# RECORD 11ddac3108d5fcc806866635ea2276160f2ca53d
# TITLE: Coastal armoring and sinking property values: the case of seawalls in California
# YEAR: 2020.0  VENUE: Environmental Economics and Policy Studies  DOI: 10.1007/s10018-020-00278-3
# AUTHORS: Arlan Brucal; John M. Lynham
# PAGES: 23  CANDIDATE TABLE PAGES (1-based): [7, 9, 10, 15, 17, 18, 19]
# ABSTRACT: Rising sea levels necessitate careful consideration of different forms of coastal protection but cost-benefit analysis is limited when important non-market social costs have not been measured. Seawalls protect individual properties but can potentially impose negative externalities on neighboring properties via accelerated beach loss. We conduct a hedonic valuation of seawalls in two coastal California counties: San Diego and Santa Cruz. We find no strong evidence to suggest that the presence of a seawall is positively correlated with the value of the home protected. However, we find that seawalls are strongly negatively correlated with the value of neighboring properties in Santa Cruz but not in San Diego county, suggesting that the effect of seawalls depend on certain geographical attributes. Our results are robust to accounting for the public-good nature of locational attributes and the potential spatial dependence of housing prices. Simulation reveals that doubling the extent of seawalls in San Diego and Santa Cruz could reduce property tax revenues by $7 million and $54 million, respectively.


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Vol.:(0123456789)
Environmental Economics and Policy Studies (2021) 23:55–77
https://doi.org/10.1007/s10018-020-00278-3
1 3
RESEARCH ARTICLE
Coastal armoring and sinking property values: the case 
of seawalls in California
Arlan Brucal1   · John Lynham2
Received: 10 September 2019 / Accepted: 4 May 2020 / Published online: 4 June 2020 
© The Author(s) 2020
Abstract
Rising sea levels necessitate careful consideration of different forms of coastal protec-
tion but cost-benefit analysis is limited when important non-market social costs have 
not been measured. Seawalls protect individual properties but can potentially impose 
negative externalities on neighboring properties via accelerated beach loss. We con-
duct a hedonic valuation of seawalls in two coastal California counties: San Diego 
and Santa Cruz. We find no strong evidence to suggest that the presence of a seawall 
is positively correlated with the value of the home protected. However, we find that 
seawalls are strongly negatively correlated with the value of neighboring properties in 
Santa Cruz but not in San Diego county, suggesting that the effect of seawalls depend 
on certain geographical attributes. Our results are robust to accounting for the public-
good nature of locational attributes and the potential spatial dependence of housing 
prices. Simulation reveals that doubling the extent of seawalls in San Diego and Santa 
Cruz could reduce property tax revenues by $7 million and $54 million, respectively.
Keywords  Seawalls · Hedonic valuation · Coastal erosion · Property values
JEL Classification  H23 · Q26 · Q51 · Q54
Arlan Brucal: Brucal acknowledges support from the Grantham Foundation and the Economic and 
Social Research Council (ESRC) through the Centre for Climate Change Economics and Policy. 
John Lynham: Lynham acknowledges support from the Center for Ocean Solutions and valuable 
research assistance from Qingran Li.
Electronic supplementary material  The online version of this article (https​://doi.org/10.1007/s1001​
8-020-00278​-3) contains supplementary material, which is available to authorized users.
 *	 Arlan Brucal 
	
a.z.brucal@lse.ac.uk
	
John Lynham 
	
lynham@hawaii.edu
1	
Grantham Research Institute on Climate Change and the Environment, London School 
of Economics and Political Science, Houghton Street, London WC2A 2AE, UK
2	
Department of Economics, University of Hawaii at Manoa, 2424 Maile Way, Saunders Hall 542, 
Honolulu, HI 96822, USA


