Price Variation in Waterfront Properties
Over the Economic Cycle
by
Randy E. Dumm
William T. Hold Professor of Risk Management and Insurance
Department of Risk Management/Insurance, Real Estate, and Legal Studies
College of Business
Florida State University
Tallahassee, Florida 32306-1110
(850) 644-7880 (Phone)
(850) 644-4077 (Fax)
[email protected]
G. Stacy Sirmans
Kenneth G. Bacheller Professor of Real Estate
Department of Risk Management/Insurance, Real Estate, and Legal Studies
College of Business
Florida State University
Tallahassee, Florida 32306-1110
(850) 644-7845 (Phone)
(850) 644-4077 (Fax)
[email protected]
Greg T. Smersh
Assistant Professor
Department of Finance
College of Business
University of South Florida
Tampa, Florida 33620
(813) 974-6239 (Phone)
(813) 974-3084 (Fax)
[email protected]
November 2014
Price Variation in Waterfront Properties Over the Economic Cycle
Abstract
Using sales data from the Tampa Bay housing market for the period from 2000 to 2012,
this paper examines price performance across the boom, bust, and post-bust phases of the most
recent real estate cycle by comparing waterfront properties to non-waterfront properties and by
comparing specific waterfront types. Waterfront properties enjoyed a 7.2 percent price premium
over non-waterfront properties but this premium was higher in the latter part of the boom stage
of the cycle and for the post-bust part of the cycle (2011 and 2012). When evaluating the
performance of specific waterfront types, properties on the bay, canal, lake, or river provided
price protection through the real estate cycle while the price performance of on-pond properties
in some years was more closely aligned with non-waterfront properties.
Key Words: waterfront, house prices, price premiums, economic cycle
Price Variation in Waterfront Properties Over the Economic Cycle
1. Introduction
The multidimensional nature of a residential property can make its valuation problematic.
Homeowners consume (and value) different sets of structural characteristics and amenities, while
a home’s immobility and proximity to various spatial characteristics (such as neighborhood
makeup and distance to city center) causes a locational effect. In addition, home values are
affected by their economic environment and cyclical behavior, of which a good example is home
price movement over the 2000s decade boom and bust cycle. An interesting question is the
extent to which these factors interact to produce observed property values. Within this context,
this study examines the movement in house prices based on these three factors: (1) different
structural characteristics, (2) spatial amenities (specifically waterfront versus non-waterfront),
and (3) the real estate market boom and bust of the 2000s decade.
Studies have confirmed that the value of residential real estate is a function of both physical
characteristics and location (see Sirmans, Macpherson, and Zietz (2005) for a review). Physical
characteristics such as square footage, age, and lot size can be easily measured and factored into
property values, and their directional effects on house prices are generally consistent across the
literature.
Locational characteristics however, may be a combination of either positive or
negative factors and extracting value effects can be more ambiguous and problematic. For
example, the positive effect of waterfront location or a water view would have a positive effect
on property value whereas being surrounded by inferior homes or a deteriorating neighborhood
would have a negative effect. In addition, across the economic cycle, the price behavior of
properties with certain amenities (such as waterfront) may differ from that of properties lacking
these amenities, and this relationship is not clearly established or understood.
Waterfront location is further complicated by the fact that it can have two distinctly different
amenity factors.
First, there are consumptive factors, or the recreational use for boating,
swimming, etc. which typically are associated with properties that are directly on the water.
Second, there are non-consumptive factors such as scenic views or perhaps a sea breeze that can
be related to properties that are either on the water or nearby. In areas with higher elevations,
this amenity can be realized for a significant distance from the water in some cases. Florida
however is very flat and in most situation, there is no view unless the property is directly on the
water. This makes the analysis of waterfront versus non-waterfront more straightforward, since
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both the recreational or scenic amenities is almost always limited to waterfront locations for
areas in Florida.
A recent study by Wyman, Hutchinson, and Tiwari (2014) provides some insight by
examining the pricing of waterfront view amenities in a South Carolina lakefront community
across the 2000s boom and bust cycle. This study considers the view amenity of vacant lots for
lakefront properties and finds price premiums vary significantly across different quality of
waterfront views. Additionally they find that the premium for waterfront view is affected by the
2000s decade recession, with higher quality waterfront properties being better protected over the
bust period. Gordon et al. (2013) also examine the pricing of waterfront view amenities by
considering the impact of locational preferences and externalities on condominium unit prices
and find that buyers are willing to pay more for the view in terms of elevation and corner
location even if considering negative externalities associated with these properties.
Although these results are informative and useful, our study provides a more comprehensive
analysis of the price effects of different types of waterfront for developed properties (not just
vacant lots) over the 2000s decade boom and bust period. Thus, our study has the advantage of
measuring not only price premiums for properties located on different types of water (bay, river,
canal, lake, and pond) but also the change in these waterfront prices over time in different
economic conditions.1 Using Tampa, Florida bay area data and examining the period 2000
through 2012, the results show an overall 7.2 percent price premium over non-waterfront
properties. Average price premiums across the 12 year time period vary greatly by type of
waterfront: bay (107%), river (62%), canal (61%), lake (15%), and pond (3.1%).
Evaluation of the performance of specific waterfront types across the economic cycle shows
that the premiums were highest at the end of the boom stage of the cycle (2006-2007) and at the
end of the recovery stage of the cycle (2011-2012). Properties on bay, canal, lake, or river
provided superior price protection through the real estate cycle relative to non-waterfront
properties. When the waterfront property types are segmented by premium and non-premium
waterfront, further differences in capitalization effects are observed.
Price premiums vary
greatly from year to year over the 2000 to 2012 period. The bay premium peaked at 177 percent
in 2005. The canal premium reached its highest level at 80 percent in 2005 while the river
premium peaked in 2008 at 79 percent. The lake premium peaked at 22 percent in 2007 and the
pond premium reached its highest level at 5 percent in 2005. Price premiums decreased for all
types of waterfront during the bust period; however by the post-bust period all the waterfront
types had seen an increase in premiums.
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2. The Value of Waterfront Properties
Previous research has established that spatial amenities such as waterfront location are
capitalized into property values (e.g., Henderson (1977) and Diamond (1980)). For example,
Garrod and Willis (1994) find that waterfront location adds a premium of 3-5 percent to house
prices. Luttik (2000) finds that houses overlooking water sold for premiums of 8-10 percent.
Other studies (e.g., Garrod and Willis (1994), Lansford and Jones (1995), and Rush and
Bruggink (2000)) have strongly supported the existence of a significant price premium for
waterfront housing for both consumptive (i.e., recreation) and non-consumptive (i.e., view) uses.
