Hurricane Vulnerability of Multi-Story Residential Buildings in Florida
G. L. Pita, J.-P. Pinelli, C.S. Subramanian
Florida Institute of Technology, 150, W. University Blvd, Melbourne, Florida 32901
United States of America, Ph.: 321-674-8085, pinelli@fit.edu
K. Gurley
University of Florida, Gainesville, Florida, United States of America
S. Hamid
Florida International University, Miami, Florida, United States of America
ABSTRACT: In recent years, a multi-disciplinary team has developed the Florida Public Hurricane Loss
Model. The model combines state-of-the-art technology in the fields of meteorology, engineering, actuarial
science, and computer science to predict hurricane-induced losses for single-family homes. The model is now
being extended to cover multi-family buildings ranging from a few stories to the high rise condominiums typically found lining the beaches of South Florida. These are engineered structures with a large variety of structural types, shapes, and heights. To address the challenge of predicting hurricane losses for these structures,
the meteorologists are developing a new 3D model of the wind field, and the engineers are working on a new
vulnerability model to incorporate the effects of height and associated air jets on the buildings’ vulnerabilities.
The first step in this process is to carry out a comprehensive survey of the multi-family buildings stock in
Florida, to identify the most prevalent characteristics of the structures, their relationship to hurricane risk, and
their geographic distribution. This paper describes the outcome of the exposure study, with an analysis of the
hurricane risk attached to multi-family, multi-story buildings and a comparison to single-family homes. The
authors conclude with a description of the strategy developed to quantify the potential hurricane losses associated with a portfolio of multi-unit structures including building and contents losses, and additional living expenses.
The first task was to perform a comprehensive
1 INTRODUCTION
survey of the current commercial-residential building stock in Florida in order to provide the vulneraHurricanes constitute a part of Floridians’ everyday
bility model with the typical buildings, their characexperience since historically they have represented a
teristics and their prevalence. The survey will
constant threat to their lives and properties. In recent
identify the building types that are representative of
years, significant efforts have been made by the State
the stock and whose vulnerability will be simulated.
of Florida to reduce hurricane-induced building lossThe resulting statistics will also be used for
es, including the development of a loss model. A
weighting or averaging the vulnerability of different
team, composed of engineers and scientists from the
building types around the state.
National Oceanographic and Atmospheric AdminTo perform the survey, the county property apistration (NOAA), Florida International University,
praisers’ databases were the only source of inforFlorida Institute of Technology and the University of
mation available. The counties that represent most of
Florida, among others, has produced the Florida
the State’s building inventory were contacted. TwenPublic Hurricane Loss Model (FPHLM). This is the
ty-two counties provided their commercialonly model specifically designed to predict residenresidential database in response to the data call. In
tial insured losses which is accessible for scrutiny by
these databases, property appraisers classify the
the scientific community and the public. The model
properties either as multi-family residential buildpredicts the losses of residential homes and has been
ings (MFR) or as condominiums. MFR buildings
certified by the Florida Commission on Hurricane
comprise duplex, triplex and quadruplex buildings.
Loss Methodology as an acceptable model for proAround 50% of the databases had some useful injecting hurricane loss costs (FCHLPM 2007). The
formation (Table 1) on MFR buildings, and around
structure of the model as well as validation and cali20% had some useful information on condos.
bration results have been described elsewhere (PiIn addition to the results of the exposure survey, a
nelli et al 2003, Powell 2005, Pinelli et al 2004,
vulnerability model is described. There is little, if
Chen et al 2004, Pinelli et al 2006, Pinelli et al
any, information published on multi-story buildings’
2007). The model is now being expanded to include
vulnerability (NIBS 2002). This situation is due to
multi-story commercial-residential buildings.
the fact that many models are proprietary so the details are not open to the public. On the other hand,
many models base their damage models in regression analysis from claim data. In this paper an engineering-based approach to calculate damage is discussed. After a threshold of exterior damage has
occurred, interior damage is triggered and then it
propagates following different rules. The aggregation of both damages accounts for the buildings’ total damage assessment. The authors are currently
working on a technique to simulate both exterior and
interior plus contents damage. A brief discussion on
their work is included.