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(ERF) is reduced to zero. To compute this variable, the erosion rate at each prop-
erty location is calculated first. Since in some areas erosion rates can be determined 
from historical records, while in others they are affected by protection devices such 
as seawalls, we applied the same method used in the Heinz Center’s erosion haz-
ard report. A detailed description of this formulation is presented in Appendix B. A 
squared logarithm of Geotime is added to the model to test for a non-linear effect. 
Built Post-FIRM is an indicator variable for whether or not the home was built post 
the publication of a Flood Insurance Rate Map (FIRM) for the surrounding area. 
Post-FIRM homes typically have lower insurance premiums if they are built above 
base flood elevation levels, therefore we expect the coefficient on this variable to be 
positive.
Table 1 presents summary statistics for all of our main variables. On the average, 
properties in Santa Cruz are generally more expensive compared to those in San 
Diego. This is probably due to bigger houses and lot sizes. Houses in San Diego are 
also situated in higher places and closer to the water, which implies that these houses 
are potentially in high cliffs above water (Griggs and Savoy 1985). The difference in 
Table 1   Summary statistics
Source of basic data: The Heinz III Center for Science, Economics, and Management (Center 2000)
San Diego
Santa Cruz
Mean
SD
Min
Max
Mean
SD
Min
Max
Price (’000, in 2014 
US$)
588.56
412.20
66.15
3120.00
699.08
600.85
71.25
3120.00
Seawall (0–1)
0.18
0.38
0.00
1.00
0.19
0.39
0.00
1.00
Percent seawalled (%)
39.59
17.79
0.00
61.54
42.49
21.00
0.00
100.00
House size (sq. ft.)
1784.05 811.17
700.00
5000.00
2571.56 2377.86 450.00
22000.00
Lot size (sq. ft.)
5843.64 5019.31 863.00
28239.00 8032.56 4873.24 399.00
28106.00
Years since last trans-
action
14.42
10.52
1.00
42.00
18.62
12.84
0.00
55.00
Years since built
40.09
19.86
2.00
109.00
44.78
20.56
1.00
104.00
Fireplace (0–1)
0.81
0.39
0.00
1.00
0.72
0.45
0.00
1.00
Waterfront (0–1)
0.47
0.50
0.00
1.00
0.44
0.50
0.00
1.00
Waterfront nourish-
ment (1–0)
0.06
0.24
0.00
1.00
0.02
0.12
0.00
1.00
Beach nourishment 
(1–0)
0.13
0.34
0.00
1.00
0.05
0.21
0.00
1.00
Bedroom
2.93
1.09
1.00
6.00
3.19
1.47
1.00
12.00
Geotime
2289.42 6609.13 0.47
45772.00 4895.74 8636.80 55.96
46059.00
Elevation (ft.)
7801.26 4220.72 −12.30 10158.70 1327.22 3360.50 − 18.00 10086.40
Distance to water (ft.)
186.75
162.45
1.00
803.35
249.77
161.57
29.05
670.38
Beach width
81.21
286.96
0.00
3000.00
38.39
179.09
0.00
2000.00
Built Post-FIRM
0.05
0.21
0.00
1.00
0.07
0.25
0.00
1.00
Year of purchase
1985
10.52
1957
1998
1980
12.84
1944
1999
Observations
132
151


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this locational attribute should provide more insights on the effect of seawalls on 
inland property. In particular, seawalls in Santa Cruz should affect inland properties 
much more than they could in San Diego. The extent of seawalled properties are 
about the same for the two counties, which makes these two cases comparable.
3  Results
3.1  OLS results
We start our analysis with an ordinary least squares (OLS) method using all the vari-
ables listed in Table A.6. We did separate analysis for San Diego and Santa Cruz 
to account for  the potential difference in hedonic price functions between these 
two market segments. We also run combined observations from the two counties 
to formally test this difference by adding a San Diego indicator variable that turns 
on when the property is situated in San Diego County. Results are summarized in 
Table 2.
For San Diego, we reject the null hypothesis of no correlation for only 4 var-
iables. In particular, we find that price increases by about 0.50% as the size of a 
house increases by one percent.9 This translates to a marginal implicit price of house 
size by about $164/sq.ft.10 Lot size has smaller marginal implicit price, with houses 
being valued more by about $37 as lot size increases by sq.ft. Waterfront is surpris-
ingly not significant. This is possibly due to fact that most houses in San Diego are 
situated in high cliffs above water and beach is not easily accessible. Surprisingly, 
homeowners value having a fireplace in their homes, considering that most areas of 
San Diego rarely require a fireplace for heating. This is possibly because we did not 
explicitly know whether it is outdoor or indoor fireplaces. Outdoor fireplaces may be 
associated with homeowners’ valuation of outdoor living, which makes this struc-
tural attribute a worthy investment.11
In Santa Cruz, house size is positively correlated with housing prices. However, 
since houses in Santa Cruz are larger on the average, the implicit marginal price of 
house size is smaller at $135/sq.ft. The same observation holds true for lot size, with 
Santa Cruz houses being valued by $32 more per additional sq.ft. Waterfront, in 
contrast to San Diego, is significantly associated with higher housing prices in Santa 
Cruz. In particular, waterfront houses in Santa Cruz are valued more by about 40%, 
9  We recognize that all of our regression coefficients should be interpreted as elasticities, although we 
remain silent on the direction of causality. See Gopalakrishnan et al. (2011) for a discussion of the endo-
geneity problem in hedonic beach value models and for a potential solution.
10  We calculate marginal implicit price ($ per unit) for the logged variables as:
where 훽k is the estimated coefficient of the regressor xk , and ̄xk and ̄p are the unconditional means of the 
regressor xk and the dependent variable p, respectively.
Marginal Implicit Price = 𝛽k
̄xk
× ̄p
11  The National Association of Realtors 2013 Home Features Survey reveals that 57% chose homes with 
at least one fireplace.


