Individually Advertised Units in Hawaii (Vacation Rentals)

CONTENTS
BACKGROUND ............................................................................................................................................ 1
OBJECTIVES ............................................................................................................................................... 1
METHODOLOGY.......................................................................................................................................... 2
DISCUSSION ................................................................................................................................................ 3
FINDINGS .................................................................................................................................................... 3
DENSITY ..................................................................................................................................................... 4
ESTIMATING TOTAL LODGING UNITS ............................................................................................................. 4
APPENDIX A: INDIVIDUALLY ADVERTISED UNITS AND HOUSING UNITS ......................................... 6
APPENDIX B: INDIVIDUALLY ADVERTISED UNITS AND HOUSING UNITS MAPS ........................... 10
APPENDIX C: STUDY METHODS ............................................................................................................ 15
SELECTING WEBSITES ............................................................................................................................... 15
DEFINITIONS.............................................................................................................................................. 15
DATA ........................................................................................................................................................ 16
Data from Internet Listings .................................................................................................................. 16
Housing Data ...................................................................................................................................... 16
DATA COLLECTION .................................................................................................................................... 16
DATA PROCESSING.................................................................................................................................... 16
DATA CLEANING ........................................................................................................................................ 17
DUPLICATE LISTINGS ................................................................................................................................. 17
LIST OF TABLES
TABLE 1: TOTAL NUMBER OF INDIVIDUALLY ADVERTISED UNITS IN 2014............................................................ 3
TABLE 2: DENSITY, RATIO OF INDIVIDUALLY ADVERTISED UNITS TO HOUSING STOCK IN HAWAI‘I, 2014 ............... 4
TABLE 3: NUMBER OF LODGING UNITS IN THE STATE OF HAWAI‘I BY TYPE, 2014 ............................................... 5
TABLE A-1: HAWAI‘I ISLAND: INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ..................................................... 6
TABLE A-2: KAUA‘I INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ................................................................... 7
TABLE A-3: MAUI INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ..................................................................... 8
TABLE A-4: MOLOKA‘I AND LĀNA‘I INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................................. 8
TABLE A-5: O‘AHU INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ................................................................... 9
LIST OF FIGURES
FIGURE B-1:
FIGURE B-2:
FIGURE B-3:
FIGURE B-4:
FIGURE B-5:
FIGURE B-6:
FIGURE B-7:
FIGURE B-8:
HAWAI‘I ISLAND NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................... 10
HAWAI‘I ISLAND: INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE ................................... 11
KAUA‘I NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................................ 12
KAUA‘I INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE ................................................. 12
MAUI COUNTY NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ................................. 13
MAUI COUNTY INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE ...................................... 13
O‘AHU NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................................. 14
O‘AHU INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE.................................................. 14
BACKGROUND
The individual vacation rental market has rapidly evolved in recent years and demand for
accommodations beyond the traditional hotel, condominium-hotel, or timeshare rooms, has
increased globally.
•
Renting out individual condominiums or homes (also known as vacation rentals,
individual vacation units, or IVU) has long existed in Hawai‘i.
•
These vacation rental units were a very small part of Hawai‘i’s total lodging supply.
Units were marketed using traditional methods such as yellow pages, print advertising,
and directories.
•
Internet marketing (individual websites) and internet-based distribution channels which
specifically market and book individual vacation units have allowed the demand to
rapidly increase.
•
Non-traditional units now represent approximately 25 percent of Hawai‘i’s total visitor
unit count.
The Hawai‘i Tourism Authority (HTA) desired to have more information about vacation rentals.
The study was conducted by SMS Research in phases at year-end 2013 and between August
and September of 2014 to determine the estimated number of vacation rental units statewide
being advertised individually on the internet.
Information reported here includes estimates of the number of individually advertised units,
number of bedrooms, unit capacity, and unit density (individually advertised units per 100
residential housing units). These are reported for the State as a whole, each of the six major
islands, and for smaller areas called “cities” which are agglomerations of zip code areas.
Finally, results are used to estimate the total number of all types of lodging units in Hawai’i in
2014.