2 BUILDING SURVEY METHODOLOGY
Previous work has been done surveying Florida’s
single family residential buildings using the information provided by the property appraisers (Intrarisk
2002, Zhang 2003). Also, California appraisers’ information has been used for damage estimation
(Kiremidjian 1994). To our knowledge, no survey
results on low, mid and high rise buildings have
been published.
For the survey, the authors classified the buildings within the range of 1-3 stories as low rise and
buildings with 4 stories or more were classified as
mid-to-high rise. MFR buildings fall under the low
rise classification, while condos could go either way.
Unanwa 1997 uses a similar definition in his study.
2.1 Collected information
The properties of interest to this study are related
with the building’s capability to resist hurricane
wind damage. In addition to external characteristics,
interior characteristics, even though not related to
strength, are also important because they are linked
to interior damage propagation. The information that
was collected includes:
 Exterior walls’ material
 Interior walls’ material
 Roof type
 Roof cover
 Floor covering
 Year built
 Number of stories
2.2 Data sources and limitations
It was not possible to obtain useful information
from every available county. For example, the most
densely built counties, Miami-Dade and Broward
counties do not record the information needed to
characterize the low-rise and mid-high rise building
stock.
In addition, there is no common data format
among the counties. Although there are similarities
in database’s structure, the way the data fields relate
with each other, the type and precision of collected
data (e.g., very few counties report Exterior Insula-
tion and Finish Systems or EIFS, as a category) and
the codes used to classify information differ from
county to county. Judgment calls were necessary in
order to make data from different counties comparable and to interpret the results.
Another issue is that some counties do not share
information on buildings with an above-the-average
dollar value due to owners’ restrictions. Usually
these buildings are high-rises with more than 9
floors. This does not mean that there are not buildings that are higher than 9 stories in the county; it is
just that the property appraisers do not share the information. This survey may not be free of error because of the aforementioned shortcomings.
As mentioned earlier the appraisers do not classify buildings as low-rise and mid-high rise but as
MFR and condos. Buildings classified as condo may
have a number of stories that range from one to
many stories while MFR have only 1 to 3 stories.
This means that condos include low-rise as well as
mid-high rise buildings while MFR include low rise
buildings only. Thus, it is necessary to split condo
building information into low rise and mid-high rise
buildings. During this process assumptions were
made since the county may have information on
MFR characteristics but not on condos, since the appraisers record information on individual condo
units but not on the buildings that house them. When
the condo information is missing for a particular
county (Table 1), it was assumed that the distribution of low rise condo characteristics such as exterior/interior wall, etc. follow the same distribution as
that of MFR. This assumption was validated against
the case when information on both MFR and condos
buildings was available. Given the aforementioned
shortcomings, the results are subject to error.
Table 1: Number of buildings (ND: no data available)
County
Palm Beach
Hillsborough
Orange
Pinellas
Duval
Brevard
Polk
Lee
Volusia
Pasco
Seminole
Collier
Marion
Lake
Leon
Alachua
Saint Lucie
Osceola
Bay
Saint Johns
Monroe
MFR
11430
14417
11563
10573
5725
3532
1068
16399
3500
1274
3663
2090
2393
2700
2051
1187
1733
1009
1750
3125
3452
Low-rise condos M-H rise condos
6372
1208
ND
7475
ND
2186
574
ND
2898
414
ND
1452
217
ND
ND
ND
ND
6192
2500
ND
ND
ND
ND
ND
ND
ND
3 LOW-RISE RESULTS
A snapshot of the 2006/2007 situation of Florida’s
building portfolio is in the next sections. The number of residential-commercial buildings is significantly smaller than for single-family residential
homes. The number of commercial-residential buildings is only around 7% of single family-residential
homes (Zhang, 2003). However, their total exposure
value is around 30% of the single homes exposure .
3.1 Exterior Walls
The existing information indicates that exterior wall
materials follow a pattern as shown in Figure 1 and
detailed in Table 2. In south-coastal Florida, there is
a belt-area where concrete block (CB) walls prevail
from 70% to 90%. This is not surprising given the
geographical situation, the population experience
with hurricanes and the building codes
In central Florida, the proportion of concrete
block varies between 40% and 70%. The smallest
proportion of concrete block exterior walls occurs in
the northern part of the peninsula and the panhandle.