Studies also have shown that the value of water proximity has distance-decay effects. Thus,
most studies measuring the value of waterfront have utilized either distance gradients or discrete
measures of distance. In some cases, distance bands about an externality (e.g., Waddell, Berry,
and Hoch (1993) have been shown to offer results superior to the distance gradient approach.
For example, Archarya and Bennett (2001), looking at 0.25 mile and one mile radii, find selling
price to be negatively correlated with distance to the ocean and lakes. However, in other studies
the distance gradient approach provides superior results. For example, Lansford and Jones
(1995) find that waterfront commands a premium but that the marginal value decreases rapidly
as distance from the water increases.
A large percentage of prior research regarding waterfront amenity value has focused on the
aspect of waterfront view. Bourassa, Hoesli and Sun (2004) provide a review of studies that
examine the impact of views on residential property prices and find that the impact of a view is
greatest at the waterfront.
Benson, Hansen, Schwartz, and Smersh (1998) find that the
willingness is quite high to pay for the amenity of a waterfront view. The authors find that
highest-quality ocean views increased the market price of an otherwise comparable home by
almost 60 percent and that the value of a view was found to vary inversely with distance from the
water. Conversely, Bourassa, Hoesli and Sun (2005) find that the increase in value for a
property with a water view was much less at about 10 percent. For lake views, Seiler, Bond, and
Seiler (2001) examine the impact of water views on property values for properties located on
Lake Erie. They find that having a lake view increased home values by about 56 percent. In a
follow-up study, Bond, Seiler and Seiler (2002) find that a lake view increased the property price
by nearly 90 percent. Doss and Taff (1996) find that a lake view increased property value by
about 44 percent.2
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3. The Data
This study analyzes single-family detached home sales in Hillsborough County, Florida over
the period 2000 to 2012. Hillsborough County’s 2010 population was just over 1.2 million
people and the county’s population density was over 1,000 people per square mile.
Approximately one-third of the houses in the sample are inside the city of Tampa and about 65
percent are owner-occupied.
Even though only a relatively small portion of Hillsborough
County makes contact with the Gulf of Mexico (Tampa Bay), roughly 17 percent of the county's
total 1,266 square miles is water. Sales of waterfront properties average about five percent of all
sales.
The data are provided by the Hillsborough County Property Appraiser’s Office and include
heated square footage, year built, and the last three sales prices and dates. All sales in the final
sample are verified as qualified sales or “arms-length” transactions. All non-qualified sales
(such as purchase by a relative, foreclosure, or any other non-arms-length transaction) were
deleted from the sample.
Supplementary property characteristics include
the number of
bedrooms, the number of bathrooms, and the number of stories, garage, fireplace, pool, air
conditioning, building condition, type of exterior wall, type of interior flooring, and type of roof
cover. Binary variables represent the years 2001 to 2012.
The Hillsborough County Property Appraiser’s Office maintains a property line file that
indicates if certain features border a property; these include industrial use, commercial use,
farmland, water, major highway, etc. Further, the codes for property lines that are bordered by
water are disaggregated into several types of waterfront border codes, including bay-front (on
Tampa Bay), canal-front, river-front (primarily on the Hillsborough River), and lake-front. The
Hillsborough River arises from the Green Swamp near the northeast corner of Hillsborough
County and flows roughly 50 miles through the county to Tampa Bay. The property code file
also identifies other rivers; including the Alafia River, Little Manatee River, Manatee River,
Palm River, and Thonotosassa River. Hillsborough County also contains well over 100 lakes;
however, most of these are relatively small. Thus, the property border codes provide the
advantage of being able to identify the location of residential properties on different types of
waterfront.
Detailed GIS analysis was used to verify the property border codes and revealed a number of
errors. Many waterfront properties were not coded and some properties coded as waterfront
were in fact not on the water. To create a dependable database, a GIS polygon layer representing
4
all water bodies in the county was created. Parcels for single family houses were selected and
exported from the Hillsborough County parcel layer to a new GIS layer. In order to better
organize the data, a file geodatabase was created for all the GIS data (water polygons and
property parcels).
A spatial query was made to select all the parcels that intersect the boundaries of the nearby
water bodies. A separate point GIS layer was created to represent these parcels. However, many
properties were still not selected even though they appeared to be (or were coded as) waterfront
properties. Many properties that are separated from a water body by a narrow road or land were
also not selected. To ensure a better representation of the waterfront designation, fifty-foot
buffers were created around all water bodies and a spatial query was made to find parcels that
intersect those buffers (instead of finding parcels that intersect water bodies directly).
Areas of all water bodies were calculated in acres, and all fields containing water
information (name, area, classification etc.) were transferred to the parcel layer. To create
classifications based on waterfront type, a tabular joined table was made between the parcel layer
and the table of waterfront property codes from the Hillsborough County Property Appraiser's
office. Properties were then identified with their correct waterfront types. GIS was also used to
create a distance variable from the coastline of Tampa Bay to all houses, providing for the use of
a single distance gradient. Hillsborough County is very flat, and in most cases, there is no scenic
view unless the home is located directly on the water. This distance variable was measured as
distance from Bay access rather than simply distance as the crow flies.
Additionally, GIS was used to dis-aggregate different waterfront types.
Canal-front
properties were differentiated by access to Tampa Bay, as determined by bridges that would not
allow a sailboat mast to past under. Premium canal indicates that a sailboat can be docked there
and that it can motor out to Tampa Bay. River-front properties were differentiated by the level
of scenic frontage and the Alafia River, Palm, and Little Manatee Rivers were selected as having
a “wild and scenic” characteristic which we presume will command a price premium over
properties on other rivers. Lakes were differentiated by size. In a typical market, waterfront
property is valued not only for the view but also for the bundle of amenities that includes
swimming, sailing, water skiing, jet skiing, and fishing. As such, houses on lakes over 100 acres
were identified as premium lake properties. Ponds usually describe small bodies of water,
generally smaller than one would require a boat to cross. Some regions of the US define a pond
as a body of water with a surface area of less than 10 acres, and in this study, we classify ponds
based on this definition.3
5
GIS was also used to identify different positive and negative amenities located in close
proximity of residential properties. For example, mobile home parks were built along rivers and
lakes many years ago in Hillsborough County – these would be expected to negatively influence
single family property values. Department of Revenue (DOR) codes identify mobile home parks
as excellent, good, average, and below average.
In addition to mobile home parks, we
investigate the influence of amenities such as golf courses, parks, cemeteries, and fitness centers.