OBJECTIVES
Objectives of this study include the following:
•
Research the evolving use of visitor accommodations in Hawai‘i;
•
Understand the impact of the internet distribution channels and the shared economy on
the availability and use of visitor accommodations;
•
To determine the number of lodging units in Hawai‘i; and
•
Quantify how these changes affect the structure of Hawai‘i’s lodging supply.
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METHODOLOGY
This study was designed to develop a reasonable estimate of independently marketed visitor
units by examining selected internet marketing sites. A summary of the methods are described
below.
•
Sites were chosen for its large number of listing, uniqueness of listings between sites,
availability of address or zip code data. Websites chosen for the project were:
– vacation
rentals
by
Owner
(VRBO):
http://www.vrbo.com/vacationrentals/usa/hawaii
– Clearstay.com: http://www.clearstay.com/Vacation+Rentals/US-Hawaii
– TripAdvisor: http://www.tripadvisor.com/hawaii-vacation-rentals.html
– Airbnb: https://www.airbnb.com/s/Hawaii--United-States
•
The units identified by this search were referred to as Individually Advertised Units.
•
The number of bedrooms was taken directly from listings on the four websites. In rare
cases the number of bedrooms was not given and was estimated from the unit capacity
under an assumption of two persons per bedroom.
•
Most listings also included unit capacity, the maximum number of guests that can be
accommodated by the unit. In some cases, the listing gave the capacity in terms of the
number of persons who can sleep there. That figure could include the use of cots or rollaway beds. In cases where only the number of bedrooms was given, the assumption
was made that each bedroom contained two beds and could sleep two people. Because
of this latter assumption, the estimate given here may be a conservative one.
•
A preliminary study was conducted at the end of 2013 and a second wave of data
collection occurred between mid-August and mid-September 2014. This report mainly
focuses on the analysis of the 2014 data.
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DISCUSSION
Findings
22,238 Individually Advertised Units in Hawai‘i were identified between mid-August and midSeptember 2014 1. Table 1 shows the number of Individually Advertised Units located on each
island.
Table 1: Total Number of Individually Advertised Units in 2014
Island
Hawai‘i
Kaua‘i
Lāna‘i
Maui
Moloka‘i
O‘ahu
State of Hawai‘i
•
1
11,155
7,466
57
15,113
605
9,103
22,238
43,499
117,607
Total Estimated
Number of
Bedrooms
Unlike traditional hotel, condominium hotel, and timeshare units, the units identified in
this study may be offered and withdrawn for visitor rental at the owners’ discretion and
are typically not available for visitor rental on a year-round basis.
–
•
4,986
3,614
22
8,840
365
4,411
Estimated Number
of Visitors that
Could Be
Accommodated
28,106
19,481
133
43,877
1,676
24,334
Number of
Individually
Advertised Units
For example, individual owners may make their units available in response to
seasonal demand or withdraw their units from the market during periods of
personal use.
22,238 units is the best estimate of the number of Individually Advertised Units in Hawai‘i
for 2014. Given the relative precision of the methods used, the estimate should be
treated as 20,000 Individually Advertised Units in high season, plus or minus 2,500 units.
–
The preliminary study done at end of 2013 yielded 18,998 units
–
The increase in identified units could have resulted from:
•
Improved data collection techniques
•
Number of listings might increase during peak summer season
•
Increasing demand has increased listings
This period represents the second phase of fieldwork. Methodology and techniques were refined between phases.
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Density
The density for Individually Advertised Units was based on existing residential housing stock
rather than an area measure such as square miles. Table 2 presents the number of Individually
Advertised Units identified, housing unit counts, and the ratio of identified units to total housing
stock on each island.
Table 2: Density, Ratio of Individually Advertised Units to Housing Stock in Hawai‘i, 2014
Island
Hawai‘i
Kaua‘i
Lāna‘i
Maui
Moloka‘i
O‘ahu
State of Hawai‘i
Total Number of
Individually Advertised
Units
4,986
3,614
22
8,840
365
4,411
22,238
Total Number of
Housing Units*
82,323
28,790
1,545
65,232
3,312
306,622
487,824
Ratio of Individually
Advertised Units to
Total Housing Units 2
6.1
12.6
1.4
13.6
11.0
1.4
4.6
•
Across the State, there were 4.6 Individually Advertised Units per 100 residential
housing units.