Thus, three different zones could be defined: southcoastal belt, central and north Florida. Overall average is: 58% CB and 39% wood.
It was found that the low-rise buildings’ exterior
wall materials’ percentages show a very similar distribution as those of the residential buildings as reported in Zhang 2003 (58% for CB).
Table 2: Exterior Wall distribution - Low rise
County
Palm Beach
Orange
Pinellas
Duval
Brevard
Lee
Volusia
Pasco
Marion
Lake
Leon
Alachua
Saint Lucie
Osceola
Bay
Saint Johns
Wood
25%
46%
24%
51%
35%
12%
56%
16%
27%
43%
57%
30%
23%
51%
48%
78%
CB
71%
49%
76%
49%
65%
84%
41%
84%
73%
55%
33%
52%
75%
49%
36%
22%
Metal
Glass
Concrete
2%
3%
Other
2%
2%
3%
1%
2%
1%
10%
17%
1%
1%
9%
6%
3.3 Roof type
The preferred roof type in the studied counties is gable/hip with an average of 90% of low-rises and a
standard deviation of 8%. See Table 3. Flat roofs
follow with an average of 4% and standard deviation
of 5%. Just three counties (Volusia, Marion and St.
Lucie) split the records between gable and hip. For
them the proportions of different roofs are seen in
Table 4.
Table 3: Roof Types - Low rise
County
Gable/Hip Flat Steel Concrete Wood Shed Other
Orange
96%
1%
2%
1%
Pinellas
91%
10%
Duval
91%
8%
Brevard
85%
11% 2%
2%
Lee
91%
4%
4%
Volusia
94%
6%
1%
Pasco
97%
2%
Marion
95%
1%
3%
Leon
94%
1%
1%
2%
1%
Saint Lucie
88%
11%
Osceola
97%
2%
Bay
95%
2%
2%
1%
Saint Johns
85%
3%
12%
Monroe
68%
18%
5%
9%
Table 4: Roof proportions - Low rise
County
Gable
Hip
Flat
Volusia
73%
21%
6%
Marion
69%
26%
1%
St. Lucie
67%
21%
The average for low rise buildings, gable roof is
70% while for hip roofs is 23%. These average gable
roof proportions match fairly well with the results of
Zhang 2003 (only surveys Brevard, gable roof 65%).
Figure 1: Exterior Wall (concrete block) distribution
3.2 Interior Walls
Interior walls show uniformity all over the studied
counties. Drywall is the preferred material with an
average of 93% of low-rises and standard deviation
of 7%.
3.4 Roof cover
Of all the studied counties, shingle cover is the most
widespread roof cover with an average of 77% of
low-rises and a standard deviation of 16%. The other
roof covers, i.e. tiles, membrane, gravel and metal
vary between 4% and 10% adding up for a total of
24%. See Table 5. For residential buildings, Zhang
(2003), presents around 67% for shingles, 20% for
tile and for Monroe, 26% Metal roof cover. In this
case, the roof cover proportions are fairly close again
with that author.
more than 50% of the low-rise building stock, but
only 30% of the exposure value. 2 story buildings
represent almost 50% of the exposure.
60%
Table 5: Roof Covers – Low rise
Tiles Gravel Membrane Metal Concrete Other
13%
9%
1%
4%
11%
1%
15%
4%
6%
1%
8%
20%
17%
10%
1%
2%
8%
1%
9%
6%
2%
2%
2%
2%
1%
1%
1%
3%
2%
6%
4%
4%
2%
16%
2%
4%
2%
1%
4%
2%
14%
4%
1%
5%
3%
4%
3%
9%
3%
44%
11%
1%
3.5 Floor finishing
% No. Buildings & % of Exposure
County
Shingle
Orange
77%
Pinellas
83%
Duval
75%
Brevard
72%
Lee
70%
Volusia
83%
Pasco
88%
Marion
95%
Lake
62%
Leon
91%
Saint Lucie
75%
Osceola
91%
Bay
81%
Saint Johns
87%
Monroe
28%
No. of buildings
Low-Rise Exposure
50%
40%
30%
20%
10%
0%
0
1
2
3
4
Number of Stories
Figure 3: No. of stories and exposure - Low rise
Table 6: Year Built - Low Rise
Carpet is the most common floor covering, as expected, with an average of 67% of low-rises and
standard deviation of 27%. Vinyl is the next with
average 13% and standard deviation of 23%. Finally
Wood floor has an average of 7%.