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Table 2   Results from simple OLS
Regression
All variables
Reduced model
Reduced model
Combined
San Diego
Santa Cruz
Combined
San Diego
Santa Cruz
Combined
San Diego
Santa Cruz
Seawall (0−1)
0.099
0.216
0.100
0.114
0.223
0.068
0.079
0.216
−0.007
(0.130)
(0.182)
(0.206)
(0.110)
(0.159)
(0.157)
(0.107)
(0.157)
(0.146)
Percent seawalled (%)
−0.080***
−0.038
−0.111**
−0.091***
−0.061
−0.100***
−0.088***
−0.065*
−0.094***
(0.030)
(0.045)
(0.050)
(0.024)
(0.038)
(0.028)
(0.024)
(0.038)
(0.028)
House size (sq. ft.)
0.516***
0.498***
0.523***
0.496***
0.455***
0.480***
0.494***
0.460***
0.504***
(0.113)
(0.125)
(0.163)
(0.089)
(0.105)
(0.131)
(0.088)
(0.104)
(0.129)
Lot size (sq. ft.)
0.150**
0.370***
0.057
0.133**
0.373***
0.079
0.137***
0.375***
0.076
(0.061)
(0.097)
(0.073)
(0.053)
(0.089)
(0.065)
(0.053)
(0.089)
(0.063)
Years since last transaction
−0.387***
−0.392***
−0.375***
−0.374***
−0.379***
−0.355***
−0.372***
−0.380***
−0.359***
(0.040)
(0.058)
(0.057)
(0.036)
(0.055)
(0.048)
(0.036)
(0.055)
(0.047)
Years since built
−0.174*
−0.134
−0.218
−0.064
−0.050
−0.106
−0.070
−0.052
−0.102
(0.095)
(0.125)
(0.174)
(0.065)
(0.099)
(0.085)
(0.065)
(0.097)
(0.087)
Fireplace (0−1)
0.232***
0.267***
0.181
0.213***
0.255**
0.187*
0.211***
0.241**
0.191*
(0.075)
(0.096)
(0.115)
(0.074)
(0.105)
(0.104)
(0.074)
(0.103)
(0.104)
Waterfront (0−1)
0.290***
0.165
0.333**
0.375***
0.196
0.463***
0.365***
0.207
0.428***
(0.106)
(0.119)
(0.159)
(0.088)
(0.134)
(0.117)
(0.086)
(0.133)
(0.112)
Waterfront nourishment (1−0)
−0.147
0.147
−0.216
−0.270
0.118
−0.447**
(0.232)
(0.620)
(0.234)
(0.205)
(0.582)
(0.204)
Beach nourishment (1−0)
0.056
−0.001
0.124
0.087
−0.205
0.209**
(0.180)
(0.456)
(0.168)
(0.137)
(0.408)
(0.104)
Bedrooms
−0.036
−0.066
0.032
(0.088)
(0.116)
(0.137)
Geotime
0.008
−0.034
−0.001
(0.066)
(0.322)
(0.076)