•
Maui’s 13.6 units per 100 residential housing units was highest for the State. Kaua‘i’s
ratio was 12.6 per 100. O‘ahu and Lāna‘i had the lowest ratio at 1.4 Individually
Advertised Units per 100 residential housing units.
The ratio of Individually Advertised Units to housing units by zip code area is presented in
Appendix A to this report.
Estimating Total Lodging Units
Results of the data collection were compared to the data reported in the 2014 Visitor Plant
Inventory Report. The objective was to estimate the total number of all types of lodging units in
Hawai’i in 2014.
•
Assessment of the data collected in this study indicated that many of the Individually
Advertised Units were also included in the 2014 Visitor Plant Inventory as VR-Condo,
VR-House, Bed & Breakfast, or as part of a traditional condominium hotel pool.
•
However, not all vacation rentals included in the Visitor Plant Inventory advertise on the
selected Internet sites.
2
Ratio is expressed as the number of Individually Advertised Units per 100 residential housing units. Does not
include housing units from geographic areas where there were no Individually Advertised Units found.
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•
The methods and definitions used in this study and the 2014 Visitor Plant Inventory are
different; however the 22,238 Individually Advertised Units identified in this study were
used as an estimate for Vacation Rentals and B&Bs reported in the Visitor Plant
Inventory.
•
Table 3 combines these Individually Advertised Units with traditional visitor units
reported in the 2014 Visitor Plant Inventory. If all of the identified units were available for
visitor use at the same time, these units would account for up to 25 percent of Hawai‘i’s
total lodging inventory.
Table 3: Number of Lodging Units in the State of Hawai‘i by Type, 2014
Lodging Type
Hotel
Condo Hotel
Timeshare
Hostel
Apartment Hotel
Other
Individually Advertised Units
(Vacation Rentals) 3
Total
3
Units
% Mix
43,575
10,560
10,647
303
325
393
49.5%
12.0%
12.1%
0.3%
0.4%
0.4%
22,238
25.3%
88,041
100.0%
Assumes all identified units are available for visitor use at the same time.
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APPENDIX A: INDIVIDUALLY ADVERTISED UNITS AND HOUSING
UNITS
Table A-1: Hawai‘i Island: Individually Advertised Units by Zip Code
Hawai‘i Island
City/Area
Waikoloa
Kailua-Kona
Zip Code
Individually
Advertised
Units
Housing
Units
Individually
Advertised Units per
100 Housing Units
96738
946
4421
21.4
96740 / 96739
2166
16843
12.9
Nīnole
96773
10
105
9.5
Volcano
96785
156
1776
8.8
Hōnaunau
96726
21
271
7.8
Kamuela
96743
417
5668
7.4
Hakalau
96710
20
275
7.3
Pāhala
96777
39
575
6.8
Hāwī
96719
43
655
6.6
Pāhoa
96778
397
6685
6.0
Captain Cook
96704
170
2938
5.8
Hōlualoa
96725
55
1469
3.7
Laupāhoehoe
96764
11
357
3.1
Nā‘ālehu
96772
29
1089
2.7
Papaaloa
96780
5
202
2.5
Honoka‘a
96727
43
1857
2.3
Pāpa‘ikou
96781
14
654
2.1
Honomu
96728
5
253
2.0
Kapa‘au
96755
25
1384
1.8
Kea‘au
96749
119
6645
1.8
Pepeekeo
96783
14
789
1.8
Kealakekua
96750
25
1466
1.7
Paauilo
96776
7
607
1.2
202
17770
1.1
Hilo
96720 / 96721
Ookala
96774
1
124
0.8
Ocean-View
96737
19
2450
0.8
Mountain View
96771
23
3660
0.6
Kurtistown
96760
4
1335
0.3
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Table A-2: Kaua‘i Individually Advertised Units by Zip Code
Kaua‘i
City/Area
Zip Code
Individually
Advertised
Units
Housing Units
Individually
Advertised Units per
100 Housing Units
Kōloa
96756
1,286