3.6 Year built – Low rise
The year of construction of a building is a key indicator of its vulnerability to hurricanes. Age will be
used later to assign a certain level of strength to
building types. The average of all the processed
counties indicates that almost 2/3 of the building
stock was built before 1983. The rest is almost linearly distributed between 1983 and the present. See
Figure 2 and Table 6.
County
Pre - 1970 1971-1983 1984-1992 1993-2002 Post 2003
Palm Beach
29%
35%
25%
7%
1%
Hillsborough
27%
36%
22%
10%
5%
Orange
13%
30%
35%
17%
4%
Pinellas
42%
34%
15%
5%
4%
Duval
85%
8%
7%
Brevard
25%
28%
35%
10%
4%
Polk
27%
35%
14%
3%
2%
Lee
9%
32%
22%
15%
22%
Volusia
19%
31%
33%
8%
9%
Pasco
24%
57%
12%
1%
7%
Seminole
13%
35%
32%
18%
2%
Collier
14%
9%
21%
49%
7%
Marion
4%
33%
41%
13%
8%
Alachua
17%
48%
18%
13%
3%
Saint Lucie
46%
30%
11%
9%
4%
Osceola
12%
24%
38%
18%
8%
Bay
16%
37%
27%
15%
6%
Saint Johns
13%
11%
25%
9%
43%
Monroe
62%
24%
8%
5%
2%
35%
30%
4 MID - HIGH-RISE BUILDING SURVEY
Proportion
25%
20%
15%
10%
5%
0%
Pre - 1970
1971-1983
1984-1992
Year Built
1993-2002
2003-2007
Figure 2: Average Year built - Low rise
3.7 Number of stories – Low rise
The 1 story structures predominate among low rise
buildings, with some local variations. In Figure 3 the
relative proportion of buildings according to number of stories is shown. 1-story buildings represent
Survey results for buildings with 4 or more stories
are more debatable than for low-rise buildings results since few counties keep useful records for high
rise buildings, from the point of view of vulnerability. It was possible to extract results only from Palm
Beach, Pinellas, Brevard and Lee counties whose
approximate number of buildings are shown in Table
1.
4.1 Mid - high rise buildings results
For mid-high rise buildings the prevalent exterior
wall is concrete block. See Table 7. For interior wall
there was no available information.
Based on the information of Lee and Brevard
County, it was found that the predominant roof type
is the flat roof. See Table 8. The average proportion
of flat roof is 66% of mid-high rises with a standard
Table 7: Exterior Walls – Med/High Rise
County
Pinellas
Brevard
Lee
Wood
2%
1%
CB
98%
98%
99%
Other
1%
50%
45%
40%
35%
30%
Proportion
deviation of 14%. On the other hand gable/hip roofs
account for 34% on average of the buildings with a
standard deviation of 14%.
In only one county (Brevard), it was possible to
study the building’s roof cover. It was found that the
preferred material are varieties of asphalt membrane
in 78% of the mid-high rises, tiles in 11% and shingle in 8% of buildings. Given the lack of data, it is
not possible to draw further conclusions. Moreover,
more research needs to be done on the roof cover of
high-rise buildings in the most important counties
since the nature of downtown windborne debris is
highly dependent on roof cover (Minor 1994).
The relative average of buildings per number of
stories (in Palm Beach, Pinellas, Brevard and Lee
County) is shown in Figure 4. As expected, the proportion decreases with the number of stories.
In Table 9 and Figure 5 a snapshot of the current
year built of building stock is shown. There, up to
45% of the buildings were built in the period of
1971-1983. This information is important to assign
strength level to the different vulnerability models.
