# RECORD f326d0249d4fa4d2ec6af5feb9cf6966a73ca545
# TITLE: The Impact of Sea Level Rise on Real Estate Prices in Coastal Georgia
# YEAR: 2020.0  VENUE: nan  DOI: 10.52324/001c.11643
# AUTHORS: Jason Beck; Meimei Lin
# PAGES: 10  CANDIDATE TABLE PAGES (1-based): [7]
# ABSTRACT: This paper adopts a hedonic pricing model to study the impact of vulnerability to inundation from sea level rise on home prices in Savannah, Georgia. We find that homes most at risk from sea level rise are associated with an approximate 3.1 percent price discount. The results are consistent with prior studies, which uses data from different locations in U.S. coastal areas. We also find that the discount is more significant in our later sample period, indicating that house buyers may be becoming more aware of the climate risk. This paper contributes to the understanding of house pricing factors in the study area regarding the sea level rise effects.


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(2020) 49, 43–52
Impacts of Sea Level Rise on Real Estate Prices in
Coastal Georgia∗
Jason Becka and Meimei Linb
a Department of Economics, Georgia Southern University, USA
b Department of Geology and Geography, Georgia Southern University, USA
Abstract: This paper adopts a hedonic pricing model to study the impact of vulnerability to inundation
from sea level rise on home prices in Savannah, Georgia. We find that homes most at risk from sea level
rise are associated with an approximate 3.1 percent price discount. The results are consistent with prior
studies, which uses data from different locations in U.S. coastal areas. We also find that the discount is
more significant in our later sample period, indicating that house buyers may be becoming more aware of
the climate risk. This paper contributes to the understanding of house pricing factors in the study area
regarding the sea level rise effects.
Keywords: coastal Georgia, house prices, hedonic model, sea level rise
JEL Codes: R30, R31, R11
1.
INTRODUCTION
It is widely accepted that human beings have altered our natural environments in ways
that could trigger irreversible consequences for global climate systems. Since the mid 20th
century, global average temperatures have risen and the resultant retreat of land ice and
thermal expansion of water molecules has caused sea level rise (SLR) worldwide. While
the exact rate at which temperature and sea level will continue to rise is unknown, SLR is
projected to be in the range of one to six feet by the end of the century and is expected to
increase at a faster rate beyond 2100 (Meyer and Pachauri, 2014).
The impact of SLR is uneven across regions with the most severe effects concentrated
in low-lying coastal areas. Due to SLR, coastal areas will likely experience flooding, sub-
mergence, and erosion during and beyond the 21st century (Meyer and Pachauri, 2014).
Exacerbating this situation further is the fact that coastal regions are often densely popu-
lated. According to “National Coastal Population Report: Population Trends from 1970 to
∗Jason Beck is an Associate Professor of Economics at Georgial Southern University, Savannah, GA. Meimei
Lin is an Assistant Professor of Geography at Georgia Souther University, Svannah, GA. Corresponding
Author: Jason Beck E-mail: jbeck@georgiasouthern.edu.
(c) Southern Regional Science Association 2020
ISSN 1553-0892, 0048-49X (online)
www.srsa.org/rrs