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Robust standard errors are in parentheses. *, **, ***Denote statistical significance at 10, 5 and 1% level.
Source of basic data: The Heinz III Center for Science, Economics, and Management
Table 2   (continued)
Regression
All variables
Reduced model
Reduced model
Combined
San Diego
Santa Cruz
Combined
San Diego
Santa Cruz
Combined
San Diego
Santa Cruz
Geotime squared
−0.005
−0.005
−0.000
(0.005)
(0.021)
(0.007)
Elevation (ft.)
0.013
−0.004
0.020
(0.016)
(0.027)
(0.023)
Distance to water (ft.)
−0.009
0.027
−0.023
(0.043)
(0.109)
(0.049)
Beach width
0.015
0.019
0.013
(0.021)
(0.031)
(0.029)
Built Post-FIRM
−0.540**
−0.580
−0.476
(0.239)
(0.356)
(0.401)
San Diego (1−0)
0.132
−0.015
−0.013
(0.104)
(0.076)
(0.075)
Constant
9.587***
7.889***
10.385***
9.380***
7.445***
10.067***
9.374***
7.414***
9.913***
(0.767)
(1.402)
(1.132)
(0.652)
(0.863)
(0.961)
(0.653)
(0.866)
(0.974)
Observations
247
130
117
281
130
151
281
130
151
R-sq.
0.557
0.623
0.550
0.516
0.574
0.497
0.513
0.572
0.486
R-sq. (adj.)
0.522
0.566
0.473
0.496
0.538
0.461
0.497
0.544
0.457


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posting a marginal implicit price of more than $232,000.12 The existence of fire-
place, while positive, is not significantly correlated with housing prices.
Overall, we find no strong evidence to suggest that there are systematic differ-
ences in housing prices between the two counties or market segmentation (Taylor 
2003). Our OLS regression finds that the San Diego dummy, while positive, is statis-
tically insignificant. We also performed Wald test for San Diego dummy and found 
the F-statistic insignificant, with the value of 1.60 and p value equivalent to 0.207.
The variables indicating flooding and erosion risks (e.g.Geotime, Elevation) do 
not appear to be significantly correlated with property prices. This is true when we 
analyze housing prices in each county separately and after combining observations 
from the two counties. This is different from the findings in Kriesel and Friedman 
(2003). This could potentially be explained by the different nature of the shorelines 
between the two regions. Unlike the Atlantic and Gulf coast, much of the Pacific 
coastline consists of narrow beaches backed by steep cliffs. Cliff erosion is site spe-
cific and episodic, which is often hidden by the low long-term average erosion rate. 
Only 45 observations in our entire sample have a Geotime less than 100 years. It is 
likely that erosion risk is not reflected in property prices till decades later.
Although the low long-term erosion rate might reduce homeowners’ perceptions 
of the erosion threat to their properties, shoreline management policies can still 
affect house buyers’ awareness of erosion risk levels. Regarding the seawall indica-
tors, we obtain remarkably similar results to Kriesel and Friedman (2003) and Krie-
sel et al. (2005). The coefficient on Seawall is positive but not statistically significant 
in either county. The imprecision of this estimate relative to Kriesel and Friedman 
(2003) could be due to our smaller sample size. We evaluate the effect of seawalls 
on housing prices using combined samples to increase the sample size and observe 
same result.
Meanwhile, we notice a significant negative effect of Percent Seawalls on prop-
erty prices in Santa Cruz. This suggests that property owners in the county are worse 
off as a result of extensive coastal armoring. Furthermore, our coefficient of −0.11 is 
more than three times larger than the −0.03 found by Kriesel and Friedman (2002). 
This suggests that, as the percentage of waterfront coastal armoring in a community 
increases by 100%, the associated value of a house located on the Santa Cruz coast 
drops by $77,597. In contrast, we do not find any strong evidence to suggest that the 
extent of armored coastline in San Diego has an effect on property values. If there 
is an effect, house values in San Diego declines by about $22,365 if the share of 
waterfront coastal armoring in a community increases by 100%. Interestingly, our 
estimate falls within the ballpark of Kriesel and Friedman (2003) who found that for 
the same percentage increase in coastal armoring, house prices on the Atlantic and 
Gulf coasts drop by $41,091 in 2014 dollars.
12  For dummy variables, the calculated marginal implicit price is expressed as:
where 훽k and p are as previously defined.
(5)
Marginal Implicit Price = (e𝛽k −1) × ̄p