4. Methodology
Real estate research has typically used hedonic regression analysis to measure the marginal
effects of housing characteristics on house prices. This provides a superior methodology for
investigating property submarkets (such as waterfront) which may have characteristics that are
significantly different from general market averages. A review by Sirmans, Macpherson and
Zietz (2005) of over 125 real estate studies that have used hedonic pricing models shows that
many types of variables have been included in these models. Such variables often include the
number of bedrooms, bathrooms, stories, as well as the existence of amenities such as garages,
pools, and fireplaces. All of those variables are included in this study. Additionally, this study
includes variables that indicate the existence of central heat and air, superior construction,
superior exterior wall, superior flooring, or superior roof cover. However, very few studies have
included direct measures of waterfront location. This study not only includes a measure of
location on the water, but further discerns between different types of waterfront.
The basic form of the hedonic pricing model is:
ln(sp) = α 0 + β i X i + ε i
where selling price (sp) is expressed in logged form, α 0 is a constant term, β i is the regression
coefficient for the ith housing characteristic, X i is a vector of structural housing characteristics,
and ε i is the residual error term. The model is expanded to include binary variables representing
several different types of waterfront and binary variables for the years 2001 to 2012, with 2000
as the base year.
With these additional variables, the hedonic model becomes:
ln(sp) = α 0 + β i X i + Ω Water + π Time + ε i
6
where Water is the designation of type of waterfront for a given property and Time is a vector of
binary variables indicating the property’s year of sale. A property may be designated as being
located on one four types of waterfront. Specifically, waterfront locations include on bay (on
Tampa Bay), on canal (man-made canals with access to Tampa Bay), on lake, and on river. If
being on the water is valued by consumers, houses built located on water should sell for higher
prices relative to houses not located on any water, other things constant. Having multiple
measures of waterfront type gives the advantage of being able to isolate the differential pricing
effects of these various types.
5. Results
5.1. Summary Statistics
The variables included in the regression model are defined in Table 1 and summary statistics
are provided in Tables 2 and 3. As shown in Table 2, houses that sold between 2000 and 2012
had an average selling price of $214,740 with an average square footage of 1,959. The average
lot size was .28 acres and the average number of stories was 1.2. The average age was 19.72
years and the average number of bathrooms was 2.23. Thirty percent of houses had a swimming
pool, twenty-two percent had a fireplace and over seventy-five percent of houses had a garage.
Almost all houses had central heating and cooling and a majority of houses (69.3%) had exterior
walls in the superior category or superior grade flooring (32.3%). A much smaller percentage of
houses were classified as superior based on the quality of construction (7.1%) or roof covering
(5.2%).
[INSERT TABLE 1 HERE]
The primary focus on this paper is on the value capitalization of waterfront location.
Overall, 16.9 percent of houses were located on some type of water.
Of the waterfront
properties, a small number were located directly on the bay (194 or .1% of the sample). The
largest category of waterfront properties were properties located on a pond (30,809 or 14% of the
sample. The next largest category was lakefront properties (2,897 or 1.4% of the sample).4
River-front properties (636 or .2% of the sample) and canal-front properties (1,673 or .7% of the
sample) were the remaining two categories.
[INSERT TABLE 2 HERE]
We would expect to see price differences between homes that are located on some type of
water versus homes that are not. These differences could be a function of several factors,
including the waterfront location, other spatial amenities, and differences in structural
characteristics. Table 3 provides a means comparison between waterfront properties and houses
7
off the water. All housing characteristics for waterfront properties are significantly superior to
those for non-waterfront. On average, the purchase price for waterfront properties was $85,064
more than that paid for property off the water. This price differential reflects differences in age
(waterfront properties newer by almost 11 years) and size (waterfront properties were just over
22% larger, over 424 square feet larger on average). Waterfront properties also had more
bedrooms and bathrooms, larger lot size, and were more likely to have a pool or garage.
Additionally, waterfront properties were more likely to be categorized as superior regardless of
category.
[INSERT TABLE 3 HERE]
Since the focus of this paper is not just on the impact of waterfront location but also on how
specific types of waterfront locations are capitalized into house prices, waterfront locations are
categorized as either on the bay (OnBay), on a canal (OnCanal), on a river (OnRiver), or on a
lake (OnLake). Table 4 and Figure 1 provide information on average prices for each specific
waterfront location. Not surprisingly, OnBay properties were the most valuable with an average
selling price of over $1.39 million. OnBay properties also had the greatest variation in price over
the decade. OnCanal properties had the next highest price with an average selling price of
almost $523,000 and the next highest variation in price over the decade. The average price for
OnLake properties ($375,000) was higher than the average price for OnRiver properties
($338,000) and the average selling price for these properties was much more stable over the
decade. As noted above, the least valuable of the specific types of waterfront properties was
OnPond properties with an average price of almost $254,000.
Table 4 provides additional price performance information for OnCanal, OnRiver, and
OnLake property types based on whether the waterfront location is classified as premium or nonpremium.5 Canal properties with bay access sold for more double the price for canal properties
that do not have bay access ($540,234 versus $250,408). Likewise, premium riverfront properties
(Alafia, Palm or Little Manatee Rivers) and premium lakefront properties (lakesize of 100 acres
or more) also show significantly higher prices with differences of over $76,000 for riverfront
properties and $228,000 for lakefront properties.
[INSERT TABLE 4 AND FIGURE 1 HERE]
5.2. Regression Results
The hedonic model is first estimated for the full sample and then for waterfront properties
only. Tables 5, 6, and 7 provide regression results for three distinct models.6 The R2 for these
models ranges from .7797 to .8460. The three models in Table 5 evaluate the impact of
8
waterfront property and progress from measuring strictly on or off water, to considering the
specific types of waterfront, to also considering the effect of specific types of waterfront in the
context of other spatial variables. Model 1A, using the indicator variable OnWater, is a basic
model measuring the general effect of having a waterfront location. Model 1B expands the base
model by replacing OnWater with specific water types: OnBay, OnCanal, OnRiver, OnLake and
OnPond. Model 1C provides a further expansion by adding spatial variables such as distance
from city center, proximity to parks, fitness centers, cemeteries, golf courses and mobile home
parks.
Models 2A and 2B in Table 6 provide a further segmentation of waterfront properties by
differentiating between premium waterfront and non-premium waterfront. Model 2A measures
the price effect of premium versus non-premium waterfront types relative to being off-water.
Model 2B uses only waterfront properties to measure the price effect of specific waterfront
property types relative to properties on the bay.