3,247
39.6
Princeville
96722
947
2,464
38.4
Hanalei
96714
355
959
37.0
Anahola
96703
80
899
8.9
Kapa‘a
96746
635
8,134
7.8
Kīlauea
96754
103
1,706
6.0
Kekaha
96752
55
1,382
4.0
Waimea
96796
22
887
2.5
96766 / 96715
97
5,296
1.8
Makaweli
96769
3
185
1.6
Kealia
96751
1
69
1.4
Lāwa‘i
96765
3
210
1.4
Kalāheo
96741
26
2,370
1.1
Hanapēpē
96716
1
982
0.1
Līhu‘e
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Table A-3: Maui Individually Advertised Units by Zip Code
Maui
City/Area
Individually
Advertised
Units
Zip Code
Housing Units
Individually
Advertised Units per
100 Housing Units
Lahaina / Kapalua
96761
3,845
11,928
32.2
Kīhei
96753
4,183
18,059
23.2
Pā‘ia
96779
151
1,292
11.7
Hāna
96713
67
964
7.0
Haiku
96708
190
4,394
4.3
Wailuku
96793
332
10,564
3.1
96768 / 96788
47
6,729
0.7
Kula
96790
23
3,664
0.6
Kahului
96732
2
7,638
0.03
Makawao / Pukalani
Table A-4: Moloka‘i and Lāna‘i Individually Advertised Units by Zip Code
Moloka‘i and Lāna‘i
City/Area
Zip Code
Individually
Advertised
Units
Housing Units
Individually
Advertised Units per
100 Housing Units
Moloka‘i: Maunaloa
96770
147
757
19.4
Moloka‘i: Kaunakakai
96748
217
2,159
10.1
Moloka‘i: Hoolehua
96729
1
396
0.3
Lānai
96763
22
1,545
1.4
387
4,857
8.0
Moloka‘i and Lāna‘i Combined
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Table A-5: O‘ahu Individually Advertised Units by Zip Code
O‘ahu
Zip Code
Individually
Advertised
Units
Housing
Units
Individually
Advertised Units per
100 Housing Units
Kahuku
96731
263
1,297
20.3
Hale‘iwa
96712
480
3,028
15.8
Hau‘ula
96717
195
1,826
10.7
Lā‘ie
96762
123
1,188
10.4
Honolulu: Waikīkī
96815
1,725
22,750
7.6
Ka‘a‘awa
96730
35
617
5.7
Waialua
96791
99
2,776
3.6
Kailua
96734
525
16,548
3.2
Kapolei
96707
372
12,461
3.0
Waimānalo
96795
73
2,494
2.9
Wai‘anae
96792
194
13,376
1.4
Honolulu: Hawaiʻi Kai
96825
67
11,592
0.6
Kāne‘ohe
96744
70
17,803
0.4
Honolulu: Kāhala & Kaimukī
96816
60
18,914
0.3
‘Ewa Beach
96706
48
18,319
0.3
Honolulu: Aina Haina & Niu Valley
96821
17
7,295
0.2
Honolulu: Ala Moana
96814
16
11,187
0.1
Honolulu: Downtown
96801
96812
96813
9
10,542
0.1
Honolulu: Mō‘ili‘ili
96826
8
15,948
0.05
Mililani
96789
8
18,650
0.04
Honolulu: Mānoa
96822
8
19,372
0.04
Aiea
96701
4
14,008
0.03
Honolulu: Nu‘uanu
96817
5
20,157
0.02
Honolulu: Moanalua
96819
3
12,399
0.02
Waipahu
96797
3
19,986
0.02
Pearl City
96782
1
12,089
0.01
City/Area
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APPENDIX B: INDIVIDUALLY ADVERTISED UNITS AND HOUSING
UNITS MAPS
Figure B-1: Hawai‘i Island Number of Individually Advertised Units by Zip Code
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Figure B-2: Hawai‘i Island: Individually Advertised Units Density by Zip Code
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Figure B-3: Kaua‘i Number of Individually Advertised Units by Zip Code
Figure B-4: Kaua‘i Individually Advertised Units Density by Zip Code
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Figure B-5: Maui County Number of Individually Advertised Units by Zip Code
Figure B-6: Maui County Individually Advertised Units Density by Zip Code
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Figure B-7: O‘ahu Number of Individually Advertised Units by Zip Code
Figure B-8: O‘ahu Individually Advertised Units Density by Zip Code
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APPENDIX C: STUDY METHODS
Selecting Websites
Internet websites that list vacation rentals
were identified and evaluated for their
effectiveness for this study.