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horizontal axis and the average prices of neighboring houses weighted by the spatial 
lag vector 퐖 in the vertical axis. We assume that the degree of spatial dependence 
of a particular house is inversely related to the geographic distance between the two 
properties. That is,
We find evidence of positive spatial dependence for houses located in Santa Cruz. 
The Moran statistic is 0.0867 and is statistically significant at 5% level. In contrast, 
there is no strong evidence to suggest that housing prices in San Diego are spatially 
dependent.
We formally test for the presence of global spatial autocorrelation by calculating 
Moran’s I (Moran 1950) and Geary c (Cliff and Ord 1970). We then determine the 
significance of these statistics following the procedure suggested by Pisati (2001). 
Table 3 summarizes the results. The tests confirm our graphical approach suggest-
ing no strong spatial dependence of house prices in San Diego. In contrast, there is 
strong evidence of spatial autocorrelation of house prices in Santa Cruz. The spatial 
autocorrelation persists even if we combined the two datasets.
3.2.2  Hedonic prices with spatial dependence
With an indication of potential spatial dependence, we re-estimate Eq. (2) using data 
on Santa Cruz county house prices. This time, we relax the assumption that 휆 and 휌 
are both zero. We estimate parameters based on models with spatial dependence in 
a maximum likelihood framework using the procedure suggested by Drukker et al. 
(7)
퐖=
⎛
⎜
⎜
⎜
⎜⎝
0
w1,2 w1,3 … w1,n
w2,1
0
w2,3 … w2,n
w3,1 w3,2
0
… w3,n
⋮
⋮
⋮
⋱
⋮
wn,1 wn,2 wn,3 …
0
⎞
⎟
⎟
⎟
⎟⎠
wi,j = 1
dij
.
Table 3   Tests for global spatial 
autocorrelation
The estimated standard deviation of each statistic are in parentheses. 
*,**,***Represents significance at 10, 5 and 1%, respectively
San Diego
Santa Cruz
Combined
Distance of nearest 
neighbor (in kms)
Mean
1.279
0.042
Min
0.931
0.006
Max
3.336
0.239
Moran’s I
0.023
0.087**
0.054*
(0.049)
(0.037)
(0.022)
Geary c
0.943
0.900**
0.924**
(0.051)
(0.040)
(0.024)


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(2015). We employ different specifications to potentially account for different forms 
of spatial autocorrelation, namely spatial lag model, spatial error model and the spa-
tial ARAR(1,1,) model.17 Then, for each model, we run three different specifications 
similar to the simple OLS regression in which we start with all the variables and 
drop some of them to slightly increase our sample. We do this to test the stability of 
our estimates. Results are presented in Table 4.
We start the analysis by looking at the estimated spatial autocorrelation param-
eters. We find no strong evidence to suggest that there exists spatial autocorrela-
tion. In particular, we fail to reject the null hypotheses that (1) 휆= 0, 휌≠0 ; (2) 
휆≠0, 휌= 0 ; and (3) 휆≠0, 휌≠0 in Eq. (6).18
The estimated effect of changes in the variables of interest, even after account-
ing for potential spatial dependence, remains robust and consistent with our base-
line results. The coefficient remains insignificant for the indicator variable Seawall. 
Nonetheless, the coefficient for the extent of coastline armoring (Percent Seawalls), 
remains negative albeit slightly smaller and statistically significant. This confirms 
our earlier result that as the percentage of waterfront coastal armoring in a beach 
community increases, the associated value of houses located in San Diego and 
Santa Cruz drops significantly. Moreover, our estimates remain stable across differ-
ent forms of assumed spatial dependence and under different sets of controls, thus 
increasing our confidence on the robustness of the results.
4  Discussion
We find that the sales price of a house located within 1,000 feet of the coast in San 
Diego and Santa Cruz counties is correlated with its structural characteristics and 
the shoreline management policies taken in the community. Although seawalls pro-
tect coastal structures from erosion damages, the negative coefficient on Percent 
Seawalls calls for attention to the welfare impacts of coastal armoring on waterfront 
and non-waterfront households.
Using our baseline estimates, we predicted the potential  change in total hous-
ing values for the two counties when the share of those that are protected by sea-
walls increases by a certain percentage (see Table  5). Results suggest that dou-
bling the current level of properties protected by seawalls may be associated with 
a $20,954.16 drop in the resale value of an average coastal home in San Diego that 
is protected by seawalls. For those that are not protected by seawalls, the magnitude 
17  Thanks to an anonymous referee, we recognized that the estimation of a SARAR model may intro-
duce more problems than solving them. Nonetheless, the employment of different specifications, includ-
ing the SARAR, is meant to show that our estimates, particularly on seawall protection, are consistent 
and robust over different specifications of spatial dependence. We refrain to interpret the actual estimates 
due to uncertainties in the type of spatial dependence.
18  We also perform diagnostic tests for spatial dependence in an OLS framework after estimating 
Eq. (2), following the procedure as suggested by Pisati (2001). Results are summarized in Table A.8. We 
find no strong evidence to suggest that there is spatial dependence amongst observed housing prices in 
each county.