[INSERT TABLE 5 HERE]
The models in Table 5 behave as expected.7 The building characteristics of square footage,
number of baths, number of stories, swimming pool, fireplace, and garage all have a positive
effect on selling price. The negative sign on bedrooms indicates that, holding square footage
constant, an additional bedroom has a diminishing marginal effect on value. Additionally, all
five of the superior housing categories have a positive effect on selling price. The variables
Y2001 through Y2012 represent the year in which the house is sold. With Y2000 as the omitted
year, the results show that house prices increased 100% (coefficient of 0.6936) through 2006
after which prices fell into a steady decline.
5.3. Waterfront Premiums
For Model 1A in Table 5, the key variable of interest, OnWater, is positive and significant.
Thus, without distinguishing between the different types of waterfront or considering any other
spatial effects or amenities, the price premium for waterfront properties is 7.2 percent
(coefficient of .0697).8
In Model 1B, the single waterfront measure is expanded to identify specific waterfront
types. The results show that the price premiums vary greatly based on the type of waterfront.
OnBay has the highest premium (115 percent), followed by OnCanal (62 percent), OnRiver (47
percent), OnLake (11.3 percent), and OnPond (3.5 percent). Thus, although a price premium
exists for being on any water, the market was clearly distinguishing between the different types
of waterfront.
9
Model 1C builds from Model 1B by adding spatial variables.9 As the results show, selling
price decreases as distance increases from the central business district. Distance from the bay
district and being with 100 yards of a golf course have no effect on price. As the results in Table
1 indicate, the quality of the mobile home park proximity impacts sales price. As seen with
MHParkExcellent, which measures proximity to a high-quality mobile home park, closer
proximity has a positive effect on price. The lesser quality mobile home parks have the reverse
effect. The coefficients for MHParkGood and MHParkAVG, which measure second-tier and
third-tier quality mobile home parks, show that closer proximity has a negative effect on price.
The coefficient for the lowest quality mobile home park, MHPark<Avg is not significant,
although the direction of effect is negative.
Proximity to a recreation park has a negative effect on price. This is likely due to the
increased traffic created by the park and the resulting increased safety concerns. Being in
proximity to a cemetery does not have a significant effect on prices. Being in proximity to a
fitness center has a positive effect on price.
As seen in Model 1C, inclusion of the other spatial variables has no effect on the premiums
for OnBay (107 percent) and OnCanal (61 percent). However, there are differences for the other
waterfront types. Including the other spatial variables has a dramatic effect on the premium for
OnRiver which increases to 62 percent. The OnLake premium increases to 15 percent while the
OnPond premium decreases to 3.1 percent.
Table 6 provides a further dissection of waterfront and a more in-depth analysis by
distinguishing between premium and non-premium waterfronts.
In Model 2A, lakefront is
segmented into premium lakefront and non-premium lakefront, riverfront is segmented into
premium riverfront and non-premium riverfront, and canal is segmented into premium canal and
non-premium canal. In addition, smaller bodies of water are distinguished as ponds.
As the results show, the premiums for OnBay and OnPond are not affected since they have
no premium or non-premium categories. The difference between premium and non-premium
river is minimal (61.8 percent compared to the premium for a non-premium river at 61.5
percent). There is a slightly greater difference between premium and non-premium canal (61
percent compared to 58.1 percent). The biggest differences across premium versus non-premium
waterfront property types is with lakefront properties. Premium lakefront shows a 25 percent
premium while non-premium lakefront is 11.3 percent.
Model 2B in Table 6 estimates the hedonic model using only waterfront properties and
further validates the results of Model 2A. The results show that all other waterfront properties
10
have lower price premiums than Bayfront properties and the reductions are consistent with the
results shown in Model 2A.
5.4. Waterfront Premiums over Time
Single-family home sales activity varied greatly from year to year over the 2000s decade,
from just over 18,000 qualified sales in 2000 to more than 27,000 qualified sales in 2005. The
number of sales decreased dramatically in 2007 to 10,631 qualified home sales. Sales activity
rebounded somewhat, increasing to 12,264 qualified sales in 2012. The average house price
doubled from just under $150,000 in 2000 to over $300,000 in 2007, and then dropped to just
over $197,500 in 2012.
To investigate waterfront property performance over the 2000s decade real estate cycle, the
hedonic model is estimated by year. Table 7 provides the yearly regression coefficients for the
OnWater variable and the other primary waterfront variables (OnBay, OnLake, OnRiver,
Oncanal, and OnPond) and Table 8 shows the price premiums associated with these coefficients.
As seen in Table 8, the price premium for OnWater properties increased from 7.29% in 2000 to a
high of 10.17% in 2005. The price premium fell to a low of 2.76% in 2008 but it increased
substantially since then to reach its highest point in 2012 at 10.95%. Table 8 also shows a
significant premium exists for OnBay properties even before the “irrational exuberance” of the
early-mid 2000s.
As the market became more active, the premium for OnBay properties
increases until 2005. The premium then decreases until 2009 when it is approximately equal to
2000 premium. As the market begins to recover, the premium increases until, in 2012, it is about
two-thirds higher than the 2000-level premium.
OnLake properties show variation in the annual premium but the premium is not on a steady
upward trend. Between 2000 and 2005, the highest premium in a given year was in 2005. The
premium then decreases in 2006 but peaks again in 2007. From there the premium decreases
annually until it increases sharply in 2012. The OnRiver premium shows a steady increase from
2000 to 2005. The premium decreases in 2006 but peaks again in 2008, drops in 2009 and then
increases steadily through 2012. The OnCanal premium increases steadily through 2005, then
decreases in 2006 and holds a somewhat even pattern through 2012. The OnPond premium
peaks in 2005 but is relatively even over the entire period 2000 through 2012.10
6. Summary and Conclusions
11
While the positive effect that waterfront location has on residential property values is
intuitive, little research has been done to examine the performance of waterfront versus nonwaterfront properties over time, or whether performance differences exists between different
types of waterfront. Using sales data from the Tampa Bay housing market for the period from
2000 to 2012, this paper has examined the variations in capitalization rates for different types of
waterfront properties and the price performance of housing across the boom, bust, and post-bust
phases of the most recent 2000s decade real estate cycle. Additionally, the price effects of
various positive and negative externalities including golf course property and properties near
cemeteries, parks, and fitness centers was examined.
The results showed that waterfront properties, on average, enjoyed a 7.20 percent price
premium over non-waterfront properties. This premium was shown to be higher in the latter part
of the boom stage of the cycle and for the post-bust part of the cycle (2011 and 2012). The price
premiums were shown to vary greatly by type of waterfront. OnBay properties had an average
premium of 107 percent over the time period while OnCanal and OnRiver had average premiums
of 61 percent and 62 percent, respectively. OnLake and OnPond premiums averaged much
lower at 15 percent and 3.1 percent, respectively. When waterfront properties are distinguished
between premium and non-premium waterfront, further differences in capitalization effects are
observed.
