# RECORD 35e9cf817db8c35d9d40580566f6516793f3cc92
# TITLE: Sea Level Rise, Homeownership, and Residential Real Estate Markets in South Florida
# YEAR: 2020.0  VENUE: Professional Geographer  DOI: 10.1080/00330124.2020.1818586
# AUTHORS: Xin-Yu Fu; J. Nijman
# PAGES: 11  CANDIDATE TABLE PAGES (1-based): [6, 7]
# ABSTRACT: This article builds on a small but rapidly growing body of research that seeks to determine the impact of sea level rise on the pricing of residential properties. Through a spatial hedonic regression analysis of real estate markets in two Florida counties (Miami–Dade and Pinellas), we assess the influence of different exposure levels on market discounts. Our article stands out in terms of its focus on two comparative case studies and its differentiation between properties that are primary homes versus nonprimary homes. We find that generally discounts are positively associated with exposure levels and overall Miami–Dade experiences higher discounts than Pinellas due to the former’s lower average elevations. We also observe different market behaviors of primary versus nonprimary home buyers and these are partially dependent on affluence. In Miami–Dade, price discounts are less for highly priced properties purchased by nonprimary owners. We attribute this to different buying motives and risk tolerance of affluent nonprimary homeowners. We argue that nonprimary ownership, particularly in high-end waterfront residential real estate, is tempering gradual market adaptation to sea level rise exposure risk, which could have detrimental longer-term consequences in terms of market volatility.


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UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)
UvA-DARE (Digital Academic Repository)
Sea Level Rise, Homeownership, and Residential Real Estate Markets in South
Florida
Fu, X.; Nijman, J.
DOI
10.1080/00330124.2020.1818586
Publication date
2021
Document Version
Final published version
Published in
The Professional Geographer
License
CC BY-NC-ND
Link to publication
Citation for published version (APA):
Fu, X., & Nijman, J. (2021). Sea Level Rise, Homeownership, and Residential Real Estate
Markets in South Florida. The Professional Geographer, 73(1), 62-71.
https://doi.org/10.1080/00330124.2020.1818586
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Download date:20 okt 2026


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terms are introduced into the prior model specifica-
tion: if the owner (buyer) of a property is a primary
homeowner, the interaction term for the PHOs
equals 1 and 0 otherwise; the same applies to the
NPHOs. All computations were processed in GeoDa,
an open source spatial econometric software.
Results
Table 3 provides summary statistics of our samples in
both counties. Note again that the mean home values
are nearly twice as high in Miami–Dade, and homes
and especially lot sizes in Miami–Dade are bigger.
The presence of very expensive coastal real estate in
Miami–Dade also causes the standard deviation to be
higher than in Pinellas. The average distance to the
coast
is
about
the
same
in
both
counties,
but
Miami–Dade properties tend to be relatively more
exposed: more homes are located in FEMA flood
zones and more are located below 6-ft elevations,
despite Pinellas having a considerably longer coastline.
The overall regression results for the two counties,
without distinction between primary and non-primary
homeownership, are shown in Table 4. Note that the
price discounts are overall higher in Miami–Dade than
in Pinellas, which can be explained by Miami–Dade’s
greater overall vulnerability due to its considerably
lower elevations. This probably also explains why a
FEMA flood plain location in Miami–Dade shows a
discount, whereas in Pinellas it does not.5 But what is
the relationship between SLR risk exposure at various
levels and pricing within the two counties?
In Pinellas, we observe a progressively higher dis-
count at increased SLR exposure levels, as hypothe-
sized. The discount is by far the highest, 12 percent,
for the most exposed properties below 2 feet. The dis-
count is 2 percent at 2–3 ft exposure. Above 3 ft, there
is actually already a price markup (negative discount):
3 percent at 3–4 ft, 6 percent at 4–5 ft, and 4 percent at
5–6 ft. In Miami–Dade, however, we observe no signif-
icant discount for the most exposed properties (<2 ft),
but we do see 3 to 7 percent discounts between 2 and 5
Table 4
Spatial hedonic regression results for Pinellas County and Miami–Dade County
Variables
Pinellas County
Miami–Dade County
Coefficient
Std. error
Coefficient
Std. error
Intercept
2.50
0.02
2.70
0.03
Number of bedrooms
0.03
7.2  10–4
0.04
0.002
Lot size, sq. ftþ
0.07
0.003
0.001
8.4  10–5
Living area, sq. ftþ
0.18
0.004
0.23
0.002
Year property built
1.20  10–4.
