545.29
$18,720.61
$20,262.57
Lot size
$169.92
$467.61
$440.99
$219.97
$585.58
$302.44
Adjacent golf
$2,783.67
$1,061.56
$6,740.19
$12,050.92
$5,590.50
$4,724.02
Distance fixed
$1,442.81
$1,374.04
$1,299.78
$1,257.91
$1,316.29
$1,407.42
Adjacent fixed
$21,854.51
$27,829.88
$23,301.11
$15,660.52
$27,423.09
$25,495.31
Distance
historic
$90.26
$18.13
$48.26
$79.94
$13.74
$77.66
Adjacent
historic
$68,554.09
$22,401.13
$43,054.24
$64,429.91
$16,453.10
$59,765.17
Adjacent
adjusted
$55,132.55
$53,640.10
$51,284.06
$50,743.48
$51,922.04
$54,123.36
Distance
railroad
$127.08
$91.15
$106.71
$128.47
$114.91
$141.64
Distance water
$167.27
$197.05
$127.38
$134.91
$194.52
$200.00
Adjacent water
$53,484.44
$67,283.97
$50,801.19
$48,338.85
$72,084.26
$68,307.48
Hicks and Queen
Valuing Historical and Open Space Amenities 59
https://doi.org/10.1017/age.2016.14 Published online by Cambridge University Press


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Table 6. Companion for Table 1
Strict Contiguity
200-meter Contiguity
400-meter Contiguity
600-meter Contiguity
Variable
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
whal
0.138453
5.045252
0.172935
5.312035
0.207123
5.144053
0.160748
4.272369
york
0.125928
3.428467
0.090187
2.112768
0.02094
0.384268
0.006473
0.110933
wall
0.009811
0.278073
0.056384
1.397484
0.057221
1.068163
0.008717
0.167595
vern
0.003119
0.097679
0.010065
0.26917
0.00173
0.036039
0.052666
1.100463
cove
0.109163
3.680348
0.126107
3.604077
0.163007
3.430927
0.153906
3.041422
magr
0.056302
1.735317
0.029236
0.779689
0.024052
0.483001
0.06993
1.266021
tabb
0.045978
1.309555
0.070859
1.722973
0.074005
1.392466
0.045185
0.822293
ston
0.023161
0.659184
0.03621
0.971315
0.057041
1.097762
0.07597
1.292011
norg
0.202905
7.24748
0.227145
6.919547
0.205097
4.839622
0.14219
3.456896
djmo
0.038174
1.46952
0.02146
0.70673
0.005436
0.143672
0.069626
1.960713
rawl
0.08651
2.868774
0.092163
2.571041
0.214272
4.635821
0.238264
5.159639
jamr
0.087196
2.634309
0.136557
3.41513
0.215454
3.841878
0.181422
2.852349
graf
0.038475
1.489601
0.01563
0.502486
0.002012
0.04746
0.058216
1.369806
clar
0.044234
1.599608
0.072745
2.264097
0.087999
2.163677
0.087324
2.165224
y1999
0.027941
2.459977
0.027715
2.48172
0.026856
2.397851
0.02615
2.284535
y2000
0.05916
5.156332
0.066009
5.856227
0.070805
6.275959
0.07367
6.401172
y2001
0.086859
7.814771
0.086544
7.906679
0.09036
8.259584
0.09216
8.256438
y2002
0.148846
13.735355
0.156104
14.5703
0.157992
14.734082
0.158751
14.527972
y2003
0.251731
19.459601
0.252182
19.755862
0.257117
20.141034
0.257481
19.790269
y2004
0.658994
12.050842
0.644023
11.883812
0.652299
12.118646
0.597727
10.929861
Hicks and Queen
Valuing Historical and Open Space Amenities 61
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average house’s sale price ($1,400 to $2,000). Thus, being located near a fixed
open space is considered a benefit. Being adjacent to a fixed open space (a golf
course, resource protection area, or park) had a slight negative effect on home
sale prices that was not significant. As expected, houses farther from railroads
were associated with higher sale prices ($150 to $250). Being close to water
was also a positive amenity even after controlling for parcels that were
adjacent to water (which command tens of thousands of dollars more). The
only adjacency measure that was significant in all of the models was
adjustable open space. Parcels abutting an adjustable open space sold for an
average of $30,000 to $50,000 less than similar nonadjacent parcels.