Price premiums were seen to vary greatly from year to year over the 2000 to 2012 period.
The OnBay premium reached its highest level at 177 percent over non-waterfront properties in
2005. The OnCanal premium reached its highest level at 80 percent in 2005 while the OnRiver
premium reached its highest level at 79 percent in 2008. The OnLake premium reached it
highest level at 22 percent in 2007 and the OnPond premium reached its highest level at 5
percent in 2005. Price premiums decreased for all types of waterfront during the bust period;
however by the post-bust period all the waterfront types had seen an increase in premiums.
12
References
Archarya, G. and L. L. Bennett. Valuing Open Space and Land-Use Patterns in Urban
Watersheds. Journal of Real Estate Finance and Economics, 2001, 22:2/3, 221-237.
Benson,E. D., J. L. Hansen, A. L. Schwartz, and G. T. Smersh. Pricing Residential Amenities:
The Value of a View. Journal of Real Estate Finance and Economics, 1998, 16:1, 55-73.
Bond, M. T., V. L. Seiler, and M. J. Seiler. Residential Real Estate Prices: A Room with a View.
Journal of Real Estate Research, 2002, 23:1/2, 129-137.
Bourassa, S., M. Hoesli, and J. Sun. What’s in a View? Environment and Planning A, 2004,
36:8, 1427- 1450.
Bourassa, S., M. Hoesli, and J. Sun. The Price of Aesthetic Externalities. Journal of Real Estate
Literature, 2005, 13:2, 167-190.
Diamond, D. The Relationship between Amenities and Urban Land Prices. Land Economics,
1980, 56, 21-32.
Dippold, T., J. Mutl, and J. Zietz. Opting for a Green Certificate: The Impact of Local Attitudes
and Economic Conditions. Working Paper, 2014.
Doss, C., and S. Taff. The Influence of Wetland Type and Wetland Proximity on Residential
Property Values. Journal of Agricultural and Resource Economics, 1996, 21:1, 120-129.
Garrod, G. and K. Willis. An Economic Estimate of the Effect of a Waterside Location on
Property Values. Environmental and Resource Economics, 1994, 4:2, 209-217.
Gordon, B., D. Winkler, J. Barrett, and L. Zumpano. The Effect of Elevation and Corner
Location on Oceanfront Condominium Value. Journal of Real Estate Research, 2013, 35:3,
345-363.
1
Henderson, J. Economic Theory and the Cities. New York: Academic Press, 1977.
Lansford, N. H. and L. L. Jones Recreational and Aesthetic Value of Water Using Hedonic Price
Analysis. Journal of Agricultural and Resource Economics, 1995, 20(2): 341-355.
Luttik, J. The Value of Trees, Water and Open Space as Reflected by House Prices in the
Netherlands. Landscape and Urban Planning, 2000, 48:3/4, 161-167.
Rush, R. and T. H. Bruggink. The Value of Ocean Proximity on Barier Island Houses. Appraisal
Journal, 2000, 68:2, 142.
Seiler, M. J., M. T. Bond, and V. L. Seiler. The Impact of World Class Great Lakes Water Views
on Residential Property Values. The Appraisal Journal, 2001, 69:3, 287-295.
Sirmans, G. S., D. A. Macpherson, and E. Zietz. The Composition of Hedonic Pricing Models.
Journal of Real Estate Literature, 2005, 13:1, 1-44.
Waddell, P., B. Berry, and I. Hoch. Residential Property Values in a Multi-Nodal Urban Area:
New Evidence on the Implicit Price of Location. Journal of Real Estate Finance and Economics,
1993, 7, 117-141.
Wyman, D., N. Hutchinson, and P. Tiwari. Testing the Waters: A Spatial Econometric Pricing
Model of Different Views. Journal of Real Estate Research, 2014, 36:3, 363-382.
2
End Notes
1. Comparing waterfront non-waterfront properties is somewhat analogous to a recent study by
Dippold, Mutl, and Zietz (2014) that examines investors’ decisions to green-certify a
property. They find that the decision to green-certify a building is responsive to both
economic conditions (just as we found that waterfront properties are sensitive to changes in
economic conditions over time) and the attitudes of the local population (just as we found
that prices are affected by the tastes and preferences of the local population).
2.
Disparate results in previous literature are likely due to differences in data collection,
modeling techniques, and the motivation for the research (i.e., view benefits versus
recreational benefits). For example, Benson, et al. (1998) collected data by driving around
the area, and focused solely on view characteristics.
3. There is no "official" definition of pond or lake. Pond usually describes small bodies of
water, generally smaller than one would require a boat to cross. Also, lakes tend to have
much more irregular shorelines, with coves and so forth, while ponds generally allow one to
take them all in visually from a single location. Given the lack of other amenities for these
small bodies of water (e.g., recreational boating) we would argue that the value added is
likely limited to the water view.
4. The terms properties and houses are used interchangeable in this paper as the data set only
contains only single family residential real estate
sales (i.e., land only sales are not
included).
5. Canal properties are classified as premium if the canal provides access to Tampa Bay. Lake
properties are classified as premium if the size of the lake is greater than or equal to 100
acres. The Alafia, Palm and Little Manatee Rivers are classified as premium rivers. OnBay
and OnPond properties do not have premium or non-premium classifications.
6. The Breusch-Pagan test was used to test the hypothesis of equal variances. Based on the test
statistic, the hypothesis was rejected (indicating the presence of heteroskedacticity). The
standard errors in Tables 5, 6, and 7 are now heteroskedasticity-consistent (HC3) following
McKinnon & White, Hayes & Cai.
7. Variance inflation factors (VIFs) were calculated for all the regression models in Tables 5
and 6. The main models (1A-C and 2A) show no indication of a multicollinearity issue
(VIFs <4.5 and mean VIFs between 1.4 and 1.9 for these models). Model 2B (onwater
property only) does show higher VIFs for the variables OnPond and NPLake. Given the
3
purpose of Model 2B and consistency of the primary results there with the other models, we
do not believe that multicollinearity is of significant concern in that model.
8. Coefficients are converted to premiums with the standard (ex-1) calculation.
9. Moran’s I statistic was calculated to evaluate spatial autocorrelation in the error terms. These
were aggregated in one-square-mile sections as the geographic level of aggregation, with
connectivity based on queen's case (edge-to-edge and vertex-to-vertex) adjacency. While
spatial dependence existed in basic models (containing no spatial variables), it was negligible
in the models shown here which incorporated distance and other spatial variables such as
waterfront location and proximity to various externalities. As such, we do not believe that
spatial dependence is of significant concern in these models.