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Table 4   Results for models with spatial dependence
Spatial lag model
Spatial error model
Spatial ARAR(1,1) model
(1)
(2)
(3)
(1)
(2)
(3)
(1)
(2)
(3)
Seawall (0−1)
0.094
0.068
−0.008
0.103
0.060
−0.011
0.084
0.046
−0.026
(0.161)
(0.138)
(0.128)
(0.163)
(0.136)
(0.126)
(0.157)
(0.132)
(0.123)
Percent seawalled (%)
−0.098*
−0.100**
−0.093**
−0.112**
−0.100***
−0.094**
−0.081*
−0.090**
−0.084**
(0.053)
(0.041)
(0.041)
(0.054)
(0.037)
(0.038)
(0.047)
(0.035)
(0.035)
House size (sq. ft.)
0.496***
0.480***
0.502***
0.519***
0.484***
0.508***
0.472***
0.468***
0.489***
(0.155)
(0.117)
(0.118)
(0.155)
(0.114)
(0.115)
(0.149)
(0.112)
(0.113)
Lot size (sq. ft.)
0.056
0.079
0.076
0.056
0.073
0.071
0.064
0.064
0.062
(0.075)
(0.060)
(0.059)
(0.077)
(0.058)
(0.058)
(0.070)
(0.055)
(0.055)
Years since last transaction
−0.379***
−0.355***
−0.360***
−0.374***
−0.358***
−0.363***
−0.382***
−0.363***
−0.367***
(0.057)
(0.053)
(0.053)
(0.058)
(0.053)
(0.053)
(0.057)
(0.053)
(0.052)
Years since built
−0.196
−0.106
−0.100
−0.210*
−0.113
−0.107
−0.212*
−0.102
−0.097
(0.124)
(0.080)
(0.081)
(0.127)
(0.079)
(0.080)
(0.125)
(0.078)
(0.079)
Fireplace (0−1)
0.176
0.187*
0.191*
0.183
0.177
0.184*
0.142
0.165
0.171
(0.116)
(0.108)
(0.108)
(0.117)
(0.108)
(0.108)
(0.119)
(0.108)
(0.108)
Waterfront (0−1)
0.333**
0.463***
0.426***
0.326**
0.480***
0.441***
0.371***
0.470***
0.434***
(0.131)
(0.101)
(0.099)
(0.136)
(0.100)
(0.098)
(0.133)
(0.096)
(0.094)
Waterfront nourishment (1−0)
−0.208
−0.446*
−0.216
−0.458*
−0.200
−0.453*
(0.263)
(0.251)
(0.264)
(0.252)
(0.264)
(0.251)
Beach nourishment (1−0)
0.132
0.209
0.127
0.220
0.128
0.196
(0.207)
(0.166)
(0.208)
(0.163)
(0.203)
(0.163)
Bedrooms
0.033
0.028
0.054
(0.134)
(0.136)
(0.132)
Geotime
0.021
0.010
−0.007
(0.080)
(0.093)
(0.076)