25%
20%
15%
10%
5%
0%
Pre - 1970 1971-1983 1984-1992 1993-2002 2003-2007
No. of Stories
Figure 5: Year built - Med/High rise
5 BUILDING EXPOSURE
The number of buildings cannot be used as the
unique criteria to weight the prevalence of a particular building type. There are buildings that have a
considerably higher monetary value although they
are less in number. Figure 6 shows the relative importance of each building classification according to
number and value.
80%
Table 8: Roof Type - Med/High rise
Flat
76%
56%
60%
50%
Proportion
County Gable/Hip
Brevard
24%
Lee
43%
40%
40%
35%
30%
30%
Proportion
Avg Money
Exposure
Avg No. of
Buildings
70%
20%
25%
10%
20%
0%
15%
MFR
LR Condo
Building Classification
M/HR Condo
10%
Figure 6: Exposure compared with No. of Buildings for
all classifications
5%
0%
4
5
6
7
8
9
>9
No. of Stories
Figure 4: No. Stories relative percentage average – Midhigh rise
Table 9: Year Built - Med/High Rise
County Pre - 1970 1971-1983 1984-1992 1993-2002 2003-2007
Palm Beach
6%
46%
40%
7%
1%
Pinellas
9%
54%
14%
12%
11%
Brevard
2%
34%
31%
22%
10%
Lee
3%
42%
15%
24%
17%
It shows that although the number of mid-high
rise buildings is smaller, they account for 30% of the
building stock, so they need to be considered carefully in the vulnerability model. As stated by Pielke
and Landsea (1998) and Pielke et al (2008), the
wealth of population and value of buildings is an
important component of vulnerability.
In the case of low-rise buildings that is true as
well. Figure 7 shows that although 2-story buildings
are almost half the number of 1-story’s, nevertheless
their exposure is higher than for 1-story buildings.
60%
Low-Rise Exposure
50%
No. of buildings
% of Exposure
40%
30%
20%
10%
0%
0
1
2
Number of Stories
3
4
Figure 7: Exposure value compared to No. of Buildings
for Low-Rise buildings in 4 counties
Number and value can be used to weight the importance of the different types of low rise as shown
in Figure 8. It shows that the 2-story buildings have
the same weight than the 1-story in the vulnerability
of the low-rise stock.
the strategy needs to be applicable to a wide variety
of buildings that differ in type of façade, number of
stories, shape, and number of units (apartments).
Given this variety of buildings, a modular approach that models individual apartment units instead of whole buildings seems appropriate. Unit
types that reflect those frequently found in Florida’s
buildings needs to be defined. That task will be done
using the survey results. The authors are currently
building and testing an external damage (ED) module that calculates the ED to an average apartment
unit. The units are classified taking into account the
following factors:
Wall type, position, size (survey results will be
shown in a future paper), and year built. The unit position will be determinant on how the wind speeds
interact with the unit an also how the interior damage propagates. There are at least two positions that
capture the above mentioned, those are: corner units
and middle units (Figure 9), with one set for intermediate floors, and one set for the top floor directly
under the roof.
60%
Weighted
Importance
% of Weighted Exposure
50%
40%
30%
20%
10%
0%
0
1
2
Number of Stories
3
4
Figure 8: Weighted exposure of Low-rise buildings
6 MODELING STRATEGIES
The survey shows that low-rise buildings have characteristics very similar to the single-family residential buildings. Accordingly, the damage modeling
strategy for low rise will be very similar, if not identical, to the strategy followed for residential single
family homes reported elsewhere (Pinelli et al 2003).
On the other hand, survey results suggest that given
the importance of mid-high rise buildings a particular vulnerability approach, different to that of lowrise buildings, needs to be created.
The new vulnerability approach for mid-high rise
buildings needs to consider the influence of the
buildings’ number of stories, as well as other characteristics reported in the survey results. In particular,
Figure 9: Building plan view with position of unit types.
Modules representing typical middle or corner
units, for different types of façade (glass, masonry,
EIFS, etc), and of an average size will be subjected
to wind speeds of varying direction and increasing
magnitude through Monte Carlo simulations. The
approach will produce vulnerability matrices for
every type of units. Given a building with a certain
number of stories and number of units per story, the
total external damage will be an aggregation of the
damage to individual units, based on the wind speed
information from the wind model.