6.8  10–5
1.40  10–4
4.2  10–5
Euclidean distance to coastline, ftþþ
0.01
9.4  10–4
0.03
0.001
Euclidean distance to major road, ftþþ
0.01
6.0  10–4
0.01
0.002
FEMA 100-year floodplain
0.05
0.004
0.03
0.004
SLR exposure less than 2 ftþþþ
0.12
0.02
0.02
0.03
SLR exposure 2 to 3 ftþþþ
0.02
0.01
0.07
0.01
SLR exposure 3 to 4 ftþþþ
0.03
0.01
0.03
0.01
SLR exposure 4 to 5 ftþþþ
0.06
0.01
0.01
0.01
SLR exposure 5 to 6 ftþþþ
0.04
0.01
0.01
0.005
q
0.45
0.003
0.47
0.004
R2
0.79
0.81
Log-likelihood
14,156.1
25,291.4
Moran’s I
308.68 (p < 0.001)
228.15 (p < 0.001)
Sample size
77,454
58,264
p < 0.001;
p < 0.01;
p < 0.05;
.p < 0.1.
Notes: Year and city/town dummies were included in the model but omitted here for simplicity;
þ1 sq. ft ¼ 0.0929 sq. m; þþ1 ft ¼ 0.3048 m; þþþ2 ft  0.6 m, 3 ft  0.9 m, 4 ft  1.2 m, 5 ft  1.5 m, 6 ft  1.8 m.
FEMA ¼ Federal Emergency Management Agency; SLR ¼ sea level rise.
Table 3
Summary statistics for regression variables for Pinellas County and Miami–Dade County
Pinellas County
(N ¼ 77,454)
Miami–Dade County
(N ¼ 58,264)
Mean
Std. dev.
Mean
Std. dev.
Housing price (103 US$, unadjusted)
292
264
555
849
Number of bedrooms
1.93
1.12
3.26
0.96
Lot size (103 sq. ftþ)
2.47
1.26
9.29
19.49
Living area (103 sq. ftþ)
1.76
0.85
2.04
1.20
Year property built (as compared to year 2019)
42.67
19.92
46
40
Distance to coast (103 ftþþ)
1.91
1.43
2.09
1.45
Distance to major road (103 ftþþ)
1.99
2.06
0.94
0.80
FEMA floodplain
0.33
0.47
0.48
0.50
SLR exposure (6 ft or 1.8 m)
0.21
0.40
0.32
0.47
Notes: þ1 sq. ft ¼ 0.0929 sq. m; þþ1 ft ¼ 0.3048 m. FEMA ¼ Federal Emergency Management Agency; SLR ¼ sea level rise.
66
Volume 73, Number 1, 2021


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ft; dropping to 1 percent above 5 ft. Miami–Dade does
not see any substantial discounts above 4 ft. Below, we
return to the apparent anomaly in Miami–Dade of no
discounts for the most exposed properties.
In the next step of the analysis, we differentiate
the impact of SLR exposure between PHOs and
NPHOs. Table 5 shows the regression results with
quite opposite patterns in the two counties. In
Miami–Dade County, discounts are mostly higher
for PHOs than for NPHOs, confirming our hypoth-
eses. However, in Pinellas County, it is NPHOs’
properties that exhibit higher discounts. For the
most exposed properties in Pinellas, the discounts
for PHOs and NPHOs are, respectively, 10 percent
versus 13 percent; at 2–3 ft exposure, they are 1 per-
cent versus 3 percent; and at 3–4 ft exposure the dif-
ferences are a 5 percent price markup versus a 1
percent price discount.
In
Miami–Dade,
the
findings
for
the
most
exposed
properties
are
especially
revealing.
Properties below 2 ft show a substantial 15 percent
discount for PHOs but no significant discount for
NPHOs. Thus, the lack of an aggregate discount for
the most exposed properties in Miami–Dade (see
Table 4) can be attributed to the large presence of
NPHOs. Our data show that 58 percent of all
buyers in this segment are NPHOs, with an average
value of NPHO properties of $1.7 m versus $1.2 m
for PHOs. These prices are much higher than in
Pinellas (NPHO properties below 2 ft in Miami
Dade are 4.6 times as expensive as in Pinellas).
Moreover, in Pinellas, NPHOs are a minority and
NPHO homes are priced lower than PHO homes.