Websites
chosen for the project were:
•
•
•
•
vacation rentals by Owner (VRBO):
http://www.vrbo.com/vacationrentals/usa/hawaii
Clearstay.com:
http://www.clearstay.com/Vacation+Rent
als/US-Hawaii
TripAdvisor:
http://www.tripadvisor.com/hawaiivacation-rentals.html
Airbnb:
https://www.airbnb.com/s/Hawaii-United-States
The
VRBO
site
was
the
most
comprehensive site in that it has a greater
number of listings than any other site.
VRBO is owned by the Homeaway and the
two lists had a very large number of the
same listings.
We eliminated the
Homeaway site from our study due to this
duplication. TripAdvisor had about half the
number of listings as VRBO. Clearstay.com
and Airbnb each had about 1,000 listings for
units in Hawai‘i. Clearstay.com and Airbnb
were selected primarily because they did
not have a high degree of overlap with
VRBO, and because they included
important data that was not available from
other websites.
Clearstay.com, for
example, lists the address of most
properties.
Airbnb listings included zip
codes for all listings in 2013.
That
information was not available in 2014, but
we decided to use Airbnb to maintain
comparable methods for the two phases of
fieldwork.
Definitions
Preparation for this report revealed that
most of the studies of vacation rentals in
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Hawai‘i have used different definitions of
terms. This stems from the fact that the
authors had different objectives, used
different methods, and applied their
methods different data sources in each
study 4. While this is understandable, it
makes comparing results of different studies
problematic. We note below the definitions
used for this study with comments on how
those definitions compare to the primary
study available at this time, the VPI.
•
Visitor Destination Area (VDA): A
geographic area with significant visitor
accommodations infrastructure, such as
Waikīkī on O‘ahu. The term VDA is
used on some islands as an official land
use
classification
and
those
classifications were applied where
available.
Where they were not
available, the district classifications for
VPI were used.
•
Individually Advertised Unit.
Any
housing unit listed for short-term rental
on the Internet sites selected for this
study.
Individually Advertised Units
include individual condominium units,
houses, villas, cottages, townhouses,
apartments, bungalows, studios, B&B
rentals, rooms in residential housing
units, and non-standard units such as
tents, converted lanais and garages.
Definitions used by the four websites for
vacation rentals and B&Bs vary from
definitions used in the VPI. There were
no units advertised on the four websites
4
Note in particular the last several years of the HTA
Visitor Plant Inventory for definitions and changes
in definitions. See also, The Kauaian Institute
(2005) Transient vacation rentals on O‘ahu: A
Comparative Analysis of the Geographic and
Economic Footprint, September 2005; The
Kauaian Institute (2005) Transient vacation rentals
on Maui: A Comparative Analysis of the
Geographic and Economic Footprint, August 2005;
The Kauaian Institute (2004) Transient vacation
rentals on Kaua‘i: A Comparative Analysis of the
Geographic and Economic Footprint, January
2004.
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that were identified as apartment hotel,
hostel, or “other” units as defined in VPI.
Data
Data from Internet Listings
For each unit listed in the four websites we
reviewed, we attempted to gather the
information listed below. Items that were
required are marked with an asterisk.
Listing that did not include the required
items were not included. There were very
few omissions.
• Website Name*
• Listing Identification Number*
• Listing Name and/or Description*
• Island Location*
• Location Indicators:
o Area or City*
o Street Address (if available)
o Zip Code (if available)
• Listing Characteristics (when published):
o Number of Bedrooms
o Number of Bathrooms
o Number of Guests that can be
accommodated
o Rates
Most of the items listed above are selfexplanatory and suited to the task at hand.