10. Due to an insufficient number of observations on a yearly basis for several of the premium
versus non-premium water type subcategories, yearly models are not produced to validate
Models 2A and 2B. Test for statistical significant differences between the yearly coefficients
for premium versus non-premium water types indicated a statistical difference in five of
twelve years for premium lakes versus non-premium lakes and for premium canals versus
non-premium canals. There was only one year where there the difference between premium
river and non-premium river coefficients was statistically significant.
4
Variable
Ln(sp)
Age
SqFt
Lotsize
Bedrooms
Baths
Stories
Pool
Fireplace
Garage
ACSuperior
ConstSuperior
ExtWallSuperior
FlooringSuperior
RoofSuperior
OnWater
OnBay
OnCanal
PremCanal
NonPremCanal
OnRiver
PremRiver
NonPremRiver
OnLake
PremLake
Table 1
Variable Definitions
Definition
Log of sale price ln(sp) = dependent variable
Age of house at the time of sale
The square footage of the house
The size of the lot in acres.
Number of bedrooms
Number of bathrooms
Number of stories
Binary variable with a value of one if the house has a pool,
zero otherwise
Binary variable with a value of one if the house has a fireplace,
zero otherwise
Binary variable with a value of one if the house has a garage,
zero otherwise
Binary variable with a value of one for central heating and
cooling, zero otherwise
Binary variable with a value of one for good or excellent
construction rating, zero otherwise
Binary variable with a value of one for superior grade exterior
wall, zero otherwise
Binary variable with a value of one for superior grade flooring,
zero otherwise
Binary variable with a value of one for superior roof cover,
zero otherwise
Binary variable for property on the water equals one, zero
otherwise
Binary variable for property on the bay equals one, zero
otherwise
Binary variable for property on a canal equals one, zero
otherwise
Binary variable for property located on a canal with access to
Tampa Bay equals one, zero otherwise.
Binary variable for property located on a canal without bay
access equals one, zero otherwise
Binary variable for property on a river equals one, zero
otherwise
Binary variable for property located on the Alafia, Palm or
Little Manatee Rivers equals one, zero otherwise
Binary variable for property located on rivers other than Alafia,
Palm or Manatee Rivers equals one, zero otherwise
Binary variable for property located on a lake equals one, zero
otherwise
Binary variable for property located on a lake greater than or
equal to 100 acres in size equals one, zero otherwise
5
NonPremLake
OnPond
CBD_Distance
BAY_Distance
GolfCourse_100yd
MHParkExcellent
MHParkGood
MHParkAvg
MHPark<Avg
RecPark
Cemetery
Fitness
Y2001 – Y2012
Binary variable for property located on a lake less than100
acres in size equals one, zero otherwise
Binary variable for property on a pond equals one, zero
otherwise
Distance from the property to the central business district
Distance from the property to the bay district
Property is located within 100 yards of a golf course equals
one, zero otherwise
Mobile home park rated as excellent equals one, zero otherwise
Mobile home park rated as good equals one, zero otherwise
Mobile home park rated as average equals one, zero otherwise
Mobile home park rated as below average equals one, zero
otherwise
Property is located within one-half mile of a recreation park
equals one, zero otherwise
Property is located with one-half mile of a cemetery equals
one, zero otherwise
Property is located with one-half mile of a fitness center equals
one, zero otherwise
Time trend variables for the years 2001 through 2012 (Y2000
is the omitted year)
6
Variable
Price
Table 2
Descriptive Statistics
(N=214,326)
Mean
StdDev
Min
Max
214740.40 165878.00 10500 6000000
Age
19.715
SqFt
1959.485
Lotsize
.278
Bedrooms
3.347
Baths
2.236
Stories
1.221
Pool
.295
Fireplace
.219
Garage
.755
ACSuperior
.990
ConstSuperior
.071
ExtWallSuperior
.693
FlooringSuperior
.323
RoofSuperior
.052
OnWater
.169
OnBay
.001
OnCanal
.008
PremCanal
.007
NonPremCanal
.000
OnRiver
.003
.001
PremRiver
.002
NonPremRiver
OnLake
.014
PremLake
.004
NonPremLake
.009
OnPond
.144
CBD_Distance
10.757
BAY_Distance
7.033
GolfCourse_100yd
.056
MHParkExcellent
3.627
MHParkGood
4.221
MHParkAvg
3.811
MHPark<Avg
1.511
Cemetery
.020
Fitness
.195
RecPark
.084
21.539
784.534
.470
.830
.745
.419
.456
.413
.430
.099
.257
.461
.467
.222
.375
.030
.088
.085
.022
.054
.031
.044
.115
.064
.097
.351
4.569