In Miami–Dade, properties between 2 and 3 ft show
discounts of 9 percent for PHOs vs. 5 percent for
NPHOs, and properties between 3 and 4 ft show
discounts of 6 percent for PHOs vs. no significant
discount for NPHOs.
Discussion and Conclusions
Our findings are generally consistent with recent
writings insofar as they indicate a price differential
based on SLR exposure (e.g., Keenan, Hill, and
Gumber 2018).6 At the same time, our study contrib-
utes important new insights in the significance of geo-
graphic variations of real estate markets (Miami–Dade
vs. Pinellas) and in the roles of nonprimary homeown-
ership in market responses to SLR exposure.
The results indicate that SLR exposure risk is
currently impacting the residential real estate mar-
kets in Pinellas County and Miami–Dade County,
with generally higher discounts for more exposed
properties. We find significant heterogeneity in the
degree and extent of the discounting effect between
the two counties: Miami–Dade shows a stronger
overall market response in price discounts than
Pinellas,
which
we
attribute
to
Miami–Dade’s
greater
county-wide
exposure
risk,
due
to
its
Table 5
Spatial hedonic regression results for PHOs and NPHOs in Pinellas County and Miami–Dade County
Variables
Pinellas County
Miami–Dade County
Coefficient
Std. error
Coefficient
Std. error
Intercept
2.50
0.02
2.70
0.03
Number of bedrooms
0.03
0.001
0.04
0.002
Lot size, sq. ftþ
0.07
0.003
0.001
8.4  10–5
Living area, sq. ftþ
0.18
0.004
0.23
0.002
Year property built
1.2  10–4.
6.8  10–5
1.4  10–4
4.2  10–5
Euclidean distance to coastline, ftþþ
0.01
0.001
0.03
0.001
Euclidean distance to major road, ftþþ
0.01
0.001
0.01
0.002
FEMA 100-year floodplain
0.05
0.004
0.03
0.004
SLR exposure less than 2 ft & PHOsþþþ
0.10
0.03
0.15
0.05
SLR exposure less than 2 ft & NPHOsþþþ
0.13
0.03
0.14
0.04
SLR exposure 2 to 3 ft & PHOsþþþ
0.01
0.01
0.09
0.02
SLR exposure 2 to 3 ft & NPHOsþþþ
0.03
0.01
0.05
0.02
SLR exposure 3 to 4 ft & PHOsþþþ
0.05
0.01
0.06
0.01
SLR exposure 3 to 4 ft & NPHOsþþþ
0.01
0.01
0.003
0.01
SLR exposure 4 to 5 ft & PHOsþþþ
0.08
0.01
0.002
0.01
SLR exposure 4 to 5 ft & NPHOsþþþ
0.002
0.01
0.01
0.01
SLR exposure 5 to 6 ft & PHOsþþþ
0.05
0.01
0.003
0.01
SLR exposure 5 to 6 ft & NPHOsþþþ
0.01.
0.01
0.02
0.01
q
0.45
0.003
0.47
0.004
R2
0.79
0.81
Log-likelihood
14,083.7
25,265.1
Moran’s I
308.55 (p < 0.001)
228.05 (p < 0.001)
Sample size
77,454
58,264
p < 0.001;
p < 0.01;
p < 0.05;
.p < 0.1.
Notes: Year and city/town dummies were included in the model but omitted here for simplicity;
þ1 sq. ft ¼ 0.0929 sq. m; þþ1 ft ¼ 0.3048 m; þþþ2 ft  0.6 m, 3 ft  0.9 m, 4 ft  1.2 m, 5 ft  1.5 m, 6 ft  1.8 m.
PHO ¼ primary homeowner; NPHO ¼ nonprimary homeowner; FEMA ¼ Federal Emergency Management Agency; SLR ¼ sea
level rise.
Sea Level Rise and Real Estate in South Florida
67


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considerably
lower
county-wide
elevations.
Miami–Dade’s average elevation is 6 ft (1.8 m), com-
pared to 30 ft (9 m) in Pinellas. Pinellas’s main cities,
Saint Petersburg and Clearwater, have average ele-
vations of 42 ft (13 m) and 32 ft (10 m), whereas the
average elevation of the City of Miami is less than 7
ft (2.1 m). Buyers in Pinellas have more options than
in Miami–Dade to purchase homes at higher eleva-
tions. Concern about SLR exposure is likely to be
more widely shared in Miami–Dade than in Pinellas,
and Miami–Dade has figured more prominently in
media reports and academic debates about sea
level rise.