Much depends on the quality of the listing
name or description. For some listings the
content may be as clear as “Fairways at
Maui, oceanfront, condominium hotel unit.”
For others, the information may be powerful
advertising, but less useful for our research.
Housing Data
The number of housing units in each area
and for each island was obtained from the
U.S. Census Bureau’s 2010 Census data.
Data Collection
Unit data were obtained directly from listings
on each of the four websites. Data were
collected electronically using custom-written
programs 5 that mined or scraped html
pages on each website, gathering unit
characteristics data into subfiles. The data
were gathered sequentially by website,
island, and region within island (city, village,
VDA, or zip code). The raw data were
written to Excel worksheets for processing.
Data Processing
Data gathered from websites were first
arranged in a common format. Separate
software was developed for each website to
eliminate duplicate entries within the site
data. A geographic locator in each file
based on whichever data were available at
that site -- city, street address, zip code or
other information.
A unit capacity measure (the maximum
number of people that could be
accommodated by a given unit) was also
generated for each file.
Shared
assumptions for this process were that there
was at least one bedroom per unit listed and
that each bedroom had two beds or could
accommodate at least two people unless
otherwise specified.
Regardless of the
information supplied by each listing, these
assumptions allowed us to calculate a
comparable unit capacity for all units.
Other programs were written for individual
files as they were needed for cleaning,
extracting information from larger fields, or
other utilities.
A sample of the listings obtained where
checked manually by SMS professionals in
order to confirm that the data obtained
electronically matched the information
published for the listings. This also served
to validate the scripts developed to pull the
data from the listing web pages. The
manual check consists of a person using a
web browser to navigate to the URL of a
given listing and then confirming that the
5
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Most routines were written in PERL, some in
SEQL.
Page 16
December, 2014
details found match the data that was
obtained programmatically.
A sample of listings identified as duplicates
were also checked manually to confirm that
they were in fact duplicates in order to
validate the process of finding and
eliminating duplicates.
Finally, data from each of the four websites
were merged and a sequential unit
identification number was generated for
each case. Initial identification numbers
from the original files were maintained.
Data Cleaning
In most cases, the exact location of the unit
or property listed was unavailable. Some
records included a ‘neighborhood’, local
area, or community name and some listed a
city or village name.
Only one site,
Clearstay.com, included an exact address
for its listings. During data cleaning the
geographic indicator was standardized
across all sources using a list of cities
recognized by the USPS and their assigned
zip codes. If the listed city or area did not
match a city with a zip code, then the
closest city with an assigned zip code was
assigned as the standardized location.
When the location data extracted from the
website was insufficient to identify a
geographic location, the listing page was
visited to determine the location based on
descriptions, maps, or images. Listings on
the
Airbnb
website
included
GPS
coordinates, which were used to obtain the
zip code which could be translated into
island and city codes.
website, and enter the relevant data to the
master data file.
Duplicate Listings
Duplicate listings were identified by
comparing case identifiers within a given
geography.
Within each of the four
component files, it was possible to identify
duplicates by their ID numbers. In each of
the four files we compared ID numbers and
“listing names” of two records. The listing
name is a property name or short
descriptive phrase or sentence describing
the rental unit. For example, if in the VRBO
site two listings had the same name and
listing ID, then they were considered
duplicates.
Duplicate records were
removed and saved for further analysis. A
sample of duplicate listings was manually
reviewed in a browser to confirm that they
were in fact listings for the same unit.
In the larger file, we compared the “listing
names” and other information for two
records.
Across records from all four
sources/websites, the identification of
duplicate listings was conducted by island
using the listing name.
When needed
additional details of the listings were used
such as number of bedrooms and
bathrooms. Using these methods, more
than 1,500 duplicate listings were identified
and removed from the database.
Similar processes were applied to important
variables such as the capacity indicator and
unit type (VR-condo, VR-house, or B&B).
Raw data extracted from the websites is
used to develop an indicator that works for
all cases. In cases where the raw data are
insufficient to generate a comparable value
for the variable under consideration, staff
members get the listing URL, visit the
VPI Ancillary Study
© SMS
Page 17
December, 2014