Being located adjacent to a historic area had no significant effect on sale
prices. As with adjacency to fixed open spaces, some buyers may consider a
shared border with a historic area as a disamenity, perhaps because of heavy
tourist traffic (from both pedestrians and cars). Decreasing distance from the
average parcel to a historic area when controlling for fixed open-space
amenities led to higher sale prices ($50 to $200). While the gains from
service flows generated by all forms of fixed open space (including historical
areas) are modest, these results show that proximity to historical and
cultural amenities yields benefits unrelated to provision of open space.
The models we estimate demonstrate the robustness of our core finding that
historic areas provide economic benefits to nearby residents. In addition, when
Figure 2. Parcels and Historical Areas
Agricultural and Resource Economics Review
62 April 2016
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Table 7. Companion for Table 2
Strict Contiguity
200-meter Contiguity
400-meter Contiguity
600-meter Contiguity
Variable
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
whal
0.1012
3.1528
0.1381
3.6750
0.1609
3.2427
0.1609
3.2427
york
0.0859
2.1546
0.0669
1.4475
0.0282
0.4630
0.0282
0.4630
wall
0.0315
0.8067
0.0109
0.2396
0.0058
0.0921
0.0058
0.0921
vern
0.0130
0.2890
0.0320
0.5943
0.0005
0.0068
0.0005
0.0068
cove
0.0829
1.7901
0.1046
1.8752
0.1337
1.6799
0.1337
1.6799
magr
0.0709
1.8868
0.0499
1.1453
0.0054
0.0893
0.0054
0.0893
tabb
0.0784
1.4680
0.1066
1.6691
0.0817
0.9344
0.0817
0.9344
ston
0.1162
2.1316
0.1196
1.8933
0.0777
0.8485
0.0777
0.8485
norg
0.2021
5.8403
0.2329
5.6316
0.2025
3.5039
0.2025
3.5039
djmo
0.0908
2.5601
0.0868
2.0846
0.0940
1.6398
0.0940
1.6398
rawl
0.0033
0.0943
0.0069
0.1637
0.1078
1.9200
0.1078
1.9200
jamr
0.0534
1.4631
0.1102
2.4996
0.1880
2.9502
0.1880
2.9502
graf
0.0281
0.8809
0.0026
0.0661
0.0020
0.0349
0.0020
0.0349
clar
0.0565
1.5640
0.0377
0.8950
0.0485
0.8710
0.0485
0.8710
y1999
0.0277
2.3341
0.0276
2.3822
0.0255
2.1830
0.0255
2.1830
y2000
0.0558
4.6527
0.0628
5.3446
0.0673
5.7214
0.0673
5.7214
y2001
0.0879
7.5504
0.0868
7.5932
0.0887
7.7584
0.0887
7.7584
y2002
0.1510
13.2183
0.1549
13.7654
0.1560
13.8726
0.1560
13.8726
y2003
0.2498
18.3534
0.2474
18.4473
0.2511
18.7509
0.2511
18.7509
y2004
0.4312
7.9690
0.4065
7.5588
0.4478
8.4271
0.4478
8.4271
Hicks and Queen
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Table 8. Companion for Table 3
Log Linear Models
Semi-log Models
600 meter (1/distance)
600 Meter (1/distance2)
600 meter (1/distance)
600 Meter (1/distance2)
Variable
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
Coefficient
t-Stat
whal
0.2075
4.5777
0.1675
4.9995
0.1622
2.7213
0.1361
3.5251
york
0.0404
0.5955
0.0994
2.2270
0.0148
0.1992
0.0638
1.3197
wall
0.0841
1.3460
0.0569
1.2996
0.0125
0.1660
0.0124
0.2531
vern
0.0122
0.2129
0.0041
0.1055
0.0285
0.2925
0.0322
0.5675
cove
0.1742
2.9201
0.1311
3.6037
0.1099
1.0924
0.0938
1.6093
magr
0.0700
1.1024
0.0482
1.2308
0.0229
0.2986
0.0646
1.4197
tabb
0.0714
1.1006
0.0413
0.9766
0.0534
0.4949
0.0775
1.1674
ston
0.0840
1.2920
0.0483
1.1620
0.0243
0.2088
0.1030
1.4874
norg
0.1956
3.8829
0.2116
6.1692
0.1702
2.3436
0.2160
4.9760
djmo
0.0687
1.6325
0.0541
1.7091
0.1922
2.7260
0.1066
2.4351
rawl
0.2169
3.9121
0.0828
2.2184
0.1020
1.4896
0.0025
0.0564
jamr
0.2270
3.0314
0.1524
3.6288
0.1821
2.1348
0.1288
2.7923
graf
0.0142
0.2769
0.0174
0.5367
