1# RECORD f326d0249d4fa4d2ec6af5feb9cf6966a73ca5452# TITLE: The Impact of Sea Level Rise on Real Estate Prices in Coastal Georgia3# YEAR: 2020.0 VENUE: nan DOI: 10.52324/001c.116434# AUTHORS: Jason Beck; Meimei Lin5# PAGES: 10 CANDIDATE TABLE PAGES (1-based): [7]6# 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.789===== PAGE 1 (score 7, numeric-paren 0, table-words 0, var-words 7) =====1011(2020) 49, 43–5212Impacts of Sea Level Rise on Real Estate Prices in13Coastal Georgia∗14Jason Becka and Meimei Linb15a Department of Economics, Georgia Southern University, USA16b Department of Geology and Geography, Georgia Southern University, USA17Abstract: This paper adopts a hedonic pricing model to study the impact of vulnerability to inundation18from sea level rise on home prices in Savannah, Georgia. We find that homes most at risk from sea level19rise are associated with an approximate 3.1 percent price discount. The results are consistent with prior20studies, which uses data from different locations in U.S. coastal areas. We also find that the discount is21more significant in our later sample period, indicating that house buyers may be becoming more aware of22the climate risk. This paper contributes to the understanding of house pricing factors in the study area23regarding the sea level rise effects.24Keywords: coastal Georgia, house prices, hedonic model, sea level rise25JEL Codes: R30, R31, R11261.27INTRODUCTION28It is widely accepted that human beings have altered our natural environments in ways29that could trigger irreversible consequences for global climate systems. Since the mid 20th30century, global average temperatures have risen and the resultant retreat of land ice and31thermal expansion of water molecules has caused sea level rise (SLR) worldwide. While32the exact rate at which temperature and sea level will continue to rise is unknown, SLR is33projected to be in the range of one to six feet by the end of the century and is expected to34increase at a faster rate beyond 2100 (Meyer and Pachauri, 2014).35The impact of SLR is uneven across regions with the most severe effects concentrated36in low-lying coastal areas. Due to SLR, coastal areas will likely experience flooding, sub-37mergence, and erosion during and beyond the 21st century (Meyer and Pachauri, 2014).38Exacerbating this situation further is the fact that coastal regions are often densely popu-39lated. According to “National Coastal Population Report: Population Trends from 1970 to40∗Jason Beck is an Associate Professor of Economics at Georgial Southern University, Savannah, GA. Meimei41Lin is an Assistant Professor of Geography at Georgia Souther University, Svannah, GA. Corresponding42Author: Jason Beck E-mail: jbeck@georgiasouthern.edu.43(c) Southern Regional Science Association 202044ISSN 1553-0892, 0048-49X (online)45www.srsa.org/rrs464748===== PAGE 7 (score 45, numeric-paren 0, table-words 5, var-words 10) =====4950BECK & LIN: IMPACTS OF SEAL LEVE RISE ON REAL ESTATE PRICES514952Table 3: OLS Estimates of the Hedonic Pricing Model for the Dependent53Variable: ln(price)54Model 1: 6 SLR55Model 2: 2 SLR Control56Control Variables (SLR1-SLR6)57Variables (HIRISK & LOWRISK)58Variable59Coef60t-stat61p-val62Coef63t-stat64p-val65SQFT2660.1546728.700680.000690.1547028.750710.00072SQFT3730.2847447.610750.000760.2857747.700780.00079SQFT4800.4498163.840820.000830.4498463.930850.00086SQFT5870.6668874.790890.000900.6669174.900920.00093ONEBEDROOM94-0.14995-8.060960.00097-0.14998-8.080990.000100TWOBEDROOMS1010.0201022.6701030.0081040.0201052.6501060.008107FOURBEDROOMS108-0.025109-5.7201100.000111-0.025112-5.7101130.000114FIV EBEDROOMS115-0.064116-6.6701170.000118-0.064119-6.6801200.000121SIXPLUSBEDROOMS122-0.105123-3.2601240.001125-0.105126-3.2501270.001128TWOFULLBATH1290.16013019.7501310.0001320.15913319.7301340.000135THREEFULLBATH1360.29813729.3401380.0001390.29814029.3201410.000142FOURFULLBATH1430.51614434.1501450.0001460.51614734.1401480.000149FIV EPLUSFULLBATH1500.73915124.9701520.0001530.73915424.9701550.000156ONEHALFBATH1570.08915821.8901590.0001600.08916121.8801620.000163TWOHALFBATH1640.1801659.4701660.0001670.1801689.4401690.000170THREEHALFBATH1710.4251723.6501730.0001740.4251753.6501760.000177FOURPLUSHALFBATH1780.1641792.8301800.0051810.1631822.8301830.005184FIREPLACE1850.09918625.4201870.0001880.09918925.4201900.000191ONEGARAGE1920.07319313.1601940.0001950.07319613.1901970.000198TWOGARAGE1990.16320031.6602010.0002020.16320331.7002040.000205THREEPLUSGARAGE2060.31620731.7402080.0002090.31621031.7702110.000212Y 2008213-0.037214-5.5702150.000216-0.037217-5.5702180.000219Y 2009220-0.117221-16.8302220.000223-0.117224-16.8302250.000226Y 2010227-0.178228-25.2902290.000230-0.178231-25.2802320.000233Y 2011234-0.250235-35.4502360.000237-0.250238-35.4502390.000240Y 2012241-0.217242-31.3002430.000244-0.217245-31.3202460.000247Y 2013248-0.134249-20.1802500.000251-0.134252-20.2002530.000254Y 2014255-0.063256-9.8002570.000258-0.063259-9.8002600.000261Y 20152620.0152632.4702640.0142650.0152662.4702670.014268Y 20162690.07427012.0102710.0002720.07427312.0102740.0002756 −10Y EARS276-0.020277-4.5302780.000279-0.020280-4.5202810.00028211 −25Y