But market responses to SLR are also limited: In
Pinellas, discounts are limited to properties below 3
ft of exposure and properties between 3 and 6 ft ele-
vation actually see a price markup. In Miami–Dade,
we observe price discounts across the different expo-
sure levels except for the most exposed properties,
below 2 ft. The risk of SLR seems to be inconse-
quential in Miami–Dade’s most exposed market seg-
ments. We attribute this to the different buyer
motives and risk tolerance in this segment: these
buyers are disproportionately affluent and nonpri-
mary homeowners.
Interestingly, we find that the role of second
homeowners
(NPHOs)
varies
between
the
two
counties: in Pinellas, second homes have higher dis-
counts than primary homes, and this appears to
align with the findings of Bernstein, Gustafson, and
Lewis (2019). Here, it is possible that a significant
number of NPHOs are in fact investors who take a
more critical look at SLR-exposed properties and
negotiate prices down and rent out the property.
Accordingly, in Pinellas, our hypothesis does not
hold up. We should note again that across all expo-
sure levels in the Pinellas sample, NPHO prices are
lower than PHO prices, indicating that PHOs in
Pinellas tend to be the more affluent buyers.
In Miami–Dade, the pattern is reversed: there,
the discounts for second homes are consistently less
than those for primary homes. In Miami–Dade, with
large numbers of second home buyers in very expen-
sive market segments, our theoretical premise seems
validated: PHOs are generally more deeply invested
in the community than NPHOs, who typically
reside elsewhere for much of the time. PHOs are
likely to be more concerned with long-term stability,
whereas NPHOs are generally invested in these
properties in the short to medium term, with an eye
on the immediate returns in terms of coastal ameni-
ties and quality of life. NPHOs are also wealthier
buyers with a higher risk tolerance; thus, they are
more financially flexible, more mobile, and less
socially invested in the community.7
Hence, in Miami–Dade County, we find that
nonprimary homeownership tends to dampen mar-
ket adjustments to present and future SLR. The
financial flexibility and mobility of many (affluent)
NPHOs underlies their market behavior: at the pres-
ent time, it allows ignoring projected SLR exposure
and continued preferences for waterfront properties,
but at some point in the foreseeable future it is likely
to trigger an exodus from the same properties.
Hence, present-day intransigence could have detri-
mental longer-term consequences in terms of mar-
ket volatility. These findings may well apply to
other coastal residential real estate markets, many of
which have a substantial share of affluent NPHOs.
What are the planning implications of our find-
ings? First, our study joins several others that
observe market reactions to SLR risk exposure. We
can expect these market reactions to continue and
probably accelerate. This signals a challenge to
many coastal cities that rely heavily on their housing
markets for tax income. Most of these communities
have yet to implement significant adaptations such
as elevating roads, changing building codes, etc. (Fu
et al. 2017, 2019; Fu 2020). Such adaptation is costly
and will become more difficult to finance if market
values of coastal properties and the tax base decline.
This means that the proverbial clock is ticking for
these
communities
to
proceed
with
adapta-
tion measures.
Second, it is striking that property owners in
Pinellas (except at the highest exposure levels, below
2 ft) appear so much less concerned with SLR com-
pared to Miami–Dade, a discrepancy we attribute in
part to Pinellas having received far less attention in
the news media and public debates about SLR com-
pared to Miami–Dade. A Google search in July 2020
on “sea level rise Miami–Dade” returned about
twelve times as many results as “sea level rise
Pinellas.” This apparent gap in risk perception calls
for targeted information for the general public and
local communities in Pinellas and similarly affected
areas. Local workshops on SLR and public engage-
ment with adaptation planning are also desired but
not yet widespread or common.
Finally, our findings suggest that governments at
various levels consider remedying present-day mar-
ket intransigence through market intervention. This
is a major challenge in terms of balancing fairness
and
effectiveness
(Chandra-Putra
and
Andrews
2019) and one that tends to go against the grain in
terms of local governments typically seeking to stim-
ulate short-term growth and expansion of the tax
base rather than the opposite. The goal would have
to be to induce a sufficient and steady long-term
market response to SLR exposure risk, and this
means gradual price discounts in the most exposed
market segments. Nonprimary homeowners across
the United States already face fiscal disincentives by
the way of denial of homestead exemptions in prop-
erty taxes, but our findings suggest that these are
too marginal to affect market behaviors where they
are most needed, on the waterfront in the most
expensive market segments. Sooner rather than
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Volume 73, Number 1, 2021