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72
	
Environmental Economics and Policy Studies (2021) 23:55–77
1 3
Table 4   (continued)
Spatial lag model
Spatial error model
Spatial ARAR(1,1) model
(1)
(2)
(3)
(1)
(2)
(3)
(1)
(2)
(3)
Geotime squared
−0.002
−0.001
−0.000
(0.006)
(0.007)
(0.006)
Elevation (ft.)
0.027
0.020
0.036*
(0.021)
(0.020)
(0.020)
Distance to water (ft.)
−0.033
−0.030
−0.009
(0.049)
(0.059)
(0.049)
Beach width
0.010
0.013
0.005
(0.022)
(0.022)
(0.023)
Built post-FIRM
−0.426
−0.449
−0.512*
(0.290)
(0.312)
(0.302)
Constant
8.372***
10.034***
9.640***
10.398***
10.114***
9.959***
6.603***
8.362***
8.102***
(2.221)
(1.904)
(1.895)
(1.043)
(0.803)
(0.809)
(2.470)
(2.122)
(2.077)
Lambda
0.156
0.003
0.022
0.294
0.146
0.155
(0.152)
(0.136)
(0.135)
(0.181)
(0.166)
(0.162)
Rho
0.056
−0.134
−0.107
−0.341
−0.312
−0.294
(0.250)
(0.204)
(0.201)
(0.339)
(0.287)
(0.280)
Observations
117
151
151
117
151
151
117
151
151
Degrees of Freedom (model)
17
10
8
17
10
8
17
10
8
Log-likelihood
−71.103
−106.390
−107.966
−71.586
−106.167
−107.833
−70.669
−105.846
−107.458
Chi-sq.
125.754
138.532
132.654
134.338
155.262
146.471
123.693
128.271
123.748
Robust standard errors are in parentheses. *, **, ***Denote statistical significance at 10, 5 and 1% level


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73
1 3
Environmental Economics and Policy Studies (2021) 23:55–77	
is larger at $22,365. Overall, the said increase in the share of protected properties 
translates to more than $718 million reduction in the value of affected houses in 
the county. In terms of property tax revenues, a very crude back-of-the-envelope 
Table 5   Potential effect of increasing the share of waterfront households protected by seawalls
I. San Diego County has 70 miles of coastline. Based on a very conservative estimate of housing density 
(4 homes per acre), this means that there are approximately 33,939 homes within 1,000 feet of the shore-
line. 42% of these homes are currently protected by seawalls. Property tax in San Diego County is a little 
more than 1%.
II. Santa Cruz County has 29 miles of coastline. Based on an even more conservative estimate of housing 
density (2 homes per acre), this means that there are approximately 7,030 homes within 1,000 feet of the 
shoreline. 40% of these homes are currently protected by seawalls. Property tax in Santa Cruz County is 
a little more than 1%.
III. The change in price for seawalled properties is calculated using the estimated coefficients of the Sea-
wall and Percent Seawall in our baseline model (i.e., OLS including all variables). The unconditional 
mean property price for each county is used as the base price ($588,000 for San Diego and $699,000 for 
Santa Cruz.). For non-seawalled properties, we just used the coefficient for Percent Seawall
% Change in 
seawalls (from 
current mean)
Change in price
No. of affected houses
Total effect
Revenue 
loss
(in $)
(in $)
Seawalled
Non-seawalled
(in ’000$)
(in ’000$)
San Diego
1.00
10.47
−223.65
14,397
19,542
−4,219.92
−42.20
5.00
7.90
−1,118.26
14,967
18,972
−21,097.37
−210.97
10.00
−95.32
−2,236.53
15,680
18,259
−42,331.75
−423.32
20.00
−635.10
−4,473.06
17,105
16,834
−86,161.88
−861.62
30.00
−1,619.35
−6,709.58
18,531
15,408
−133,391.07
−1,333.91
40.00
−3,048.07
−8,946.11
19,956
13,983
−185,919.98
−1,859.20
50.00
−4,921.25
−11,182.64
21,382
12,557
−245,649.31
−2,456.49
60.00
−7,238.90
−13,419.17
22,807
11,132
−314,479.74
−3,144.80
70.00
−10,001.02
−15,655.70
24,232
9,707
−394,311.94
−3,943.12
80.00
−13,207.60
−17,892.22
25,658
8,281
−487,046.53
−4,870.47
90.00
−16,858.64
−20,128.75
27,083
6,856
−594,584.26
−5,945.84
100.00
−20,954.16
−22,365.28
28,509
5,430
−718,825.86
−7,188.26
Santa Cruz
1.00
−0.74
−775.98
2,840.12
4,189.88
−32,533.71
−325.34
5.00
−158.54
−3,879.89
2,952.60
4,077.40
−162,879.83
−1,628.80
10.00
−704.13
−7,759.79
3,093.20
3,936.80
−327,267.40
−3,272.67
20.00
−2,956.45
−15,519.58
3,374.40
3,655.60
−667,096.04
−6,670.96
30.00
−6,756.96
−23,279.37
3,655.60
3,374.40
−1,032,546.40
−10,325.46
40.00
−12,105.67
−31,039.15
3,936.80
3,093.20
−1,436,679.04
−14,366.79
50.00
−19,002.57
−38,798.94
4,218.00
2,812.00
−1,892,554.72
−18,925.55
60.00
−27,447.66
−46,558.73
4,499.20
2,530.80
−2,413,233.60
−24,132.34
70.00
−37,440.95
−54,318.52
4,780.40
2,249.60
−3,011,776.64
−30,117.77
80.00
−48,982.43
−62,078.30
5,061.60
1,968.40
−3,701,244.16
−37,012.44
90.00
−62,072.11
−69,838.09
5,342.80
1,687.20
−4,494,696.64
−44,946.97
100.00
−76,709.97
−77,597.88
5,624.00
1,406.00
−5,405,194.88
−54,051.95