EARS283-0.048284-9.6702850.000286-0.048287-9.7002880.00028926 −50Y EARS290-0.108291-16.0602920.000293-0.108294-16.0602950.00029651 −100Y EARS297-0.059298-5.4902990.000300-0.059301-5.5003020.000303101 + Y EARS3040.0473052.4503060.0143070.0473082.4503090.014310POOL3110.08931212.4403130.0003140.08931512.4703160.000317WATERFRONT3180.19831934.6003200.0003210.19832234.5503230.000324CONDO325-0.092326-13.3803270.000328-0.091329-13.4903300.000331SLR1332-0.025333-1.8603340.062335SLR2336-0.077337-3.4503380.001339SLR3340-0.022341-1.4203420.156343SLR43440.0003450.0403460.965347SLR53480.0063490.6903500.487351SLR63520.0053530.6303540.528355HIRISK356-0.031357-3.1703580.002359LOWRISK3600.0043610.6903620.493363R23640.7883650.788366F36757.23036856.340369N3703480737134807372Note: 167 6-digit zip code fixed effects are present but not reported373of the square footage variables and is consistent with other research (see Salter et al. (2012),374Waller and Jubran (2012), Seagraves and Gallimore (2013) Allen et al. (2015), among others).375Both the national decline in home values and the subsequent rebound over the last376decade are visible in these results. Relative to 2007, Savannah housing prices appear to have377bottomed out in 2011 before trending back upwards and approaching their 2007 levels by378c⃝Southern Regional Science Association 2020.379380381===== PAGE 8 (score 5, numeric-paren 0, table-words 0, var-words 5) =====38238350384The Review of Regional Studies 50(1)3852016.386Turning now to our variables of interest, we see that the coefficients on SLR1 and SLR2387are negative and statistically significant.388They roughly exhibit a tapering effect, mostly389decreasing in magnitude and statistical significance as the level of risk decreases. Properties390that are the most vulnerable to sea level rise (i.e. those that would be inundated with a rise391in sea level of one foot or less) are associated with a 2.5 percent penalty relative to those392that are under little/no risk of being inundated. This result is statistically significant at the39310 percent level. Properties that would be inundated with a two-foot or three-foot sea level394rise are associated with a statistically significant 7.7 percent discount. Homes inundated395with a three to six foot rise were not associated with a statistically significant different sales396price than safe homes. It is unclear why the associated coefficient on SLR2 is larger than397the coefficient on SLR1. Despite the fairly rich set of control variables that are provided398in the MLS data, unobservable heterogeneity is always a possibility and it could be that399the homes that are most directly threatened by SLR are also associated with unobservable400positive home characteristics such as beach access or an ocean view. Examined as a whole, a401general picture emerges of an association between degree of property vulnerability and lower402sales price.403A similar model (Model 2) was also estimated with the seven sea level rise variables404(SLR1-SLR6 and “no risk”) collapsed into three categories: no risk (as previously defined),405low risk (homes that would be inundated with a four to six foot increase in sea level), and406high risk (homes that would be inundated with a SLR of three feet or fewer). Relative to the407reference group (“no risk”), homes designated as high risk were associated with a statistically408significant 3.1 percent lower price. Low-risk homes saw no noticeable impact on sales price.409Over time, information levels regarding the risk of SLR may increase. Additionally, the410time before the most direct consequences of SLR will be felt grows shorter. It is thus possible411that the effects of SLR risk on housing prices may change over time. To examine this, we412divide the ten years of available data into two five-year periods: 2007-2011 and 2012-2016.413In estimating Model 2 on each of the two five-year sub-samples, we find that the discount414associated with the highest risk properties increases from 3.4 percent to 4 percent across the415two periods. This difference is statistically significant.4164.417CONCLUSION418Since any public policy action will consider the costs and benefit of action, information on the419costs of SLR is important when formulating a policy response. One of the many costs of SLR420will be home devaluation in vulnerable areas. This paper combines MLS home transaction421data from 2007-2016 for Savannah, Georgia with GIS mapping to explore the connection422between properties at risk of inundation as a result of SLR and their observed price. The423results suggest that properties that would be inundated with a sea level rise of three feet424or less were associated with a 3.1 percent discount. Properties that would be inundated425with a 4-6 feet SLR saw no discount relative to safe homes. Additionally, we find that the426discount was statistically larger in the period of 2012-2016 than it was in 2007-2011. These427findings suggest that buyers may be aware of the risk of vulnerable properties, are bidding428accordingly, and this effect has grown over time. Our results are in line with other research,429c⃝Southern Regional Science Association 2020.430