4.756
.231
2.130
2.150
2.520
1.133
.139
.396
.278
7
0
770
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
125
9921
40
9
10
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
27
26
1
13
14
12
8
1
1
1
Table 3
Means Comparison of Housing Characteristics:
On and Off Water Properties
(N=214,326)
Mean
Mean
Variable
OnWater=1 OnWater=0
Diff
283704.70
198640.90 85063.80
Price
10.96
21.49
-10.53
Age
2312.11
1887.80
424.31
SqFt
.36
.26
.10
Lotsize
3.62
3.29
.32
Bedrooms
2.54
2.17
.36
Baths
1.32
1.20
.12
Stories
.40
.27
.13
Pool
.22
.22
.00
Fireplace
.92
.72
.20
Garage
ACSuperior
1.00
.99
.01
ConstSuperior
.04
.08
-.04
ExtWallSuperior
.85
.66
.19
FlooringSuperior
.30
.33
-.02
RoofSuperior
.12
.04
.08
12.58
10.39
2.19
CBD_Distance
7.13
7.01
.11
BAY_Distance
.08
.05
.03
GolfCourse_100yd
4.00
3.55
.45
MHParkExcellent
4.32
4.20
.12
MHParkGood
4.45
3.68
.77
MHParkAvg
1.94
1.43
.51
MHPark<Avg
.00
.02
-.02
Cemetery
.12
.21
-.09
Fitness
.06
.09
-.03
RecPark
36211
178115
N
***- Significant at the .01 level
8
Sig
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
Table 4
Mean Price by Waterfront Property Type
Mean
StdDev
Min
1388218
963382.6
102600
Variable
OnBay
N
196
OnCanal
• PremCanal
• NonPremCanal
1673
1572
101
522737.2
540234.1
250408.9
514196.2
524635.3
130221.7
80000
82000
80000
5600000
5600000
860000
OnRiver
• PremRiver
• NonPremRiver
636
212
424
338355.1
389125.6
312969.8
207818.3
237623.5
186360.3
25000
75000
25000
1395000
1395000
1100000
OnLake
• PremLake
• NonPremLake
2897
868
2029
374965.2
534915.1
306539.2
319015.9
304506.3
300090.8
38000
44000
38000
3937500
2300000
3937500
OnPond
30809
253988.6
161224.5
15000
4500000
Figure 1
Mean Price Movement Over Time by Waterfront Property Type
9
Max
6500000
Table 5
Regression Model Output: Location and Distance from Bay
Dependent Variable: LnPrice
MODEL 1A
MODEL 1B
MODEL 1C
Coefficient
Coefficient
Coefficient
VARIABLE
(Std. Error)**
(Std. Error)**
(Std. Error)**
Constant
10.4091*
10.4238*
10.4695*
(.0435)
(.0438)
(.0416)
Age
-.0007
-.0009
-.0020*
(.0006)
(.0006)
(.0004)
Sqft
.0004*
.0004*
.0004*
(.0000)
(.0000)
(.0000)
Lotsize
.0353*
.0346*
.0621*
(.0078)
(.0077)
(.0083)
Bedrooms
-.0329*
-.0297*
-.0235*
(.0056)
(.0055)
(.0037)
Baths
.0768*
.0753*
.0639*
(.0069)
(.0066)
(.0049)
Stories
-.0111
-.0157
-.0396*
(.0131)
(.0132)
(.0091)
Pool
.1279*
.1234*
.1121*
(.0076)
(.0073)
(.0048)
Fireplace
.0863*
.0871*
.0848*
(.0120)
(.0120)
(.0097)
Garage
.1748*
.1715*
.1978*
(.0121)
(.0121)
(.0097)
ACSuperior
.2569*
.2538*
.2258*
(.0194)
(.0194)
(.0159)
ConstSuperior
.1892*
.1878*
.1517*
(.0276)
(.0279)
(.0205)
ExtWallSuperior
.0600*
.0583*
.0549*
(.0081)
(.0081)
(.0077)
FlooringSuperior
.0926*
.0913*
.0735*
(.0109)
(.0110)
(.0070)
RoofSuperior
.1984*
.1739*
.1480*
(.0225)
(.0194)
(.0185)
OnWater
.0697*
(.0122)
OnBay
.7633*
.7290*
(.0532)
(.0589)
OnLake
.1068*
.1410*
(.0354)
(.0319)
OnRiver
.3839*
.4796*
(.0354)
(.0416)
OnCanal
.4822*
.4746*
(.0343)
(.0353)
OnPond
.0344*
.0305*
(.0100)
(.0087)
CBD_Distance
-.0117*
(.0023)
10
Bay_Distance
GolfCourse_100yd
MHParkExcellent
MHParkGood
MHParkAvg
MHPark<Avg
RecPark
Cemetery
Fitness
Y2001
N
.0792*
(.0040)
.1413*
(.0054)
.2264*
(.0069)
.3555*
(.0080)
.5597*
(.0095)
.6936*
(.0111)
.6214*
(.0109)
.4153*
(.0091)
.1395*
(.0132)
.1415*
(.0109)
.0706*
(.0128)
.0477*
(.0151)
214326
R2
.7795
Y2002
Y2003
Y2004
Y2005
Y2006
Y2007
Y2008
Y2009
Y2010
Y2011
Y2012
.0793*
(.0040)
.1416*
(.0054)
.2276*
(.0069)
.3571*
(.0081)
.5621*
(.0095)
.6976*
(.0112)
.6253*
(.0108)
.4189*
(.0091)
.1423*
(.0133)
.1441*
(.0110)
.0735*
(.0130)
.0497*
(.0152)
214326
.7857
Bold- Significant at the 5% level; *- Significant at the 1% level
**- Standard errors adjusted for clusters in STR
11
.0012
(.0020)
.0488*
(.0195)
-.0186*
(.0041)
.0210*
(.0040)
.0291*
(.0032)
.0084
(.0080)
-.0455
(.0214)
-.0448
(.0397)
.0593*
(.0192)
.0846*
(.0036)
.1475*
(.0051)
.2370*
(.0065)
.3718*
(.0074)
.5812*
(.0085)
.7263*
(.0089)
.6535*
(.0087)
.4420*
(.0077)
.1703*
(.0142)
.1717*
(.0117)
.0996*
(.0140)
.0808*
(.0165)
214236
.8184
Table 6
Regression Model Output: Water Location and Time
Dependent Variable: LnPrice
MODEL 2A (Full Sample)
MODEL 2B (Waterfront Only)
Coefficient
Coefficient
VARIABLE
(Std. Error)**
(Std. Error)**
Constant
10.4704*
11.7109*
(.0417)
(.1233)
Age
-.0020*
-.0027*
(.0004)
(.0009)
Sqft
.0004*
.0004*
(.0000)
(.0000)
Lotsize
.0623*
.0483*
(.0083)
(.0070)
Bedrooms
-.0234*
-.0225*
(.0037)
(.0054)
Baths
.0636*
.0444*
(.0049)
(.0080)
Stories
-.0395*
-.0669*
(.0091)
(.0082)
Pool
.1122*
.1343*
(.0048)
(.0067)