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74
	
Environmental Economics and Policy Studies (2021) 23:55–77
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calculation suggests that this increase would cost the government $7 million in 
terms of revenue loss and the discounted present value of this annual loss is $143 
million.19
For Santa Cruz County, the potential effect of increasing the current share of 
seawalled properties by 100% is significantly greater, reaching to about a $77,000 
decline in home resale values for protected properties. For non-seawalled proper-
ties, the effect is about similar at $78,000. This implies that there is no positive net 
benefit of putting up an additional seawall from the current level. The increase in the 
seawalled-properties is associated with the loss in the total value of coastal homes in 
the county by more than $5.4 billion. This translates to a government revenue loss of 
about $54 million, which is equivalent to a discounted present value of about $1.08 
billion.
Although our estimates are large, the associated welfare impacts are consist-
ent with the existing literature. Our findings are in line with Kriesel and Friedman 
(2003) and Kriesel et al. (2005) who find negative, albeit smaller, effects on prop-
erty prices. Landry et al. (2003) also found a negative effect, but their estimated 
effect was statistically insignificant. However, their contingent valuation study 
strongly suggests that beachgoers do not like beaches with seawalls. The existing 
literature suggests that our estimates might even be underestimated. Gopalakrishnan 
et al. (2011) correct for endogeneity bias and find that the beach width effect on 
property prices is almost five times larger. Furthermore, they find that the long- term 
net value of coastal residential property can fall by as much as 52% when the erosion 
rate triples. To the extent that seawalls can potentially double or triple erosion rates 
on neighboring non-protected properties, our estimates are very much in line with 
their results.
Our paper does not come without limitations. Aside from potential measurement 
problems associated with the survey-based price variable and the inability to control 
for unobserved systematic patterns across beach communities, our study may also 
be confounded by the potential immunity of the property market to climate risks, 
which may be coupled with households’ potential downward bias against these risks. 
To be more precise, if property buyers underestimate the risk of floods/erosion, then 
the benefits of seawalls will be vastly undervalued while the negative consequences 
would remain. Moreover, we find that variables indicating flooding and erosion risks 
are not significantly correlated with property prices, which could indicate that home 
buyers are myopic. Another possibility is that home buyers assess the risk accurately 
but that it is quite low or even non-existent. Both possibilities could be because 
there were no flood episodes in the study areas in the years prior to the survey. Most 
of the prior flood episodes were in Northern California. There has been no major 
impacts in Central California until 2017 when Hurricane Marie caused flooding in 
the region.
Even in the absence of flooding and erosion, more recent information of pro-
gressing climate change, climate change reporting, and extreme events might have 
19  Property tax in San Diego County is a little more than 1%. Using the annual discount rate of 5%, an 
infinite stream of annual losses of $7.188 million would be $7.188 m/0.05 = $143.765 m.