0.0167
0.2369
0.0023
0.0574
clar
0.0766
1.6375
0.0483
1.4459
0.0670
1.0015
0.0524
1.1867
y1999
0.0291
2.6248
0.0283
2.5515
0.0283
2.4582
0.0309
2.6933
y2000
0.0722
6.4534
0.0658
5.9084
0.0678
5.8246
0.0634
5.4740
y2001
0.0889
8.1792
0.0815
7.5275
0.0872
7.6958
0.0830
7.3541
y2002
0.1585
14.8534
0.1494
14.0765
0.1560
13.9752
0.1504
13.5201
y2003
0.2542
20.1089
0.2443
19.4059
0.2492
18.8085
0.2426
18.3782
y2004
0.6090
11.5127
0.6218
11.8388
0.3873
7.4071
0.3683
7.1110
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64 April 2016
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we consider the results for all other parameters (excluding historical and open
space amenities), we find a remarkable degree of consistency across models.10
The differences are in the magnitudes of the coefficients, the signs, and the
significance levels. When we focus on the set of models that are most
consistent with prior studies (strict contiguity, 200-meter contiguity, and 1/
d2 weight matrices), the results are even more robust in terms of estimating
the value of historical and other spatial amenities.
Conclusion
This study is the first to demonstrate economic benefits from local historical
and cultural resources beyond drawing visitors to historic sites, and the
results confirm that owners of nearby residential properties also benefit from
historic amenities. Quantification of these benefits using revealed-preference
nonmarket valuation techniques is an important step in addressing the
public-policy problem of prioritizing and choosing appropriate levels of
historic preservation. Our results show that, for historic areas such as
Williamsburg, Virginia, policies should consider the benefits provided by both
open space and historical resources to account fully for the flow of services.
We find significant spatial dependence in the error structure in our model, a
problem that is common when analyzing spatial amenities. The ordinary least
squares model was rejected in all of the specifications considered. By using
several types of spatial weighting matrices, we determine that historic
amenities are valuable resources for homebuyers and that the amenity value
is not particularly sensitive to specification of the functional form or the
error structure. Our results also confirm the presence of spatial heterogeneity
in
estimating
the
value
of
historic
areas.
In
particular,
the
average
homeowner does not consider sharing a border with a historic area as
beneficial but does value living near one. This result is consistent with the
large volume of visitors and traffic associated with historic sites in the
Williamsburg area at certain times of year.
By carefully considering the services offered by historic areas, we isolate the
marginal value of the flow of services solely from historical amenities from
other types of amenities such historic areas may also provide, such as open
space. We find weak evidence that properties adjacent to a historic area are
valued less by buyers, indicating that living in a tourism district is viewed as
a disamenity. We find strong evidence that buyers value living near a
historical area rather than directly next to one.
10 Bell and Bockstael (2000) reported that such results are sensitive to differences in
assumptions regarding spatial weight matrices.
Hicks and Queen
Valuing Historical and Open Space Amenities 65
https://doi.org/10.1017/age.2016.14 Published online by Cambridge University Press