6.1 Propagation criteria
In addition to calculating and aggregating the ED
of mid-high rise buildings units, the modular approach will include a propagation rule for interior
plus utilities damage (IUD). Total average damage
Di has three components, namely, average exterior
damage DiE , average interior damage DiI and the
average utilities damage DiU such that
Di  DiE  DiI  DiU
(1)
A propagation scheme which accounts for the
IUD damage triggered by exterior damage and the
interaction of ID damage between adjacent units, is
currently under study. The principal object is to analyze how the exterior damage triggers the internal
damage and how this propagates inside buildings of
any given number of stories and units per floor (survey results to be shown in a future paper).
The overall procedure is shown in Figure 10. At
the end of the process a 4-dimensions tensor that
will account for all the combined possibilities will
be available. To assess the internal damage of a single building from an insurance portfolio it will be
necessary to know 1) the number of stories, 2) the
number of units per floor, 3) exterior damage.
Figure 11: Finite differences grid. Every node is a condo
unit
0
0
0
0.03 0.02
0
0.10 0.06
0
1
0.33 0.11
0
0
0.09 0.05
0
Units damage
0
0
Exterior
Damage
0
Figure 12: Building plan view layout in Excel ®
Figure 10: Interior damage propagation scheme
The propagation engine is at the heart of the interior damage model. A first approach is presented
here. At present it deals with water propagation only.
The main questions to be addressed are the location
of the damage and its magnitude. It can be shown
that the location is a function of the magnitude.
The model first determines the places where the
water penetrates. Second, in those places where there
is water, it computes the internal damage as a function of water content. A finite differences version of
the Laplace equation was adopted to model water
propagation (Figure 11). It was tested on a preliminary simple discretized building plan-layout (Figure
12) implemented in a spreadsheet in Excel® (Figure
13). The yellow cells represent the layout of a typical condo floor with 6 appartment units. The orange cell indicate whether the unit has external damage (1) or not (0). The numbers in each yellow cell
represent the percentage of IUD. They are computed
based on equation (2), where φi,j represents the water
content in the unit i,j (represented as a node)
The implementation of the algorithm is shown in
Figure 13. Preliminary assessment indicates that an
acceptable error is reached with 5 iterations or less.
 i, j 
1
 i1, j   i1, j   i, j 1   i, j 1 
4
(2)
Figure 13: Detail of the equation in Excel®
A relationship (still under consideration) to account for vertical propagation (leakage) was also developed. It intends to capture the water propagation
from top floor to bottom floor. Some challenges are
being addressed currently like the apparentpermeability of the buildings. This is a work in progress and additional results will be shown in subsequent papers.
7 CONCLUSIONS
The results of a survey carried out on Florida’s low
rise and mid-high rise buildings by the engineering
team of the FPHLM have been presented and discussed. The survey results are valuable for defining
building types to be analyzed by the vulnerability
model and also to average the resulting vulnerabilities when no specific structural information is available for a given building.
The quality of this type of survey would greatly
improve if certain uniform data collection and archiving measures would be adopted to enhance the
data collection in such a way that it will be useful
not only for tax assessment purposes, but also for the
catastrophe modeling community.
The authors also presented a novel modular approach to assess exterior and interior hurricaneinduced damage for mid to high rise building of any
type and number of stories. The advantages of the
approach are its simplicity and its versatility. It is a
work in progress. The final challenge will be to validate the approach against actual hurricane losses.
8 ACKNOWLEDGMENTS
The authors want to acknowledge the invaluable
help of the staff of the different Florida counties tax
property appraiser offices. The help of Tim Johnson
(FIT), Fausto Fleites (FIU), HsinYu Ha (FIU) and
Paul Austin (FIT) is gratefully appreciated. This research is supported by the State of Florida through a Department of Financial Services (FDFS) grant to the Florida International University International Hurricane
Research Center. The opinions, findings, and conclu-
sions expressed in this paper are not necessarily
those of the FDFS.
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