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BECK & LIN: IMPACTS OF SEAL LEVE RISE ON REAL ESTATE PRICES
49
Table 3: OLS Estimates of the Hedonic Pricing Model for the Dependent
Variable: ln(price)
Model 1: 6 SLR
Model 2: 2 SLR Control
Control Variables (SLR1-SLR6)
Variables (HIRISK & LOWRISK)
Variable
Coef
t-stat
p-val
Coef
t-stat
p-val
SQFT2
0.154
28.700
0.000
0.154
28.750
0.000
SQFT3
0.284
47.610
0.000
0.285
47.700
0.000
SQFT4
0.449
63.840
0.000
0.449
63.930
0.000
SQFT5
0.666
74.790
0.000
0.666
74.900
0.000
ONEBEDROOM
-0.149
-8.060
0.000
-0.149
-8.080
0.000
TWOBEDROOMS
0.020
2.670
0.008
0.020
2.650
0.008
FOURBEDROOMS
-0.025
-5.720
0.000
-0.025
-5.710
0.000
FIV EBEDROOMS
-0.064
-6.670
0.000
-0.064
-6.680
0.000
SIXPLUSBEDROOMS
-0.105
-3.260
0.001
-0.105
-3.250
0.001
TWOFULLBATH
0.160
19.750
0.000
0.159
19.730
0.000
THREEFULLBATH
0.298
29.340
0.000
0.298
29.320
0.000
FOURFULLBATH
0.516
34.150
0.000
0.516
34.140
0.000
FIV EPLUSFULLBATH
0.739
24.970
0.000
0.739
24.970
0.000
ONEHALFBATH
0.089
21.890
0.000
0.089
21.880
0.000
TWOHALFBATH
0.180
9.470
0.000
0.180
9.440
0.000
THREEHALFBATH
0.425
3.650
0.000
0.425
3.650
0.000
FOURPLUSHALFBATH
0.164
2.830
0.005
0.163
2.830
0.005
FIREPLACE
0.099
25.420
0.000
0.099
25.420
0.000
ONEGARAGE
0.073
13.160
0.000
0.073
13.190
0.000
TWOGARAGE
0.163
31.660
0.000
0.163
31.700
0.000
THREEPLUSGARAGE
0.316
31.740
0.000
0.316
31.770
0.000
Y 2008
-0.037
-5.570
0.000
-0.037
-5.570
0.000
Y 2009
-0.117
-16.830
0.000
-0.117
-16.830
0.000
Y 2010
-0.178
-25.290
0.000
-0.178
-25.280
0.000
Y 2011
-0.250
-35.450
0.000
-0.250
-35.450
0.000
Y 2012
-0.217
-31.300
0.000
-0.217
-31.320
0.000
Y 2013
-0.134
-20.180
0.000
-0.134
-20.200
0.000
Y 2014
-0.063
-9.800
0.000
-0.063
-9.800
0.000
Y 2015
0.015
2.470
0.014
0.015
2.470
0.014
Y 2016
0.074
12.010
0.000
0.074
12.010
0.000
6 −10Y EARS
-0.020
-4.530
0.000
-0.020
-4.520
0.000
11 −25Y EARS
-0.048
-9.670
0.000
-0.048
-9.700
0.000
26 −50Y EARS
-0.108
-16.060
0.000
-0.108
-16.060
0.000
51 −100Y EARS
-0.059
-5.490
0.000
-0.059
-5.500
0.000
101 + Y EARS
0.047
2.450
0.014
0.047
2.450
0.014
POOL
0.089
12.440
0.000
0.089
12.470
0.000
WATERFRONT
0.198
34.600
0.000
0.198
34.550
0.000
CONDO
-0.092
-13.380
0.000
-0.091
-13.490
0.000
SLR1
-0.025
-1.860
0.062
SLR2
-0.077
-3.450
0.001
SLR3
-0.022
-1.420
0.156
SLR4
0.000
0.040
0.965
SLR5
0.006
0.690
0.487
SLR6
0.005
0.630
0.528
HIRISK
-0.031
-3.170
0.002
LOWRISK
0.004
0.690
0.493
R2
0.788
0.788
F
57.230
56.340
N
34807
34807
Note: 167 6-digit zip code fixed effects are present but not reported
of the square footage variables and is consistent with other research (see Salter et al. (2012),
Waller and Jubran (2012), Seagraves and Gallimore (2013) Allen et al. (2015), among others).
Both the national decline in home values and the subsequent rebound over the last
decade are visible in these results. Relative to 2007, Savannah housing prices appear to have
bottomed out in 2011 before trending back upwards and approaching their 2007 levels by
c⃝Southern Regional Science Association 2020.


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50
The Review of Regional Studies 50(1)
2016.
Turning now to our variables of interest, we see that the coefficients on SLR1 and SLR2
are negative and statistically significant.
They roughly exhibit a tapering effect, mostly
decreasing in magnitude and statistical significance as the level of risk decreases. Properties
that are the most vulnerable to sea level rise (i.e. those that would be inundated with a rise
in sea level of one foot or less) are associated with a 2.5 percent penalty relative to those
that are under little/no risk of being inundated. This result is statistically significant at the
10 percent level. Properties that would be inundated with a two-foot or three-foot sea level
rise are associated with a statistically significant 7.7 percent discount. Homes inundated
with a three to six foot rise were not associated with a statistically significant different sales
price than safe homes. It is unclear why the associated coefficient on SLR2 is larger than
the coefficient on SLR1. Despite the fairly rich set of control variables that are provided
in the MLS data, unobservable heterogeneity is always a possibility and it could be that
the homes that are most directly threatened by SLR are also associated with unobservable
positive home characteristics such as beach access or an ocean view. Examined as a whole, a
general picture emerges of an association between degree of property vulnerability and lower
sales price.
A similar model (Model 2) was also estimated with the seven sea level rise variables
(SLR1-SLR6 and “no risk”) collapsed into three categories: no risk (as previously defined),
low risk (homes that would be inundated with a four to six foot increase in sea level), and
high risk (homes that would be inundated with a SLR of three feet or fewer). Relative to the
reference group (“no risk”), homes designated as high risk were associated with a statistically
significant 3.1 percent lower price. Low-risk homes saw no noticeable impact on sales price.
Over time, information levels regarding the risk of SLR may increase. Additionally, the
time before the most direct consequences of SLR will be felt grows shorter. It is thus possible
that the effects of SLR risk on housing prices may change over time. To examine this, we
divide the ten years of available data into two five-year periods: 2007-2011 and 2012-2016.
In estimating Model 2 on each of the two five-year sub-samples, we find that the discount
associated with the highest risk properties increases from 3.4 percent to 4 percent across the
two periods. This difference is statistically significant.
4.
CONCLUSION
Since any public policy action will consider the costs and benefit of action, information on the
costs of SLR is important when formulating a policy response. One of the many costs of SLR
will be home devaluation in vulnerable areas. This paper combines MLS home transaction
data from 2007-2016 for Savannah, Georgia with GIS mapping to explore the connection
between properties at risk of inundation as a result of SLR and their observed price. The
results suggest that properties that would be inundated with a sea level rise of three feet
or less were associated with a 3.1 percent discount. Properties that would be inundated
with a 4-6 feet SLR saw no discount relative to safe homes. Additionally, we find that the
discount was statistically larger in the period of 2012-2016 than it was in 2007-2011. These
findings suggest that buyers may be aware of the risk of vulnerable properties, are bidding
accordingly, and this effect has grown over time. Our results are in line with other research,
c⃝Southern Regional Science Association 2020.