Fireplace
.0847*
.0641*
(.0097)
(.0097)
Garage
.1981*
.1439*
(.0097)
(.0206)
ACSuperior
.2261*
.0607*
(.0159)
(.0718)
ConstSuperior
.1515*
.1059*
(.0204)
(.0210)
ExtWallSuperior
.0308
.0552*
(.0076)
(.0266)
FlooringSuperior
.0735*
.0374*
(.0070)
(.0092)
RoofSuperior
.1462*
.1422*
(.0187)
(.0241)
OnBay
.7304*
(.0587)
PremLake
.2215*
-.6080*
(.0903)
(.1028)
NonPremLake
.1072*
-.7359*
(.0252)
(.0603)
PremRiver
.4809*
-.3500*
(.0438)
(.0673)
NonPremRiver
.4793*
-.4012*
(.0578)
(.0753)
PremCanal
.4764*
-.3150*
(.0371)
(.0496)
NonPremCanal
.4579*
-.3982*
(.0824)
(.0963)
OnPond
.0306*
-.8209*
12
(.0088)
-.0117*
(.0023)
BAY_Distance
.0011
(.0020)
GolfCourse_100yd
.0493
(.0195)
MHParkExcellent
-.0187*
(.0041)
MHParkGood
.0210*
(.0040)
MHParkAvg
.0292*
(.0032)
MHPark<Avg
.0084
(.0080)
RecPark
-.0456
(.0214)
Cemetery
-.0447
(.0396)
Fitness
.0593*
(.0192)
Y2001
.0846*
(.0036)
Y2002
.1476*
(.0051)
Y2003
.2370*
(.0064)
Y2004
.3718*
(.0074)
Y2005
.5810*
(.0085)
Y2006
.7261*
(.0089)
Y2007
.6535*
(.0088)
Y2008
.4419*
(.0077)
Y2009
.1701*
(.0142)
Y2010
.1717*
(.0117)
Y2011
.0997*
(.0140)
Y2012
.0808*
(.0165)
N
214236
R2
.8186
Bold- Significant at the 1% level; *- Significant at the 5% level
**- Standard errors adjusted for clusters in STR
CBD_Distance
13
(.0645)
-.0100*
(.0037)
-.0001
(.0045)
.0485*
(.0182)
-.0043
(.0078)
.0014
(.0053)
.0331*
(.0030)
.0000
(.0097)
-.0234
(.0324)
-.1227
(.0821)
-.0305
(.0170)
.0767*
(.0064)
.1287*
(.0088)
.2245*
(.0108)
.3706*
(.0141)
.5886*
(.0172)
.7056*
(.0168)
.6264*
(.0135)
.4183*
(.0132)
.2449*
(.0147)
.2329*
(.0151)
.1843*
(.0150)
.2119*
(.0153)
36211
.8460
Table 7
Regression Models 1A and 1C: By Year (Reporting Only Water Type Variables)
Dependent Variable: LNPRICE
(**Standard errors adjusted for clusters in STR)
OnWater
OnBay
OnLake
OnRiver
OnCanal
OnPond
N
R2
2000
COEF
(SE)**
.0704*
(.0097)
.4578
(.2018)
.1672*
(.0256)
.3190*
(.0597)
.3697*
(.0478)
.0344*
(.0076)
18046
.8215
2001
COEF
(SE)**
.0716*
(.0125)
.6869*
(.0728)
.1578*
(.0296)
.3843*
(.0456)
.3941*
(.0443)
.0291*
(.0098)
18736
.8271
2002
COEF
(SE)**
.0614*
(.0126)
.6549*
(.0786)
.1331*
(.0352)
.4145*
(.0537)
.4387*
(.0374)
.0191
(.0091)
19702
.8208
2003
COEF
(SE)**
.0632*
(.0125)
.6386*
(.1057)
.1339*
(.0407)
.4991*
(.0658)
.4506*
(.0301)
.0254*
(.0091)
21808
.8367
2004
COEF
(SE)**
.0842*
(.0162)
.7159*
(.1275)
.1407*
(.0451)
.5383*
(.0469)
.5723*
(.0354)
.0380*
(.0125)
23890
.8346
2005
COEF
(SE)**
.0968*
(.0180)
1.0169*
(.0740)
.1920*
(.0415)
.5578*
(.0480)
.5903*
(.0336)
.0481*
(.0145)
27239
.7968
14
2006
COEF
(SE)**
.0580*
(.0175)
.7576*
(.0775)
.1183*
(.0427)
.3919*
(.0818)
.5113*
(.0435)
.0347
(.0144)
19840
.8367
2007
COEF
(SE)**
.0520*
(.0138)
.8424*
(.0677)
.2025*
(.0481)
.5210*
(.0687)
.4455*
(.0428)
.0301*
(.0112)
10631
.8393
2008
COEF
(SE)**
.0272
(.0149)
.5918*
(.1191)
.1510*
(.0472)
.5839*
(.0841)
.4798*
(.0549)
.0097
(.0116)
9341
.7998
2009
COEF
(SE)**
.0548*
(.0158)
.4495
(.3072)
.1295*
(.0465)
.4252
(.1719)
.4550*
(.0521)
.0212
(.0124)
11000
.7698
2010
COEF
(SE)**
.0596*
(.0160)
.6050*
(.1042)
.0918*
(.0344)
.5367*
(.0949)
.4014*
(.0621)
.0224
(.0128)
10273
.8012
2011
COEF
(SE)**
.0716*
(.0158)
.6813*
(.0936)
.0787
(.0394)
.5451*
(.0739)
.4199*
(.0588)
.0252
(.0112)
10916
.8058
2012
COEF
(SE)**
.1039*
(.0189)
.7788*
(.1220)
.2115*
(.0493)
.5947*
(.0706)
.4449*
(.0571)
.0297
(.0137)
12264
.7928
Table 8
Price Premiums for Waterfront Properties Over the 2000s Decade*
OnWater
OnBay
Year
PCT
#Sales
PCT
#Sales
2000
7.29%
2823
58.06%
12
2001
7.42%
2907
98.75%
15
2002
6.34%
3067
92.50%
21
2003
6.52%
3780
89.38%
11
2004
8.79%
4043
104.60%
20
2005
10.17%
4551
176.46%
17
2006
5.97%
3154
113.32%
14
2007
5.34%
1960
132.19%
12
2008
2.76%
1801
80.72%
8
2009
5.64%
2037
56.75%
10
2010
6.14%
1958
83.13%
16
2011
7.42%
2056
97.64%
19
2012
10.95%
2074
117.89%
21
Total
36,211
196
*Converted from the regression coefficients in Table 7
OnCanal
PCT
#Sales
44.73%
180
48.30%
168
55.07%
173
56.93%
162
77.23%
165
80.45%
170
66.75%
62
56.13%
65
61.58%
70
57.62%
110
49.39%
101
52.18%
107
56.03%
140
1,673
15
OnRiver
PCT
#Sales
37.58%
59
46.86%
69
51.36%
62
64.72%
65
71.31%
62
74.68%
72
47.98%
45
68.37%
25
79.30%
27
52.99%
30
71.04%
33
72.48%
36
81.25%
51
636
OnLake
PCT
#Sales
18.20%
194
17.09%
197
14.24%
233
14.33%
375
15.11%
375
21.17%
365
12.56%
248
22.45%
147
16.30%
133
13.83%
146
9.61%
152
8.19%
167
23.55%
165
2,897
OnPond
PCT #Sales
3.50% 2378
2.95% 2458
1.93% 2578
2.57% 3167
3.87% 3421
4.93% 3927
3.53% 2785
3.06% 1711
0.97% 1563
2.14% 1741
2.27% 1656
2.55% 1727
3.01% 1697
30809